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2. Author Correction: Trans-ancestral genome-wide association study of longitudinal pubertal height growth and shared heritability with adult health outcomes

Author

Bradfeld, Jonathan P., Kember, Rachel L., Ulrich, Anna, Balkhiyarova, Zhanna, Alyass, Akram, Aris, Izzuddin M., Bell, Joshua A., Broadaway, K. Alaine, Chen, Zhanghua, Chai, Jin-Fang, Davies, Neil M., Fernandez-Orth, Dietmar, Bustamante, Mariona, Fore, Ruby, Ganguli, Amitavo, Heiskala, Anni, Hottenga, Jouke-Jan, Íñiguez, Carmen, Kobes, Sayuko, Leinonen, Jaakko, Lowry, Estelle, Lyytikainen, Leo-Pekka, Mahajan, Anubha, Pitkänen, Niina, Schnurr, Theresia M., Have, Christian Theil, Strachan, David P., Thiering, Elisabeth, Vogelezang, Suzanne, Wade, Kaitlin H., Wang, Carol A., Wong, Andrew, Holm, Louise Aas, Chesi, Alessandra, Choong, Catherine, Cruz, Miguel, Elliott, Paul, Franks, Steve, Frithiof-Bøjsøe, Christine, Gauderman, W. James, Glessner, Joseph T., Gilsanz, Vicente, Griesman, Kendra, Hanson, Robert L., Kaakinen, Marika, Kalkwarf, Heidi, Kelly, Andrea, Kindler, Joseph, Kähönen, Mika, Lanca, Carla, Lappe, Joan, Lee, Nanette R., McCormack, Shana, Mentch, Frank D., Mitchell, Jonathan A., Mononen, Nina, Niinikoski, Harri, Oken, Emily, Pahkala, Katja, Sim, Xueling, Teo, Yik-Ying, Baier, Leslie J., van Beijsterveldt, Toos, Adair, Linda S., Boomsma, Dorret I., de Geus, Eco, Guxens, Mònica, Eriksson, Johan G., Felix, Janine F., Gilliland, Frank D., Hansen, Torben, Hardy, Rebecca, Hivert, Marie-France, Holm, Jens-Christian, Jaddoe, Vincent W. V., Järvelin, Marjo-Riitta, Lehtimäki, Terho, Mackey, David A., Meyre, David, Mohlke, Karen L., Mykkänen, Juha, Oberfeld, Sharon, Pennell, Craig E., Perry, John R. B., Raitakari, Olli, Rivadeneira, Fernando, Saw, Seang-Mei, Sebert, Sylvain, Shepherd, John A., Standl, Marie, Sørensen, Thorkild I. A., Timpson, Nicholas J., Torrent, Maties, Willemsen, Gonneke, Hypponen, Elina, Power, Chris, McCarthy, Mark I., Freathy, Rachel M., Widén, Elisabeth, Hakonarson, Hakon, Prokopenko, Inga, Voight, Benjamin F., Zemel, Babette S., Grant, Struan F. A., and Cousminer, Diana L.

Published
2024
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3. Trans-ancestral genome-wide association study of longitudinal pubertal height growth and shared heritability with adult health outcomes

Author

Bradfield, Jonathan P., Kember, Rachel L., Ulrich, Anna, Balkhiyarova, Zhanna, Alyass, Akram, Aris, Izzuddin M., Bell, Joshua A., Broadaway, K. Alaine, Chen, Zhanghua, Chai, Jin-Fang, Davies, Neil M., Fernandez-Orth, Dietmar, Bustamante, Mariona, Fore, Ruby, Ganguli, Amitavo, Heiskala, Anni, Hottenga, Jouke-Jan, Íñiguez, Carmen, Kobes, Sayuko, Leinonen, Jaakko, Lowry, Estelle, Lyytikainen, Leo-Pekka, Mahajan, Anubha, Pitkänen, Niina, Schnurr, Theresia M., Have, Christian Theil, Strachan, David P., Thiering, Elisabeth, Vogelezang, Suzanne, Wade, Kaitlin H., Wang, Carol A., Wong, Andrew, Holm, Louise Aas, Chesi, Alessandra, Choong, Catherine, Cruz, Miguel, Elliott, Paul, Franks, Steve, Frithioff-Bøjsøe, Christine, Gauderman, W. James, Glessner, Joseph T., Gilsanz, Vicente, Griesman, Kendra, Hanson, Robert L., Kaakinen, Marika, Kalkwarf, Heidi, Kelly, Andrea, Kindler, Joseph, Kähönen, Mika, Lanca, Carla, Lappe, Joan, Lee, Nanette R., McCormack, Shana, Mentch, Frank D., Mitchell, Jonathan A., Mononen, Nina, Niinikoski, Harri, Oken, Emily, Pahkala, Katja, Sim, Xueling, Teo, Yik-Ying, Baier, Leslie J., van Beijsterveldt, Toos, Adair, Linda S., Boomsma, Dorret I., de Geus, Eco, Guxens, Mònica, Eriksson, Johan G., Felix, Janine F., Gilliland, Frank D., Biobank, Penn Medicine, Hansen, Torben, Hardy, Rebecca, Hivert, Marie-France, Holm, Jens-Christian, Jaddoe, Vincent W. V., Järvelin, Marjo-Riitta, Lehtimäki, Terho, Mackey, David A., Meyre, David, Mohlke, Karen L., Mykkänen, Juha, Oberfield, Sharon, Pennell, Craig E., Perry, John R. B., Raitakari, Olli, Rivadeneira, Fernando, Saw, Seang-Mei, Sebert, Sylvain, Shepherd, John A., Standl, Marie, Sørensen, Thorkild I. A., Timpson, Nicholas J., Torrent, Maties, Willemsen, Gonneke, Hypponen, Elina, Power, Chris, McCarthy, Mark I., Freathy, Rachel M., Widén, Elisabeth, Hakonarson, Hakon, Prokopenko, Inga, Voight, Benjamin F., Zemel, Babette S., Grant, Struan F. A., and Cousminer, Diana L.

Published
2024
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6. Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium

Author

Lin, Bridget M, Grinde, Kelsey E, Brody, Jennifer A, Breeze, Charles E, Raffield, Laura M, Mychaleckyj, Josyf C, Thornton, Timothy A, Perry, James A, Baier, Leslie J, de las Fuentes, Lisa, Guo, Xiuqing, Heavner, Benjamin D, Hanson, Robert L, Hung, Yi-Jen, Qian, Huijun, Hsiung, Chao A, Hwang, Shih-Jen, Irvin, Margaret R, Jain, Deepti, Kelly, Tanika N, Kobes, Sayuko, Lange, Leslie, Lash, James P, Li, Yun, Liu, Xiaoming, Mi, Xuenan, Musani, Solomon K, Papanicolaou, George J, Parsa, Afshin, Reiner, Alex P, Salimi, Shabnam, Sheu, Wayne H-H, Shuldiner, Alan R, Taylor, Kent D, Smith, Albert V, Smith, Jennifer A, Tin, Adrienne, Vaidya, Dhananjay, Wallace, Robert B, Yamamoto, Kenichi, Sakaue, Saori, Matsuda, Koichi, Kamatani, Yoichiro, Momozawa, Yukihide, Yanek, Lisa R, Young, Betsi A, Zhao, Wei, Okada, Yukinori, Abecasis, Gonzalo, Psaty, Bruce M, Arnett, Donna K, Boerwinkle, Eric, Cai, Jianwen, Chen, Ida Yii-Der, Correa, Adolfo, Cupples, L Adrienne, He, Jiang, Kardia, Sharon LR, Kooperberg, Charles, Mathias, Rasika A, Mitchell, Braxton D, Nickerson, Deborah A, Turner, Steve T, Ramachandran, Vasan S, Rotter, Jerome I, Levy, Daniel, Kramer, Holly J, Köttgen, Anna, Consortium, NHLBI Trans-Omics for Precision Medicine, Group, TOPMed Kidney Working, Rich, Stephen S, Lin, Dan-Yu, Browning, Sharon R, and Franceschini, Nora

Subjects
Epidemiology, Health Sciences, Biotechnology, Human Genome, Genetics, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Alleles, Gene Frequency, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Glomerular Filtration Rate, Humans, Male, National Heart, Lung, and Blood Institute (U.S.), Polymorphism, Single Nucleotide, Precision Medicine, Public Health Surveillance, Quantitative Trait, Heritable, United States, Whole Genome Sequencing, Whole genome sequencing, Kidney traits, Rare variants, Ancestry-specific variants, Clinical Sciences, Public Health and Health Services, Clinical sciences
Abstract

BackgroundGenetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants.MethodsWe combined whole genome sequencing (WGS) data from 23,732 participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) Program multi-ethnic studies to identify novel loci for estimated glomerular filtration rate (eGFR). Participants included European, African, East Asian, and Hispanic ancestries. We applied linear mixed models using a genetic relationship matrix estimated from the WGS data and adjusted for age, sex, study, and ethnicity.FindingsWhen testing single variants, we identified three novel loci driven by low frequency variants more commonly observed in non-European ancestry (PRKAA2, rs180996919, minor allele frequency [MAF] 0.04%, P = 6.1 × 10-11; METTL8, rs116951054, MAF 0.09%, P = 4.5 × 10-9; and MATK, rs539182790, MAF 0.05%, P = 3.4 × 10-9). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P = 1.2 × 10-9, nearest gene GATM, and rs71147340, MAF=0.34, P = 3.3 × 10-9, CDK12). Testing aggregated variants within a gene identified the MAF gene. A statistical approach based on local ancestry helped to identify replication samples for ancestry-specific variants.InterpretationThis study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry.

Published
2021

9. Erratum: Large meta-analysis of genome-wide association studies identifies five loci for lean body mass.

Author

Zillikens, M Carola, Demissie, Serkalem, Hsu, Yi-Hsiang, Yerges-Armstrong, Laura M, Chou, Wen-Chi, Stolk, Lisette, Livshits, Gregory, Broer, Linda, Johnson, Toby, Koller, Daniel L, Kutalik, Zoltán, Luan, Jian'an, Malkin, Ida, Ried, Janina S, Smith, Albert V, Thorleifsson, Gudmar, Vandenput, Liesbeth, Hua Zhao, Jing, Zhang, Weihua, Aghdassi, Ali, Åkesson, Kristina, Amin, Najaf, Baier, Leslie J, Barroso, Inês, Bennett, David A, Bertram, Lars, Biffar, Rainer, Bochud, Murielle, Boehnke, Michael, Borecki, Ingrid B, Buchman, Aron S, Byberg, Liisa, Campbell, Harry, Campos Obanda, Natalia, Cauley, Jane A, Cawthon, Peggy M, Cederberg, Henna, Chen, Zhao, Cho, Nam H, Jin Choi, Hyung, Claussnitzer, Melina, Collins, Francis, Cummings, Steven R, De Jager, Philip L, Demuth, Ilja, Dhonukshe-Rutten, Rosalie AM, Diatchenko, Luda, Eiriksdottir, Gudny, Enneman, Anke W, Erdos, Mike, Eriksson, Johan G, Eriksson, Joel, Estrada, Karol, Evans, Daniel S, Feitosa, Mary F, Fu, Mao, Garcia, Melissa, Gieger, Christian, Girke, Thomas, Glazer, Nicole L, Grallert, Harald, Grewal, Jagvir, Han, Bok-Ghee, Hanson, Robert L, Hayward, Caroline, Hofman, Albert, Hoffman, Eric P, Homuth, Georg, Hsueh, Wen-Chi, Hubal, Monica J, Hubbard, Alan, Huffman, Kim M, Husted, Lise B, Illig, Thomas, Ingelsson, Erik, Ittermann, Till, Jansson, John-Olov, Jordan, Joanne M, Jula, Antti, Karlsson, Magnus, Khaw, Kay-Tee, Kilpeläinen, Tuomas O, Klopp, Norman, Kloth, Jacqueline SL, Koistinen, Heikki A, Kraus, William E, Kritchevsky, Stephen, Kuulasmaa, Teemu, Kuusisto, Johanna, Laakso, Markku, Lahti, Jari, Lang, Thomas, Langdahl, Bente L, Launer, Lenore J, Lee, Jong-Young, Lerch, Markus M, Lewis, Joshua R, Lind, Lars, Lindgren, Cecilia, and Liu, Yongmei

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics
Abstract

A correction to this article has been published and is linked from the HTML version of this article.

Published
2017

11. Large meta-analysis of genome-wide association studies identifies five loci for lean body mass.

Author

Zillikens, M Carola, Demissie, Serkalem, Hsu, Yi-Hsiang, Yerges-Armstrong, Laura M, Chou, Wen-Chi, Stolk, Lisette, Livshits, Gregory, Broer, Linda, Johnson, Toby, Koller, Daniel L, Kutalik, Zoltán, Luan, Jian'an, Malkin, Ida, Ried, Janina S, Smith, Albert V, Thorleifsson, Gudmar, Vandenput, Liesbeth, Hua Zhao, Jing, Zhang, Weihua, Aghdassi, Ali, Åkesson, Kristina, Amin, Najaf, Baier, Leslie J, Barroso, Inês, Bennett, David A, Bertram, Lars, Biffar, Rainer, Bochud, Murielle, Boehnke, Michael, Borecki, Ingrid B, Buchman, Aron S, Byberg, Liisa, Campbell, Harry, Campos Obanda, Natalia, Cauley, Jane A, Cawthon, Peggy M, Cederberg, Henna, Chen, Zhao, Cho, Nam H, Jin Choi, Hyung, Claussnitzer, Melina, Collins, Francis, Cummings, Steven R, De Jager, Philip L, Demuth, Ilja, Dhonukshe-Rutten, Rosalie AM, Diatchenko, Luda, Eiriksdottir, Gudny, Enneman, Anke W, Erdos, Mike, Eriksson, Johan G, Eriksson, Joel, Estrada, Karol, Evans, Daniel S, Feitosa, Mary F, Fu, Mao, Garcia, Melissa, Gieger, Christian, Girke, Thomas, Glazer, Nicole L, Grallert, Harald, Grewal, Jagvir, Han, Bok-Ghee, Hanson, Robert L, Hayward, Caroline, Hofman, Albert, Hoffman, Eric P, Homuth, Georg, Hsueh, Wen-Chi, Hubal, Monica J, Hubbard, Alan, Huffman, Kim M, Husted, Lise B, Illig, Thomas, Ingelsson, Erik, Ittermann, Till, Jansson, John-Olov, Jordan, Joanne M, Jula, Antti, Karlsson, Magnus, Khaw, Kay-Tee, Kilpeläinen, Tuomas O, Klopp, Norman, Kloth, Jacqueline SL, Koistinen, Heikki A, Kraus, William E, Kritchevsky, Stephen, Kuulasmaa, Teemu, Kuusisto, Johanna, Laakso, Markku, Lahti, Jari, Lang, Thomas, Langdahl, Bente L, Launer, Lenore J, Lee, Jong-Young, Lerch, Markus M, Lewis, Joshua R, Lind, Lars, Lindgren, Cecilia, and Liu, Yongmei

Subjects
Humans, Thinness, 17-Hydroxysteroid Dehydrogenases, Aldehyde Oxidoreductases, Extracellular Matrix Proteins, Body Composition, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Regulatory Elements, Transcriptional, Versicans, Genome-Wide Association Study, Insulin Receptor Substrate Proteins, ADAMTS Proteins, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Human Genome, Genetics, 1.1 Normal biological development and functioning
Abstract

Lean body mass, consisting mostly of skeletal muscle, is important for healthy aging. We performed a genome-wide association study for whole body (20 cohorts of European ancestry with n = 38,292) and appendicular (arms and legs) lean body mass (n = 28,330) measured using dual energy X-ray absorptiometry or bioelectrical impedance analysis, adjusted for sex, age, height, and fat mass. Twenty-one single-nucleotide polymorphisms were significantly associated with lean body mass either genome wide (p

Published
2017

12. Selecting SNPs informative for African, American Indian and European Ancestry: application to the Family Investigation of Nephropathy and Diabetes (FIND)

Author

Williams, Robert C, Elston, Robert C, Kumar, Pankaj, Knowler, William C, Abboud, Hanna E, Adler, Sharon, Bowden, Donald W, Divers, Jasmin, Freedman, Barry I, Igo, Robert P, Ipp, Eli, Iyengar, Sudha K, Kimmel, Paul L, Klag, Michael J, Kohn, Orly, Langefeld, Carl D, Leehey, David J, Nelson, Robert G, Nicholas, Susanne B, Pahl, Madeleine V, Parekh, Rulan S, Rotter, Jerome I, Schelling, Jeffrey R, Sedor, John R, Shah, Vallabh O, Smith, Michael W, Taylor, Kent D, Thameem, Farook, Thornley-Brown, Denyse, Winkler, Cheryl A, Guo, Xiuqing, Zager, Phillip, Hanson, Robert L, and the FIND Research Group

Subjects
Biological Sciences, Genetics, Diabetes, Human Genome, American Indian or Alaska Native, Health Disparities, Minority Health, Metabolic and endocrine, Black or African American, Algorithms, Chromosome Mapping, Diabetic Nephropathies, Genetic Markers, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Indians, North American, Likelihood Functions, Mexican Americans, Models, Genetic, Oligonucleotide Array Sequence Analysis, Polymorphism, Single Nucleotide, Principal Component Analysis, United States, White People, Individual genetic ancestry, Population structure, SNP, Diabetic nephropathy, FIND Research Group, Information and Computing Sciences, Medical and Health Sciences, Bioinformatics, Biological sciences, Biomedical and clinical sciences
Abstract

BackgroundThe presence of population structure in a sample may confound the search for important genetic loci associated with disease. Our four samples in the Family Investigation of Nephropathy and Diabetes (FIND), European Americans, Mexican Americans, African Americans, and American Indians are part of a genome- wide association study in which population structure might be particularly important. We therefore decided to study in detail one component of this, individual genetic ancestry (IGA). From SNPs present on the Affymetrix 6.0 Human SNP array, we identified 3 sets of ancestry informative markers (AIMs), each maximized for the information in one the three contrasts among ancestral populations: Europeans (HAPMAP, CEU), Africans (HAPMAP, YRI and LWK), and Native Americans (full heritage Pima Indians). We estimate IGA and present an algorithm for their standard errors, compare IGA to principal components, emphasize the importance of balancing information in the ancestry informative markers (AIMs), and test the association of IGA with diabetic nephropathy in the combined sample.ResultsA fixed parental allele maximum likelihood algorithm was applied to the FIND to estimate IGA in four samples: 869 American Indians; 1385 African Americans; 1451 Mexican Americans; and 826 European Americans. When the information in the AIMs is unbalanced, the estimates are incorrect with large error. Individual genetic admixture is highly correlated with principle components for capturing population structure. It takes ~700 SNPs to reduce the average standard error of individual admixture below 0.01. When the samples are combined, the resulting population structure creates associations between IGA and diabetic nephropathy.ConclusionsThe identified set of AIMs, which include American Indian parental allele frequencies, may be particularly useful for estimating genetic admixture in populations from the Americas. Failure to balance information in maximum likelihood, poly-ancestry models creates biased estimates of individual admixture with large error. This also occurs when estimating IGA using the Bayesian clustering method as implemented in the program STRUCTURE. Odds ratios for the associations of IGA with disease are consistent with what is known about the incidence and prevalence of diabetic nephropathy in these populations.

Published
2016

13. Trans-ancestral genome-wide association study of longitudinal pubertal height growth and shared heritability with adult health outcomes

Author

Bradfield, Jonathan P, Kember, Rachel L, Ulrich, Anna, Balkiyarova, Zhanna, Alyass, Akram, Aris, Izzuddin M, Bell, Joshua A, Broadaway, K Alaine, Chen, Zhanghua, Chai, Jin-Fang, Davies, Neil M, Fernandez-Orth, Dietmar, Bustamante, Mariona, Fore, Ruby, Ganguli, Amitavo, Heiskala, Anni, Hottenga, Jouke-Jan, Íñiguez, Carmen, Kobes, Sayuko, Leinonen, Jaakko, Lowry, Estelle, Lyytikainen, Leo-Pekka, Mahajan, Anubha, Pitkänen, Niina, Schnurr, Theresia M, Have, Christian Theil, Strachan, David P, Thiering, Elisabeth, Vogelezang, Suzanne, Wade, Kaitlin H, Wang, Carol A, Wong, Andrew, Holm, Louise Aas, Chesi, Alessandra, Choong, Catherine, Cruz, Miguel, Elliott, Paul, Franks, Steve, Frithioff-Bøjsøe, Christine, Gauderman, W James, Glessner, Joseph T, Gilsanz, Vicente, Griesman, Kendra, Hanson, Robert L, Kaakinen, Marika, Kalkwarf, Heidi, Kelly, Andrea, Hansen, Torben, Holm, Jens-Christian, Sørensen, Thorkild I A, Grant, Struan F A, Cousminer, Diana L, Bradfield, Jonathan P, Kember, Rachel L, Ulrich, Anna, Balkiyarova, Zhanna, Alyass, Akram, Aris, Izzuddin M, Bell, Joshua A, Broadaway, K Alaine, Chen, Zhanghua, Chai, Jin-Fang, Davies, Neil M, Fernandez-Orth, Dietmar, Bustamante, Mariona, Fore, Ruby, Ganguli, Amitavo, Heiskala, Anni, Hottenga, Jouke-Jan, Íñiguez, Carmen, Kobes, Sayuko, Leinonen, Jaakko, Lowry, Estelle, Lyytikainen, Leo-Pekka, Mahajan, Anubha, Pitkänen, Niina, Schnurr, Theresia M, Have, Christian Theil, Strachan, David P, Thiering, Elisabeth, Vogelezang, Suzanne, Wade, Kaitlin H, Wang, Carol A, Wong, Andrew, Holm, Louise Aas, Chesi, Alessandra, Choong, Catherine, Cruz, Miguel, Elliott, Paul, Franks, Steve, Frithioff-Bøjsøe, Christine, Gauderman, W James, Glessner, Joseph T, Gilsanz, Vicente, Griesman, Kendra, Hanson, Robert L, Kaakinen, Marika, Kalkwarf, Heidi, Kelly, Andrea, Hansen, Torben, Holm, Jens-Christian, Sørensen, Thorkild I A, Grant, Struan F A, and Cousminer, Diana L

Abstract

Background: Pubertal growth patterns correlate with future health outcomes. How‑ever, the genetic mechanisms mediating growth trajectories remain largely unknown. Here, we modeled longitudinal height growth with Super-Imposition by Transla‑tion And Rotation (SITAR) growth curve analysis on~56,000 trans-ancestry samples with repeated height measurements from age 5 years to adulthood. We performed genetic analysis on six phenotypes representing the magnitude, timing, and intensity of the pubertal growth spurt. To investigate the lifelong impact of genetic variants associated with pubertal growth trajectories, we performed genetic correlation analy‑ses and phenome-wide association studies in the Penn Medicine BioBank and the UK Biobank. Results: Large-scale growth modeling enables an unprecedented view of adoles‑ cent growth across contemporary and 20th-century pediatric cohorts. We identify 26 genome-wide signifcant loci and leverage trans-ancestry data to perform fnemapping. Our data reveals genetic relationships between pediatric height growth and health across the life course, with diferent growth trajectories correlated with dif‑ferent outcomes. For instance, a faster tempo of pubertal growth correlates with higher bone mineral density, HOMA-IR, fasting insulin, type 2 diabetes, and lung cancer, whereas being taller at early puberty, taller across puberty, and having quicker pubertal growth were associated with higher risk for atrial fbrillation. Conclusion: We report novel genetic associations with the tempo of pubertal growth and fnd that genetic determinants of growth are correlated with reproductive, glyce‑mic, respiratory, and cardiac traits in adulthood. These results aid in identifying specifc growth trajectories impacting lifelong health and show that there may not be a single “optimal” pubertal growth pattern., BACKGROUND: Pubertal growth patterns correlate with future health outcomes. However, the genetic mechanisms mediating growth trajectories remain largely unknown. Here, we modeled longitudinal height growth with Super-Imposition by Translation And Rotation (SITAR) growth curve analysis on ~ 56,000 trans-ancestry samples with repeated height measurements from age 5 years to adulthood. We performed genetic analysis on six phenotypes representing the magnitude, timing, and intensity of the pubertal growth spurt. To investigate the lifelong impact of genetic variants associated with pubertal growth trajectories, we performed genetic correlation analyses and phenome-wide association studies in the Penn Medicine BioBank and the UK Biobank.RESULTS: Large-scale growth modeling enables an unprecedented view of adolescent growth across contemporary and 20th-century pediatric cohorts. We identify 26 genome-wide significant loci and leverage trans-ancestry data to perform fine-mapping. Our data reveals genetic relationships between pediatric height growth and health across the life course, with different growth trajectories correlated with different outcomes. For instance, a faster tempo of pubertal growth correlates with higher bone mineral density, HOMA-IR, fasting insulin, type 2 diabetes, and lung cancer, whereas being taller at early puberty, taller across puberty, and having quicker pubertal growth were associated with higher risk for atrial fibrillation.CONCLUSION: We report novel genetic associations with the tempo of pubertal growth and find that genetic determinants of growth are correlated with reproductive, glycemic, respiratory, and cardiac traits in adulthood. These results aid in identifying specific growth trajectories impacting lifelong health and show that there may not be a single "optimal" pubertal growth pattern.

Published
2024

15. Genome-wide Association Study of Platelet Count Identifies Ancestry-Specific Loci in Hispanic/Latino Americans

Author

Schick, Ursula M, Jain, Deepti, Hodonsky, Chani J, Morrison, Jean V, Davis, James P, Brown, Lisa, Sofer, Tamar, Conomos, Matthew P, Schurmann, Claudia, McHugh, Caitlin P, Nelson, Sarah C, Vadlamudi, Swarooparani, Stilp, Adrienne, Plantinga, Anna, Baier, Leslie, Bien, Stephanie A, Gogarten, Stephanie M, Laurie, Cecelia A, Taylor, Kent D, Liu, Yongmei, Auer, Paul L, Franceschini, Nora, Szpiro, Adam, Rice, Ken, Kerr, Kathleen F, Rotter, Jerome I, Hanson, Robert L, Papanicolaou, George, Rich, Stephen S, Loos, Ruth JF, Browning, Brian L, Browning, Sharon R, Weir, Bruce S, Laurie, Cathy C, Mohlke, Karen L, North, Kari E, Thornton, Timothy A, and Reiner, Alex P

Subjects
Biological Sciences, Genetics, Human Genome, Clinical Research, Hematology, Underpinning research, 1.1 Normal biological development and functioning, Blood, Actinin, Adolescent, Adult, Aged, Alleles, Gene Frequency, Genetic Association Studies, Genetic Loci, Genotype, Genotyping Techniques, Hispanic or Latino, Humans, MEF2 Transcription Factors, Membrane Proteins, Middle Aged, Phenotype, Platelet Count, Polymorphism, Single Nucleotide, Receptors, GABA-B, Young Adult, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Platelets play an essential role in hemostasis and thrombosis. We performed a genome-wide association study of platelet count in 12,491 participants of the Hispanic Community Health Study/Study of Latinos by using a mixed-model method that accounts for admixture and family relationships. We discovered and replicated associations with five genes (ACTN1, ETV7, GABBR1-MOG, MEF2C, and ZBTB9-BAK1). Our strongest association was with Amerindian-specific variant rs117672662 (p value = 1.16 × 10(-28)) in ACTN1, a gene implicated in congenital macrothrombocytopenia. rs117672662 exhibited allelic differences in transcriptional activity and protein binding in hematopoietic cells. Our results underscore the value of diverse populations to extend insights into the allelic architecture of complex traits.

Published
2016

17. Further evidence supporting a potential role for ADH1B in obesity

Author

Morales, Liza D., Cromack, Douglas T., Tripathy, Devjit, Fourcaudot, Marcel, Kumar, Satish, Curran, Joanne E., Carless, Melanie, Göring, Harald H. H., Hu, Shirley L., Lopez-Alvarenga, Juan Carlos, Garske, Kristina M., Pajukanta, Päivi, Small, Kerrin S., Glastonbury, Craig A., Das, Swapan K., Langefeld, Carl, Hanson, Robert L., Hsueh, Wen-Chi, Norton, Luke, Arya, Rector, Mummidi, Srinivas, Blangero, John, DeFronzo, Ralph A., Duggirala, Ravindranath, and Jenkinson, Christopher P.

Published
2021
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19. Genome-Wide Association and Trans-ethnic Meta-Analysis for Advanced Diabetic Kidney Disease: Family Investigation of Nephropathy and Diabetes (FIND).

Author

Iyengar, Sudha K, Sedor, John R, Freedman, Barry I, Kao, WH Linda, Kretzler, Matthias, Keller, Benjamin J, Abboud, Hanna E, Adler, Sharon G, Best, Lyle G, Bowden, Donald W, Burlock, Allison, Chen, Yii-Der Ida, Cole, Shelley A, Comeau, Mary E, Curtis, Jeffrey M, Divers, Jasmin, Drechsler, Christiane, Duggirala, Ravi, Elston, Robert C, Guo, Xiuqing, Huang, Huateng, Hoffmann, Michael Marcus, Howard, Barbara V, Ipp, Eli, Kimmel, Paul L, Klag, Michael J, Knowler, William C, Kohn, Orly F, Leak, Tennille S, Leehey, David J, Li, Man, Malhotra, Alka, März, Winfried, Nair, Viji, Nelson, Robert G, Nicholas, Susanne B, O'Brien, Stephen J, Pahl, Madeleine V, Parekh, Rulan S, Pezzolesi, Marcus G, Rasooly, Rebekah S, Rotimi, Charles N, Rotter, Jerome I, Schelling, Jeffrey R, Seldin, Michael F, Shah, Vallabh O, Smiles, Adam M, Smith, Michael W, Taylor, Kent D, Thameem, Farook, Thornley-Brown, Denyse P, Truitt, Barbara J, Wanner, Christoph, Weil, E Jennifer, Winkler, Cheryl A, Zager, Philip G, Igo, Robert P, Hanson, Robert L, Langefeld, Carl D, and Family Investigation of Nephropathy and Diabetes (FIND)

Subjects
Family Investigation of Nephropathy and Diabetes, Humans, Diabetic Nephropathies, Diabetes Mellitus, Type 2, Genetic Predisposition to Disease, RNA-Binding Proteins, African Americans, Indians, North American, European Continental Ancestry Group, Hispanic Americans, United States, Genome-Wide Association Study, Diabetes, Genetics, Human Genome, Clinical Research, Kidney Disease, American Indian or Alaska Native, Prevention, 2.1 Biological and endogenous factors, Metabolic and endocrine, Renal and urogenital, Developmental Biology
Abstract

Diabetic kidney disease (DKD) is the most common etiology of chronic kidney disease (CKD) in the industrialized world and accounts for much of the excess mortality in patients with diabetes mellitus. Approximately 45% of U.S. patients with incident end-stage kidney disease (ESKD) have DKD. Independent of glycemic control, DKD aggregates in families and has higher incidence rates in African, Mexican, and American Indian ancestral groups relative to European populations. The Family Investigation of Nephropathy and Diabetes (FIND) performed a genome-wide association study (GWAS) contrasting 6,197 unrelated individuals with advanced DKD with healthy and diabetic individuals lacking nephropathy of European American, African American, Mexican American, or American Indian ancestry. A large-scale replication and trans-ethnic meta-analysis included 7,539 additional European American, African American and American Indian DKD cases and non-nephropathy controls. Within ethnic group meta-analysis of discovery GWAS and replication set results identified genome-wide significant evidence for association between DKD and rs12523822 on chromosome 6q25.2 in American Indians (P = 5.74x10-9). The strongest signal of association in the trans-ethnic meta-analysis was with a SNP in strong linkage disequilibrium with rs12523822 (rs955333; P = 1.31x10-8), with directionally consistent results across ethnic groups. These 6q25.2 SNPs are located between the SCAF8 and CNKSR3 genes, a region with DKD relevant changes in gene expression and an eQTL with IPCEF1, a gene co-translated with CNKSR3. Several other SNPs demonstrated suggestive evidence of association with DKD, within and across populations. These data identify a novel DKD susceptibility locus with consistent directions of effect across diverse ancestral groups and provide insight into the genetic architecture of DKD.

Published
2015

20. Association of urinary KIM-1, L-FABP, NAG and NGAL with incident end-stage renal disease and mortality in American Indians with type 2 diabetes mellitus

Author

Fufaa, Gudeta D, Weil, E Jennifer, Nelson, Robert G, Hanson, Robert L, Bonventre, Joseph V, Sabbisetti, Venkata, Waikar, Sushrut S, Mifflin, Theodore E, Zhang, Xiaoming, Xie, Dawei, Hsu, Chi-yuan, Feldman, Harold I, Coresh, Josef, Vasan, Ramachandran S, Kimmel, Paul L, Liu, Kathleen D, and for the Chronic Kidney Disease Biomarkers Consortium Investigators

Subjects
Epidemiology, Biomedical and Clinical Sciences, Health Sciences, Kidney Disease, Renal and urogenital, Good Health and Well Being, Acute-Phase Proteins, Adolescent, Adult, Aged, Diabetes Mellitus, Type 2, Diabetic Nephropathies, Fatty Acid-Binding Proteins, Female, Hepatitis A Virus Cellular Receptor 1, Humans, Incidence, Indians, North American, Kidney Failure, Chronic, Lipocalin-2, Lipocalins, Male, Membrane Glycoproteins, Middle Aged, Neoplasm Proteins, Proto-Oncogene Proteins, Receptors, Virus, Young Adult, Biomarkers, End-stage renal disease, Mortality, Type 2 diabetes, Chronic Kidney Disease Biomarkers Consortium Investigators, Clinical Sciences, Paediatrics and Reproductive Medicine, Public Health and Health Services, Endocrinology & Metabolism, Clinical sciences, Public health
Abstract

Aims/hypothesisKidney injury molecule 1 (KIM-1), liver fatty acid-binding protein (L-FABP), N-acetyl-β-D-glucosaminidase (NAG) and neutrophil gelatinase-associated lipocalin (NGAL) are urinary biomarkers of renal tubular injury. We examined their association with incident end-stage renal disease (ESRD) and all-cause mortality in American Indians with type 2 diabetes.MethodsBiomarker concentrations were measured in baseline urine samples in 260 Pima Indians who were followed for a median of 14 years. HRs were reported per SD of creatinine (Cr)-normalised log-transformed KIM-1, NAG and NGAL, and for three categories of L-FABP.ResultsDuring follow-up, 74 participants developed ESRD and 101 died. Median concentrations of KIM-1/Cr, NAG/Cr and NGAL/Cr and the proportion of detectable L-FABP were highest in those with macroalbuminuria (p

Published
2015

21. A Genome-Wide Search for Linkage of Estimated Glomerular Filtration Rate (eGFR) in the Family Investigation of Nephropathy and Diabetes (FIND)

Author

Thameem, Farook, Igo, Robert P, Freedman, Barry I, Langefeld, Carl, Hanson, Robert L, Schelling, Jeffrey R, Elston, Robert C, Duggirala, Ravindranath, Nicholas, Susanne B, Goddard, Katrina A. B, Divers, Jasmin, Guo, Xiuqing, Ipp, Eli, Kimmel, Paul L, Meoni, Lucy A, Shah, Vallabh O, Smith, Michael W, Winkler, Cheryl A, Zager, Philip G, Knowler, William C, Nelson, Robert G, Pahl, Madeline V, Parekh, Rulan S, Kao, W. H. Linda, Rasooly, Rebekah S, Adler, Sharon G, Abboud, Hanna E, Iyengar, Sudha K, Sedor, John R, and Cai, Tao

Subjects
Chronic Kidney-Disease, Renal-Function, Mexican-Americans, Genetic-Variants, Serum Creatinine, Identifies 6, Association, Loci, Albuminuria, Traits
Published
2013

25. Genomewide linkage scan for diabetic renal failure and albuminuria: the FIND study.

Author

Igo, Robert P, Jr, Iyengar, Sudha K, Nicholas, Susanne B, Goddard, Katrina A B, Langefeld, Carl D, Hanson, Robert L, Duggirala, Ravindranath, Divers, Jasmin, Abboud, Hanna, Adler, Sharon G, Arar, Nedal H, Horvath, Amanda, Elston, Robert C, Bowden, Donald W, Guo, Xiuqing, Ipp, Eli, Kao, W H Linda, Kimmel, Paul L, Knowler, William C, Meoni, Lucy A, Molineros, Julio, Nelson, Robert G, Pahl, Madeline V, Parekh, Rulan S, Rasooly, Rebekah S, Schelling, Jeffrey R, Shah, Vallabh O, Smith, Michael W, Winkler, Cheryl A, Zager, Philip G, Sedor, John R, and Freedman, Barry I

Subjects
Aged, Albuminuria: genetics, metabolism, Chromosome Mapping, Diabetic Nephropathies: genetics, metabolism, Ethnic Groups, Female, Genetic Linkage, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Middle Aged, Pedigree, Polymorphism, Single Nucleotide, Renal Insufficiency: genetics, metabolism, Risk, Time Factors
Abstract

Diabetic nephropathy (DN) is a leading cause of mortality and morbidity in patients with type 1 and type 2 diabetes. The multicenter FIND consortium aims to identify genes for DN and its associated quantitative traits, e.g. the urine albumin:creatinine ratio (ACR). Herein, the results of whole-genome linkage analysis and a sparse association scan for ACR and a dichotomous DN phenotype are reported in diabetic individuals.A genomewide scan comprising more than 5,500 autosomal single nucleotide polymorphism markers (average spacing of 0.6 cM) was performed on 1,235 nuclear and extended pedigrees (3,972 diabetic participants) ascertained for DN from African-American (AA), American-Indian (AI), European-American (EA) and Mexican-American (MA) populations.Strong evidence for linkage to DN was detected on chromosome 6p (p = 8.0 × 10(-5), LOD = 3.09) in EA families as well as suggestive evidence for linkage to chromosome 7p in AI families. Regions on chromosomes 3p in AA, 7q in EA, 16q in AA and 22q in MA displayed suggestive evidence of linkage for urine ACR. The linkage peak on chromosome 22q overlaps the MYH9/APOL1 gene region, previously implicated in AA diabetic and nondiabetic nephropathies.These results strengthen the evidence for previously identified genomic regions and implicate several novel loci potentially involved in the pathogenesis of DN.

Published
2011

28. The Effect of Interventions to Prevent Type 2 Diabetes on the Development of Diabetic Retinopathy: The DPP/DPPOS Experience

Author

White, Neil H., Pan, Qing, Knowler, William C., Schroeder, Emily B., Dabelea, Dana, Chew, Emily Y., Blodi, Barbara, Goldberg, Ronald B., Pi-Sunyer, Xavier, Darwin, Christine, Schlögl, Mathias, Nathan, David M., Goldstein, Barry J., Furlong, Kevin, Smith, Kellie A., Mendoza, Jewel, Wildman, Wendi, Simmons, Marsha, Jensen, Genine, Liberoni, Renee, Spandorfer, John, Pepe, Constance, Donahue, Richard P., Prineas, Ronald, Rowe, Patricia, Giannella, Anna, Calles, Jeanette, Sanguily, Juliet, Cassanova-Romero, Paul, Castillo-Florez, Sumaya, Florez, Hermes J., Garg, Rajesh, Kirby, Lascelles, Lara, Olga, Larreal, Carmen, McLymont, Valerie, Mendez, Jadell, Perry, Arlette, Saab, Patrice, Veciana, Bertha, Haffner, Steven M., Hazuda, Helen P., Montez, Maria G., Isaac, Juan, Hattaway, Kathy, Lorenzo, Carlos, Martinez, Arlene, Salazar, Monica, Walker, Tatiana, Hamman, Richard F., Nash, Patricia V., Steinke, Sheila C., Testaverde, Lisa, Truong, Jennifer, Anderson, Denise R., Ballonoff, Larry B., Bouffard, Alexis, Boxer, Rebecca S., Bucca, Brian, Calonge, B. Ned, Delve, Lynne, Farago, Martha, Hill, James O., Hoyer, Shelley R., Jenkins, Tonya, Jortberg, Bonnie T., Lenz, Dione, Miller, Marsha, Nilan, Thomas, Perreault, Leigh, Price, David W., Regensteiner, Judith G., Seagle, Helen, Smith, Carissa M., VanDorsten, Brent, Horton, Edward S., Munshi, Medha, Lawton, Kathleen E., Poirier, Catherine S., Swift, Kati, Jackson, Sharon D., Arky, Ronald A., Bryant, Marybeth, Burke, Jacqueline P., Caballero, Enrique, Callaphan, Karen M., Fargnoli, Barbara, Franklin, Therese, Ganda, Om P., Guidi, Ashley, Guido, Mathew, Jacobsen, Alan M., Kula, Lyn M., Kocal, Margaret, Lambert, Lori, Ledbury, Sarah, Malloy, Maureen A., Middelbeek, Roeland J.W., Nicosia, Maryanne, Oldmixon, Cathryn F., Pan, Jocelyn, Quitingon, Marizel, Rainville, Riley, Rubtchinsky, Stacy, Seely, Ellen W., Sansoucy, Jessica, Schweizer, Dana, Simonson, Donald, Smith, Fannie, Solomon, Caren G., Spellman, Jeanne, Warram, James, Kahn, Steven E., Montgomery, Brenda K., Fattaleh, Basma, Colegrove, Celeste, Fujimoto, Wilfred, Knopp, Robert H., Lipkin, Edward W., Marr, Michelle, Morgan-Taggart, Ivy, Murillo, Anne, O’Neal, Kayla, Trence, Dace, Taylor, Lonnese, Thomas, April, Tsai, Elaine C., Kitabchi, Abbas E., Dagogo-Jack, Samuel, Murphy, Mary E., Taylor, Laura, Dolgoff, Jennifer, Hampton, Ethel Faye, Applegate, William B., Bryer-Ash, Michael, Clark, Debra, Frieson, Sandra L., Ibebuogu, Uzoma, Imseis, Raed, Lambeth, Helen, Lichtermann, Lynne C., Oktaei, Hooman, Ricks, Harriet, Rutledge, Lily M.K., Sherman, Amy R., Smith, Clara M., Soberman, Judith E., Williamsleaves, Beverly, Patel, Avnisha, Nyenwe, Ebenezer A., Metzger, Boyd E., Molitch, Mark E., Wallia, Amisha, Johnson, Mariana K., VanderMolen, Sarah, Adelman, Daphne T., Behrends, Catherine, Cook, Michelle, Fitzgibbon, Marian, Giles, Mimi M., Hartmuller, Monica, Johnson, Cheryl K.H., Larsen, Diane, Lowe, Anne, Lyman, Megan, McPherson, David, Penn, Samsam C., Pitts, Thomas, Reinhart, Renee, Roston, Susan, Schinleber, Pamela A., McKitrick, Charles, Turgeon, Heather, Larkin, Mary, Mugford, Marielle, Thangthaeng, Nopporn, Leander, Fernelle, Abbott, Kathy, Anderson, Ellen, Bissett, Laurie, Bondi, Kristy, Cagliero, Enrico, Florez, Jose C., Delahanty, Linda, Goldman, Valerie, Grassa, Elaine, Gurry, Lindsey, D’Anna, Kali, Leandre, Fernelle, Lou, Peter, Poulos, Alexandra, Raymond, Elyse, Ripley, Valerie, Stevens, Christine, Tseng, Beverly, Olefsky, Jerrold M., Barrettonnor, Elizabeth, Mudaliar, Sunder, Rosario Araneta, Maria, Carrion-Petersen, Mary Lou, Vejvoda, Karen, Bassiouni, Sarah, Beltran, Madeline, Claravall, Lauren N., Dowden, Jonalle M., Edelman, Steven V., Garimella, Pranav, Henry, Robert R., Horne, Javiva, Lamkin, Marycie, Szerdi Janesch, Simona, Leos, Diana, Polonsky, William, Ruiz, Rosa, Smith, Jean, Torio-Hurley, Jennifer, Pi-Sunyer, F. Xavier, Laferrere, Blandine, Lee, Jane E., Hagamen, Susan, Kelly-Dinham, Kim, Allison, David B., Agharanya, Nnenna, Aronoff, Nancy J., Baldo, Maria, Crandall, Jill P., Foo, Sandra T., Luchsinger, Jose A., Pal, Carmen, Parkes, Kathy, Pena, Mary Beth, Roman, Julie, Rooney, Ellen S., VanWye, Gretchen E.H., Viscovich, Kristine A., Prince, Melvin J., Marrero, David G., Mather, Kieren J., De Groot, Mary, Kelly, Susie M., Jackson, Marcia A., McAtee, Gina, Putenney, Paula, Ackermann, Ronald T., Cantrell, Carolyn M., Dotson, Yolanda F., Fineberg, Edwin S., Fultz, Megan, Guare, John C., Hadden, Angela, Ignaut, James M., Kirkman, Marion S., O’Kelly Phillips, Erin, Pinner, Kisha L., Porter, Beverly D., Roach, Paris J., Rowland, Nancy D., Wheeler, Madelyn L., Ratner, Robert E., Aroda, Vanita, Magee, Michelle, Youssef, Gretchen, Shapiro, Sue, Andon, Natalie, Bavido-Arrage, Catherine, Boggs, Geraldine, Bronsord, Marjorie, Brown, Ernestine, Love Burkott, Holly, Cheatham, Wayman W., Cola, Susan, Evans, Cindy, Gibbs, Peggy, Kellum, Tracy, Leon, Lilia, Lagarda, Milvia, Levatan, Claresa, Lindsay, Milajurine, Nair, Asha K., Park, Jean, Passaro, Maureen, Silverman, Angela, Uwaifo, Gabriel, Wells-Thayer, Debra, Wiggins, Renee, Saad, Mohammed F., Watson, Karol, Budget, Maria, Jinagouda, Sujata, Botrous, Medhat, Sosa, Anthony, Tadros, Sameh, Akbar, Khan, Conzues, Claudia, Magpuri, Perpetua, Ngo, Kathy, Rassam, Amer, Waters, Debra, Xapthalamous, Kathy, Santiago, Julio V., Brown, Angela L., Santiago, Ana, Das, Samia, Khare-Ranade, Prajakta, Stich, Tamara, Fisher, Edwin, Hurt, Emma, Jones, Jackie, Jones, Tracy, Kerr, Michelle, McCowan, Sherri, Ryder, Lucy, Wernimont, Cormarie, Saudek, Christopher D., Hill Golden, Sherita, Bradley, Vanessa, Sullivan, Emily, Whittington, Tracy, Abbas, Caroline, Allen, Adrienne, Brancati, Frederick L., Cappelli, Sharon, Clark, Jeanne M., Charleston, Jeanne B., Freel, Janice, Horak, Katherine, Greene, Alicia, Jiggetts, Dawn, Johnson, Delois, Joseph, Hope, Kalyani, Rita, Loman, Kimberly, Mathioudakis, Nestoras, Maruthur, Nisa, Mosley, Henry, Reusing, John, Rubin, Richard R., Samuels, Alafia, Shields, Thomas, Stephens, Shawne, Stewart, Kerry J., Thomas, LeeLana, Utsey, Evonne, Williamson, Paula, Schade, David S., Adams, Karwyn S., Johannes, Carolyn, Hemphill, Claire, Hyde, Penny, Canady, Janene L., Atler, Leslie F., Boyle, Patrick J., Burge, Mark R., Chai, Lisa, Colleran, Kathleen, Fondino, Ateka, Gonzales, Ysela, Hernandez-McGinnis, Doris A., Katz, Patricia, King, Carolyn, Middendorf, Julia, Rubinchik, Sofya, Senter, Willette, Shamoon, Harry, Crandall, Jill, Brown, Janet O., Trandafirescu, Gilda, Powell, Danielle, Adorno, Elsie, Cox, Liane, Duffy, Helena, Engel, Samuel, Friedler, Allison, Goldstein, Angela, Howardentury, Crystal J., Lukin, Jennifer, Kloiber, Stacey, Longchamp, Nadege, Martinez, Helen, Pompi, Dorothy, Scheindlin, Jonathan, Tomuta, Norica, Violino, Elissa, Walker, Elizabeth A., Wylie-Rosett, Judith, Zimmerman, Elise, Zonszein, Joel, Wing, Rena R., Orchard, Trevor, Venditti, Elizabeth, Koenning, Gaye, Kramer, M. Kaye, Smith, Marie, Jeffries, Susan, Weinzierl, Valarie, Barr, Susan, Benchoff, Catherine, Boraz, Miriam, Clifford, Lisa, Culyba, Rebecca, Frazier, Marlene, Gilligan, Ryan, Guimond, Stephanie, Harrier, Susan, Harris, Louann, Kriska, Andrea, Manjoo, Qurashia, Mullen, Monica, Noel, Alicia, Otto, Amy, Pettigrew, Jessica, Rockette-Wagner, Bonny, Rubinstein, Debra, Semler, Linda, Smith, Cheryl F., Williams, Katherine V., Wilson, Tara, Arakaki, Richard F., Mau, Marjorie K., Latimer, Renee W., Isonaga, Mae K., Baker-Ladao, Narleen K., Bow, Ralph, Bermudez, Nina E., Dias, Lorna, Inouye, Jillian, Melish, John S., Mikami, Kathy, Mohideen, Pharis, Odom, Sharon K., Perry, Raynette U., Yamamoto, Robin E., Hanson, Robert L., Shah, Vallabh, Hoskin, Mary A., Percy, Carol A., Cooeyate, Norman, Natewa, Camille, Dodge, Charlotte, Enote, Alvera, Anderson, Harelda, Acton, Kelly J., Andre, Vickie L., Barber, Rosalyn, Begay, Shandiin, Bennett, Peter H., Benson, Mary Beth, Bird, Evelyn C., Broussard, Brenda A., Bucca, Brian C., Chavez, Marcella, Cook, Sherron, Curtis, Jeff, Dacawyma, Tara, Doughty, Matthew S., Duncan, Roberta, Edgerton, Cyndy, Ghahate, Jacqueline M., Glass, Justin, Glass, Martia, Gohdes, Dorothy, Grant, Wendy, Horse, Ellie, Ingraham, Louise E., Jackson, Merry, Jay, Priscilla, Kaskalla, Roylen S., Kavena, Karen, Kessler, David, Kobus, Kathleen M., Krakoff, Jonathan, Kurland, Jason, Manus, Catherine, McCabe, Cherie, Michaels, Sara, Morgan, Tina, Nashboo, Yolanda, Nelson, Julie A., Poirier, Steven, Polczynski, Evette, Piromalli, Christopher, Reidy, Mike, Roumain, Jeanine, Rowse, Debra, Roy, Robert J., Sangster, Sandra, Sewenemewa, Janet, Smart, Miranda, Spencer, Chelsea, Tonemah, Darryl, Williams, Rachel, Wilson, Charlton, Yazzie, Michelle, Bain, Raymond, Fowler, Sarah, Larsen, Michael D., Jablonski, Kathleen, Temprosa, Marinella, Brenneman, Tina, Edelstein, Sharon L., Abebe, Solome, Bamdad, Julie, Barkalow, Melanie, Bethepu, Joel, Bezabeh, Tsedenia, Bowers, Anna, Butler, Nicole, Callaghan, Jackie, Carter, Caitlin E., Christophi, Costas, Dwyer, Gregory M., Foulkes, Mary, Gao, Yuping, Gooding, Robert, Gottlieb, Adrienne, Grimes, Kristina L., Grover-Fairchild, Nisha, Haffner, Lori, Hoffman, Heather, Jones, Steve, Jones, Tara L., Katz, Richard, Kolinjivadi, Preethy, Lachin, John M., Ma, Yong, Mucik, Pamela, Orlosky, Robert, Reamer, Susan, Rochon, James, Sapozhnikova, Alla, Sherif, Hanna, Stimpson, Charlotte, Hogan Tjaden, Ashley, Walker-Murray, Fredricka, Venditti, Elizabeth M., Kriska, Andrea M., Weinzierl, Valerie, Marcovina, Santica, Aldrich, F. Alan, Harting, Jessica, Albers, John, Strylewicz, Greg, Killeen, Anthony, Gabrielson, Deanna, Eastman, R., Fradkin, Judith, Garfield, Sanford, Lee, Christine, Gregg, Edward, Zhang, Ping, O’Leary, Dan, Evans, Gregory, Budoff, Matthew, Dailing, Chris, Stamm, Elizabeth, Schwartz, Ann, Navy, Caroline, Palermo, Lisa, Rautaharju, Pentti, Prineas, Ronald J., Soliman, Elsayed Z., Alexander, Teresa, Campbell, Charles, Hall, Sharon, Li, Yabing, Mills, Margaret, Pemberton, Nancy, Rautaharju, Farida, Zhang, Zhuming, Hu, Julie, Hensley, Susan, Keasler, Lisa, Taylor, Tonya, Danis, Ronald, Davis, Matthew, Hubbard, Larry, Endres, Ryan, Elsas, Deborah, Johnson, Samantha, Myers, Dawn, Barrett, Nancy, Baumhauer, Heather, Benz, Wendy, Cohn, Holly, Corkery, Ellie, Dohm, Kristi, Domalpally, Amitha, Gama, Vonnie, Goulding, Anne, Ewen, Andy, Hurtenbach, Cynthia, Lawrence, Daniel, McDaniel, Kyle, Pak, Jeong, Reimers, James, Shaw, Ruth, Swift, Maria, Vargo, Pamela, Watson, Sheila, Manly, Jennifer, Mayer-Davis, Elizabeth, Moran, Robert R., Ganiats, Ted, David, Kristin, Sarkin, Andrew J., Groessl, Erik, Katzir, Naomi, Chong, Helen, Herman, William H., Brändle, Michael, Brown, Morton B., Altshuler, David, Billings, Liana K., Chen, Ling, Harden, Maegan, Pollin, Toni I., Shuldiner, Alan R., Franks, Paul W., and Hivert, Marie-France

Subjects
Advanced and Specialized Nursing, Endocrinology, Diabetes and Metabolism, Internal Medicine, Pathophysiology/Complications
Abstract

OBJECTIVE To determine whether interventions that slow or prevent the development of type 2 diabetes in those at risk reduce the subsequent prevalence of diabetic retinopathy. RESEARCH DESIGN AND METHODS The Diabetes Prevention Program (DPP) randomized subjects at risk for developing type 2 diabetes because of overweight/obesity and dysglycemia to metformin (MET), intensive lifestyle intervention (ILS), or placebo (PLB) to assess the prevention of diabetes. During the DPP and DPP Outcome Study (DPPOS), we performed fundus photography over time on study participants, regardless of their diabetes status. Fundus photographs were graded using the Early Treatment Diabetic Retinopathy Study grading system, with diabetic retinopathy defined as typical lesions of diabetic retinopathy (microaneurysms, exudates, or hemorrhage, or worse) in either eye. RESULTS Despite reduced progression to diabetes in the ILS and MET groups compared with PLB, there was no difference in the prevalence of diabetic retinopathy between treatment groups after 1, 5, 11, or 16 years of follow-up. No treatment group differences in retinopathy were found within prespecified subgroups (baseline age, sex, race/ethnicity, baseline BMI). In addition, there was no difference in the prevalence of diabetic retinopathy between those exposed to metformin and those not exposed to metformin, regardless of treatment group assignment. CONCLUSIONS Interventions that delay or prevent the onset of type 2 diabetes in overweight/obese subjects with dysglycemia who are at risk for diabetes do not reduce the development of diabetic retinopathy for up to 20 years.

Published
2022

32. Identification of Genetic Variation Influencing Metformin Response in a Multiancestry Genome-Wide Association Study in the Diabetes Prevention Program (DPP).

Author

Li, Josephine H., Perry, James A., Jablonski, Kathleen A., Srinivasan, Shylaja, Chen, Ling, Todd, Jennifer N., Harden, Maegan, Mercader, Josep M., Pan, Qing, Dawed, Adem Y., Yee, Sook Wah, Pearson, Ewan R., Giacomini, Kathleen M., Giri, Ayush, Hung, Adriana M., Xiao, Shujie, Williams, L. Keoki, Franks, Paul W., Hanson, Robert L., and Kahn, Steven E.

Subjects
GENOME-wide association studies, GENETIC variation, GLYCOSYLATED hemoglobin, TYPE 2 diabetes, PROPORTIONAL hazards models
Abstract

Genome-wide significant loci for metformin response in type 2 diabetes reported elsewhere have not been replicated in the Diabetes Prevention Program (DPP). To assess pharmacogenetic interactions in prediabetes, we conducted a genome-wide association study (GWAS) in the DPP. Cox proportional hazards models tested associations with diabetes incidence in the metformin (MET; n = 876) and placebo (PBO; n = 887) arms. Multiple linear regression assessed association with 1-year change in metformin-related quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-treatment interaction. No significant associations emerged for diabetes incidence. We identified four genome-wide significant variants after correcting for correlated traits (P < 9 × 10−9). In the MET arm, rs144322333 near ENOSF1 (minor allele frequency [MAF]AFR = 0.07; MAFEUR = 0.002) was associated with an increase in percentage of glycated hemoglobin (per minor allele, β = 0.39 [95% CI 0.28, 0.50]; P = 2.8 × 10−12). rs145591055 near OMSR (MAF = 0.10 in American Indians) was associated with weight loss (kilograms) (per G allele, β = −7.55 [95% CI −9.88, −5.22]; P = 3.2 × 10−10) in the MET arm. Neither variant was significant in PBO; gene-by-treatment interaction was significant for both variants [P(G×T) < 1.0 × 10−4]. Replication in individuals with diabetes did not yield significant findings. A GWAS for metformin response in prediabetes revealed novel ethnic-specific associations that require further investigation but may have implications for tailored therapy. [ABSTRACT FROM AUTHOR]

Published
2023
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48. Weight Loss, Lifestyle Intervention, and Metformin Affect Longitudinal Relationship of Insulin Secretion and Sensitivity.

Author

Arreola, Elsa Vazquez, Knowler, William C., and Hanson, Robert L.

Abstract

Context: Insulin secretion and sensitivity regulate glycemia, with inadequately compensated deficiencies leading to diabetes. Objective: We investigated effects of weight loss, an intensive lifestyle intervention (ILS), and metformin on the relationship between insulin secretion and sensitivity using repository data from 2931 participants in the Diabetes Prevention Program clinical trial in adults at high risk of developing type 2 diabetes. Methods: Insulin secretion and sensitivity were estimated from insulin and glucose concentrations in fasting and 30-minute postload serum samples at baseline and 1, 2, and 3 years after randomization, during the active intervention phase. The nonlinear relationship of secretion and sensitivity was evaluated by standardized major axis regression to account for variability in both variables. Insulin secretory demand and compensatory insulin secretion were characterized by distances along and away from the regression line, respectively. Results: ILS and metformin decreased secretory demand while increasing compensatory insulin secretion, with greater effects of ILS. Improvements were directly related to weight loss; decreased weight significantly reduced secretory demand (b=-0.144 SD; 95% CI (-0.162, -0.125)/5 kg loss) and increased compensatory insulin secretion (b=0.287 SD, 95% CI (0.261, 0.314)/5 kg loss). In time-dependent hazard models, increasing compensatory insulin secretion (hazard ratio [HR]=0.166 per baseline SD, 95% CI 0.133, 0.206) and weight loss (HR=0.710 per 5 kg loss, 95% CI 0.613, 0.819) predicted lower diabetes risk. Conclusion: Diabetes risk reduction was directly related to the amount of weight loss, an effect mediated by lowered insulin secretory demand (due to increased insulin sensitivity) coupled with improved compensatory insulin secretion. [ABSTRACT FROM AUTHOR]

Published
2022
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