%0 Journal Article %J Transl Psychiatry %D 2012 %T Genome-wide meta-analyses of smoking behaviors in African Americans. %A David, S P %A Hamidovic, A %A Chen, G K %A Bergen, A W %A Wessel, J %A Kasberger, J L %A Brown, W M %A Petruzella, S %A Thacker, E L %A Kim, Y %A Nalls, M A %A Tranah, G J %A Sung, Y J %A Ambrosone, C B %A Arnett, D %A Bandera, E V %A Becker, D M %A Becker, L %A Berndt, S I %A Bernstein, L %A Blot, W J %A Broeckel, U %A Buxbaum, S G %A Caporaso, N %A Casey, G %A Chanock, S J %A Deming, S L %A Diver, W R %A Eaton, C B %A Evans, D S %A Evans, M K %A Fornage, M %A Franceschini, N %A Harris, T B %A Henderson, B E %A Hernandez, D G %A Hitsman, B %A Hu, J J %A Hunt, S C %A Ingles, S A %A John, E M %A Kittles, R %A Kolb, S %A Kolonel, L N %A Le Marchand, L %A Liu, Y %A Lohman, K K %A McKnight, B %A Millikan, R C %A Murphy, A %A Neslund-Dudas, C %A Nyante, S %A Press, M %A Psaty, B M %A Rao, D C %A Redline, S %A Rodriguez-Gil, J L %A Rybicki, B A %A Signorello, L B %A Singleton, A B %A Smoller, J %A Snively, B %A Spring, B %A Stanford, J L %A Strom, S S %A Swan, G E %A Taylor, K D %A Thun, M J %A Wilson, A F %A Witte, J S %A Yamamura, Y %A Yanek, L R %A Yu, K %A Zheng, W %A Ziegler, R G %A Zonderman, A B %A Jorgenson, E %A Haiman, C A %A Furberg, H %K Adult %K African Americans %K Aged %K Chromosomes, Human, Pair 10 %K Chromosomes, Human, Pair 15 %K Female %K Genetic Loci %K Genetic Predisposition to Disease %K Genetic Variation %K Genome-Wide Association Study %K Genotype %K Humans %K Male %K Middle Aged %K Nerve Tissue Proteins %K Phenotype %K Polymorphism, Single Nucleotide %K Proteoglycans %K Receptors, Nicotinic %K Smoking %K Statistics as Topic %X

The identification and exploration of genetic loci that influence smoking behaviors have been conducted primarily in populations of the European ancestry. Here we report results of the first genome-wide association study meta-analysis of smoking behavior in African Americans in the Study of Tobacco in Minority Populations Genetics Consortium (n = 32,389). We identified one non-coding single-nucleotide polymorphism (SNP; rs2036527[A]) on chromosome 15q25.1 associated with smoking quantity (cigarettes per day), which exceeded genome-wide significance (β = 0.040, s.e. = 0.007, P = 1.84 × 10(-8)). This variant is present in the 5'-distal enhancer region of the CHRNA5 gene and defines the primary index signal reported in studies of the European ancestry. No other SNP reached genome-wide significance for smoking initiation (SI, ever vs never smoking), age of SI, or smoking cessation (SC, former vs current smoking). Informative associations that approached genome-wide significance included three modestly correlated variants, at 15q25.1 within PSMA4, CHRNA5 and CHRNA3 for smoking quantity, which are associated with a second signal previously reported in studies in European ancestry populations, and a signal represented by three SNPs in the SPOCK2 gene on chr10q22.1. The association at 15q25.1 confirms this region as an important susceptibility locus for smoking quantity in men and women of African ancestry. Larger studies will be needed to validate the suggestive loci that did not reach genome-wide significance and further elucidate the contribution of genetic variation to disparities in cigarette consumption, SC and smoking-attributable disease between African Americans and European Americans.

%B Transl Psychiatry %V 2 %P e119 %8 2012 %G eng %1 http://www.ncbi.nlm.nih.gov/pubmed/22832964?dopt=Abstract %R 10.1038/tp.2012.41 %0 Journal Article %J Nat Commun %D 2016 %T A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape. %A Ried, Janina S %A Jeff M, Janina %A Chu, Audrey Y %A Bragg-Gresham, Jennifer L %A van Dongen, Jenny %A Huffman, Jennifer E %A Ahluwalia, Tarunveer S %A Cadby, Gemma %A Eklund, Niina %A Eriksson, Joel %A Esko, Tõnu %A Feitosa, Mary F %A Goel, Anuj %A Gorski, Mathias %A Hayward, Caroline %A Heard-Costa, Nancy L %A Jackson, Anne U %A Jokinen, Eero %A Kanoni, Stavroula %A Kristiansson, Kati %A Kutalik, Zoltán %A Lahti, Jari %A Luan, Jian'an %A Mägi, Reedik %A Mahajan, Anubha %A Mangino, Massimo %A Medina-Gómez, Carolina %A Monda, Keri L %A Nolte, Ilja M %A Perusse, Louis %A Prokopenko, Inga %A Qi, Lu %A Rose, Lynda M %A Salvi, Erika %A Smith, Megan T %A Snieder, Harold %A Stančáková, Alena %A Ju Sung, Yun %A Tachmazidou, Ioanna %A Teumer, Alexander %A Thorleifsson, Gudmar %A van der Harst, Pim %A Walker, Ryan W %A Wang, Sophie R %A Wild, Sarah H %A Willems, Sara M %A Wong, Andrew %A Zhang, Weihua %A Albrecht, Eva %A Couto Alves, Alexessander %A Bakker, Stephan J L %A Barlassina, Cristina %A Bartz, Traci M %A Beilby, John %A Bellis, Claire %A Bergman, Richard N %A Bergmann, Sven %A Blangero, John %A Blüher, Matthias %A Boerwinkle, Eric %A Bonnycastle, Lori L %A Bornstein, Stefan R %A Bruinenberg, Marcel %A Campbell, Harry %A Chen, Yii-Der Ida %A Chiang, Charleston W K %A Chines, Peter S %A Collins, Francis S %A Cucca, Fracensco %A Cupples, L Adrienne %A D'Avila, Francesca %A de Geus, Eco J C %A Dedoussis, George %A Dimitriou, Maria %A Döring, Angela %A Eriksson, Johan G %A Farmaki, Aliki-Eleni %A Farrall, Martin %A Ferreira, Teresa %A Fischer, Krista %A Forouhi, Nita G %A Friedrich, Nele %A Gjesing, Anette Prior %A Glorioso, Nicola %A Graff, Mariaelisa %A Grallert, Harald %A Grarup, Niels %A Gräßler, Jürgen %A Grewal, Jagvir %A Hamsten, Anders %A Harder, Marie Neergaard %A Hartman, Catharina A %A Hassinen, Maija %A Hastie, Nicholas %A Hattersley, Andrew Tym %A Havulinna, Aki S %A Heliövaara, Markku %A Hillege, Hans %A Hofman, Albert %A Holmen, Oddgeir %A Homuth, Georg %A Hottenga, Jouke-Jan %A Hui, Jennie %A Husemoen, Lise Lotte %A Hysi, Pirro G %A Isaacs, Aaron %A Ittermann, Till %A Jalilzadeh, Shapour %A James, Alan L %A Jørgensen, Torben %A Jousilahti, Pekka %A Jula, Antti %A Marie Justesen, Johanne %A Justice, Anne E %A Kähönen, Mika %A Karaleftheri, Maria %A Tee Khaw, Kay %A Keinanen-Kiukaanniemi, Sirkka M %A Kinnunen, Leena %A Knekt, Paul B %A Koistinen, Heikki A %A Kolcic, Ivana %A Kooner, Ishminder K %A Koskinen, Seppo %A Kovacs, Peter %A Kyriakou, Theodosios %A Laitinen, Tomi %A Langenberg, Claudia %A Lewin, Alexandra M %A Lichtner, Peter %A Lindgren, Cecilia M %A Lindström, Jaana %A Linneberg, Allan %A Lorbeer, Roberto %A Lorentzon, Mattias %A Luben, Robert %A Lyssenko, Valeriya %A Männistö, Satu %A Manunta, Paolo %A Leach, Irene Mateo %A McArdle, Wendy L %A McKnight, Barbara %A Mohlke, Karen L %A Mihailov, Evelin %A Milani, Lili %A Mills, Rebecca %A Montasser, May E %A Morris, Andrew P %A Müller, Gabriele %A Musk, Arthur W %A Narisu, Narisu %A Ong, Ken K %A Oostra, Ben A %A Osmond, Clive %A Palotie, Aarno %A Pankow, James S %A Paternoster, Lavinia %A Penninx, Brenda W %A Pichler, Irene %A Pilia, Maria G %A Polasek, Ozren %A Pramstaller, Peter P %A Raitakari, Olli T %A Rankinen, Tuomo %A Rao, D C %A Rayner, Nigel W %A Ribel-Madsen, Rasmus %A Rice, Treva K %A Richards, Marcus %A Ridker, Paul M %A Rivadeneira, Fernando %A Ryan, Kathy A %A Sanna, Serena %A Sarzynski, Mark A %A Scholtens, Salome %A Scott, Robert A %A Sebert, Sylvain %A Southam, Lorraine %A Sparsø, Thomas Hempel %A Steinthorsdottir, Valgerdur %A Stirrups, Kathleen %A Stolk, Ronald P %A Strauch, Konstantin %A Stringham, Heather M %A Swertz, Morris A %A Swift, Amy J %A Tönjes, Anke %A Tsafantakis, Emmanouil %A van der Most, Peter J %A van Vliet-Ostaptchouk, Jana V %A Vandenput, Liesbeth %A Vartiainen, Erkki %A Venturini, Cristina %A Verweij, Niek %A Viikari, Jorma S %A Vitart, Veronique %A Vohl, Marie-Claude %A Vonk, Judith M %A Waeber, Gérard %A Widen, Elisabeth %A Willemsen, Gonneke %A Wilsgaard, Tom %A Winkler, Thomas W %A Wright, Alan F %A Yerges-Armstrong, Laura M %A Hua Zhao, Jing %A Zillikens, M Carola %A Boomsma, Dorret I %A Bouchard, Claude %A Chambers, John C %A Chasman, Daniel I %A Cusi, Daniele %A Gansevoort, Ron T %A Gieger, Christian %A Hansen, Torben %A Hicks, Andrew A %A Hu, Frank %A Hveem, Kristian %A Jarvelin, Marjo-Riitta %A Kajantie, Eero %A Kooner, Jaspal S %A Kuh, Diana %A Kuusisto, Johanna %A Laakso, Markku %A Lakka, Timo A %A Lehtimäki, Terho %A Metspalu, Andres %A Njølstad, Inger %A Ohlsson, Claes %A Oldehinkel, Albertine J %A Palmer, Lyle J %A Pedersen, Oluf %A Perola, Markus %A Peters, Annette %A Psaty, Bruce M %A Puolijoki, Hannu %A Rauramaa, Rainer %A Rudan, Igor %A Salomaa, Veikko %A Schwarz, Peter E H %A Shudiner, Alan R %A Smit, Jan H %A Sørensen, Thorkild I A %A Spector, Timothy D %A Stefansson, Kari %A Stumvoll, Michael %A Tremblay, Angelo %A Tuomilehto, Jaakko %A Uitterlinden, André G %A Uusitupa, Matti %A Völker, Uwe %A Vollenweider, Peter %A Wareham, Nicholas J %A Watkins, Hugh %A Wilson, James F %A Zeggini, Eleftheria %A Abecasis, Goncalo R %A Boehnke, Michael %A Borecki, Ingrid B %A Deloukas, Panos %A van Duijn, Cornelia M %A Fox, Caroline %A Groop, Leif C %A Heid, Iris M %A Hunter, David J %A Kaplan, Robert C %A McCarthy, Mark I %A North, Kari E %A O'Connell, Jeffrey R %A Schlessinger, David %A Thorsteinsdottir, Unnur %A Strachan, David P %A Frayling, Timothy %A Hirschhorn, Joel N %A Müller-Nurasyid, Martina %A Loos, Ruth J F %K Anthropometry %K Body Size %K Genome-Wide Association Study %K Genotype %K Humans %K Models, Genetic %K Principal Component Analysis %X

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.

%B Nat Commun %V 7 %P 13357 %8 2016 11 23 %G eng %R 10.1038/ncomms13357 %0 Journal Article %J Mol Psychiatry %D 2016 %T Rare, low frequency and common coding variants in CHRNA5 and their contribution to nicotine dependence in European and African Americans. %A Olfson, E %A Saccone, N L %A Johnson, E O %A Chen, L-S %A Culverhouse, R %A Doheny, K %A Foltz, S M %A Fox, L %A Gogarten, S M %A Hartz, S %A Hetrick, K %A Laurie, C C %A Marosy, B %A Amin, N %A Arnett, D %A Barr, R G %A Bartz, T M %A Bertelsen, S %A Borecki, I B %A Brown, M R %A Chasman, D I %A van Duijn, C M %A Feitosa, M F %A Fox, E R %A Franceschini, N %A Franco, O H %A Grove, M L %A Guo, X %A Hofman, A %A Kardia, S L R %A Morrison, A C %A Musani, S K %A Psaty, B M %A Rao, D C %A Reiner, A P %A Rice, K %A Ridker, P M %A Rose, L M %A Schick, U M %A Schwander, K %A Uitterlinden, A G %A Vojinovic, D %A Wang, J-C %A Ware, E B %A Wilson, G %A Yao, J %A Zhao, W %A Breslau, N %A Hatsukami, D %A Stitzel, J A %A Rice, J %A Goate, A %A Bierut, L J %X

The common nonsynonymous variant rs16969968 in the α5 nicotinic receptor subunit gene (CHRNA5) is the strongest genetic risk factor for nicotine dependence in European Americans and contributes to risk in African Americans. To comprehensively examine whether other CHRNA5 coding variation influences nicotine dependence risk, we performed targeted sequencing on 1582 nicotine-dependent cases (Fagerström Test for Nicotine Dependence score⩾4) and 1238 non-dependent controls, with independent replication of common and low frequency variants using 12 studies with exome chip data. Nicotine dependence was examined using logistic regression with individual common variants (minor allele frequency (MAF)⩾0.05), aggregate low frequency variants (0.05>MAF⩾0.005) and aggregate rare variants (MAF<0.005). Meta-analysis of primary results was performed with replication studies containing 12 174 heavy and 11 290 light smokers. Next-generation sequencing with 180 × coverage identified 24 nonsynonymous variants and 2 frameshift deletions in CHRNA5, including 9 novel variants in the 2820 subjects. Meta-analysis confirmed the risk effect of the only common variant (rs16969968, European ancestry: odds ratio (OR)=1.3, P=3.5 × 10(-11); African ancestry: OR=1.3, P=0.01) and demonstrated that three low frequency variants contributed an independent risk (aggregate term, European ancestry: OR=1.3, P=0.005; African ancestry: OR=1.4, P=0.0006). The remaining 22 rare coding variants were associated with increased risk of nicotine dependence in the European American primary sample (OR=12.9, P=0.01) and in the same risk direction in African Americans (OR=1.5, P=0.37). Our results indicate that common, low frequency and rare CHRNA5 coding variants are independently associated with nicotine dependence risk. These newly identified variants likely influence the risk for smoking-related diseases such as lung cancer.

%B Mol Psychiatry %V 21 %P 601-7 %8 2016 May %G eng %N 5 %1 http://www.ncbi.nlm.nih.gov/pubmed/26239294?dopt=Abstract %R 10.1038/mp.2015.105 %0 Journal Article %J PLoS Genet %D 2017 %T 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. %A Ng, Maggie C Y %A Graff, Mariaelisa %A Lu, Yingchang %A Justice, Anne E %A Mudgal, Poorva %A Liu, Ching-Ti %A Young, Kristin %A Yanek, Lisa R %A Feitosa, Mary F %A Wojczynski, Mary K %A Rand, Kristin %A Brody, Jennifer A %A Cade, Brian E %A Dimitrov, Latchezar %A Duan, Qing %A Guo, Xiuqing %A Lange, Leslie A %A Nalls, Michael A %A Okut, Hayrettin %A Tajuddin, Salman M %A Tayo, Bamidele O %A Vedantam, Sailaja %A Bradfield, Jonathan P %A Chen, Guanjie %A Chen, Wei-Min %A Chesi, Alessandra %A Irvin, Marguerite R %A Padhukasahasram, Badri %A Smith, Jennifer A %A Zheng, Wei %A Allison, Matthew A %A Ambrosone, Christine B %A Bandera, Elisa V %A Bartz, Traci M %A Berndt, Sonja I %A Bernstein, Leslie %A Blot, William J %A Bottinger, Erwin P %A Carpten, John %A Chanock, Stephen J %A Chen, Yii-Der Ida %A Conti, David V %A Cooper, Richard S %A Fornage, Myriam %A Freedman, Barry I %A Garcia, Melissa %A Goodman, Phyllis J %A Hsu, Yu-Han H %A Hu, Jennifer %A Huff, Chad D %A Ingles, Sue A %A John, Esther M %A Kittles, Rick %A Klein, Eric %A Li, Jin %A McKnight, Barbara %A Nayak, Uma %A Nemesure, Barbara %A Ogunniyi, Adesola %A Olshan, Andrew %A Press, Michael F %A Rohde, Rebecca %A Rybicki, Benjamin A %A Salako, Babatunde %A Sanderson, Maureen %A Shao, Yaming %A Siscovick, David S %A Stanford, Janet L %A Stevens, Victoria L %A Stram, Alex %A Strom, Sara S %A Vaidya, Dhananjay %A Witte, John S %A Yao, Jie %A Zhu, Xiaofeng %A Ziegler, Regina G %A Zonderman, Alan B %A Adeyemo, Adebowale %A Ambs, Stefan %A Cushman, Mary %A Faul, Jessica D %A Hakonarson, Hakon %A Levin, Albert M %A Nathanson, Katherine L %A Ware, Erin B %A Weir, David R %A Zhao, Wei %A Zhi, Degui %A Arnett, Donna K %A Grant, Struan F A %A Kardia, Sharon L R %A Oloapde, Olufunmilayo I %A Rao, D C %A Rotimi, Charles N %A Sale, Michèle M %A Williams, L Keoki %A Zemel, Babette S %A Becker, Diane M %A Borecki, Ingrid B %A Evans, Michele K %A Harris, Tamara B %A Hirschhorn, Joel N %A Li, Yun %A Patel, Sanjay R %A Psaty, Bruce M %A Rotter, Jerome I %A Wilson, James G %A Bowden, Donald W %A Cupples, L Adrienne %A Haiman, Christopher A %A Loos, Ruth J F %A North, Kari E %X

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.

%B PLoS Genet %V 13 %P e1006719 %8 2017 Apr 21 %G eng %N 4 %R 10.1371/journal.pgen.1006719 %0 Journal Article %J Circ Cardiovasc Genet %D 2017 %T Multiancestry Study of Gene-Lifestyle Interactions for Cardiovascular Traits in 610 475 Individuals From 124 Cohorts: Design and Rationale. %A Rao, D C %A Sung, Yun J %A Winkler, Thomas W %A Schwander, Karen %A Borecki, Ingrid %A Cupples, L Adrienne %A Gauderman, W James %A Rice, Kenneth %A Munroe, Patricia B %A Psaty, Bruce M %X

BACKGROUND: Several consortia have pursued genome-wide association studies for identifying novel genetic loci for blood pressure, lipids, hypertension, etc. They demonstrated the power of collaborative research through meta-analysis of study-specific results.

METHODS AND RESULTS: The Gene-Lifestyle Interactions Working Group was formed to facilitate the first large, concerted, multiancestry study to systematically evaluate gene-lifestyle interactions. In stage 1, genome-wide interaction analysis is performed in 53 cohorts with a total of 149 684 individuals from multiple ancestries. In stage 2 involving an additional 71 cohorts with 460 791 individuals from multiple ancestries, focused analysis is performed for a subset of the most promising variants from stage 1. In all, the study involves up to 610 475 individuals. Current focus is on cardiovascular traits including blood pressure and lipids, and lifestyle factors including smoking, alcohol, education (as a surrogate for socioeconomic status), physical activity, psychosocial variables, and sleep. The total sample sizes vary among projects because of missing data. Large-scale gene-lifestyle or more generally gene-environment interaction (G×E) meta-analysis studies can be cumbersome and challenging. This article describes the design and some of the approaches pursued in the interaction projects.

CONCLUSIONS: The Gene-Lifestyle Interactions Working Group provides an excellent framework for understanding the lifestyle context of genetic effects and to identify novel trait loci through analysis of interactions. An important and novel feature of our study is that the gene-lifestyle interaction (G×E) results may improve our knowledge about the underlying mechanisms for novel and already known trait loci.

%B Circ Cardiovasc Genet %V 10 %8 2017 Jun %G eng %N 3 %R 10.1161/CIRCGENETICS.116.001649 %0 Journal Article %J PLoS Genet %D 2017 %T Single-trait and multi-trait genome-wide association analyses identify novel loci for blood pressure in African-ancestry populations. %A Liang, Jingjing %A Le, Thu H %A Edwards, Digna R Velez %A Tayo, Bamidele O %A Gaulton, Kyle J %A Smith, Jennifer A %A Lu, Yingchang %A Jensen, Richard A %A Chen, Guanjie %A Yanek, Lisa R %A Schwander, Karen %A Tajuddin, Salman M %A Sofer, Tamar %A Kim, Wonji %A Kayima, James %A McKenzie, Colin A %A Fox, Ervin %A Nalls, Michael A %A Young, J Hunter %A Sun, Yan V %A Lane, Jacqueline M %A Cechova, Sylvia %A Zhou, Jie %A Tang, Hua %A Fornage, Myriam %A Musani, Solomon K %A Wang, Heming %A Lee, Juyoung %A Adeyemo, Adebowale %A Dreisbach, Albert W %A Forrester, Terrence %A Chu, Pei-Lun %A Cappola, Anne %A Evans, Michele K %A Morrison, Alanna C %A Martin, Lisa W %A Wiggins, Kerri L %A Hui, Qin %A Zhao, Wei %A Jackson, Rebecca D %A Ware, Erin B %A Faul, Jessica D %A Reiner, Alex P %A Bray, Michael %A Denny, Joshua C %A Mosley, Thomas H %A Palmas, Walter %A Guo, Xiuqing %A Papanicolaou, George J %A Penman, Alan D %A Polak, Joseph F %A Rice, Kenneth %A Taylor, Ken D %A Boerwinkle, Eric %A Bottinger, Erwin P %A Liu, Kiang %A Risch, Neil %A Hunt, Steven C %A Kooperberg, Charles %A Zonderman, Alan B %A Laurie, Cathy C %A Becker, Diane M %A Cai, Jianwen %A Loos, Ruth J F %A Psaty, Bruce M %A Weir, David R %A Kardia, Sharon L R %A Arnett, Donna K %A Won, Sungho %A Edwards, Todd L %A Redline, Susan %A Cooper, Richard S %A Rao, D C %A Rotter, Jerome I %A Rotimi, Charles %A Levy, Daniel %A Chakravarti, Aravinda %A Zhu, Xiaofeng %A Franceschini, Nora %K African Americans %K Animals %K Basic Helix-Loop-Helix Transcription Factors %K Blood Pressure %K Cadherins %K Case-Control Studies %K Female %K Genetic Loci %K Genome-Wide Association Study %K Humans %K Hypertension %K Male %K Membrane Proteins %K Mice %K Multifactorial Inheritance %K Polymorphism, Single Nucleotide %X

Hypertension is a leading cause of global disease, mortality, and disability. While individuals of African descent suffer a disproportionate burden of hypertension and its complications, they have been underrepresented in genetic studies. To identify novel susceptibility loci for blood pressure and hypertension in people of African ancestry, we performed both single and multiple-trait genome-wide association analyses. We analyzed 21 genome-wide association studies comprised of 31,968 individuals of African ancestry, and validated our results with additional 54,395 individuals from multi-ethnic studies. These analyses identified nine loci with eleven independent variants which reached genome-wide significance (P < 1.25×10-8) for either systolic and diastolic blood pressure, hypertension, or for combined traits. Single-trait analyses identified two loci (TARID/TCF21 and LLPH/TMBIM4) and multiple-trait analyses identified one novel locus (FRMD3) for blood pressure. At these three loci, as well as at GRP20/CDH17, associated variants had alleles common only in African-ancestry populations. Functional annotation showed enrichment for genes expressed in immune and kidney cells, as well as in heart and vascular cells/tissues. Experiments driven by these findings and using angiotensin-II induced hypertension in mice showed altered kidney mRNA expression of six genes, suggesting their potential role in hypertension. Our study provides new evidence for genes related to hypertension susceptibility, and the need to study African-ancestry populations in order to identify biologic factors contributing to hypertension.

%B PLoS Genet %V 13 %P e1006728 %8 2017 May %G eng %N 5 %R 10.1371/journal.pgen.1006728 %0 Journal Article %J Pharmacogenomics J %D 2019 %T Genome-wide meta-analysis of variant-by-diuretic interactions as modulators of lipid traits in persons of European and African ancestry. %A de Las Fuentes, L %A Sung, Y J %A Sitlani, C M %A Avery, C L %A Bartz, T M %A Keyser, C de %A Evans, D S %A Li, X %A Musani, S K %A Ruiter, R %A Smith, A V %A Sun, F %A Trompet, S %A Xu, H %A Arnett, D K %A Bis, J C %A Broeckel, U %A Busch, E L %A Chen, Y-D I %A Correa, A %A Cummings, S R %A Floyd, J S %A Ford, I %A Guo, X %A Harris, T B %A Ikram, M A %A Lange, L %A Launer, L J %A Reiner, A P %A Schwander, K %A Smith, N L %A Sotoodehnia, N %A Stewart, J D %A Stott, D J %A Stürmer, T %A Taylor, K D %A Uitterlinden, A %A Vasan, R S %A Wiggins, K L %A Cupples, L A %A Gudnason, V %A Heckbert, S R %A Jukema, J W %A Liu, Y %A Psaty, B M %A Rao, D C %A Rotter, J I %A Stricker, B %A Wilson, J G %A Whitsel, E A %X

Hypertension (HTN) is a significant risk factor for cardiovascular morbidity and mortality. Metabolic abnormalities, including adverse cholesterol and triglycerides (TG) profiles, are frequent comorbid findings with HTN and contribute to cardiovascular disease. Diuretics, which are used to treat HTN and heart failure, have been associated with worsening of fasting lipid concentrations. Genome-wide meta-analyses with 39,710 European-ancestry (EA) individuals and 9925 African-ancestry (AA) individuals were performed to identify genetic variants that modify the effect of loop or thiazide diuretic use on blood lipid concentrations. Both longitudinal and cross sectional data were used to compute cohort-specific interaction results, which were then combined through meta-analysis in each ancestry. These ancestry-specific results were further combined through trans-ancestry meta-analysis. Analysis of EA data identified two genome-wide significant (p < 5 × 10) loci with single nucleotide variant (SNV)-loop diuretic interaction on TG concentrations (including COL11A1). Analysis of AA data identified one genome-wide significant locus adjacent to BMP2 with SNV-loop diuretic interaction on TG concentrations. Trans-ancestry analysis strengthened evidence of association for SNV-loop diuretic interaction at two loci (KIAA1217 and BAALC). There were few significant SNV-thiazide diuretic interaction associations on TG concentrations and for either diuretic on cholesterol concentrations. Several promising loci were identified that may implicate biologic pathways that contribute to adverse metabolic side effects from diuretic therapy.

%B Pharmacogenomics J %8 2019 Dec 06 %G eng %R 10.1038/s41397-019-0132-y %0 Journal Article %J Nat Commun %D 2021 %T Chromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices. %A Natarajan, Pradeep %A Pampana, Akhil %A Graham, Sarah E %A Ruotsalainen, Sanni E %A Perry, James A %A de Vries, Paul S %A Broome, Jai G %A Pirruccello, James P %A Honigberg, Michael C %A Aragam, Krishna %A Wolford, Brooke %A Brody, Jennifer A %A Antonacci-Fulton, Lucinda %A Arden, Moscati %A Aslibekyan, Stella %A Assimes, Themistocles L %A Ballantyne, Christie M %A Bielak, Lawrence F %A Bis, Joshua C %A Cade, Brian E %A Do, Ron %A Doddapaneni, Harsha %A Emery, Leslie S %A Hung, Yi-Jen %A Irvin, Marguerite R %A Khan, Alyna T %A Lange, Leslie %A Lee, Jiwon %A Lemaitre, Rozenn N %A Martin, Lisa W %A Metcalf, Ginger %A Montasser, May E %A Moon, Jee-Young %A Muzny, Donna %A O'Connell, Jeffrey R %A Palmer, Nicholette D %A Peralta, Juan M %A Peyser, Patricia A %A Stilp, Adrienne M %A Tsai, Michael %A Wang, Fei Fei %A Weeks, Daniel E %A Yanek, Lisa R %A Wilson, James G %A Abecasis, Goncalo %A Arnett, Donna K %A Becker, Lewis C %A Blangero, John %A Boerwinkle, Eric %A Bowden, Donald W %A Chang, Yi-Cheng %A Chen, Yii-der I %A Choi, Won Jung %A Correa, Adolfo %A Curran, Joanne E %A Daly, Mark J %A Dutcher, Susan K %A Ellinor, Patrick T %A Fornage, Myriam %A Freedman, Barry I %A Gabriel, Stacey %A Germer, Soren %A Gibbs, Richard A %A He, Jiang %A Hveem, Kristian %A Jarvik, Gail P %A Kaplan, Robert C %A Kardia, Sharon L R %A Kenny, Eimear %A Kim, Ryan W %A Kooperberg, Charles %A Laurie, Cathy C %A Lee, Seonwook %A Lloyd-Jones, Don M %A Loos, Ruth J F %A Lubitz, Steven A %A Mathias, Rasika A %A Martinez, Karine A Viaud %A McGarvey, Stephen T %A Mitchell, Braxton D %A Nickerson, Deborah A %A North, Kari E %A Palotie, Aarno %A Park, Cheol Joo %A Psaty, Bruce M %A Rao, D C %A Redline, Susan %A Reiner, Alexander P %A Seo, Daekwan %A Seo, Jeong-Sun %A Smith, Albert V %A Tracy, Russell P %A Vasan, Ramachandran S %A Kathiresan, Sekar %A Cupples, L Adrienne %A Rotter, Jerome I %A Morrison, Alanna C %A Rich, Stephen S %A Ripatti, Samuli %A Willer, Cristen %A Peloso, Gina M %X

Autosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P = 8.5 × 10), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P = 1.7 × 10), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P = 1.4 × 10). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids.

%B Nat Commun %V 12 %P 2182 %8 2021 04 12 %G eng %N 1 %R 10.1038/s41467-021-22339-1 %0 Journal Article %J Am J Hum Genet %D 2021 %T Discovery and fine-mapping of height loci via high-density imputation of GWASs in individuals of African ancestry. %A Graff, Mariaelisa %A Justice, Anne E %A Young, Kristin L %A Marouli, Eirini %A Zhang, Xinruo %A Fine, Rebecca S %A Lim, Elise %A Buchanan, Victoria %A Rand, Kristin %A Feitosa, Mary F %A Wojczynski, Mary K %A Yanek, Lisa R %A Shao, Yaming %A Rohde, Rebecca %A Adeyemo, Adebowale A %A Aldrich, Melinda C %A Allison, Matthew A %A Ambrosone, Christine B %A Ambs, Stefan %A Amos, Christopher %A Arnett, Donna K %A Atwood, Larry %A Bandera, Elisa V %A Bartz, Traci %A Becker, Diane M %A Berndt, Sonja I %A Bernstein, Leslie %A Bielak, Lawrence F %A Blot, William J %A Bottinger, Erwin P %A Bowden, Donald W %A Bradfield, Jonathan P %A Brody, Jennifer A %A Broeckel, Ulrich %A Burke, Gregory %A Cade, Brian E %A Cai, Qiuyin %A Caporaso, Neil %A Carlson, Chris %A Carpten, John %A Casey, Graham %A Chanock, Stephen J %A Chen, Guanjie %A Chen, Minhui %A Chen, Yii-der I %A Chen, Wei-Min %A Chesi, Alessandra %A Chiang, Charleston W K %A Chu, Lisa %A Coetzee, Gerry A %A Conti, David V %A Cooper, Richard S %A Cushman, Mary %A Demerath, Ellen %A Deming, Sandra L %A Dimitrov, Latchezar %A Ding, Jingzhong %A Diver, W Ryan %A Duan, Qing %A Evans, Michele K %A Falusi, Adeyinka G %A Faul, Jessica D %A Fornage, Myriam %A Fox, Caroline %A Freedman, Barry I %A Garcia, Melissa %A Gillanders, Elizabeth M %A Goodman, Phyllis %A Gottesman, Omri %A Grant, Struan F A %A Guo, Xiuqing %A Hakonarson, Hakon %A Haritunians, Talin %A Harris, Tamara B %A Harris, Curtis C %A Henderson, Brian E %A Hennis, Anselm %A Hernandez, Dena G %A Hirschhorn, Joel N %A McNeill, Lorna Haughton %A Howard, Timothy D %A Howard, Barbara %A Hsing, Ann W %A Hsu, Yu-Han H %A Hu, Jennifer J %A Huff, Chad D %A Huo, Dezheng %A Ingles, Sue A %A Irvin, Marguerite R %A John, Esther M %A Johnson, Karen C %A Jordan, Joanne M %A Kabagambe, Edmond K %A Kang, Sun J %A Kardia, Sharon L %A Keating, Brendan J %A Kittles, Rick A %A Klein, Eric A %A Kolb, Suzanne %A Kolonel, Laurence N %A Kooperberg, Charles %A Kuller, Lewis %A Kutlar, Abdullah %A Lange, Leslie %A Langefeld, Carl D %A Le Marchand, Loïc %A Leonard, Hampton %A Lettre, Guillaume %A Levin, Albert M %A Li, Yun %A Li, Jin %A Liu, Yongmei %A Liu, Youfang %A Liu, Simin %A Lohman, Kurt %A Lotay, Vaneet %A Lu, Yingchang %A Maixner, William %A Manson, JoAnn E %A McKnight, Barbara %A Meng, Yan %A Monda, Keri L %A Monroe, Kris %A Moore, Jason H %A Mosley, Thomas H %A Mudgal, Poorva %A Murphy, Adam B %A Nadukuru, Rajiv %A Nalls, Mike A %A Nathanson, Katherine L %A Nayak, Uma %A N'diaye, Amidou %A Nemesure, Barbara %A Neslund-Dudas, Christine %A Neuhouser, Marian L %A Nyante, Sarah %A Ochs-Balcom, Heather %A Ogundiran, Temidayo O %A Ogunniyi, Adesola %A Ojengbede, Oladosu %A Okut, Hayrettin %A Olopade, Olufunmilayo I %A Olshan, Andrew %A Padhukasahasram, Badri %A Palmer, Julie %A Palmer, Cameron D %A Palmer, Nicholette D %A Papanicolaou, George %A Patel, Sanjay R %A Pettaway, Curtis A %A Peyser, Patricia A %A Press, Michael F %A Rao, D C %A Rasmussen-Torvik, Laura J %A Redline, Susan %A Reiner, Alex P %A Rhie, Suhn K %A Rodriguez-Gil, Jorge L %A Rotimi, Charles N %A Rotter, Jerome I %A Ruiz-Narvaez, Edward A %A Rybicki, Benjamin A %A Salako, Babatunde %A Sale, Michèle M %A Sanderson, Maureen %A Schadt, Eric %A Schreiner, Pamela J %A Schurmann, Claudia %A Schwartz, Ann G %A Shriner, Daniel A %A Signorello, Lisa B %A Singleton, Andrew B %A Siscovick, David S %A Smith, Jennifer A %A Smith, Shad %A Speliotes, Elizabeth %A Spitz, Margaret %A Stanford, Janet L %A Stevens, Victoria L %A Stram, Alex %A Strom, Sara S %A Sucheston, Lara %A Sun, Yan V %A Tajuddin, Salman M %A Taylor, Herman %A Taylor, Kira %A Tayo, Bamidele O %A Thun, Michael J %A Tucker, Margaret A %A Vaidya, Dhananjay %A Van Den Berg, David J %A Vedantam, Sailaja %A Vitolins, Mara %A Wang, Zhaoming %A Ware, Erin B %A Wassertheil-Smoller, Sylvia %A Weir, David R %A Wiencke, John K %A Williams, Scott M %A Williams, L Keoki %A Wilson, James G %A Witte, John S %A Wrensch, Margaret %A Wu, Xifeng %A Yao, Jie %A Zakai, Neil %A Zanetti, Krista %A Zemel, Babette S %A Zhao, Wei %A Zhao, Jing Hua %A Zheng, Wei %A Zhi, Degui %A Zhou, Jie %A Zhu, Xiaofeng %A Ziegler, Regina G %A Zmuda, Joe %A Zonderman, Alan B %A Psaty, Bruce M %A Borecki, Ingrid B %A Cupples, L Adrienne %A Liu, Ching-Ti %A Haiman, Christopher A %A Loos, Ruth %A Ng, Maggie C Y %A North, Kari E %X

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.

%B Am J Hum Genet %V 108 %P 564-582 %8 2021 Apr 01 %G eng %N 4 %R 10.1016/j.ajhg.2021.02.011 %0 Journal Article %J Nature %D 2021 %T Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. %A Taliun, Daniel %A Harris, Daniel N %A Kessler, Michael D %A Carlson, Jedidiah %A Szpiech, Zachary A %A Torres, Raul %A Taliun, Sarah A Gagliano %A Corvelo, André %A Gogarten, Stephanie M %A Kang, Hyun Min %A Pitsillides, Achilleas N %A LeFaive, Jonathon %A Lee, Seung-Been %A Tian, Xiaowen %A Browning, Brian L %A Das, Sayantan %A Emde, Anne-Katrin %A Clarke, Wayne E %A Loesch, Douglas P %A Shetty, Amol C %A Blackwell, Thomas W %A Smith, Albert V %A Wong, Quenna %A Liu, Xiaoming %A Conomos, Matthew P %A Bobo, Dean M %A Aguet, Francois %A Albert, Christine %A Alonso, Alvaro %A Ardlie, Kristin G %A Arking, Dan E %A Aslibekyan, Stella %A Auer, Paul L %A Barnard, John %A Barr, R Graham %A Barwick, Lucas %A Becker, Lewis C %A Beer, Rebecca L %A Benjamin, Emelia J %A Bielak, Lawrence F %A Blangero, John %A Boehnke, Michael %A Bowden, Donald W %A Brody, Jennifer A %A Burchard, Esteban G %A Cade, Brian E %A Casella, James F %A Chalazan, Brandon %A Chasman, Daniel I %A Chen, Yii-Der Ida %A Cho, Michael H %A Choi, Seung Hoan %A Chung, Mina K %A Clish, Clary B %A Correa, Adolfo %A Curran, Joanne E %A Custer, Brian %A Darbar, Dawood %A Daya, Michelle %A de Andrade, Mariza %A DeMeo, Dawn L %A Dutcher, Susan K %A Ellinor, Patrick T %A Emery, Leslie S %A Eng, Celeste %A Fatkin, Diane %A Fingerlin, Tasha %A Forer, Lukas %A Fornage, Myriam %A Franceschini, Nora %A Fuchsberger, Christian %A Fullerton, Stephanie M %A Germer, Soren %A Gladwin, Mark T %A Gottlieb, Daniel J %A Guo, Xiuqing %A Hall, Michael E %A He, Jiang %A Heard-Costa, Nancy L %A Heckbert, Susan R %A Irvin, Marguerite R %A Johnsen, Jill M %A Johnson, Andrew D %A Kaplan, Robert %A Kardia, Sharon L R %A Kelly, Tanika %A Kelly, Shannon %A Kenny, Eimear E %A Kiel, Douglas P %A Klemmer, Robert %A Konkle, Barbara A %A Kooperberg, Charles %A Köttgen, Anna %A Lange, Leslie A %A Lasky-Su, Jessica %A Levy, Daniel %A Lin, Xihong %A Lin, Keng-Han %A Liu, Chunyu %A Loos, Ruth J F %A Garman, Lori %A Gerszten, Robert %A Lubitz, Steven A %A Lunetta, Kathryn L %A Mak, Angel C Y %A Manichaikul, Ani %A Manning, Alisa K %A Mathias, Rasika A %A McManus, David D %A McGarvey, Stephen T %A Meigs, James B %A Meyers, Deborah A %A Mikulla, Julie L %A Minear, Mollie A %A Mitchell, Braxton D %A Mohanty, Sanghamitra %A Montasser, May E %A Montgomery, Courtney %A Morrison, Alanna C %A Murabito, Joanne M %A Natale, Andrea %A Natarajan, Pradeep %A Nelson, Sarah C %A North, Kari E %A O'Connell, Jeffrey R %A Palmer, Nicholette D %A Pankratz, Nathan %A Peloso, Gina M %A Peyser, Patricia A %A Pleiness, Jacob %A Post, Wendy S %A Psaty, Bruce M %A Rao, D C %A Redline, Susan %A Reiner, Alexander P %A Roden, Dan %A Rotter, Jerome I %A Ruczinski, Ingo %A Sarnowski, Chloe %A Schoenherr, Sebastian %A Schwartz, David A %A Seo, Jeong-Sun %A Seshadri, Sudha %A Sheehan, Vivien A %A Sheu, Wayne H %A Shoemaker, M Benjamin %A Smith, Nicholas L %A Smith, Jennifer A %A Sotoodehnia, Nona %A Stilp, Adrienne M %A Tang, Weihong %A Taylor, Kent D %A Telen, Marilyn %A Thornton, Timothy A %A Tracy, Russell P %A Van Den Berg, David J %A Vasan, Ramachandran S %A Viaud-Martinez, Karine A %A Vrieze, Scott %A Weeks, Daniel E %A Weir, Bruce S %A Weiss, Scott T %A Weng, Lu-Chen %A Willer, Cristen J %A Zhang, Yingze %A Zhao, Xutong %A Arnett, Donna K %A Ashley-Koch, Allison E %A Barnes, Kathleen C %A Boerwinkle, Eric %A Gabriel, Stacey %A Gibbs, Richard %A Rice, Kenneth M %A Rich, Stephen S %A Silverman, Edwin K %A Qasba, Pankaj %A Gan, Weiniu %A Papanicolaou, George J %A Nickerson, Deborah A %A Browning, Sharon R %A Zody, Michael C %A Zöllner, Sebastian %A Wilson, James G %A Cupples, L Adrienne %A Laurie, Cathy C %A Jaquish, Cashell E %A Hernandez, Ryan D %A O'Connor, Timothy D %A Abecasis, Goncalo R %X

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes). In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.

%B Nature %V 590 %P 290-299 %8 2021 02 %G eng %N 7845 %R 10.1038/s41586-021-03205-y %0 Journal Article %J Am J Hum Genet %D 2022 %T Rare coding variants in 35 genes associate with circulating lipid levels-A multi-ancestry analysis of 170,000 exomes. %A Hindy, George %A Dornbos, Peter %A Chaffin, Mark D %A Liu, Dajiang J %A Wang, Minxian %A Selvaraj, Margaret Sunitha %A Zhang, David %A Park, Joseph %A Aguilar-Salinas, Carlos A %A Antonacci-Fulton, Lucinda %A Ardissino, Diego %A Arnett, Donna K %A Aslibekyan, Stella %A Atzmon, Gil %A Ballantyne, Christie M %A Barajas-Olmos, Francisco %A Barzilai, Nir %A Becker, Lewis C %A Bielak, Lawrence F %A Bis, Joshua C %A Blangero, John %A Boerwinkle, Eric %A Bonnycastle, Lori L %A Bottinger, Erwin %A Bowden, Donald W %A Bown, Matthew J %A Brody, Jennifer A %A Broome, Jai G %A Burtt, Noel P %A Cade, Brian E %A Centeno-Cruz, Federico %A Chan, Edmund %A Chang, Yi-Cheng %A Chen, Yii-der I %A Cheng, Ching-Yu %A Choi, Won Jung %A Chowdhury, Rajiv %A Contreras-Cubas, Cecilia %A Córdova, Emilio J %A Correa, Adolfo %A Cupples, L Adrienne %A Curran, Joanne E %A Danesh, John %A de Vries, Paul S %A DeFronzo, Ralph A %A Doddapaneni, Harsha %A Duggirala, Ravindranath %A Dutcher, Susan K %A Ellinor, Patrick T %A Emery, Leslie S %A Florez, Jose C %A Fornage, Myriam %A Freedman, Barry I %A Fuster, Valentin %A Garay-Sevilla, Ma Eugenia %A García-Ortiz, Humberto %A Germer, Soren %A Gibbs, Richard A %A Gieger, Christian %A Glaser, Benjamin %A Gonzalez, Clicerio %A Gonzalez-Villalpando, Maria Elena %A Graff, Mariaelisa %A Graham, Sarah E %A Grarup, Niels %A Groop, Leif C %A Guo, Xiuqing %A Gupta, Namrata %A Han, Sohee %A Hanis, Craig L %A Hansen, Torben %A He, Jiang %A Heard-Costa, Nancy L %A Hung, Yi-Jen %A Hwang, Mi Yeong %A Irvin, Marguerite R %A Islas-Andrade, Sergio %A Jarvik, Gail P %A Kang, Hyun Min %A Kardia, Sharon L R %A Kelly, Tanika %A Kenny, Eimear E %A Khan, Alyna T %A Kim, Bong-Jo %A Kim, Ryan W %A Kim, Young Jin %A Koistinen, Heikki A %A Kooperberg, Charles %A Kuusisto, Johanna %A Kwak, Soo Heon %A Laakso, Markku %A Lange, Leslie A %A Lee, Jiwon %A Lee, Juyoung %A Lee, Seonwook %A Lehman, Donna M %A Lemaitre, Rozenn N %A Linneberg, Allan %A Liu, Jianjun %A Loos, Ruth J F %A Lubitz, Steven A %A Lyssenko, Valeriya %A Ma, Ronald C W %A Martin, Lisa Warsinger %A Martínez-Hernández, Angélica %A Mathias, Rasika A %A McGarvey, Stephen T %A McPherson, Ruth %A Meigs, James B %A Meitinger, Thomas %A Melander, Olle %A Mendoza-Caamal, Elvia %A Metcalf, Ginger A %A Mi, Xuenan %A Mohlke, Karen L %A Montasser, May E %A Moon, Jee-Young %A Moreno-Macias, Hortensia %A Morrison, Alanna C %A Muzny, Donna M %A Nelson, Sarah C %A Nilsson, Peter M %A O'Connell, Jeffrey R %A Orho-Melander, Marju %A Orozco, Lorena %A Palmer, Colin N A %A Palmer, Nicholette D %A Park, Cheol Joo %A Park, Kyong Soo %A Pedersen, Oluf %A Peralta, Juan M %A Peyser, Patricia A %A Post, Wendy S %A Preuss, Michael %A Psaty, Bruce M %A Qi, Qibin %A Rao, D C %A Redline, Susan %A Reiner, Alexander P %A Revilla-Monsalve, Cristina %A Rich, Stephen S %A Samani, Nilesh %A Schunkert, Heribert %A Schurmann, Claudia %A Seo, Daekwan %A Seo, Jeong-Sun %A Sim, Xueling %A Sladek, Rob %A Small, Kerrin S %A So, Wing Yee %A Stilp, Adrienne M %A Tai, E Shyong %A Tam, Claudia H T %A Taylor, Kent D %A Teo, Yik Ying %A Thameem, Farook %A Tomlinson, Brian %A Tsai, Michael Y %A Tuomi, Tiinamaija %A Tuomilehto, Jaakko %A Tusié-Luna, Teresa %A Udler, Miriam S %A van Dam, Rob M %A Vasan, Ramachandran S %A Viaud Martinez, Karine A %A Wang, Fei Fei %A Wang, Xuzhi %A Watkins, Hugh %A Weeks, Daniel E %A Wilson, James G %A Witte, Daniel R %A Wong, Tien-Yin %A Yanek, Lisa R %A Kathiresan, Sekar %A Rader, Daniel J %A Rotter, Jerome I %A Boehnke, Michael %A McCarthy, Mark I %A Willer, Cristen J %A Natarajan, Pradeep %A Flannick, Jason A %A Khera, Amit V %A Peloso, Gina M %K Alleles %K Blood Glucose %K Case-Control Studies %K Computational Biology %K Databases, Genetic %K Diabetes Mellitus, Type 2 %K Exome %K Genetic Predisposition to Disease %K Genetic Variation %K Genetics, Population %K Genome-Wide Association Study %K Humans %K Lipid Metabolism %K Lipids %K Liver %K Molecular Sequence Annotation %K Multifactorial Inheritance %K Open Reading Frames %K Phenotype %K Polymorphism, Single Nucleotide %X

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.

%B Am J Hum Genet %V 109 %P 81-96 %8 2022 01 06 %G eng %N 1 %R 10.1016/j.ajhg.2021.11.021 %0 Journal Article %J medRxiv %D 2023 %T Genome-Wide Interaction Analysis with DASH Diet Score Identified Novel Loci for Systolic Blood Pressure. %A Guirette, Melanie %A Lan, Jessie %A McKeown, Nicola %A Brown, Michael R %A Chen, Han %A de Vries, Paul S %A Kim, Hyunju %A Rebholz, Casey M %A Morrison, Alanna C %A Bartz, Traci M %A Fretts, Amanda M %A Guo, Xiuqing %A Lemaitre, Rozenn N %A Liu, Ching-Ti %A Noordam, Raymond %A de Mutsert, Renée %A Rosendaal, Frits R %A Wang, Carol A %A Beilin, Lawrence %A Mori, Trevor A %A Oddy, Wendy H %A Pennell, Craig E %A Chai, Jin Fang %A Whitton, Clare %A van Dam, Rob M %A Liu, Jianjun %A Tai, E Shyong %A Sim, Xueling %A Neuhouser, Marian L %A Kooperberg, Charles %A Tinker, Lesley %A Franceschini, Nora %A Huan, Tianxiao %A Winkler, Thomas W %A Bentley, Amy R %A Gauderman, W James %A Heerkens, Luc %A Tanaka, Toshiko %A van Rooij, Jeroen %A Munroe, Patricia B %A Warren, Helen R %A Voortman, Trudy %A Chen, Honglei %A Rao, D C %A Levy, Daniel %A Ma, Jiantao %X

OBJECTIVE: We examined interactions between genotype and a Dietary Approaches to Stop Hypertension (DASH) diet score in relation to systolic blood pressure (SBP).

METHODS: We analyzed up to 9,420,585 biallelic imputed single nucleotide polymorphisms (SNPs) in up to 127,282 individuals of six population groups (91% of European population) from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (CHARGE; n=35,660) and UK Biobank (n=91,622) and performed European population-specific and cross-population meta-analyses.

RESULTS: We identified three loci in European-specific analyses and an additional four loci in cross-population analyses at P for interaction < 5e-8. We observed a consistent interaction between rs117878928 at 15q25.1 (minor allele frequency = 0.03) and the DASH diet score (P for interaction = 4e-8; P for heterogeneity = 0.35) in European population, where the interaction effect size was 0.42±0.09 mm Hg (P for interaction = 9.4e-7) and 0.20±0.06 mm Hg (P for interaction = 0.001) in CHARGE and the UK Biobank, respectively. The 1 Mb region surrounding rs117878928 was enriched with -expression quantitative trait loci (eQTL) variants (P = 4e-273) and -DNA methylation quantitative trait loci (mQTL) variants (P = 1e-300). While the closest gene for rs117878928 is , the highest narrow sense heritability accounted by SNPs potentially interacting with the DASH diet score in this locus was for gene at 15q25.1.

CONCLUSION: We demonstrated gene-DASH diet score interaction effects on SBP in several loci. Studies with larger diverse populations are needed to validate our findings.

%B medRxiv %8 2023 Nov 11 %G eng %R 10.1101/2023.11.10.23298402 %0 Journal Article %J medRxiv %D 2023 %T WHOLE GENOME SEQUENCING ANALYSIS OF BODY MASS INDEX IDENTIFIES NOVEL AFRICAN ANCESTRY-SPECIFIC RISK ALLELE. %A Zhang, Xinruo %A Brody, Jennifer A %A Graff, Mariaelisa %A Highland, Heather M %A Chami, Nathalie %A Xu, Hanfei %A Wang, Zhe %A Ferrier, Kendra %A Chittoor, Geetha %A Josyula, Navya S %A Li, Xihao %A Li, Zilin %A Allison, Matthew A %A Becker, Diane M %A Bielak, Lawrence F %A Bis, Joshua C %A Boorgula, Meher Preethi %A Bowden, Donald W %A Broome, Jai G %A Buth, Erin J %A Carlson, Christopher S %A Chang, Kyong-Mi %A Chavan, Sameer %A Chiu, Yen-Feng %A Chuang, Lee-Ming %A Conomos, Matthew P %A DeMeo, Dawn L %A Du, Margaret %A Duggirala, Ravindranath %A Eng, Celeste %A Fohner, Alison E %A Freedman, Barry I %A Garrett, Melanie E %A Guo, Xiuqing %A Haiman, Chris %A Heavner, Benjamin D %A Hidalgo, Bertha %A Hixson, James E %A Ho, Yuk-Lam %A Hobbs, Brian D %A Hu, Donglei %A Hui, Qin %A Hwu, Chii-Min %A Jackson, Rebecca D %A Jain, Deepti %A Kalyani, Rita R %A Kardia, Sharon L R %A Kelly, Tanika N %A Lange, Ethan M %A LeNoir, Michael %A Li, Changwei %A Marchand, Loic Le %A McDonald, Merry-Lynn N %A McHugh, Caitlin P %A Morrison, Alanna C %A Naseri, Take %A O'Connell, Jeffrey %A O'Donnell, Christopher J %A Palmer, Nicholette D %A Pankow, James S %A Perry, James A %A Peters, Ulrike %A Preuss, Michael H %A Rao, D C %A Regan, Elizabeth A %A Reupena, Sefuiva M %A Roden, Dan M %A Rodriguez-Santana, Jose %A Sitlani, Colleen M %A Smith, Jennifer A %A Tiwari, Hemant K %A Vasan, Ramachandran S %A Wang, Zeyuan %A Weeks, Daniel E %A Wessel, Jennifer %A Wiggins, Kerri L %A Wilkens, Lynne R %A Wilson, Peter W F %A Yanek, Lisa R %A Yoneda, Zachary T %A Zhao, Wei %A Zöllner, Sebastian %A Arnett, Donna K %A Ashley-Koch, Allison E %A Barnes, Kathleen C %A Blangero, John %A Boerwinkle, Eric %A Burchard, Esteban G %A Carson, April P %A Chasman, Daniel I %A Chen, Yii-Der Ida %A Curran, Joanne E %A Fornage, Myriam %A Gordeuk, Victor R %A He, Jiang %A Heckbert, Susan R %A Hou, Lifang %A Irvin, Marguerite R %A Kooperberg, Charles %A Minster, Ryan L %A Mitchell, Braxton D %A Nouraie, Mehdi %A Psaty, Bruce M %A Raffield, Laura M %A Reiner, Alexander P %A Rich, Stephen S %A Rotter, Jerome I %A Shoemaker, M Benjamin %A Smith, Nicholas L %A Taylor, Kent D %A Telen, Marilyn J %A Weiss, Scott T %A Zhang, Yingze %A Costa, Nancy Heard- %A Sun, Yan V %A Lin, Xihong %A Cupples, L Adrienne %A Lange, Leslie A %A Liu, Ching-Ti %A Loos, Ruth J F %A North, Kari E %A Justice, Anne E %X

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals ( < 5 × 10 ). Notably, we identified and replicated a novel low frequency single nucleotide polymorphism (SNP) in that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the and loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.

%B medRxiv %8 2023 Aug 22 %G eng %R 10.1101/2023.08.21.23293271