%0 Journal Article %J Genome Biol %D 2016 %T DNA methylation signatures of chronic low-grade inflammation are associated with complex diseases. %A Ligthart, Symen %A Marzi, Carola %A Aslibekyan, Stella %A Mendelson, Michael M %A Conneely, Karen N %A Tanaka, Toshiko %A Colicino, Elena %A Waite, Lindsay L %A Joehanes, Roby %A Guan, Weihua %A Brody, Jennifer A %A Elks, Cathy %A Marioni, Riccardo %A Jhun, Min A %A Agha, Golareh %A Bressler, Jan %A Ward-Caviness, Cavin K %A Chen, Brian H %A Huan, Tianxiao %A Bakulski, Kelly %A Salfati, Elias L %A Fiorito, Giovanni %A Wahl, Simone %A Schramm, Katharina %A Sha, Jin %A Hernandez, Dena G %A Just, Allan C %A Smith, Jennifer A %A Sotoodehnia, Nona %A Pilling, Luke C %A Pankow, James S %A Tsao, Phil S %A Liu, Chunyu %A Zhao, Wei %A Guarrera, Simonetta %A Michopoulos, Vasiliki J %A Smith, Alicia K %A Peters, Marjolein J %A Melzer, David %A Vokonas, Pantel %A Fornage, Myriam %A Prokisch, Holger %A Bis, Joshua C %A Chu, Audrey Y %A Herder, Christian %A Grallert, Harald %A Yao, Chen %A Shah, Sonia %A McRae, Allan F %A Lin, Honghuang %A Horvath, Steve %A Fallin, Daniele %A Hofman, Albert %A Wareham, Nicholas J %A Wiggins, Kerri L %A Feinberg, Andrew P %A Starr, John M %A Visscher, Peter M %A Murabito, Joanne M %A Kardia, Sharon L R %A Absher, Devin M %A Binder, Elisabeth B %A Singleton, Andrew B %A Bandinelli, Stefania %A Peters, Annette %A Waldenberger, Melanie %A Matullo, Giuseppe %A Schwartz, Joel D %A Demerath, Ellen W %A Uitterlinden, André G %A van Meurs, Joyce B J %A Franco, Oscar H %A Chen, Yii-Der Ida %A Levy, Daniel %A Turner, Stephen T %A Deary, Ian J %A Ressler, Kerry J %A Dupuis, Josée %A Ferrucci, Luigi %A Ong, Ken K %A Assimes, Themistocles L %A Boerwinkle, Eric %A Koenig, Wolfgang %A Arnett, Donna K %A Baccarelli, Andrea A %A Benjamin, Emelia J %A Dehghan, Abbas %X

BACKGROUND: Chronic low-grade inflammation reflects a subclinical immune response implicated in the pathogenesis of complex diseases. Identifying genetic loci where DNA methylation is associated with chronic low-grade inflammation may reveal novel pathways or therapeutic targets for inflammation.

RESULTS: We performed a meta-analysis of epigenome-wide association studies (EWAS) of serum C-reactive protein (CRP), which is a sensitive marker of low-grade inflammation, in a large European population (n = 8863) and trans-ethnic replication in African Americans (n = 4111). We found differential methylation at 218 CpG sites to be associated with CRP (P < 1.15 × 10(-7)) in the discovery panel of European ancestry and replicated (P < 2.29 × 10(-4)) 58 CpG sites (45 unique loci) among African Americans. To further characterize the molecular and clinical relevance of the findings, we examined the association with gene expression, genetic sequence variants, and clinical outcomes. DNA methylation at nine (16%) CpG sites was associated with whole blood gene expression in cis (P < 8.47 × 10(-5)), ten (17%) CpG sites were associated with a nearby genetic variant (P < 2.50 × 10(-3)), and 51 (88%) were also associated with at least one related cardiometabolic entity (P < 9.58 × 10(-5)). An additive weighted score of replicated CpG sites accounted for up to 6% inter-individual variation (R2) of age-adjusted and sex-adjusted CRP, independent of known CRP-related genetic variants.

CONCLUSION: We have completed an EWAS of chronic low-grade inflammation and identified many novel genetic loci underlying inflammation that may serve as targets for the development of novel therapeutic interventions for inflammation.

%B Genome Biol %V 17 %P 255 %8 2016 Dec 12 %G eng %N 1 %R 10.1186/s13059-016-1119-5 %0 Journal Article %J Circ Cardiovasc Genet %D 2016 %T Epigenetic Signatures of Cigarette Smoking. %A Joehanes, Roby %A Just, Allan C %A Marioni, Riccardo E %A Pilling, Luke C %A Reynolds, Lindsay M %A Mandaviya, Pooja R %A Guan, Weihua %A Xu, Tao %A Elks, Cathy E %A Aslibekyan, Stella %A Moreno-Macias, Hortensia %A Smith, Jennifer A %A Brody, Jennifer A %A Dhingra, Radhika %A Yousefi, Paul %A Pankow, James S %A Kunze, Sonja %A Shah, Sonia H %A McRae, Allan F %A Lohman, Kurt %A Sha, Jin %A Absher, Devin M %A Ferrucci, Luigi %A Zhao, Wei %A Demerath, Ellen W %A Bressler, Jan %A Grove, Megan L %A Huan, Tianxiao %A Liu, Chunyu %A Mendelson, Michael M %A Yao, Chen %A Kiel, Douglas P %A Peters, Annette %A Wang-Sattler, Rui %A Visscher, Peter M %A Wray, Naomi R %A Starr, John M %A Ding, Jingzhong %A Rodriguez, Carlos J %A Wareham, Nicholas J %A Irvin, Marguerite R %A Zhi, Degui %A Barrdahl, Myrto %A Vineis, Paolo %A Ambatipudi, Srikant %A Uitterlinden, André G %A Hofman, Albert %A Schwartz, Joel %A Colicino, Elena %A Hou, Lifang %A Vokonas, Pantel S %A Hernandez, Dena G %A Singleton, Andrew B %A Bandinelli, Stefania %A Turner, Stephen T %A Ware, Erin B %A Smith, Alicia K %A Klengel, Torsten %A Binder, Elisabeth B %A Psaty, Bruce M %A Taylor, Kent D %A Gharib, Sina A %A Swenson, Brenton R %A Liang, Liming %A DeMeo, Dawn L %A O'Connor, George T %A Herceg, Zdenko %A Ressler, Kerry J %A Conneely, Karen N %A Sotoodehnia, Nona %A Kardia, Sharon L R %A Melzer, David %A Baccarelli, Andrea A %A van Meurs, Joyce B J %A Romieu, Isabelle %A Arnett, Donna K %A Ong, Ken K %A Liu, Yongmei %A Waldenberger, Melanie %A Deary, Ian J %A Fornage, Myriam %A Levy, Daniel %A London, Stephanie J %X

BACKGROUND: DNA methylation leaves a long-term signature of smoking exposure and is one potential mechanism by which tobacco exposure predisposes to adverse health outcomes, such as cancers, osteoporosis, lung, and cardiovascular disorders.

METHODS AND RESULTS: To comprehensively determine the association between cigarette smoking and DNA methylation, we conducted a meta-analysis of genome-wide DNA methylation assessed using the Illumina BeadChip 450K array on 15 907 blood-derived DNA samples from participants in 16 cohorts (including 2433 current, 6518 former, and 6956 never smokers). Comparing current versus never smokers, 2623 cytosine-phosphate-guanine sites (CpGs), annotated to 1405 genes, were statistically significantly differentially methylated at Bonferroni threshold of P<1×10(-7) (18 760 CpGs at false discovery rate <0.05). Genes annotated to these CpGs were enriched for associations with several smoking-related traits in genome-wide studies including pulmonary function, cancers, inflammatory diseases, and heart disease. Comparing former versus never smokers, 185 of the CpGs that differed between current and never smokers were significant P<1×10(-7) (2623 CpGs at false discovery rate <0.05), indicating a pattern of persistent altered methylation, with attenuation, after smoking cessation. Transcriptomic integration identified effects on gene expression at many differentially methylated CpGs.

CONCLUSIONS: Cigarette smoking has a broad impact on genome-wide methylation that, at many loci, persists many years after smoking cessation. Many of the differentially methylated genes were novel genes with respect to biological effects of smoking and might represent therapeutic targets for prevention or treatment of tobacco-related diseases. Methylation at these sites could also serve as sensitive and stable biomarkers of lifetime exposure to tobacco smoke.

%B Circ Cardiovasc Genet %V 9 %P 436-447 %8 2016 Oct %G eng %N 5 %R 10.1161/CIRCGENETICS.116.001506 %0 Journal Article %J Am J Clin Nutr %D 2016 %T Interaction of methylation-related genetic variants with circulating fatty acids on plasma lipids: a meta-analysis of 7 studies and methylation analysis of 3 studies in the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium. %A Ma, Yiyi %A Follis, Jack L %A Smith, Caren E %A Tanaka, Toshiko %A Manichaikul, Ani W %A Chu, Audrey Y %A Samieri, Cecilia %A Zhou, Xia %A Guan, Weihua %A Wang, Lu %A Biggs, Mary L %A Chen, Yii-der I %A Hernandez, Dena G %A Borecki, Ingrid %A Chasman, Daniel I %A Rich, Stephen S %A Ferrucci, Luigi %A Irvin, Marguerite Ryan %A Aslibekyan, Stella %A Zhi, Degui %A Tiwari, Hemant K %A Claas, Steven A %A Sha, Jin %A Kabagambe, Edmond K %A Lai, Chao-Qiang %A Parnell, Laurence D %A Lee, Yu-Chi %A Amouyel, Philippe %A Lambert, Jean-Charles %A Psaty, Bruce M %A King, Irena B %A Mozaffarian, Dariush %A McKnight, Barbara %A Bandinelli, Stefania %A Tsai, Michael Y %A Ridker, Paul M %A Ding, Jingzhong %A Mstat, Kurt Lohmant %A Liu, Yongmei %A Sotoodehnia, Nona %A Barberger-Gateau, Pascale %A Steffen, Lyn M %A Siscovick, David S %A Absher, Devin %A Arnett, Donna K %A Ordovas, Jose M %A Lemaitre, Rozenn N %K Apolipoproteins E %K ATP Binding Cassette Transporter 1 %K Cholesterol, HDL %K Cohort Studies %K Diet %K DNA Methylation %K Eicosapentaenoic Acid %K Epigenesis, Genetic %K Fatty Acids %K Gene Expression Regulation %K Humans %K Lipids %K Polymorphism, Single Nucleotide %K Promoter Regions, Genetic %K Triglycerides %X

BACKGROUND: DNA methylation is influenced by diet and single nucleotide polymorphisms (SNPs), and methylation modulates gene expression.

OBJECTIVE: We aimed to explore whether the gene-by-diet interactions on blood lipids act through DNA methylation.

DESIGN: We selected 7 SNPs on the basis of predicted relations in fatty acids, methylation, and lipids. We conducted a meta-analysis and a methylation and mediation analysis with the use of data from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium and the ENCODE (Encyclopedia of DNA Elements) consortium.

RESULTS: On the basis of the meta-analysis of 7 cohorts in the CHARGE consortium, higher plasma HDL cholesterol was associated with fewer C alleles at ATP-binding cassette subfamily A member 1 (ABCA1) rs2246293 (β = -0.6 mg/dL, P = 0.015) and higher circulating eicosapentaenoic acid (EPA) (β = 3.87 mg/dL, P = 5.62 × 10(21)). The difference in HDL cholesterol associated with higher circulating EPA was dependent on genotypes at rs2246293, and it was greater for each additional C allele (β = 1.69 mg/dL, P = 0.006). In the GOLDN (Genetics of Lipid Lowering Drugs and Diet Network) study, higher ABCA1 promoter cg14019050 methylation was associated with more C alleles at rs2246293 (β = 8.84%, P = 3.51 × 10(18)) and lower circulating EPA (β = -1.46%, P = 0.009), and the mean difference in methylation of cg14019050 that was associated with higher EPA was smaller with each additional C allele of rs2246293 (β = -2.83%, P = 0.007). Higher ABCA1 cg14019050 methylation was correlated with lower ABCA1 expression (r = -0.61, P = 0.009) in the ENCODE consortium and lower plasma HDL cholesterol in the GOLDN study (r = -0.12, P = 0.0002). An additional mediation analysis was meta-analyzed across the GOLDN study, Cardiovascular Health Study, and the Multi-Ethnic Study of Atherosclerosis. Compared with the model without the adjustment of cg14019050 methylation, the model with such adjustment provided smaller estimates of the mean plasma HDL cholesterol concentration in association with both the rs2246293 C allele and EPA and a smaller difference by rs2246293 genotypes in the EPA-associated HDL cholesterol. However, the differences between 2 nested models were NS (P > 0.05).

CONCLUSION: We obtained little evidence that the gene-by-fatty acid interactions on blood lipids act through DNA methylation.

%B Am J Clin Nutr %V 103 %P 567-78 %8 2016 Feb %G eng %N 2 %1 http://www.ncbi.nlm.nih.gov/pubmed/26791180?dopt=Abstract %R 10.3945/ajcn.115.112987 %0 Journal Article %J Am J Hum Genet %D 2017 %T DNA Methylation Analysis Identifies Loci for Blood Pressure Regulation. %A Richard, Melissa A %A Huan, Tianxiao %A Ligthart, Symen %A Gondalia, Rahul %A Jhun, Min A %A Brody, Jennifer A %A Irvin, Marguerite R %A Marioni, Riccardo %A Shen, Jincheng %A Tsai, Pei-Chien %A Montasser, May E %A Jia, Yucheng %A Syme, Catriona %A Salfati, Elias L %A Boerwinkle, Eric %A Guan, Weihua %A Mosley, Thomas H %A Bressler, Jan %A Morrison, Alanna C %A Liu, Chunyu %A Mendelson, Michael M %A Uitterlinden, André G %A van Meurs, Joyce B %A Franco, Oscar H %A Zhang, Guosheng %A Li, Yun %A Stewart, James D %A Bis, Joshua C %A Psaty, Bruce M %A Chen, Yii-Der Ida %A Kardia, Sharon L R %A Zhao, Wei %A Turner, Stephen T %A Absher, Devin %A Aslibekyan, Stella %A Starr, John M %A McRae, Allan F %A Hou, Lifang %A Just, Allan C %A Schwartz, Joel D %A Vokonas, Pantel S %A Menni, Cristina %A Spector, Tim D %A Shuldiner, Alan %A Damcott, Coleen M %A Rotter, Jerome I %A Palmas, Walter %A Liu, Yongmei %A Paus, Tomáš %A Horvath, Steve %A O'Connell, Jeffrey R %A Guo, Xiuqing %A Pausova, Zdenka %A Assimes, Themistocles L %A Sotoodehnia, Nona %A Smith, Jennifer A %A Arnett, Donna K %A Deary, Ian J %A Baccarelli, Andrea A %A Bell, Jordana T %A Whitsel, Eric %A Dehghan, Abbas %A Levy, Daniel %A Fornage, Myriam %K Aged %K Blood Pressure %K CpG Islands %K Cross-Sectional Studies %K DNA Methylation %K Epigenesis, Genetic %K Genetic Variation %K Genome-Wide Association Study %K Humans %K Mendelian Randomization Analysis %K Middle Aged %K Nerve Tissue Proteins %K Quantitative Trait Loci %K Tetraspanins %X

Genome-wide association studies have identified hundreds of genetic variants associated with blood pressure (BP), but sequence variation accounts for a small fraction of the phenotypic variance. Epigenetic changes may alter the expression of genes involved in BP regulation and explain part of the missing heritability. We therefore conducted a two-stage meta-analysis of the cross-sectional associations of systolic and diastolic BP with blood-derived genome-wide DNA methylation measured on the Infinium HumanMethylation450 BeadChip in 17,010 individuals of European, African American, and Hispanic ancestry. Of 31 discovery-stage cytosine-phosphate-guanine (CpG) dinucleotides, 13 replicated after Bonferroni correction (discovery: N = 9,828, p < 1.0 × 10-7; replication: N = 7,182, p < 1.6 × 10-3). The replicated methylation sites are heritable (h2 > 30%) and independent of known BP genetic variants, explaining an additional 1.4% and 2.0% of the interindividual variation in systolic and diastolic BP, respectively. Bidirectional Mendelian randomization among up to 4,513 individuals of European ancestry from 4 cohorts suggested that methylation at cg08035323 (TAF1B-YWHAQ) influences BP, while BP influences methylation at cg00533891 (ZMIZ1), cg00574958 (CPT1A), and cg02711608 (SLC1A5). Gene expression analyses further identified six genes (TSPAN2, SLC7A11, UNC93B1, CPT1A, PTMS, and LPCAT3) with evidence of triangular associations between methylation, gene expression, and BP. Additional integrative Mendelian randomization analyses of gene expression and DNA methylation suggested that the expression of TSPAN2 is a putative mediator of association between DNA methylation at cg23999170 and BP. These findings suggest that heritable DNA methylation plays a role in regulating BP independently of previously known genetic variants.

%B Am J Hum Genet %V 101 %P 888-902 %8 2017 Dec 07 %G eng %N 6 %R 10.1016/j.ajhg.2017.09.028 %0 Journal Article %J Mol Nutr Food Res %D 2017 %T Genome-Wide Interactions with Dairy Intake for Body Mass Index in Adults of European Descent. %A Smith, Caren E %A Follis, Jack L %A Dashti, Hassan S %A Tanaka, Toshiko %A Graff, Mariaelisa %A Fretts, Amanda M %A Kilpeläinen, Tuomas O %A Wojczynski, Mary K %A Richardson, Kris %A Nalls, Mike A %A Schulz, Christina-Alexandra %A Liu, Yongmei %A Frazier-Wood, Alexis C %A van Eekelen, Esther %A Wang, Carol %A de Vries, Paul S %A Mikkilä, Vera %A Rohde, Rebecca %A Psaty, Bruce M %A Hansen, Torben %A Feitosa, Mary F %A Lai, Chao-Qiang %A Houston, Denise K %A Ferruci, Luigi %A Ericson, Ulrika %A Wang, Zhe %A de Mutsert, Renée %A Oddy, Wendy H %A de Jonge, Ester A L %A Seppälä, Ilkka %A Justice, Anne E %A Lemaitre, Rozenn N %A Sørensen, Thorkild I A %A Province, Michael A %A Parnell, Laurence D %A Garcia, Melissa E %A Bandinelli, Stefania %A Orho-Melander, Marju %A Rich, Stephen S %A Rosendaal, Frits R %A Pennell, Craig E %A Kiefte-de Jong, Jessica C %A Kähönen, Mika %A Young, Kristin L %A Pedersen, Oluf %A Aslibekyan, Stella %A Rotter, Jerome I %A Mook-Kanamori, Dennis O %A Zillikens, M Carola %A Raitakari, Olli T %A North, Kari E %A Overvad, Kim %A Arnett, Donna K %A Hofman, Albert %A Lehtimäki, Terho %A Tjønneland, Anne %A Uitterlinden, André G %A Rivadeneira, Fernando %A Franco, Oscar H %A German, J Bruce %A Siscovick, David S %A Cupples, L Adrienne %A Ordovas, Jose M %X

SCOPE: Body weight responds variably to the intake of dairy foods. Genetic variation may contribute to inter-individual variability in associations between body weight and dairy consumption.

METHODS AND RESULTS: A genome-wide interaction study to discover genetic variants that account for variation in BMI in the context of low-fat, high-fat and total dairy intake in cross-sectional analysis was conducted. Data from nine discovery studies (up to 25 513 European descent individuals) were meta-analyzed. Twenty-six genetic variants reached the selected significance threshold (p-interaction <10-7) , and six independent variants (LINC01512-rs7751666, PALM2/AKAP2-rs914359, ACTA2-rs1388, PPP1R12A-rs7961195, LINC00333-rs9635058, AC098847.1-rs1791355) were evaluated meta-analytically for replication of interaction in up to 17 675 individuals. Variant rs9635058 (128 kb 3' of LINC00333) was replicated (p-interaction = 0.004). In the discovery cohorts, rs9635058 interacted with dairy (p-interaction = 7.36 × 10-8) such that each serving of low-fat dairy was associated with 0.225 kg m-2 lower BMI per each additional copy of the effect allele (A). A second genetic variant (ACTA2-rs1388) approached interaction replication significance for low-fat dairy exposure.

CONCLUSION: Body weight responses to dairy intake may be modified by genotype, in that greater dairy intake may protect a genetic subgroup from higher body weight.

%B Mol Nutr Food Res %8 2017 Sep 21 %G eng %R 10.1002/mnfr.201700347 %0 Journal Article %J Am J Hum Genet %D 2018 %T Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders. %A Ligthart, Symen %A Vaez, Ahmad %A Võsa, Urmo %A Stathopoulou, Maria G %A de Vries, Paul S %A Prins, Bram P %A van der Most, Peter J %A Tanaka, Toshiko %A Naderi, Elnaz %A Rose, Lynda M %A Wu, Ying %A Karlsson, Robert %A Barbalic, Maja %A Lin, Honghuang %A Pool, Rene %A Zhu, Gu %A Mace, Aurelien %A Sidore, Carlo %A Trompet, Stella %A Mangino, Massimo %A Sabater-Lleal, Maria %A Kemp, John P %A Abbasi, Ali %A Kacprowski, Tim %A Verweij, Niek %A Smith, Albert V %A Huang, Tao %A Marzi, Carola %A Feitosa, Mary F %A Lohman, Kurt K %A Kleber, Marcus E %A Milaneschi, Yuri %A Mueller, Christian %A Huq, Mahmudul %A Vlachopoulou, Efthymia %A Lyytikäinen, Leo-Pekka %A Oldmeadow, Christopher %A Deelen, Joris %A Perola, Markus %A Zhao, Jing Hua %A Feenstra, Bjarke %A Amini, Marzyeh %A Lahti, Jari %A Schraut, Katharina E %A Fornage, Myriam %A Suktitipat, Bhoom %A Chen, Wei-Min %A Li, Xiaohui %A Nutile, Teresa %A Malerba, Giovanni %A Luan, Jian'an %A Bak, Tom %A Schork, Nicholas %A del Greco M, Fabiola %A Thiering, Elisabeth %A Mahajan, Anubha %A Marioni, Riccardo E %A Mihailov, Evelin %A Eriksson, Joel %A Ozel, Ayse Bilge %A Zhang, Weihua %A Nethander, Maria %A Cheng, Yu-Ching %A Aslibekyan, Stella %A Ang, Wei %A Gandin, Ilaria %A Yengo, Loic %A Portas, Laura %A Kooperberg, Charles %A Hofer, Edith %A Rajan, Kumar B %A Schurmann, Claudia %A den Hollander, Wouter %A Ahluwalia, Tarunveer S %A Zhao, Jing %A Draisma, Harmen H M %A Ford, Ian %A Timpson, Nicholas %A Teumer, Alexander %A Huang, Hongyan %A Wahl, Simone %A Liu, Yongmei %A Huang, Jie %A Uh, Hae-Won %A Geller, Frank %A Joshi, Peter K %A Yanek, Lisa R %A Trabetti, Elisabetta %A Lehne, Benjamin %A Vozzi, Diego %A Verbanck, Marie %A Biino, Ginevra %A Saba, Yasaman %A Meulenbelt, Ingrid %A O'Connell, Jeff R %A Laakso, Markku %A Giulianini, Franco %A Magnusson, Patrik K E %A Ballantyne, Christie M %A Hottenga, Jouke Jan %A Montgomery, Grant W %A Rivadineira, Fernando %A Rueedi, Rico %A Steri, Maristella %A Herzig, Karl-Heinz %A Stott, David J %A Menni, Cristina %A Frånberg, Mattias %A St Pourcain, Beate %A Felix, Stephan B %A Pers, Tune H %A Bakker, Stephan J L %A Kraft, Peter %A Peters, Annette %A Vaidya, Dhananjay %A Delgado, Graciela %A Smit, Johannes H %A Großmann, Vera %A Sinisalo, Juha %A Seppälä, Ilkka %A Williams, Stephen R %A Holliday, Elizabeth G %A Moed, Matthijs %A Langenberg, Claudia %A Räikkönen, Katri %A Ding, Jingzhong %A Campbell, Harry %A Sale, Michèle M %A Chen, Yii-der I %A James, Alan L %A Ruggiero, Daniela %A Soranzo, Nicole %A Hartman, Catharina A %A Smith, Erin N %A Berenson, Gerald S %A Fuchsberger, Christian %A Hernandez, Dena %A Tiesler, Carla M T %A Giedraitis, Vilmantas %A Liewald, David %A Fischer, Krista %A Mellström, Dan %A Larsson, Anders %A Wang, Yunmei %A Scott, William R %A Lorentzon, Matthias %A Beilby, John %A Ryan, Kathleen A %A Pennell, Craig E %A Vuckovic, Dragana %A Balkau, Beverly %A Concas, Maria Pina %A Schmidt, Reinhold %A Mendes de Leon, Carlos F %A Bottinger, Erwin P %A Kloppenburg, Margreet %A Paternoster, Lavinia %A Boehnke, Michael %A Musk, A W %A Willemsen, Gonneke %A Evans, David M %A Madden, Pamela A F %A Kähönen, Mika %A Kutalik, Zoltán %A Zoledziewska, Magdalena %A Karhunen, Ville %A Kritchevsky, Stephen B %A Sattar, Naveed %A Lachance, Genevieve %A Clarke, Robert %A Harris, Tamara B %A Raitakari, Olli T %A Attia, John R %A van Heemst, Diana %A Kajantie, Eero %A Sorice, Rossella %A Gambaro, Giovanni %A Scott, Robert A %A Hicks, Andrew A %A Ferrucci, Luigi %A Standl, Marie %A Lindgren, Cecilia M %A Starr, John M %A Karlsson, Magnus %A Lind, Lars %A Li, Jun Z %A Chambers, John C %A Mori, Trevor A %A de Geus, Eco J C N %A Heath, Andrew C %A Martin, Nicholas G %A Auvinen, Juha %A Buckley, Brendan M %A de Craen, Anton J M %A Waldenberger, Melanie %A Strauch, Konstantin %A Meitinger, Thomas %A Scott, Rodney J %A McEvoy, Mark %A Beekman, Marian %A Bombieri, Cristina %A Ridker, Paul M %A Mohlke, Karen L %A Pedersen, Nancy L %A Morrison, Alanna C %A Boomsma, Dorret I %A Whitfield, John B %A Strachan, David P %A Hofman, Albert %A Vollenweider, Peter %A Cucca, Francesco %A Jarvelin, Marjo-Riitta %A Jukema, J Wouter %A Spector, Tim D %A Hamsten, Anders %A Zeller, Tanja %A Uitterlinden, André G %A Nauck, Matthias %A Gudnason, Vilmundur %A Qi, Lu %A Grallert, Harald %A Borecki, Ingrid B %A Rotter, Jerome I %A März, Winfried %A Wild, Philipp S %A Lokki, Marja-Liisa %A Boyle, Michael %A Salomaa, Veikko %A Melbye, Mads %A Eriksson, Johan G %A Wilson, James F %A Penninx, Brenda W J H %A Becker, Diane M %A Worrall, Bradford B %A Gibson, Greg %A Krauss, Ronald M %A Ciullo, Marina %A Zaza, Gianluigi %A Wareham, Nicholas J %A Oldehinkel, Albertine J %A Palmer, Lyle J %A Murray, Sarah S %A Pramstaller, Peter P %A Bandinelli, Stefania %A Heinrich, Joachim %A Ingelsson, Erik %A Deary, Ian J %A Mägi, Reedik %A Vandenput, Liesbeth %A van der Harst, Pim %A Desch, Karl C %A Kooner, Jaspal S %A Ohlsson, Claes %A Hayward, Caroline %A Lehtimäki, Terho %A Shuldiner, Alan R %A Arnett, Donna K %A Beilin, Lawrence J %A Robino, Antonietta %A Froguel, Philippe %A Pirastu, Mario %A Jess, Tine %A Koenig, Wolfgang %A Loos, Ruth J F %A Evans, Denis A %A Schmidt, Helena %A Smith, George Davey %A Slagboom, P Eline %A Eiriksdottir, Gudny %A Morris, Andrew P %A Psaty, Bruce M %A Tracy, Russell P %A Nolte, Ilja M %A Boerwinkle, Eric %A Visvikis-Siest, Sophie %A Reiner, Alex P %A Gross, Myron %A Bis, Joshua C %A Franke, Lude %A Franco, Oscar H %A Benjamin, Emelia J %A Chasman, Daniel I %A Dupuis, Josée %A Snieder, Harold %A Dehghan, Abbas %A Alizadeh, Behrooz Z %X

C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. The genetic determinants of chronic inflammation remain largely unknown, and the causal role of CRP in several clinical outcomes is debated. We performed two genome-wide association studies (GWASs), on HapMap and 1000 Genomes imputed data, of circulating amounts of CRP by using data from 88 studies comprising 204,402 European individuals. Additionally, we performed in silico functional analyses and Mendelian randomization analyses with several clinical outcomes. The GWAS meta-analyses of CRP revealed 58 distinct genetic loci (p < 5 × 10). After adjustment for body mass index in the regression analysis, the associations at all except three loci remained. The lead variants at the distinct loci explained up to 7.0% of the variance in circulating amounts of CRP. We identified 66 gene sets that were organized in two substantially correlated clusters, one mainly composed of immune pathways and the other characterized by metabolic pathways in the liver. Mendelian randomization analyses revealed a causal protective effect of CRP on schizophrenia and a risk-increasing effect on bipolar disorder. Our findings provide further insights into the biology of inflammation and could lead to interventions for treating inflammation and its clinical consequences.

%B Am J Hum Genet %V 103 %P 691-706 %8 2018 Nov 01 %G eng %N 5 %R 10.1016/j.ajhg.2018.09.009 %0 Journal Article %J PLoS One %D 2018 %T Genome-wide association meta-analysis of circulating odd-numbered chain saturated fatty acids: Results from the CHARGE Consortium. %A de Oliveira Otto, Marcia C %A Lemaitre, Rozenn N %A Sun, Qi %A King, Irena B %A Wu, Jason H Y %A Manichaikul, Ani %A Rich, Stephen S %A Tsai, Michael Y %A Chen, Y D %A Fornage, Myriam %A Weihua, Guan %A Aslibekyan, Stella %A Irvin, Marguerite R %A Kabagambe, Edmond K %A Arnett, Donna K %A Jensen, Majken K %A McKnight, Barbara %A Psaty, Bruce M %A Steffen, Lyn M %A Smith, Caren E %A Riserus, Ulf %A Lind, Lars %A Hu, Frank B %A Rimm, Eric B %A Siscovick, David S %A Mozaffarian, Dariush %K Fatty Acids %K Genome-Wide Association Study %K Humans %K Introns %K Lactase %K Myosins %K Polymorphism, Single Nucleotide %K Sphingomyelins %K Sphingosine N-Acyltransferase %K Tumor Suppressor Proteins %X

BACKGROUND: Odd-numbered chain saturated fatty acids (OCSFA) have been associated with potential health benefits. Although some OCSFA (e.g., C15:0 and C17:0) are found in meats and dairy products, sources and metabolism of C19:0 and C23:0 are relatively unknown, and the influence of non-dietary determinants, including genetic factors, on circulating levels of OCSFA is not established.

OBJECTIVE: To elucidate the biological processes that influence circulating levels of OCSFA by investigating associations between genetic variation and OCSFA.

DESIGN: We performed a meta-analysis of genome-wide association studies (GWAS) of plasma phospholipid/erythrocyte levels of C15:0, C17:0, C19:0, and C23:0 among 11,494 individuals of European descent. We also investigated relationships between specific single nucleotide polymorphisms (SNPs) in the lactase (LCT) gene, associated with adult-onset lactase intolerance, with circulating levels of dairy-derived OCSFA, and evaluated associations of candidate sphingolipid genes with C23:0 levels.

RESULTS: We found no genome-wide significant evidence that common genetic variation is associated with circulating levels of C15:0 or C23:0. In two cohorts with available data, we identified one intronic SNP (rs13361131) in myosin X gene (MYO10) associated with C17:0 level (P = 1.37×10-8), and two intronic SNP (rs12874278 and rs17363566) in deleted in lymphocytic leukemia 1 (DLEU1) region associated with C19:0 level (P = 7.07×10-9). In contrast, when using a candidate-gene approach, we found evidence that three SNPs in LCT (rs11884924, rs16832067, and rs3816088) are associated with circulating C17:0 level (adjusted P = 4×10-2). In addition, nine SNPs in the ceramide synthase 4 (CERS4) region were associated with circulating C23:0 levels (adjusted P<5×10-2).

CONCLUSIONS: Our findings suggest that circulating levels of OCSFA may be predominantly influenced by non-genetic factors. SNPs associated with C17:0 level in the LCT gene may reflect genetic influence in dairy consumption or in metabolism of dairy foods. SNPs associated with C23:0 may reflect a role of genetic factors in the synthesis of sphingomyelin.

%B PLoS One %V 13 %P e0196951 %8 2018 %G eng %N 5 %R 10.1371/journal.pone.0196951 %0 Journal Article %J Am J Hum Genet %D 2018 %T A Large-Scale Multi-ancestry Genome-wide Study Accounting for Smoking Behavior Identifies Multiple Significant Loci for Blood Pressure. %A Sung, Yun J %A Winkler, Thomas W %A de Las Fuentes, Lisa %A Bentley, Amy R %A Brown, Michael R %A Kraja, Aldi T %A Schwander, Karen %A Ntalla, Ioanna %A Guo, Xiuqing %A Franceschini, Nora %A Lu, Yingchang %A Cheng, Ching-Yu %A Sim, Xueling %A Vojinovic, Dina %A Marten, Jonathan %A Musani, Solomon K %A Li, Changwei %A Feitosa, Mary F %A Kilpeläinen, Tuomas O %A Richard, Melissa A %A Noordam, Raymond %A Aslibekyan, Stella %A Aschard, Hugues %A Bartz, Traci M %A Dorajoo, Rajkumar %A Liu, Yongmei %A Manning, Alisa K %A Rankinen, Tuomo %A Smith, Albert Vernon %A Tajuddin, Salman M %A Tayo, Bamidele O %A Warren, Helen R %A Zhao, Wei %A Zhou, Yanhua %A Matoba, Nana %A Sofer, Tamar %A Alver, Maris %A Amini, Marzyeh %A Boissel, Mathilde %A Chai, Jin Fang %A Chen, Xu %A Divers, Jasmin %A Gandin, Ilaria %A Gao, Chuan %A Giulianini, Franco %A Goel, Anuj %A Harris, Sarah E %A Hartwig, Fernando Pires %A Horimoto, Andrea R V R %A Hsu, Fang-Chi %A Jackson, Anne U %A Kähönen, Mika %A Kasturiratne, Anuradhani %A Kuhnel, Brigitte %A Leander, Karin %A Lee, Wen-Jane %A Lin, Keng-Hung %A 'an Luan, Jian %A McKenzie, Colin A %A Meian, He %A Nelson, Christopher P %A Rauramaa, Rainer %A Schupf, Nicole %A Scott, Robert A %A Sheu, Wayne H H %A Stančáková, Alena %A Takeuchi, Fumihiko %A van der Most, Peter J %A Varga, Tibor V %A Wang, Heming %A Wang, Yajuan %A Ware, Erin B %A Weiss, Stefan %A Wen, Wanqing %A Yanek, Lisa R %A Zhang, Weihua %A Zhao, Jing Hua %A Afaq, Saima %A Alfred, Tamuno %A Amin, Najaf %A Arking, Dan %A Aung, Tin %A Barr, R Graham %A Bielak, Lawrence F %A Boerwinkle, Eric %A Bottinger, Erwin P %A Braund, Peter S %A Brody, Jennifer A %A Broeckel, Ulrich %A Cabrera, Claudia P %A Cade, Brian %A Caizheng, Yu %A Campbell, Archie %A Canouil, Mickaël %A Chakravarti, Aravinda %A Chauhan, Ganesh %A Christensen, Kaare %A Cocca, Massimiliano %A Collins, Francis S %A Connell, John M %A de Mutsert, Renée %A de Silva, H Janaka %A Debette, Stephanie %A Dörr, Marcus %A Duan, Qing %A Eaton, Charles B %A Ehret, Georg %A Evangelou, Evangelos %A Faul, Jessica D %A Fisher, Virginia A %A Forouhi, Nita G %A Franco, Oscar H %A Friedlander, Yechiel %A Gao, He %A Gigante, Bruna %A Graff, Misa %A Gu, C Charles %A Gu, Dongfeng %A Gupta, Preeti %A Hagenaars, Saskia P %A Harris, Tamara B %A He, Jiang %A Heikkinen, Sami %A Heng, Chew-Kiat %A Hirata, Makoto %A Hofman, Albert %A Howard, Barbara V %A Hunt, Steven %A Irvin, Marguerite R %A Jia, Yucheng %A Joehanes, Roby %A Justice, Anne E %A Katsuya, Tomohiro %A Kaufman, Joel %A Kerrison, Nicola D %A Khor, Chiea Chuen %A Koh, Woon-Puay %A Koistinen, Heikki A %A Komulainen, Pirjo %A Kooperberg, Charles %A Krieger, Jose E %A Kubo, Michiaki %A Kuusisto, Johanna %A Langefeld, Carl D %A Langenberg, Claudia %A Launer, Lenore J %A Lehne, Benjamin %A Lewis, Cora E %A Li, Yize %A Lim, Sing Hui %A Lin, Shiow %A Liu, Ching-Ti %A Liu, Jianjun %A Liu, Jingmin %A Liu, Kiang %A Liu, Yeheng %A Loh, Marie %A Lohman, Kurt K %A Long, Jirong %A Louie, Tin %A Mägi, Reedik %A Mahajan, Anubha %A Meitinger, Thomas %A Metspalu, Andres %A Milani, Lili %A Momozawa, Yukihide %A Morris, Andrew P %A Mosley, Thomas H %A Munson, Peter %A Murray, Alison D %A Nalls, Mike A %A Nasri, Ubaydah %A Norris, Jill M %A North, Kari %A Ogunniyi, Adesola %A Padmanabhan, Sandosh %A Palmas, Walter R %A Palmer, Nicholette D %A Pankow, James S %A Pedersen, Nancy L %A Peters, Annette %A Peyser, Patricia A %A Polasek, Ozren %A Raitakari, Olli T %A Renstrom, Frida %A Rice, Treva K %A Ridker, Paul M %A Robino, Antonietta %A Robinson, Jennifer G %A Rose, Lynda M %A Rudan, Igor %A Sabanayagam, Charumathi %A Salako, Babatunde L %A Sandow, Kevin %A Schmidt, Carsten O %A Schreiner, Pamela J %A Scott, William R %A Seshadri, Sudha %A Sever, Peter %A Sitlani, Colleen M %A Smith, Jennifer A %A Snieder, Harold %A Starr, John M %A Strauch, Konstantin %A Tang, Hua %A Taylor, Kent D %A Teo, Yik Ying %A Tham, Yih Chung %A Uitterlinden, André G %A Waldenberger, Melanie %A Wang, Lihua %A Wang, Ya X %A Wei, Wen Bin %A Williams, Christine %A Wilson, Gregory %A Wojczynski, Mary K %A Yao, Jie %A Yuan, Jian-Min %A Zonderman, Alan B %A Becker, Diane M %A Boehnke, Michael %A Bowden, Donald W %A Chambers, John C %A Chen, Yii-Der Ida %A de Faire, Ulf %A Deary, Ian J %A Esko, Tõnu %A Farrall, Martin %A Forrester, Terrence %A Franks, Paul W %A Freedman, Barry I %A Froguel, Philippe %A Gasparini, Paolo %A Gieger, Christian %A Horta, Bernardo Lessa %A Hung, Yi-Jen %A Jonas, Jost B %A Kato, Norihiro %A Kooner, Jaspal S %A Laakso, Markku %A Lehtimäki, Terho %A Liang, Kae-Woei %A Magnusson, Patrik K E %A Newman, Anne B %A Oldehinkel, Albertine J %A Pereira, Alexandre C %A Redline, Susan %A Rettig, Rainer %A Samani, Nilesh J %A Scott, James %A Shu, Xiao-Ou %A van der Harst, Pim %A Wagenknecht, Lynne E %A Wareham, Nicholas J %A Watkins, Hugh %A Weir, David R %A Wickremasinghe, Ananda R %A Wu, Tangchun %A Zheng, Wei %A Kamatani, Yoichiro %A Laurie, Cathy C %A Bouchard, Claude %A Cooper, Richard S %A Evans, Michele K %A Gudnason, Vilmundur %A Kardia, Sharon L R %A Kritchevsky, Stephen B %A Levy, Daniel %A O'Connell, Jeff R %A Psaty, Bruce M %A van Dam, Rob M %A Sims, Mario %A Arnett, Donna K %A Mook-Kanamori, Dennis O %A Kelly, Tanika N %A Fox, Ervin R %A Hayward, Caroline %A Fornage, Myriam %A Rotimi, Charles N %A Province, Michael A %A van Duijn, Cornelia M %A Tai, E Shyong %A Wong, Tien Yin %A Loos, Ruth J F %A Reiner, Alex P %A Rotter, Jerome I %A Zhu, Xiaofeng %A Bierut, Laura J %A Gauderman, W James %A Caulfield, Mark J %A Elliott, Paul %A Rice, Kenneth %A Munroe, Patricia B %A Morrison, Alanna C %A Cupples, L Adrienne %A Rao, Dabeeru C %A Chasman, Daniel I %X

Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactions in 610,091 individuals. Stage 1 analysis examined ∼18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-up analysis of promising variants in 480,178 additional individuals from five ancestries. We identified 15 loci that were genome-wide significant (p < 5 × 10) in stage 1 and formally replicated in stage 2. A combined stage 1 and 2 meta-analysis identified 66 additional genome-wide significant loci (13, 35, and 18 loci in European, African, and trans-ancestry, respectively). A total of 56 known BP loci were also identified by our results (p < 5 × 10). Of the newly identified loci, ten showed significant interaction with smoking status, but none of them were replicated in stage 2. Several loci were identified in African ancestry, highlighting the importance of genetic studies in diverse populations. The identified loci show strong evidence for regulatory features and support shared pathophysiology with cardiometabolic and addiction traits. They also highlight a role in BP regulation for biological candidates such as modulators of vascular structure and function (CDKN1B, BCAR1-CFDP1, PXDN, EEA1), ciliopathies (SDCCAG8, RPGRIP1L), telomere maintenance (TNKS, PINX1, AKTIP), and central dopaminergic signaling (MSRA, EBF2).

%B Am J Hum Genet %V 102 %P 375-400 %8 2018 Mar 01 %G eng %N 3 %R 10.1016/j.ajhg.2018.01.015 %0 Journal Article %J Circulation %D 2019 %T Biomarkers of Dietary Omega-6 Fatty Acids and Incident Cardiovascular Disease and Mortality. %A Marklund, Matti %A Wu, Jason H Y %A Imamura, Fumiaki %A Del Gobbo, Liana C %A Fretts, Amanda %A de Goede, Janette %A Shi, Peilin %A Tintle, Nathan %A Wennberg, Maria %A Aslibekyan, Stella %A Chen, Tzu-An %A de Oliveira Otto, Marcia C %A Hirakawa, Yoichiro %A Eriksen, Helle Højmark %A Kröger, Janine %A Laguzzi, Federica %A Lankinen, Maria %A Murphy, Rachel A %A Prem, Kiesha %A Samieri, Cecilia %A Virtanen, Jyrki %A Wood, Alexis C %A Wong, Kerry %A Yang, Wei-Sin %A Zhou, Xia %A Baylin, Ana %A Boer, Jolanda M A %A Brouwer, Ingeborg A %A Campos, Hannia %A Chaves, Paulo H M %A Chien, Kuo-Liong %A de Faire, Ulf %A Djoussé, Luc %A Eiriksdottir, Gudny %A El-Abbadi, Naglaa %A Forouhi, Nita G %A Michael Gaziano, J %A Geleijnse, Johanna M %A Gigante, Bruna %A Giles, Graham %A Guallar, Eliseo %A Gudnason, Vilmundur %A Harris, Tamara %A Harris, William S %A Helmer, Catherine %A Hellenius, Mai-Lis %A Hodge, Allison %A Hu, Frank B %A Jacques, Paul F %A Jansson, Jan-Håkan %A Kalsbeek, Anya %A Khaw, Kay-Tee %A Koh, Woon-Puay %A Laakso, Markku %A Leander, Karin %A Lin, Hung-Ju %A Lind, Lars %A Luben, Robert %A Luo, Juhua %A McKnight, Barbara %A Mursu, Jaakko %A Ninomiya, Toshiharu %A Overvad, Kim %A Psaty, Bruce M %A Rimm, Eric %A Schulze, Matthias B %A Siscovick, David %A Skjelbo Nielsen, Michael %A Smith, Albert V %A Steffen, Brian T %A Steffen, Lyn %A Sun, Qi %A Sundström, Johan %A Tsai, Michael Y %A Tunstall-Pedoe, Hugh %A Uusitupa, Matti I J %A van Dam, Rob M %A Veenstra, Jenna %A Monique Verschuren, W M %A Wareham, Nick %A Willett, Walter %A Woodward, Mark %A Yuan, Jian-Min %A Micha, Renata %A Lemaitre, Rozenn N %A Mozaffarian, Dariush %A Riserus, Ulf %X

BACKGROUND: Global dietary recommendations for and cardiovascular effects of linoleic acid, the major dietary omega-6 fatty acid, and its major metabolite, arachidonic acid, remain controversial. To address this uncertainty and inform international recommendations, we evaluated how in vivo circulating and tissue levels of linoleic acid (LA) and arachidonic acid (AA) relate to incident cardiovascular disease (CVD) across multiple international studies.

METHODS: We performed harmonized, de novo, individual-level analyses in a global consortium of 30 prospective observational studies from 13 countries. Multivariable-adjusted associations of circulating and adipose tissue LA and AA biomarkers with incident total CVD and subtypes (coronary heart disease, ischemic stroke, cardiovascular mortality) were investigated according to a prespecified analytic plan. Levels of LA and AA, measured as the percentage of total fatty acids, were evaluated linearly according to their interquintile range (ie, the range between the midpoint of the first and fifth quintiles), and categorically by quintiles. Study-specific results were pooled using inverse-variance-weighted meta-analysis. Heterogeneity was explored by age, sex, race, diabetes mellitus, statin use, aspirin use, omega-3 levels, and fatty acid desaturase 1 genotype (when available).

RESULTS: In 30 prospective studies with medians of follow-up ranging 2.5 to 31.9 years, 15 198 incident cardiovascular events occurred among 68 659 participants. Higher levels of LA were significantly associated with lower risks of total CVD, cardiovascular mortality, and ischemic stroke, with hazard ratios per interquintile range of 0.93 (95% CI, 0.88-0.99), 0.78 (0.70-0.85), and 0.88 (0.79-0.98), respectively, and nonsignificantly with lower coronary heart disease risk (0.94; 0.88-1.00). Relationships were similar for LA evaluated across quintiles. AA levels were not associated with higher risk of cardiovascular outcomes; in a comparison of extreme quintiles, higher levels were associated with lower risk of total CVD (0.92; 0.86-0.99). No consistent heterogeneity by population subgroups was identified in the observed relationships.

CONCLUSIONS: In pooled global analyses, higher in vivo circulating and tissue levels of LA and possibly AA were associated with lower risk of major cardiovascular events. These results support a favorable role for LA in CVD prevention.

%B Circulation %V 139 %P 2422-2436 %8 2019 May 21 %G eng %N 21 %R 10.1161/CIRCULATIONAHA.118.038908 %0 Journal Article %J Mol Genet Genomic Med %D 2019 %T Genome-wide meta-analysis of SNP and antihypertensive medication interactions on left ventricular traits in African Americans. %A Do, Anh N %A Zhao, Wei %A Baldridge, Abigail S %A Raffield, Laura M %A Wiggins, Kerri L %A Shah, Sanjiv J %A Aslibekyan, Stella %A Tiwari, Hemant K %A Limdi, Nita %A Zhi, Degui %A Sitlani, Colleen M %A Taylor, Kent D %A Psaty, Bruce M %A Sotoodehnia, Nona %A Brody, Jennifer A %A Rasmussen-Torvik, Laura J %A Lloyd-Jones, Donald %A Lange, Leslie A %A Wilson, James G %A Smith, Jennifer A %A Kardia, Sharon L R %A Mosley, Thomas H %A Vasan, Ramachandran S %A Arnett, Donna K %A Irvin, Marguerite R %K African Americans %K Angiotensin-Converting Enzyme Inhibitors %K Antihypertensive Agents %K Calcium Channel Blockers %K Humans %K Observational Studies as Topic %K Pharmacogenomic Variants %K Polymorphism, Single Nucleotide %K Sodium Chloride Symporter Inhibitors %K Ventricular Dysfunction, Left %X

BACKGROUND: Left ventricular (LV) hypertrophy affects up to 43% of African Americans (AAs). Antihypertensive treatment reduces LV mass (LVM). However, interindividual variation in LV traits in response to antihypertensive treatments exists. We hypothesized that genetic variants may modify the association of antihypertensive treatment class with LV traits measured by echocardiography.

METHODS: We evaluated the main effects of the three most common antihypertensive treatments for AAs as well as the single nucleotide polymorphism (SNP)-by-drug interaction on LVM and relative wall thickness (RWT) in 2,068 participants across five community-based cohorts. Treatments included thiazide diuretics (TDs), angiotensin converting enzyme inhibitors (ACE-Is), and dihydropyridine calcium channel blockers (dCCBs) and were compared in a pairwise manner. We performed fixed effects inverse variance weighted meta-analyses of main effects of drugs and 2.5 million SNP-by-drug interaction estimates.

RESULTS: We observed that dCCBs versus TDs were associated with higher LVM after adjusting for covariates (p = 0.001). We report three SNPs at a single locus on chromosome 20 that modified the association between RWT and treatment when comparing dCCBs to ACE-Is with consistent effects across cohorts (smallest p = 4.7 × 10 , minor allele frequency range 0.09-0.12). This locus has been linked to LV hypertrophy in a previous study. A marginally significant locus in BICD1 (rs326641) was validated in an external population.

CONCLUSIONS: Our study identified one locus having genome-wide significant SNP-by-drug interaction effect on RWT among dCCB users in comparison to ACE-I users. Upon additional validation in future studies, our findings can enhance the precision of medical approaches in hypertension treatment.

%B Mol Genet Genomic Med %V 7 %P e00788 %8 2019 10 %G eng %N 10 %R 10.1002/mgg3.788 %0 Journal Article %J Nat Commun %D 2019 %T Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration. %A Noordam, Raymond %A Bos, Maxime M %A Wang, Heming %A Winkler, Thomas W %A Bentley, Amy R %A Kilpeläinen, Tuomas O %A de Vries, Paul S %A Sung, Yun Ju %A Schwander, Karen %A Cade, Brian E %A Manning, Alisa %A Aschard, Hugues %A Brown, Michael R %A Chen, Han %A Franceschini, Nora %A Musani, Solomon K %A Richard, Melissa %A Vojinovic, Dina %A Aslibekyan, Stella %A Bartz, Traci M %A de Las Fuentes, Lisa %A Feitosa, Mary %A Horimoto, Andrea R %A Ilkov, Marjan %A Kho, Minjung %A Kraja, Aldi %A Li, Changwei %A Lim, Elise %A Liu, Yongmei %A Mook-Kanamori, Dennis O %A Rankinen, Tuomo %A Tajuddin, Salman M %A van der Spek, Ashley %A Wang, Zhe %A Marten, Jonathan %A Laville, Vincent %A Alver, Maris %A Evangelou, Evangelos %A Graff, Maria E %A He, Meian %A Kuhnel, Brigitte %A Lyytikäinen, Leo-Pekka %A Marques-Vidal, Pedro %A Nolte, Ilja M %A Palmer, Nicholette D %A Rauramaa, Rainer %A Shu, Xiao-Ou %A Snieder, Harold %A Weiss, Stefan %A Wen, Wanqing %A Yanek, Lisa R %A Adolfo, Correa %A Ballantyne, Christie %A Bielak, Larry %A Biermasz, Nienke R %A Boerwinkle, Eric %A Dimou, Niki %A Eiriksdottir, Gudny %A Gao, Chuan %A Gharib, Sina A %A Gottlieb, Daniel J %A Haba-Rubio, José %A Harris, Tamara B %A Heikkinen, Sami %A Heinzer, Raphael %A Hixson, James E %A Homuth, Georg %A Ikram, M Arfan %A Komulainen, Pirjo %A Krieger, Jose E %A Lee, Jiwon %A Liu, Jingmin %A Lohman, Kurt K %A Luik, Annemarie I %A Mägi, Reedik %A Martin, Lisa W %A Meitinger, Thomas %A Metspalu, Andres %A Milaneschi, Yuri %A Nalls, Mike A %A O'Connell, Jeff %A Peters, Annette %A Peyser, Patricia %A Raitakari, Olli T %A Reiner, Alex P %A Rensen, Patrick C N %A Rice, Treva K %A Rich, Stephen S %A Roenneberg, Till %A Rotter, Jerome I %A Schreiner, Pamela J %A Shikany, James %A Sidney, Stephen S %A Sims, Mario %A Sitlani, Colleen M %A Sofer, Tamar %A Strauch, Konstantin %A Swertz, Morris A %A Taylor, Kent D %A Uitterlinden, André G %A van Duijn, Cornelia M %A Völzke, Henry %A Waldenberger, Melanie %A Wallance, Robert B %A van Dijk, Ko Willems %A Yu, Caizheng %A Zonderman, Alan B %A Becker, Diane M %A Elliott, Paul %A Esko, Tõnu %A Gieger, Christian %A Grabe, Hans J %A Lakka, Timo A %A Lehtimäki, Terho %A North, Kari E %A Penninx, Brenda W J H %A Vollenweider, Peter %A Wagenknecht, Lynne E %A Wu, Tangchun %A Xiang, Yong-Bing %A Zheng, Wei %A Arnett, Donna K %A Bouchard, Claude %A Evans, Michele K %A Gudnason, Vilmundur %A Kardia, Sharon %A Kelly, Tanika N %A Kritchevsky, Stephen B %A Loos, Ruth J F %A Pereira, Alexandre C %A Province, Mike %A Psaty, Bruce M %A Rotimi, Charles %A Zhu, Xiaofeng %A Amin, Najaf %A Cupples, L Adrienne %A Fornage, Myriam %A Fox, Ervin F %A Guo, Xiuqing %A Gauderman, W James %A Rice, Kenneth %A Kooperberg, Charles %A Munroe, Patricia B %A Liu, Ching-Ti %A Morrison, Alanna C %A Rao, Dabeeru C %A van Heemst, Diana %A Redline, Susan %X

Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.

%B Nat Commun %V 10 %P 5121 %8 2019 Nov 12 %G eng %N 1 %R 10.1038/s41467-019-12958-0 %0 Journal Article %J Nat Commun %D 2019 %T Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity. %A Kilpeläinen, Tuomas O %A Bentley, Amy R %A Noordam, Raymond %A Sung, Yun Ju %A Schwander, Karen %A Winkler, Thomas W %A Jakupović, Hermina %A Chasman, Daniel I %A Manning, Alisa %A Ntalla, Ioanna %A Aschard, Hugues %A Brown, Michael R %A de Las Fuentes, Lisa %A Franceschini, Nora %A Guo, Xiuqing %A Vojinovic, Dina %A Aslibekyan, Stella %A Feitosa, Mary F %A Kho, Minjung %A Musani, Solomon K %A Richard, Melissa %A Wang, Heming %A Wang, Zhe %A Bartz, Traci M %A Bielak, Lawrence F %A Campbell, Archie %A Dorajoo, Rajkumar %A Fisher, Virginia %A Hartwig, Fernando P %A Horimoto, Andrea R V R %A Li, Changwei %A Lohman, Kurt K %A Marten, Jonathan %A Sim, Xueling %A Smith, Albert V %A Tajuddin, Salman M %A Alver, Maris %A Amini, Marzyeh %A Boissel, Mathilde %A Chai, Jin Fang %A Chen, Xu %A Divers, Jasmin %A Evangelou, Evangelos %A Gao, Chuan %A Graff, Mariaelisa %A Harris, Sarah E %A He, Meian %A Hsu, Fang-Chi %A Jackson, Anne U %A Zhao, Jing Hua %A Kraja, Aldi T %A Kuhnel, Brigitte %A Laguzzi, Federica %A Lyytikäinen, Leo-Pekka %A Nolte, Ilja M %A Rauramaa, Rainer %A Riaz, Muhammad %A Robino, Antonietta %A Rueedi, Rico %A Stringham, Heather M %A Takeuchi, Fumihiko %A van der Most, Peter J %A Varga, Tibor V %A Verweij, Niek %A Ware, Erin B %A Wen, Wanqing %A Li, Xiaoyin %A Yanek, Lisa R %A Amin, Najaf %A Arnett, Donna K %A Boerwinkle, Eric %A Brumat, Marco %A Cade, Brian %A Canouil, Mickaël %A Chen, Yii-Der Ida %A Concas, Maria Pina %A Connell, John %A de Mutsert, Renée %A de Silva, H Janaka %A de Vries, Paul S %A Demirkan, Ayse %A Ding, Jingzhong %A Eaton, Charles B %A Faul, Jessica D %A Friedlander, Yechiel %A Gabriel, Kelley P %A Ghanbari, Mohsen %A Giulianini, Franco %A Gu, Chi Charles %A Gu, Dongfeng %A Harris, Tamara B %A He, Jiang %A Heikkinen, Sami %A Heng, Chew-Kiat %A Hunt, Steven C %A Ikram, M Arfan %A Jonas, Jost B %A Koh, Woon-Puay %A Komulainen, Pirjo %A Krieger, Jose E %A Kritchevsky, Stephen B %A Kutalik, Zoltán %A Kuusisto, Johanna %A Langefeld, Carl D %A Langenberg, Claudia %A Launer, Lenore J %A Leander, Karin %A Lemaitre, Rozenn N %A Lewis, Cora E %A Liang, Jingjing %A Liu, Jianjun %A Mägi, Reedik %A Manichaikul, Ani %A Meitinger, Thomas %A Metspalu, Andres %A Milaneschi, Yuri %A Mohlke, Karen L %A Mosley, Thomas H %A Murray, Alison D %A Nalls, Mike A %A Nang, Ei-Ei Khaing %A Nelson, Christopher P %A Nona, Sotoodehnia %A Norris, Jill M %A Nwuba, Chiamaka Vivian %A O'Connell, Jeff %A Palmer, Nicholette D %A Papanicolau, George J %A Pazoki, Raha %A Pedersen, Nancy L %A Peters, Annette %A Peyser, Patricia A %A Polasek, Ozren %A Porteous, David J %A Poveda, Alaitz %A Raitakari, Olli T %A Rich, Stephen S %A Risch, Neil %A Robinson, Jennifer G %A Rose, Lynda M %A Rudan, Igor %A Schreiner, Pamela J %A Scott, Robert A %A Sidney, Stephen S %A Sims, Mario %A Smith, Jennifer A %A Snieder, Harold %A Sofer, Tamar %A Starr, John M %A Sternfeld, Barbara %A Strauch, Konstantin %A Tang, Hua %A Taylor, Kent D %A Tsai, Michael Y %A Tuomilehto, Jaakko %A Uitterlinden, André G %A van der Ende, M Yldau %A van Heemst, Diana %A Voortman, Trudy %A Waldenberger, Melanie %A Wennberg, Patrik %A Wilson, Gregory %A Xiang, Yong-Bing %A Yao, Jie %A Yu, Caizheng %A Yuan, Jian-Min %A Zhao, Wei %A Zonderman, Alan B %A Becker, Diane M %A Boehnke, Michael %A Bowden, Donald W %A de Faire, Ulf %A Deary, Ian J %A Elliott, Paul %A Esko, Tõnu %A Freedman, Barry I %A Froguel, Philippe %A Gasparini, Paolo %A Gieger, Christian %A Kato, Norihiro %A Laakso, Markku %A Lakka, Timo A %A Lehtimäki, Terho %A Magnusson, Patrik K E %A Oldehinkel, Albertine J %A Penninx, Brenda W J H %A Samani, Nilesh J %A Shu, Xiao-Ou %A van der Harst, Pim %A van Vliet-Ostaptchouk, Jana V %A Vollenweider, Peter %A Wagenknecht, Lynne E %A Wang, Ya X %A Wareham, Nicholas J %A Weir, David R %A Wu, Tangchun %A Zheng, Wei %A Zhu, Xiaofeng %A Evans, Michele K %A Franks, Paul W %A Gudnason, Vilmundur %A Hayward, Caroline %A Horta, Bernardo L %A Kelly, Tanika N %A Liu, Yongmei %A North, Kari E %A Pereira, Alexandre C %A Ridker, Paul M %A Tai, E Shyong %A van Dam, Rob M %A Fox, Ervin R %A Kardia, Sharon L R %A Liu, Ching-Ti %A Mook-Kanamori, Dennis O %A Province, Michael A %A Redline, Susan %A van Duijn, Cornelia M %A Rotter, Jerome I %A Kooperberg, Charles B %A Gauderman, W James %A Psaty, Bruce M %A Rice, Kenneth %A Munroe, Patricia B %A Fornage, Myriam %A Cupples, L Adrienne %A Rotimi, Charles N %A Morrison, Alanna C %A Rao, Dabeeru C %A Loos, Ruth J F %K Adolescent %K Adult %K African Continental Ancestry Group %K Aged %K Aged, 80 and over %K Asian Continental Ancestry Group %K Brazil %K Calcium-Binding Proteins %K Cholesterol %K Cholesterol, HDL %K Cholesterol, LDL %K European Continental Ancestry Group %K Exercise %K Female %K Genetic Loci %K Genome-Wide Association Study %K Genotype %K Hispanic Americans %K Humans %K LIM-Homeodomain Proteins %K Lipid Metabolism %K Lipids %K Male %K Membrane Proteins %K Microtubule-Associated Proteins %K Middle Aged %K Muscle Proteins %K Nerve Tissue Proteins %K Transcription Factors %K Triglycerides %K Young Adult %X

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels.

%B Nat Commun %V 10 %P 376 %8 2019 01 22 %G eng %N 1 %R 10.1038/s41467-018-08008-w %0 Journal Article %J Nature %D 2020 %T Inherited causes of clonal haematopoiesis in 97,691 whole genomes. %A Bick, Alexander G %A Weinstock, Joshua S %A Nandakumar, Satish K %A Fulco, Charles P %A Bao, Erik L %A Zekavat, Seyedeh M %A Szeto, Mindy D %A Liao, Xiaotian %A Leventhal, Matthew J %A Nasser, Joseph %A Chang, Kyle %A Laurie, Cecelia %A Burugula, Bala Bharathi %A Gibson, Christopher J %A Lin, Amy E %A Taub, Margaret A %A Aguet, Francois %A Ardlie, Kristin %A Mitchell, Braxton D %A Barnes, Kathleen C %A Moscati, Arden %A Fornage, Myriam %A Redline, Susan %A Psaty, Bruce M %A Silverman, Edwin K %A Weiss, Scott T %A Palmer, Nicholette D %A Vasan, Ramachandran S %A Burchard, Esteban G %A Kardia, Sharon L R %A He, Jiang %A Kaplan, Robert C %A Smith, Nicholas L %A Arnett, Donna K %A Schwartz, David A %A Correa, Adolfo %A de Andrade, Mariza %A Guo, Xiuqing %A Konkle, Barbara A %A Custer, Brian %A Peralta, Juan M %A Gui, Hongsheng %A Meyers, Deborah A %A McGarvey, Stephen T %A Chen, Ida Yii-Der %A Shoemaker, M Benjamin %A Peyser, Patricia A %A Broome, Jai G %A Gogarten, Stephanie M %A Wang, Fei Fei %A Wong, Quenna %A Montasser, May E %A Daya, Michelle %A Kenny, Eimear E %A North, Kari E %A Launer, Lenore J %A Cade, Brian E %A Bis, Joshua C %A Cho, Michael H %A Lasky-Su, Jessica %A Bowden, Donald W %A Cupples, L Adrienne %A Mak, Angel C Y %A Becker, Lewis C %A Smith, Jennifer A %A Kelly, Tanika N %A Aslibekyan, Stella %A Heckbert, Susan R %A Tiwari, Hemant K %A Yang, Ivana V %A Heit, John A %A Lubitz, Steven A %A Johnsen, Jill M %A Curran, Joanne E %A Wenzel, Sally E %A Weeks, Daniel E %A Rao, Dabeeru C %A Darbar, Dawood %A Moon, Jee-Young %A Tracy, Russell P %A Buth, Erin J %A Rafaels, Nicholas %A Loos, Ruth J F %A Durda, Peter %A Liu, Yongmei %A Hou, Lifang %A Lee, Jiwon %A Kachroo, Priyadarshini %A Freedman, Barry I %A Levy, Daniel %A Bielak, Lawrence F %A Hixson, James E %A Floyd, James S %A Whitsel, Eric A %A Ellinor, Patrick T %A Irvin, Marguerite R %A Fingerlin, Tasha E %A Raffield, Laura M %A Armasu, Sebastian M %A Wheeler, Marsha M %A Sabino, Ester C %A Blangero, John %A Williams, L Keoki %A Levy, Bruce D %A Sheu, Wayne Huey-Herng %A Roden, Dan M %A Boerwinkle, Eric %A Manson, JoAnn E %A Mathias, Rasika A %A Desai, Pinkal %A Taylor, Kent D %A Johnson, Andrew D %A Auer, Paul L %A Kooperberg, Charles %A Laurie, Cathy C %A Blackwell, Thomas W %A Smith, Albert V %A Zhao, Hongyu %A Lange, Ethan %A Lange, Leslie %A Rich, Stephen S %A Rotter, Jerome I %A Wilson, James G %A Scheet, Paul %A Kitzman, Jacob O %A Lander, Eric S %A Engreitz, Jesse M %A Ebert, Benjamin L %A Reiner, Alexander P %A Jaiswal, Siddhartha %A Abecasis, Goncalo %A Sankaran, Vijay G %A Kathiresan, Sekar %A Natarajan, Pradeep %X

Age is the dominant risk factor for most chronic human diseases, but the mechanisms through which ageing confers this risk are largely unknown. The age-related acquisition of somatic mutations that lead to clonal expansion in regenerating haematopoietic stem cell populations has recently been associated with both haematological cancer and coronary heart disease-this phenomenon is termed clonal haematopoiesis of indeterminate potential (CHIP). Simultaneous analyses of germline and somatic whole-genome sequences provide the opportunity to identify root causes of CHIP. Here we analyse high-coverage whole-genome sequences from 97,691 participants of diverse ancestries in the National Heart, Lung, and Blood Institute Trans-omics for Precision Medicine (TOPMed) programme, and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid and inflammatory traits that are specific to different CHIP driver genes. Association of a genome-wide set of germline genetic variants enabled the identification of three genetic loci associated with CHIP status, including one locus at TET2 that was specific to individuals of African ancestry. In silico-informed in vitro evaluation of the TET2 germline locus enabled the identification of a causal variant that disrupts a TET2 distal enhancer, resulting in increased self-renewal of haematopoietic stem cells. Overall, we observe that germline genetic variation shapes haematopoietic stem cell function, leading to CHIP through mechanisms that are specific to clonal haematopoiesis as well as shared mechanisms that lead to somatic mutations across tissues.

%B Nature %V 586 %P 763-768 %8 2020 10 %G eng %N 7831 %R 10.1038/s41586-020-2819-2 %0 Journal Article %J Circ Genom Precis Med %D 2020 %T Whole Blood DNA Methylation Signatures of Diet Are Associated With Cardiovascular Disease Risk Factors and All-Cause Mortality. %A Ma, Jiantao %A Rebholz, Casey M %A Braun, Kim V E %A Reynolds, Lindsay M %A Aslibekyan, Stella %A Xia, Rui %A Biligowda, Niranjan G %A Huan, Tianxiao %A Liu, Chunyu %A Mendelson, Michael M %A Joehanes, Roby %A Hu, Emily A %A Vitolins, Mara Z %A Wood, Alexis C %A Lohman, Kurt %A Ochoa-Rosales, Carolina %A van Meurs, Joyce %A Uitterlinden, Andre %A Liu, Yongmei %A Elhadad, Mohamed A %A Heier, Margit %A Waldenberger, Melanie %A Peters, Annette %A Colicino, Elena %A Whitsel, Eric A %A Baldassari, Antoine %A Gharib, Sina A %A Sotoodehnia, Nona %A Brody, Jennifer A %A Sitlani, Colleen M %A Tanaka, Toshiko %A Hill, W David %A Corley, Janie %A Deary, Ian J %A Zhang, Yan %A Schöttker, Ben %A Brenner, Hermann %A Walker, Maura E %A Ye, Shumao %A Nguyen, Steve %A Pankow, Jim %A Demerath, Ellen W %A Zheng, Yinan %A Hou, Lifang %A Liang, Liming %A Lichtenstein, Alice H %A Hu, Frank B %A Fornage, Myriam %A Voortman, Trudy %A Levy, Daniel %X

BACKGROUND: DNA methylation patterns associated with habitual diet have not been well studied.

METHODS: Diet quality was characterized using a Mediterranean-style diet score and the Alternative Healthy Eating Index score. We conducted ethnicity-specific and trans-ethnic epigenome-wide association analyses for diet quality and leukocyte-derived DNA methylation at over 400 000 CpGs (cytosine-guanine dinucleotides) in 5 population-based cohorts including 6662 European ancestry, 2702 African ancestry, and 360 Hispanic ancestry participants. For diet-associated CpGs identified in epigenome-wide analyses, we conducted Mendelian randomization (MR) analysis to examine their relations to cardiovascular disease risk factors and examined their longitudinal associations with all-cause mortality.

RESULTS: We identified 30 CpGs associated with either Mediterranean-style diet score or Alternative Healthy Eating Index, or both, in European ancestry participants. Among these CpGs, 12 CpGs were significantly associated with all-cause mortality (Bonferroni corrected <1.6×10). Hypermethylation of cg18181703 () was associated with higher scores of both Mediterranean-style diet score and Alternative Healthy Eating Index and lower risk for all-cause mortality (=5.7×10). Ten additional diet-associated CpGs were nominally associated with all-cause mortality (<0.05). MR analysis revealed 8 putatively causal associations for 6 CpGs with 4 cardiovascular disease risk factors (body mass index, triglycerides, high-density lipoprotein cholesterol concentrations, and type 2 diabetes mellitus; Bonferroni corrected MR <4.5×10). For example, hypermethylation of cg11250194 () was associated with lower triglyceride concentrations (MR, =1.5×10).and hypermethylation of cg02079413 (; ) was associated with body mass index (corrected MR, =1×10).

CONCLUSIONS: Habitual diet quality was associated with differential peripheral leukocyte DNA methylation levels of 30 CpGs, most of which were also associated with multiple health outcomes, in European ancestry individuals. These findings demonstrate that integrative genomic analysis of dietary information may reveal molecular targets for disease prevention and treatment.

%B Circ Genom Precis Med %V 13 %P e002766 %8 2020 Aug %G eng %N 4 %R 10.1161/CIRCGEN.119.002766 %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 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 Epidemiol %D 2021 %T A System for Phenotype Harmonization in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. %A Stilp, Adrienne M %A Emery, Leslie S %A Broome, Jai G %A Buth, Erin J %A Khan, Alyna T %A Laurie, Cecelia A %A Wang, Fei Fei %A Wong, Quenna %A Chen, Dongquan %A D'Augustine, Catherine M %A Heard-Costa, Nancy L %A Hohensee, Chancellor R %A Johnson, William Craig %A Juarez, Lucia D %A Liu, Jingmin %A Mutalik, Karen M %A Raffield, Laura M %A Wiggins, Kerri L %A de Vries, Paul S %A Kelly, Tanika N %A Kooperberg, Charles %A Natarajan, Pradeep %A Peloso, Gina M %A Peyser, Patricia A %A Reiner, Alex P %A Arnett, Donna K %A Aslibekyan, Stella %A Barnes, Kathleen C %A Bielak, Lawrence F %A Bis, Joshua C %A Cade, Brian E %A Chen, Ming-Huei %A Correa, Adolfo %A Cupples, L Adrienne %A de Andrade, Mariza %A Ellinor, Patrick T %A Fornage, Myriam %A Franceschini, Nora %A Gan, Weiniu %A Ganesh, Santhi K %A Graffelman, Jan %A Grove, Megan L %A Guo, Xiuqing %A Hawley, Nicola L %A Hsu, Wan-Ling %A Jackson, Rebecca D %A Jaquish, Cashell E %A Johnson, Andrew D %A Kardia, Sharon L R %A Kelly, Shannon %A Lee, Jiwon %A Mathias, Rasika A %A McGarvey, Stephen T %A Mitchell, Braxton D %A Montasser, May E %A Morrison, Alanna C %A North, Kari E %A Nouraie, Seyed Mehdi %A Oelsner, Elizabeth C %A Pankratz, Nathan %A Rich, Stephen S %A Rotter, Jerome I %A Smith, Jennifer A %A Taylor, Kent D %A Vasan, Ramachandran S %A Weeks, Daniel E %A Weiss, Scott T %A Wilson, Carla G %A Yanek, Lisa R %A Psaty, Bruce M %A Heckbert, Susan R %A Laurie, Cathy C %X

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data sharing mechanisms. This system was developed for the National Heart, Lung and Blood Institute's Trans-Omics for Precision Medicine program, which is generating genomic and other omics data for >80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants from up to 17 studies per phenotype (participants recruited 1948-2012). We discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled-access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include (1) the code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify or extend these harmonizations to additional studies; and (2) results of labeling thousands of phenotype variables with controlled vocabulary terms.

%B Am J Epidemiol %8 2021 Apr 16 %G eng %R 10.1093/aje/kwab115 %0 Journal Article %J Hypertension %D 2022 %T Insights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension. %A Kelly, Tanika N %A Sun, Xiao %A He, Karen Y %A Brown, Michael R %A Taliun, Sarah A Gagliano %A Hellwege, Jacklyn N %A Irvin, Marguerite R %A Mi, Xuenan %A Brody, Jennifer A %A Franceschini, Nora %A Guo, Xiuqing %A Hwang, Shih-Jen %A de Vries, Paul S %A Gao, Yan %A Moscati, Arden %A Nadkarni, Girish N %A Yanek, Lisa R %A Elfassy, Tali %A Smith, Jennifer A %A Chung, Ren-Hua %A Beitelshees, Amber L %A Patki, Amit %A Aslibekyan, Stella %A Blobner, Brandon M %A Peralta, Juan M %A Assimes, Themistocles L %A Palmas, Walter R %A Liu, Chunyu %A Bress, Adam P %A Huang, Zhijie %A Becker, Lewis C %A Hwa, Chii-Min %A O'Connell, Jeffrey R %A Carlson, Jenna C %A Warren, Helen R %A Das, Sayantan %A Giri, Ayush %A Martin, Lisa W %A Craig Johnson, W %A Fox, Ervin R %A Bottinger, Erwin P %A Razavi, Alexander C %A Vaidya, Dhananjay %A Chuang, Lee-Ming %A Chang, Yen-Pei C %A Naseri, Take %A Jain, Deepti %A Kang, Hyun Min %A Hung, Adriana M %A Srinivasasainagendra, Vinodh %A Snively, Beverly M %A Gu, Dongfeng %A Montasser, May E %A Reupena, Muagututi'a Sefuiva %A Heavner, Benjamin D %A LeFaive, Jonathon %A Hixson, James E %A Rice, Kenneth M %A Wang, Fei Fei %A Nielsen, Jonas B %A Huang, Jianfeng %A Khan, Alyna T %A Zhou, Wei %A Nierenberg, Jovia L %A Laurie, Cathy C %A Armstrong, Nicole D %A Shi, Mengyao %A Pan, Yang %A Stilp, Adrienne M %A Emery, Leslie %A Wong, Quenna %A Hawley, Nicola L %A Minster, Ryan L %A Curran, Joanne E %A Munroe, Patricia B %A Weeks, Daniel E %A North, Kari E %A Tracy, Russell P %A Kenny, Eimear E %A Shimbo, Daichi %A Chakravarti, Aravinda %A Rich, Stephen S %A Reiner, Alex P %A Blangero, John %A Redline, Susan %A Mitchell, Braxton D %A Rao, Dabeeru C %A Ida Chen, Yii-Der %A Kardia, Sharon L R %A Kaplan, Robert C %A Mathias, Rasika A %A He, Jiang %A Psaty, Bruce M %A Fornage, Myriam %A Loos, Ruth J F %A Correa, Adolfo %A Boerwinkle, Eric %A Rotter, Jerome I %A Kooperberg, Charles %A Edwards, Todd L %A Abecasis, Goncalo R %A Zhu, Xiaofeng %A Levy, Daniel %A Arnett, Donna K %A Morrison, Alanna C %X

BACKGROUND: The availability of whole-genome sequencing data in large studies has enabled the assessment of coding and noncoding variants across the allele frequency spectrum for their associations with blood pressure.

METHODS: We conducted a multiancestry whole-genome sequencing analysis of blood pressure among 51 456 Trans-Omics for Precision Medicine and Centers for Common Disease Genomics program participants (stage-1). Stage-2 analyses leveraged array data from UK Biobank (N=383 145), Million Veteran Program (N=318 891), and Reasons for Geographic and Racial Differences in Stroke (N=10 643) participants, along with whole-exome sequencing data from UK Biobank (N=199 631) participants.

RESULTS: Two blood pressure signals achieved genome-wide significance in meta-analyses of stage-1 and stage-2 single variant findings (<5×10). Among them, a rare intergenic variant at novel locus, , was associated with lower systolic blood pressure in stage-1 (beta [SE]=-32.6 [6.0]; =4.99×10) but not stage-2 analysis (=0.11). Furthermore, a novel common variant at the known locus was suggestively associated with diastolic blood pressure in stage-1 (beta [SE]=-0.36 [0.07]; =4.18×10) and attained genome-wide significance in stage-2 (beta [SE]=-0.29 [0.03]; =7.28×10). Nineteen additional signals suggestively associated with blood pressure in meta-analysis of single and aggregate rare variant findings (<1×10 and <1×10, respectively).

DISCUSSION: We report one promising but unconfirmed rare variant for blood pressure and, more importantly, contribute insights for future blood pressure sequencing studies. Our findings suggest promise of aggregate analyses to complement single variant analysis strategies and the need for larger, diverse samples, and family studies to enable robust rare variant identification.

%B Hypertension %P 101161HYPERTENSIONAHA12219324 %8 2022 Jun 02 %G eng %R 10.1161/HYPERTENSIONAHA.122.19324 %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 Commun Biol %D 2022 %T Whole genome sequence association analysis of fasting glucose and fasting insulin levels in diverse cohorts from the NHLBI TOPMed program. %A DiCorpo, Daniel %A Gaynor, Sheila M %A Russell, Emily M %A Westerman, Kenneth E %A Raffield, Laura M %A Majarian, Timothy D %A Wu, Peitao %A Sarnowski, Chloe %A Highland, Heather M %A Jackson, Anne %A Hasbani, Natalie R %A de Vries, Paul S %A Brody, Jennifer A %A Hidalgo, Bertha %A Guo, Xiuqing %A Perry, James A %A O'Connell, Jeffrey R %A Lent, Samantha %A Montasser, May E %A Cade, Brian E %A Jain, Deepti %A Wang, Heming %A D'Oliveira Albanus, Ricardo %A Varshney, Arushi %A Yanek, Lisa R %A Lange, Leslie %A Palmer, Nicholette D %A Almeida, Marcio %A Peralta, Juan M %A Aslibekyan, Stella %A Baldridge, Abigail S %A Bertoni, Alain G %A Bielak, Lawrence F %A Chen, Chung-Shiuan %A Chen, Yii-Der Ida %A Choi, Won Jung %A Goodarzi, Mark O %A Floyd, James S %A Irvin, Marguerite R %A Kalyani, Rita R %A Kelly, Tanika N %A Lee, Seonwook %A Liu, Ching-Ti %A Loesch, Douglas %A Manson, JoAnn E %A Minster, Ryan L %A Naseri, Take %A Pankow, James S %A Rasmussen-Torvik, Laura J %A Reiner, Alexander P %A Reupena, Muagututi'a Sefuiva %A Selvin, Elizabeth %A Smith, Jennifer A %A Weeks, Daniel E %A Xu, Huichun %A Yao, Jie %A Zhao, Wei %A Parker, Stephen %A Alonso, Alvaro %A Arnett, Donna K %A Blangero, John %A Boerwinkle, Eric %A Correa, Adolfo %A Cupples, L Adrienne %A Curran, Joanne E %A Duggirala, Ravindranath %A He, Jiang %A Heckbert, Susan R %A Kardia, Sharon L R %A Kim, Ryan W %A Kooperberg, Charles %A Liu, Simin %A Mathias, Rasika A %A McGarvey, Stephen T %A Mitchell, Braxton D %A Morrison, Alanna C %A Peyser, Patricia A %A Psaty, Bruce M %A Redline, Susan %A Shuldiner, Alan R %A Taylor, Kent D %A Vasan, Ramachandran S %A Viaud-Martinez, Karine A %A Florez, Jose C %A Wilson, James G %A Sladek, Robert %A Rich, Stephen S %A Rotter, Jerome I %A Lin, Xihong %A Dupuis, Josée %A Meigs, James B %A Wessel, Jennifer %A Manning, Alisa K %K Diabetes Mellitus, Type 2 %K Fasting %K Glucose %K Humans %K Insulin %K National Heart, Lung, and Blood Institute (U.S.) %K Nerve Tissue Proteins %K Polymorphism, Single Nucleotide %K Precision Medicine %K Receptors, Immunologic %K United States %X

The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI's Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits.

%B Commun Biol %V 5 %P 756 %8 2022 07 28 %G eng %N 1 %R 10.1038/s42003-022-03702-4 %0 Journal Article %J Front Genet %D 2023 %T Gene-educational attainment interactions in a multi-population genome-wide meta-analysis identify novel lipid loci. %A de Las Fuentes, Lisa %A Schwander, Karen L %A Brown, Michael R %A Bentley, Amy R %A Winkler, Thomas W %A Sung, Yun Ju %A Munroe, Patricia B %A Miller, Clint L %A Aschard, Hugo %A Aslibekyan, Stella %A Bartz, Traci M %A Bielak, Lawrence F %A Chai, Jin Fang %A Cheng, Ching-Yu %A Dorajoo, Rajkumar %A Feitosa, Mary F %A Guo, Xiuqing %A Hartwig, Fernando P %A Horimoto, Andrea %A Kolcic, Ivana %A Lim, Elise %A Liu, Yongmei %A Manning, Alisa K %A Marten, Jonathan %A Musani, Solomon K %A Noordam, Raymond %A Padmanabhan, Sandosh %A Rankinen, Tuomo %A Richard, Melissa A %A Ridker, Paul M %A Smith, Albert V %A Vojinovic, Dina %A Zonderman, Alan B %A Alver, Maris %A Boissel, Mathilde %A Christensen, Kaare %A Freedman, Barry I %A Gao, Chuan %A Giulianini, Franco %A Harris, Sarah E %A He, Meian %A Hsu, Fang-Chi %A Kuhnel, Brigitte %A Laguzzi, Federica %A Li, Xiaoyin %A Lyytikäinen, Leo-Pekka %A Nolte, Ilja M %A Poveda, Alaitz %A Rauramaa, Rainer %A Riaz, Muhammad %A Robino, Antonietta %A Sofer, Tamar %A Takeuchi, Fumihiko %A Tayo, Bamidele O %A van der Most, Peter J %A Verweij, Niek %A Ware, Erin B %A Weiss, Stefan %A Wen, Wanqing %A Yanek, Lisa R %A Zhan, Yiqiang %A Amin, Najaf %A Arking, Dan E %A Ballantyne, Christie %A Boerwinkle, Eric %A Brody, Jennifer A %A Broeckel, Ulrich %A Campbell, Archie %A Canouil, Mickaël %A Chai, Xiaoran %A Chen, Yii-Der Ida %A Chen, Xu %A Chitrala, Kumaraswamy Naidu %A Concas, Maria Pina %A de Faire, Ulf %A de Mutsert, Renée %A de Silva, H Janaka %A de Vries, Paul S %A Do, Ahn %A Faul, Jessica D %A Fisher, Virginia %A Floyd, James S %A Forrester, Terrence %A Friedlander, Yechiel %A Girotto, Giorgia %A Gu, C Charles %A Hallmans, Göran %A Heikkinen, Sami %A Heng, Chew-Kiat %A Homuth, Georg %A Hunt, Steven %A Ikram, M Arfan %A Jacobs, David R %A Kavousi, Maryam %A Khor, Chiea Chuen %A Kilpeläinen, Tuomas O %A Koh, Woon-Puay %A Komulainen, Pirjo %A Langefeld, Carl D %A Liang, Jingjing %A Liu, Kiang %A Liu, Jianjun %A Lohman, Kurt %A Mägi, Reedik %A Manichaikul, Ani W %A McKenzie, Colin A %A Meitinger, Thomas %A Milaneschi, Yuri %A Nauck, Matthias %A Nelson, Christopher P %A O'Connell, Jeffrey R %A Palmer, Nicholette D %A Pereira, Alexandre C %A Perls, Thomas %A Peters, Annette %A Polasek, Ozren %A Raitakari, Olli T %A Rice, Kenneth %A Rice, Treva K %A Rich, Stephen S %A Sabanayagam, Charumathi %A Schreiner, Pamela J %A Shu, Xiao-Ou %A Sidney, Stephen %A Sims, Mario %A Smith, Jennifer A %A Starr, John M %A Strauch, Konstantin %A Tai, E Shyong %A Taylor, Kent D %A Tsai, Michael Y %A Uitterlinden, André G %A van Heemst, Diana %A Waldenberger, Melanie %A Wang, Ya-Xing %A Wei, Wen-Bin %A Wilson, Gregory %A Xuan, Deng %A Yao, Jie %A Yu, Caizheng %A Yuan, Jian-Min %A Zhao, Wei %A Becker, Diane M %A Bonnefond, Amélie %A Bowden, Donald W %A Cooper, Richard S %A Deary, Ian J %A Divers, Jasmin %A Esko, Tõnu %A Franks, Paul W %A Froguel, Philippe %A Gieger, Christian %A Jonas, Jost B %A Kato, Norihiro %A Lakka, Timo A %A Leander, Karin %A Lehtimäki, Terho %A Magnusson, Patrik K E %A North, Kari E %A Ntalla, Ioanna %A Penninx, Brenda %A Samani, Nilesh J %A Snieder, Harold %A Spedicati, Beatrice %A van der Harst, Pim %A Völzke, Henry %A Wagenknecht, Lynne E %A Weir, David R %A Wojczynski, Mary K %A Wu, Tangchun %A Zheng, Wei %A Zhu, Xiaofeng %A Bouchard, Claude %A Chasman, Daniel I %A Evans, Michele K %A Fox, Ervin R %A Gudnason, Vilmundur %A Hayward, Caroline %A Horta, Bernardo L %A Kardia, Sharon L R %A Krieger, Jose Eduardo %A Mook-Kanamori, Dennis O %A Peyser, Patricia A %A Province, Michael M %A Psaty, Bruce M %A Rudan, Igor %A Sim, Xueling %A Smith, Blair H %A van Dam, Rob M %A van Duijn, Cornelia M %A Wong, Tien Yin %A Arnett, Donna K %A Rao, Dabeeru C %A Gauderman, James %A Liu, Ching-Ti %A Morrison, Alanna C %A Rotter, Jerome I %A Fornage, Myriam %X

Educational attainment, widely used in epidemiologic studies as a surrogate for socioeconomic status, is a predictor of cardiovascular health outcomes. A two-stage genome-wide meta-analysis of low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglyceride (TG) levels was performed while accounting for gene-educational attainment interactions in up to 226,315 individuals from five population groups. We considered two educational attainment variables: "Some College" (yes/no, for any education beyond high school) and "Graduated College" (yes/no, for completing a 4-year college degree). Genome-wide significant ( < 5 × 10) and suggestive ( < 1 × 10) variants were identified in Stage 1 (in up to 108,784 individuals) through genome-wide analysis, and those variants were followed up in Stage 2 studies (in up to 117,531 individuals). In combined analysis of Stages 1 and 2, we identified 18 novel lipid loci (nine for LDL, seven for HDL, and two for TG) by two degree-of-freedom (2 DF) joint tests of main and interaction effects. Four loci showed significant interaction with educational attainment. Two loci were significant only in cross-population analyses. Several loci include genes with known or suggested roles in adipose (), brain (), and liver () biology, highlighting the potential importance of brain-adipose-liver communication in the regulation of lipid metabolism. An investigation of the potential druggability of genes in identified loci resulted in five gene targets shown to interact with drugs approved by the Food and Drug Administration, including genes with roles in adipose and brain tissue. Genome-wide interaction analysis of educational attainment identified novel lipid loci not previously detected by analyses limited to main genetic effects.

%B Front Genet %V 14 %P 1235337 %8 2023 %G eng %R 10.3389/fgene.2023.1235337