A reference panel of 64,976 haplotypes for genotype imputation

McCarthy, Shane; S, Das; Kretzschmar, Warren W.; Delaneau, Olivier; Wood, Andrew R.; Teumer, Alexander; Kang, Hyun Min; Fuchsberger, Christian; Danecek, Petr; Sharp, Kevin; Luo, Yang; Sidore, Carlo; Kwong, Alan; Timpson, Nicholas J.; Koskinen, Seppo; Vrieze, Scott; Scott, Laura J.; Zhang, He; Mahajan, Anubha; Veldink, Jan H.; Peters, Ulrike; Pato, Carlos N.; Duijn, Cornelia M. van; Gillies, Christopher E.; Gandin, Ilaria; Mezzavilla, Massimo; Gilly, Arthur; Cocca, Massimiliano; Traglia, Michela; Angius, Andrea; Barrett, Jeffrey C.; Boomsma, Dorret I.; Branham, Kari; Breen, Gerome; Brummett, Chad M.; Busonero, Fabio; Campbell, Harry; Chan, Andrew T.; Chen, Sai; Chew, Emily Y.; Collins, Francis S.; Corbin, Laura J; Smith, George Davey; Dedoussis, George; Dörr, Marcus; Farmaki, Aliki Eleni; Ferrucci, Luigi; Forer, Lukas; Fraser, Ross M.; Gabriel, Stacey; Levy, Shawn; Groop, Leif; Harrison, Tabitha A.; Hattersley, Andrew T.; Holmen, Oddgeir L.; Hveem, Kristian; Kretzler, Matthias; Lee, James C.; McGue, Matt; Meitinger, Thomas; Melzer, David; Min, Josine L.; Mohlke, Karen L.; Vincent, John B.; Nauck, Matthias; Nickerson, Deborah A.; Palotie, Aarno; Pato, Michele T.; Pirastu, Nicola; McInnis, Melvin G.; Richards, J. Brent; Sala, Cinzia; Salomaa, Veikko; Schlessinger, David; Schoenherr, Sebastian; Slagboom, P. Eline; Small, Kerrin S.; Spector, Timothy D.; Stambolian, Dwight; Tuke, Marcus A.; Tuomilehto, Jaakko; Berg, Leonard H. van den; Rheenen, Wouter van; Völker, Uwe; Wijmenga, Cisca; Toniolo, Daniela; Zeggini, Eleftheria; Gasparini, Paolo; Sampson, Matthew G.; Wilson, James F.; Frayling, Timothy M.; Bakker, Paul I. W. de; Swertz, Morris A.; McCarroll, Steven A.; Kooperberg, Charles; Dekker, Annelot M.; Altshuler, David; Willer, Cristen J.; Iacono, William G.; Ripatti, Samuli; Soranzo, Nicole; Walter, Klaudia; Swaroop, Anand; Cucca, Francesco; Anderson, Carl A.; Myers, R; Boehnke, Michael; McCarthy, Mark; Durbin, Richard; Abecasis, Gonçalo R.; Marchini, Jonathan

doi:10.1038/ng.3643

Cited by 2,442 publications

(1,175 citation statements)

References 24 publications

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“…QC steps were performed by each study and are described in Supplementary Table 18 . Most studies with European ancestry samples performed imputation with the HRC reference v1.1 39 panel on the Michigan Imputation Server v1.0.1. 40 Studies without available HRC imputation were included based on imputation to the 1000 Genomes Phase 1 integrated v3 panel (March 2012).…”

Section: Methodsmentioning

confidence: 99%

Multi-ethnic genome-wide association study for atrial fibrillation

et al. 2018

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Atrial fibrillation (AF) affects over 33 million individuals worldwide1 and has a complex heritability.2 We conducted the largest meta-analysis of genome-wide association studies for AF to date, consisting of over half a million individuals including 65,446 with AF. In total, we identified 97 loci significantly associated with AF including 67 of which were novel in a combined-ancestry analysis, and 3 in a European specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait loci (eQTL) analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci. The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile and structural pathways. These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF.

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Section: Methodsmentioning

confidence: 99%

Multi-ethnic genome-wide association study for atrial fibrillation

et al. 2018

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“…We used Eagle (version 2.3) to prephase haplotypes on the basis of genotype data. 13,14 We did the imputation using the Michigan Imputation Server. 15 Following imputation, we kept variants passing a standard imputation quality threshold ( R 2 ≥0·3) for further analysis.…”

Section: Methodsmentioning

confidence: 99%

Investigating the genetic architecture of dementia with Lewy bodies: a two-stage genome-wide association study

Guerreiro

Ross

Kun‐Rodrigues

et al. 2018

The Lancet Neurology

196

225

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Summary Background Dementia with Lewy bodies is the second most common form of dementia in elderly people but has been overshadowed in the research field, partly because of similarities between dementia with Lewy bodies, Parkinson’s disease, and Alzheimer’s disease. So far, to our knowledge, no large-scale genetic study of dementia with Lewy bodies has been done. To better understand the genetic basis of dementia with Lewy bodies, we have done a genome-wide association study with the aim of identifying genetic risk factors for this disorder. Methods In this two-stage genome-wide association study, we collected samples from white participants of European ancestry who had been diagnosed with dementia with Lewy bodies according to established clinical or pathological criteria. In the discovery stage (with the case cohort recruited from 22 centres in ten countries and the controls derived from two publicly available database of Genotypes and Phenotypes studies [phs000404.v1.p1 and phs000982.v1.p1] in the USA), we performed genotyping and exploited the recently established Haplotype Reference Consortium panel as the basis for imputation. Pathological samples were ascertained following autopsy in each individual brain bank, whereas clinical samples were collected after participant examination. There was no specific timeframe for collection of samples. We did association analyses in all participants with dementia with Lewy bodies, and also only in participants with pathological diagnosis. In the replication stage, we performed genotyping of significant and suggestive results from the discovery stage. Lastly, we did a meta-analysis of both stages under a fixed-effects model and used logistic regression to test for association in each stage. Findings This study included 1743 patients with dementia with Lewy bodies (1324 with pathological diagnosis) and 4454 controls (1216 patients with dementia with Lewy bodies vs 3791 controls in the discovery stage; 527 vs 663 in the replication stage). Results confirm previously reported associations: APOE (rs429358; odds ratio [OR] 2·40, 95% CI 2·14–2·70; p=1·05 × 10−48), SNCA (rs7681440; OR 0·73, 0·66–0·81; p=6·39 × 10−10), and GBA (rs35749011; OR 2·55, 1·88–3·46; p=1·78 × 10−9). They also provide some evidence for a novel candidate locus, namely CNTN1 (rs7314908; OR 1·51, 1·27–1·79; p=2·32 × 10−6); further replication will be important. Additionally, we estimate the heritable component of dementia with Lewy bodies to be about 36%. Interpretation Despite the small sample size for a genome-wide association study, and acknowledging the potential biases from ascertaining samples from multiple locations, we present the most comprehensive and well powered genetic study in dementia with Lewy bodies so far. These data show that common genetic variability has a role in the disease.

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“…We found no pairs of closely related individuals (third degree or less) in the data sets because we had previously used another genotyping array (Illumina ExomeChip) to label related individuals in the MHI Biobank. We imputed autosomal variants on this quality‐controlled genome‐wide genotyping data set using haplotype from the Haplotype Reference Consortium18 and the Michigan Imputation Server (https://imputationserver.sph.umich.edu). …”

Section: Methodsmentioning

confidence: 99%

Variants at the APOE/C1/C2/C4 Locus Modulate Cholesterol Efflux Capacity Independently of High‐Density Lipoprotein Cholesterol

Low‐Kam

Rhainds

et al. 2018

JAHA

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BackgroundMacrophage cholesterol efflux to high‐density lipoproteins (HDLs) is the first step of reverse cholesterol transport. The cholesterol efflux capacity (CEC) of HDL particles is a protective risk factor for coronary artery disease independent of HDL cholesterol levels. Using a genome‐wide association study approach, we aimed to identify pathways that regulate CEC in humans.Methods and ResultsWe measured CEC in 5293 French Canadians. We tested the genetic association between 4 CEC measures and genotypes at >9 million common autosomal DNA sequence variants. These analyses yielded 10 genome‐wide significant signals (P<6.25×10−9) representing 7 loci. Five of these loci harbor genes with important roles in lipid biology (CETP,LIPC,LPL,APOA1/C3/A4/A5, and APOE /C1/C2/C4). Except for the APOE/C1/C2/C4 variant (rs141622900, P nonadjusted=1.0×10−11; P adjusted=8.8×10−9), the association signals disappear when correcting for HDL cholesterol and triglyceride levels. The additional 2 significant signals were near the PPP1CB/PLB1 and RBFOX3/ENPP7 genes. In secondary analyses, we considered candidate functional variants for 58 genes implicated in HDL biology, as well as 239 variants associated with blood lipid levels and/or coronary artery disease risk by genome‐wide association study. These analyses identified 27 significant CEC associations, implicating 5 additional loci (GCKR,LIPG,PLTP,PPARA, and TRIB1).ConclusionsOur genome‐wide association study identified common genetic variation at the APOE/C1/C2/C4 locus as a major determinant of CEC that acts largely independently of HDL cholesterol. We predict that HDL‐based therapies aiming at increasing CEC will be modulated by changes in the expression of apolipoproteins in this gene cluster.

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A reference panel of 64,976 haplotypes for genotype imputation

Cited by 2,442 publications

References 24 publications

Multi-ethnic genome-wide association study for atrial fibrillation

Multi-ethnic genome-wide association study for atrial fibrillation

Investigating the genetic architecture of dementia with Lewy bodies: a two-stage genome-wide association study

Variants at the APOE/C1/C2/C4 Locus Modulate Cholesterol Efflux Capacity Independently of High‐Density Lipoprotein Cholesterol

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