Publications by authors named "Adam E Locke"

51 Publications

Sequencing of 640,000 exomes identifies variants associated with protection from obesity.

Science 2021 07;373(6550)

Geisinger Obesity Institute, Geisinger Health System, Danville, PA 17882, USA.

Large-scale human exome sequencing can identify rare protein-coding variants with a large impact on complex traits such as body adiposity. We sequenced the exomes of 645,626 individuals from the United Kingdom, the United States, and Mexico and estimated associations of rare coding variants with body mass index (BMI). We identified 16 genes with an exome-wide significant association with BMI, including those encoding five brain-expressed G protein-coupled receptors (, , , , and ). Protein-truncating variants in were observed in ~4/10,000 sequenced individuals and were associated with 1.8 kilograms per square meter lower BMI and 54% lower odds of obesity in the heterozygous state. Knock out of in mice resulted in resistance to weight gain and improved glycemic control in a high-fat diet model. Inhibition of GPR75 may provide a therapeutic strategy for obesity.
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http://dx.doi.org/10.1126/science.abf8683DOI Listing
July 2021

Pan-ancestry exome-wide association analyses of COVID-19 outcomes in 586,157 individuals.

Am J Hum Genet 2021 07 3;108(7):1350-1355. Epub 2021 Jun 3.

Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, UK.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), a respiratory illness that can result in hospitalization or death. We used exome sequence data to investigate associations between rare genetic variants and seven COVID-19 outcomes in 586,157 individuals, including 20,952 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome wide or when specifically focusing on (1) 13 interferon pathway genes in which rare deleterious variants have been reported in individuals with severe COVID-19, (2) 281 genes located in susceptibility loci identified by the COVID-19 Host Genetics Initiative, or (3) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, and results are publicly available through the Regeneron Genetics Center COVID-19 Results Browser.
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http://dx.doi.org/10.1016/j.ajhg.2021.05.017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8173480PMC
July 2021

Heterozygous Variants of CLPB are a Cause of Severe Congenital Neutropenia.

Blood 2021 Jun 11. Epub 2021 Jun 11.

Washington University School of Medicine, St. Louis, Missouri, United States.

Severe congenital neutropenia (SCN) is an inborn disorder of granulopoiesis. Approximately one-third of cases do not have a known genetic cause. Exome sequencing of 104 persons with congenital neutropenia identified heterozygous missense variants of CLPB (caseinolytic peptidase B) in 5 SCN cases, with 5 more cases identified through additional sequencing efforts or clinical sequencing. CLPB encodes an adenosine triphosphatase (ATPase) implicated in protein folding and mitochondrial function. Prior studies showed that biallelic mutations of CLPB are associated with a syndrome of 3-methylglutaconic aciduria, cataracts, neurologic disease, and variable neutropenia. However, 3-methylglutaconic aciduria was not observed and, other than neutropenia, these clinical features were uncommon in our series. Moreover, the CLPB variants are distinct, consisting of heterozygous variants that cluster near the ATP-binding pocket. Both genetic loss of CLPB and expression of CLPB variants results in impaired granulocytic differentiation of human hematopoietic progenitors and increased apoptosis. These CLPB variants associate with wildtype CLPB and inhibit its ATPase and disaggregase activity in a dominant-negative fashion. Finally, expression of CLPB variants is associated with impaired mitochondrial function but does not render cells more sensitive to endoplasmic reticulum stress. Together, these data show that heterozygous CLPB variants are a new and relatively common cause of congenital neutropenia and should be considered in the evaluation of patients with congenital neutropenia.
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http://dx.doi.org/10.1182/blood.2021010762DOI Listing
June 2021

Association of structural variation with cardiometabolic traits in Finns.

Am J Hum Genet 2021 04;108(4):583-596

McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA; Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA. Electronic address:

The contribution of genome structural variation (SV) to quantitative traits associated with cardiometabolic diseases remains largely unknown. Here, we present the results of a study examining genetic association between SVs and cardiometabolic traits in the Finnish population. We used sensitive methods to identify and genotype 129,166 high-confidence SVs from deep whole-genome sequencing (WGS) data of 4,848 individuals. We tested the 64,572 common and low-frequency SVs for association with 116 quantitative traits and tested candidate associations using exome sequencing and array genotype data from an additional 15,205 individuals. We discovered 31 genome-wide significant associations at 15 loci, including 2 loci at which SVs have strong phenotypic effects: (1) a deletion of the ALB promoter that is greatly enriched in the Finnish population and causes decreased serum albumin level in carriers (p = 1.47 × 10) and is also associated with increased levels of total cholesterol (p = 1.22 × 10) and 14 additional cholesterol-related traits, and (2) a multi-allelic copy number variant (CNV) at PDPR that is strongly associated with pyruvate (p = 4.81 × 10) and alanine (p = 6.14 × 10) levels and resides within a structurally complex genomic region that has accumulated many rearrangements over evolutionary time. We also confirmed six previously reported associations, including five led by stronger signals in single nucleotide variants (SNVs) and one linking recurrent HP gene deletion and cholesterol levels (p = 6.24 × 10), which was also found to be strongly associated with increased glycoprotein level (p = 3.53 × 10). Our study confirms that integrating SVs in trait-mapping studies will expand our knowledge of genetic factors underlying disease risk.
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http://dx.doi.org/10.1016/j.ajhg.2021.03.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8059371PMC
April 2021

Investigating rare pathogenic/likely pathogenic exonic variation in bipolar disorder.

Mol Psychiatry 2021 Jan 22. Epub 2021 Jan 22.

HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA.

Bipolar disorder (BD) is a serious mental illness with substantial common variant heritability. However, the role of rare coding variation in BD is not well established. We examined the protein-coding (exonic) sequences of 3,987 unrelated individuals with BD and 5,322 controls of predominantly European ancestry across four cohorts from the Bipolar Sequencing Consortium (BSC). We assessed the burden of rare, protein-altering, single nucleotide variants classified as pathogenic or likely pathogenic (P-LP) both exome-wide and within several groups of genes with phenotypic or biologic plausibility in BD. While we observed an increased burden of rare coding P-LP variants within 165 genes identified as BD GWAS regions in 3,987 BD cases (meta-analysis OR = 1.9, 95% CI = 1.3-2.8, one-sided p = 6.0 × 10), this enrichment did not replicate in an additional 9,929 BD cases and 14,018 controls (OR = 0.9, one-side p = 0.70). Although BD shares common variant heritability with schizophrenia, in the BSC sample we did not observe a significant enrichment of P-LP variants in SCZ GWAS genes, in two classes of neuronal synaptic genes (RBFOX2 and FMRP) associated with SCZ or in loss-of-function intolerant genes. In this study, the largest analysis of exonic variation in BD, individuals with BD do not carry a replicable enrichment of rare P-LP variants across the exome or in any of several groups of genes with biologic plausibility. Moreover, despite a strong shared susceptibility between BD and SCZ through common genetic variation, we do not observe an association between BD risk and rare P-LP coding variants in genes known to modulate risk for SCZ.
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http://dx.doi.org/10.1038/s41380-020-01006-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8295400PMC
January 2021

Adiponectin GWAS loci harboring extensive allelic heterogeneity exhibit distinct molecular consequences.

PLoS Genet 2020 09 11;16(9):e1009019. Epub 2020 Sep 11.

Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America.

Loci identified in genome-wide association studies (GWAS) can include multiple distinct association signals. We sought to identify the molecular basis of multiple association signals for adiponectin, a hormone involved in glucose regulation secreted almost exclusively from adipose tissue, identified in the Metabolic Syndrome in Men (METSIM) study. With GWAS data for 9,262 men, four loci were significantly associated with adiponectin: ADIPOQ, CDH13, IRS1, and PBRM1. We performed stepwise conditional analyses to identify distinct association signals, a subset of which are also nearly independent (lead variant pairwise r2<0.01). Two loci exhibited allelic heterogeneity, ADIPOQ and CDH13. Of seven association signals at the ADIPOQ locus, two signals colocalized with adipose tissue expression quantitative trait loci (eQTLs) for three transcripts: trait-increasing alleles at one signal were associated with increased ADIPOQ and LINC02043, while trait-increasing alleles at the other signal were associated with decreased ADIPOQ-AS1. In reporter assays, adiponectin-increasing alleles at two signals showed corresponding directions of effect on transcriptional activity. Putative mechanisms for the seven ADIPOQ signals include a missense variant (ADIPOQ G90S), a splice variant, a promoter variant, and four enhancer variants. Of two association signals at the CDH13 locus, the first signal consisted of promoter variants, including the lead adipose tissue eQTL variant for CDH13, while a second signal included a distal intron 1 enhancer variant that showed ~2-fold allelic differences in transcriptional reporter activity. Fine-mapping and experimental validation demonstrated that multiple, distinct association signals at these loci can influence multiple transcripts through multiple molecular mechanisms.
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http://dx.doi.org/10.1371/journal.pgen.1009019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7511027PMC
September 2020

Colocalization of GWAS and eQTL signals at loci with multiple signals identifies additional candidate genes for body fat distribution.

Hum Mol Genet 2019 12;28(24):4161-4172

Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.

Integration of genome-wide association study (GWAS) signals with expression quantitative trait loci (eQTL) studies enables identification of candidate genes. However, evaluating whether nearby signals may share causal variants, termed colocalization, is affected by the presence of allelic heterogeneity, different variants at the same locus impacting the same phenotype. We previously identified eQTL in subcutaneous adipose tissue from 770 participants in the Metabolic Syndrome in Men (METSIM) study and detected 15 eQTL signals that colocalized with GWAS signals for waist-hip ratio adjusted for body mass index (WHRadjBMI) from the Genetic Investigation of Anthropometric Traits consortium. Here, we reevaluated evidence of colocalization using two approaches, conditional analysis and the Bayesian test COLOC, and show that providing COLOC with approximate conditional summary statistics at multi-signal GWAS loci can reconcile disagreements in colocalization classification between the two tests. Next, we performed conditional analysis on the METSIM subcutaneous adipose tissue data to identify conditionally distinct or secondary eQTL signals. We used the two approaches to test for colocalization with WHRadjBMI GWAS signals and evaluated the differences in colocalization classification between the two tests. Through these analyses, we identified four GWAS signals colocalized with secondary eQTL signals for FAM13A, SSR3, GRB14 and FMO1. Thus, at loci with multiple eQTL and/or GWAS signals, analyzing each signal independently enabled additional candidate genes to be identified.
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http://dx.doi.org/10.1093/hmg/ddz263DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7202621PMC
December 2019

Adipose Tissue Gene Expression Associations Reveal Hundreds of Candidate Genes for Cardiometabolic Traits.

Am J Hum Genet 2019 10 26;105(4):773-787. Epub 2019 Sep 26.

Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA. Electronic address:

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic traits including type 2 diabetes (T2D), lipid levels, body fat distribution, and adiposity, although most causal genes remain unknown. We used subcutaneous adipose tissue RNA-seq data from 434 Finnish men from the METSIM study to identify 9,687 primary and 2,785 secondary cis-expression quantitative trait loci (eQTL; <1 Mb from TSS, FDR < 1%). Compared to primary eQTL signals, secondary eQTL signals were located further from transcription start sites, had smaller effect sizes, and were less enriched in adipose tissue regulatory elements compared to primary signals. Among 2,843 cardiometabolic GWAS signals, 262 colocalized by LD and conditional analysis with 318 transcripts as primary and conditionally distinct secondary cis-eQTLs, including some across ancestries. Of cardiometabolic traits examined for adipose tissue eQTL colocalizations, waist-hip ratio (WHR) and circulating lipid traits had the highest percentage of colocalized eQTLs (15% and 14%, respectively). Among alleles associated with increased cardiometabolic GWAS risk, approximately half (53%) were associated with decreased gene expression level. Mediation analyses of colocalized genes and cardiometabolic traits within the 434 individuals provided further evidence that gene expression influences variant-trait associations. These results identify hundreds of candidate genes that may act in adipose tissue to influence cardiometabolic traits.
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http://dx.doi.org/10.1016/j.ajhg.2019.09.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6817527PMC
October 2019

Exome sequencing of Finnish isolates enhances rare-variant association power.

Nature 2019 08 31;572(7769):323-328. Epub 2019 Jul 31.

Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.

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.
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http://dx.doi.org/10.1038/s41586-019-1457-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697530PMC
August 2019

Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution.

Nat Genet 2019 03 18;51(3):452-469. Epub 2019 Feb 18.

Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF <5%) coding novel variants. Pathway/gene set enrichment analyses identified lipid particle, adiponectin, abnormal white adipose tissue physiology and bone development and morphology as important contributors to fat distribution, while cross-trait associations highlight cardiometabolic traits. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). We implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants.
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http://dx.doi.org/10.1038/s41588-018-0334-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6560635PMC
March 2019

Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.

Nat Genet 2018 11 8;50(11):1505-1513. Epub 2018 Oct 8.

Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.

We expanded GWAS discovery for type 2 diabetes (T2D) by combining data from 898,130 European-descent individuals (9% cases), after imputation to high-density reference panels. With these data, we (i) extend the inventory of T2D-risk variants (243 loci, 135 newly implicated in T2D predisposition, comprising 403 distinct association signals); (ii) enrich discovery of lower-frequency risk alleles (80 index variants with minor allele frequency <5%, 14 with estimated allelic odds ratio >2); (iii) substantially improve fine-mapping of causal variants (at 51 signals, one variant accounted for >80% posterior probability of association (PPA)); (iv) extend fine-mapping through integration of tissue-specific epigenomic information (islet regulatory annotations extend the number of variants with PPA >80% to 73); (v) highlight validated therapeutic targets (18 genes with associations attributable to coding variants); and (vi) demonstrate enhanced potential for clinical translation (genome-wide chip heritability explains 18% of T2D risk; individuals in the extremes of a T2D polygenic risk score differ more than ninefold in prevalence).
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http://dx.doi.org/10.1038/s41588-018-0241-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6287706PMC
November 2018

Extremely rare variants reveal patterns of germline mutation rate heterogeneity in humans.

Nat Commun 2018 09 14;9(1):3753. Epub 2018 Sep 14.

Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.

A detailed understanding of the genome-wide variability of single-nucleotide germline mutation rates is essential to studying human genome evolution. Here, we use ~36 million singleton variants from 3560 whole-genome sequences to infer fine-scale patterns of mutation rate heterogeneity. Mutability is jointly affected by adjacent nucleotide context and diverse genomic features of the surrounding region, including histone modifications, replication timing, and recombination rate, sometimes suggesting specific mutagenic mechanisms. Remarkably, GC content, DNase hypersensitivity, CpG islands, and H3K36 trimethylation are associated with both increased and decreased mutation rates depending on nucleotide context. We validate these estimated effects in an independent dataset of ~46,000 de novo mutations, and confirm our estimates are more accurate than previously published results based on ancestrally older variants without considering genomic features. Our results thus provide the most refined portrait to date of the factors contributing to genome-wide variability of the human germline mutation rate.
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http://dx.doi.org/10.1038/s41467-018-05936-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138700PMC
September 2018

Quantifying the Impact of Rare and Ultra-rare Coding Variation across the Phenotypic Spectrum.

Am J Hum Genet 2018 06 31;102(6):1204-1211. Epub 2018 May 31.

Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 17176, Sweden.

There is a limited understanding about the impact of rare protein-truncating variants across multiple phenotypes. We explore the impact of this class of variants on 13 quantitative traits and 10 diseases using whole-exome sequencing data from 100,296 individuals. Protein-truncating variants in genes intolerant to this class of mutations increased risk of autism, schizophrenia, bipolar disorder, intellectual disability, and ADHD. In individuals without these disorders, there was an association with shorter height, lower education, increased hospitalization, and reduced age at enrollment. Gene sets implicated from GWASs did not show a significant protein-truncating variants burden beyond what was captured by established Mendelian genes. In conclusion, we provide a thorough investigation of the impact of rare deleterious coding variants on complex traits, suggesting widespread pleiotropic risk.
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http://dx.doi.org/10.1016/j.ajhg.2018.05.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5992130PMC
June 2018

Publisher Correction: Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity.

Nat Genet 2018 05;50(5):766-767

Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.

In the version of this article originally published, one of the two authors with the name Wei Zhao was omitted from the author list and the affiliations for both authors were assigned to the single Wei Zhao in the author list. In addition, the ORCID for Wei Zhao (Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA) was incorrectly assigned to author Wei Zhou. The errors have been corrected in the HTML and PDF versions of the article.
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http://dx.doi.org/10.1038/s41588-018-0082-3DOI Listing
May 2018

Identification of seven novel loci associated with amino acid levels using single-variant and gene-based tests in 8545 Finnish men from the METSIM study.

Hum Mol Genet 2018 05;27(9):1664-1674

Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.

Comprehensive metabolite profiling captures many highly heritable traits, including amino acid levels, which are potentially sensitive biomarkers for disease pathogenesis. To better understand the contribution of genetic variation to amino acid levels, we performed single variant and gene-based tests of association between nine serum amino acids (alanine, glutamine, glycine, histidine, isoleucine, leucine, phenylalanine, tyrosine, and valine) and 16.6 million genotyped and imputed variants in 8545 non-diabetic Finnish men from the METabolic Syndrome In Men (METSIM) study with replication in Northern Finland Birth Cohort (NFBC1966). We identified five novel loci associated with amino acid levels (P = < 5×10-8): LOC157273/PPP1R3B with glycine (rs9987289, P = 2.3×10-26); ZFHX3 (chr16:73326579, minor allele frequency (MAF) = 0.42%, P = 3.6×10-9), LIPC (rs10468017, P = 1.5×10-8), and WWOX (rs9937914, P = 3.8×10-8) with alanine; and TRIB1 with tyrosine (rs28601761, P = 8×10-9). Gene-based tests identified two novel genes harboring missense variants of MAF <1% that show aggregate association with amino acid levels: PYCR1 with glycine (Pgene = 1.5×10-6) and BCAT2 with valine (Pgene = 7.4×10-7); neither gene was implicated by single variant association tests. These findings are among the first applications of gene-based tests to identify new loci for amino acid levels. In addition to the seven novel gene associations, we identified five independent signals at established amino acid loci, including two rare variant signals at GLDC (rs138640017, MAF=0.95%, Pconditional = 5.8×10-40) with glycine levels and HAL (rs141635447, MAF = 0.46%, Pconditional = 9.4×10-11) with histidine levels. Examination of all single variant association results in our data revealed a strong inverse relationship between effect size and MAF (Ptrend<0.001). These novel signals provide further insight into the molecular mechanisms of amino acid metabolism and potentially, their perturbations in disease.
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http://dx.doi.org/10.1093/hmg/ddy067DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5905595PMC
May 2018

Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees.

Proc Natl Acad Sci U S A 2018 01 26;115(2):379-384. Epub 2017 Dec 26.

Department of Statistics, Seoul National University, Seoul 08826, Republic of Korea.

A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, our experiment was powered to detect association. Using gene expression data on 21,677 transcripts for 643 pedigree members, we identified evidence for large-effect rare-variant -expression quantitative trait loci that could not be detected in population studies, validating our approach. However, we did not identify any rare variants of large effect associated with T2D, or the related traits of fasting glucose and insulin, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families. Reliable identification of large-effect rare variants will require larger samples of extended pedigrees or different study designs that further enrich for such variants.
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http://dx.doi.org/10.1073/pnas.1705859115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5777025PMC
January 2018

Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity.

Nat Genet 2018 01 22;50(1):26-41. Epub 2017 Dec 22.

Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.
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http://dx.doi.org/10.1038/s41588-017-0011-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5945951PMC
January 2018

Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

Sci Data 2017 12 19;4:170179. Epub 2017 Dec 19.

Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.
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http://dx.doi.org/10.1038/sdata.2017.179DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5735917PMC
December 2017

Common, low-frequency, and rare genetic variants associated with lipoprotein subclasses and triglyceride measures in Finnish men from the METSIM study.

PLoS Genet 2017 Oct 30;13(10):e1007079. Epub 2017 Oct 30.

Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.

Lipid and lipoprotein subclasses are associated with metabolic and cardiovascular diseases, yet the genetic contributions to variability in subclass traits are not fully understood. We conducted single-variant and gene-based association tests between 15.1M variants from genome-wide and exome array and imputed genotypes and 72 lipid and lipoprotein traits in 8,372 Finns. After accounting for 885 variants at 157 previously identified lipid loci, we identified five novel signals near established loci at HIF3A, ADAMTS3, PLTP, LCAT, and LIPG. Four of the signals were identified with a low-frequency (0.005
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http://dx.doi.org/10.1371/journal.pgen.1007079DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5679656PMC
October 2017

Interaction between the gene, body mass index and depression: meta-analysis of 13701 individuals.

Br J Psychiatry 2017 08 22;211(2):70-76. Epub 2017 Jun 22.

Margarita Rivera, PhD, Department of Biochemistry and Molecular Biology II and Institute of Neurosciences, Center for Biomedical Research, University of Granada, Granada, Spain, and MRC Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, Kinǵs College London, UK; Adam E. Locke, PhD, Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA; Tanguy Corre, PhD, Department of Medical Genetics, University of Lausanne, Lausanne, and Swiss Institute of Bioinformatics, Lausanne, Switzerland; Darina Czamara, PhD, Christiane Wolf, PhD, Max Planck Institute of Psychiatry, Munich, Germany; Ana Ching-Lopez, Department of Psychiatry, School of Medicine, University of Granada, and Institute of Neurosciences Federico Olóriz, Centra de Investigación Biomédica, University of Granada, Spain; Yuri Milaneschi, PhD, Department of Psychiatry and EMGO Institute for Health and Care Research, VU University Medical Center/GGZ in Geest, Amsterdam, The Netherlands; Stefan Kloiber, MD, Max Planck Institute of Psychiatry, Munich, Germany; Sara Cohen-Woods, PhD, School of Psychology, Flinders University, Adelaide, South Australia, Australia; James Rucker, MD, PhD, MRC Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; Katherine J. Aitchison, MD, PhD, Department of Psychiatry, University of Alberta, Alberta, Canada; Sven Bergmann, PhD, Department of Medical Genetics, University of Lausanne, Lausanne, and Swiss Institute of Bioinformatics, Lausanne, Switzerland; Dorret I. Boomsma, PhD, Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands; Nick Craddock, MB, PhD, FMedSci, Department of Psychological Medicine and Neurology, Cardiff University School of Medicine, Henry Wellcome Building, Cardiff, UK; Michael Gill, MD, Department of Psychiatry, Trinity Centre for Health Sciences, Dublin 8, Ireland; Florian Holsboer, MD, PhD, Max Planck Institute of Psychiatry, Munich, Germany; Jouke-Jan Hottenga, PhD, Department of Psychiatry, University of Alberta, Alberta, Canada; Ania Korszun, PhD, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK; Zoltan Kutalik, PhD, Department of Medical Genetics, University of Lausanne, Lausanne, and Swiss Institute of Bioinformatics, Lausanne, Switzerland; Susanne Lucae, MD, PhD, Max Planck Institute of Psychiatry, Munich, Germany; Wolfgang Maier, MD, Department of Psychiatry, University of Bonn, Bonn, Germany; Ole Mors, MD, PhD, Research Department P, Aarhus University Hospital, Risskov, Denmark; Bertram Müller-Myhsok MD, Max Planck Institute of Psychiatry, Munich, Germany; Michael J. Owen, MB, PhD, FMedSci, MRC Centre for Neuropsychiatry Genetics and Genomics, Department of Psychological Medicine and Neurology, School of Medicine, Cardiff University, Cardiff, UK; Brenda W. J. H. Penninx, PhD, Department of Psychiatry and EMGO Institute for Health and Care Research, VU University Medical Center/GGZ in Geest, Amsterdam, The Netherlands; Martin Preisig, MD, Department of Psychiatry, Lausanne University Hospital, 1008 Prilly-Lausanne, Switzerland; John Rice, PhD, Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri, USA; Marcella Rietschel, MD, Central Institute of Mental Health, Mannheim, Germany; Federica Tozzi, MD, Genetics Division, Drug Discovery, GlaxoSmithKline Research and Development, Verona, Italy; Rudolf Uher, MD, PhD, MRC Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK, and Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada; Peter Vollenweider, MD, PhD, Gerard Waeber, MD, PhD, Division of Internal Medicine, CHUV, Lausanne, Switzerland; Gonneke Willemsen, PhD, Department of Psychiatry, University of Alberta, Alberta, Canada; Ian W. Craig, PhD, Anne E. Farmer, MD, MRC Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; Cathryn M. Lewis, PhD, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, and Department of Medical and Molecular Genetics, School of Medicine, King's College London, UK; Gerome Breen, PhD, Peter McGuffin, MB, PhD, FMedSci, MRC Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK.

Depression and obesity are highly prevalent, and major impacts on public health frequently co-occur. Recently, we reported that having depression moderates the effect of the gene, suggesting its implication in the association between depression and obesity.To confirm these findings by investigating the polymorphism rs9939609 in new cohorts, and subsequently in a meta-analysis.The sample consists of 6902 individuals with depression and 6799 controls from three replication cohorts and two original discovery cohorts. Linear regression models were performed to test for association between rs9939609 and body mass index (BMI), and for the interaction between rs9939609 and depression status for an effect on BMI. Fixed and random effects meta-analyses were performed using METASOFT.In the replication cohorts, we observed a significant interaction between , BMI and depression with fixed effects meta-analysis (β = 0.12, = 2.7 × 10) and with the Han/Eskin random effects method ( = 1.4 × 10) but not with traditional random effects (β = 0.1, = 0.35). When combined with the discovery cohorts, random effects meta-analysis also supports the interaction (β = 0.12, = 0.027) being highly significant based on the Han/Eskin model ( = 6.9 × 10). On average, carriers of the risk allele who have depression have a 2.2% higher BMI for each risk allele, over and above the main effect of This meta-analysis provides additional support for a significant interaction between , depression and BMI, indicating that depression increases the effect of on BMI. The findings provide a useful starting point in understanding the biological mechanism involved in the association between obesity and depression.
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http://dx.doi.org/10.1192/bjp.bp.116.183475DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5537566PMC
August 2017

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

Diabetes 2017 07 24;66(7):2019-2032. Epub 2017 Mar 24.

Diabetes and Endocrinology Unit, Department of Clinical Sciences Malmö, Lund University Diabetes Centre, Malmö, Sweden.

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 and fasting plasma insulin (FI), 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 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-h 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 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of .
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http://dx.doi.org/10.2337/db16-1329DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5482074PMC
July 2017

Rare and low-frequency coding variants alter human adult height.

Nature 2017 02 1;542(7640):186-190. Epub 2017 Feb 1.

Netherlands Comprehensive Cancer Organisation, Utrecht, 3501 DB, The Netherlands.

Height is a highly heritable, classic polygenic trait with approximately 700 common associated variants identified through genome-wide association studies so far. Here, we report 83 height-associated coding variants with lower minor-allele frequencies (in the range of 0.1-4.8%) and effects of up to 2 centimetres per allele (such as those in IHH, STC2, AR and CRISPLD2), greater than ten times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (giving an increase of 1-2 centimetres per allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes that are mutated in monogenic growth disorders and highlight new biological candidates (such as ADAMTS3, IL11RA and NOX4) and pathways (such as proteoglycan and glycosaminoglycan synthesis) involved in growth. Our results demonstrate that sufficiently large sample sizes can uncover rare and low-frequency variants of moderate-to-large effect associated with polygenic human phenotypes, and that these variants implicate relevant genes and pathways.
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http://dx.doi.org/10.1038/nature21039DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5302847PMC
February 2017

Next-generation genotype imputation service and methods.

Nat Genet 2016 10 29;48(10):1284-1287. Epub 2016 Aug 29.

Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA.

Genotype imputation is a key component of genetic association studies, where it increases power, facilitates meta-analysis, and aids interpretation of signals. Genotype imputation is computationally demanding and, with current tools, typically requires access to a high-performance computing cluster and to a reference panel of sequenced genomes. Here we describe improvements to imputation machinery that reduce computational requirements by more than an order of magnitude with no loss of accuracy in comparison to standard imputation tools. We also describe a new web-based service for imputation that facilitates access to new reference panels and greatly improves user experience and productivity.
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http://dx.doi.org/10.1038/ng.3656DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5157836PMC
October 2016

Across-cohort QC analyses of GWAS summary statistics from complex traits.

Eur J Hum Genet 2016 01 24;25(1):137-146. Epub 2016 Aug 24.

Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia.

Genome-wide association studies (GWASs) have been successful in discovering SNP trait associations for many quantitative traits and common diseases. Typically, the effect sizes of SNP alleles are very small and this requires large genome-wide association meta-analyses (GWAMAs) to maximize statistical power. A trend towards ever-larger GWAMA is likely to continue, yet dealing with summary statistics from hundreds of cohorts increases logistical and quality control problems, including unknown sample overlap, and these can lead to both false positive and false negative findings. In this study, we propose four metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We propose methods to examine the concordance between demographic information, and summary statistics and methods to investigate sample overlap. (I) We use the population genetics F statistic to verify the genetic origin of each cohort and their geographic location, and demonstrate using GWAMA data from the GIANT Consortium that geographic locations of cohorts can be recovered and outlier cohorts can be detected. (II) We conduct principal component analysis based on reported allele frequencies, and are able to recover the ancestral information for each cohort. (III) We propose a new statistic that uses the reported allelic effect sizes and their standard errors to identify significant sample overlap or heterogeneity between pairs of cohorts. (IV) To quantify unknown sample overlap across all pairs of cohorts, we propose a method that uses randomly generated genetic predictors that does not require the sharing of individual-level genotype data and does not breach individual privacy.
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http://dx.doi.org/10.1038/ejhg.2016.106DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5159754PMC
January 2016

The genetic architecture of type 2 diabetes.

Nature 2016 08 11;536(7614):41-47. Epub 2016 Jul 11.

Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.
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http://dx.doi.org/10.1038/nature18642DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5034897PMC
August 2016
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