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2. Genetic insights into biological mechanisms governing human ovarian ageing.

Author

Ruth K.S., Day F.R., Hussain J., Martinez-Marchal A., Aiken C.E., Azad A., Thompson D.J., Knoblochova L., Abe H., Tarry-Adkins J.L., Gonzalez J.M., Fontanillas P., Claringbould A., Bakker O.B., Sulem P., Walters R.G., Terao C., Turon S., Horikoshi M., Lin K., Onland-Moret N.C., Sankar A., Hertz E.P.T., Timshel P.N., Shukla V., Borup R., Olsen K.W., Aguilera P., Ferrer-Roda M., Huang Y., Stankovic S., Timmers P.R.H.J., Ahearn T.U., Alizadeh B.Z., Naderi E., Andrulis I.L., Arnold A.M., Aronson K.J., Augustinsson A., Bandinelli S., Barbieri C.M., Beaumont R.N., Becher H., Beckmann M.W., Benonisdottir S., Bergmann S., Bochud M., Boerwinkle E., Bojesen S.E., Bolla M.K., Boomsma D.I., Bowker N., Brody J.A., Broer L., Buring J.E., Campbell A., Campbell H., Castelao J.E., Catamo E., Chanock S.J., Chenevix-Trench G., Ciullo M., Corre T., Couch F.J., Cox A., Crisponi L., Cross S.S., Cucca F., Czene K., Smith G.D., de Geus E.J.C.N., de Mutsert R., De Vivo I., Demerath E.W., Dennis J., Dunning A.M., Dwek M., Eriksson M., Esko T., Fasching P.A., Faul J.D., Ferrucci L., Franceschini N., Frayling T.M., Gago-Dominguez M., Mezzavilla M., Garcia-Closas M., Gieger C., Giles G.G., Grallert H., Gudbjartsson D.F., Gudnason V., Guenel P., Haiman C.A., Hakansson N., Hall P., Hayward C., He C., He W., Heiss G., Hoffding M.K., Hopper J.L., Hottenga J.J., Hu F., Hunter D., Ikram M.A., Jackson R.D., Joaquim M.D.R., John E.M., Joshi P.K., Karasik D., Kardia S.L.R., Kartsonaki C., Karlsson R., Kitahara C.M., Kolcic I., Kooperberg C., Kraft P., Kurian A.W., Kutalik Z., La Bianca M., LaChance G., Langenberg C., Launer L.J., Laven J.S.E., Lawlor D.A., Le Marchand L., Li J., Lindblom A., Lindstrom S., Lindstrom T., Linet M., Liu Y.M., Liu S., Luan J., Magi R., Magnusson P.K.E., Mangino M., Mannermaa A., Marco B., Marten J., Martin N.G., Mbarek H., McKnight B., Medland S.E., Meisinger C., Meitinger T., Menni C., Metspalu A., Milani L., Milne R.L., Montgomery G.W., Mook-Kanamori D.O., Mulas A., Mulligan A.M., Nalls M.A., Newman A., Noordam R., Nutile T., Nyholt D.R., Olshan A.F., Olsson H., Painter J.N., Patel A.V., Pedersen N.L., Perjakova N., Peters A., Peters U., Pharoah P.D.P., Polasek O., Porcu E., Psaty B.M., Rahman I., Rennert G., Rennert H.S., Ridker P.M., Ring S.M., Robino A., Rose L.M., Rosendaal F.R., Rossouw J., Rudan I., Rueedi R., Ruggiero D., Sala C.F., Saloustros E., Sandler D.P., Sanna S., Sawyer E.J., Sarnowski C., Schlessinger D., Schmidt M.K., Schoemaker M.J., Schraut K.E., Scott C., Shekari S., Shrikhande A., Smith A.V., Smith B.H., Smith J.A., Sorice R., Southey M.C., Spector T.D., Spinelli J.J., Stampfer M., Stockl D., van Meurs J.B.J., Strauch K., Styrkarsdottir U., Swerdlow A.J., Tanaka T., Teras L.R., Teumer A., Thorsteinsdottir U., Timpson N.J., Toniolo D., Traglia M., Troester M.A., Truong T., Tyrrell J., Uitterlinden A.G., Ulivi S., Vachon C.M., Vitart V., Volker U., Vollenweider P., Volzke H., Wang Q., Wareham N.J., Weinberg C.R., Weir D.R., Wilcox A.N., van Dijk K.W., Willemsen G., Wilson J.F., Wolffenbuttel B.H.R., Wolk A., Wood A.R., Zhao W., Zygmunt M., Chen Z., Li L., Franke L., Burgess S., Deelen P., Pers T.H., Grondahl M.L., Andersen C.Y., Pujol A., Lopez-Contreras A.J., Daniel J.A., Stefansson K., Chang-Claude J., van der Schouw Y.T., Lunetta K.L., Chasman D.I., Easton D.F., Visser J.A., Ozanne S.E., Namekawa S.H., Solc P., Murabito J.M., Ong K.K., Hoffmann E.R., Murray A., Roig I., Perry J.R.B., Ruth K.S., Day F.R., Hussain J., Martinez-Marchal A., Aiken C.E., Azad A., Thompson D.J., Knoblochova L., Abe H., Tarry-Adkins J.L., Gonzalez J.M., Fontanillas P., Claringbould A., Bakker O.B., Sulem P., Walters R.G., Terao C., Turon S., Horikoshi M., Lin K., Onland-Moret N.C., Sankar A., Hertz E.P.T., Timshel P.N., Shukla V., Borup R., Olsen K.W., Aguilera P., Ferrer-Roda M., Huang Y., Stankovic S., Timmers P.R.H.J., Ahearn T.U., Alizadeh B.Z., Naderi E., Andrulis I.L., Arnold A.M., Aronson K.J., Augustinsson A., Bandinelli S., Barbieri C.M., Beaumont R.N., Becher H., Beckmann M.W., Benonisdottir S., Bergmann S., Bochud M., Boerwinkle E., Bojesen S.E., Bolla M.K., Boomsma D.I., Bowker N., Brody J.A., Broer L., Buring J.E., Campbell A., Campbell H., Castelao J.E., Catamo E., Chanock S.J., Chenevix-Trench G., Ciullo M., Corre T., Couch F.J., Cox A., Crisponi L., Cross S.S., Cucca F., Czene K., Smith G.D., de Geus E.J.C.N., de Mutsert R., De Vivo I., Demerath E.W., Dennis J., Dunning A.M., Dwek M., Eriksson M., Esko T., Fasching P.A., Faul J.D., Ferrucci L., Franceschini N., Frayling T.M., Gago-Dominguez M., Mezzavilla M., Garcia-Closas M., Gieger C., Giles G.G., Grallert H., Gudbjartsson D.F., Gudnason V., Guenel P., Haiman C.A., Hakansson N., Hall P., Hayward C., He C., He W., Heiss G., Hoffding M.K., Hopper J.L., Hottenga J.J., Hu F., Hunter D., Ikram M.A., Jackson R.D., Joaquim M.D.R., John E.M., Joshi P.K., Karasik D., Kardia S.L.R., Kartsonaki C., Karlsson R., Kitahara C.M., Kolcic I., Kooperberg C., Kraft P., Kurian A.W., Kutalik Z., La Bianca M., LaChance G., Langenberg C., Launer L.J., Laven J.S.E., Lawlor D.A., Le Marchand L., Li J., Lindblom A., Lindstrom S., Lindstrom T., Linet M., Liu Y.M., Liu S., Luan J., Magi R., Magnusson P.K.E., Mangino M., Mannermaa A., Marco B., Marten J., Martin N.G., Mbarek H., McKnight B., Medland S.E., Meisinger C., Meitinger T., Menni C., Metspalu A., Milani L., Milne R.L., Montgomery G.W., Mook-Kanamori D.O., Mulas A., Mulligan A.M., Nalls M.A., Newman A., Noordam R., Nutile T., Nyholt D.R., Olshan A.F., Olsson H., Painter J.N., Patel A.V., Pedersen N.L., Perjakova N., Peters A., Peters U., Pharoah P.D.P., Polasek O., Porcu E., Psaty B.M., Rahman I., Rennert G., Rennert H.S., Ridker P.M., Ring S.M., Robino A., Rose L.M., Rosendaal F.R., Rossouw J., Rudan I., Rueedi R., Ruggiero D., Sala C.F., Saloustros E., Sandler D.P., Sanna S., Sawyer E.J., Sarnowski C., Schlessinger D., Schmidt M.K., Schoemaker M.J., Schraut K.E., Scott C., Shekari S., Shrikhande A., Smith A.V., Smith B.H., Smith J.A., Sorice R., Southey M.C., Spector T.D., Spinelli J.J., Stampfer M., Stockl D., van Meurs J.B.J., Strauch K., Styrkarsdottir U., Swerdlow A.J., Tanaka T., Teras L.R., Teumer A., Thorsteinsdottir U., Timpson N.J., Toniolo D., Traglia M., Troester M.A., Truong T., Tyrrell J., Uitterlinden A.G., Ulivi S., Vachon C.M., Vitart V., Volker U., Vollenweider P., Volzke H., Wang Q., Wareham N.J., Weinberg C.R., Weir D.R., Wilcox A.N., van Dijk K.W., Willemsen G., Wilson J.F., Wolffenbuttel B.H.R., Wolk A., Wood A.R., Zhao W., Zygmunt M., Chen Z., Li L., Franke L., Burgess S., Deelen P., Pers T.H., Grondahl M.L., Andersen C.Y., Pujol A., Lopez-Contreras A.J., Daniel J.A., Stefansson K., Chang-Claude J., van der Schouw Y.T., Lunetta K.L., Chasman D.I., Easton D.F., Visser J.A., Ozanne S.E., Namekawa S.H., Solc P., Murabito J.M., Ong K.K., Hoffmann E.R., Murray A., Roig I., and Perry J.R.B.

Abstract

Reproductive longevity is essential for fertility and influences healthy ageing in women1,2, but insights into its underlying biological mechanisms and treatments to preserve it are limited. Here we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in about 200,000 women of European ancestry. These common alleles were associated with clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations3. The identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR-associated genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increases fertility and extends reproductive life in mice. Causal inference analyses using the identified genetic variants indicate that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases the risk of hormone-sensitive cancers. These findings provide insight into the mechanisms that govern ovarian ageing, when they act, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease.Copyright © 2021, The Author(s), under exclusive licence to Springer Nature Limited.

Published
2021

3. Genetic insights into biological mechanisms governing human ovarian ageing

Author

Ruth, KS, Day, FR, Hussain, J, Martinez-Marchal, A, Aiken, CE, Azad, A, Thompson, DJ, Knoblochova, L, Abe, H, Tarry-Adkins, JL, Gonzalez, JM, Fontanillas, P, Claringbould, A, Bakker, OB, Sulem, P, Walters, RG, Terao, C, Turon, S, Horikoshi, M, Lin, K, Onland-Moret, NC, Sankar, A, Hertz, EPT, Timshel, PN, Shukla, V, Borup, R, Olsen, KW, Aguilera, P, Ferrer-Roda, M, Huang, Y, Stankovic, S, Timmers, PRHJ, Ahearn, TU, Alizadeh, BZ, Naderi, E, Andrulis, IL, Arnold, AM, Aronson, KJ, Augustinsson, A, Bandinelli, S, Barbieri, CM, Beaumont, RN, Becher, H, Beckmann, MW, Benonisdottir, S, Bergmann, S, Bochud, M, Boerwinkle, E, Bojesen, SE, Bolla, MK, Boomsma, DI, Bowker, N, Brody, JA, Broer, L, Buring, JE, Campbell, A, Campbell, H, Castelao, JE, Catamo, E, Chanock, SJ, Chenevix-Trench, G, Ciullo, M, Corre, T, Couch, FJ, Cox, A, Crisponi, L, Cross, SS, Cucca, F, Czene, K, Smith, GD, de Geus, EJCN, de Mutsert, R, De Vivo, I, Demerath, EW, Dennis, J, Dunning, AM, Dwek, M, Eriksson, M, Esko, T, Fasching, PA, Faul, JD, Ferrucci, L, Franceschini, N, Frayling, TM, Gago-Dominguez, M, Mezzavilla, M, Garcia-Closas, M, Gieger, C, Giles, GG, Grallert, H, Gudbjartsson, DF, Gudnason, V, Guenel, P, Haiman, CA, Hakansson, N, Hall, P, Hayward, C, He, C, He, W, Heiss, G, Hoffding, MK, Hopper, JL, Hottenga, JJ, Hu, F, Hunter, D, Ikram, MA, Jackson, RD, Joaquim, MDR, John, EM, Joshi, PK, Karasik, D, Kardia, SLR, Kartsonaki, C, Karlsson, R, Kitahara, CM, Kolcic, I, Kooperberg, C, Kraft, P, Kurian, AW, Kutalik, Z, La Bianca, M, LaChance, G, Langenberg, C, Launer, LJ, Laven, JSE, Lawlor, DA, Le Marchand, L, Li, J, Lindblom, A, Lindstrom, S, Lindstrom, T, Linet, M, Liu, Y, Liu, S, Luan, J, Magi, R, Magnusson, PKE, Mangino, M, Mannermaa, A, Marco, B, Marten, J, Martin, NG, Mbarek, H, McKnight, B, Medland, SE, Meisinger, C, Meitinger, T, Menni, C, Metspalu, A, Milani, L, Milne, RL, Montgomery, GW, Mook-Kanamori, DO, Mulas, A, Mulligan, AM, Murray, A, Nalls, MA, Newman, A, Noordam, R, Nutile, T, Nyholt, DR, Olshan, AF, Olsson, H, Painter, JN, Patel, AV, Pedersen, NL, Perjakova, N, Peters, A, Peters, U, Pharoah, PDP, Polasek, O, Porcu, E, Psaty, BM, Rahman, I, Rennert, G, Rennert, HS, Ridker, PM, Ring, SM, Robino, A, Rose, LM, Rosendaal, FR, Rossouw, J, Rudan, I, Rueedi, R, Ruggiero, D, Sala, CF, Saloustros, E, Sandler, DP, Sanna, S, Sawyer, EJ, Sarnowski, C, Schlessinger, D, Schmidt, MK, Schoemaker, MJ, Schraut, KE, Scott, C, Shekari, S, Shrikhande, A, Smith, AV, Smith, BH, Smith, JA, Sorice, R, Southey, MC, Spector, TD, Spinelli, JJ, Stampfer, M, Stoeckl, D, van Meurs, JBJ, Strauch, K, Styrkarsdottir, U, Swerdlow, AJ, Tanaka, T, Teras, LR, Teumer, A, thorsteinsdottir, U, Timpson, NJ, Toniolo, D, Traglia, M, Troester, MA, Truong, T, Tyrrell, J, Uitterlinden, AG, Ulivi, S, Vachon, CM, Vitart, V, Voelker, U, Vollenweider, P, Voelzke, H, Wang, Q, Wareham, NJ, Weinberg, CR, Weir, DR, Wilcox, AN, van Dijk, KW, Willemsen, G, Wilson, JF, Wolffenbuttel, BHR, Wolk, A, Wood, AR, Zhao, W, Zygmunt, M, Chen, Z, Li, L, Franke, L, Burgess, S, Deelen, P, Pers, TH, Grondahl, ML, Andersen, CY, Pujol, A, Lopez-Contreras, AJ, Daniel, JA, Stefansson, K, Chang-Claude, J, van der Schouw, YT, Lunetta, KL, Chasman, DI, Easton, DF, Visser, JA, Ozanne, SE, Namekawa, SH, Solc, P, Murabito, JM, Ong, KK, Hoffmann, ER, Roig, I, Perry, JRB, Ruth, KS, Day, FR, Hussain, J, Martinez-Marchal, A, Aiken, CE, Azad, A, Thompson, DJ, Knoblochova, L, Abe, H, Tarry-Adkins, JL, Gonzalez, JM, Fontanillas, P, Claringbould, A, Bakker, OB, Sulem, P, Walters, RG, Terao, C, Turon, S, Horikoshi, M, Lin, K, Onland-Moret, NC, Sankar, A, Hertz, EPT, Timshel, PN, Shukla, V, Borup, R, Olsen, KW, Aguilera, P, Ferrer-Roda, M, Huang, Y, Stankovic, S, Timmers, PRHJ, Ahearn, TU, Alizadeh, BZ, Naderi, E, Andrulis, IL, Arnold, AM, Aronson, KJ, Augustinsson, A, Bandinelli, S, Barbieri, CM, Beaumont, RN, Becher, H, Beckmann, MW, Benonisdottir, S, Bergmann, S, Bochud, M, Boerwinkle, E, Bojesen, SE, Bolla, MK, Boomsma, DI, Bowker, N, Brody, JA, Broer, L, Buring, JE, Campbell, A, Campbell, H, Castelao, JE, Catamo, E, Chanock, SJ, Chenevix-Trench, G, Ciullo, M, Corre, T, Couch, FJ, Cox, A, Crisponi, L, Cross, SS, Cucca, F, Czene, K, Smith, GD, de Geus, EJCN, de Mutsert, R, De Vivo, I, Demerath, EW, Dennis, J, Dunning, AM, Dwek, M, Eriksson, M, Esko, T, Fasching, PA, Faul, JD, Ferrucci, L, Franceschini, N, Frayling, TM, Gago-Dominguez, M, Mezzavilla, M, Garcia-Closas, M, Gieger, C, Giles, GG, Grallert, H, Gudbjartsson, DF, Gudnason, V, Guenel, P, Haiman, CA, Hakansson, N, Hall, P, Hayward, C, He, C, He, W, Heiss, G, Hoffding, MK, Hopper, JL, Hottenga, JJ, Hu, F, Hunter, D, Ikram, MA, Jackson, RD, Joaquim, MDR, John, EM, Joshi, PK, Karasik, D, Kardia, SLR, Kartsonaki, C, Karlsson, R, Kitahara, CM, Kolcic, I, Kooperberg, C, Kraft, P, Kurian, AW, Kutalik, Z, La Bianca, M, LaChance, G, Langenberg, C, Launer, LJ, Laven, JSE, Lawlor, DA, Le Marchand, L, Li, J, Lindblom, A, Lindstrom, S, Lindstrom, T, Linet, M, Liu, Y, Liu, S, Luan, J, Magi, R, Magnusson, PKE, Mangino, M, Mannermaa, A, Marco, B, Marten, J, Martin, NG, Mbarek, H, McKnight, B, Medland, SE, Meisinger, C, Meitinger, T, Menni, C, Metspalu, A, Milani, L, Milne, RL, Montgomery, GW, Mook-Kanamori, DO, Mulas, A, Mulligan, AM, Murray, A, Nalls, MA, Newman, A, Noordam, R, Nutile, T, Nyholt, DR, Olshan, AF, Olsson, H, Painter, JN, Patel, AV, Pedersen, NL, Perjakova, N, Peters, A, Peters, U, Pharoah, PDP, Polasek, O, Porcu, E, Psaty, BM, Rahman, I, Rennert, G, Rennert, HS, Ridker, PM, Ring, SM, Robino, A, Rose, LM, Rosendaal, FR, Rossouw, J, Rudan, I, Rueedi, R, Ruggiero, D, Sala, CF, Saloustros, E, Sandler, DP, Sanna, S, Sawyer, EJ, Sarnowski, C, Schlessinger, D, Schmidt, MK, Schoemaker, MJ, Schraut, KE, Scott, C, Shekari, S, Shrikhande, A, Smith, AV, Smith, BH, Smith, JA, Sorice, R, Southey, MC, Spector, TD, Spinelli, JJ, Stampfer, M, Stoeckl, D, van Meurs, JBJ, Strauch, K, Styrkarsdottir, U, Swerdlow, AJ, Tanaka, T, Teras, LR, Teumer, A, thorsteinsdottir, U, Timpson, NJ, Toniolo, D, Traglia, M, Troester, MA, Truong, T, Tyrrell, J, Uitterlinden, AG, Ulivi, S, Vachon, CM, Vitart, V, Voelker, U, Vollenweider, P, Voelzke, H, Wang, Q, Wareham, NJ, Weinberg, CR, Weir, DR, Wilcox, AN, van Dijk, KW, Willemsen, G, Wilson, JF, Wolffenbuttel, BHR, Wolk, A, Wood, AR, Zhao, W, Zygmunt, M, Chen, Z, Li, L, Franke, L, Burgess, S, Deelen, P, Pers, TH, Grondahl, ML, Andersen, CY, Pujol, A, Lopez-Contreras, AJ, Daniel, JA, Stefansson, K, Chang-Claude, J, van der Schouw, YT, Lunetta, KL, Chasman, DI, Easton, DF, Visser, JA, Ozanne, SE, Namekawa, SH, Solc, P, Murabito, JM, Ong, KK, Hoffmann, ER, Roig, I, and Perry, JRB

Abstract

Reproductive longevity is essential for fertility and influences healthy ageing in women1,2, but insights into its underlying biological mechanisms and treatments to preserve it are limited. Here we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in about 200,000 women of European ancestry. These common alleles were associated with clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations3. The identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR-associated genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increases fertility and extends reproductive life in mice. Causal inference analyses using the identified genetic variants indicate that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases the risk of hormone-sensitive cancers. These findings provide insight into the mechanisms that govern ovarian ageing, when they act, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease.

Published
2021

5. Sociological implications of an epidemiological study of eczema in the City of Birmingham.

Author

Bowker, N. C., Cross, K. W., Iairburn, E. A., and M. Wall

Subjects
SKIN inflammation, SKIN diseases, ECZEMA, HEALTH facilities, PATIENTS
Abstract

An epidemiological study has been made of 4696 cases of eczematous dermatitis referred to hospital outpatient clinics in the City of Birmingham over a 3-year period. Some sociological implications of the investigation are discussed. The need for further sociological accounts of skin disease is stressed. [ABSTRACT FROM AUTHOR]

Published
1976
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9. Deciphering Genetic Susceptibility to Tuberculous Meningitis.

Author

Schurz H, Glanzmann B, Bowker N, van Toorn R, Solomons R, Schoeman J, van Helden PD, Kinnear CJ, Hoal EG, and Möller M

Abstract

Tuberculous meningitis (TBM) is the most severe form of extrapulmonary tuberculosis (TB) that arises when a caseating meningeal granuloma discharges its contents into the subarachnoid space. It accounts for ~1% of all disease caused by Mycobacterium tuberculosis and the age of peak incidence is from 2-4 years. The exact pathogenesis of TBM is still not fully understood and the mechanism(s) by which the bacilli initially invade the blood-brain-barrier are still to be elucidated. This study investigated the involvement of the host genome in TBM susceptibility, by considering common variants (minor allele frequency (MAF) >5%) using microarray genotyping and rare variants (MAF <1%) via exome sequencing. A total of 123 TBM cases, 400 pulmonary TB (pTB) cases and 477 healthy controls were genotyped on the MEGA array. A genome-wide association study (GWAS) comparing 114 TBM cases to 395 healthy controls showed no association with TBM susceptibility. A second analysis comparing 114 TBM cases to 382 pTB cases was conducted to investigate variants associated with different TB phenotypes. No significant associations were found with progression from pTB to TBM. Ten TBM cases and 10 healthy controls were exome sequenced. Gene set association tests SKAT-O and SKAT Common Rare were used to assess the association of rare SNPs and the cumulative effect of both common and rare SNPs with susceptibility to TBM, respectively. Ingenuity Pathway Analysis (IPA) of the top-hits of the SKAT-O analysis showed that NOD2 and CYP4F2 are both important in TBM pathogenesis and highlighted these as targets for future study. For the SKAT Common Rare analysis Centriolar Coiled-Coil Protein 110 ( CCP110) was nominally associated ( p = 5.89x10 -6 ) with TBM susceptibility. In addition, several top-hit genes ascribed to the development of the central nervous system (CNS) and innate immune system regulation were identified. Exome sequencing and GWAS of our TBM cohort has identified a single previously undescribed association of CCP110 with TBM susceptibility. These results advance our understanding of TBM in terms of both variants and genes that influence susceptibility. In addition, several candidate genes involved in innate immunity have been identified for further genotypic and functional investigation., Competing Interests: NB is currently an employee and shareholder of GlaxoSmithKline (Stevenage, UK). The remaining 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 Schurz, Glanzmann, Bowker, van Toorn, Solomons, Schoeman, van Helden, Kinnear, Hoal and Möller.)

Published
2022
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10. Identification of Rare Loss-of-Function Genetic Variation Regulating Body Fat Distribution.

Author

Koprulu M, Zhao Y, Wheeler E, Dong L, Rocha N, Li C, Griffin JD, Patel S, Van de Streek M, Glastonbury CA, Stewart ID, Day FR, Luan J, Bowker N, Wittemans LBL, Kerrison ND, Cai L, Lucarelli DME, Barroso I, McCarthy MI, Scott RA, Saudek V, Small KS, Wareham NJ, Semple RK, Perry JRB, O'Rahilly S, Lotta LA, Langenberg C, and Savage DB

Subjects
Activin Receptors, Type I genetics, Body Fat Distribution, Exome, Genetic Variation, Genome-Wide Association Study, Humans, Diabetes Mellitus, Type 2 genetics
Abstract

Context: Biological and translational insights from large-scale, array-based genetic studies of fat distribution, a key determinant of metabolic health, have been limited by the difficulty in linking predominantly noncoding variants to specific gene targets. Rare coding variant analyses provide greater confidence that a specific gene is involved, but do not necessarily indicate whether gain or loss of function (LoF) would be of most therapeutic benefit., Objective: This work aimed to identify genes/proteins involved in determining fat distribution., Methods: We combined the power of genome-wide analysis of array-based rare, nonsynonymous variants in 450 562 individuals in the UK Biobank with exome-sequence-based rare LoF gene burden testing in 184 246 individuals., Results: The data indicate that the LoF of 4 genes (PLIN1 [LoF variants, P = 5.86 × 10-7], INSR [LoF variants, P = 6.21 × 10-7], ACVR1C [LoF + moderate impact variants, P = 1.68 × 10-7; moderate impact variants, P = 4.57 × 10-7], and PDE3B [LoF variants, P = 1.41 × 10-6]) is associated with a beneficial effect on body mass index-adjusted waist-to-hip ratio and increased gluteofemoral fat mass, whereas LoF of PLIN4 (LoF variants, P = 5.86 × 10-7 adversely affects these parameters. Phenotypic follow-up suggests that LoF of PLIN1, PDE3B, and ACVR1C favorably affects metabolic phenotypes (eg, triglycerides [TGs] and high-density lipoprotein [HDL] cholesterol concentrations) and reduces the risk of cardiovascular disease, whereas PLIN4 LoF has adverse health consequences. INSR LoF is associated with lower TG and HDL levels but may increase the risk of type 2 diabetes., Conclusion: This study robustly implicates these genes in the regulation of fat distribution, providing new and in some cases somewhat counterintuitive insight into the potential consequences of targeting these molecules therapeutically., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Endocrine Society.)

Published
2022
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11. Evidence for Shared Genetic Aetiology Between Schizophrenia, Cardiometabolic, and Inflammation-Related Traits: Genetic Correlation and Colocalization Analyses.

Author

Perry BI, Bowker N, Burgess S, Wareham NJ, Upthegrove R, Jones PB, Langenberg C, and Khandaker GM

Abstract

Background: Schizophrenia commonly co-occurs with cardiometabolic and inflammation-related traits. It is unclear to what extent the comorbidity could be explained by shared genetic aetiology., Methods: We used GWAS data to estimate shared genetic aetiology between schizophrenia, cardiometabolic, and inflammation-related traits: fasting insulin (FI), fasting glucose, glycated haemoglobin, glucose tolerance, type 2 diabetes (T2D), lipids, body mass index (BMI), coronary artery disease (CAD), and C-reactive protein (CRP). We examined genome-wide correlation using linkage disequilibrium score regression (LDSC); stratified by minor-allele frequency using genetic covariance analyzer (GNOVA); then refined to locus-level using heritability estimation from summary statistics (ρ-HESS). Regions with local correlation were used in hypothesis prioritization multi-trait colocalization to examine for colocalisation, implying common genetic aetiology., Results: We found evidence for weak genome-wide negative correlation of schizophrenia with T2D (r g = -0.07; 95% C.I., -0.03,0.12; P = .002) and BMI (r g = -0.09; 95% C.I., -0.06, -0.12; P = 1.83 × 10 -5 ). We found a trend of evidence for positive genetic correlation between schizophrenia and cardiometabolic traits confined to lower-frequency variants. This was underpinned by 85 regions of locus-level correlation with evidence of opposing mechanisms. Ten loci showed strong evidence of colocalization. Four of those (rs6265 ( BDNF ); rs8192675 ( SLC2A2 ); rs3800229 ( FOXO3 ); rs17514846 ( FURIN )) are implicated in brain-derived neurotrophic factor (BDNF)-related pathways., Conclusions: LDSC may lead to downwardly-biased genetic correlation estimates between schizophrenia, cardiometabolic, and inflammation-related traits. Common genetic aetiology for these traits could be confined to lower-frequency common variants and involve opposing mechanisms. Genes related to BDNF and glucose transport amongst others may partly explain the comorbidity between schizophrenia and cardiometabolic disorders., (© The Author(s) 2022. Published by Oxford University Press on behalf of the University of Maryland's school of medicine, Maryland Psychiatric Research Center.)

Published
2022
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12. Genetically Predicted Glucose-Dependent Insulinotropic Polypeptide (GIP) Levels and Cardiovascular Disease Risk Are Driven by Distinct Causal Variants in the GIPR Region.

Author

Bowker N, Hansford R, Burgess S, Foley CN, Auyeung VPW, Erzurumluoglu AM, Stewart ID, Wheeler E, Pietzner M, Gribble F, Reimann F, Bhatnagar P, Coghlan MP, Wareham NJ, and Langenberg C

Subjects
Adult, Aged, Diabetes Mellitus, Type 2 genetics, Diabetes Mellitus, Type 2 metabolism, Female, Finland, Gastric Inhibitory Polypeptide genetics, Gastric Inhibitory Polypeptide metabolism, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Male, Middle Aged, Receptors, Gastrointestinal Hormone genetics, Risk Factors, United Kingdom, Cardiovascular Diseases blood, Diabetes Mellitus, Type 2 blood, Gastric Inhibitory Polypeptide blood, Genetic Predisposition to Disease, Receptors, Gastrointestinal Hormone metabolism
Abstract

There is considerable interest in GIPR agonism to enhance the insulinotropic and extrapancreatic effects of GIP, thereby improving glycemic and weight control in type 2 diabetes (T2D) and obesity. Recent genetic epidemiological evidence has implicated higher GIPR-mediated GIP levels in raising coronary artery disease (CAD) risk, a potential safety concern for GIPR agonism. We therefore aimed to quantitatively assess whether the association between higher GIPR-mediated fasting GIP levels and CAD risk is mediated via GIPR or is instead the result of linkage disequilibrium (LD) confounding between variants at the GIPR locus. Using Bayesian multitrait colocalization, we identified a GIPR missense variant, rs1800437 (G allele; E354), as the putatively causal variant shared among fasting GIP levels, glycemic traits, and adiposity-related traits (posterior probability for colocalization [PP coloc ] > 0.97; PP explained by the candidate variant [PP explained ] = 1) that was independent from a cluster of CAD and lipid traits driven by a known missense variant in APOE (rs7412; distance to E354 ∼770 Kb; R 2 with E354 = 0.004; PP coloc > 0.99; PP explained = 1). Further, conditioning the association between E354 and CAD on the residual LD with rs7412, we observed slight attenuation in association, but it remained significant (odds ratio [OR] per copy of E354 after adjustment 1.03; 95% CI 1.02, 1.04; P = 0.003). Instead, E354's association with CAD was completely attenuated when conditioning on an additional established CAD signal, rs1964272 ( R 2 with E354 = 0.27), an intronic variant in SNRPD2 (OR for E354 after adjustment for rs1964272: 1.01; 95% CI 0.99, 1.03; P = 0.06). We demonstrate that associations with GIP and anthropometric and glycemic traits are driven by genetic signals distinct from those driving CAD and lipid traits in the GIPR region and that higher E354-mediated fasting GIP levels are not associated with CAD risk. These findings provide evidence that the inclusion of GIPR agonism in dual GIPR/GLP1R agonists could potentiate the protective effect of GLP-1 agonists on diabetes without undue CAD risk, an aspect that has yet to be assessed in clinical trials., (© 2021 by the American Diabetes Association.)

Published
2021
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13. Genetic insights into biological mechanisms governing human ovarian ageing.

Author

Ruth KS, Day FR, Hussain J, Martínez-Marchal A, Aiken CE, Azad A, Thompson DJ, Knoblochova L, Abe H, Tarry-Adkins JL, Gonzalez JM, Fontanillas P, Claringbould A, Bakker OB, Sulem P, Walters RG, Terao C, Turon S, Horikoshi M, Lin K, Onland-Moret NC, Sankar A, Hertz EPT, Timshel PN, Shukla V, Borup R, Olsen KW, Aguilera P, Ferrer-Roda M, Huang Y, Stankovic S, Timmers PRHJ, Ahearn TU, Alizadeh BZ, Naderi E, Andrulis IL, Arnold AM, Aronson KJ, Augustinsson A, Bandinelli S, Barbieri CM, Beaumont RN, Becher H, Beckmann MW, Benonisdottir S, Bergmann S, Bochud M, Boerwinkle E, Bojesen SE, Bolla MK, Boomsma DI, Bowker N, Brody JA, Broer L, Buring JE, Campbell A, Campbell H, Castelao JE, Catamo E, Chanock SJ, Chenevix-Trench G, Ciullo M, Corre T, Couch FJ, Cox A, Crisponi L, Cross SS, Cucca F, Czene K, Smith GD, de Geus EJCN, de Mutsert R, De Vivo I, Demerath EW, Dennis J, Dunning AM, Dwek M, Eriksson M, Esko T, Fasching PA, Faul JD, Ferrucci L, Franceschini N, Frayling TM, Gago-Dominguez M, Mezzavilla M, García-Closas M, Gieger C, Giles GG, Grallert H, Gudbjartsson DF, Gudnason V, Guénel P, Haiman CA, Håkansson N, Hall P, Hayward C, He C, He W, Heiss G, Høffding MK, Hopper JL, Hottenga JJ, Hu F, Hunter D, Ikram MA, Jackson RD, Joaquim MDR, John EM, Joshi PK, Karasik D, Kardia SLR, Kartsonaki C, Karlsson R, Kitahara CM, Kolcic I, Kooperberg C, Kraft P, Kurian AW, Kutalik Z, La Bianca M, LaChance G, Langenberg C, Launer LJ, Laven JSE, Lawlor DA, Le Marchand L, Li J, Lindblom A, Lindstrom S, Lindstrom T, Linet M, Liu Y, Liu S, Luan J, Mägi R, Magnusson PKE, Mangino M, Mannermaa A, Marco B, Marten J, Martin NG, Mbarek H, McKnight B, Medland SE, Meisinger C, Meitinger T, Menni C, Metspalu A, Milani L, Milne RL, Montgomery GW, Mook-Kanamori DO, Mulas A, Mulligan AM, Murray A, Nalls MA, Newman A, Noordam R, Nutile T, Nyholt DR, Olshan AF, Olsson H, Painter JN, Patel AV, Pedersen NL, Perjakova N, Peters A, Peters U, Pharoah PDP, Polasek O, Porcu E, Psaty BM, Rahman I, Rennert G, Rennert HS, Ridker PM, Ring SM, Robino A, Rose LM, Rosendaal FR, Rossouw J, Rudan I, Rueedi R, Ruggiero D, Sala CF, Saloustros E, Sandler DP, Sanna S, Sawyer EJ, Sarnowski C, Schlessinger D, Schmidt MK, Schoemaker MJ, Schraut KE, Scott C, Shekari S, Shrikhande A, Smith AV, Smith BH, Smith JA, Sorice R, Southey MC, Spector TD, Spinelli JJ, Stampfer M, Stöckl D, van Meurs JBJ, Strauch K, Styrkarsdottir U, Swerdlow AJ, Tanaka T, Teras LR, Teumer A, Þorsteinsdottir U, Timpson NJ, Toniolo D, Traglia M, Troester MA, Truong T, Tyrrell J, Uitterlinden AG, Ulivi S, Vachon CM, Vitart V, Völker U, Vollenweider P, Völzke H, Wang Q, Wareham NJ, Weinberg CR, Weir DR, Wilcox AN, van Dijk KW, Willemsen G, Wilson JF, Wolffenbuttel BHR, Wolk A, Wood AR, Zhao W, Zygmunt M, Chen Z, Li L, Franke L, Burgess S, Deelen P, Pers TH, Grøndahl ML, Andersen CY, Pujol A, Lopez-Contreras AJ, Daniel JA, Stefansson K, Chang-Claude J, van der Schouw YT, Lunetta KL, Chasman DI, Easton DF, Visser JA, Ozanne SE, Namekawa SH, Solc P, Murabito JM, Ong KK, Hoffmann ER, Murray A, Roig I, and Perry JRB

Subjects
Adult, Alleles, Animals, Bone and Bones metabolism, Checkpoint Kinase 1 genetics, Checkpoint Kinase 2 genetics, Diabetes Mellitus, Type 2, Diet, Europe ethnology, Asia, Eastern ethnology, Female, Fertility genetics, Fragile X Mental Retardation Protein genetics, Genetic Predisposition to Disease, Genome-Wide Association Study, Healthy Aging genetics, Humans, Longevity genetics, Menopause genetics, Menopause, Premature genetics, Mice, Mice, Inbred C57BL, Middle Aged, Primary Ovarian Insufficiency genetics, Uterus, Aging genetics, Ovary metabolism
Abstract

Reproductive longevity is essential for fertility and influences healthy ageing in women 1,2 , but insights into its underlying biological mechanisms and treatments to preserve it are limited. Here we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in about 200,000 women of European ancestry. These common alleles were associated with clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations 3 . The identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR-associated genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increases fertility and extends reproductive life in mice. Causal inference analyses using the identified genetic variants indicate that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases the risk of hormone-sensitive cancers. These findings provide insight into the mechanisms that govern ovarian ageing, when they act, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Published
2021
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14. Meta-analysis investigating the role of interleukin-6 mediated inflammation in type 2 diabetes.

Author

Bowker N, Shah RL, Sharp SJ, Luan J, Stewart ID, Wheeler E, Ferreira MAR, Baras A, Wareham NJ, Langenberg C, and Lotta LA

Subjects
Adiposity genetics, Biomarkers, Blood Glucose, Body Weights and Measures, Cytokines metabolism, Diabetes Mellitus, Type 2 genetics, Diabetes Mellitus, Type 2 metabolism, Genetic Predisposition to Disease, Humans, Inflammation Mediators metabolism, Odds Ratio, Receptors, Interleukin-6 genetics, Receptors, Interleukin-6 metabolism, Risk Factors, Signal Transduction, Diabetes Mellitus, Type 2 complications, Disease Susceptibility, Inflammation etiology, Inflammation metabolism, Interleukin-6 metabolism
Abstract

Background: Evidence from animal models and observational epidemiology points to a role for chronic inflammation, in which interleukin 6 (IL-6) is a key player, in the pathophysiology of type 2 diabetes (T2D). However, it is unknown whether IL-6 mediated inflammation is implicated in the pathophysiology of T2D., Methods: We performed a meta-analysis of 15 prospective studies to investigate associations between IL-6 levels and incident T2D including 5,421 cases and 31,562 non-cases. We also estimated the association of a loss-of-function missense variant (Asp358Ala) in the IL-6 receptor gene (IL6R), previously shown to mimic the effects of IL-6R inhibition, in a large trans-ethnic meta-analysis of six T2D case-control studies including 260,614 cases and 1,350,640 controls., Findings: In a meta-analysis of 15 prospective studies, higher levels of IL-6 (per log pg/mL) were significantly associated with a higher risk of incident T2D (1·24 95% CI, 1·17, 1·32; P = 1 × 10 -12 ). In a trans-ethnic meta-analysis of 260,614 cases and 1,350,640 controls, the IL6R Asp358Ala missense variant was associated with lower odds of T2D (OR, 0·98; 95% CI, 0·97, 0·99; P = 2 × 10 -7 ). This association was not due to diagnostic misclassification and was consistent across ethnic groups. IL-6 levels mediated up to 5% of the association between higher body mass index and T2D., Interpretation: Large-scale human prospective and genetic data provide evidence that IL-6 mediated inflammation is implicated in the etiology of T2D but suggest that the impact of this pathway on disease risk in the general population is likely to be small., Funding: The EPICNorfolk study has received funding from the Medical Research Council (MRC) (MR/N003284/1, MC-UU&#95;12015/1 and MC&#95;PC&#95;13048) and Cancer Research UK (C864/A14136). The Fenland Study is funded by the MRC (MC&#95;UU&#95;12015/1 and MC&#95;PC&#95;13046)., Competing Interests: Declaration of Competing Interest M.A.R.F., A.B., L.A.L. are employees and shareholders of Regeneron Pharmaceuticals. N.B., R.L.S., S.J.S., J.L., I.D.S, E.W., N.J.W. and C.L. have nothing to declare., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Published
2020
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15. Human Gain-of-Function MC4R Variants Show Signaling Bias and Protect against Obesity.

Author

Lotta LA, Mokrosiński J, Mendes de Oliveira E, Li C, Sharp SJ, Luan J, Brouwers B, Ayinampudi V, Bowker N, Kerrison N, Kaimakis V, Hoult D, Stewart ID, Wheeler E, Day FR, Perry JRB, Langenberg C, Wareham NJ, and Farooqi IS

Subjects
Adult, Aged, Body Mass Index, Coronary Artery Disease complications, Coronary Artery Disease metabolism, Coronary Artery Disease pathology, Cyclic AMP metabolism, Databases, Factual, Diabetes Mellitus, Type 2 complications, Diabetes Mellitus, Type 2 metabolism, Diabetes Mellitus, Type 2 pathology, Female, GTP-Binding Protein alpha Subunits, Gs metabolism, Genetic Predisposition to Disease, Genotype, Humans, Male, Middle Aged, Obesity complications, Obesity metabolism, Polymorphism, Single Nucleotide, Receptor, Melanocortin, Type 4 chemistry, Receptor, Melanocortin, Type 4 metabolism, beta-Arrestins metabolism, Gain of Function Mutation genetics, Obesity pathology, Receptor, Melanocortin, Type 4 genetics, Signal Transduction
Abstract

The melanocortin 4 receptor (MC4R) is a G protein-coupled receptor whose disruption causes obesity. We functionally characterized 61 MC4R variants identified in 0.5 million people from UK Biobank and examined their associations with body mass index (BMI) and obesity-related cardiometabolic diseases. We found that the maximal efficacy of β-arrestin recruitment to MC4R, rather than canonical Gα s -mediated cyclic adenosine-monophosphate production, explained 88% of the variance in the association of MC4R variants with BMI. While most MC4R variants caused loss of function, a subset caused gain of function; these variants were associated with significantly lower BMI and lower odds of obesity, type 2 diabetes, and coronary artery disease. Protective associations were driven by MC4R variants exhibiting signaling bias toward β-arrestin recruitment and increased mitogen-activated protein kinase pathway activation. Harnessing β-arrestin-biased MC4R signaling may represent an effective strategy for weight loss and the treatment of obesity-related cardiometabolic diseases., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Published
2019
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16. Association of Genetic Variants Related to Gluteofemoral vs Abdominal Fat Distribution With Type 2 Diabetes, Coronary Disease, and Cardiovascular Risk Factors.

Author

Lotta LA, Wittemans LBL, Zuber V, Stewart ID, Sharp SJ, Luan J, Day FR, Li C, Bowker N, Cai L, De Lucia Rolfe E, Khaw KT, Perry JRB, O'Rahilly S, Scott RA, Savage DB, Burgess S, Wareham NJ, and Langenberg C

Subjects
Body Mass Index, Cardiovascular Diseases genetics, Female, Genome-Wide Association Study, Humans, Male, Middle Aged, Risk Factors, Abdominal Fat, Adiposity genetics, Coronary Disease genetics, Diabetes Mellitus, Type 2 genetics, Genetic Predisposition to Disease, Genetic Variation, Waist-Hip Ratio
Abstract

Importance: Body fat distribution, usually measured using waist-to-hip ratio (WHR), is an important contributor to cardiometabolic disease independent of body mass index (BMI). Whether mechanisms that increase WHR via lower gluteofemoral (hip) or via higher abdominal (waist) fat distribution affect cardiometabolic risk is unknown., Objective: To identify genetic variants associated with higher WHR specifically via lower gluteofemoral or higher abdominal fat distribution and estimate their association with cardiometabolic risk., Design, Setting, and Participants: Genome-wide association studies (GWAS) for WHR combined data from the UK Biobank cohort and summary statistics from previous GWAS (data collection: 2006-2018). Specific polygenic scores for higher WHR via lower gluteofemoral or via higher abdominal fat distribution were derived using WHR-associated genetic variants showing specific association with hip or waist circumference. Associations of polygenic scores with outcomes were estimated in 3 population-based cohorts, a case-cohort study, and summary statistics from 6 GWAS (data collection: 1991-2018)., Exposures: More than 2.4 million common genetic variants (GWAS); polygenic scores for higher WHR (follow-up analyses)., Main Outcomes and Measures: BMI-adjusted WHR and unadjusted WHR (GWAS); compartmental fat mass measured by dual-energy x-ray absorptiometry, systolic and diastolic blood pressure, low-density lipoprotein cholesterol, triglycerides, fasting glucose, fasting insulin, type 2 diabetes, and coronary disease risk (follow-up analyses)., Results: Among 452 302 UK Biobank participants of European ancestry, the mean (SD) age was 57 (8) years and the mean (SD) WHR was 0.87 (0.09). In genome-wide analyses, 202 independent genetic variants were associated with higher BMI-adjusted WHR (n = 660 648) and unadjusted WHR (n = 663 598). In dual-energy x-ray absorptiometry analyses (n = 18 330), the hip- and waist-specific polygenic scores for higher WHR were specifically associated with lower gluteofemoral and higher abdominal fat, respectively. In follow-up analyses (n = 636 607), both polygenic scores were associated with higher blood pressure and triglyceride levels and higher risk of diabetes (waist-specific score: odds ratio [OR], 1.57 [95% CI, 1.34-1.83], absolute risk increase per 1000 participant-years [ARI], 4.4 [95% CI, 2.7-6.5], P < .001; hip-specific score: OR, 2.54 [95% CI, 2.17-2.96], ARI, 12.0 [95% CI, 9.1-15.3], P < .001) and coronary disease (waist-specific score: OR, 1.60 [95% CI, 1.39-1.84], ARI, 2.3 [95% CI, 1.5-3.3], P < .001; hip-specific score: OR, 1.76 [95% CI, 1.53-2.02], ARI, 3.0 [95% CI, 2.1-4.0], P < .001), per 1-SD increase in BMI-adjusted WHR., Conclusions and Relevance: Distinct genetic mechanisms may be linked to gluteofemoral and abdominal fat distribution that are the basis for the calculation of the WHR. These findings may improve risk assessment and treatment of diabetes and coronary disease.

Published
2018
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17. Association of Genetically Enhanced Lipoprotein Lipase-Mediated Lipolysis and Low-Density Lipoprotein Cholesterol-Lowering Alleles With Risk of Coronary Disease and Type 2 Diabetes.

Author

Lotta LA, Stewart ID, Sharp SJ, Day FR, Burgess S, Luan J, Bowker N, Cai L, Li C, Wittemans LBL, Kerrison ND, Khaw KT, McCarthy MI, O'Rahilly S, Scott RA, Savage DB, Perry JRB, Langenberg C, and Wareham NJ

Subjects
Aged, Angiopoietin-Like Protein 4 genetics, Female, Genetic Association Studies, Genetic Predisposition to Disease, Humans, Hydroxymethylglutaryl CoA Reductases genetics, Lipolysis, Male, Membrane Proteins genetics, Membrane Transport Proteins, Middle Aged, Odds Ratio, Proprotein Convertase 9 genetics, Cholesterol, LDL genetics, Coronary Artery Disease genetics, Diabetes Mellitus, Type 2 genetics, Lipoprotein Lipase genetics
Abstract

Importance: Pharmacological enhancers of lipoprotein lipase (LPL) are in preclinical or early clinical development for cardiovascular prevention. Studying whether these agents will reduce cardiovascular events or diabetes risk when added to existing lipid-lowering drugs would require large outcome trials. Human genetics studies can help prioritize or deprioritize these resource-demanding endeavors., Objective: To investigate the independent and combined associations of genetically determined differences in LPL-mediated lipolysis and low-density lipoprotein cholesterol (LDL-C) metabolism with risk of coronary disease and diabetes., Design, Setting, and Participants: In this genetic association study, individual-level genetic data from 392 220 participants from 2 population-based cohort studies and 1 case-cohort study conducted in Europe were included. Data were collected from January 1991 to July 2018, and data were analyzed from July 2014 to July 2018., Exposures: Six conditionally independent triglyceride-lowering alleles in LPL, the p.Glu40Lys variant in ANGPTL4, rare loss-of-function variants in ANGPTL3, and LDL-C-lowering polymorphisms at 58 independent genomic regions, including HMGCR, NPC1L1, and PCSK9., Main Outcomes and Measures: Odds ratio for coronary artery disease and type 2 diabetes., Results: Of the 392 220 participants included, 211 915 (54.0%) were female, and the mean (SD) age was 57 (8) years. Triglyceride-lowering alleles in LPL were associated with protection from coronary disease (approximately 40% lower odds per SD of genetically lower triglycerides) and type 2 diabetes (approximately 30% lower odds) in people above or below the median of the population distribution of LDL-C-lowering alleles at 58 independent genomic regions, HMGCR, NPC1L1, or PCSK9. Associations with lower risk were consistent in quintiles of the distribution of LDL-C-lowering alleles and 2 × 2 factorial genetic analyses. The 40Lys variant in ANGPTL4 was associated with protection from coronary disease and type 2 diabetes in groups with genetically higher or lower LDL-C. For a genetic difference of 0.23 SDs in LDL-C, ANGPTL3 loss-of-function variants, which also have beneficial associations with LPL lipolysis, were associated with greater protection against coronary disease than other LDL-C-lowering genetic mechanisms (ANGPTL3 loss-of-function variants: odds ratio, 0.66; 95% CI, 0.52-0.83; 58 LDL-C-lowering variants: odds ratio, 0.90; 95% CI, 0.89-0.91; P for heterogeneity = .009)., Conclusions and Relevance: Triglyceride-lowering alleles in the LPL pathway are associated with lower risk of coronary disease and type 2 diabetes independently of LDL-C-lowering genetic mechanisms. These findings provide human genetics evidence to support the development of agents that enhance LPL-mediated lipolysis for further clinical benefit in addition to LDL-C-lowering therapy.

Published
2018
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18. Polymorphisms in the Pattern Recognition Receptor Mincle Gene (CLEC4E) and Association with Tuberculosis.

Author

Bowker N, Salie M, Schurz H, van Helden PD, Kinnear CJ, Hoal EG, and Möller M

Subjects
Adult, Case-Control Studies, Cord Factors immunology, Female, Genotype, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide, Tuberculosis, Pulmonary immunology, Young Adult, Genetic Predisposition to Disease, Lectins, C-Type genetics, Mycobacterium tuberculosis immunology, Receptors, Immunologic genetics, Tuberculosis, Pulmonary genetics
Abstract

The mechanisms involved in interactions between Mycobacterium tuberculosis and host innate immune cells determine outcome. Antigen-presenting cells, including macrophages and dendritic cells, express many pattern recognition receptors to identify pathogen-associated molecular patterns, thereby initiating an immune response. A major mycobacterial virulence factor, trehalose-6',6-dimycolate, is recognised by the macrophage-inducible C-type lectin, Mincle, which leads to the activation of the Syk-Card9 signalling pathway in macrophages. Mincle is encoded by CLEC4E, and we investigated polymorphisms in this gene to assess its role in tuberculosis susceptibility. Four tagging single nucleotide polymorphisms (SNPs) (rs10841845, rs10841847, rs10841856 and rs4620776) were genotyped using TaqMan(®) SNP assays in 416 tuberculosis cases and 405 healthy controls. Logistic regression models were used for analysis. No association was detected with any of the SNPs analysed. This research highlights tuberculosis disease complexity where recognition proteins which specifically bind mycobacterial glycolipids cannot be conclusively associated with the disease in genetic studies.

Published
2016
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19. Are women occupying positions of power online? Demographics of chat room operators.

Author

Bowker NI and Liu JH

Subjects
Adolescent, Adult, Child, Communication, Demography, Female, Humans, Interpersonal Relations, Male, Middle Aged, Surveys and Questionnaires, Internet
Abstract

Internet statistics indicate a reduction in the gender discrepancy online. Yet, what is the situation within specific online communities like Internet Relay Chat (IRC)? Likewise, what is the gender status of those occupying positions of power online? An exploratory study of chat room operators (those who govern chat rooms) was conducted to investigate gender differences in operator's demographic characteristics and IRC experience. Whether those less satisfied with their real-life occupation were attracted to chat room operator positions was also investigated. A survey of 423 chat room operators was administered, comprising 25% women. Real-life occupations of chat room operators covered a broad spectrum, from professional and managerial to service, sales, and production workers, as well as those not employed. The most common occupational category cited was student, with very similar proportions of men and women occupying high-status positions. Of the occupations listed, 23% fell within the IT industry, with significantly more male than female operators working in this area. Majorities of both genders were satisfied with their real-life occupation. There was no relationship between job satisfaction and IRC experience or time spent as chat room operator. There were no gender differences for IRC experience. Majorities of both genders had been using IRC for 1 to 3 years or more, used IRC daily, and spent most or all of their time on IRC as operators. Ages ranged from 11 to 66 years, with the mean age 25 years. Women were significantly older than men. A significant proportion of men and women were from North America.

Published
2001
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