Imperial College London

DrWeihuaZhang

Faculty of MedicineSchool of Public Health

Honorary Research Associate
 
 
 
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Contact

 

+44 (0)20 7594 1612weihua.zhang

 
 
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Location

 

165Medical SchoolSt Mary's Campus

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Summary

 

Publications

Publication Type
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143 results found

Patel R, Kooner J, Zhang W, 2023, Comorbidities associated with the severity of COVID-19, and differences across ethnic groups: a UK Biobank cohort study, BMC Public Health, Vol: 23, Pages: 1-13, ISSN: 1471-2458

BackgroundDisparities in COVID-19 outcomes exist on the basis of ethnicity and comorbidities. Minority ethnic groups in the UK are known to have poorer COVID-19 outcomes, but also an increased prevelance of certain comorbidities associated with severe outcomes. Additionally, despite the prevalence of certain psychiatric disorders there is a lack of research establishing their relationship with COVID-19 outcomes.MethodsWe used UK Biobank data, involving 472,182 participants, to test for an association between comorbidities and COVID-19 diagnosis (n = 30,901); and to test for an association between comorbidities and severe COVID-19 (n = 3182). This was done by performing univariable and multivariable logistic regression analysis, estimating odds ratios (ORs) and their 95% confidence intervals (95% CIs). The comorbidities studied were coronary heart disease (CHD), hypertension, type II diabetes mellitus (T2DM), obesity, chronic kidney disease (CKD), depression and anxiety. Multivariable models were adjusted for various socioeconomic, demographic and health-related confounders. We then performed sub-group analysis by common UK ethnic groups (White, South Asian, and Black).ResultsIncreased prevalence of all studied comorbidities was seen in both outcomes, compared to the rest of the cohort. All studied comorbidities were associated with an increased risk of COVID-19 infection and severity across all models. For example, the adjusted ORs (95% CI) for depression were 1.112 (1.083 – 1.161) for COVID-19 diagnosis and 2.398 (2.163 – 2.658) for severe COVID-19. Sub-group analysis revealed stronger associations of COVID-19 diagnosis and severe COVID-19 for South-Asian participants for CHD (OR 1.585 [95% CI 1.194–2.105] for COVID-19 diagnosis and 3.021 [1.683–5.390] for severe COVID-19), hypertension (1.488 [1.231–1.799]; 3.399 [1.862–6.206]) and T2DM (1.671 [1.346–2.076]; 5.412 [3.130–9.357]) compared t

Journal article

van de Vegte Y, Eppinga RP, van der Ende MY, Hagemeijer Y, Mahendran YV, Salfati EY, Smith AE, Tan V, Arking DV, Ntalla I, Appel EA, Schurmann C, Brody J, Rueedi R, Polasek O, Sveinbjornsson G, Lecoeur C, Ladenvall C, Zhao JH, Isaacs A, Wang L, Luan J, Hwang S-J, Mononen NU, Auro KF, Jackson A, Bielak L, Zeng L, Shah N, Nethander M, Campbell A, Rankinen T, Pechlivanis S, Qi L, Zhao W, Rizzi F, Tanaka T, Robino A, Cocca M, Lange L, Mueller-Nurasyid M, Roselli CE, Zhang W, Kleber MJ, Guo X, Lin HE, Pavani F, Galesloot T, Noordam RE, Milaneschi Y, Schraut K, den Hoed M, Degenhardt FE, Trompet S, van den Berg M, Pistis G, Tham Y-CS, Weiss SL, Sim XJ, Li HM, van der Most P, Nolte I, Lyytikaeinen L-PR, Said MA, Witte D, Iribarren CM, Launer LS, Ring S, de Vries P, Sever PP, Linneberg A, Bottinger EM, Padmanabhan S, Psaty B, Sotoodehnia N, Kolcic I, Roshandel DD, Paterson AO, Arnar DF, Gudbjartsson D, Holm H, Balkau BT, Silva CH, Newton-Cheh C, Nikus K, Salo PL, Mohlke KA, Peyser P, Schunkert H, Lorentzon M, Lahti JC, Rao DC, Cornelis MD, Faul JA, Smith J, Stolarz-Skrzypek K, Bandinelli S, Concas MP, Sinagra G, Meitinger T, Waldenberger MF, Sinner M, Strauch KE, Delgado GD, Taylor K, Yao J, Foco L, Melander O, de Graaf J, de Mutsert R, de Geus EJC, Johansson AK, Joshi PK, Lind L, Franke AW, Macfarlane PV, Tarasov K, Tan NB, Felix S, Tai E-SQ, Quek D, Snieder H, Ormel J, Ingelsson M, Lindgren CP, Morris AT, Raitakari O, Hansen T, Assimes T, Gudnason VJ, Timpson NC, Morrison AB, Munroe PP, Strachan D, Grarup N, Loos RJFR, Heckbert S, Vollenweider P, Hayward C, Stefansson K, Froguel P, Groop LJ, Wareham NM, van Duijn CF, Feitosa MJ, O'Donnell C, Kaehoenen M, Perola M, Boehnke M, Kardia SLR, Erdmann J, Palmer CNA, Ohlsson CJ, Porteous DG, Eriksson J, Bouchard C, Moebus S, Kraft PR, Weir D, Cusi D, Ferrucci L, Ulivi S, Girotto G, Correa A, Kaeaeb S, Peters AC, Chambers JS, Kooner J, Maerz WI, Rotter JA, Hicks A, Smith JG, Kiemeney LALMO, Mook-Kanamori D, Penninx BWJH, Gyllensteet al., 2023, Genetic insights into resting heart rate and its role in cardiovascular disease, Nature Communications, Vol: 14, ISSN: 2041-1723

Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.

Journal article

Li JH, Brenner LN, Kaur V, Figueroa K, Schroeder P, Huerta-Chagoya A, MAGIC Investigators, Diabetes Prevention Program DPP Research Group, Udler MS, Leong A, Mercader JM, Florez JCet al., 2023, Genome-wide association analysis identifies ancestry-specific genetic variation associated with acute response to metformin and glipizide in SUGAR-MGH., Diabetologia, Vol: 66, Pages: 1260-1272

AIMS/HYPOTHESIS: Characterisation of genetic variation that influences the response to glucose-lowering medications is instrumental to precision medicine for treatment of type 2 diabetes. The Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH) examined the acute response to metformin and glipizide in order to identify new pharmacogenetic associations for the response to common glucose-lowering medications in individuals at risk of type 2 diabetes. METHODS: One thousand participants at risk for type 2 diabetes from diverse ancestries underwent sequential glipizide and metformin challenges. A genome-wide association study was performed using the Illumina Multi-Ethnic Genotyping Array. Imputation was performed with the TOPMed reference panel. Multiple linear regression using an additive model tested for association between genetic variants and primary endpoints of drug response. In a more focused analysis, we evaluated the influence of 804 unique type 2 diabetes- and glycaemic trait-associated variants on SUGAR-MGH outcomes and performed colocalisation analyses to identify shared genetic signals. RESULTS: Five genome-wide significant variants were associated with metformin or glipizide response. The strongest association was between an African ancestry-specific variant (minor allele frequency [MAFAfr]=0.0283) at rs149403252 and lower fasting glucose at Visit 2 following metformin (p=1.9×10-9); carriers were found to have a 0.94 mmol/l larger decrease in fasting glucose. rs111770298, another African ancestry-specific variant (MAFAfr=0.0536), was associated with a reduced response to metformin (p=2.4×10-8), where carriers had a 0.29 mmol/l increase in fasting glucose compared with non-carriers, who experienced a 0.15 mmol/l decrease. This finding was validated in the Diabetes Prevention Program, where rs111770298 was associated with a worse glycaemic response to metformin: heterozygous carriers had an increa

Journal article

Farooqi R, Zhang W, Kooner J, 2023, Associations between polygenic risk score and COVID-19 susceptibility and severity across ethnic groups: UK biobank analysis, BMC Medical Genomics, Vol: 16, Pages: 1-15, ISSN: 1755-8794

BackgroundCOVID-19 manifests with huge heterogeneity in susceptibility and severity outcomes. UK Black Asian and Minority Ethnic (BAME) groups have demonstrated disproportionate burdens. Some variability remains unexplained, suggesting potential genetic contribution. Polygenic Risk Scores (PRS) can determine genetic predisposition to disease based on Single Nucleotide Polymorphisms (SNPs) within the genome. COVID-19 PRS analyses within non-European samples are extremely limited. We applied a multi-ethnic PRS to a UK-based cohort to understand genetic contribution to COVID-19 variability.MethodsWe constructed two PRS for susceptibility and severity outcomes based on leading risk-variants from the COVID-19 Host Genetics Initiative. Scores were applied to 447,382 participants from the UK-Biobank. Associations with COVID-19 outcomes were assessed using binary logistic regression and discriminative power was validated using incremental area under receiver operating curve (ΔAUC). Variance explained was compared between ethnic groups via incremental pseudo-R2 (ΔR2).ResultsCompared to those at low genetic risk, those at high risk had a significantly greater risk of severe COVID-19 for White (odds ratio [OR] 1.57, 95% confidence interval [CI] 1.42–1.74), Asian (OR 2.88, 95% CI 1.63–5.09) and Black (OR 1.98, 95% CI 1.11–3.53) ethnic groups. Severity PRS performed best within Asian (ΔAUC 0.9%, ΔR2 0.98%) and Black (ΔAUC 0.6%, ΔR2 0.61%) cohorts. For susceptibility, higher genetic risk was significantly associated with COVID-19 infection risk for the White cohort (OR 1.31, 95% CI 1.26–1.36), but not for Black or Asian groups.ConclusionsSignificant associations between PRS and COVID-19 outcomes were elicited, establishing a genetic basis for variability in COVID-19. PRS showed utility in identifying high-risk individuals. The multi-ethnic approach allowed applicability of PRS to diverse populations, with the severity mo

Journal article

Graham SE, Clarke SL, Wu K-HH, Kanoni S, Zajac GJM, Ramdas S, Surakka I, Ntalla I, Vedantam S, Winkler TW, Locke AE, Marouli E, Hwang MY, Han S, Narita A, Choudhury A, Bentley AR, Ekoru K, Verma A, Trivedi B, Martin HC, Hunt KA, Hui Q, Klarin D, Zhu X, Thorleifsson G, Helgadottir A, Gudbjartsson DF, Holm H, Olafsson I, Akiyama M, Sakaue S, Terao C, Kanai M, Zhou W, Brumpton BM, Rasheed H, Ruotsalainen SE, Havulinna AS, Veturi Y, Feng Q, Rosenthal EA, Lingren T, Pacheco JA, Pendergrass SA, Haessler J, Giulianini F, Bradford Y, Miller JE, Campbell A, Lin K, Millwood IY, Hindy G, Rasheed A, Faul JD, Zhao W, Weir DR, Turman C, Huang H, Graff M, Mahajan A, Brown MR, Zhang W, Yu K, Schmidt EM, Pandit A, Gustafsson S, Yin X, Luan J, Zhao J-H, Matsuda F, Jang H-M, Yoon K, Medina-Gomez C, Pitsillides A, Hottenga JJ, Willemsen G, Wood AR, Ji Y, Gao Z, Haworth S, Mitchell RE, Chai JF, Aadahl M, Yao J, Manichaikul A, Warren HR, Ramirez J, Bork-Jensen J, Karhus LL, Goel A, Sabater-Lleal M, Noordam R, Sidore C, Fiorillo E, McDaid AF, Marques-Vidal P, Wielscher M, Trompet S, Sattar N, Mollehave LT, Thuesen BH, Munz M, Zeng L, Huang J, Yang B, Poveda A, Kurbasic A, Lamina C, Forer L, Scholz M, Galesloot TE, Bradfield JP, Daw EW, Zmuda JM, Mitchell JS, Fuchsberger C, Christensen H, Brody JA, Feitosa MF, Wojczynski MK, Preuss M, Mangino M, Christofidou P, Verweij N, Benjamins JW, Engmann J, Kember RL, Slieker RC, Lo KS, Zilhao NR, Le P, Kleber ME, Delgado GE, Huo S, Ikeda DD, Iha H, Yang J, Liu J, Leonard HL, Marten J, Schmidt B, Arendt M, Smyth LJ, Canadas-Garre M, Wang C, Nakatochi M, Wong A, Hutri-Kahonen N, Sim X, Xia R, Huerta-Chagoya A, Fernandez-Lopez JC, Lyssenko V, Ahmed M, Jackson AU, Yousri NA, Irvin MR, Oldmeadow C, Kim H-N, Ryu S, Timmers PRHJ, Arbeeva L, Dorajoo R, Lange LA, Chai X, Prasad G, Lores-Motta L, Pauper M, Long J, Li X, Theusch E, Takeuchi F, Spracklen CN, Loukola A, Bollepalli S, Warner SC, Wang YX, Wei WB, Nutile T, Ruggiero D, Sung YJ, Hung Y-J, Chen S, Liet al., 2023, Author Correction: The power of genetic diversity in genome-wide association studies of lipids, Nature, Vol: 618, Pages: E19-E20, ISSN: 0028-0836

Journal article

McAllan L, Baranasic D, Villicaña S, Brown S, Zhang W, Lehne B, Adamo M, Jenkinson A, Elkalaawy M, Mohammadi B, Hashemi M, Fernandes N, Lambie N, Williams R, Christiansen C, Yang Y, Zudina L, Lagou V, Tan S, Castillo-Fernandez J, King JWD, Soong R, Elliott P, Scott J, Prokopenko I, Cebola I, Loh M, Lenhard B, Batterham RL, Bell JT, Chambers JC, Kooner JS, Scott WRet al., 2023, Integrative genomic analyses in adipocytes implicate DNA methylation in human obesity and diabetes, Nature Communications, Vol: 14, Pages: 1-20, ISSN: 2041-1723

DNA methylation variations are prevalent in human obesity but evidence of a causative role in disease pathogenesis is limited. Here, we combine epigenome-wide association and integrative genomics to investigate the impact of adipocyte DNA methylation variations in human obesity. We discover extensive DNA methylation changes that are robustly associated with obesity (N = 190 samples, 691 loci in subcutaneous and 173 loci in visceral adipocytes, P < 1 × 10-7). We connect obesity-associated methylation variations to transcriptomic changes at >500 target genes, and identify putative methylation-transcription factor interactions. Through Mendelian Randomisation, we infer causal effects of methylation on obesity and obesity-induced metabolic disturbances at 59 independent loci. Targeted methylation sequencing, CRISPR-activation and gene silencing in adipocytes, further identifies regional methylation variations, underlying regulatory elements and novel cellular metabolic effects. Our results indicate DNA methylation is an important determinant of human obesity and its metabolic complications, and reveal mechanisms through which altered methylation may impact adipocyte functions.

Journal article

Kanoni S, Graham SE, Wang Y, Surakka I, Ramdas S, Zhu X, Clarke SL, Bhatti KF, Vedantam S, Winkler TW, Locke AE, Marouli E, Zajac GJM, Wu K-HH, Ntalla I, Hui Q, Klarin D, Hilliard AT, Wang Z, Xue C, Thorleifsson G, Helgadottir A, Gudbjartsson DF, Holm H, Olafsson I, Hwang MY, Han S, Akiyama M, Sakaue S, Terao C, Kanai M, Zhou W, Brumpton BM, Rasheed H, Havulinna AS, Veturi Y, Pacheco JA, Rosenthal EA, Lingren T, Feng Q, Kullo IJ, Narita A, Takayama J, Martin HC, Hunt KA, Trivedi B, Haessler J, Giulianini F, Bradford Y, Miller JE, Campbell A, Lin K, Millwood IY, Rasheed A, Hindy G, Faul JD, Zhao W, Weir DR, Turman C, Huang H, Graf M, Choudhury A, Sengupta D, Mahajan A, Brown MR, Zhang W, Yu K, Schmidt EM, Pandit A, Gustafsson S, Yin X, Luan J, Zhao J-H, Matsuda F, Jang H-M, Yoon K, Medina-Gomez C, Pitsillides A, Hottenga JJ, Wood AR, Ji Y, Gao Z, Haworth S, Yousri NA, Mitchell RE, Chai JF, Aadahl M, Bjerregaard AA, Yao J, Manichaikul A, Hwu C-M, Hung Y-J, Warren HR, Ramirez J, Bork-Jensen J, Karhus LL, Goel A, Sabater-Lleal M, Noordam R, Mauro P, Matteo F, McDaid AF, Marques-Vidal P, Wielscher M, Trompet S, Sattar N, Mollehave LT, Munz M, Zeng L, Huang J, Yang B, Poveda A, Kurbasic A, Lamina C, Forer L, Scholz M, Galesloot TE, Bradfeld JP, Ruotsalainen SE, Daw E, Zmuda JM, Mitchell JS, Fuchsberger C, Christensen H, Brody JA, Vazquez-Moreno M, Feitosa MF, Wojczynski MK, Wang Z, Preuss MH, Mangino M, Christofdou P, Verweij N, Benjamins JW, Engmann J, Tsao NL, Verma A, Slieker RC, Lo KS, Zilhao NR, Le P, Kleber ME, Delgado GE, Huo S, Ikeda DD, Iha H, Yang J, Liu J, Leonard HL, Marten J, Frank M, Schmidt B, Smyth LJ, Canadas-Garre M, Wang C, Nakatochi M, Wong A, Hutri-Kahonen N, Sim X, Xia R, Huerta-Chagoya A, Fernandez-Lopez JC, Lyssenko V, Nongmaithem SS, Bayyana S, Stringham HM, Irvin MR, Oldmeadow C, Kim H-N, Ryu S, Timmers PRHJ, Arbeeva L, Dorajoo R, Lange LA, Prasad G, Lores-Motta L, Pauper M, Long J, Li X, Theusch E, Takeuchi F, Spracklen CN, Loukola A, Bollepalliet al., 2022, Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis, Genome Biology, Vol: 23, ISSN: 1474-7596

BackgroundGenetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery.ResultsTo expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism.ConclusionsTaken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

Journal article

Reeves JJ, Kooner JS, Zhang W, 2022, Accelerated ageing is associated with increased Covid-19 severity and differences across ethnic groups may exist, Frontiers in Public Health, Vol: 10, Pages: 1-12, ISSN: 2296-2565

Background:While increased age is an established risk factor for COVID-19, there is great heterogeneity in outcomes within age groups. This isbecause chronological age does not reflect health, unlike biological age. We intend to investigate the association betweenaccelerated ageing and COVID-19 outcomes through the lens of three measures, namely phenotypic age acceleration(PhenoAgeAccel), telomere length (Adjusted T/S Ratio) and facial ageing, and to examine whether there are differences acrossethnic groups.Methods:Taking participants from the UK Biobank, we associated accelerated ageing with severe COVID-19 outcomes, defined as COVID-related hospitalisation or death. Separate logistic regressions models were created for age and the three accelerated ageing-related variables, adjusting for a variety of covariates in each model. Multivariable logistic regression models were also createdwithin White, Black, Asian and Other ethnic groups to assess for potential differing associations. Forward likelihood ratio logisticregression models were created to evaluate importance of the variables and to assess for patterns of association across the totalpopulation and ethnic groups.Results:After adjusting for all covariates, the odds ratio (OR) and 95% confidence interval (95% CI) of COVID-19 severe outcomes for age was1.080 (1.074-1.086). After further adjusting age for the accelerated ageing variables, the ORs were 1.029 (1.020-1.039) forPhenoAgeAccel and 0.847 (0.772-0.929) for Facial Ageing’s ‘Younger Than You Are’ while Adjusted T/S ratio and ‘Older Than You Are’were statistically insignificant. The OR for age remained similar across ethnic groups. Both PhenoAgeAccel and younger facial agesin the White population and PhenoAgeAccel in the Black population had ORs of 1.031 (1.021-1.042), 0.853 (0.774-0.939), and 1.049(1.001-1.100), respectively. Both Adjusted T/S Ratio and older facial ages showed statistical insignificance in all ethnicities. In

Journal article

Yengo L, Vedantam S, Marouli E, Sidorenko J, Bartell E, Sakaue S, Graff M, Eliasen AU, Jiang Y, Raghavan S, Miao J, Arias JD, Graham SE, Mukamel RE, Spracklen CN, Yin X, Chen S-H, Ferreira T, Highland HH, Ji Y, Karaderi T, Lin K, Lull K, Malden DE, Medina-Gomez C, Machado M, Moore A, Rueger S, Sim X, Vrieze S, Ahluwalia TS, Akiyama M, Allison MA, Alvarez M, Andersen MK, Ani A, Appadurai V, Arbeeva L, Bhaskar S, Bielak LF, Bollepalli S, Bonnycastle LL, Bork-Jensen J, Bradfield JP, Bradford Y, Braund PS, Brody JA, Burgdorf KS, Cade BE, Cai H, Cai Q, Campbell A, Canadas-Garre M, Catamo E, Chai J-F, Chai X, Chang L-C, Chang Y-C, Chen C-H, Chesi A, Choi SH, Chung R-H, Cocca M, Concas MP, Couture C, Cuellar-Partida G, Danning R, Daw EW, Degenhard F, Delgado GE, Delitala A, Demirkan A, Deng X, Devineni P, Dietl A, Dimitriou M, Dimitrov L, Dorajoo R, Ekici AB, Engmann JE, Fairhurst-Hunter Z, Farmaki A-E, Faul JD, Fernandez-Lopez J-C, Forer L, Francescatto M, Freitag-Wolf S, Fuchsberger C, Galesloot TE, Gao Y, Gao Z, Geller F, Giannakopoulou O, Giulianini F, Gjesing AP, Goel A, Gordon SD, Gorski M, Grove J, Guo X, Gustafsson S, Haessler J, Hansen TF, Havulinna AS, Haworth SJ, He J, Heard-Costa N, Hebbar P, Hindy G, Ho Y-LA, Hofer E, Holliday E, Horn K, Hornsby WE, Hottenga J-J, Huang H, Huang J, Huerta-Chagoya A, Huffman JE, Hung Y-J, Huo S, Hwang MY, Iha H, Ikeda DD, Isono M, Jackson AU, Jager S, Jansen IE, Johansson I, Jonas JB, Jonsson A, Jorgensen T, Kalafati I-P, Kanai M, Kanoni S, Karhus LL, Kasturiratne A, Katsuya T, Kawaguchi T, Kember RL, Kentistou KA, Kim H-N, Kim YJ, Kleber ME, Knol MJ, Kurbasic A, Lauzon M, Le P, Lea R, Lee J-Y, Leonard HL, Li SA, Li X, Li X, Liang J, Lin H, Lin S-Y, Liu J, Liu X, Lo KS, Long J, Lores-Motta L, Luan J, Lyssenko V, Lyytikainen L-P, Mahajan A, Mamakou V, Mangino M, Manichaikul A, Marten J, Mattheisen M, Mavarani L, McDaid AF, Meidtner K, Melendez TL, Mercader JM, Milaneschi Y, Miller JE, Millwood IY, Mishra PP, Mitchell RE, Mollehavet al., 2022, A saturated map of common genetic variants associated with human height, NATURE, Vol: 610, Pages: 704-+, ISSN: 0028-0836

Journal article

Ramdas S, Judd J, Graham SE, Kanoni S, Wang Y, Surakka I, Wenz B, Clarke SL, Chesi A, Wells A, Bhatti KF, Vedantam S, Winkler TW, Locke AE, Marouli E, Zajac GJM, Wu K-HH, Ntalla I, Hui Q, Klarin D, Hilliard AT, Wang Z, Xue C, Thorleifsson G, Helgadottir A, Gudbjartsson DF, Holm H, Olafsson I, Hwang MY, Han S, Akiyama M, Sakaue S, Terao C, Kanai M, Zhou W, Brumpton BM, Rasheed H, Havulinna AS, Veturi Y, Pacheco JA, Rosenthal EA, Lingren T, Feng Q, Kullo IJ, Narita A, Takayama J, Martin HC, Hunt KA, Trivedi B, Haessler J, Giulianini F, Bradford Y, Miller JE, Campbell A, Lin K, Millwood IY, Rasheed A, Hindy G, Faul JD, Zhao W, Weir DR, Turman C, Huang H, Graff M, Choudhury A, Sengupta D, Mahajan A, Brown MR, Zhang W, Yu K, Schmidt EM, Pandit A, Gustafsson S, Yin X, Luan J, Zhao J-H, Matsuda F, Jang H-M, Yoon K, Medina-Gomez C, Pitsillides A, Hottenga JJ, Wood AR, Ji Y, Gao Z, Haworth S, Mitchell RE, Chai JF, Aadahl M, Bjerregaard AA, Yao J, Manichaikul A, Lee W-J, Hsiung CA, Warren HR, Ramirez J, Bork-Jensen J, Karhus LL, Goel A, Sabater-Lleal M, Noordam R, Mauro P, Matteo F, McDaid AF, Marques-Vidal P, Wielscher M, Trompet S, Sattar N, Mollehave LT, Munz M, Zeng L, Huang J, Yang B, Poveda A, Kurbasic A, Schonherr S, Forer L, Scholz M, Galesloot TE, Bradfield JP, Ruotsalainen SE, Daw EW, Zmuda JM, Mitchell JS, Fuchsberger C, Christensen H, Brody JA, Le P, Feitosa MF, Wojczynski MK, Hemerich D, Preuss M, Mangino M, Christofidou P, Verweij N, Benjamins JW, Engmann J, Noah TL, Verma A, Slieker RC, Lo KS, Zilhao NR, Kleber ME, Delgado GE, Huo S, Ikeda DD, Iha H, Yang J, Liu J, Demirkan A, Leonard HL, Marten J, Emmel C, Schmidt B, Smyth LJ, Canadas-Garre M, Wang C, Nakatochi M, Wong A, Hutri-Kahonen N, Sim X, Xia R, Huerta-Chagoya A, Fernandez-Lopez JC, Lyssenko V, Nongmaithem SS, Sankareswaran A, Irvin MR, Oldmeadow C, Kim H-N, Ryu S, Timmers PRHJ, Arbeeva L, Dorajoo R, Lange LA, Prasad G, Lores-Motta L, Pauper M, Long J, Li X, Theusch E, Takeuchi F, Spracklen CN, Loukolaet al., 2022, A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids, American Journal of Human Genetics, Vol: 109, Pages: 1366-1387, ISSN: 0002-9297

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

Journal article

Winkler TW, Rasheed H, Teumer A, Gorski M, Rowan BX, Stanzick KJ, Thomas LF, Tin A, Hoppmann A, Chu AY, Tayo B, Thio CHL, Cusi D, Chai J-F, Sieber KB, Horn K, Li M, Scholz M, Cocca M, Wuttke M, van der Most PJ, Yang Q, Ghasemi S, Nutile T, Li Y, Pontali G, Guenther F, Dehghan A, Correa A, Parsa A, Feresin A, de Vries APJ, Zonderman AB, Smith A, Oldehinkel AJ, De Grandi A, Rosenkranz AR, Franke A, Teren A, Metspalu A, Hicks AA, Morris AP, Toenjes A, Morgan A, Podgornaia A, Peters A, Koerner A, Mahajan A, Campbell A, Freedman B, Spedicati B, Ponte B, Schoettker B, Brumpton B, Banas B, Kraemer BK, Jung B, Asvold BO, Smith BH, Ning B, Penninx BWJH, Vanderwerff BR, Psaty BM, Kammerer CM, Langefeld CD, Hayward C, Spracklen CN, Robinson-Cohen C, Hartman CA, Lindgren CM, Wang C, Sabanayagam C, Heng C-K, Lanzani C, Khor C-C, Cheng C-Y, Fuchsberger C, Gieger C, Shaffer CM, Schulz C-A, Willer CJ, Chasman D, Gudbjartsson DF, Ruggiero D, Toniolo D, Czamara D, Porteous DJ, Waterworth DM, Mascalzoni D, Mook-Kanamori DO, Reilly DF, Daw EW, Hofer E, Boerwinkle E, Salvi E, Bottinger EP, Tai E-S, Catamo E, Rizzi F, Guo F, Rivadeneira F, Guilianini F, Sveinbjornsson G, Ehret G, Waeber G, Biino G, Girotto G, Pistis G, Nadkarni GN, Delgado GE, Montgomery GW, Snieder H, Campbell H, White HD, Gao H, Stringham HM, Schmidt H, Li H, Brenner H, Holm H, Kirsten H, Kramer H, Rudan I, Nolte IM, Tzoulaki I, Olafsson I, Martins J, Cook JP, Wilson JF, Halbritter J, Felix JF, Divers J, Kooner JS, Lee JJ-M, O'Connell J, Rotter J, Liu J, Xu J, Thiery J, Arnlov J, Kuusisto J, Jakobsdottir J, Tremblay J, Chambers JC, Whitfield JB, Gaziano JM, Marten J, Coresh J, Jonas JB, Mychaleckyj JC, Christensen K, Eckardt K-U, Mohlke KL, Endlich K, Dittrich K, Ryan KA, Rice KM, Taylor KD, Ho K, Nikus K, Matsuda K, Strauch K, Miliku K, Hveem K, Lind L, Wallentin L, Yerges-Armstrong LM, Raffield LM, Phillips LS, Launer LJ, Lyytikainen L-P, Lange LA, Citterio L, Klaric L, Ikram MA, Ising M, Kleber ME, Francescatto M Cet al., 2022, Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals, Communications Biology, Vol: 5, ISSN: 2399-3642

Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM.

Journal article

Loh M, Zhang W, Ng HK, Schmid K, Lamri A, Tong L, Ahmad M, Lee J-J, Ng MCY, Petty LE, Spracklen CN, Takeuchi F, Islam MT, Jasmine F, Kasturiratne A, Kibriya M, Mohlke KL, Pare G, Prasad G, Shahriar M, Chee ML, de Silva HJ, Engert JC, Gerstein HC, Mani KR, Sabanayagam C, Vujkovic M, Wickremasinghe AR, Wong TY, Yajnik CS, Yusuf S, Ahsan H, Bharadwaj D, Anand SS, Below JE, Boehnke M, Bowden DW, Chandak GR, Cheng C-Y, Kato N, Mahajan A, Sim X, McCarthy MI, Morris AP, Kooner JS, Saleheen D, Chambers JCet al., 2022, Identification of genetic effects underlying type 2 diabetes in South Asian and European populations (vol 5, 329, 2022), COMMUNICATIONS BIOLOGY, Vol: 5

Journal article

Mahajan A, Spracklen CN, Zhang W, Ng MCY, Petty LE, Kitajima H, Yu GZ, Rueger S, Speidel L, Kim YJ, Horikoshi M, Mercader JM, Taliun D, Moon S, Kwak S-H, Robertson NR, Rayner NW, Loh M, Kim B-J, Chiou J, Miguel-Escalada I, Parolo PDB, Lin K, Bragg F, Preuss MH, Takeuchi F, Nano J, Guo X, Lamri A, Nakatochi M, Scott RA, Lee J-J, Huerta-Chagoya A, Graff M, Chai J-F, Parra EJ, Yao J, Bielak LF, Tabara Y, Hai Y, Steinthorsdottir V, Cook JP, Kals M, Grarup N, Schmidt EM, Pan I, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Long J, Sun M, Tong L, Chen W-M, Ahmad M, Noordam R, Lim VJY, Tam CHT, Joo YY, Chen C-H, Raffield LM, Lecoeur C, Prins BP, Nicolas A, Yanek LR, Chen G, Jensen RA, Tajuddin S, Kabagambe EK, An P, Xiang AH, Choi HS, Cade BE, Tan J, Flanagan J, Abaitua F, Adair LS, Adeyemo A, Aguilar-Salinas CA, Akiyama M, Anand SS, Bertoni A, Bian Z, Bork-Jensen J, Brandslund I, Brody JA, Brummett CM, Buchanan TA, Canouil M, Chan JCN, Chang L-C, Chee M-L, Chen J, Chen S-H, Chen Y-T, Chen Z, Chuang L-M, Cushman M, Das SK, de Silva HJ, Dedoussis G, Dimitrov L, Doumatey AP, Du S, Duan Q, Eckardt K-U, Emery LS, Evans DS, Evans MK, Fischer K, Floyd JS, Ford I, Fornage M, Franco OH, Frayling TM, Freedman B, Fuchsberger C, Genter P, Gerstein HC, Giedraitis V, Gonzalez-Villalpando C, Gonzalez-Villalpando ME, Goodarzi MO, Gordon-Larsen P, Gorkin D, Gross M, Guo Y, Hackinger S, Han S, Hattersley AT, Herder C, Howard A-G, Hsueh W, Huang M, Huang W, Hung Y-J, Hwang MY, Hwu C-M, Ichihara S, Ikram MA, Ingelsson M, Islam MT, Isono M, Jang H-M, Jasmine F, Jiang G, Jonas JB, Jorgensen ME, Jorgensen T, Kamatani Y, Kandeel FR, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton JM, Kho AN, Khor C-C, Kibriya MG, Kim D-H, Kohara K, Kriebel J, Kronenberg F, Kuusisto J, Lall K, Lange LA, Lee M-S, Lee NR, Leong A, Li L, Li Y, Li-Gao R, Ligthart S, Lindgren CM, Linneberg A, Liu C-T, Liu J, Locke AE, Louie T, Luan J, Luk AO, Luo X, Lv J, Lyssenko V, Mamakou V, Mani KR, Meitinet al., 2022, Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation, NATURE GENETICS, Vol: 54, Pages: 560-+, ISSN: 1061-4036

Journal article

Loh M, Zhang W, Ng HK, Schmid K, Lamri A, Tong L, Ahmad M, Lee J-J, Ng MCY, Petty LE, Spracklen CN, Takeuchi F, Islam MT, Jasmine F, Kasturiratne A, Kibriya M, Mohlke KL, Pare G, Prasad G, Shahriar M, Chee ML, de Silva HJ, Engert JC, Gerstein HC, Mani KR, Sabanayagam C, Vujkovic M, Wickremasinghe AR, Wong TY, Yajnik CS, Yusuf S, Ahsan H, Bharadwaj D, Anand SS, Below JE, Boehnke M, Bowden DW, Chandak GR, Cheng C-Y, Kato N, Mahajan A, Sim X, McCarthy MI, Morris AP, Kooner JS, Saleheen D, Chambers Jet al., 2022, Identification of genetic effects underlying Type 2 Diabetes in South Asian and European populations, Communications Biology, Vol: 5, ISSN: 2399-3642

South Asians are at high risk of developing type 2 diabetes (T2D). We carried out a genome-wide association meta-analysis with South Asian T2D cases (n=16,677) and controls (n=33,856), followed by combined analyses with Europeans (neff=231,420). We identify 21 novel genetic loci for significant association with T2D (P=4.7x10-8 to 5.2x10-12), to the best of our knowledge at the point of analysis. The loci are enriched for regulatory features, including DNA methylation and gene expression in relevant tissues, and highlight CHMP4B, PDHB, LRIG1 and other genes linked to adiposity and glucose metabolism. A polygenic risk score based on South Asian-derived summary statistics shows ~4-fold higher risk for T2D between the top and bottom quartile. Our results provide further insights into the genetic mechanisms underlying T2D, and highlight the opportunities for discovery from joint analysis of data from across ancestral populations.

Journal article

Hawe JS, Wilson R, Schmid KT, Zhou L, Lakshmanan LN, Lehne BC, Kuehnel B, Scott WR, Wielscher M, Yew YW, Baumbach C, Lee DP, Marouli E, Bernard M, Pfeiffer L, Matias-Garcia PR, Autio M, Bourgeois S, Herder C, Karhunen V, Meitinger T, Prokisch H, Rathmann W, Roden M, Sebert S, Shin J, Strauch K, Zhang W, Tan WLW, Hauck SM, Merl-Pham J, Grallert H, Barbosa EG, Illig T, Peters A, Paus T, Pausova Z, Deloukas P, Foo RSY, Jarvelin M-R, Kooner JS, Loh M, Heinig M, Gieger C, Waldenberger M, Chambers JCet al., 2022, Genetic variation influencing DNA methylation provides insights into molecular mechanisms regulating genomic function, NATURE GENETICS, Vol: 54, Pages: 18-+, ISSN: 1061-4036

Journal article

Graham SE, Clarke SL, Wu K-HH, Kanoni S, Zajac GJM, Ramdas S, Surakka I, Ntalla I, Vedantam S, Winkler TW, Locke AE, Marouli E, Hwang MY, Han S, Narita A, Choudhury A, Bentley AR, Ekoru K, Verma A, Trivedi B, Martin HC, Hunt KA, Hui Q, Klarin D, Zhu X, Thorleifsson G, Helgadottir A, Gudbjartsson DF, Holm H, Olafsson I, Akiyama M, Sakaue S, Terao C, Kanai M, Zhou W, Brumpton BM, Rasheed H, Ruotsalainen SE, Havulinna AS, Veturi Y, Feng Q, Rosenthal EA, Lingren T, Pacheco JA, Pendergrass SA, Haessler J, Giulianini F, Bradford Y, Miller JE, Campbell A, Lin K, Millwood IY, Hindy G, Rasheed A, Faul JD, Zhao W, Weir DR, Turman C, Huang H, Graff M, Mahajan A, Brown MR, Zhang W, Yu K, Schmidt EM, Pandit A, Gustafsson S, Yin X, Luan J, Zhao J-H, Matsuda F, Jang H-M, Yoon K, Medina-Gomez C, Pitsillides A, Hottenga JJ, Willemsen G, Wood AR, Ji Y, Gao Z, Haworth S, Mitchell RE, Chai JF, Aadahl M, Yao J, Manichaikul A, Warren HR, Ramirez J, Bork-Jensen J, Karhus LL, Goel A, Sabater-Lleal M, Noordam R, Sidore C, Fiorillo E, McDaid AF, Marques-Vidal P, Wielscher M, Trompet S, Sattar N, Mollehave LT, Thuesen BH, Munz M, Zeng L, Huang J, Yang B, Poveda A, Kurbasic A, Lamina C, Forer L, Scholz M, Galesloot TE, Bradfield JP, Daw EW, Zmuda JM, Mitchell JS, Fuchsberger C, Christensen H, Brody JA, Feitosa MF, Wojczynski MK, Preuss M, Mangino M, Christofidou P, Verweij N, Benjamins JW, Engmann J, Kember RL, Slieker RC, Lo KS, Zilhao NR, Phuong L, Kleber ME, Delgado GE, Huo S, Ikeda DD, Iha H, Yang J, Liu J, Leonard HL, Marten J, Schmidt B, Arendt M, Smyth LJ, Canadas-Garre M, Wang C, Nakatochi M, Wong A, Hutri-Kahonen N, Sim X, Xia R, Huerta-Chagoya A, Fernandez-Lopez JC, Lyssenko V, Ahmed M, Jackson AU, Irvin MR, Oldmeadow C, Kim H-N, Ryu S, Timmers PRHJ, Arbeeva L, Dorajoo R, Lange LA, Chai X, Prasad G, Lores-Motta L, Pauper M, Long J, Li X, Theusch E, Takeuchi F, Spracklen CN, Loukola A, Bollepalli S, Warner SC, Wang YX, Wei WB, Nutile T, Ruggiero D, Sung YJ, Hung Y-J, Chen S, Liu F, Yaet al., 2021, The power of genetic diversity in genome-wide association studies of lipids, NATURE, Vol: 600, Pages: 675-+, ISSN: 0028-0836

Journal article

Goyal S, Tanigawa Y, Zhang W, Chai J-F, Almeida M, Sim X, Lerner M, Chainakul J, Ramiu JG, Seraphin C, Apple B, Vaughan A, Muniu J, Peralta J, Lehman DM, Ralhan S, Wander GS, Singh JR, Mehra NK, Sidorov E, Peyton MD, Blackett PR, Curran JE, Tai ES, van Dam R, Cheng C-Y, Duggirala R, Blangero J, Chambers JC, Sabanayagam C, Kooner JS, Rivas MA, Aston CE, Sanghera DKet al., 2021, <i>APOC3</i> genetic variation, serum triglycerides, and risk of coronary artery disease in Asian Indians, Europeans, and other ethnic groups, LIPIDS IN HEALTH AND DISEASE, Vol: 20

Journal article

Chen J, Spracklen CN, Marenne G, Varshney A, Corbin LJ, Luan J, Willems SM, Wu Y, Zhang X, Horikoshi M, Boutin TS, Magi R, Waage J, Li-Gao R, Chan KHK, Yao J, Anasanti MD, Chu AY, Claringbould A, Heikkinen J, Hong J, Hottenga J-J, Huo S, Kaakinen MA, Louie T, Maerz W, Moreno-Macias H, Ndungu A, Nelson SC, Nolte IM, North KE, Raulerson CK, Ray D, Rohde R, Rybin D, Schurmann C, Sim X, Southam L, Stewart ID, Wang CA, Wang Y, Wu P, Zhang W, Ahluwalia TS, Appel EVR, Bielak LF, Brody JA, Burtt NP, Cabrera CP, Cade BE, Chai JF, Chai X, Chang L-C, Chen C-H, Chen BH, Chitrala KN, Chiu Y-F, de Haan HG, Delgado GE, Demirkan A, Duan Q, Engmann J, Fatumo SA, Gayan J, Giulianini F, Gong JH, Gustafsson S, Hai Y, Hartwig FP, He J, Heianza Y, Huang T, Huerta-Chagoya A, Hwang MY, Jensen RA, Kawaguchi T, Kentistou KA, Kim YJ, Kleber ME, Kooner IK, Lai S, Lange LA, Langefeld CD, Lauzon M, Li M, Ligthart S, Liu J, Loh M, Long J, Lyssenko V, Mangino M, Marzi C, Montasser ME, Nag A, Nakatochi M, Noce D, Noordam R, Pistis G, Preuss M, Raffield L, Rasmussen-Torvik LJ, Rich SS, Robertson NR, Rueedi R, Ryan K, Sanna S, Saxena R, Schraut KE, Sennblad B, Setoh K, Smith AV, Sparso T, Strawbridge RJ, Takeuchi F, Tan J, Trompet S, van den Akker E, van der Most PJ, Verweij N, Vogel M, Wang H, Wang C, Wang N, Warren HR, Wen W, Wilsgaard T, Wong A, Wood AR, Xie T, Zafarmand MH, Zhao J-H, Zhao W, Amin N, Arzumanyan Z, Astrup A, Bakker SJL, Baldassarre D, Beekman M, Bergman RN, Bertoni A, Blueher M, Bonnycastle LL, Bornstein SR, Bowden DW, Cai Q, Campbell A, Campbell H, Chang YC, de Geus EJC, Dehghan A, Du S, Eiriksdottir G, Farmaki AE, Franberg M, Fuchsberger C, Gao Y, Gjesing AP, Goel A, Han S, Hartman CA, Herder C, Hicks AA, Hsieh C-H, Hsueh WA, Ichihara S, Igase M, Ikram MA, Johnson WC, Jorgensen ME, Joshi PK, Kalyani RR, Kandeel FR, Katsuya T, Khor CC, Kiess W, Kolcic I, Kuulasmaa T, Kuusisto J, Lall K, Lam K, Lawlor DA, Lee NR, Lemaitre RN, Li H, Lin S-Y, Lindstrom J, Linneberg A, Liu J, Lorenzoet al., 2021, The trans-ancestral genomic architecture of glycemic traits, Nature Genetics, Vol: 53, Pages: 840-860, ISSN: 1061-4036

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10−8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.

Journal article

Surendran P, Feofanova EV, Lahrouchi N, Ntalla I, Karthikeyan S, Cook J, Chen L, Mifsud B, Yao C, Kraja AT, Cartwright JH, Hellwege JN, Giri A, Tragante V, Thorleifsson G, Liu DJ, Prins BP, Stewart ID, Cabrera CP, Eales JM, Akbarov A, Auer PL, Bielak LF, Bis JC, Braithwaite VS, Brody JA, Daw EW, Warren HR, Drenos F, Nielsen SF, Faul JD, Fauman EB, Fava C, Ferreira T, Foley CN, Franceschini N, Gao H, Giannakopoulou O, Giulianini F, Gudbjartsson DF, Guo X, Harris SE, Havulinna AS, Helgadottir A, Huffman JE, Hwang S-J, Kanoni S, Kontto J, Larson MG, Li-Gao R, Lindstrom J, Lotta LA, Lu Y, Luan J, Mahajan A, Malerba G, Masca NGD, Mei H, Menni C, Mook-Kanamori DO, Mosen-Ansorena D, Muller-Nurasyid M, Pare G, Paul DS, Perola M, Poveda A, Rauramaa R, Richard M, Richardson TG, Sepulveda N, Sim X, Smith AV, Smith JA, Staley JR, Stanakova A, Sulem P, Theriault S, Thorsteinsdottir U, Trompet S, Varga TV, Velez Edwards DR, Veronesi G, Weiss S, Willems SM, Yao J, Young R, Yu B, Zhang W, Zhao J-H, Zhao W, Zhao W, Evangelou E, Aeschbacher S, Asllanaj E, Blankenberg S, Bonnycastle LL, Bork-Jensen J, Brandslund I, Braund PS, Burgess S, Cho K, Christensen C, Connell J, de Mutsert R, Dominiczak AF, Dorr M, Eiriksdottir G, Farmaki A-E, Gaziano JM, Grarup N, Grove ML, Hallmans G, Hansen T, Have CT, Heiss G, Jorgensen ME, Jousilahti P, Kajantie E, Kamat M, Karajamaki A, Karpe F, Koistinen HA, Kovesdy CP, Kuulasmaa K, Laatikainen I, Lannfelt L, Lee I-T, Lee W-J, Linneberg A, Martin LW, Moitry M, Nadkarni G, Neville MJ, Palmer CNA, Papanicolaou GJ, Pedersen O, Peters J, Poulter N, Rasheed A, Rasmussen KL, Rayner NW, Magi R, Renstrom F, Rettig R, Rossouw J, Schreiner PJ, Sever PS, Sigurdsson EL, Skaaby T, Sun YV, Sundstrom J, Thorgeirsson G, Esko T, Trabetti E, Tsao PS, Tuomi T, Turner ST, Tzoulaki I, Vaartjes I, Vergnaud A-C, Willer CJ, Wilson PWF, Witte DR, Yonova-Doing E, Zhang H, Aliya N, Almgren P, Amouyel P, Asselbergs FW, Barnes MR, Blakemore AI, Boehnke M, Bots ML, Bottinger EP, Buriet al., 2021, Publisher Correction: Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals, Nature Genetics, Vol: 53, Pages: 1-2, ISSN: 1061-4036

Journal article

Gomez-Alonso MDC, Kretschmer A, Wilson R, Pfeiffer L, Karhunen V, Seppala I, Zhang W, Mittelstrass K, Wahl S, Matias-Garcia PR, Prokisch H, Horn S, Meitinger T, Serrano-Garcia LR, Sebert S, Raitakari O, Loh M, Rathmann W, Mueller-Nurasyid M, Herder C, Roden M, Hurme M, Jarvelin M-R, Ala-Korpela M, Kooner JS, Peters A, Lehtimaki T, Chambers JC, Gieger C, Kettunen J, Waldenberger Met al., 2021, DNA methylation and lipid metabolism: an EWAS of 226 metabolic measures, CLINICAL EPIGENETICS, Vol: 13, ISSN: 1868-7075

Journal article

Surendran P, Gao H, Zhang W, Evangelou E, Poulter N, Sever PJ, Vergnaud A, Chambers JC, Elliott P, Jarvelin M-R, Kooner JS, Howson Jet al., 2020, Discovery of rare variants associated with blood pressure regulation trhough meta-analaysis of 1.3 million individuals, Nature Genetics, Vol: 52, Pages: 1314-1332, ISSN: 1061-4036

Genetic studies of blood pressure (BP) to date have mainly analyzed common variants (minor allele frequency, MAF > 0.05). In a meta-analysis of up to >1.3 million participants, we discovered 106 new BP-associated genomic regions and 87 rare (MAF≤ 0.01) variant BP associations (P < 5 × 10-8), of which 32 were in new BP-associated loci and 55 were independent BP-associated SNVs within known BP-associated regions. Average effects of rare variants (44% coding) were ~8 times larger than common variant effects and indicate potential candidate causal genes at new and known loci (e.g.GATA5, PLCB3). BP-associated variants (including rare and common) were enriched in regions of active chromatin in fetal tissues, potentially linking fetal development with BP regulation in later life. Multivariable Mendelian randomization suggested possible inverse effects of elevated systolic and diastolic BP on large artery stroke. Our study demonstrates the utility of rare variant analyses for identifying candidate genes and the results highlight potential therapeutic targets.

Journal article

Mahajan A, Spracklen CN, Zhang W, Ng MCY, Petty LE, Kitajima H, Yu GZ, Rüeger S, Speidel L, Kim YJ, Horikoshi M, Mercader JM, Taliun D, Moon S, Kwak S-H, Robertson NR, Rayner NW, Loh M, Kim B-J, Chiou J, Miguel-Escalada I, Briotta Parolo PD, Lin K, Bragg F, Preuss MH, Takeuchi F, Nano J, Guo X, Lamri A, Nakatochi M, Scott RA, Lee J-J, Huerta-Chagoya A, Graff M, Chai J-F, Parra EJ, Yao J, Bielak LF, Tabara Y, Hai Y, Steinthorsdottir V, Cook JP, Kals M, Grarup N, Schmidt EM, Pan I, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Long J, Sun M, Tong L, Chen W-M, Ahmad M, Noordam R, Lim VJY, Tam CHT, Joo YY, Chen C-H, Raffield LM, Lecoeur C, Maruthur NM, Prins BP, Nicolas A, Yanek LR, Chen G, Jensen RA, Tajuddin S, Kabagambe E, An P, Xiang AH, Choi HS, Cade BE, Tan J, Abaitua F, Adair LS, Adeyemo A, Aguilar-Salinas CA, Akiyama M, Anand SS, Bertoni A, Bian Z, Bork-Jensen J, Brandslund I, Brody JA, Brummett CM, Buchanan TA, Canouil M, Chan JCN, Chang L-C, Chee M-L, Chen J, Chen S-H, Chen Y-T, Chen Z, Chuang L-M, Cushman M, Das SK, de Silva HJ, Dedoussis G, Dimitrov L, Doumatey AP, Du S, Duan Q, Eckardt K-U, Emery LS, Evans DS, Evans MK, Fischer K, Floyd JS, Ford I, Fornage M, Franco OH, Frayling TM, Freedman BI, Fuchsberger C, Genter P, Gerstein HC, Giedraitis V, González-Villalpando C, González-Villalpando ME, Goodarzi MO, Gordon-Larsen P, Gorkin D, Gross M, Guo Y, Hackinger S, Han S, Hattersley AT, Herder C, Howard A-G, Hsueh W, Huang M, Huang W, Hung Y-J, Hwang MY, Hwu C-M, Ichihara S, Ikram MA, Ingelsson M, Islam MT, Isono M, Jang H-M, Jasmine F, Jiang G, Jonas JB, Jørgensen ME, Jørgensen T, Kamatani Y, Kandeel FR, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton JM, Kho AN, Khor C-C, Kibriya MG, Kim D-H, Kohara K, Kriebel J, Kronenberg F, Kuusisto J, Läll K, Lange LA, Lee M-S, Lee NR, Leong A, Li L, Li Y, Li-Gao R, Ligthart S, Lindgren CM, Linneberg A, Liu C-T, Liu J, Locke AE, Louie T, Luan J, Luk AO, Luo X, Lv J, Lyssenko V, Mamakou V, Mani KRet al., 2020, Trans-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation

<jats:title>ABSTRACT</jats:title><jats:p>We assembled an ancestrally diverse collection of genome-wide association studies of type 2 diabetes (T2D) in 180,834 cases and 1,159,055 controls (48.9% non-European descent). We identified 277 loci at genome-wide significance (<jats:italic>p</jats:italic>&lt;5×10<jats:sup>-8</jats:sup>), including 237 attaining a more stringent trans-ancestry threshold (<jats:italic>p</jats:italic>&lt;5×10<jats:sup>-9</jats:sup>), which were delineated to 338 distinct association signals. Trans-ancestry meta-regression offered substantial enhancements to fine-mapping, with 58.6% of associations more precisely localised due to population diversity, and 54.4% of signals resolved to a single variant with &gt;50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying foundations for functional investigations. Trans-ancestry genetic risk scores enhanced transferability across diverse populations, providing a step towards more effective clinical translation to improve global health.</jats:p>

Journal article

Spracklen CN, Horikoshi M, Kim YJ, Lin K, Bragg F, Moon S, Suzuki K, Tam CHT, Tabara Y, Kwak S-H, Takeuchi F, Long J, Lim VJY, Chai J-F, Chen C-H, Nakatochi M, Yao J, Choi HS, Iyengar AK, Perrin HJ, Brotman SM, Van De Bunt M, Gloyn AL, Below JE, Boehnke M, Bowden DW, Chambers JC, Mahajan A, McCarthy MI, Ng MCY, Petty LE, Zhang W, Morris AP, Adair LS, Akiyama M, Bian Z, Chan JCN, Chang L-C, Chee M-L, Chen Y-DI, Chen Y-T, Chen Z, Chuang L-M, Du S, Gordon-Larsen P, Gross M, Guo X, Guo Y, Han S, Howard A-G, Huang W, Hung Y-J, Hwang MY, Hwu C-M, Ichihara S, Isono M, Jang H-M, Jiang G, Jonas JB, Kamatani Y, Katsuya T, Kawaguchi T, Khor C-C, Kohara K, Lee M-S, Lee NR, Li L, Liu J, Luk AO, Lv J, Okada Y, Pereira MA, Sabanayagam C, Shi J, Shin DM, So WY, Takahashi A, Tomlinson B, Tsai F-J, van Dam RM, Xiang Y-B, Yamamoto K, Yamauchi T, Yoon K, Yu C, Yuan J-M, Zhang L, Zheng W, Igase M, Cho YS, Rotter JI, Wang Y-X, Sheu WHH, Yokota M, Wu J-Y, Cheng C-Y, Wong T-Y, Shu X-O, Kato N, Park K-S, Tai E-S, Matsuda F, Koh W-P, Ma RCW, Maeda S, Millwood IY, Lee J, Kadowaki T, Walters RG, Kim B-J, Mohlke KL, Sim Xet al., 2020, Identification of type 2 diabetes loci in 433,540 East Asian individuals, NATURE, Vol: 582, Pages: 240-+, ISSN: 0028-0836

Journal article

Ochoa-Rosales C, Portilla-Fernandez E, Nano J, Wilson R, Lehne B, Mishra PP, Gao X, Ghanbari M, Rueda-Ochoa OL, Juvinao-Quintero D, Loh M, Zhang W, Kooner JS, Grabe HJ, Felix SB, Schoettker B, Zhang Y, Gieger C, Mueller-Nurasyid M, Heier M, Peters A, Lehtimaki T, Teumer A, Brenner H, Waldenberger M, Ikram MA, van Meurs JBJ, Franco OH, Voortman T, Chambers J, Stricker BH, Muka Tet al., 2020, Epigenetic Link Between Statin Therapy and Type 2 Diabetes, DIABETES CARE, Vol: 43, Pages: 875-884, ISSN: 0149-5992

Journal article

Clark DW, Zhang W, Gao H, Afaq S, Elliott P, Elliott J, Poulter N, Scott W, Sever P, Tzoulaki I, Lehne B, Chambers J, Evangelou E, Kooner J, Walters R, Wilson Jet al., 2019, Associations of autozygosity with a broad range of human phenotypes, Nature Communications, Vol: 10, ISSN: 2041-1723

In many species, the offspring of related parents suffer reduced vigor, survival and reproductive success, a phenomenon known as inbreeding depression1. In humans, the importance of this effect has remained unclear2, partly because reproduction between close relatives is both rare in many cultures and frequently associated with confounding social factors3. Here, using genomic inbreeding coefficients4 (FROH) for >1.3 million individuals, we show that FROH is significantly associated (P < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. Increased FROH is associated with reduced reproductive success (decreased number and likelihood of having children, older age at first sex and first birth, decreased number of sexual partners), as well as reduced risk-taking behaviour (alcohol intake, ever-smoked, self-reported risk taking) and increased disease risk (self-reported overall health, and risk factors including grip strength and heart rate). The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. These effects are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants causing inbreeding depression are predominantly rare. For a subset of traits, the effect of FROH differs significantly between men and women. Indeed, an increased FROH is associated with decreased total and LDL cholesterol in men, raising the possibility that increases in these traits may have benefited evolutionary fitness, despite being known coronary risk factors. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of environmental confounding. We conclude that inbreeding depression influences a broad range of human phenotypes through the action of rare, recessive variants.

Journal article

Erzurumluoglu AM, Chambers JC, Elliott P, Evangelou E, Kooner JS, Poulter N, Sever P, Zhang W, Howson JMM, Wells Jet al., 2019, Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci, Molecular Psychiatry, Vol: 25, Pages: 2392-2409, ISSN: 1359-4184

Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10−8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10−8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10−3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.

Journal article

Tin A, Marten J, Kuhns VLH, Li Y, Wuttke M, Kirsten H, Sieber KB, Qiu C, Gorski M, Yu Z, Giri A, Sveinbjornsson G, Li M, Chu AY, Hoppmann A, O'Connor LJ, Prins B, Nutile T, Noce D, Akiyama M, Cocca M, Ghasemi S, van Der Most PJ, Horn K, Xu Y, Fuchsberger C, Sedaghat S, Afaq S, Amin N, Arnlov J, Bakker SJL, Bansal N, Baptista D, Bergmann S, Biggs ML, Biino G, Boerwinkle E, Bottinger EP, Boutin TS, Brumat M, Burkhardt R, Campana E, Campbell A, Campbell H, Carroll RJ, Catamo E, Chambers JC, Ciullo M, Concas MP, Coresh J, Corre T, Cusi D, Felicita SC, de Borst MH, De Grandi A, de Mutsert R, de Vries APJ, Delgado G, Demirkan A, Devuyst O, Dittrich K, Eckardt K-U, Ehret G, Endlich K, Evans MK, Gansevoort RT, Gasparini P, Giedraitis V, Gieger C, Girotto G, Ggele M, Gordon SD, Gudbjartsson DF, Gudnason V, Haller T, Hamet P, Harris TB, Hayward C, Hicks AA, Hofer E, Holm H, Huang W, Hutri-Kahonen N, Hwang S-J, Ikram MA, Lewis RM, Ingelsson E, Jakobsdottir J, Jonsdottir I, Jonsson H, Joshi PK, Josyula NS, Jung B, Kahonen M, Kamatani Y, Kanai M, Kerr SM, Kiess W, Kleber ME, Koenig W, Kooner JS, Korner A, Kovacs P, Kramer BK, Kronenberg F, Kubo M, Kuhnel B, La Bianca M, Lange LA, Lehne B, Lehtimaki T, Liu J, Loeffler M, Loos RJF, Lyytikainen L-P, Magi R, Mahajan A, Martin NG, Marz W, Mascalzoni D, Matsuda K, Meisinger C, Meitinger T, Metspalu A, Milaneschi Y, ODonnell CJ, Wilson OD, Gaziano JM, Mishra PP, Mohlke KL, Mononen N, Montgomery GW, Mook-Kanamori DO, Mueller-Nurasyid M, Nadkarni GN, Nalls MA, Nauck M, Nikus K, Ning B, Nolte IM, Noordam R, O'Connell JR, Olafsson I, Padmanabhan S, Penninx BWJH, Perls T, Peters A, Pirastu M, Pirastu N, Pistis G, Polasek O, Ponte B, Porteous DJ, Poulain T, Preuss MH, Rabelink TJ, Raffield LM, Raitakari OT, Rettig R, Rheinberger M, Rice KM, Rizzi F, Robino A, Rudan I, Krajcoviechova A, Cifkova R, Rueedi R, Ruggiero D, Ryan KA, Saba Y, Salvi E, Schmidt H, Schmidt R, Shaffer CM, Smith A, Smith BH, Spracklen CN, Strauch K, Stumvoll M, Sulem Pet al., 2019, Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels, Nature Genetics, Vol: 51, Pages: 1459-1474, ISSN: 1061-4036

Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urate-associated loci and colocalization with gene expression in 47 tissues implicated the kidney and liver as the main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in the liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A transactivated the promoter of ABCG2, encoding a major urate transporter, in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional coregulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardiometabolic traits.

Journal article

Sung YJ, de Las Fuentes L, Winkler TW, Chasman DI, Bentley AR, Kraja AT, Ntalla I, Warren HR, Guo X, Schwander K, Manning AK, Brown MR, Aschard H, Feitosa MF, Franceschini N, Lu Y, Cheng C-Y, Sim X, Vojinovic D, Marten J, Musani SK, Kilpeläinen TO, Richard MA, Aslibekyan S, Bartz TM, Dorajoo R, Li C, Liu Y, Rankinen T, Smith AV, Tajuddin SM, Tayo BO, Zhao W, Zhou Y, Matoba N, Sofer T, Alver M, Amini M, Boissel M, Chai JF, Chen X, Divers J, Gandin I, Gao C, Giulianini F, Goel A, Harris SE, Hartwig FP, He M, Horimoto ARVR, Hsu F-C, Jackson AU, Kammerer CM, Kasturiratne A, Komulainen P, Kühnel B, Leander K, Lee W-J, Lin K-H, Luan J, Lyytikäinen L-P, McKenzie CA, Nelson CP, Noordam R, Scott RA, Sheu WHH, Stančáková A, Takeuchi F, van der Most PJ, Varga TV, Waken RJ, Wang H, Wang Y, Ware EB, Weiss S, Wen W, Yanek LR, Zhang W, Zhao JH, Afaq S, Alfred T, Amin N, Arking DE, Aung T, Barr RG, Bielak LF, Boerwinkle E, Bottinger EP, Braund PS, Brody JA, Broeckel U, Cade B, Campbell A, Canouil M, Chakravarti A, Cocca M, Collins FS, Connell JM, de Mutsert R, de Silva HJ, Dörr M, Duan Q, Eaton CB, Ehret G, Evangelou E, Faul JD, Forouhi NG, Franco OH, Friedlander Y, Gao H, Gigante B, Gu CC, Gupta P, Hagenaars SP, Harris TB, He J, Heikkinen S, Heng C-K, Hofman A, Howard BV, Hunt SC, Irvin MR, Jia Y, Katsuya T, Kaufman J, Kerrison ND, Khor CC, Koh W-P, Koistinen HA, Kooperberg CB, Krieger JE, Kubo M, Kutalik Z, Kuusisto J, Lakka TA, Langefeld CD, Langenberg C, Launer LJ, Lee JH, Lehne B, Levy D, Lewis CE, Li Y, Lifelines Cohort Study, Lim SH, Liu C-T, Liu J, Liu J, Liu Y, Loh M, Lohman KK, Louie T, Mägi R, Matsuda K, Meitinger T, Metspalu A, Milani L, Momozawa Y, Mosley TH, Nalls MA, Nasri U, O'Connell JR, Ogunniyi A, Palmas WR, Palmer ND, Pankow JS, Pedersen NL, Peters A, Peyser PA, Polasek O, Porteous D, Raitakari OT, Renström F, Rice TK, Ridker PM, Robino A, Robinson JG, Rose LM, Rudan I, Sabanayagam C, Salako BL, Sandow K, Schmidt CO, Schreiner PJ, Scott WR, Sever P, Sims M, Sitet al., 2019, A multi-ancestry genome-wide study incorporating gene-smoking interactions identifies multiple new loci for pulse pressure and mean arterial pressure, Human Molecular Genetics, Vol: 28, Pages: 2615-2633, ISSN: 0964-6906

Elevated blood pressure (BP), a leading cause of global morbidity and mortality, is influenced by both genetic and lifestyle factors. Cigarette smoking is one such lifestyle factor. Across five ancestries, we performed a genome-wide gene–smoking interaction study of mean arterial pressure (MAP) and pulse pressure (PP) in 129 913 individuals in stage 1 and follow-up analysis in 480 178 additional individuals in stage 2. We report here 136 loci significantly associated with MAP and/or PP. Of these, 61 were previously published through main-effect analysis of BP traits, 37 were recently reported by us for systolic BP and/or diastolic BP through gene–smoking interaction analysis and 38 were newly identified (P < 5 × 10−8, false discovery rate < 0.05). We also identified nine new signals near known loci. Of the 136 loci, 8 showed significant interaction with smoking status. They include CSMD1 previously reported for insulin resistance and BP in the spontaneously hypertensive rats. Many of the 38 new loci show biologic plausibility for a role in BP regulation. SLC26A7 encodes a chloride/bicarbonate exchanger expressed in the renal outer medullary collecting duct. AVPR1A is widely expressed, including in vascular smooth muscle cells, kidney, myocardium and brain. FHAD1 is a long non-coding RNA overexpressed in heart failure. TMEM51 was associated with contractile function in cardiomyocytes. CASP9 plays a central role in cardiomyocyte apoptosis. Identified only in African ancestry were 30 novel loci. Our findings highlight the value of multi-ancestry investigations, particularly in studies of interaction with lifestyle factors, where genomic and lifestyle differences may contribute to novel findings.

Journal article

Turcot V, Lu Y, Highland HM, Schurmann C, Justice AE, Fine RS, Bradfield JP, Esko T, Giri A, Graff M, Guo X, Hendricks AE, Karaderi T, Lempradl A, Locke AE, Mahajan A, Marouli E, Sivapalaratnam S, Young KL, Alfred T, Feitosa MF, Masca NGD, Manning AK, Medina-Gomez C, Mudgal P, Ng MCY, Reiner AP, Vedantam S, Willems SM, Winkler TW, Abecasis G, Aben KK, Alam DS, Alharthi SE, Allison M, Amouyel P, Asselbergs FW, Auer PL, Balkau B, Bang LE, Barroso I, Bastarache L, Benn M, Bergmann S, Bielak LF, Bluher M, Boehnke M, Boeing H, Boerwinkle E, Boger CA, Bork-Jensen J, Bots ML, Bottinger EP, Bowden DW, Brandslund I, Breen G, Brilliant MH, Broer L, Brumat M, Burt AA, Butterworth AS, Campbell PT, Cappellani S, Carey DJ, Catamo E, Caulfield MJ, Chambers JC, Chasman DI, Chen Y-DI, Chowdhury R, Christensen C, Chu AY, Cocca M, Collins FS, Cook JP, Corley J, Galbany JC, Cox AJ, Crosslin DS, Cuellar-Partida G, D'Eustacchio A, Danesh J, Davies G, Bakker PIW, Groot MCH, Mutsert R, Deary IJ, Dedoussis G, Demerath EW, Heijer M, Hollander AI, Ruijter HM, Dennis JG, Denny JC, Di Angelantonio E, Drenos F, Du M, Dube M-P, Dunning AM, Easton DF, Edwards TL, Ellinghaus D, Ellinor PT, Elliott P, Evangelou E, Farmaki A-E, Farooqi IS, Faul JD, Fauser S, Feng S, Ferrannini E, Ferrieres J, Florez JC, Ford I, Fornage M, Franco OH, Franke A, Franks PW, Friedrich N, Frikke-Schmidt R, Galesloot TE, Gan W, Gandin I, Gasparini P, Gibson J, Giedraitis V, Gjesing AP, Gordon-Larsen P, Gorski M, Grabe H-J, Grant SFA, Grarup N, Griffiths HL, Grove ML, Gudnason V, Gustafsson S, Haessler J, Hakonarson H, Hammerschlag AR, Hansen T, Harris KM, Harris TB, Hattersley AT, Have CT, Hayward C, He L, Heard-Costa NL, Heath AC, Heid IM, Helgeland O, Hernesniemi J, Hewitt AW, Holmen OL, Hovingh GK, Howson JMM, Hu Y, Huang PL, Huffman JE, Ikram MA, Ingelsson E, Jackson AU, Jansson J-H, Jarvik GP, Jensen GB, Jia Y, Johansson S, Jorgensen ME, Jorgensen T, Jukema JW, Kahali B, Kahn RS, Kahonen M, Kamstrup PR, Kanoni S, Kapriet al., 2019, Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity, Nature Genetics, Vol: 51, Pages: 1191-1192, ISSN: 1061-4036

In the HTML version of this article initially published, the author groups ‘CHD Exome+ Consortium’, ‘EPIC-CVD Consortium’, ‘ExomeBP Consortium’, ‘Global Lipids Genetic Consortium’, ‘GoT2D Genes Consortium’, ‘EPIC InterAct Consortium’, ‘INTERVAL Study’, ‘ReproGen Consortium’, ‘T2D-Genes Consortium’, ‘The MAGIC Investigators’ and ‘Understanding Society Scientific Group’ appeared at the end of the author list but should have appeared earlier in the list, after author Krina T. Zondervan. The errors have been corrected in the HTML version of the article.

Journal article

Brazel DM, Jiang Y, Hughey JM, Turcot V, Zhan X, Gong J, Batini C, Weissenkampen JD, Liu MZ, Surendran P, Young R, Barnes DR, Nielsen SF, Rasheed A, Samuel M, Zhao W, Kontto J, Perola M, Caslake M, de Craen AJM, Trompet S, Uria-Nickelsen M, Malarstig A, Reily DF, Hoek M, Vogt T, Jukema JW, Sattar N, Ford I, Packard CJ, Alam DS, Majumder AAS, Di Angelantonio E, Chowdhury R, Amouyel P, Arveiler D, Blankenberg S, Ferrières J, Kee F, Kuulasmaa K, Müller-Nurasyid M, Veronesi G, Virtamo J, EPIC-CVD Consortium, Frossard P, Nordestgaard BG, Saleheen D, Danesh J, Butterworth AS, Howson JMM, Erzurumluoglu AM, Jackson VE, Melbourne CA, Varga TV, Warren HR, Tragante V, Tachmazidou I, Harris SE, Evangelou E, Marten J, Zhang W, Altmaier E, Luan J, Langenberg C, Scott RA, Yaghootkar H, Stirrups K, Kanoni S, Marouli E, Karpe F, Dominiczak AF, Sever P, Poulter N, Rolandsson O, Baumbach C, Afaq S, Chambers JC, Kooner JS, Wareham NJ, Renström F, Hallmans G, Marioni RE, Corley J, Starr JM, Verweij N, de Boer RA, van der Meer P, Yavas E, Vaartjes I, Bots ML, Asselbergs FW, Grabe HJ, Völzke Het al., 2019, Exome chip meta-analysis fine maps causal variants and elucidates the genetic architecture of rare coding variants in smoking and alcohol use, Biological Psychiatry, Vol: 85, Pages: 946-955, ISSN: 0006-3223

Background: Smoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences and to contribute to disease risk. Methods: We analyzed ∼250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack-years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss-of-function coding variants. We performed a novel fine-mapping analysis to winnow the number of putative causal variants within associated loci. Results: Meta-analytic sample sizes ranged from 152,348 to 433,216, depending on the phenotype. Rare coding variation explained 1.1% to 2.2% of phenotypic variance, reflecting 11% to 18% of the total single nucleotide polymorphism heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between three and 10 variants for 65 loci. Twenty loci contained rare coding variants in the 95% credible intervals. Conclusions: Rare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine-mapping genome-wide association study loci identifies specific variants contributing to the biological etiology of substance use behavior.

Journal article

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