Exome sequencing of Finnish isolates enhances rare-variant association power

Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power

Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci

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 withP <5 x 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 x 10(-8)) in the discovery samples. Ten novel SNVs, including rs12616219 nearTMEM182, were followed-up and five of them (rs462779 inREV3L, rs12780116 inCNNM2, rs1190736 inGPR101, rs11539157 inPJA1, and rs12616219 nearTMEM182) replicated at a Bonferroni significance threshold (P <4.5 x 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, inCCDC141and two low-frequency SNVs inCEP350andHDGFRP2. Functional follow-up implied that decreased expression ofREV3Lmay 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.Peer reviewe

Genome-wide physical activity interactions in adiposity. A meta-analysis of 200,452 adults

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by similar to 30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.Peer reviewe

The trans-ancestral genomic architecture of glycemic traits

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

Identification and Functional Characterization of G6PC2 Coding Variants Influencing Glycemic Traits Define an Effector Transcript at the G6PC2-ABCB11 Locus

This is the final version. Available on open access from the Public Library of Science via the DOI in this recordData Availability: This is a meta-analysis that was conducted on summary level results. Individual level data was not shared amongst the authors of the manuscript, and the corresponding authors are not in a position to make the individual level data available. For most of the samples included, individual level data deposition is precluded by existing consents, and other issues related to individual privacy. Summary level data from the meta-analysis are available from the DIAGRAM (http://www.diagram-consortium.org/Mahajan_2014_ExomeChip/) and LocusZoom (http://csg.sph.umich.edu/locuszoom/) website.Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights

GWAS of random glucose in 476,326 individuals provide insights into diabetes pathophysiology, complications and treatment stratification

This is the final version. Available on open access from Nature Research via the DOI in this recordData availability: Meta-analysis summary statistics for the GWAS presented in this manuscript are available on the MAGIC website (magicinvestigators.org) and through the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/downloads/summary-statistics, GCP ID: GCP000666; with study accession codes for Europeans-only meta-analysis: GCST90271557; cross-ancestry meta-analysis: GCST90271558; and sex-dimorphic meta-analysis: GCST90271559). UK Biobank individual-level data can be obtained through a data access application available at https://www.ukbiobank.ac.uk/. In this study, we made use of data made available by: 1000 Genomes project (https://www.genome.gov/27528684/1000-genomes-project); SNPsnap (https://data.broadinstitute.org/mpg/snpsnap/index.html); Tabula Muris (https://www.czbiohub.org/tabula-muris/); GTEx Consortium (https://gtexportal.org/home/); microbiome GWAS (https://mibiogen.gcc.rug.nl/); Human Gut Microbiome Atlas (https://www.microbiomeatlas.org); eQTLGen Consortium (https://www.eqtlgen.org/); TIGER expression data (http://tiger.bsc.es/) and LDHub database (http://ldsc.broadinstitute.org/ldhub/).Conventional measurements of fasting and postprandial blood glucose levels investigated in genome-wide association studies (GWAS) cannot capture the effects of DNA variability on ‘around the clock’ glucoregulatory processes. Here we show that GWAS meta-analysis of glucose measurements under nonstandardized conditions (random glucose (RG)) in 476,326 individuals of diverse ancestries and without diabetes enables locus discovery and innovative pathophysiological observations. We discovered 120 RG loci represented by 150 distinct signals, including 13 with sex-dimorphic effects, two cross-ancestry and seven rare frequency signals. Of these, 44 loci are new for glycemic traits. Regulatory, glycosylation and metagenomic annotations highlight ileum and colon tissues, indicating an underappreciated role of the gastrointestinal tract in controlling blood glucose. Functional follow-up and molecular dynamics simulations of lower frequency coding variants in glucagon-like peptide-1 receptor (GLP1R), a type 2 diabetes treatment target, reveal that optimal selection of GLP-1R agonist therapy will benefit from tailored genetic stratification. We also provide evidence from Mendelian randomization that lung function is modulated by blood glucose and that pulmonary dysfunction is a diabetes complication. Our investigation yields new insights into the biology of glucose regulation, diabetes complications and pathways for treatment stratification

The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals

To dissect the genetic architecture of blood pressure and assess effects on target organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry, and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure–associated loci, of which 17 were new; 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target organ damage in multiple tissues but with minor effects in the kidney. Our findings expand current knowledge of blood pressure–related pathways and highlight tissues beyond the classical renal system in blood pressure regulation

A Low-Frequency Inactivating Akt2 Variant Enriched in the Finnish Population is Associated With Fasting Insulin Levels and Type 2 Diabetes Risk

To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting insulin, a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in fasting plasma insulin (FI) levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-hour insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio=1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2.Academy of Finland (129293, 128315, 129330, 131593, 139635, 139635, 121584, 126925, 124282, 129378, 258753); Action on Hearing Loss (G51); Ahokas Foundation; American Diabetes Association (#7-12-MN-02); Atlantic Canada Opportunities Agency; Augustinus foundation; Becket foundation; Benzon Foundation; Biomedical Research Council; British Heart Foundation (SP/04/002); Canada Foundation for Innovation; Commission of the European Communities, Directorate C-Public Health (2004310); Copenhagen County; Danish Centre for Evaluation and Health Technology Assessment; Danish Council for Independent Research; Danish Heart Foundation (07-10-R61-A1754-B838-22392F); Danish Medical Research Council; Danish Pharmaceutical Association; Emil Aaltonen Foundation; European Research Council Advanced Research Grant; European Union FP7 (EpiMigrant, 279143; FP7/2007-2013; 259749); Finland's Slottery Machine Association; Finnish Cultural Foundation; Finnish Diabetes Research Foundation; Finnish Foundation for Cardiovascular Research; Finnish Foundation of Cardiovascular Research; Finnish Medical Society; Finnish National Public Health Institute; Finska Läkaresällskapet; Folkhälsan Research Foundation; Foundation for Life and Health in Finland; German Center for Diabetes Research (DZD) ; German Federal Ministry of Education and Research; Health Care Centers in Vasa, Närpes and Korsholm; Health Insurance Foundation (2012B233) ; Helsinki University Central Hospital Research Foundation; Hospital districts of Pirkanmaa, Southern Ostrobothnia, North Ostrobothnia, Central Finland, and Northern Savo; Ib Henriksen foundation; Juho Vainio Foundation; Korea Centers for Disease Control and Prevention (4845–301); Korea National Institute of Health (2012-N73002-00); Li Ka Shing Foundation; Liv och Hälsa; Lundbeck Foundation; Marie-Curie Fellowship (PIEF-GA-2012-329156); Medical Research Council (G0601261, G0900747-91070, G0601966, G0700931); Ministry of Education in Finland; Ministry of Social Affairs and Health in Finland; MRC-PHE Centre for Environment and Health;Municipal Heath Care Center and Hospital in Jakobstad; Närpes Health Care Foundation; National Institute for Health Research (RP-PG-0407-10371); National Institutes of Health (U01 DK085526, U01 DK085501, U01 DK085524, U01 DK085545, U01 DK085584, U01 DK088389, RC2-DK088389, DK085545, DK098032, HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, HHSN, R01MH107666 and K12CA139160268201300050C, U01 DK062370, R01 DK066358, U01DK085501, R01HL102830, R01DK073541, PO1AG027734, R01AG046949, 1R01AG042188, P30AG038072, R01 MH101820, R01MH090937, P30DK020595, R01 DK078616, NIDDK K24 DK080140, 1RC2DK088389, T32GM007753); National Medical Research Council; National Research Foundation of Korea (NRF-2012R1A2A1A03006155); Nordic Center of Excellence in Disease Genetics; Novo Nordisk; Ollqvist Foundation; OrionFarmos Research Foundation; Paavo Nurmi Foundation; Perklén Foundation; Samfundet Folkhälsan; Signe and Ane Gyllenberg Foundation; Sigrid Juselius Foundation; Social Insurance Institution of Finland; South East Norway Health Authority (2011060); Swedish Cultural Foundation in Finland; Swedish Heart-Lung Foundation; Swedish Research Council; Swedish Research Council (Linné and Strategic Research Grant); The American Federation for Aging Research; The Einstein Glenn Center; The European Commission (HEALTH-F4-2007-201413); The Finnish Diabetes Association; The Folkhälsan Research Foundation; The Påhlssons Foundation; The provinces of Newfoundland and Labrador, Nova Scotia, and New Brunswick; The Sigrid Juselius Foundation; The Skåne Regional Health Authority; The Swedish Heart-Lung Foundation; Timber Merchant Vilhelm Bang’s Foundation; Turku University Foundation; Uppsala University; Wellcome Trust (064890, 083948, 085475, 086596, 090367, 090532, 092447, 095101/Z/10/Z, 200837/Z/16/Z, 095552, 098017, 098381, 098051, 084723, 072960/2/ 03/2, 086113/Z/08/Z, WT098017, WT064890, WT090532, WT098017, 098051, WT086596/Z/08/A and 086596/Z/08/Z). Detailed acknowledgment of funding sources is provided in the Additional Acknowledgements section of the Supplementary Materials

Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution