A Large Multi-Ethnic Genome-Wide Association Study of Adult Body Mass Index Identifies
Total Page：16
File Type：pdf, Size：1020Kb
Genetics: Early Online, published on August 14, 2018 as 10.1534/genetics.118.301479 1 A large multi-ethnic genome-wide association study of adult body mass index identifies 2 novel loci 3 4 Thomas J Hoffmann*,†,1, Hélène Choquet‡, Jie Yin‡, Yambazi Banda*, Mark N Kvale*, 5 Maria Glymour†, Catherine Schaefer‡, Neil Risch*,†,‡, and Eric Jorgenson‡,1 6 7 *Institute for Human Genetics, University of California San Francisco, San Francisco, 8 CA, 94143 9 †Department of Epidemiology and Biostatistics, University of California San Francisco, 10 San Francisco, CA 94158 11 ‡Division of Research, Kaiser Permanente, Northern California, Oakland, CA 94612 12 13 GO Project IRB: CN-09CScha-06-H. 14 15 1 Copyright 2018. 16 Short running title: GWAS of BMI 17 18 Keywords: adult body mass index; adult BMI; genome-wide association study; obesity 19 20 1Corresponding authors: Eric Jorgenson, Division of Research, Kaiser Permanente, 21 Northern California, 2000 Broadway, Oakland, CA 94612, Tel: 510-891-3473, e-mail: 22 [email protected], or Thomas J. Hoffmann, University of California San Francisco, 23 513 Parnassus Ave, San Francisco, CA 94117, Tel: 415-476-2475, e-mail: 24 [email protected]. 25 2 26 Abstract 27 Body mass index (BMI), a proxy measure for obesity, is determined by both 28 environmental (including ethnicity, age, and sex) and genetic factors with over 400 BMI- 29 associated loci identified to date. However, the impact, interplay, and underlying 30 biological mechanisms among BMI, environment, genetics, and ancestry are not 31 completely understood. To further examine these relationships, we utilized 427,509 32 calendar-year averaged BMI measurements on 100,418 adults from the single large 33 multi-ethnic Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. 34 We observed substantial independent ancestry and nationality differences, including 35 ancestry principal component interactions and non-linear effects. To increase the list of 36 BMI-associated variants before assessing other differences, we conducted a genome- 37 wide association study (GWAS) in GERA, with replication in Genetic Investigation of 38 Anthropomorphic Traits (GIANT) combined with UK Biobank (UKB), followed by GWAS 39 in GERA combined with GIANT, with replication in UKB. We discovered 30 novel 40 independent BMI loci (P<5.0x10-8) that replicated. We then assessed in UKB the 41 proportion of BMI variance explained by sex using previously- compared to previously- 42 and newly-identified loci and found slight increases: from 3.0% to 3.3% for males, and 43 from 2.7% to 3.0% for females. Further, the variance explained by previously- and 44 newly-identified variants decreased with increasing age in GERA and UKB, echoed in 45 the variance explained by the entire genome, which also showed gene-age interaction 46 effects. Finally, we conducted a tissue eQTL enrichment analysis, which revealed that 47 GWAS BMI-associated variants were enriched in the cerebellum, consistent with prior 48 work in humans and mice. 3 49 Introduction 50 Body mass index (BMI) is a proxy measure for obesity, and high BMI (≥30 kg/m2) 51 increases the risk of many health problems (Bhaskaran et al. 2014; Ortega et al. 2016; 52 Benjamin et al. 2017). BMI is determined by both genetic and environmental factors, 53 though their individual and combined contributions to risk are not completely 54 understood. Recent BMI heritability estimates are around 40%, over half of which is due 55 to common genetic variation (Hemani et al. 2013; Yang et al. 2015). To date, meta- 56 analyses of genome-wide association studies (GWAS) have identified 426 independent 57 BMI-associated variants (Liu et al. 2008; Thorleifsson et al. 2009; Willer et al. 2009; 58 Speliotes et al. 2010; Kim et al. 2011; Okada et al. 2012; Wen et al. 2012, 2014; Ng et 59 al. 2012, 2017; Yang et al. 2012, 2014; Berndt et al. 2013; Monda et al. 2013; Gong et 60 al. 2013; Scannell Bryan et al. 2014; Locke et al. 2015; Horikoshi et al. 2015; Winkler et 61 al. 2015; Hägg et al. 2015; Minster et al. 2016; Ried et al. 2016; Ahmad et al. 2016; 62 Wang et al. 2016; Salinas et al. 2016; Bakshi et al. 2016; Nagy et al. 2017; Justice et al. 63 2017; Graff et al. 2017; Tachmazidou et al. 2017; Akiyama et al. 2017; Turcot et al. 64 2018), and 676 independent variants associated with measures of adiposity 65 phenotypes, including BMI (Scuteri et al. 2007; Chambers et al. 2008; Meyre et al. 66 2009; Heard-Costa et al. 2009; Lindgren et al. 2009; Cotsapas et al. 2009; Scherag et 67 al. 2010; Heid et al. 2010; Kraja et al. 2011; Wang et al. 2011; Kilpeläinen et al. 2011; 68 Jiao et al. 2011; Paternoster et al. 2011; Comuzzie et al. 2012; Melka et al. 2012; 69 Bradfield et al. 2012; Liu et al. 2013; Namjou et al. 2013; Wheeler et al. 2013; Graff et 70 al. 2013; Pei et al. 2014, 2017; Shungin et al. 2015; Felix et al. 2016; Wen et al. 2016; 71 Sung et al. 2016; Chu et al. 2017; Southam et al. 2017). These variants account for only 4 72 ~3% of the variance for this complex trait (Speliotes et al. 2010; Wen et al. 2014; Locke 73 et al. 2015; Horikoshi et al. 2015). The vast majority of these loci were identified in 74 studies of European- or Asian-ancestry (Okada et al. 2012; Wen et al. 2012, 2014; 75 Scannell Bryan et al. 2014; Yang et al. 2014; Justice et al. 2017; Graff et al. 2017; 76 Akiyama et al. 2017; Turcot et al. 2018) populations, as sample sizes have been 77 somewhat smaller in Hispanic/Latino- (Salinas et al. 2016) or African-ancestry 78 populations (Ng et al. 2012, 2017; Monda et al. 2013; Gong et al. 2013; Salinas et al. 79 2016). Previous work has implicated ancestral differences, with some conflicting results 80 (Hu et al. 2015), but has not yet assessed ancestry variation within nationality 81 subgroups. 82 Gene-environment interaction may explain an additional portion of the missing 83 heritability. Previous work has discovered several variants differ between sexes (Locke 84 et al. 2015) and age (Winkler et al. 2015), as well as overall heritability differences in 85 age (Robinson et al. 2017), but found that this was driven only by young (ages 18-40) 86 vs. old (age≥60) in the AHTHEL cohort, with no evidence of interaction between ages 87 46-73 in the UK Biobank (UKB). 88 To further investigate the relationships among BMI, ancestry, sex, and age, we 89 utilized 427,509 calendar-year average BMI measurements from electronic health 90 records (EHRs) to 100,418 members from the Genetic Epidemiology Research in Adult 91 Health and Aging (GERA) cohort. Our goal was to utilize advantages of having a single 92 large multiethnic cohort to obtain a more comprehensive picture of the genetic 93 landscape of BMI. To this end, we first comprehensively assessed ancestry and 94 nationality effects on BMI in GERA. Then, in order to test for ancestry differences in 5 95 polygenic risk factors and further characterize SNPs, including age and sex differences, 96 we first attempted to increase the list of associated variants by searching for additional 97 BMI-associated variants in GERA, and further meta-analyzed GERA with Genetic 98 Investigation of Anthropomorphic Traits (GIANT) for improved discovery. We then used 99 previously-reported plus our novel variants to test for age, sex, and ancestry 100 differences, by both testing the individual variants themselves and testing them together 101 in polygenic risk scores and through gene-age co-heritability estimates. 102 6 103 Materials and Methods 104 All statistical tests were two-sided. 105 106 Participants and phenotype 107 Our primary analysis used data on adult RPGEH GERA cohort participants (Banda et 108 al. 2015; Kvale et al. 2015) who were, on average, 62.7 yr old (at specimen collection) 109 and members of KPNC for 23 yr, and had comprehensive electronic health records 110 (EHR) available for retrieval of height and weight. Each individual's height was 111 calculated as the mode (or median if no mode), and outpatient weight measurements 112 from 2005-2010 were averaged within each calendar year, excluding outlier weights <70 113 or >500 pounds (<31.7 or >226.8 kgs). Within a calendar year, if the range exceeded 114 100 pounds (45.3 kgs) (3,759 occurrences), weights ≥75 pounds (34.0 kgs) from the 115 median were discarded. Across calendar years, if a difference between the average of 116 two consecutive years was >175 lbs (79.4 kgs), the one further from the median was 117 excluded. BMI was calculated by definition, weight (kg) / height (m)2. We further 118 excluded BMI measures <10 or >100 kg/m2, where age <18 yr, 6 months prior/after 119 childbirth, or after bariatric surgery. In total, 427,509 calendar-year BMI measurements 120 were available on 100,418 individuals. To define nationality/geography within ethnicity 121 groups, participants endorsed all applicable from 23 groups (Banda et al. 2015). The 122 Kaiser Permanente Northern California Institutional Review Board and the University of 123 California San Francisco Human Research Protection Program Committee on Human 124 Health approved of this project. Written informed consent was obtained from all 125 subjects. 7 126 127 Genotyping, quality control, and imputation 128 Individuals were genotyped at over 650,000 single nucleotide polymorphisms (SNPs) on 129 four custom Affymetrix arrays optimized for individuals of European, Latino, East Asian 130 and African American ancestry (Hoffmann et al. 2011a; b), and South Asians were 131 genotyped on the European array.