211 results on '"Hanis, CL"'
Search Results
2. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls.
- Author
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Flannick, J, Mercader, JM, Fuchsberger, C, Udler, MS, Mahajan, A, Wessel, J, Teslovich, TM, Caulkins, L, Koesterer, R, Barajas-Olmos, F, Blackwell, TW, Boerwinkle, E, Brody, JA, Centeno-Cruz, F, Chen, L, Chen, S, Contreras-Cubas, C, Córdova, E, Correa, A, Cortes, M, DeFronzo, RA, Dolan, L, Drews, KL, Elliott, A, Floyd, JS, Gabriel, S, Garay-Sevilla, ME, García-Ortiz, H, Gross, M, Han, S, Heard-Costa, NL, Jackson, AU, Jørgensen, ME, Kang, HM, Kelsey, M, Kim, B-J, Koistinen, HA, Kuusisto, J, Leader, JB, Linneberg, A, Liu, C-T, Liu, J, Lyssenko, V, Manning, AK, Marcketta, A, Malacara-Hernandez, JM, Martínez-Hernández, A, Matsuo, K, Mayer-Davis, E, Mendoza-Caamal, E, Mohlke, KL, Morrison, AC, Ndungu, A, Ng, MCY, O'Dushlaine, C, Payne, AJ, Pihoker, C, Broad Genomics Platform, Post, WS, Preuss, M, Psaty, BM, Vasan, RS, Rayner, NW, Reiner, AP, Revilla-Monsalve, C, Robertson, NR, Santoro, N, Schurmann, C, So, WY, Soberón, X, Stringham, HM, Strom, TM, Tam, CHT, Thameem, F, Tomlinson, B, Torres, JM, Tracy, RP, van Dam, RM, Vujkovic, M, Wang, S, Welch, RP, Witte, DR, Wong, T-Y, Atzmon, G, Barzilai, N, Blangero, J, Bonnycastle, LL, Bowden, DW, Chambers, JC, Chan, E, Cheng, C-Y, Cho, YS, Collins, FS, de Vries, PS, Duggirala, R, Glaser, B, Gonzalez, C, Gonzalez, ME, Groop, L, Kooner, JS, Kwak, SH, Laakso, M, Lehman, DM, Nilsson, P, Spector, TD, Tai, ES, Tuomi, T, Tuomilehto, J, Wilson, JG, Aguilar-Salinas, CA, Bottinger, E, Burke, B, Carey, DJ, Chan, JCN, Dupuis, J, Frossard, P, Heckbert, SR, Hwang, MY, Kim, YJ, Kirchner, HL, Lee, J-Y, Lee, J, Loos, RJF, Ma, RCW, Morris, AD, O'Donnell, CJ, Palmer, CNA, Pankow, J, Park, KS, Rasheed, A, Saleheen, D, Sim, X, Small, KS, Teo, YY, Haiman, C, Hanis, CL, Henderson, BE, Orozco, L, Tusié-Luna, T, Dewey, FE, Baras, A, Gieger, C, Meitinger, T, Strauch, K, Lange, L, Grarup, N, Hansen, T, Pedersen, O, Zeitler, P, Dabelea, D, Abecasis, G, Bell, GI, Cox, NJ, Seielstad, M, Sladek, R, Meigs, JB, Rich, SS, Rotter, JI, DiscovEHR Collaboration, CHARGE, LuCamp, ProDiGY, GoT2D, ESP, SIGMA-T2D, T2D-GENES, AMP-T2D-GENES, Altshuler, D, Burtt, NP, Scott, LJ, Morris, AP, Florez, JC, McCarthy, MI, Boehnke, M, Flannick, J, Mercader, JM, Fuchsberger, C, Udler, MS, Mahajan, A, Wessel, J, Teslovich, TM, Caulkins, L, Koesterer, R, Barajas-Olmos, F, Blackwell, TW, Boerwinkle, E, Brody, JA, Centeno-Cruz, F, Chen, L, Chen, S, Contreras-Cubas, C, Córdova, E, Correa, A, Cortes, M, DeFronzo, RA, Dolan, L, Drews, KL, Elliott, A, Floyd, JS, Gabriel, S, Garay-Sevilla, ME, García-Ortiz, H, Gross, M, Han, S, Heard-Costa, NL, Jackson, AU, Jørgensen, ME, Kang, HM, Kelsey, M, Kim, B-J, Koistinen, HA, Kuusisto, J, Leader, JB, Linneberg, A, Liu, C-T, Liu, J, Lyssenko, V, Manning, AK, Marcketta, A, Malacara-Hernandez, JM, Martínez-Hernández, A, Matsuo, K, Mayer-Davis, E, Mendoza-Caamal, E, Mohlke, KL, Morrison, AC, Ndungu, A, Ng, MCY, O'Dushlaine, C, Payne, AJ, Pihoker, C, Broad Genomics Platform, Post, WS, Preuss, M, Psaty, BM, Vasan, RS, Rayner, NW, Reiner, AP, Revilla-Monsalve, C, Robertson, NR, Santoro, N, Schurmann, C, So, WY, Soberón, X, Stringham, HM, Strom, TM, Tam, CHT, Thameem, F, Tomlinson, B, Torres, JM, Tracy, RP, van Dam, RM, Vujkovic, M, Wang, S, Welch, RP, Witte, DR, Wong, T-Y, Atzmon, G, Barzilai, N, Blangero, J, Bonnycastle, LL, Bowden, DW, Chambers, JC, Chan, E, Cheng, C-Y, Cho, YS, Collins, FS, de Vries, PS, Duggirala, R, Glaser, B, Gonzalez, C, Gonzalez, ME, Groop, L, Kooner, JS, Kwak, SH, Laakso, M, Lehman, DM, Nilsson, P, Spector, TD, Tai, ES, Tuomi, T, Tuomilehto, J, Wilson, JG, Aguilar-Salinas, CA, Bottinger, E, Burke, B, Carey, DJ, Chan, JCN, Dupuis, J, Frossard, P, Heckbert, SR, Hwang, MY, Kim, YJ, Kirchner, HL, Lee, J-Y, Lee, J, Loos, RJF, Ma, RCW, Morris, AD, O'Donnell, CJ, Palmer, CNA, Pankow, J, Park, KS, Rasheed, A, Saleheen, D, Sim, X, Small, KS, Teo, YY, Haiman, C, Hanis, CL, Henderson, BE, Orozco, L, Tusié-Luna, T, Dewey, FE, Baras, A, Gieger, C, Meitinger, T, Strauch, K, Lange, L, Grarup, N, Hansen, T, Pedersen, O, Zeitler, P, Dabelea, D, Abecasis, G, Bell, GI, Cox, NJ, Seielstad, M, Sladek, R, Meigs, JB, Rich, SS, Rotter, JI, DiscovEHR Collaboration, CHARGE, LuCamp, ProDiGY, GoT2D, ESP, SIGMA-T2D, T2D-GENES, AMP-T2D-GENES, Altshuler, D, Burtt, NP, Scott, LJ, Morris, AP, Florez, JC, McCarthy, MI, and Boehnke, M
- Abstract
Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 × 10-3) and candidate genes from knockout mice (P = 5.2 × 10-3). Within our study, the strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000-185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts.
- Published
- 2019
3. Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.
- Author
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Flannick, J, Fuchsberger, C, Mahajan, A, Teslovich, TM, Agarwala, V, Gaulton, KJ, Caulkins, L, Koesterer, R, Ma, C, Moutsianas, L, McCarthy, DJ, Rivas, MA, Perry, JRB, Sim, X, Blackwell, TW, Robertson, NR, Rayner, NW, Cingolani, P, Locke, AE, Tajes, JF, Highland, HM, Dupuis, J, Chines, PS, Lindgren, CM, Hartl, C, Jackson, AU, Chen, H, Huyghe, JR, van de Bunt, M, Pearson, RD, Kumar, A, Mueller-Nurasyid, M, Grarup, N, Stringham, HM, Gamazon, ER, Lee, J, Chen, Y, Scott, RA, Below, JE, Chen, P, Huang, J, Go, MJ, Stitzel, ML, Pasko, D, Parker, SCJ, Varga, TV, Green, T, Beer, NL, Day-Williams, AG, Ferreira, T, Fingerlin, T, Horikoshi, M, Hu, C, Huh, I, Ikram, MK, Kim, B-J, Kim, Y, Kim, YJ, Kwon, M-S, Lee, S, Lin, K-H, Maxwell, TJ, Nagai, Y, Wang, X, Welch, RP, Yoon, J, Zhang, W, Barzilai, N, Voight, BF, Han, B-G, Jenkinson, CP, Kuulasmaa, T, Kuusisto, J, Manning, A, Ng, MCY, Palmer, ND, Balkau, B, Stancakova, A, Abboud, HE, Boeing, H, Giedraitis, V, Prabhakaran, D, Gottesman, O, Scott, J, Carey, J, Kwan, P, Grant, G, Smith, JD, Neale, BM, Purcell, S, Butterworth, AS, Howson, JMM, Lee, HM, Lu, Y, Kwak, S-H, Zhao, W, Danesh, J, Lam, VKL, Park, KS, Saleheen, D, So, WY, Tam, CHT, Afzal, U, Aguilar, D, Arya, R, Aung, T, Chan, E, Navarro, C, Cheng, C-Y, Palli, D, Correa, A, Curran, JE, Rybin, D, Farook, VS, Fowler, SP, Freedman, BI, Griswold, M, Hale, DE, Hicks, PJ, Khor, C-C, Kumar, S, Lehne, B, Thuillier, D, Lim, WY, Liu, J, Loh, M, Musani, SK, Puppala, S, Scott, WR, Yengo, L, Tan, S-T, Taylor, HA, Thameem, F, Wilson, G, Wong, TY, Njolstad, PR, Levy, JC, Mangino, M, Bonnycastle, LL, Schwarzmayr, T, Fadista, J, Surdulescu, GL, Herder, C, Groves, CJ, Wieland, T, Bork-Jensen, J, Brandslund, I, Christensen, C, Koistinen, HA, Doney, ASF, Kinnunen, L, Esko, T, Farmer, AJ, Hakaste, L, Hodgkiss, D, Kravic, J, Lyssenko, V, Hollensted, M, Jorgensen, ME, Jorgensen, T, Ladenvall, C, Justesen, JM, Karajamaki, A, Kriebel, J, Rathmann, W, Lannfelt, L, Lauritzen, T, Narisu, N, Linneberg, A, Melander, O, Milani, L, Neville, M, Orho-Melander, M, Qi, L, Qi, Q, Roden, M, Rolandsson, O, Swift, A, Rosengren, AH, Stirrups, K, Wood, AR, Mihailov, E, Blancher, C, Carneiro, MO, Maguire, J, Poplin, R, Shakir, K, Fennell, T, DePristo, M, de Angelis, MH, Deloukas, P, Gjesing, AP, Jun, G, Nilsson, PM, Murphy, J, Onofrio, R, Thorand, B, Hansen, T, Meisinger, C, Hu, FB, Isomaa, B, Karpe, F, Liang, L, Peters, A, Huth, C, O'Rahilly, SP, Palmer, CNA, Pedersen, O, Rauramaa, R, Tuomilehto, J, Salomaa, V, Watanabe, RM, Syvanen, A-C, Bergman, RN, Bharadwaj, D, Bottinger, EP, Cho, YS, Chandak, GR, Chan, JC, Chia, KS, Daly, MJ, Ebrahim, SB, Langenberg, C, Elliott, P, Jablonski, KA, Lehman, DM, Jia, W, Ma, RCW, Pollin, TI, Sandhu, M, Tandon, N, Froguel, P, Barroso, I, Teo, YY, Zeggini, E, Loos, RJF, Small, KS, Ried, JS, DeFronzo, RA, Grallert, H, Glaser, B, Metspalu, A, Wareham, NJ, Walker, M, Banks, E, Gieger, C, Ingelsson, E, Im, HK, Illig, T, Franks, PW, Buck, G, Trakalo, J, Buck, D, Prokopenko, I, Magi, R, Lind, L, Farjoun, Y, Owen, KR, Gloyn, AL, Strauch, K, Tuomi, T, Kooner, JS, Lee, J-Y, Park, T, Donnelly, P, Morris, AD, Hattersley, AT, Bowden, DW, Collins, FS, Atzmon, G, Chambers, JC, Spector, TD, Laakso, M, Strom, TM, Bell, GI, Blangero, J, Duggirala, R, Tai, E, McVean, G, Hanis, CL, Wilson, JG, Seielstad, M, Frayling, TM, Meigs, JB, Cox, NJ, Sladek, R, Lander, ES, Gabriel, S, Mohlke, KL, Meitinger, T, Groop, L, Abecasis, G, Scott, LJ, Morris, AP, Kang, HM, Altshuler, D, Burtt, NP, Florez, JC, Boehnke, M, McCarthy, MI, Flannick, J, Fuchsberger, C, Mahajan, A, Teslovich, TM, Agarwala, V, Gaulton, KJ, Caulkins, L, Koesterer, R, Ma, C, Moutsianas, L, McCarthy, DJ, Rivas, MA, Perry, JRB, Sim, X, Blackwell, TW, Robertson, NR, Rayner, NW, Cingolani, P, Locke, AE, Tajes, JF, Highland, HM, Dupuis, J, Chines, PS, Lindgren, CM, Hartl, C, Jackson, AU, Chen, H, Huyghe, JR, van de Bunt, M, Pearson, RD, Kumar, A, Mueller-Nurasyid, M, Grarup, N, Stringham, HM, Gamazon, ER, Lee, J, Chen, Y, Scott, RA, Below, JE, Chen, P, Huang, J, Go, MJ, Stitzel, ML, Pasko, D, Parker, SCJ, Varga, TV, Green, T, Beer, NL, Day-Williams, AG, Ferreira, T, Fingerlin, T, Horikoshi, M, Hu, C, Huh, I, Ikram, MK, Kim, B-J, Kim, Y, Kim, YJ, Kwon, M-S, Lee, S, Lin, K-H, Maxwell, TJ, Nagai, Y, Wang, X, Welch, RP, Yoon, J, Zhang, W, Barzilai, N, Voight, BF, Han, B-G, Jenkinson, CP, Kuulasmaa, T, Kuusisto, J, Manning, A, Ng, MCY, Palmer, ND, Balkau, B, Stancakova, A, Abboud, HE, Boeing, H, Giedraitis, V, Prabhakaran, D, Gottesman, O, Scott, J, Carey, J, Kwan, P, Grant, G, Smith, JD, Neale, BM, Purcell, S, Butterworth, AS, Howson, JMM, Lee, HM, Lu, Y, Kwak, S-H, Zhao, W, Danesh, J, Lam, VKL, Park, KS, Saleheen, D, So, WY, Tam, CHT, Afzal, U, Aguilar, D, Arya, R, Aung, T, Chan, E, Navarro, C, Cheng, C-Y, Palli, D, Correa, A, Curran, JE, Rybin, D, Farook, VS, Fowler, SP, Freedman, BI, Griswold, M, Hale, DE, Hicks, PJ, Khor, C-C, Kumar, S, Lehne, B, Thuillier, D, Lim, WY, Liu, J, Loh, M, Musani, SK, Puppala, S, Scott, WR, Yengo, L, Tan, S-T, Taylor, HA, Thameem, F, Wilson, G, Wong, TY, Njolstad, PR, Levy, JC, Mangino, M, Bonnycastle, LL, Schwarzmayr, T, Fadista, J, Surdulescu, GL, Herder, C, Groves, CJ, Wieland, T, Bork-Jensen, J, Brandslund, I, Christensen, C, Koistinen, HA, Doney, ASF, Kinnunen, L, Esko, T, Farmer, AJ, Hakaste, L, Hodgkiss, D, Kravic, J, Lyssenko, V, Hollensted, M, Jorgensen, ME, Jorgensen, T, Ladenvall, C, Justesen, JM, Karajamaki, A, Kriebel, J, Rathmann, W, Lannfelt, L, Lauritzen, T, Narisu, N, Linneberg, A, Melander, O, Milani, L, Neville, M, Orho-Melander, M, Qi, L, Qi, Q, Roden, M, Rolandsson, O, Swift, A, Rosengren, AH, Stirrups, K, Wood, AR, Mihailov, E, Blancher, C, Carneiro, MO, Maguire, J, Poplin, R, Shakir, K, Fennell, T, DePristo, M, de Angelis, MH, Deloukas, P, Gjesing, AP, Jun, G, Nilsson, PM, Murphy, J, Onofrio, R, Thorand, B, Hansen, T, Meisinger, C, Hu, FB, Isomaa, B, Karpe, F, Liang, L, Peters, A, Huth, C, O'Rahilly, SP, Palmer, CNA, Pedersen, O, Rauramaa, R, Tuomilehto, J, Salomaa, V, Watanabe, RM, Syvanen, A-C, Bergman, RN, Bharadwaj, D, Bottinger, EP, Cho, YS, Chandak, GR, Chan, JC, Chia, KS, Daly, MJ, Ebrahim, SB, Langenberg, C, Elliott, P, Jablonski, KA, Lehman, DM, Jia, W, Ma, RCW, Pollin, TI, Sandhu, M, Tandon, N, Froguel, P, Barroso, I, Teo, YY, Zeggini, E, Loos, RJF, Small, KS, Ried, JS, DeFronzo, RA, Grallert, H, Glaser, B, Metspalu, A, Wareham, NJ, Walker, M, Banks, E, Gieger, C, Ingelsson, E, Im, HK, Illig, T, Franks, PW, Buck, G, Trakalo, J, Buck, D, Prokopenko, I, Magi, R, Lind, L, Farjoun, Y, Owen, KR, Gloyn, AL, Strauch, K, Tuomi, T, Kooner, JS, Lee, J-Y, Park, T, Donnelly, P, Morris, AD, Hattersley, AT, Bowden, DW, Collins, FS, Atzmon, G, Chambers, JC, Spector, TD, Laakso, M, Strom, TM, Bell, GI, Blangero, J, Duggirala, R, Tai, E, McVean, G, Hanis, CL, Wilson, JG, Seielstad, M, Frayling, TM, Meigs, JB, Cox, NJ, Sladek, R, Lander, ES, Gabriel, S, Mohlke, KL, Meitinger, T, Groop, L, Abecasis, G, Scott, LJ, Morris, AP, Kang, HM, Altshuler, D, Burtt, NP, Florez, JC, Boehnke, M, and McCarthy, MI
- Abstract
This corrects the article DOI: 10.1038/sdata.2017.179.
- Published
- 2018
4. Data Descriptor: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls
- Author
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Flannick, J, Fuchsberger, C, Mahajan, A, Teslovich, TM, Agarwala, V, Gaulton, KJ, Caulkins, L, Koesterer, R, Ma, C, Moutsianas, L, McCarthy, DJ, Rivas, MA, Perry, JRB, Sim, X, Blackwell, TW, Robertson, NR, Rayner, NW, Cingolani, P, Locke, AE, Tajes, JF, Highland, HM, Dupuis, J, Chines, PS, Lindgren, CM, Hartl, C, Jackson, AU, Chen, H, Huyghe, JR, De Bunt, MV, Pearson, RD, Kumar, A, Muller-Nurasyid, M, Grarup, N, Stringham, HM, Gamazon, ER, Lee, J, Chen, Y, Scott, RA, Below, JE, Chen, P, Huang, J, Go, MJ, Stitzel, ML, Pasko, D, Parker, SCJ, Varga, TV, Green, T, Beer, NL, Day-Williams, AG, Ferreira, T, Fingerlin, T, Horikoshi, M, Hu, C, Huh, I, Ikram, MK, Kim, B-J, Kim, Y, Kim, YJ, Kwon, M-S, Lee, S, Lin, K-H, Maxwell, TJ, Nagai, Y, Wang, X, Welch, RP, Yoon, J, Zhang, W, Barzilai, N, Voight, BF, Han, B-G, Jenkinson, CP, Kuulasmaa, T, Kuusisto, J, Manning, A, Ng, MCY, Palmer, ND, Balkau, B, Stancakova, A, Abboud, HE, Boeing, H, Giedraitis, V, Prabhakaran, D, Gottesman, O, Scott, J, Carey, J, Kwan, P, Grant, G, Smith, JD, Neale, BM, Purcell, S, Butterworth, AS, Howson, JMM, Lee, HM, Lu, Y, Kwak, S-H, Zhao, W, Danesh, J, Lam, VKL, Park, KS, Saleheen, D, So, WY, Tam, CHT, Afzal, U, Aguilar, D, Arya, R, Aung, T, Chan, E, Navarro, C, Cheng, C-Y, Palli, D, Correa, A, Curran, JE, Rybin, D, Farook, VS, Fowler, SP, Freedman, BI, Griswold, M, Hale, DE, Hicks, PJ, Khor, C-C, Kumar, S, Lehne, B, Thuillier, D, Lim, WY, Liu, J, Loh, M, Musani, SK, Puppala, S, Scott, WR, Yengo, L, Tan, S-T, Taylor, HA, Thameem, F, Wilson, G, Wong, TY, Njolstad, PR, Levy, JC, Mangino, M, Bonnycastle, LL, Schwarzmayr, T, Fadista, J, Surdulescu, GL, Herder, C, Groves, CJ, Wieland, T, Bork-Jensen, J, Brandslund, I, Christensen, C, Koistinen, HA, Doney, ASF, Kinnunen, L, Esko, T, Farmer, AJ, Hakaste, L, Hodgkiss, D, Kravic, J, Lyssenko, V, Hollensted, M, Jorgensen, ME, Jorgensen, T, Ladenvall, C, Justesen, JM, Karajamaki, A, Kriebel, J, Rathmann, W, Lannfelt, L, Lauritzen, T, Narisu, N, Linneberg, A, Melander, O, Milani, L, Neville, M, Orho-Melander, M, Qi, L, Qi, Q, Roden, M, Rolandsson, O, Swift, A, Rosengren, AH, Stirrups, K, Wood, AR, Mihailov, E, Blancher, C, Carneiro, MO, Maguire, J, Poplin, R, Shakir, K, Fennell, T, DePristo, M, De Angelis, MH, Deloukas, P, Gjesing, AP, Jun, G, Nilsson, PM, Murphy, J, Onofrio, R, Thorand, B, Hansen, T, Meisinger, C, Hu, FB, Isomaa, B, Karpe, F, Liang, L, Peters, A, Huth, C, O'Rahilly, SP, Palmer, CNA, Pedersen, O, Rauramaa, R, Tuomilehto, J, Salomaa, V, Watanabe, RM, Syvanen, A-C, Bergman, RN, Bharadwaj, D, Bottinger, EP, Cho, YS, Chandak, GR, Chan, JC, Chia, KS, Daly, MJ, Ebrahim, SB, Langenberg, C, Elliott, P, Jablonski, KA, Lehman, DM, Jia, W, Ma, RC, Pollin, TI, Sandhu, M, Tandon, N, Froguel, P, Barroso, I, Teo, YY, Zeggini, E, Loos, RJF, Small, KS, Ried, JS, DeFronzo, RA, Grallert, H, Glaser, B, Metspalu, A, Wareham, NJ, Walker, M, Banks, E, Gieger, C, Ingelsson, E, Im, HK, Illig, T, Franks, PW, Buck, G, Trakalo, J, Buck, D, Prokopenko, I, Magi, R, Lind, L, Farjoun, Y, Owen, KR, Gloyn, AL, Strauch, K, Tuomi, T, Kooner, JS, Lee, J-Y, Park, T, Donnelly, P, Morris, AD, Hattersley, AT, Bowden, DW, Collins, FS, Atzmon, G, Chambers, JC, Spector, TD, Laakso, M, Strom, TM, Bell, GI, Blangero, J, Duggirala, R, Tai, E, McVean, G, Hanis, CL, Wilson, JG, Seielstad, M, Frayling, TM, Meigs, JB, Cox, NJ, Sladek, R, Lander, ES, Gabriel, S, Mohlke, KL, Meitinger, T, Groop, L, Abecasis, G, Scott, LJ, Morris, AP, Kang, HM, Altshuler, D, Burtt, NP, Florez, JC, Boehnke, M, McCarthy, MI, Flannick, J, Fuchsberger, C, Mahajan, A, Teslovich, TM, Agarwala, V, Gaulton, KJ, Caulkins, L, Koesterer, R, Ma, C, Moutsianas, L, McCarthy, DJ, Rivas, MA, Perry, JRB, Sim, X, Blackwell, TW, Robertson, NR, Rayner, NW, Cingolani, P, Locke, AE, Tajes, JF, Highland, HM, Dupuis, J, Chines, PS, Lindgren, CM, Hartl, C, Jackson, AU, Chen, H, Huyghe, JR, De Bunt, MV, Pearson, RD, Kumar, A, Muller-Nurasyid, M, Grarup, N, Stringham, HM, Gamazon, ER, Lee, J, Chen, Y, Scott, RA, Below, JE, Chen, P, Huang, J, Go, MJ, Stitzel, ML, Pasko, D, Parker, SCJ, Varga, TV, Green, T, Beer, NL, Day-Williams, AG, Ferreira, T, Fingerlin, T, Horikoshi, M, Hu, C, Huh, I, Ikram, MK, Kim, B-J, Kim, Y, Kim, YJ, Kwon, M-S, Lee, S, Lin, K-H, Maxwell, TJ, Nagai, Y, Wang, X, Welch, RP, Yoon, J, Zhang, W, Barzilai, N, Voight, BF, Han, B-G, Jenkinson, CP, Kuulasmaa, T, Kuusisto, J, Manning, A, Ng, MCY, Palmer, ND, Balkau, B, Stancakova, A, Abboud, HE, Boeing, H, Giedraitis, V, Prabhakaran, D, Gottesman, O, Scott, J, Carey, J, Kwan, P, Grant, G, Smith, JD, Neale, BM, Purcell, S, Butterworth, AS, Howson, JMM, Lee, HM, Lu, Y, Kwak, S-H, Zhao, W, Danesh, J, Lam, VKL, Park, KS, Saleheen, D, So, WY, Tam, CHT, Afzal, U, Aguilar, D, Arya, R, Aung, T, Chan, E, Navarro, C, Cheng, C-Y, Palli, D, Correa, A, Curran, JE, Rybin, D, Farook, VS, Fowler, SP, Freedman, BI, Griswold, M, Hale, DE, Hicks, PJ, Khor, C-C, Kumar, S, Lehne, B, Thuillier, D, Lim, WY, Liu, J, Loh, M, Musani, SK, Puppala, S, Scott, WR, Yengo, L, Tan, S-T, Taylor, HA, Thameem, F, Wilson, G, Wong, TY, Njolstad, PR, Levy, JC, Mangino, M, Bonnycastle, LL, Schwarzmayr, T, Fadista, J, Surdulescu, GL, Herder, C, Groves, CJ, Wieland, T, Bork-Jensen, J, Brandslund, I, Christensen, C, Koistinen, HA, Doney, ASF, Kinnunen, L, Esko, T, Farmer, AJ, Hakaste, L, Hodgkiss, D, Kravic, J, Lyssenko, V, Hollensted, M, Jorgensen, ME, Jorgensen, T, Ladenvall, C, Justesen, JM, Karajamaki, A, Kriebel, J, Rathmann, W, Lannfelt, L, Lauritzen, T, Narisu, N, Linneberg, A, Melander, O, Milani, L, Neville, M, Orho-Melander, M, Qi, L, Qi, Q, Roden, M, Rolandsson, O, Swift, A, Rosengren, AH, Stirrups, K, Wood, AR, Mihailov, E, Blancher, C, Carneiro, MO, Maguire, J, Poplin, R, Shakir, K, Fennell, T, DePristo, M, De Angelis, MH, Deloukas, P, Gjesing, AP, Jun, G, Nilsson, PM, Murphy, J, Onofrio, R, Thorand, B, Hansen, T, Meisinger, C, Hu, FB, Isomaa, B, Karpe, F, Liang, L, Peters, A, Huth, C, O'Rahilly, SP, Palmer, CNA, Pedersen, O, Rauramaa, R, Tuomilehto, J, Salomaa, V, Watanabe, RM, Syvanen, A-C, Bergman, RN, Bharadwaj, D, Bottinger, EP, Cho, YS, Chandak, GR, Chan, JC, Chia, KS, Daly, MJ, Ebrahim, SB, Langenberg, C, Elliott, P, Jablonski, KA, Lehman, DM, Jia, W, Ma, RC, Pollin, TI, Sandhu, M, Tandon, N, Froguel, P, Barroso, I, Teo, YY, Zeggini, E, Loos, RJF, Small, KS, Ried, JS, DeFronzo, RA, Grallert, H, Glaser, B, Metspalu, A, Wareham, NJ, Walker, M, Banks, E, Gieger, C, Ingelsson, E, Im, HK, Illig, T, Franks, PW, Buck, G, Trakalo, J, Buck, D, Prokopenko, I, Magi, R, Lind, L, Farjoun, Y, Owen, KR, Gloyn, AL, Strauch, K, Tuomi, T, Kooner, JS, Lee, J-Y, Park, T, Donnelly, P, Morris, AD, Hattersley, AT, Bowden, DW, Collins, FS, Atzmon, G, Chambers, JC, Spector, TD, Laakso, M, Strom, TM, Bell, GI, Blangero, J, Duggirala, R, Tai, E, McVean, G, Hanis, CL, Wilson, JG, Seielstad, M, Frayling, TM, Meigs, JB, Cox, NJ, Sladek, R, Lander, ES, Gabriel, S, Mohlke, KL, Meitinger, T, Groop, L, Abecasis, G, Scott, LJ, Morris, AP, Kang, HM, Altshuler, D, Burtt, NP, Florez, JC, Boehnke, M, and McCarthy, MI
- Abstract
To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.
- Published
- 2017
5. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility
- Author
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Mahajan, A, Go, MJ, Zhang, W, Below, JE, Gaulton, KJ, Ferreira, T, Horikoshi, M, Johnson, AD, Ng, MCY, Prokopenko, I, Saleheen, D, Wang, X, Zeggini, E, Abecasis, GR, Adair, LS, Almgren, P, Atalay, M, Aung, T, Baldassarre, D, Balkau, B, Bao, Y, Barnett, AH, Barroso, I, Basit, A, Been, LF, Beilby, J, Bell, GI, Benediktsson, R, Bergman, RN, Boehm, BO, Boerwinkle, E, Bonnycastle, LL, Burtt, N, Cai, Q, Campbell, H, Carey, J, Cauchi, S, Caulfield, M, Chan, JCN, Chang, L-C, Chang, T-J, Chang, Y-C, Charpentier, G, Chen, C-H, Chen, H, Chen, Y-T, Chia, K-S, Chidambaram, M, Chines, PS, Cho, NH, Cho, YM, Chuang, L-M, Collins, FS, Cornelis, MC, Couper, DJ, Crenshaw, AT, van Dam, RM, Danesh, J, Das, D, de Faire, U, Dedoussis, G, Deloukas, P, Dimas, AS, Dina, C, Doney, ASF, Donnelly, PJ, Dorkhan, M, van Duijn, C, Dupuis, J, Edkins, S, Elliott, P, Emilsson, V, Erbel, R, Eriksson, JG, Escobedo, J, Esko, T, Eury, E, Florez, JC, Fontanillas, P, Forouhi, NG, Forsen, T, Fox, C, Fraser, RM, Frayling, TM, Froguel, P, Frossard, P, Gao, Y, Gertow, K, Gieger, C, Gigante, B, Grallert, H, Grant, GB, Groop, LC, Groves, CJ, Grundberg, E, Guiducci, C, Hamsten, A, Han, B-G, Hara, K, Hassanali, N, Hattersley, AT, Hayward, C, Hedman, AK, Herder, C, Hofman, A, Holmen, OL, Hovingh, K, Hreidarsson, AB, Hu, C, Hu, FB, Hui, J, Humphries, SE, Hunt, SE, Hunter, DJ, Hveem, K, Hydrie, ZI, Ikegami, H, Illig, T, Ingelsson, E, Islam, M, Isomaa, B, Jackson, AU, Jafar, T, James, A, Jia, W, Joeckel, K-H, Jonsson, A, Jowett, JBM, Kadowaki, T, Kang, HM, Kanoni, S, Kao, WHL, Kathiresan, S, Kato, N, Katulanda, P, Keinanen-Kiukaanniemi, SM, Kelly, AM, Khan, H, Khaw, K-T, Khor, C-C, Kim, H-L, Kim, S, Kim, YJ, Kinnunen, L, Klopp, N, Kong, A, Korpi-Hyovalti, E, Kowlessur, S, Kraft, P, Kravic, J, Kristensen, MM, Krithika, S, Kumar, A, Kumate, J, Kuusisto, J, Kwak, SH, Laakso, M, Lagou, V, Lakka, TA, Langenberg, C, Langford, C, Lawrence, R, Leander, K, Lee, J-M, Lee, NR, Li, M, Li, X, Li, Y, Liang, J, Liju, S, Lim, W-Y, Lind, L, Lindgren, CM, Lindholm, E, Liu, C-T, Liu, JJ, Lobbens, S, Long, J, Loos, RJF, Lu, W, Luan, J, Lyssenko, V, Ma, RCW, Maeda, S, Maegi, R, Mannisto, S, Matthews, DR, Meigs, JB, Melander, O, Metspalu, A, Meyer, J, Mirza, G, Mihailov, E, Moebus, S, Mohan, V, Mohlke, KL, Morris, AD, Muehleisen, TW, Mueller-Nurasyid, M, Musk, B, Nakamura, J, Nakashima, E, Navarro, P, Peng-Keat, N, Nica, AC, Nilsson, PM, Njolstad, I, Noethen, MM, Ohnaka, K, Ong, TH, Owen, KR, Palmer, CNA, Pankow, JS, Park, KS, Parkin, M, Pechlivanis, S, Pedersen, NL, Peltonen, L, Perry, JRB, Peters, A, Pinidiyapathirage, JM, Platou, CGP, Potter, S, Price, JF, Qi, L, Radha, V, Rallidis, L, Rasheed, A, Rathmann, W, Rauramaa, R, Raychaudhuri, S, Rayner, NW, Rees, SD, Rehnberg, E, Ripatti, S, Robertson, N, Roden, M, Rossin, EJ, Rudan, I, Rybin, D, Saaristo, TE, Salomaa, V, Saltevo, J, Samuel, M, Sanghera, DK, Saramies, J, Scott, J, Scott, LJ, Scott, RA, Segre, AV, Sehmi, J, Sennblad, B, Shah, N, Shah, S, Shera, AS, Shu, XO, Shuldiner, AR, Sigurdsson, G, Sijbrands, E, Silveira, A, Sim, X, Sivapalaratnam, S, Small, KS, So, WY, Stancakova, A, Stefansson, K, Steinbach, G, Steinthorsdottir, V, Stirrups, K, Strawbridge, RJ, Stringham, HM, Sun, Q, Suo, C, Syvanen, A-C, Takayanagi, R, Takeuchi, F, Tay, WT, Teslovich, TM, Thorand, B, Thorleifsson, G, Thorsteinsdottir, U, Tikkanen, E, Trakalo, J, Tremoli, E, Trip, MD, Tsai, FJ, Tuomi, T, Tuomilehto, J, Uitterlinden, AG, Valladares-Salgado, A, Vedantam, S, Veglia, F, Voight, BF, Wang, C, Wareham, NJ, Wennauer, R, Wickremasinghe, AR, Wilsgaard, T, Wilson, JF, Wiltshire, S, Winckler, W, Wong, TY, Wood, AR, Wu, J-Y, Wu, Y, Yamamoto, K, Yamauchi, T, Yang, M, Yengo, L, Yokota, M, Young, R, Zabaneh, D, Zhang, F, Zhang, R, Zheng, W, Zimmet, PZ, Altshuler, D, Bowden, DW, Cho, YS, Cox, NJ, Cruz, M, Hanis, CL, Kooner, J, Lee, J-Y, Seielstad, M, Teo, YY, Boehnke, M, Parra, EJ, Chambers, JC, Tai, ES, McCarthy, MI, Morris, AP, Mahajan, A, Go, MJ, Zhang, W, Below, JE, Gaulton, KJ, Ferreira, T, Horikoshi, M, Johnson, AD, Ng, MCY, Prokopenko, I, Saleheen, D, Wang, X, Zeggini, E, Abecasis, GR, Adair, LS, Almgren, P, Atalay, M, Aung, T, Baldassarre, D, Balkau, B, Bao, Y, Barnett, AH, Barroso, I, Basit, A, Been, LF, Beilby, J, Bell, GI, Benediktsson, R, Bergman, RN, Boehm, BO, Boerwinkle, E, Bonnycastle, LL, Burtt, N, Cai, Q, Campbell, H, Carey, J, Cauchi, S, Caulfield, M, Chan, JCN, Chang, L-C, Chang, T-J, Chang, Y-C, Charpentier, G, Chen, C-H, Chen, H, Chen, Y-T, Chia, K-S, Chidambaram, M, Chines, PS, Cho, NH, Cho, YM, Chuang, L-M, Collins, FS, Cornelis, MC, Couper, DJ, Crenshaw, AT, van Dam, RM, Danesh, J, Das, D, de Faire, U, Dedoussis, G, Deloukas, P, Dimas, AS, Dina, C, Doney, ASF, Donnelly, PJ, Dorkhan, M, van Duijn, C, Dupuis, J, Edkins, S, Elliott, P, Emilsson, V, Erbel, R, Eriksson, JG, Escobedo, J, Esko, T, Eury, E, Florez, JC, Fontanillas, P, Forouhi, NG, Forsen, T, Fox, C, Fraser, RM, Frayling, TM, Froguel, P, Frossard, P, Gao, Y, Gertow, K, Gieger, C, Gigante, B, Grallert, H, Grant, GB, Groop, LC, Groves, CJ, Grundberg, E, Guiducci, C, Hamsten, A, Han, B-G, Hara, K, Hassanali, N, Hattersley, AT, Hayward, C, Hedman, AK, Herder, C, Hofman, A, Holmen, OL, Hovingh, K, Hreidarsson, AB, Hu, C, Hu, FB, Hui, J, Humphries, SE, Hunt, SE, Hunter, DJ, Hveem, K, Hydrie, ZI, Ikegami, H, Illig, T, Ingelsson, E, Islam, M, Isomaa, B, Jackson, AU, Jafar, T, James, A, Jia, W, Joeckel, K-H, Jonsson, A, Jowett, JBM, Kadowaki, T, Kang, HM, Kanoni, S, Kao, WHL, Kathiresan, S, Kato, N, Katulanda, P, Keinanen-Kiukaanniemi, SM, Kelly, AM, Khan, H, Khaw, K-T, Khor, C-C, Kim, H-L, Kim, S, Kim, YJ, Kinnunen, L, Klopp, N, Kong, A, Korpi-Hyovalti, E, Kowlessur, S, Kraft, P, Kravic, J, Kristensen, MM, Krithika, S, Kumar, A, Kumate, J, Kuusisto, J, Kwak, SH, Laakso, M, Lagou, V, Lakka, TA, Langenberg, C, Langford, C, Lawrence, R, Leander, K, Lee, J-M, Lee, NR, Li, M, Li, X, Li, Y, Liang, J, Liju, S, Lim, W-Y, Lind, L, Lindgren, CM, Lindholm, E, Liu, C-T, Liu, JJ, Lobbens, S, Long, J, Loos, RJF, Lu, W, Luan, J, Lyssenko, V, Ma, RCW, Maeda, S, Maegi, R, Mannisto, S, Matthews, DR, Meigs, JB, Melander, O, Metspalu, A, Meyer, J, Mirza, G, Mihailov, E, Moebus, S, Mohan, V, Mohlke, KL, Morris, AD, Muehleisen, TW, Mueller-Nurasyid, M, Musk, B, Nakamura, J, Nakashima, E, Navarro, P, Peng-Keat, N, Nica, AC, Nilsson, PM, Njolstad, I, Noethen, MM, Ohnaka, K, Ong, TH, Owen, KR, Palmer, CNA, Pankow, JS, Park, KS, Parkin, M, Pechlivanis, S, Pedersen, NL, Peltonen, L, Perry, JRB, Peters, A, Pinidiyapathirage, JM, Platou, CGP, Potter, S, Price, JF, Qi, L, Radha, V, Rallidis, L, Rasheed, A, Rathmann, W, Rauramaa, R, Raychaudhuri, S, Rayner, NW, Rees, SD, Rehnberg, E, Ripatti, S, Robertson, N, Roden, M, Rossin, EJ, Rudan, I, Rybin, D, Saaristo, TE, Salomaa, V, Saltevo, J, Samuel, M, Sanghera, DK, Saramies, J, Scott, J, Scott, LJ, Scott, RA, Segre, AV, Sehmi, J, Sennblad, B, Shah, N, Shah, S, Shera, AS, Shu, XO, Shuldiner, AR, Sigurdsson, G, Sijbrands, E, Silveira, A, Sim, X, Sivapalaratnam, S, Small, KS, So, WY, Stancakova, A, Stefansson, K, Steinbach, G, Steinthorsdottir, V, Stirrups, K, Strawbridge, RJ, Stringham, HM, Sun, Q, Suo, C, Syvanen, A-C, Takayanagi, R, Takeuchi, F, Tay, WT, Teslovich, TM, Thorand, B, Thorleifsson, G, Thorsteinsdottir, U, Tikkanen, E, Trakalo, J, Tremoli, E, Trip, MD, Tsai, FJ, Tuomi, T, Tuomilehto, J, Uitterlinden, AG, Valladares-Salgado, A, Vedantam, S, Veglia, F, Voight, BF, Wang, C, Wareham, NJ, Wennauer, R, Wickremasinghe, AR, Wilsgaard, T, Wilson, JF, Wiltshire, S, Winckler, W, Wong, TY, Wood, AR, Wu, J-Y, Wu, Y, Yamamoto, K, Yamauchi, T, Yang, M, Yengo, L, Yokota, M, Young, R, Zabaneh, D, Zhang, F, Zhang, R, Zheng, W, Zimmet, PZ, Altshuler, D, Bowden, DW, Cho, YS, Cox, NJ, Cruz, M, Hanis, CL, Kooner, J, Lee, J-Y, Seielstad, M, Teo, YY, Boehnke, M, Parra, EJ, Chambers, JC, Tai, ES, McCarthy, MI, and Morris, AP
- Abstract
To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS), including 26,488 cases and 83,964 controls of European, east Asian, south Asian and Mexican and Mexican American ancestry. We observed a significant excess in the directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven new T2D susceptibility loci. Furthermore, we observed considerable improvements in the fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterization of complex trait loci and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry.
- Published
- 2014
6. Missed Opportunities for Diagnosis and Treatment of Diabetes, Hypertension, and Hypercholesterolemia in a Mexican American Population, Cameron County Hispanic Cohort, 2003–2008
- Author
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Fisher-Hoch, SP, primary, Vatcheva, KP, additional, Laing, ST, additional, Hossain, MM, additional, Rahbar, MH, additional, and Hanis, CL, additional
- Published
- 2012
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7. Variants in CXADR and F2RL1 are associated with blood pressure and obesity in African-Americans in regions identified through admixture mapping.
- Author
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Shetty PB, Tang H, Tayo BO, Morrison AC, Hanis CL, Rao DC, Young JH, Fox ER, Boerwinkle E, Cooper RS, Risch NJ, Zhu X, Candidate Gene Association Resource (CARe) Consortium, Shetty, Priya B, Tang, Hua, Tayo, Bamidele O, Morrison, Alanna C, Hanis, Craig L, Rao, Dabeeru C, and Young, Jeffery H
- Published
- 2012
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8. Identification of diabetic retinopathy genes through a genome-wide association study among Mexican-Americans from Starr County, Texas.
- Author
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Fu Y, Hallman DM, Gonzalez VH, Klein BEK, Klein R, Hayes MG, Cox NJ, Bell GI, and Hanis CL
- Abstract
To identify genetic loci for severe diabetic retinopathy, 286 Mexican-Americans with type 2 diabetes from Starr County, Texas, completed physical examinations including fundus photography for diabetic retinopathy grading. Individuals with moderate-to-severe non-proliferative and proliferative diabetic retinopathy were defined as cases. Direct genotyping was performed using the Affymetrix GeneChip Human Mapping 100 K Set, and SNPs passing quality control criteria were used to impute markers available in HapMap Phase III Mexican population (MXL) in Los Angeles, California. Two directly genotyped markers were associated with severe diabetic retinopathy at a P-value less than .0001: SNP rs2300782 (P = 6.04 × 10
-5 ) mapped to an intron region of CAMK4 (calcium/calmodulin-dependent protein kinase IV) on chromosome 5, and SNP rs10519765 (P = 6.21 × 10-5 ) on chromosomal 15q13 in the FMN1 (formin 1) gene. Using well-imputed markers based on the HapMap III Mexican population, we identified an additional 32 SNPs located in 11 chromosomal regions with nominal association with severe diabetic retinopathy at P-value less than .0001. None of these markers were located in traditional candidate genes for diabetic retinopathy or diabetes itself. However, these signals implicate genes involved in inflammation, oxidative stress and cell adhesion for the development and progression of diabetic retinopathy. [ABSTRACT FROM AUTHOR]- Published
- 2010
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9. Gene by smoking interaction in hypertension: identification of a major quantitative trait locus on chromosome 15q for systolic blood pressure in Mexican-Americans.
- Author
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Montasser ME, Shimmin LC, Hanis CL, Boerwinkle E, Hixson JE, Montasser, May E, Shimmin, Lawrence C, Hanis, Craig L, Boerwinkle, Eric, and Hixson, James E
- Published
- 2009
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10. Health beliefs of Mexican Americans with type 2 diabetes: the Starr County Border Health Initiative.
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Brown SA, Blozis SA, Kouzekanani K, Garcia AA, Winchell M, and Hanis CL
- Abstract
PURPOSE: The purpose of this study was to compare 2 culturally competent diabetes self-management interventions designed for Mexican Americans: an original extended program (24 hours of education, 28 hours of support groups) versus a shorter, more resource-efficient compressed strategy (16 hours of education, 6 hours of support groups). The effects of the interventions on health beliefs are compared. METHODS: The authors recruited 216 persons between 35 and 70 years of age diagnosed with type 2 diabetes for at least 1 year. Intervention groups of 8 participants and 8 support persons were randomly assigned to 1 of the interventions. RESULTS: Mean health belief scores on each subscale improved for both intervention groups. Both intervention groups reported significant improvements in perceptions of control of their diabetes. Improvements in health beliefs were more sustained at 12 months for individuals in the longer, extended program. The health belief subscale control was the most significant predictor of HbA1c levels at 12 months. CONCLUSIONS: Both culturally competent diabetes self-management education interventions were effective in promotingmore positive health beliefs. These findings on health beliefs indicate a dosage effect of the intervention and support the importance of ongoing contact through support groups to attain more sustainable improvements in health beliefs. [ABSTRACT FROM AUTHOR]
- Published
- 2007
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11. Dosage effects of diabetes self-management education for Mexican Americans: the Starr County Border Health Initiative.
- Author
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Brown SA, Blozis SA, Kouzekanani K, Garcia AA, Winchell M, Hanis CL, Brown, Sharon A, Blozis, Shelley A, Kouzekanani, Kamiar, Garcia, Alexandra A, Winchell, Maria, and Hanis, Craig L
- Abstract
Objective: The objective of this study was to compare two diabetes self-management interventions designed for Mexican Americans: "extended" (24 h of education, 28 h of support groups) and "compressed" (16 h of education, 6 h of support groups). Both interventions were culturally competent regarding language, diet, social emphasis, family participation, and incorporating cultural beliefs.Research Design and Methods: We recruited 216 persons between 35 and 70 years of age diagnosed with type 2 diabetes >/=1 year. Intervention groups of eight participants and eight support persons were randomly assigned to the compressed or extended conditions. The interventions differed in total number of contact hours over the year-long intervention period, with the major difference being the number of support group sessions held. The same information provided in the educational sessions of the extended intervention was compressed into fewer sessions, thus providing more information during each group meeting.Results: The interventions were not statistically different in reducing HbA(1c); however, both were effective. A "dosage effect" of attendance was detected with the largest HbA(1c) reductions achieved by those who attended more of the extended intervention. For individuals who attended >/=50% of the intervention, baseline to 12-month HbA(1c) change was -0.6 percentage points for the compressed group and -1.7 percentage points for the extended group.Conclusions: Both culturally competent diabetes self-management education interventions were effective in promoting improved metabolic control and diabetes knowledge. A dosage effect was evident; attending more sessions resulted in greater improvements in metabolic control. [ABSTRACT FROM AUTHOR]- Published
- 2005
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12. Measuring health beliefs in Spanish-speaking Mexican Americans with type 2 diabetes: adapting an existing instrument.
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Brown SA, Becker HA, Garcia AA, Barton SA, and Hanis CL
- Published
- 2002
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13. Culturally competent diabetes self-management education for Mexican Americans: the Starr County border health initiative.
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Brown SA, Garcia AA, Kouzekanani K, Hanis CL, Brown, Sharon A, Garcia, Alexandra A, Kouzekanani, Kamiar, and Hanis, Craig L
- Abstract
Objective: To determine the effects of a culturally competent diabetes self-management intervention in Mexican Americans with type 2 diabetes.Research Design and Methods: A prospective, randomized, repeated measures study was conducted on the Texas-Mexico border in Starr County. A total of 256 randomly selected individuals with type 2 diabetes between 35 and 70 years of age, diagnosed with type 2 diabetes after 35 years of age, and accompanied by a family member or friend were included. The intervention consisted of 52 contact hours over 12 months and was provided by bilingual Mexican American nurses, dietitians, and community workers. The intervention involved 3 months of weekly instructional sessions on nutrition, self-monitoring of blood glucose, exercise, and other self-care topics and 6 months of biweekly support group sessions to promote behavior changes. The approach was culturally competent in terms of language, diet, social emphasis, family participation, and incorporation of cultural health beliefs. Outcomes included indicators of metabolic control (HbA(1c) and fasting blood glucose), diabetes knowledge, and diabetes-related health beliefs.Results: Experimental groups showed significantly lower levels of HbA(1c) and fasting blood glucose at 6 and 12 months and higher diabetes knowledge scores. At 6 months, the mean HbA(1c) of the experimental subjects was 1.4% below the mean of the control group; however, the mean level of the experimental subjects was still high (>10%).Conclusions: This study confirms the effectiveness of culturally competent diabetes self-management education on improving health outcomes of Mexican Americans, particularly for those individuals with HbA(1c) levels >10%. [ABSTRACT FROM AUTHOR]- Published
- 2002
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14. Evaluation of culturally appropriate intervention to increase physical activity.
- Author
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Poston WSC II, Haddock CK, Olvera NE, Suminski RR, Reeves RS, Dunn JK, Hanis CL, and Foreyt JP
- Abstract
OBJECTIVE: To evaluate a culturally appropriate intervention to increase activity in overweight Mexican American women. METHODS: Participants were randomly assigned to a physical activity program or wait-list control. RESULTS: Treated participants were not more active than controls at 6 or 12 months. In addition, we found no significant differences in the proportion of individuals who met an objective criterion for physical activity from baseline to 6 months in the treatment or control groups. CONCLUSION: The intervention did not increase physical activity in this population. Differences in baseline activity and contamination of the control group may partially account for the outcome. [ABSTRACT FROM AUTHOR]
- Published
- 2001
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15. The Starr County Diabetes Education Study: development of the Spanish-language diabetes knowledge questionnaire.
- Author
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Garcia AA, Villagomez ET, Brown SA, Kouzekanani K, Hanis CL, Garcia, A A, Villagomez, E T, Brown, S A, Kouzekanani, K, and Hanis, C L
- Abstract
Objective: This study reports the psychometric properties of the 24-item version of the Diabetes Knowledge Questionnaire (DKQ).Research Design and Methods: The original 60-item DKQ was administered to 502 adult Mexican-Americans with type 2 diabetes who are part of the Starr County Diabetes Education Study. The sample was composed of 252 participants and 250 support partners. The subjects were randomly assigned to the educational and social support intervention (n = 250) or to the wait-listed control group (n = 252). A shortened 24-item version of the DKQ was derived from the original instrument after data collection was completed. Reliability was assessed by means of Cronbach's coefficient alpha. To determine validity, differentiation between the experimental and control groups was conducted at baseline and after the educational portion of the intervention.Results: The 24-item version of the DKQ (DKQ-24) attained a reliability coefficient of 0.78, indicating internal consistency, and showed sensitivity to the intervention, suggesting construct validation.Conclusions: The DKQ-24 is a reliable and valid measure of diabetes-related knowledge that is relatively easy to administer to either English or Spanish speakers. [ABSTRACT FROM AUTHOR]- Published
- 2001
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16. Change in level of physical activity and risk of all-cause mortality or reinfarction: The Corpus Christi Heart Project.
- Author
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Steffen-Batey L, Nichaman MZ, Goff DC Jr., Frankowski RF, Hanis CL, Ramsey DJ, Labarthe DR, Steffen-Batey, L, Nichaman, M Z, Goff, D C Jr, Frankowski, R F, Hanis, C L, Ramsey, D J, and Labarthe, D R
- Published
- 2000
17. Gender and treatment differences in knowledge, health beliefs, and metabolic control in Mexican Americans with type 2 diabetes.
- Author
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Brown SA, Harrist RB, Villagomez ET, Segura M, Barton SA, and Hanis CL
- Abstract
PURPOSE: The purpose of this project was to describe metabolic control, knowledge, and health beliefs of Mexican Americans with type 2 diabetes. METHODS: The study site was Starr County, Texas, a border community located on the Rio Grande River and bordering northern Mexico. Of the total sample of 360 persons, 252 agreed to participate in this intervention study and were randomized either to the treatment group or the control group that waited 1 year to begin the intervention. RESULTS: The majority of individual were Spanish-speaking females with a mean age of 54 years and a mean diabetes duration of 8 years. For those treated with diet only, males exhibited higher fasting blood glucose levels than females. Gender effects were seen for cholesterol level, with females exhibiting higher level than males. Males expressed stronger perceptions of control and social support for diet. Bivariate relationships were found between acculturation and diabetes knowledge. The health belief subscales of control and impact on job together explained 16% of the variance in HbA1c values. CONCLUSIONS: Males and females held differing beliefs about ability to control their diabetes and degree of social support for diet. The impact of gender differences on ability to integrate diabetes self-care and on effectiveness of diabetes programs has not been determined but should be considered in future research. [ABSTRACT FROM AUTHOR]
- Published
- 2000
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18. Culturally competent diabetes education for Mexican Americans: the Starr County Study.
- Author
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Brown SA and Hanis CL
- Abstract
PURPOSE: Few culturally competent health programs have been designed for Mexican Americans, a group that bears a disproportionate burden of Type 2 diabetes. In Starr County, a Texas-Mexico border community, investigators designed and tested a culturally competent intervention aimed at improving the health of this target population. The purpose of this article is to describe the development process of this diabetes education and support group intervention. METHODS: The development stages were (1) community assessment, (2) intervention design, (3) selection or development of outcomes, (4) pilot testing, and (5) a randomized clinical investigation. RESULTS: Focus group participants identified knowledge deficits regarding diabetes and self-management strategies, and suggested characteristics of an effective intervention for Mexican Americans. Outcome measures included metabolic control indicators, a newly developed knowledge instrument, and an existing health belief instrument. Preliminary analyses indicated that the intervention was successful in significantly improving metabolic control in the target population. CONCLUSIONS: Developing successful diabetes interventions for minority groups requires a number of stages, careful planning, assessment of cultural characteristics of the target population, and a systematic approach to implementation. [ABSTRACT FROM AUTHOR]
- Published
- 1999
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19. Evaluation of the factor structure and psychometric characteristic of the General Well-Being Schedule (GWB) with Mexican American women.
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Poston WSC II, Olvera NE, Yanez C, Haddock CK, Dunn JK, Hanis CL, and Foreyt JP
- Abstract
The General Well-Being Schedule (GWB) is a brief, reliable, and valid measure of subjective well-being that is widely used in research as an indicator of psychological health and dysfunction. The GWB is hypothesized to have six subscales or dimensions (anxiety, depression, positive well-being, self-control, vitality, and general health), but previous research has not yielded a consistent factor structure. Little attention has been paid to the reliability and validity of the GWB with Mexican-Americans, the fastest growing minority group in the U.S. The purpose of this study was to evaluate the reliability and validity of the GWB schedule with Mexican-American women involved in a community-based weight-loss study. Factor analysis indicated a four-factor solution. The GWB and the resulting factors demonstrated acceptable reliability and discriminability. [ABSTRACT FROM AUTHOR]
- Published
- 1998
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20. Symptom-related self-care of Mexican Americans with type 2 diabetes: preliminary findings of the Starr County Diabetes Education Study.
- Author
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Brown SA, Upchurch SL, Garcia AA, Barton SA, and Hanis CL
- Abstract
Starr County, Texas, a Texas-Mexico border community, was the site of a study involving culturally-appropriate education and group support for Mexican Americans with type 2 diabetes. Data were collected from 63 subjects on frequency of diabetes-related symptoms during the previous month and on self-care symptom treatments. On average, subjects were 57-year-old females, diagnosed with diabetes for 10 years, and exhibiting HbA1c levels of 12.5%. Almost 50% experienced excessive urination, excessive thirst, shakiness/nervousness, and numbness and/or tingling in their extremities. More than 50% of those who experienced symptoms did not view them as serious. Only one subject checked blood sugar levels when symptoms occurred. Significantly higher mean glycosylated hemoglobin levels were found for individuals who experienced dizziness and/or chest pain compared with those who did not. A variety of self-care treatments were employed, including over-the-counter medications and home remedies. [ABSTRACT FROM AUTHOR]
- Published
- 1998
21. A community-based, culturally sensitive education and group-support intervention for Mexican Americans with NIDDM: a pilot study of efficacy.
- Author
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Brown SA and Hanis CL
- Abstract
The purpose of this study was to determine the feasibility of providing a diabetes patient education and group-support intervention that was directed by a Mexican-American clinical nurse specialist (CNS), dietitian, and community worker; consistent with national standards; and designed for the Mexican-American culture. In a rural Texas-Mexico border community, subjects with diabetes were randomly selected to participate in the intervention, and a family member of each subject participated as a support person. The intervention involved 8 weeks of educational sessions with instruction on nutrition, blood glucose self-monitoring, exercise, and other diabetes self-management topics, and provided group support. Group discussion was facilitated using a series of Spanish-language videotapes that had been developed and previously tested in the target Mexican-American community. Results suggested statistically significant improvements in diabetes knowledge, fasting blood sugar levels, and glycosylated hemoglobin levels. The study documented the feasibility and potential benefits of the intervention. [ABSTRACT FROM AUTHOR]
- Published
- 1995
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22. Simultaneous expression of COX-2 and mPGES-1 in mouse gastrointestinal hamartomas.
- Author
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Garcia AA, Brown SA, Winchell M, Hanis CL, Takeda, H, Miyoshi, H, Tamai, Y, Oshima, M, and Taketo, M M
- Abstract
Cyclo-oxygenase (COX)-2 is induced in various types of cancer tissues. Here, we demonstrate stromal expression of both COX-2 and microsomal prostaglandin E(2) synthase (mPGES)-1 in gastrointestinal hamartomas developed in Lkb1(+/-), Smad4(+/-) and Cdx2(+/-)mice. These results suggest that PGE(2) produced by COX-2 and mPGES-1 plays an important role in hamartoma development regardless of the mutated genes causing hamartomas. [ABSTRACT FROM AUTHOR]
- Published
- 2003
23. Accelerated Longitudinal Glycemic Changes in Relation to Urinary Toxic/Essential Metals and Metal Mixtures Among Mexican Americans Living in Starr County, Texas.
- Author
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Weiss MC, Sun J, Jackson BP, Turyk ME, Wang L, Brown EL, Aguilar D, Brown SA, Hanis CL, Argos M, and Sargis RM
- Subjects
- Humans, Female, Male, Texas epidemiology, Middle Aged, Adult, Diabetes Mellitus, Type 2 urine, Diabetes Mellitus, Type 2 ethnology, Diabetes Mellitus, Type 2 epidemiology, Metals urine, Arsenic urine, Glycated Hemoglobin metabolism, Glycated Hemoglobin analysis, Longitudinal Studies, Mexican Americans statistics & numerical data, Blood Glucose metabolism
- Abstract
Objective: Metal and metalloid exposures (hereafter "metals") are associated with adverse health outcomes, including type 2 diabetes; however, previous studies were largely cross-sectional or underpowered. Furthermore, underserved racial and ethnic groups are underrepresented in environmental health research despite having higher rates of type 2 diabetes and a greater risk of metal exposures. Consequently, we evaluated continuous glycemic traits in relation to baseline urinary toxic metal, essential metal, and metal mixtures in a cohort of Mexican American adults., Research Design and Methods: A total of 510 participants were selected based upon self-reported diabetes status and followed over 3 years. Urinary metals were assessed at baseline. Linear mixed-effects models were used to estimate per-month changes in hemoglobin A1c, fasting plasma glucose, and postload glucose in relation to urinary metal levels. Multiple statistical approaches were used to assess the associations between glycemic traits and metal mixtures., Results: After adjustment, higher urinary levels of arsenic, selenium, copper, molybdenum, nickel, and tin were associated with faster increases in measures of glycemia. The toxic metal mixture composed of arsenic, lead, cadmium, nickel, and tin was associated with faster increases in postload glucose. Using postload glucose criteria, highest versus lowest arsenic was predicted to accelerate conversion of normoglycemia to prediabetes and diabetes by 23 and 65 months, respectively., Conclusions: In this underrepresented, high-risk Mexican American population, exposure to toxic metals and alterations in essential metal homeostasis were associated with faster increases in glycemia over time that may accelerate type 2 diabetes development., (© 2024 by the American Diabetes Association.)
- Published
- 2024
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24. Predictors of Toxic Metal/Metalloid Exposures Among Mexican Americans in Starr County, Texas.
- Author
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Weiss MC, Sun J, Jackson BP, Turyk ME, Wang L, Brown EL, Aguilar D, Hanis CL, Argos M, and Sargis RM
- Abstract
Background: Arsenic, cadmium, and lead are toxic elements that widely contaminate our environment. These toxicants are associated with acute and chronic health problems, and evidence suggests that minority communities, including Hispanic/Latino Americans, are disproportionately exposed. Few studies have assessed culturally specific predictors of exposure to understand the potential drivers of racial/ethnic exposure disparities., Objective: We sought to evaluate acculturation measures as predictors of metal/metalloid (hereafter "metal") concentrations among Mexican American adults to illuminate potential exposure sources that may be targeted for interventions., Methods: As part of a longitudinal cohort, 510 adults, aged 35 to 69 years, underwent baseline interview, physical examination, and urine sample collection. Self-reported acculturation was assessed across various domains using the Short Acculturation Scale for Hispanics (SASH). Multivariable linear regression was used to assess associations between acculturation and urinary concentrations of arsenic, cadmium, and lead. Ordinal logistic regression was utilized to assess associations between acculturation and a metal mixture score. Lastly, best subset selection was used to build a prediction model for each toxic metal with a combination of the acculturation predictors., Results: After adjustment, immigration factors were positively associated with arsenic and lead concentrations. For lead alone, English language and American media and food preferences were associated with lower levels. Immigration and parental heritage from Mexico were positively associated with the metal mixture, while preferences for English language, media, and food were negatively associated., Conclusion: Acculturation-related predictors of exposure provide information about potential sources of toxic metals, including international travel, foods, and consumer products. The findings in this research study provide information to empower future efforts to identify and address specific acculturation-associated toxicant exposures in order to promote health equity through clinical guidance, patient education, and public policy., (© 2024. W. Montague Cobb-NMA Health Institute.)
- Published
- 2024
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25. Transitioning From an In-Person Intervention to Augmented Text Messaging During COVID-19 in Mexican Americans With Prediabetes: The Starr County Diabetes Prevention Randomized Clinical Trial.
- Author
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Brown SA, Winter MA, Becker HA, García AA, Velasquez MM, Tanaka H, Perkison WB, Brown EL, Aguilar D, and Hanis CL
- Subjects
- Adult, Female, Humans, Male, COVID-19, Glycated Hemoglobin, Mexican Americans, Diabetes Mellitus prevention & control, Prediabetic State therapy, Text Messaging
- Abstract
Purpose: The purpose of the study was to explore the feasibility of using commonly available technology, such as text messaging, for diabetes prevention in rural Mexican American communities during COVID-19., Methods: Participants were selected from a diabetes prevention study funded by the National Institutes of Health that, prior to COVID-19, involved in-person group intervention sessions. Participants were predominantly female adults born in Mexico and Spanish-speaking. A subsample (n = 140) was divided into 3 cohorts: (1) 50 who completed the initial in-person intervention prior to the COVID-19 research pause, (2) 60 who needed additional support sessions to complete the intervention and thus received 10 text messages with links to relevant online diabetes prevention videos (TM+), and (3) 30 who received enhanced usual care involving health guidance offered during data collection (control). Repeated measures analysis of covariance was used to evaluate cohort differences at 24 months post baseline., Results: No significant cohort differences were found for depression, eating self-efficacy, alcohol intake, fat avoidance, or sedentary behaviors. Differences in A1C showed both in-person and TM+ cohorts having lower mean A1C levels (5.5%) than the control cohort (5.7%). The TM+ cohort had lower body mass index than other cohorts and a lower diabetes conversion rate (22.2%) compared to the control cohort (28%). Participants indicated preferences for in-person/TM+ combination interventions. The strongest positive feedback was for the TM+ intervention cooking demonstration videos., Conclusions: Augmented text messaging combined with in-person sessions had similar outcomes to the all in-person strategy and thus has the potential for expanding the reach of diabetes prevention to many Mexican American communities., Competing Interests: Conflicts of InterestThe authors declare that there are no conflicts of interest.
- Published
- 2024
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26. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.
- Author
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Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, Mandla R, Huerta-Chagoya A, Melloni GEM, Kanoni S, Rayner NW, Bocher O, Arruda AL, Sonehara K, Namba S, Lee SSK, Preuss MH, Petty LE, Schroeder P, Vanderwerff B, Kals M, Bragg F, Lin K, Guo X, Zhang W, Yao J, Kim YJ, Graff M, Takeuchi F, Nano J, Lamri A, Nakatochi M, Moon S, Scott RA, Cook JP, Lee JJ, Pan I, Taliun D, Parra EJ, Chai JF, Bielak LF, Tabara Y, Hai Y, Thorleifsson G, Grarup N, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Kwak SH, Long J, Sun M, Tong L, Chen WM, Nongmaithem SS, Noordam R, Lim VJY, Tam CHT, Joo YY, Chen CH, Raffield LM, Prins BP, Nicolas A, Yanek LR, Chen G, Brody JA, Kabagambe E, An P, Xiang AH, Choi HS, Cade BE, Tan J, Broadaway KA, Williamson A, Kamali Z, Cui J, Thangam M, Adair LS, Adeyemo A, Aguilar-Salinas CA, Ahluwalia TS, Anand SS, Bertoni A, Bork-Jensen J, Brandslund I, Buchanan TA, Burant CF, Butterworth AS, Canouil M, Chan JCN, Chang LC, Chee ML, Chen J, Chen SH, Chen YT, Chen Z, Chuang LM, Cushman M, Danesh J, Das SK, de Silva HJ, Dedoussis G, Dimitrov L, Doumatey AP, Du S, Duan Q, Eckardt KU, Emery LS, Evans DS, Evans MK, Fischer K, Floyd JS, Ford I, Franco OH, Frayling TM, Freedman BI, Genter P, Gerstein HC, Giedraitis V, González-Villalpando C, González-Villalpando ME, Gordon-Larsen P, Gross M, Guare LA, Hackinger S, Hakaste L, Han S, Hattersley AT, Herder C, Horikoshi M, Howard AG, Hsueh W, Huang M, Huang W, Hung YJ, Hwang MY, Hwu CM, Ichihara S, Ikram MA, Ingelsson M, Islam MT, Isono M, Jang HM, Jasmine F, Jiang G, Jonas JB, Jørgensen T, Kamanu FK, Kandeel FR, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton JM, Kho AN, Khor CC, Kibriya MG, Kim DH, Kronenberg F, Kuusisto J, Läll K, Lange LA, Lee KM, Lee MS, Lee NR, Leong A, Li L, Li Y, Li-Gao R, Ligthart S, Lindgren CM, Linneberg A, Liu CT, Liu J, Locke AE, Louie T, Luan J, Luk AO, Luo X, Lv J, Lynch JA, Lyssenko V, Maeda S, Mamakou V, Mansuri SR, Matsuda K, Meitinger T, Melander O, Metspalu A, Mo H, Morris AD, Moura FA, Nadler JL, Nalls MA, Nayak U, Ntalla I, Okada Y, Orozco L, Patel SR, Patil S, Pei P, Pereira MA, Peters A, Pirie FJ, Polikowsky HG, Porneala B, Prasad G, Rasmussen-Torvik LJ, Reiner AP, Roden M, Rohde R, Roll K, Sabanayagam C, Sandow K, Sankareswaran A, Sattar N, Schönherr S, Shahriar M, Shen B, Shi J, Shin DM, Shojima N, Smith JA, So WY, Stančáková A, Steinthorsdottir V, Stilp AM, Strauch K, Taylor KD, Thorand B, Thorsteinsdottir U, Tomlinson B, Tran TC, Tsai FJ, Tuomilehto J, Tusie-Luna T, Udler MS, Valladares-Salgado A, van Dam RM, van Klinken JB, Varma R, Wacher-Rodarte N, Wheeler E, Wickremasinghe AR, van Dijk KW, Witte DR, Yajnik CS, Yamamoto K, Yamamoto K, Yoon K, Yu C, Yuan JM, Yusuf S, Zawistowski M, Zhang L, Zheng W, Raffel LJ, Igase M, Ipp E, Redline S, Cho YS, Lind L, Province MA, Fornage M, Hanis CL, Ingelsson E, Zonderman AB, Psaty BM, Wang YX, Rotimi CN, Becker DM, Matsuda F, Liu Y, Yokota M, Kardia SLR, Peyser PA, Pankow JS, Engert JC, Bonnefond A, Froguel P, Wilson JG, Sheu WHH, Wu JY, Hayes MG, Ma RCW, Wong TY, Mook-Kanamori DO, Tuomi T, Chandak GR, Collins FS, Bharadwaj D, Paré G, Sale MM, Ahsan H, Motala AA, Shu XO, Park KS, Jukema JW, Cruz M, Chen YI, Rich SS, McKean-Cowdin R, Grallert H, Cheng CY, Ghanbari M, Tai ES, Dupuis J, Kato N, Laakso M, Köttgen A, Koh WP, Bowden DW, Palmer CNA, Kooner JS, Kooperberg C, Liu S, North KE, Saleheen D, Hansen T, Pedersen O, Wareham NJ, Lee J, Kim BJ, Millwood IY, Walters RG, Stefansson K, Ahlqvist E, Goodarzi MO, Mohlke KL, Langenberg C, Haiman CA, Loos RJF, Florez JC, Rader DJ, Ritchie MD, Zöllner S, Mägi R, Marston NA, Ruff CT, van Heel DA, Finer S, Denny JC, Yamauchi T, Kadowaki T, Chambers JC, Ng MCY, Sim X, Below JE, Tsao PS, Chang KM, McCarthy MI, Meigs JB, Mahajan A, Spracklen CN, Mercader JM, Boehnke M, Rotter JI, Vujkovic M, Voight BF, Morris AP, and Zeggini E
- Subjects
- Humans, Adipocytes metabolism, Chromatin genetics, Chromatin metabolism, Coronary Artery Disease complications, Coronary Artery Disease genetics, Diabetic Nephropathies complications, Diabetic Nephropathies genetics, Endothelial Cells metabolism, Enteroendocrine Cells, Epigenomics, Islets of Langerhans metabolism, Multifactorial Inheritance genetics, Peripheral Arterial Disease complications, Peripheral Arterial Disease genetics, Single-Cell Analysis, Diabetes Mellitus, Type 2 classification, Diabetes Mellitus, Type 2 complications, Diabetes Mellitus, Type 2 genetics, Diabetes Mellitus, Type 2 pathology, Diabetes Mellitus, Type 2 physiopathology, Disease Progression, Genetic Predisposition to Disease genetics, Genome-Wide Association Study
- Abstract
Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes
1,2 and molecular mechanisms that are often specific to cell type3,4 . Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8 ) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care., (© 2024. The Author(s).)- Published
- 2024
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27. C-Reactive Protein Levels Correlate with Measures of Dysglycemia and Gut Microbiome Profiles.
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Brown EL, Essigmann HT, Hoffman KL, Petrosino J, Jun G, Brown SA, Aguilar D, and Hanis CL
- Subjects
- Humans, C-Reactive Protein, Inflammation, Gastrointestinal Microbiome, Diabetes Mellitus, Type 2, Microbiota
- Abstract
C-reactive protein (CRP) is a commonly used marker of low-grade inflammation as well as a marker of acute infection. CRP levels are elevated in those with diabetes and increased CRP concentrations are a risk factor for developing type 2 diabetes. Gut microbiome effects on metabolism and immune responses can impact chronic inflammation, including affecting CRP levels, that in turn can lead to the development and maintenance of dysglycemia. Using a high-sensitivity C-reactive protein (hsCRP) assay capable of detecting subtle changes in C-reactive protein, we show that higher hsCRP levels specifically correlate with worsening glycemia, reduced microbial richness and evenness, and with a reduction in the Firmicutes/Bacteroidota ratio. These data demonstrate a pivotal role for CRP not only in the context of worsening glycemia and changes to the gut microbiota, but also highlight CRP as a potential target for mitigating type 2 diabetes progression or as a therapeutic target that could be manipulated through the microbiome. Understanding these processes will provide insights into the etiology of type 2 diabetes in addition to opening doors leading to possible novel diagnostic strategies and therapeutics., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
- Published
- 2023
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28. Arsenic metabolism, diabetes prevalence, and insulin resistance among Mexican Americans: A mendelian randomization approach.
- Author
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Weiss MC, Shih YH, Bryan MS, Jackson BP, Aguilar D, Brown EL, Jun G, Hanis CL, Argos M, and Sargis RM
- Abstract
Background: Differences in arsenic metabolism capacity may influence risk for type 2 diabetes, but the mechanistic drivers are unclear. We evaluated the associations between arsenic metabolism with overall diabetes prevalence and with static and dynamic measures of insulin resistance among Mexican Americans living in Starr County, Texas., Methods: We utilized data from cross-sectional studies conducted in Starr County, Texas, from 2010-2014. A Mendelian randomization approach was utilized to evaluate the associations between arsenic metabolism and type 2 diabetes prevalence using the intronic variant in the arsenic methylating gene, rs9527, as the instrumental variable for arsenic metabolism. To further assess mechanisms for diabetes pathogenesis, proportions of the urinary arsenic metabolites were employed to assess the association between arsenic metabolism and insulin resistance among participants without diabetes. Urinary biomarkers of arsenic metabolites were modeled as individual proportions of the total. Arsenic metabolism was evaluated both with a static outcome of insulin resistance, homeostatic measure of assessment (HOMA-IR), and a dynamic measure of insulin sensitivity, Matsuda Index., Results: Among 475 Mexican American participants from Starr County, higher metabolism capacity for arsenic is associated with higher diabetes prevalence driven by worse insulin resistance. Presence of the minor T allele of rs9527 is independently associated with an increase in the proportion of monomethylated arsenic (MMA%) and is associated with an odds ratio of 0.50 (95% CI: 0.24, 0.90) for type 2 diabetes. This association was conserved after potential covariate adjustment. Furthermore, among participants without type 2 diabetes, the highest quartile of MMA% was associated with 22% (95% CI: -33.5%, -9.07%) lower HOMA-IR and 56% (95% CI: 28.3%, 91.3%) higher Matsuda Index for insulin sensitivity., Conclusions: Arsenic metabolism capacity, indicated by a lower proportion of monomethylated arsenic, is associated with increased diabetes prevalence driven by an insulin resistant phenotype among Mexican Americans living in Starr County, Texas.
- Published
- 2023
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29. The effects of gender and country of origin on acculturation, psychological factors, lifestyle factors, and diabetes-related physiological outcomes among Mexican Americans: The Starr County diabetes prevention initiative.
- Author
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Brown SA, Becker HA, García AA, Velasquez MM, Tanaka H, Winter MA, Perkison WB, Brown EL, Aguilar D, and Hanis CL
- Subjects
- Male, Humans, Female, Mexican Americans, Acculturation, Life Style, Insulin Resistance, Diabetes Mellitus
- Abstract
Objectives: Examine acculturation and psychological, lifestyle, and physiological factors based on gender and country of origin (U.S. vs. Mexico)., Methods: Baseline data from the Starr County diabetes prevention study ( N = 300) were analyzed - acculturation ( language ), psychological factors ( depression ), lifestyle factors ( sedentary behaviors ), and diabetes-related physiological outcomes ( insulin resistance ). MANOVA and linear regression were used to examine variable relationships based on gender and country of origin and identify predictors of depression and insulin resistance., Results: Participants were: predominantly female (73%); 51 years of age, on average; born in Mexico (71%); and Spanish-speaking. Individuals spent 11 of their waking hours (range = 0-18 h) in sedentary activities. Compared to females, more males spoke English and reported fewer hours in sedentary activities. Compared to participants born in Mexico, those born in the U.S. were more likely to: speak English; report depressive symptoms; and exhibit elevated BMI and insulin resistance rates. Two distinct models significantly predicted depression (R
2 = 14.5%) and insulin resistance (R2 = 26.8%), with acculturation-language entering into both models., Discussion: Significant gender and country-of-origin differences were found. Future research on diabetes prevention should examine other Hispanic subgroups and strategies for addressing individual differences, while employing cost-effective group interventions that incorporate these differences and reach more at-risk individuals.- Published
- 2023
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30. Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications.
- Author
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Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, Mandla R, Huerta-Chagoya A, Rayner NW, Bocher O, Arruda ALSV, Sonehara K, Namba S, Lee SSK, Preuss MH, Petty LE, Schroeder P, Vanderwerff B, Kals M, Bragg F, Lin K, Guo X, Zhang W, Yao J, Kim YJ, Graff M, Takeuchi F, Nano J, Lamri A, Nakatochi M, Moon S, Scott RA, Cook JP, Lee JJ, Pan I, Taliun D, Parra EJ, Chai JF, Bielak LF, Tabara Y, Hai Y, Thorleifsson G, Grarup N, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Kwak SH, Long J, Sun M, Tong L, Chen WM, Nongmaithem SS, Noordam R, Lim VJY, Tam CHT, Joo YY, Chen CH, Raffield LM, Prins BP, Nicolas A, Yanek LR, Chen G, Brody JA, Kabagambe E, An P, Xiang AH, Choi HS, Cade BE, Tan J, Broadaway KA, Williamson A, Kamali Z, Cui J, Adair LS, Adeyemo A, Aguilar-Salinas CA, Ahluwalia TS, Anand SS, Bertoni A, Bork-Jensen J, Brandslund I, Buchanan TA, Burant CF, Butterworth AS, Canouil M, Chan JCN, Chang LC, Chee ML, Chen J, Chen SH, Chen YT, Chen Z, Chuang LM, Cushman M, Danesh J, Das SK, de Silva HJ, Dedoussis G, Dimitrov L, Doumatey AP, Du S, Duan Q, Eckardt KU, Emery LS, Evans DS, Evans MK, Fischer K, Floyd JS, Ford I, Franco OH, Frayling TM, Freedman BI, Genter P, Gerstein HC, Giedraitis V, González-Villalpando C, González-Villalpando ME, Gordon-Larsen P, Gross M, Guare LA, Hackinger S, Han S, Hattersley AT, Herder C, Horikoshi M, Howard AG, Hsueh W, Huang M, Huang W, Hung YJ, Hwang MY, Hwu CM, Ichihara S, Ikram MA, Ingelsson M, Islam MT, Isono M, Jang HM, Jasmine F, Jiang G, Jonas JB, Jørgensen T, Kandeel FR, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton JM, Kho AN, Khor CC, Kibriya MG, Kim DH, Kronenberg F, Kuusisto J, Läll K, Lange LA, Lee KM, Lee MS, Lee NR, Leong A, Li L, Li Y, Li-Gao R, Lithgart S, Lindgren CM, Linneberg A, Liu CT, Liu J, Locke AE, Louie T, Luan J, Luk AO, Luo X, Lv J, Lynch JA, Lyssenko V, Maeda S, Mamakou V, Mansuri SR, Matsuda K, Meitinger T, Metspalu A, Mo H, Morris AD, Nadler JL, Nalls MA, Nayak U, Ntalla I, Okada Y, Orozco L, Patel SR, Patil S, Pei P, Pereira MA, Peters A, Pirie FJ, Polikowsky HG, Porneala B, Prasad G, Rasmussen-Torvik LJ, Reiner AP, Roden M, Rohde R, Roll K, Sabanayagam C, Sandow K, Sankareswaran A, Sattar N, Schönherr S, Shahriar M, Shen B, Shi J, Shin DM, Shojima N, Smith JA, So WY, Stančáková A, Steinthorsdottir V, Stilp AM, Strauch K, Taylor KD, Thorand B, Thorsteinsdottir U, Tomlinson B, Tran TC, Tsai FJ, Tuomilehto J, Tusie-Luna T, Udler MS, Valladares-Salgado A, van Dam RM, van Klinken JB, Varma R, Wacher-Rodarte N, Wheeler E, Wickremasinghe AR, van Dijk KW, Witte DR, Yajnik CS, Yamamoto K, Yamamoto K, Yoon K, Yu C, Yuan JM, Yusuf S, Zawistowski M, Zhang L, Zheng W, Raffel LJ, Igase M, Ipp E, Redline S, Cho YS, Lind L, Province MA, Fornage M, Hanis CL, Ingelsson E, Zonderman AB, Psaty BM, Wang YX, Rotimi CN, Becker DM, Matsuda F, Liu Y, Yokota M, Kardia SLR, Peyser PA, Pankow JS, Engert JC, Bonnefond A, Froguel P, Wilson JG, Sheu WHH, Wu JY, Hayes MG, Ma RCW, Wong TY, Mook-Kanamori DO, Tuomi T, Chandak GR, Collins FS, Bharadwaj D, Paré G, Sale MM, Ahsan H, Motala AA, Shu XO, Park KS, Jukema JW, Cruz M, Chen YI, Rich SS, McKean-Cowdin R, Grallert H, Cheng CY, Ghanbari M, Tai ES, Dupuis J, Kato N, Laakso M, Köttgen A, Koh WP, Bowden DW, Palmer CNA, Kooner JS, Kooperberg C, Liu S, North KE, Saleheen D, Hansen T, Pedersen O, Wareham NJ, Lee J, Kim BJ, Millwood IY, Walters RG, Stefansson K, Goodarzi MO, Mohlke KL, Langenberg C, Haiman CA, Loos RJF, Florez JC, Rader DJ, Ritchie MD, Zöllner S, Mägi R, Denny JC, Yamauchi T, Kadowaki T, Chambers JC, Ng MCY, Sim X, Below JE, Tsao PS, Chang KM, McCarthy MI, Meigs JB, Mahajan A, Spracklen CN, Mercader JM, Boehnke M, Rotter JI, Vujkovic M, Voight BF, Morris AP, and Zeggini E
- Abstract
Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. We identify 1,289 independent association signals at genome-wide significance ( P <5×10
-8 ) that map to 611 loci, of which 145 loci are previously unreported. We define eight non-overlapping clusters of T2D signals characterised by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial, and enteroendocrine cells. We build cluster-specific partitioned genetic risk scores (GRS) in an additional 137,559 individuals of diverse ancestry, including 10,159 T2D cases, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned GRS are more strongly associated with coronary artery disease and end-stage diabetic nephropathy than an overall T2D GRS across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings demonstrate the value of integrating multi-ancestry GWAS with single-cell epigenomics to disentangle the aetiological heterogeneity driving the development and progression of T2D, which may offer a route to optimise global access to genetically-informed diabetes care., Competing Interests: R.A.S. is now an employee of GlaxoSmithKline. G.T. is an employee of deCODE genetics/Amgen Inc. A.S.B. reports institutional grants from AstraZeneca, Bayer, Biogen, BioMarin, Bioverativ, Novartis, Regeneron and Sanofi. J.Danesh serves on scientific advisory boards for AstraZeneca, Novartis, and UK Biobank, and has received multiple grants from academic, charitable and industry sources outside of the submitted work. L.S.E. is now an employee of Bristol Myers Squibb. J.S.F. has consulted for Shionogi Inc. T.M.F. has consulted for Sanofi, Boehringer Ingelheim, and received funding from GlaxoSmithKline. H.C.G. holds the McMaster-Sanofi Population Health Institute Chair in Diabetes Research and Care; reports research grants from Eli Lilly, AstraZeneca, Merck, Novo Nordisk and Sanofi; honoraria for speaking from AstraZeneca, Boehringer Ingelheim, Eli Lilly, Novo Nordisk, DKSH, Zuellig, Roche, and Sanofi; and consulting fees from Abbott, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Merck, Novo Nordisk, Pfizer, Sanofi, Kowa and Hanmi.lth Institute Chair in Diabetes Research and Care; reports research grants from Eli Lilly, AstraZeneca, Merck, Novo Nordisk and Sanofi; honoraria for speaking from AstraZeneca, Boehringer Ingelheim, Eli Lilly, Novo Nordisk, and Sanofi; and consulting fees from Abbott, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Merck, Novo Nordisk, Janssen, Sanofi, and Kowa. M.Ingelsson is a paid consultant to BioArctic AB. R.L.-G. is a part-time consultant of Metabolon Inc. A.E.L. is now an employee of Regeneron Genetics Center LLC and holds shares in Regeneron Pharmaceuticals. M.A.N. currently serves on the scientific advisory board for Clover Therapeutics and is an advisor to Neuron23 Inc. S.R.P. has received grant funding from Bayer Pharmaceuticals, Philips Respironics and Respicardia. N.Sattar has consulted for or been on speakers bureau for Abbott, Amgen, Astrazeneca, Boehringer Ingelheim, Eli Lilly, Hanmi, Novartis, Novo Nordisk, Sanofi and Pfizer and has received grant funding from Astrazeneca, Boehringer Ingelheim, Novartis and Roche Diagnostics. V.S. is now an employee of deCODE genetics/Amgen Inc. A.M.S. receives funding from Seven Bridges Genomics to develop tools for the NHLBI BioData Catalyst consortium. U.T. is an employee of deCODE genetics/Amgen Inc. E.Ingelsson is now an employee of GlaxoSmithKline. B.M.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. R.C.W.M. reports research funding from AstraZeneca, Bayer, Novo Nordisk, Pfizer, Tricida Inc. and Sanofi, and has consulted for or received speakers fees from AstraZeneca, Bayer, Boehringer Ingelheim, all of which have been donated to the Chinese University of Hong Kong to support diabetes research. D.O.M.-K. is a part-time clinical research consultant for Metabolon Inc. S.Liu reports consulting payments and honoraria or promises of the same for scientific presentations or reviews at numerous venues, including but not limited to Barilla, by-Health Inc, Ausa Pharmed Co.LTD, Fred Hutchinson Cancer Center, Harvard University, University of Buffalo, Guangdong General Hospital and Academy of Medical Sciences, Consulting member for Novo Nordisk, Inc; member of the Data Safety and Monitoring Board for a trial of pulmonary hypertension in diabetes patients at Massachusetts General Hospital; receives royalties from UpToDate; receives an honorarium from the American Society for Nutrition for his duties as Associate Editor. K.Stefansson is an employee of deCODE genetics/Amgen Inc. M.I.M. has served on advisory panels for Pfizer, NovoNordisk and Zoe Global, has received honoraria from Merck, Pfizer, Novo Nordisk and Eli Lilly, and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda; and is now an employee of Genentech and a holder of Roche stock. J.B.M. is an Academic Associate for Quest Diagnostics R&D. A.Mahajan is an employee of Genentech, and a holder of Roche stock.- Published
- 2023
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31. Acculturation, Dietary Behaviors, and Macronutrient Intake Among Mexican Americans With Prediabetes: The Starr County Diabetes Prevention Initiative.
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Brown SA, Becker HA, García AA, Velasquez MM, Tanaka H, Winter MA, Perkison WB, Brown EL, Aguilar D, and Hanis CL
- Subjects
- Male, Humans, Female, Texas epidemiology, Acculturation, Eating, Diet, Mexican Americans, Prediabetic State
- Abstract
Purpose: The purpose of the study was to examine the influences of sex and acculturation on dietary behaviors, macronutrient intake, and dietary quality in participants enrolled in a diabetes prevention initiative in Starr County, Texas., Methods: Baseline data from the Starr County diabetes prevention study (N = 300) were analyzed-acculturation (country of origin, years in Starr County, language and food preferences), depressive symptoms (Patient Health Questionnaire-9), healthy eating self-efficacy (Weight Efficacy Lifestyle Questionnaire-Short Form), diet quality (USDA Healthy Eating Index), fat avoidance (Fat Avoidance Scale, Spanish version), and macronutrients. Descriptive statistics and univariate analysis of covariance were used to examine differences based on acculturation, controlling for sex., Results: Participants were predominantly female (73%) and, on average, 51 years of age. Language and food preferences favored Spanish language and Hispanic foods, respectively. The majority (71%) was born in Mexico but had resided in Starr County for 33 years, on average. Depressive symptoms were moderate, and eating self-efficacy scores suggested low confidence in making healthy food choices, particularly for saturated fats. Spanish language preference was associated with worse dietary habits. The mean dietary quality score was lower than the national average (54 vs 59 nationally); females had slightly higher dietary quality than males and a higher mean fat avoidance score, although differences were not clinically significant. Intakes of carbohydrate, saturated fats, and cholesterol were higher than recommended daily allowances., Conclusions: The overall preference for speaking Spanish and the influence of language on dietary intake should inform future dietary interventions. Accommodating cultural norms and food preferences remain major challenges to improving dietary quality among the diverse Hispanic ethnic groups.
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- 2023
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32. Relationships Between Urinary Metals and Diabetes Traits Among Mexican Americans in Starr County, Texas, USA.
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Weiss MC, Shih YH, Bryan MS, Jackson BP, Aguilar D, Hanis CL, Argos M, and Sargis RM
- Subjects
- Adult, Humans, Blood Glucose, Insulins blood, Mexican Americans, Texas, Arsenic urine, Copper urine, Diabetes Mellitus, Type 2 ethnology, Diabetes Mellitus, Type 2 urine, Insulin Resistance, Molybdenum urine
- Abstract
Hispanics/Latinos have higher rates of type 2 diabetes (T2D), and the origins of these disparities are poorly understood. Environmental endocrine-disrupting chemicals (EDCs), including some metals and metalloids, are implicated as diabetes risk factors. Data indicate that Hispanics/Latinos may be disproportionately exposed to EDCs, yet they remain understudied with respect to environmental exposures and diabetes. The objective of this study is to determine how metal exposures contribute to T2D progression by evaluating the associations between 8 urinary metals and measures of glycemic status in 414 normoglycemic or prediabetic adults living in Starr County, Texas, a Hispanic/Latino community with high rates of diabetes and diabetes-associated mortality. We used multivariable linear regression to quantify the differences in homeostatic model assessments for pancreatic β-cell function, insulin resistance, and insulin sensitivity (HOMA-β, HOMA-IR, HOMA-S, respectively), plasma insulin, plasma glucose, and hemoglobin A1c (HbA1c) associated with increasing urinary metal concentrations. Quantile-based g-computation was utilized to assess mixture effects. After multivariable adjustment, urinary arsenic and molybdenum were associated with lower HOMA-β, HOMA-IR, and plasma insulin levels and higher HOMA-S. Additionally, higher urinary copper levels were associated with a reduced HOMA-β. Lastly, a higher concentration of the 8 metal mixtures was associated with lower HOMA-β, HOMA-IR, and plasma insulin levels as well as higher HOMA-S. Our data indicate that arsenic, molybdenum, copper, and this metal mixture are associated with alterations in measures of glucose homeostasis among non-diabetics in Starr County. This study is one of the first to comprehensively evaluate associations of urinary metals with glycemic measures in a high-risk Mexican American population., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
- Published
- 2023
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33. IgA-Biome Profiles Correlate with Clinical Parkinson's Disease Subtypes.
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Brown EL, Essigmann HT, Hoffman KL, Alexander AS, Newmark M, Jiang ZD, Suescun J, Schiess MC, Hanis CL, and DuPont HL
- Subjects
- Humans, Tremor etiology, Disease Progression, Immunoglobulin A, Parkinson Disease complications, Gastrointestinal Microbiome physiology
- Abstract
Background: Parkinson's disease is a heterogeneous neurodegenerative disorder with distinctive gut microbiome patterns suggesting that interventions targeting the gut microbiota may prevent, slow, or reverse disease progression and severity., Objective: Because secretory IgA (SIgA) plays a key role in shaping the gut microbiota, characterization of the IgA-Biome of individuals classified into either the akinetic rigid (AR) or tremor dominant (TD) Parkinson's disease clinical subtypes was used to further define taxa unique to these distinct clinical phenotypes., Methods: Flow cytometry was used to separate IgA-coated and -uncoated bacteria from stool samples obtained from AR and TD patients followed by amplification and sequencing of the V4 region of the 16 S rDNA gene on the MiSeq platform (Illumina)., Results: IgA-Biome analyses identified significant alpha and beta diversity differences between the Parkinson's disease phenotypes and the Firmicutes/Bacteroides ratio was significantly higher in those with TD compared to those with AR. In addition, discriminant taxa analyses identified a more pro-inflammatory bacterial profile in the IgA+ fraction of those with the AR clinical subclass compared to IgA-Biome analyses of those with the TD subclass and with the taxa identified in the unsorted control samples., Conclusion: IgA-Biome analyses underscores the importance of the host immune response in shaping the gut microbiome potentially affecting disease progression and presentation. In the present study, IgA-Biome analyses identified a unique proinflammatory microbial signature in the IgA+ fraction of those with AR that would have otherwise been undetected using conventional microbiome analysis approaches.
- Published
- 2023
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34. Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits.
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Chun S, Akle S, Teodosiadis A, Cade BE, Wang H, Sofer T, Evans DS, Stone KL, Gharib SA, Mukherjee S, Palmer LJ, Hillman D, Rotter JI, Hanis CL, Stamatoyannopoulos JA, Redline S, Cotsapas C, and Sunyaev SR
- Subjects
- Humans, Phenotype, Genetic Association Studies, Sleep, Genetic Pleiotropy, Polymorphism, Single Nucleotide, DNA Primase, Genome-Wide Association Study methods, Sleep Apnea, Obstructive
- Abstract
Genetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and burden of the required phenotyping. This reduces statistical power and limits discovery of multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology. Specifically, we combine a colocalization test with a locus-level test of pleiotropy. In simulations, we show that this approach is highly selective for identifying true pleiotropy driven by the same causative variant, thereby improves the chance to replicate the associations in underpowered validation cohorts and leads to higher interpretability. Here, as an exemplar, we use Obstructive Sleep Apnea (OSA), a common disorder diagnosed using overnight multi-channel physiological testing. We leverage pleiotropy with relevant cellular and cardio-metabolic phenotypes and gene expression traits to map new risk loci in an underpowered OSA GWAS. We identify several pleiotropic loci harboring suggestive associations to OSA and genome-wide significant associations to other traits, and show that their OSA association replicates in independent cohorts of diverse ancestries. By investigating pleiotropic loci, our strategy allows proposing new hypotheses about OSA pathobiology across many physiological layers. For example, we identify and replicate the pleiotropy across the plateletcrit, OSA and an eQTL of DNA primase subunit 1 (PRIM1) in immune cells. We find suggestive links between OSA, a measure of lung function (FEV1/FVC), and an eQTL of matrix metallopeptidase 15 (MMP15) in lung tissue. We also link a previously known genome-wide significant peak for OSA in the hexokinase 1 (HK1) locus to hematocrit and other red blood cell related traits. Thus, the analysis of pleiotropic associations has the potential to assemble diverse phenotypes into a chain of mechanistic hypotheses that provide insight into the pathogenesis of complex human diseases., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Chun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
- Published
- 2022
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35. Worsening Glycemia Increases the Odds of Intermittent but Not Persistent Staphylococcus aureus Nasal Carriage in Two Cohorts of Mexican American Adults.
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Essigmann HT, Hanis CL, DeSantis SM, Perkison WB, Aguilar DA, Jun G, Robinson DA, and Brown EL
- Subjects
- Adult, Blood Glucose, Carrier State epidemiology, Glycated Hemoglobin, Humans, Mexican Americans, Staphylococcal Infections diagnosis, Staphylococcal Infections epidemiology, Staphylococcus aureus genetics
- Abstract
Numerous host and environmental factors contribute to persistent and intermittent nasal Staphylococcus aureus carriage in humans. The effects of worsening glycemia on the odds of S. aureus intermittent and persistent nasal carriage was established in two cohorts from an adult Mexican American population living in Starr County, Texas. The anterior nares were sampled at two time points and the presence of S. aureus determined by laboratory culture and spa -typing. Persistent carriers were defined by the presence of S. aureus of the same spa -type at both time points, intermittent carriers were S. aureus-positive for 1 of 2 swabs, and noncarriers were negative for S. aureus at both time points. Diabetes status was obtained through personal interview and physical examination that included a blood draw for the determination of percent glycated hemoglobin A1c (%HbA1c), fasting plasma glucose, and other blood chemistry values. Using logistic regression and general estimating equations, the odds of persistent and intermittent nasal carriage compared to noncarriers across the glycemic spectrum was determined controlling for covariates. Increasing fasting plasma glucose and %HbA1c in the primary and replication cohort, respectively, were significantly associated with increasing odds of S. aureus intermittent, but not persistent nasal carriage. These data suggest that increasing dysglycemia is a risk factor for intermittent S. aureus nasal carriage potentially placing those with poorly controlled diabetes at an increased risk of acquiring an S. aureus infection. IMPORTANCE Factors affecting nasal S. aureus colonization have been studied primarily in the context of persistent carriage. In contrast, few studies have examined factors affecting intermittent nasal carriage with this pathogen. This study demonstrates that the odds of intermittent but not persistent nasal carriage of S. aureus significantly increases with worsening measures of dysglycemia. This is important in the context of poorly controlled diabetes since the risk of becoming colonized with one of the primary organisms associated with diabetic foot infections can lead to increased morbidity and mortality.
- Published
- 2022
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36. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation.
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Mahajan A, Spracklen CN, Zhang W, Ng MCY, Petty LE, Kitajima H, Yu GZ, Rüeger S, Speidel L, Kim YJ, Horikoshi M, Mercader JM, Taliun D, Moon S, Kwak SH, Robertson NR, Rayner NW, Loh M, Kim BJ, Chiou J, Miguel-Escalada I, Della Briotta Parolo P, Lin K, Bragg F, Preuss MH, Takeuchi F, Nano J, Guo X, Lamri A, Nakatochi M, Scott RA, Lee JJ, Huerta-Chagoya A, Graff M, Chai JF, Parra EJ, Yao J, Bielak LF, Tabara Y, Hai Y, Steinthorsdottir V, Cook JP, Kals M, Grarup N, Schmidt EM, Pan I, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Long J, Sun M, Tong L, Chen WM, Ahmad M, Noordam R, Lim VJY, Tam CHT, Joo YY, Chen CH, Raffield LM, Lecoeur C, Prins BP, Nicolas A, Yanek LR, Chen G, Jensen RA, Tajuddin S, Kabagambe EK, An P, Xiang AH, Choi HS, Cade BE, Tan J, Flanagan J, Abaitua F, Adair LS, Adeyemo A, Aguilar-Salinas CA, Akiyama M, Anand SS, Bertoni A, Bian Z, Bork-Jensen J, Brandslund I, Brody JA, Brummett CM, Buchanan TA, Canouil M, Chan JCN, Chang LC, Chee ML, Chen J, Chen SH, Chen YT, Chen Z, Chuang LM, Cushman M, Das SK, de Silva HJ, Dedoussis G, Dimitrov L, Doumatey AP, Du S, Duan Q, Eckardt KU, Emery LS, Evans DS, Evans MK, Fischer K, Floyd JS, Ford I, Fornage M, Franco OH, Frayling TM, Freedman BI, Fuchsberger C, Genter P, Gerstein HC, Giedraitis V, González-Villalpando C, González-Villalpando ME, Goodarzi MO, Gordon-Larsen P, Gorkin D, Gross M, Guo Y, Hackinger S, Han S, Hattersley AT, Herder C, Howard AG, Hsueh W, Huang M, Huang W, Hung YJ, Hwang MY, Hwu CM, Ichihara S, Ikram MA, Ingelsson M, Islam MT, Isono M, Jang HM, Jasmine F, Jiang G, Jonas JB, Jørgensen ME, Jørgensen T, Kamatani Y, Kandeel FR, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton JM, Kho AN, Khor CC, Kibriya MG, Kim DH, Kohara K, Kriebel J, Kronenberg F, Kuusisto J, Läll K, Lange LA, Lee MS, Lee NR, Leong A, Li L, Li Y, Li-Gao R, Ligthart S, Lindgren CM, Linneberg A, Liu CT, Liu J, Locke AE, Louie T, Luan J, Luk AO, Luo X, Lv J, Lyssenko V, Mamakou V, Mani KR, Meitinger T, Metspalu A, Morris AD, Nadkarni GN, Nadler JL, Nalls MA, Nayak U, Nongmaithem SS, Ntalla I, Okada Y, Orozco L, Patel SR, Pereira MA, Peters A, Pirie FJ, Porneala B, Prasad G, Preissl S, Rasmussen-Torvik LJ, Reiner AP, Roden M, Rohde R, Roll K, Sabanayagam C, Sander M, Sandow K, Sattar N, Schönherr S, Schurmann C, Shahriar M, Shi J, Shin DM, Shriner D, Smith JA, So WY, Stančáková A, Stilp AM, Strauch K, Suzuki K, Takahashi A, Taylor KD, Thorand B, Thorleifsson G, Thorsteinsdottir U, Tomlinson B, Torres JM, Tsai FJ, Tuomilehto J, Tusie-Luna T, Udler MS, Valladares-Salgado A, van Dam RM, van Klinken JB, Varma R, Vujkovic M, Wacher-Rodarte N, Wheeler E, Whitsel EA, Wickremasinghe AR, van Dijk KW, Witte DR, Yajnik CS, Yamamoto K, Yamauchi T, Yengo L, Yoon K, Yu C, Yuan JM, Yusuf S, Zhang L, Zheng W, Raffel LJ, Igase M, Ipp E, Redline S, Cho YS, Lind L, Province MA, Hanis CL, Peyser PA, Ingelsson E, Zonderman AB, Psaty BM, Wang YX, Rotimi CN, Becker DM, Matsuda F, Liu Y, Zeggini E, Yokota M, Rich SS, Kooperberg C, Pankow JS, Engert JC, Chen YI, Froguel P, Wilson JG, Sheu WHH, Kardia SLR, Wu JY, Hayes MG, Ma RCW, Wong TY, Groop L, Mook-Kanamori DO, Chandak GR, Collins FS, Bharadwaj D, Paré G, Sale MM, Ahsan H, Motala AA, Shu XO, Park KS, Jukema JW, Cruz M, McKean-Cowdin R, Grallert H, Cheng CY, Bottinger EP, Dehghan A, Tai ES, Dupuis J, Kato N, Laakso M, Köttgen A, Koh WP, Palmer CNA, Liu S, Abecasis G, Kooner JS, Loos RJF, North KE, Haiman CA, Florez JC, Saleheen D, Hansen T, Pedersen O, Mägi R, Langenberg C, Wareham NJ, Maeda S, Kadowaki T, Lee J, Millwood IY, Walters RG, Stefansson K, Myers SR, Ferrer J, Gaulton KJ, Meigs JB, Mohlke KL, Gloyn AL, Bowden DW, Below JE, Chambers JC, Sim X, Boehnke M, Rotter JI, McCarthy MI, and Morris AP
- Subjects
- Ethnicity, Genetic Predisposition to Disease, Humans, Polymorphism, Single Nucleotide genetics, Risk Factors, Diabetes Mellitus, Type 2 epidemiology, Genome-Wide Association Study
- Abstract
We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10
-9 ), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)- Published
- 2022
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37. Epidemiology of Antibiotic Use and Drivers of Cross-Border Procurement in a Mexican American Border Community.
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Essigmann HT, Aguilar DA, Perkison WB, Bay KG, Deaton MR, Brown SA, Hanis CL, and Brown EL
- Subjects
- Health Services Accessibility, Humans, Longitudinal Studies, Mexico, Texas, Anti-Bacterial Agents supply & distribution, Anti-Bacterial Agents therapeutic use, Mexican Americans
- Abstract
Background: The U.S.-Mexico Border is an area of opportunity for improved health care access; however, gaps remain as to how and where U.S. border residents, particularly those who are underinsured, obtain care. Antibiotics are one of the most common reported drivers of cross-border healthcare access and a medication of particular concern since indiscriminate or inappropriate use is associated with antimicrobial resistance. In addition, many studies assessing preferences for Mexican pharmaceuticals and healthcare in U.S. border residents were done prior to 2010 when many prescription medications, including antibiotics, were available over the counter in Mexico., Methods: Data used in this study were collected during the baseline examination of an ongoing longitudinal cohort study in Starr Country, Texas, one of 14 counties on the Texas-Mexico border. Participants self-reported the name, date of use, and the source country of each antibiotic used in the past 12 months. Logistic regression was used to determine social, cultural, and clinical features associated with cross-border procurement of antibiotics., Results: Over 10% of the study cohort reported using antibiotics in the past 30 days with over 60% of all rounds used in the past 12 months sourced from Mexico. A lack of health insurance and generation score, a measure of acculturation, were the strongest predictors of cross-border procurement of antibiotics., Conclusions: Factors previously associated with cross-border acquisition of antibiotics are still present despite changes in 2010 to prescription drug regulations in Mexico. These results may be used to inform future public health initiatives to provide culturally sensitive education about responsible antibiotic stewardship and to address barriers to U.S. healthcare and pharmaceutical access in medically underserved, impoverished U.S.-Mexico border communities., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Essigmann, Aguilar, Perkison, Bay, Deaton, Brown, Hanis and Brown.)
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- 2022
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38. Rare coding variants in 35 genes associate with circulating lipid levels-A multi-ancestry analysis of 170,000 exomes.
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Hindy G, Dornbos P, Chaffin MD, Liu DJ, Wang M, Selvaraj MS, Zhang D, Park J, Aguilar-Salinas CA, Antonacci-Fulton L, Ardissino D, Arnett DK, Aslibekyan S, Atzmon G, Ballantyne CM, Barajas-Olmos F, Barzilai N, Becker LC, Bielak LF, Bis JC, Blangero J, Boerwinkle E, Bonnycastle LL, Bottinger E, Bowden DW, Bown MJ, Brody JA, Broome JG, Burtt NP, Cade BE, Centeno-Cruz F, Chan E, Chang YC, Chen YI, Cheng CY, Choi WJ, Chowdhury R, Contreras-Cubas C, Córdova EJ, Correa A, Cupples LA, Curran JE, Danesh J, de Vries PS, DeFronzo RA, Doddapaneni H, Duggirala R, Dutcher SK, Ellinor PT, Emery LS, Florez JC, Fornage M, Freedman BI, Fuster V, Garay-Sevilla ME, García-Ortiz H, Germer S, Gibbs RA, Gieger C, Glaser B, Gonzalez C, Gonzalez-Villalpando ME, Graff M, Graham SE, Grarup N, Groop LC, Guo X, Gupta N, Han S, Hanis CL, Hansen T, He J, Heard-Costa NL, Hung YJ, Hwang MY, Irvin MR, Islas-Andrade S, Jarvik GP, Kang HM, Kardia SLR, Kelly T, Kenny EE, Khan AT, Kim BJ, Kim RW, Kim YJ, Koistinen HA, Kooperberg C, Kuusisto J, Kwak SH, Laakso M, Lange LA, Lee J, Lee J, Lee S, Lehman DM, Lemaitre RN, Linneberg A, Liu J, Loos RJF, Lubitz SA, Lyssenko V, Ma RCW, Martin LW, Martínez-Hernández A, Mathias RA, McGarvey ST, McPherson R, Meigs JB, Meitinger T, Melander O, Mendoza-Caamal E, Metcalf GA, Mi X, Mohlke KL, Montasser ME, Moon JY, Moreno-Macías H, Morrison AC, Muzny DM, Nelson SC, Nilsson PM, O'Connell JR, Orho-Melander M, Orozco L, Palmer CNA, Palmer ND, Park CJ, Park KS, Pedersen O, Peralta JM, Peyser PA, Post WS, Preuss M, Psaty BM, Qi Q, Rao DC, Redline S, Reiner AP, Revilla-Monsalve C, Rich SS, Samani N, Schunkert H, Schurmann C, Seo D, Seo JS, Sim X, Sladek R, Small KS, So WY, Stilp AM, Tai ES, Tam CHT, Taylor KD, Teo YY, Thameem F, Tomlinson B, Tsai MY, Tuomi T, Tuomilehto J, Tusié-Luna T, Udler MS, van Dam RM, Vasan RS, Viaud Martinez KA, Wang FF, Wang X, Watkins H, Weeks DE, Wilson JG, Witte DR, Wong TY, Yanek LR, Kathiresan S, Rader DJ, Rotter JI, Boehnke M, McCarthy MI, Willer CJ, Natarajan P, Flannick JA, Khera AV, and Peloso GM
- Subjects
- Alleles, Blood Glucose genetics, Case-Control Studies, Computational Biology methods, Databases, Genetic, Diabetes Mellitus, Type 2 genetics, Diabetes Mellitus, Type 2 metabolism, Genetic Predisposition to Disease, Genetics, Population, Humans, Lipid Metabolism genetics, Liver metabolism, Liver pathology, Molecular Sequence Annotation, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Exome, Genetic Variation, Genome-Wide Association Study methods, Lipids blood, Open Reading Frames
- Abstract
Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels., Competing Interests: Declaration of interests The authors declare no competing interests for the present work. P.N. reports investigator-initiated grants from Amgen, Apple, and Boston Scientific; is a scientific advisor to Apple, Blackstone Life Sciences, and Novartis; and has spousal employment at Vertex, all unrelated to the present work. A.V.K. has served as a scientific advisor to Sanofi, Medicines Company, Maze Pharmaceuticals, Navitor Pharmaceuticals, Verve Therapeutics, Amgen, and Color; received speaking fees from Illumina, MedGenome, Amgen, and the Novartis Institute for Biomedical Research; received sponsored research agreements from the Novartis Institute for Biomedical Research and IBM Research; and reports a patent related to a genetic risk predictor (20190017119). C.J.W.’s spouse is employed at Regeneron. L.E.S. is currently an employee of Celgene/Bristol Myers Squibb. Celgene/Bristol Myers Squibb had no role in the funding, design, conduct, and interpretation of this study. M.E.M. receives funding from Regeneron unrelated to this work. E.E.K. has received speaker honoraria from Illumina, Inc and Regeneron Pharmaceuticals. B.M.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. L.A.C. has consulted with the Dyslipidemia Foundation on lipid projects in the Framingham Heart Study. P.T.E. is supported by a grant from Bayer AG to the Broad Institute focused on the genetics and therapeutics of cardiovascular disease. P.T.E. has consulted for Bayer AG, Novartis, MyoKardia, and Quest Diagnostics. S.A.L. receives sponsored research support from Bristol Myers Squibb/Pfizer, Bayer AG, Boehringer Ingelheim, Fitbit, and IBM and has consulted for Bristol Myers Squibb/Pfizer, Bayer AG, and Blackstone Life Sciences. The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. M.I.M. has served on advisory panels for Pfizer, NovoNordisk, and Zoe Global and has received honoraria from Merck, Pfizer, Novo Nordisk, and Eli Lilly and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. As of June 2019, M.I.M. is an employee of Genentech and a holder of Roche stock. M.E.J. holds shares in Novo Nordisk A/S. H.M.K. is an employee of Regeneron Pharmaceuticals; he owns stock and stock options for Regeneron Pharmaceuticals. M.E.J. has received research grants form Astra Zeneca, Boehringer Ingelheim, Amgen, and Sanofi. S.K. is founder of Verve Therapeutics., (Copyright © 2021 American Society of Human Genetics. All rights reserved.)
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- 2022
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39. The impact of the Th17:Treg axis on the IgA-Biome across the glycemic spectrum.
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Essigmann HT, Hoffman KL, Petrosino JF, Jun G, Aguilar D, Hanis CL, DuPont HL, and Brown EL
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- Adult, Bacteria genetics, Bacteria isolation & purification, DNA, Bacterial genetics, DNA, Ribosomal genetics, Diabetes Mellitus, Type 2 microbiology, Epigenesis, Genetic, Female, Gastrointestinal Microbiome, Humans, Male, Mexican Americans, Middle Aged, Phylogeny, Bacteria classification, Diabetes Mellitus, Type 2 immunology, Immunoglobulin A, Secretory metabolism, RNA, Ribosomal, 16S genetics, Sequence Analysis, DNA methods, T-Lymphocytes, Regulatory immunology, Th17 Cells immunology
- Abstract
Secretory IgA (SIgA) is released into mucosal surfaces where its function extends beyond that of host defense to include the shaping of resident microbial communities by mediating exclusion/inclusion of respective microbes and regulating bacterial gene expression. In this capacity, SIgA acts as the fulcrum on which host immunity and the health of the microbiota are balanced. We recently completed an analysis of the gut and salivary IgA-Biomes (16S rDNA sequencing of SIgA-coated/uncoated bacteria) in Mexican-American adults that identified IgA-Biome differences across the glycemic spectrum. As Th17:Treg ratio imbalances are associated with gut microbiome dysbiosis and chronic inflammatory conditions such as type 2 diabetes, the present study extends our prior work by examining the impact of Th17:Treg ratios (pro-inflammatory:anti-inflammatory T-cell ratios) and the SIgA response (Th17:Treg-SIgA axis) in shaping microbial communities. Examining the impact of Th17:Treg ratios (determined by epigenetic qPCR lymphocyte subset quantification) on the IgA-Biome across diabetes phenotypes identified a proportional relationship between Th17:Treg ratios and alpha diversity in the stool IgA-Biome of those with dysglycemia, significant changes in community composition of the stool and salivary microbiomes across glycemic profiles, and genera preferentially abundant by T-cell inflammatory phenotype. This is the first study to associate epigenetically quantified Th17:Treg ratios with both the larger and SIgA-fractionated microbiome, assess these associations in the context of a chronic inflammatory disease, and offers a novel frame through which to evaluate mucosal microbiomes in the context of host responses and inflammation., Competing Interests: No authors have competing interests.
- Published
- 2021
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40. Whole-genome association analyses of sleep-disordered breathing phenotypes in the NHLBI TOPMed program.
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Cade BE, Lee J, Sofer T, Wang H, Zhang M, Chen H, Gharib SA, Gottlieb DJ, Guo X, Lane JM, Liang J, Lin X, Mei H, Patel SR, Purcell SM, Saxena R, Shah NA, Evans DS, Hanis CL, Hillman DR, Mukherjee S, Palmer LJ, Stone KL, Tranah GJ, Abecasis GR, Boerwinkle EA, Correa A, Cupples LA, Kaplan RC, Nickerson DA, North KE, Psaty BM, Rotter JI, Rich SS, Tracy RP, Vasan RS, Wilson JG, Zhu X, and Redline S
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- Alleles, Chromatin Immunoprecipitation Sequencing, Female, Gene Expression Regulation, Genotype, Humans, Male, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Precision Medicine methods, Research, Signal Transduction, Sleep Apnea Syndromes metabolism, United States, Genetic Association Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Sleep Apnea Syndromes diagnosis, Sleep Apnea Syndromes etiology, Whole Genome Sequencing
- Abstract
Background: Sleep-disordered breathing is a common disorder associated with significant morbidity. The genetic architecture of sleep-disordered breathing remains poorly understood. Through the NHLBI Trans-Omics for Precision Medicine (TOPMed) program, we performed the first whole-genome sequence analysis of sleep-disordered breathing., Methods: The study sample was comprised of 7988 individuals of diverse ancestry. Common-variant and pathway analyses included an additional 13,257 individuals. We examined five complementary traits describing different aspects of sleep-disordered breathing: the apnea-hypopnea index, average oxyhemoglobin desaturation per event, average and minimum oxyhemoglobin saturation across the sleep episode, and the percentage of sleep with oxyhemoglobin saturation < 90%. We adjusted for age, sex, BMI, study, and family structure using MMSKAT and EMMAX mixed linear model approaches. Additional bioinformatics analyses were performed with MetaXcan, GIGSEA, and ReMap., Results: We identified a multi-ethnic set-based rare-variant association (p = 3.48 × 10
-8 ) on chromosome X with ARMCX3. Additional rare-variant associations include ARMCX3-AS1, MRPS33, and C16orf90. Novel common-variant loci were identified in the NRG1 and SLC45A2 regions, and previously associated loci in the IL18RAP and ATP2B4 regions were associated with novel phenotypes. Transcription factor binding site enrichment identified associations with genes implicated with respiratory and craniofacial traits. Additional analyses identified significantly associated pathways., Conclusions: We have identified the first gene-based rare-variant associations with objectively measured sleep-disordered breathing traits. Our results increase the understanding of the genetic architecture of sleep-disordered breathing and highlight associations in genes that modulate lung development, inflammation, respiratory rhythmogenesis, and HIF1A-mediated hypoxic response., (© 2021. The Author(s).)- Published
- 2021
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41. Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes.
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Goodrich JK, Singer-Berk M, Son R, Sveden A, Wood J, England E, Cole JB, Weisburd B, Watts N, Caulkins L, Dornbos P, Koesterer R, Zappala Z, Zhang H, Maloney KA, Dahl A, Aguilar-Salinas CA, Atzmon G, Barajas-Olmos F, Barzilai N, Blangero J, Boerwinkle E, Bonnycastle LL, Bottinger E, Bowden DW, Centeno-Cruz F, Chambers JC, Chami N, Chan E, Chan J, Cheng CY, Cho YS, Contreras-Cubas C, Córdova E, Correa A, DeFronzo RA, Duggirala R, Dupuis J, Garay-Sevilla ME, García-Ortiz H, Gieger C, Glaser B, González-Villalpando C, Gonzalez ME, Grarup N, Groop L, Gross M, Haiman C, Han S, Hanis CL, Hansen T, Heard-Costa NL, Henderson BE, Hernandez JMM, Hwang MY, Islas-Andrade S, Jørgensen ME, Kang HM, Kim BJ, Kim YJ, Koistinen HA, Kooner JS, Kuusisto J, Kwak SH, Laakso M, Lange L, Lee JY, Lee J, Lehman DM, Linneberg A, Liu J, Loos RJF, Lyssenko V, Ma RCW, Martínez-Hernández A, Meigs JB, Meitinger T, Mendoza-Caamal E, Mohlke KL, Morris AD, Morrison AC, Ng MCY, Nilsson PM, O'Donnell CJ, Orozco L, Palmer CNA, Park KS, Post WS, Pedersen O, Preuss M, Psaty BM, Reiner AP, Revilla-Monsalve C, Rich SS, Rotter JI, Saleheen D, Schurmann C, Sim X, Sladek R, Small KS, So WY, Spector TD, Strauch K, Strom TM, Tai ES, Tam CHT, Teo YY, Thameem F, Tomlinson B, Tracy RP, Tuomi T, Tuomilehto J, Tusié-Luna T, van Dam RM, Vasan RS, Wilson JG, Witte DR, Wong TY, Burtt NP, Zaitlen N, McCarthy MI, Boehnke M, Pollin TI, Flannick J, Mercader JM, O'Donnell-Luria A, Baxter S, Florez JC, MacArthur DG, and Udler MS
- Subjects
- Adult, Biological Variation, Population, Biomarkers metabolism, Diabetes Mellitus, Type 2 metabolism, Dyslipidemias metabolism, Exome genetics, Genotype, Humans, Multifactorial Inheritance, Penetrance, Risk Assessment, Diabetes Mellitus, Type 2 genetics, Dyslipidemias genetics, Genetic Predisposition to Disease genetics
- Abstract
Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers average 60% or lower for most conditions. We assess epidemiologic and genetic factors contributing to risk prediction in monogenic variant carriers, demonstrating that inclusion of polygenic variation significantly improves biomarker estimation for two monogenic dyslipidemias.
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- 2021
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42. Impact of Diabetes on the Gut and Salivary IgA Microbiomes.
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Brown EL, Essigmann HT, Hoffman KL, Palm NW, Gunter SM, Sederstrom JM, Petrosino JF, Jun G, Aguilar D, Perkison WB, Hanis CL, and DuPont HL
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- Adult, Bacteria classification, Classification, Diabetes Mellitus, Type 2 immunology, Discriminant Analysis, Dysbiosis, Feces microbiology, Female, Humans, Immunoglobulin A, Secretory immunology, Male, Middle Aged, RNA, Ribosomal, 16S genetics, Saliva microbiology, Bacteria genetics, Diabetes Mellitus, Type 2 microbiology, Gastrointestinal Microbiome genetics, Immunoglobulin A, Secretory analysis
- Abstract
Mucosal surfaces like those present in the lung, gut, and mouth interface with distinct external environments. These mucosal gateways are not only portals of entry for potential pathogens but also homes to microbial communities that impact host health. Secretory immunoglobulin A (SIgA) is the single most abundant acquired immune component secreted onto mucosal surfaces and, via the process of immune exclusion, shapes the architecture of these microbiomes. Not all microorganisms at mucosal surfaces are targeted by SIgA; therefore, a better understanding of the SIgA-coated fraction may identify the microbial constituents that stimulate host immune responses in the context of health and disease. Chronic diseases like type 2 diabetes are associated with altered microbial communities (dysbiosis) that in turn affect immune-mediated homeostasis. 16S rRNA gene sequencing of SIgA-coated/uncoated bacteria (IgA-Biome) was conducted on stool and saliva samples of normoglycemic participants and individuals with prediabetes or diabetes ( n = 8/group). These analyses demonstrated shifts in relative abundance in the IgA-Biome profiles between normoglycemic, prediabetic, or diabetic samples distinct from that of the overall microbiome. Differences in IgA-Biome alpha diversity were apparent for both stool and saliva, while overarching bacterial community differences (beta diversity) were also observed in saliva. These data suggest that IgA-Biome analyses can be used to identify novel microbial signatures associated with diabetes and support the need for further studies exploring these communities. Ultimately, an understanding of the IgA-Biome may promote the development of novel strategies to restructure the microbiome as a means of preventing or treating diseases associated with dysbiosis at mucosal surfaces., (Copyright © 2020 American Society for Microbiology.)
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- 2020
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43. Erratum. Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control. Diabetes 2019;68:441-456.
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Pollack S, Igo RP Jr, Jensen RA, Christiansen M, Li X, Cheng CY, Ng MCY, Smith AV, Rossin EJ, Segrè AV, Davoudi S, Tan GS, Ida Chen YD, Kuo JZ, Dimitrov LM, Stanwyck LK, Meng W, Hosseini SM, Imamura M, Nousome D, Kim J, Hai Y, Jia Y, Ahn J, Leong A, Shah K, Park KH, Guo X, Ipp E, Taylor KD, Adler SG, Sedor JR, Freedman BI, Lee IT, Sheu WH, Kubo M, Takahashi A, Hadjadj S, Marre M, Tregouet DA, Mckean-Cowdin R, Varma R, McCarthy MI, Groop L, Ahlqvist E, Lyssenko V, Agardh E, Morris A, Doney ASF, Colhoun HM, Toppila I, Sandholm N, Groop PH, Maeda S, Hanis CL, Penman A, Chen CJ, Hancock H, Mitchell P, Craig JE, Chew EY, Paterson AD, Grassi MA, Palmer C, Bowden DW, Yaspan BL, Siscovick D, Cotch MF, Wang JJ, Burdon KP, Wong TY, Klein BEK, Klein R, Rotter JI, Iyengar SK, Price AL, and Sobrin L
- Published
- 2020
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44. Metabolomic profiles associated with subtypes of prediabetes among Mexican Americans in Starr County, Texas, USA.
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Jun G, Aguilar D, Evans C, Burant CF, and Hanis CL
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- Adult, Aged, Amino Acids, Branched-Chain blood, Amino Acids, Branched-Chain metabolism, Blood Glucose metabolism, Diabetes Mellitus, Type 2 blood, Diabetes Mellitus, Type 2 metabolism, Fasting blood, Glycated Hemoglobin metabolism, Humans, Mexican Americans, Middle Aged, Multivariate Analysis, Prediabetic State blood, Prediabetic State metabolism, Texas, United States, Young Adult, Metabolomics methods
- Abstract
Aims/hypothesis: To understand the complex metabolic changes that occur long before the diagnosis of type 2 diabetes, we investigated differences in metabolomic profiles in plasma between prediabetic and normoglycaemic individuals for subtypes of prediabetes defined by fasting glucose, 2 h glucose and HbA
1c measures., Methods: Untargeted metabolomics data were obtained from 155 plasma samples from 127 Mexican American individuals from Starr County, TX, USA. None had type 2 diabetes at the time of sample collection and 69 had prediabetes by at least one criterion. We tested statistical associations of amino acids and other metabolites with each subtype of prediabetes., Results: We identified distinctive differences in amino acid profiles between prediabetic and normoglycaemic individuals, with further differences in amino acid levels among subtypes of prediabetes. When testing all named metabolites, several fatty acids were also significantly associated with 2 h glucose levels. Multivariate discriminative analyses show that untargeted metabolomic data have considerable potential for identifying metabolic differences among subtypes of prediabetes., Conclusions/interpretation: People with each subtype of prediabetes have a distinctive metabolomic signature, beyond the well-known differences in branched-chain amino acids., Data Availability: Metabolomics data are available through the NCBI database of Genotypes and Phenotypes (dbGaP, accession number phs001166; www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001166.v1.p1).- Published
- 2020
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45. Sleep apnea and galectin-3: possible sex-specific relationship.
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Singh M, Hanis CL, Redline S, Ballantyne CM, Hamzeh I, and Aguilar D
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- Adult, Aged, Cardiovascular Diseases etiology, Cohort Studies, Correlation of Data, Female, Humans, Male, Middle Aged, Polysomnography, Risk Factors, Sex Factors, Sleep Apnea Syndromes complications, Biomarkers blood, Cardiovascular Diseases blood, Galectin 3 blood, Sleep Apnea Syndromes physiopathology
- Abstract
Purpose: Sleep apnea is associated with increased risk of cardiovascular disease. Elevated plasma galectin-3 levels, a biomarker associated with myocardial fibrosis, are also associated with adverse cardiovascular events, including heart failure. Our objective was to determine the relationship between severity of sleep apnea and plasma levels of galectin-3 and to determine whether this relationship was modified by sex., Methods: We performed a cross-sectional study of 471 Mexican Americans from Starr County, TX who underwent an overnight, in-home sleep evaluation, and plasma measurement of galectin-3. Severity of sleep apnea was based on apnea hypopnea index (AHI). Multivariable linear regression modeling was used to determine the association between categories of sleep apnea and galectin-3. We also tested for interactions by sex., Results: The mean age was 53 years, and 74% of the cohort was female. The prevalence of moderate to severe sleep apnea (AHI > 15 apnea-hypopnea events per hour) was 36.7%. Moderate to severe sleep apnea was associated with increased levels of galectin-3 in the entire population, but we identified a statistically significant interaction between galectin-3 levels and category of sleep apnea by sex (p for interaction = 0.02). Plasma galectin levels were significantly higher in women with moderate or severe sleep apnea than women with no/mild sleep apnea (multivariable adjusted p < 0.001), but not in men (p = 0.5)., Conclusions: Sleep apnea is associated elevated galectin-3 levels in women but not men. Our findings highlight a possible sex-specific relationship between sleep apnea and galectin-3, a biomarker of potential myocardial fibrosis that has been associated with increased cardiovascular risk.
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- 2019
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46. A Genome-Wide Association Study Identifies Blood Disorder-Related Variants Influencing Hemoglobin A 1c With Implications for Glycemic Status in U.S. Hispanics/Latinos.
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Moon JY, Louie TL, Jain D, Sofer T, Schurmann C, Below JE, Lai CQ, Aviles-Santa ML, Talavera GA, Smith CE, Petty LE, Bottinger EP, Chen YI, Taylor KD, Daviglus ML, Cai J, Wang T, Tucker KL, Ordovás JM, Hanis CL, Loos RJF, Schneiderman N, Rotter JI, Kaplan RC, and Qi Q
- Subjects
- Adult, Alleles, Blood Glucose metabolism, Diabetes Mellitus ethnology, Fasting blood, Female, Genome-Wide Association Study, Glucose Tolerance Test, Hematologic Diseases ethnology, Humans, Hyperglycemia epidemiology, Hyperglycemia ethnology, Hyperglycemia genetics, Male, Middle Aged, Phenotype, Prediabetic State ethnology, Prediabetic State genetics, Prevalence, United States epidemiology, Diabetes Mellitus genetics, Genetic Variation genetics, Glycated Hemoglobin genetics, Hematologic Diseases genetics, Hispanic or Latino genetics
- Abstract
Objective: We aimed to identify hemoglobin A
1c (HbA1c )-associated genetic variants and examine their implications for glycemic status evaluated by HbA1c in U.S. Hispanics/Latinos with diverse genetic ancestries., Research Design and Methods: We conducted a genome-wide association study (GWAS) of HbA1c in 9,636 U.S. Hispanics/Latinos without diabetes from the Hispanic Community Health Study/Study of Latinos, followed by a replication among 4,729 U.S. Hispanics/Latinos from three independent studies., Results: Our GWAS and replication analyses showed 10 previously known and novel loci associated with HbA1c at genome-wide significance levels ( P < 5.0 × 10-8 ). In particular, two African ancestry-specific variants, HBB- rs334 and G6PD -rs1050828, which are causal mutations for sickle cell disease and G6PD deficiency, respectively, had ∼10 times larger effect sizes on HbA1c levels (β = -0.31% [-3.4 mmol/mol]) and -0.35% [-3.8 mmol/mol] per minor allele, respectively) compared with other HbA1c -associated variants (0.03-0.04% [0.3-0.4 mmol/mol] per allele). A novel Amerindian ancestry-specific variant, HBM -rs145546625, was associated with HbA1c and hematologic traits but not with fasting glucose. The prevalence of hyperglycemia (prediabetes and diabetes) defined using fasting glucose or oral glucose tolerance test 2-h glucose was similar between carriers of HBB- rs334 or G6PD -rs1050828 HbA1c -lowering alleles and noncarriers, whereas the prevalence of hyperglycemia defined using HbA1c was significantly lower in carriers than in noncarriers (12.2% vs. 28.4%, P < 0.001). After recalibration of the HbA1c level taking HBB -rs334 and G6PD -rs1050828 into account, the prevalence of hyperglycemia in carriers was similar to noncarriers (31.3% vs. 28.4%, P = 0.28)., Conclusions: This study in U.S. Hispanics/Latinos found several ancestry-specific alleles associated with HbA1c through erythrocyte-related rather than glycemic-related pathways. The potential influences of these nonglycemic-related variants need to be considered when the HbA1c test is performed., (© 2019 by the American Diabetes Association.)- Published
- 2019
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47. GWAS of QRS duration identifies new loci specific to Hispanic/Latino populations.
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Swenson BR, Louie T, Lin HJ, Méndez-Giráldez R, Below JE, Laurie CC, Kerr KF, Highland H, Thornton TA, Ryckman KK, Kooperberg C, Soliman EZ, Seyerle AA, Guo X, Taylor KD, Yao J, Heckbert SR, Darbar D, Petty LE, McKnight B, Cheng S, Bello NA, Whitsel EA, Hanis CL, Nalls MA, Evans DS, Rotter JI, Sofer T, Avery CL, and Sotoodehnia N
- Subjects
- Humans, Middle Aged, Molecular Sequence Annotation, Phenotype, Polymorphism, Single Nucleotide genetics, Electrocardiography, Genetic Loci, Genome-Wide Association Study, Hispanic or Latino genetics
- Abstract
Background: The electrocardiographically quantified QRS duration measures ventricular depolarization and conduction. QRS prolongation has been associated with poor heart failure prognosis and cardiovascular mortality, including sudden death. While previous genome-wide association studies (GWAS) have identified 32 QRS SNPs across 26 loci among European, African, and Asian-descent populations, the genetics of QRS among Hispanics/Latinos has not been previously explored., Methods: We performed a GWAS of QRS duration among Hispanic/Latino ancestry populations (n = 15,124) from four studies using 1000 Genomes imputed genotype data (adjusted for age, sex, global ancestry, clinical and study-specific covariates). Study-specific results were combined using fixed-effects, inverse variance-weighted meta-analysis., Results: We identified six loci associated with QRS (P<5x10-8), including two novel loci: MYOCD, a nuclear protein expressed in the heart, and SYT1, an integral membrane protein. The top SNP in the MYOCD locus, intronic SNP rs16946539, was found in Hispanics/Latinos with a minor allele frequency (MAF) of 0.04, but is monomorphic in European and African descent populations. The most significant QRS duration association was with intronic SNP rs3922344 (P = 1.19x10-24) in SCN5A/SCN10A. Three other previously identified loci, CDKN1A, VTI1A, and HAND1, also exceeded the GWAS significance threshold among Hispanics/Latinos. A total of 27 of 32 previously identified QRS duration SNPs were shown to generalize in Hispanics/Latinos., Conclusions: Our QRS duration GWAS, the first in Hispanic/Latino populations, identified two new loci, underscoring the utility of extending large scale genomic studies to currently under-examined populations., Competing Interests: Dr. Nalls consults for Illumina Inc., the Michael J. Fox Foundation and University of California Healthcare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
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- 2019
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48. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls.
- Author
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Flannick J, Mercader JM, Fuchsberger C, Udler MS, Mahajan A, Wessel J, Teslovich TM, Caulkins L, Koesterer R, Barajas-Olmos F, Blackwell TW, Boerwinkle E, Brody JA, Centeno-Cruz F, Chen L, Chen S, Contreras-Cubas C, Córdova E, Correa A, Cortes M, DeFronzo RA, Dolan L, Drews KL, Elliott A, Floyd JS, Gabriel S, Garay-Sevilla ME, García-Ortiz H, Gross M, Han S, Heard-Costa NL, Jackson AU, Jørgensen ME, Kang HM, Kelsey M, Kim BJ, Koistinen HA, Kuusisto J, Leader JB, Linneberg A, Liu CT, Liu J, Lyssenko V, Manning AK, Marcketta A, Malacara-Hernandez JM, Martínez-Hernández A, Matsuo K, Mayer-Davis E, Mendoza-Caamal E, Mohlke KL, Morrison AC, Ndungu A, Ng MCY, O'Dushlaine C, Payne AJ, Pihoker C, Post WS, Preuss M, Psaty BM, Vasan RS, Rayner NW, Reiner AP, Revilla-Monsalve C, Robertson NR, Santoro N, Schurmann C, So WY, Soberón X, Stringham HM, Strom TM, Tam CHT, Thameem F, Tomlinson B, Torres JM, Tracy RP, van Dam RM, Vujkovic M, Wang S, Welch RP, Witte DR, Wong TY, Atzmon G, Barzilai N, Blangero J, Bonnycastle LL, Bowden DW, Chambers JC, Chan E, Cheng CY, Cho YS, Collins FS, de Vries PS, Duggirala R, Glaser B, Gonzalez C, Gonzalez ME, Groop L, Kooner JS, Kwak SH, Laakso M, Lehman DM, Nilsson P, Spector TD, Tai ES, Tuomi T, Tuomilehto J, Wilson JG, Aguilar-Salinas CA, Bottinger E, Burke B, Carey DJ, Chan JCN, Dupuis J, Frossard P, Heckbert SR, Hwang MY, Kim YJ, Kirchner HL, Lee JY, Lee J, Loos RJF, Ma RCW, Morris AD, O'Donnell CJ, Palmer CNA, Pankow J, Park KS, Rasheed A, Saleheen D, Sim X, Small KS, Teo YY, Haiman C, Hanis CL, Henderson BE, Orozco L, Tusié-Luna T, Dewey FE, Baras A, Gieger C, Meitinger T, Strauch K, Lange L, Grarup N, Hansen T, Pedersen O, Zeitler P, Dabelea D, Abecasis G, Bell GI, Cox NJ, Seielstad M, Sladek R, Meigs JB, Rich SS, Rotter JI, Altshuler D, Burtt NP, Scott LJ, Morris AP, Florez JC, McCarthy MI, and Boehnke M
- Subjects
- Animals, Case-Control Studies, Decision Support Techniques, Female, Gene Frequency, Genome-Wide Association Study, Humans, Male, Mice, Mice, Knockout, Diabetes Mellitus, Type 2 genetics, Exome genetics, Exome Sequencing
- Abstract
Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 × 10
-3 ) and candidate genes from knockout mice (P = 5.2 × 10-3 ). Within our study, the strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000-185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts.- Published
- 2019
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49. Associations of variants In the hexokinase 1 and interleukin 18 receptor regions with oxyhemoglobin saturation during sleep.
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Cade BE, Chen H, Stilp AM, Louie T, Ancoli-Israel S, Arens R, Barfield R, Below JE, Cai J, Conomos MP, Evans DS, Frazier-Wood AC, Gharib SA, Gleason KJ, Gottlieb DJ, Hillman DR, Johnson WC, Lederer DJ, Lee J, Loredo JS, Mei H, Mukherjee S, Patel SR, Post WS, Purcell SM, Ramos AR, Reid KJ, Rice K, Shah NA, Sofer T, Taylor KD, Thornton TA, Wang H, Yaffe K, Zee PC, Hanis CL, Palmer LJ, Rotter JI, Stone KL, Tranah GJ, Wilson JG, Sunyaev SR, Laurie CC, Zhu X, Saxena R, Lin X, and Redline S
- Subjects
- Adolescent, Adult, Aged, Aged, 80 and over, Cell Adhesion Molecules, Neuronal genetics, Computational Biology, Extracellular Matrix Proteins genetics, Female, Gene Regulatory Networks, Genetic Variation, Genome-Wide Association Study, Humans, Hypoxia blood, Hypoxia genetics, Male, Middle Aged, NLR Family, Pyrin Domain-Containing 3 Protein genetics, Nerve Tissue Proteins genetics, Oxygen blood, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Reelin Protein, Serine Endopeptidases genetics, Sleep Apnea Syndromes blood, Sleep Apnea Syndromes genetics, Young Adult, Hexokinase genetics, Interleukin-18 Receptor alpha Subunit genetics, Oxyhemoglobins metabolism, Sleep genetics
- Abstract
Sleep disordered breathing (SDB)-related overnight hypoxemia is associated with cardiometabolic disease and other comorbidities. Understanding the genetic bases for variations in nocturnal hypoxemia may help understand mechanisms influencing oxygenation and SDB-related mortality. We conducted genome-wide association tests across 10 cohorts and 4 populations to identify genetic variants associated with three correlated measures of overnight oxyhemoglobin saturation: average and minimum oxyhemoglobin saturation during sleep and the percent of sleep with oxyhemoglobin saturation under 90%. The discovery sample consisted of 8,326 individuals. Variants with p < 1 × 10(-6) were analyzed in a replication group of 14,410 individuals. We identified 3 significantly associated regions, including 2 regions in multi-ethnic analyses (2q12, 10q22). SNPs in the 2q12 region associated with minimum SpO2 (rs78136548 p = 2.70 × 10(-10)). SNPs at 10q22 were associated with all three traits including average SpO2 (rs72805692 p = 4.58 × 10(-8)). SNPs in both regions were associated in over 20,000 individuals and are supported by prior associations or functional evidence. Four additional significant regions were detected in secondary sex-stratified and combined discovery and replication analyses, including a region overlapping Reelin, a known marker of respiratory complex neurons.These are the first genome-wide significant findings reported for oxyhemoglobin saturation during sleep, a phenotype of high clinical interest. Our replicated associations with HK1 and IL18R1 suggest that variants in inflammatory pathways, such as the biologically-plausible NLRP3 inflammasome, may contribute to nocturnal hypoxemia., Competing Interests: The authors have declared that no competing interests exist.
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- 2019
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50. Functionally oriented analysis of cardiometabolic traits in a trans-ethnic sample.
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Petty LE, Highland HM, Gamazon ER, Hu H, Karhade M, Chen HH, de Vries PS, Grove ML, Aguilar D, Bell GI, Huff CD, Hanis CL, Doddapaneni H, Munzy DM, Gibbs RA, Ma J, Parra EJ, Cruz M, Valladares-Salgado A, Arking DE, Barbeira A, Im HK, Morrison AC, Boerwinkle E, and Below JE
- Subjects
- Adult, Aged, Blood Pressure, Body Mass Index, Chromosome Mapping methods, Ethnicity genetics, Female, Genetic Association Studies methods, Genome-Wide Association Study methods, Humans, Male, Middle Aged, Multifactorial Inheritance genetics, Phenotype, Polymorphism, Single Nucleotide genetics, Transcriptome genetics, White People genetics, Forecasting methods, Metabolome genetics, Metabolome physiology
- Abstract
Interpretation of genetic association results is difficult because signals often lack biological context. To generate hypotheses of the functional genetic etiology of complex cardiometabolic traits, we estimated the genetically determined component of gene expression from common variants using PrediXcan (1) and determined genes with differential predicted expression by trait. PrediXcan imputes tissue-specific expression levels from genetic variation using variant-level effect on gene expression in transcriptome data. To explore the value of imputed genetically regulated gene expression (GReX) models across different ancestral populations, we evaluated imputed expression levels for predictive accuracy genome-wide in RNA sequence data in samples drawn from European-ancestry and African-ancestry populations and identified substantial predictive power using European-derived models in a non-European target population. We then tested the association of GReX on 15 cardiometabolic traits including blood lipid levels, body mass index, height, blood pressure, fasting glucose and insulin, RR interval, fibrinogen level, factor VII level and white blood cell and platelet counts in 15 755 individuals across three ancestry groups, resulting in 20 novel gene-phenotype associations reaching experiment-wide significance across ancestries. In addition, we identified 18 significant novel gene-phenotype associations in our ancestry-specific analyses. Top associations were assessed for additional support via query of S-PrediXcan (2) results derived from publicly available genome-wide association studies summary data. Collectively, these findings illustrate the utility of transcriptome-based imputation models for discovery of cardiometabolic effect genes in a diverse dataset., (© The Author(s) 2019. Published by Oxford University Press.)
- Published
- 2019
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