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9 results on '"P. Chesi"'

1. A saturated map of common genetic variants associated with human height

Author

Yengo, Loïc, Vedantam, Sailaja, Marouli, Eirini, Sidorenko, Julia, Bartell, Eric, Sakaue, Saori, Graff, Marielisa, Eliasen, Anders U, Jiang, Yunxuan, Raghavan, Sridharan, Miao, Jenkai, Arias, Joshua D, Graham, Sarah E, Mukamel, Ronen E, Spracklen, Cassandra N, Yin, Xianyong, Chen, Shyh-Huei, Ferreira, Teresa, Highland, Heather H, Ji, Yingjie, Karaderi, Tugce, Lin, Kuang, Lüll, Kreete, Malden, Deborah E, Medina-Gomez, Carolina, Machado, Moara, Moore, Amy, Rüeger, Sina, Sim, Xueling, Vrieze, Scott, Ahluwalia, Tarunveer S, Akiyama, Masato, Allison, Matthew A, Alvarez, Marcus, Andersen, Mette K, Ani, Alireza, Appadurai, Vivek, Arbeeva, Liubov, Bhaskar, Seema, Bielak, Lawrence F, Bollepalli, Sailalitha, Bonnycastle, Lori L, Bork-Jensen, Jette, Bradfield, Jonathan P, Bradford, Yuki, Braund, Peter S, Brody, Jennifer A, Burgdorf, Kristoffer S, Cade, Brian E, Cai, Hui, Cai, Qiuyin, Campbell, Archie, Cañadas-Garre, Marisa, Catamo, Eulalia, Chai, Jin-Fang, Chai, Xiaoran, Chang, Li-Ching, Chang, Yi-Cheng, Chen, Chien-Hsiun, Chesi, Alessandra, Choi, Seung Hoan, Chung, Ren-Hua, Cocca, Massimiliano, Concas, Maria Pina, Couture, Christian, Cuellar-Partida, Gabriel, Danning, Rebecca, Daw, E Warwick, Degenhard, Frauke, Delgado, Graciela E, Delitala, Alessandro, Demirkan, Ayse, Deng, Xuan, Devineni, Poornima, Dietl, Alexander, Dimitriou, Maria, Dimitrov, Latchezar, Dorajoo, Rajkumar, Ekici, Arif B, Engmann, Jorgen E, Fairhurst-Hunter, Zammy, Farmaki, Aliki-Eleni, Faul, Jessica D, Fernandez-Lopez, Juan-Carlos, Forer, Lukas, Francescatto, Margherita, Freitag-Wolf, Sandra, Fuchsberger, Christian, Galesloot, Tessel E, Gao, Yan, Gao, Zishan, Geller, Frank, Giannakopoulou, Olga, Giulianini, Franco, Gjesing, Anette P, Goel, Anuj, Gordon, Scott D, Gorski, Mathias, Grove, Jakob, and Guo, Xiuqing

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Humans, Body Height, Gene Frequency, Genome, Human, Genome-Wide Association Study, Haplotypes, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Europe, Sample Size, Phenotype, Chromosome Mapping, 23andMe Research Team, VA Million Veteran Program, DiscovEHR, eMERGE, Lifelines Cohort Study, PRACTICAL Consortium, Understanding Society Scientific Group, General Science & Technology
Abstract

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

Published
2022

2. A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

Author

Ramdas, Shweta, Judd, Jonathan, Graham, Sarah E, Kanoni, Stavroula, Wang, Yuxuan, Surakka, Ida, Wenz, Brandon, Clarke, Shoa L, Chesi, Alessandra, Wells, Andrew, Bhatti, Konain Fatima, Vedantam, Sailaja, Winkler, Thomas W, Locke, Adam E, Marouli, Eirini, Zajac, Greg JM, Wu, Kuan-Han H, Ntalla, Ioanna, Hui, Qin, Klarin, Derek, Hilliard, Austin T, Wang, Zeyuan, Xue, Chao, Thorleifsson, Gudmar, Helgadottir, Anna, Gudbjartsson, Daniel F, Holm, Hilma, Olafsson, Isleifur, Hwang, Mi Yeong, Han, Sohee, Akiyama, Masato, Sakaue, Saori, Terao, Chikashi, Kanai, Masahiro, Zhou, Wei, Brumpton, Ben M, Rasheed, Humaira, Havulinna, Aki S, Veturi, Yogasudha, Pacheco, Jennifer Allen, Rosenthal, Elisabeth A, Lingren, Todd, Feng, QiPing, Kullo, Iftikhar J, Narita, Akira, Takayama, Jun, Martin, Hilary C, Hunt, Karen A, Trivedi, Bhavi, Haessler, Jeffrey, Giulianini, Franco, Bradford, Yuki, Miller, Jason E, Campbell, Archie, Lin, Kuang, Millwood, Iona Y, Rasheed, Asif, Hindy, George, Faul, Jessica D, Zhao, Wei, Weir, David R, Turman, Constance, Huang, Hongyan, Graff, Mariaelisa, Choudhury, Ananyo, Sengupta, Dhriti, Mahajan, Anubha, Brown, Michael R, Zhang, Weihua, Yu, Ketian, Schmidt, Ellen M, Pandit, Anita, Gustafsson, Stefan, Yin, Xianyong, Luan, Jian’an, Zhao, Jing-Hua, Matsuda, Fumihiko, Jang, Hye-Mi, Yoon, Kyungheon, Medina-Gomez, Carolina, Pitsillides, Achilleas, Hottenga, Jouke Jan, Wood, Andrew R, Ji, Yingji, Gao, Zishan, Haworth, Simon, Mitchell, Ruth E, Chai, Jin Fang, Aadahl, Mette, Bjerregaard, Anne A, Yao, Jie, Manichaikul, Ani, Lee, Wen-Jane, Hsiung, Chao Agnes, Warren, Helen R, Ramirez, Julia, Bork-Jensen, Jette, Kårhus, Line L, Goel, Anuj, and Sabater-Lleal, Maria

Subjects
Human Genome, Biotechnology, Genetics, 2.1 Biological and endogenous factors, Aetiology, Chromatin, Genome-Wide Association Study, Genomics, Humans, Lipids, Polymorphism, Single Nucleotide, Million Veterans Program, Global Lipids Genetics Consortium, complex traits, fine-mapping, functional genomics, lipid biology, post-GWAS, regulatory mechanism, variant prioritization, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.

Published
2022

3. Discovery and fine-mapping of height loci via high-density imputation of GWASs in individuals of African ancestry

Author

Graff, Mariaelisa, Justice, Anne E, Young, Kristin L, Marouli, Eirini, Zhang, Xinruo, Fine, Rebecca S, Lim, Elise, Buchanan, Victoria, Rand, Kristin, Feitosa, Mary F, Wojczynski, Mary K, Yanek, Lisa R, Shao, Yaming, Rohde, Rebecca, Adeyemo, Adebowale A, Aldrich, Melinda C, Allison, Matthew A, Ambrosone, Christine B, Ambs, Stefan, Amos, Christopher, Arnett, Donna K, Atwood, Larry, Bandera, Elisa V, Bartz, Traci, Becker, Diane M, Berndt, Sonja I, Bernstein, Leslie, Bielak, Lawrence F, Blot, William J, Bottinger, Erwin P, Bowden, Donald W, Bradfield, Jonathan P, Brody, Jennifer A, Broeckel, Ulrich, Burke, Gregory, Cade, Brian E, Cai, Qiuyin, Caporaso, Neil, Carlson, Chris, Carpten, John, Casey, Graham, Chanock, Stephen J, Chen, Guanjie, Chen, Minhui, Chen, Yii-Der I, Chen, Wei-Min, Chesi, Alessandra, Chiang, Charleston WK, Chu, Lisa, Coetzee, Gerry A, Conti, David V, Cooper, Richard S, Cushman, Mary, Demerath, Ellen, Deming, Sandra L, Dimitrov, Latchezar, Ding, Jingzhong, Diver, W Ryan, Duan, Qing, Evans, Michele K, Falusi, Adeyinka G, Faul, Jessica D, Fornage, Myriam, Fox, Caroline, Freedman, Barry I, Garcia, Melissa, Gillanders, Elizabeth M, Goodman, Phyllis, Gottesman, Omri, Grant, Struan FA, Guo, Xiuqing, Hakonarson, Hakon, Haritunians, Talin, Harris, Tamara B, Harris, Curtis C, Henderson, Brian E, Hennis, Anselm, Hernandez, Dena G, Hirschhorn, Joel N, McNeill, Lorna Haughton, Howard, Timothy D, Howard, Barbara, Hsing, Ann W, Hsu, Yu-Han H, Hu, Jennifer J, Huff, Chad D, Huo, Dezheng, Ingles, Sue A, Irvin, Marguerite R, John, Esther M, Johnson, Karen C, Jordan, Joanne M, Kabagambe, Edmond K, Kang, Sun J, Kardia, Sharon L, Keating, Brendan J, Kittles, Rick A, Klein, Eric A, Kolb, Suzanne, and Kolonel, Laurence N

Subjects
Human Genome, Genetics, Africa, Black or African American, Black People, Body Height, Europe, Female, Genome-Wide Association Study, Humans, Male, Polymorphism, Single Nucleotide, African ancestry, fine-mapping, genome-wide, height, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Although many loci have been associated with height in European ancestry populations, very few have been identified in African ancestry individuals. Furthermore, many of the known loci have yet to be generalized to and fine-mapped within a large-scale African ancestry sample. We performed sex-combined and sex-stratified meta-analyses in up to 52,764 individuals with height and genome-wide genotyping data from the African Ancestry Anthropometry Genetics Consortium (AAAGC). We additionally combined our African ancestry meta-analysis results with published European genome-wide association study (GWAS) data. In the African ancestry analyses, we identified three novel loci (SLC4A3, NCOA2, ECD/FAM149B1) in sex-combined results and two loci (CRB1, KLF6) in women only. In the African plus European sex-combined GWAS, we identified an additional three novel loci (RCCD1, G6PC3, CEP95) which were equally driven by AAAGC and European results. Among 39 genome-wide significant signals at known loci, conditioning index SNPs from European studies identified 20 secondary signals. Two of the 20 new secondary signals and none of the 8 novel loci had minor allele frequencies (MAF) < 5%. Of 802 known European height signals, 643 displayed directionally consistent associations with height, of which 205 were nominally significant (p < 0.05) in the African ancestry sex-combined sample. Furthermore, 148 of 241 loci contained ≤20 variants in the credible sets that jointly account for 99% of the posterior probability of driving the associations. In summary, trans-ethnic meta-analyses revealed novel signals and further improved fine-mapping of putative causal variants in loci shared between African and European ancestry populations.

Published
2021

4. Life-Course Genome-wide Association Study Meta-analysis of Total Body BMD and Assessment of Age-Specific Effects

Author

Medina-Gomez, Carolina, Kemp, John P, Trajanoska, Katerina, Luan, Jian’an, Chesi, Alessandra, Ahluwalia, Tarunveer S, Mook-Kanamori, Dennis O, Ham, Annelies, Hartwig, Fernando P, Evans, Daniel S, Joro, Raimo, Nedeljkovic, Ivana, Zheng, Hou-Feng, Zhu, Kun, Atalay, Mustafa, Liu, Ching-Ti, Nethander, Maria, Broer, Linda, Porleifsson, Gudmar, Mullin, Benjamin H, Handelman, Samuel K, Nalls, Mike A, Jessen, Leon E, Heppe, Denise HM, Richards, J Brent, Wang, Carol, Chawes, Bo, Schraut, Katharina E, Amin, Najaf, Wareham, Nick, Karasik, David, Van der Velde, Nathalie, Ikram, M Arfan, Zemel, Babette S, Zhou, Yanhua, Carlsson, Christian J, Liu, Yongmei, McGuigan, Fiona E, Boer, Cindy G, Bønnelykke, Klaus, Ralston, Stuart H, Robbins, John A, Walsh, John P, Zillikens, M Carola, Langenberg, Claudia, Li-Gao, Ruifang, Williams, Frances MK, Harris, Tamara B, Akesson, Kristina, Jackson, Rebecca D, Sigurdsson, Gunnar, Heijer, Martin den, van der Eerden, Bram CJ, van de Peppel, Jeroen, Spector, Timothy D, Pennell, Craig, Horta, Bernardo L, Felix, Janine F, Zhao, Jing Hua, Wilson, Scott G, de Mutsert, Renée, Bisgaard, Hans, Styrkársdóttir, Unnur, Jaddoe, Vincent W, Orwoll, Eric, Lakka, Timo A, Scott, Robert, Grant, Struan FA, Lorentzon, Mattias, van Duijn, Cornelia M, Wilson, James F, Stefansson, Kari, Psaty, Bruce M, Kiel, Douglas P, Ohlsson, Claes, Ntzani, Evangelia, van Wijnen, Andre J, Forgetta, Vincenzo, Ghanbari, Mohsen, Logan, John G, Williams, Graham R, Bassett, JH Duncan, Croucher, Peter I, Evangelou, Evangelos, Uitterlinden, Andre G, Ackert-Bicknell, Cheryl L, Tobias, Jonathan H, Evans, David M, and Rivadeneira, Fernando

Subjects
Rare Diseases, Aging, Human Genome, Osteoporosis, Clinical Research, Genetics, Aetiology, 2.1 Biological and endogenous factors, Musculoskeletal, Good Health and Well Being, Adolescent, Age Factors, Animals, Bone Density, Child, Child, Preschool, Genetic Loci, Genome-Wide Association Study, Humans, Infant, Infant, Newborn, Mice, Knockout, Polymorphism, Single Nucleotide, Quantitative Trait, Heritable, Regression Analysis, BMD, CREB3L1, ESR1, GWASs, RANKL, age-dependent effects, bone mineral density, fracture, genetic correlation, genome-wide association studies, meta-regression, total-body DXA, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Bone mineral density (BMD) assessed by DXA is used to evaluate bone health. In children, total body (TB) measurements are commonly used; in older individuals, BMD at the lumbar spine (LS) and femoral neck (FN) is used to diagnose osteoporosis. To date, genetic variants in more than 60 loci have been identified as associated with BMD. To investigate the genetic determinants of TB-BMD variation along the life course and test for age-specific effects, we performed a meta-analysis of 30 genome-wide association studies (GWASs) of TB-BMD including 66,628 individuals overall and divided across five age strata, each spanning 15 years. We identified variants associated with TB-BMD at 80 loci, of which 36 have not been previously identified; overall, they explain approximately 10% of the TB-BMD variance when combining all age groups and influence the risk of fracture. Pathway and enrichment analysis of the association signals showed clustering within gene sets implicated in the regulation of cell growth and SMAD proteins, overexpressed in the musculoskeletal system, and enriched in enhancer and promoter regions. These findings reveal TB-BMD as a relevant trait for genetic studies of osteoporosis, enabling the identification of variants and pathways influencing different bone compartments. Only variants in ESR1 and close proximity to RANKL showed a clear effect dependency on age. This most likely indicates that the majority of genetic variants identified influence BMD early in life and that their effect can be captured throughout the life course.

Published
2018

5. Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium.

Author

Ng, Maggie CY, Graff, Mariaelisa, Lu, Yingchang, Justice, Anne E, Mudgal, Poorva, Liu, Ching-Ti, Young, Kristin, Yanek, Lisa R, Feitosa, Mary F, Wojczynski, Mary K, Rand, Kristin, Brody, Jennifer A, Cade, Brian E, Dimitrov, Latchezar, Duan, Qing, Guo, Xiuqing, Lange, Leslie A, Nalls, Michael A, Okut, Hayrettin, Tajuddin, Salman M, Tayo, Bamidele O, Vedantam, Sailaja, Bradfield, Jonathan P, Chen, Guanjie, Chen, Wei-Min, Chesi, Alessandra, Irvin, Marguerite R, Padhukasahasram, Badri, Smith, Jennifer A, Zheng, Wei, Allison, Matthew A, Ambrosone, Christine B, Bandera, Elisa V, Bartz, Traci M, Berndt, Sonja I, Bernstein, Leslie, Blot, William J, Bottinger, Erwin P, Carpten, John, Chanock, Stephen J, Chen, Yii-Der Ida, Conti, David V, Cooper, Richard S, Fornage, Myriam, Freedman, Barry I, Garcia, Melissa, Goodman, Phyllis J, Hsu, Yu-Han H, Hu, Jennifer, Huff, Chad D, Ingles, Sue A, John, Esther M, Kittles, Rick, Klein, Eric, Li, Jin, McKnight, Barbara, Nayak, Uma, Nemesure, Barbara, Ogunniyi, Adesola, Olshan, Andrew, Press, Michael F, Rohde, Rebecca, Rybicki, Benjamin A, Salako, Babatunde, Sanderson, Maureen, Shao, Yaming, Siscovick, David S, Stanford, Janet L, Stevens, Victoria L, Stram, Alex, Strom, Sara S, Vaidya, Dhananjay, Witte, John S, Yao, Jie, Zhu, Xiaofeng, Ziegler, Regina G, Zonderman, Alan B, Adeyemo, Adebowale, Ambs, Stefan, Cushman, Mary, Faul, Jessica D, Hakonarson, Hakon, Levin, Albert M, Nathanson, Katherine L, Ware, Erin B, Weir, David R, Zhao, Wei, Zhi, Degui, Bone Mineral Density in Childhood Study (BMDCS) Group, Arnett, Donna K, Grant, Struan FA, Kardia, Sharon LR, Oloapde, Olufunmilayo I, Rao, DC, Rotimi, Charles N, Sale, Michele M, Williams, L Keoki, Zemel, Babette S, Becker, Diane M, and Borecki, Ingrid B

Subjects
Bone Mineral Density in Childhood Study (BMDCS) Group, Humans, Obesity, Genetic Predisposition to Disease, Serine Endopeptidases, Anthropometry, Body Mass Index, Waist-Hip Ratio, Chromosome Mapping, Gene Frequency, Linkage Disequilibrium, Polymorphism, Single Nucleotide, African Continental Ancestry Group, European Continental Ancestry Group, Female, Male, Adiposity, Genome-Wide Association Study, Transcription Factor 7-Like 2 Protein, Genetics, Human Genome, 2.1 Biological and endogenous factors, Developmental Biology
Abstract

Genome-wide association studies (GWAS) have identified >300 loci associated with measures of adiposity including body mass index (BMI) and waist-to-hip ratio (adjusted for BMI, WHRadjBMI), but few have been identified through screening of the African ancestry genomes. We performed large scale meta-analyses and replications in up to 52,895 individuals for BMI and up to 23,095 individuals for WHRadjBMI from the African Ancestry Anthropometry Genetics Consortium (AAAGC) using 1000 Genomes phase 1 imputed GWAS to improve coverage of both common and low frequency variants in the low linkage disequilibrium African ancestry genomes. In the sex-combined analyses, we identified one novel locus (TCF7L2/HABP2) for WHRadjBMI and eight previously established loci at P < 5×10-8: seven for BMI, and one for WHRadjBMI in African ancestry individuals. An additional novel locus (SPRYD7/DLEU2) was identified for WHRadjBMI when combined with European GWAS. In the sex-stratified analyses, we identified three novel loci for BMI (INTS10/LPL and MLC1 in men, IRX4/IRX2 in women) and four for WHRadjBMI (SSX2IP, CASC8, PDE3B and ZDHHC1/HSD11B2 in women) in individuals of African ancestry or both African and European ancestry. For four of the novel variants, the minor allele frequency was low (

Published
2017

6. Role of Pirh2 in mediating the regulation of p53 and c-Myc.

Author

Hakem, Anne, Bohgaki, Miyuki, Lemmers, Bénédicte, Tai, Elisabeth, Salmena, Leonardo, Matysiak-Zablocki, Elzbieta, Jung, Yong-Sam, Karaskova, Jana, Kaustov, Lilia, Duan, Shili, Madore, Jason, Boutros, Paul, Sheng, Yi, Chesi, Marta, Bergsagel, P Leif, Perez-Ordonez, Bayardo, Mes-Masson, Anne-Marie, Penn, Linda, Squire, Jeremy, Chen, Xinbin, Jurisica, Igor, Arrowsmith, Cheryl, Sanchez, Otto, Benchimol, Samuel, and Hakem, Razqallah

Subjects
Animals, Mice, Inbred C57BL, Humans, Mice, Neoplasms, Cell Transformation, Neoplastic, Ubiquitin-Protein Ligases, Proto-Oncogene Proteins c-myc, Gene Expression Regulation, Neoplastic, Genes, Tumor Suppressor, Radiation Tolerance, Tumor Suppressor Protein p53, Ubiquitination, Kaplan-Meier Estimate, HEK293 Cells, Proteolysis, Inbred C57BL, Cell Transformation, Neoplastic, Gene Expression Regulation, Genes, Tumor Suppressor, Genetics, Developmental Biology
Abstract

Ubiquitylation is fundamental for the regulation of the stability and function of p53 and c-Myc. The E3 ligase Pirh2 has been reported to polyubiquitylate p53 and to mediate its proteasomal degradation. Here, using Pirh2 deficient mice, we report that Pirh2 is important for the in vivo regulation of p53 stability in response to DNA damage. We also demonstrate that c-Myc is a novel interacting protein for Pirh2 and that Pirh2 mediates its polyubiquitylation and proteolysis. Pirh2 mutant mice display elevated levels of c-Myc and are predisposed for plasma cell hyperplasia and tumorigenesis. Consistent with the role p53 plays in suppressing c-Myc-induced oncogenesis, its deficiency exacerbates tumorigenesis of Pirh2(-/-) mice. We also report that low expression of human PIRH2 in lung, ovarian, and breast cancers correlates with decreased patients' survival. Collectively, our data reveal the in vivo roles of Pirh2 in the regulation of p53 and c-Myc stability and support its role as a tumor suppressor.

Published
2011

9. Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium

Author

Ng, Maggie C. Y., Graff, Mariaelisa, Lu, Yingchang, Justice, Anne E., Mudgal, Poorva, Liu, Ching-Ti, Young, Kristin, Yanek, Lisa R., Feitosa, Mary F., Wojczynski, Mary K., Rand, Kristin, Brody, Jennifer A., Cade, Brian E., Dimitrov, Latchezar, Duan, Qing, Guo, Xiuqing, Lange, Leslie A., Nalls, Michael A., Okut, Hayrettin, Tajuddin, Salman M., Tayo, Bamidele O., Vedantam, Sailaja, Bradfield, Jonathan P., Chen, Guanjie, Chen, Wei-Min, Chesi, Alessandra, Irvin, Marguerite R., Padhukasahasram, Badri, Smith, Jennifer A., Zheng, Wei, Allison, Matthew A., Ambrosone, Christine B., Bandera, Elisa V., Bartz, Traci M., Berndt, Sonja I., Bernstein, Leslie, Blot, William J., Bottinger, Erwin P., Carpten, John, Chanock, Stephen J., Chen, Yii-Der Ida, Conti, David V., Cooper, Richard S., Fornage, Myriam, Freedman, Barry I., Garcia, Melissa, Goodman, Phyllis J., Hsu, Yu-Han H., Hu, Jennifer, Huff, Chad D., Ingles, Sue A., John, Esther M., Kittles, Rick, Klein, Eric, Li, Jin, McKnight, Barbara, Nayak, Uma, Nemesure, Barbara, Ogunniyi, Adesola, Olshan, Andrew, Press, Michael F., Rohde, Rebecca, Rybicki, Benjamin A., Salako, Babatunde, Sanderson, Maureen, Shao, Yaming, Siscovick, David S., Stanford, Janet L., Stevens, Victoria L., Stram, Alex, Strom, Sara S., Vaidya, Dhananjay, Witte, John S., Yao, Jie, Zhu, Xiaofeng, Ziegler, Regina G., Zonderman, Alan B., Adeyemo, Adebowale, Ambs, Stefan, Cushman, Mary, Faul, Jessica D., Hakonarson, Hakon, Levin, Albert M., Nathanson, Katherine L., Ware, Erin B., Weir, David R., Zhao, Wei, Zhi, Degui, Arnett, Donna K., Grant, Struan F. A., Kardia, Sharon L. R., Oloapde, Olufunmilayo I., Rao, D. C., Rotimi, Charles N., Sale, Michele M., Williams, L. Keoki, Zemel, Babette S., Becker, Diane M., Borecki, Ingrid B., Evans, Michele K., Harris, Tamara B., Hirschhorn, Joel N., Li, Yun, Patel, Sanjay R., Psaty, Bruce M., Rotter, Jerome I., Wilson, James G., Bowden, Donald W., Cupples, L. Adrienne, Haiman, Christopher A., Loos, Ruth J. F., and North, Kari E.

Subjects
Biology and Life Sciences, Computational Biology, Genome Analysis, Genome-Wide Association Studies, Genetics, Genomics, Human Genetics, Mathematical and Statistical Techniques, Statistical Methods, Meta-Analysis, Physical Sciences, Mathematics, Statistics (Mathematics), Genetic Loci, Genomics Statistics, People and Places, Population Groupings, Ethnicities, Africans, Alleles, Trait Locus Analysis, Cell Biology, Signal Transduction, Cell Signaling, Genomic Signal Processing
Abstract

Genome-wide association studies (GWAS) have identified >300 loci associated with measures of adiposity including body mass index (BMI) and waist-to-hip ratio (adjusted for BMI, WHRadjBMI), but few have been identified through screening of the African ancestry genomes. We performed large scale meta-analyses and replications in up to 52,895 individuals for BMI and up to 23,095 individuals for WHRadjBMI from the African Ancestry Anthropometry Genetics Consortium (AAAGC) using 1000 Genomes phase 1 imputed GWAS to improve coverage of both common and low frequency variants in the low linkage disequilibrium African ancestry genomes. In the sex-combined analyses, we identified one novel locus (TCF7L2/HABP2) for WHRadjBMI and eight previously established loci at P < 5×10−8: seven for BMI, and one for WHRadjBMI in African ancestry individuals. An additional novel locus (SPRYD7/DLEU2) was identified for WHRadjBMI when combined with European GWAS. In the sex-stratified analyses, we identified three novel loci for BMI (INTS10/LPL and MLC1 in men, IRX4/IRX2 in women) and four for WHRadjBMI (SSX2IP, CASC8, PDE3B and ZDHHC1/HSD11B2 in women) in individuals of African ancestry or both African and European ancestry. For four of the novel variants, the minor allele frequency was low (<5%). In the trans-ethnic fine mapping of 47 BMI loci and 27 WHRadjBMI loci that were locus-wide significant (P < 0.05 adjusted for effective number of variants per locus) from the African ancestry sex-combined and sex-stratified analyses, 26 BMI loci and 17 WHRadjBMI loci contained ≤ 20 variants in the credible sets that jointly account for 99% posterior probability of driving the associations. The lead variants in 13 of these loci had a high probability of being causal. As compared to our previous HapMap imputed GWAS for BMI and WHRadjBMI including up to 71,412 and 27,350 African ancestry individuals, respectively, our results suggest that 1000 Genomes imputation showed modest improvement in identifying GWAS loci including low frequency variants. Trans-ethnic meta-analyses further improved fine mapping of putative causal variants in loci shared between the African and European ancestry populations.

Published
2017
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