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Genome-wide association studies identify 77 loci for suicidality and provide novel biological insights

Colbert SM, Koromina M, Hatoum AS, Stephenson M, Edwards AC, Johnson EC, Qin X, Shabalin AA, Ito LT, O’Connell KS, Harder A, Hjerling-Leffler J, Kim MJ, Otsuka I, Vilar-Ribó L, Agrawal A, Alda M, Alfredsson L, Aliev F, Andlauer TF, Arango C, Arntz A, Awasthi S, Ayinde OO, Bacanu S, Barr PB, Bau CH, Baune BT, Beckham JC, Bejan CA, Belangero S, Berger K, Biernacka JM, Bogdan R, Boomsma DI, Børglum AD, Bourassa KJ, Braff DL, Braun A, Bressan RA, Brueckl TM, Budde M, Cabrera-Mendoza B, Carpiniello B, Ceja Z, Cervilla JA, Chang C, Chaumette B, Cheon MJ, Cichon S, Coleman JR, Coon H, Copeland WE, Czamara D, Dalkner N, Dannlowski U, David FS, Demontis D, Di Florio A, Diaconu CC, Dick DM, Dikeos D, Djurovic S, Edenberg HJ, Erlangsen A, Euler S, Falkai P, Fanelli G, Fellendorf FT, Ferentinos P, Fernandez-Aranda F, Forstner AJ, Frei O, Fries GR, Fullerton JM, Gaine ME, Galfalvy HC, Galimberti M, Garcia-Aymerich J, Garrett ME, Gawlik M, Gelernter J, Gordon-Smith K, Gorelik AJ, Gorwood P, Grabe HJ, Green MJ, Grigoroiu-Serbanescu M, Gupta P, Gutiérrez B, Guzman-Parra J, Han S, Haram M, Hauser ER, Heilbronner U, Herpertz SC, Herrera-Imbroda J, Hesselbrock VM, Hishimoto A, Holla B, Izotova A, Jang Y, Jimenez-Murcia S, Jones LA, Jonsson L, Kang J, Kang JH, Keel PK, Kim J, Kim D, Kircher T, Kirov G, Kraft J, Kramer J, Kranzler HR, Kuo P, Kurvits S, Lai D, Lake MT, Landén M, Lannoy S, Law MH, Lee B, Lee YK, Lehto K, Levey DF, Lewis CM, Li QS, Liao C, Lind PA, Lochner C, Lori A, Maes HH, Mahadevan J, Manchia M, Mars B, Martin NG, Martorell L, McIntosh AM, McMain SF, McQuillin A, Medland SE, Mitchell PB, Molina E, Monson ET, Mufford MS, Muntané G, Musil R, Myung W, Neagu AI, Nielsen TT, Nöthen MM, Nunez YZ, Nurnberger JI, Okazaki S, Olsen CM, Ophoff RA, Owen MJ, Pan PM, Papiol S, Pascual JC, Patrinos GP, Pawlak JM, Penninx BW, Pérez-Gutiérrez AM, Perroud N, Peterson RE, Pisanu C, Pistis G, Porjesz B, Posthuma D, Powers A, Preisig M, Purushottam M, Reif A, Reininghaus EZ, Rentería ME, Ripke S, Ripperger MA, Rivera M, Roberts EK, Roberts G, Rødevand L, Roepke S, Rovaris DL, Salum GA, Sanders AR, Santoro ML, Sawyers C, Scherer SW, Schilling C, Schmahl C, Schofield PR, Schulze TG, Scott LJ, Serretti A, Shadrin A, Shirai T, Smeland OB, Smoller JW, Sokolowski M, Sonuga-Barke EJ, Squassina A, Starnawska A, Steen NE, Stein DJ, Stein F, Stein MB, Streit F, Sud R, Sullivan PF, Terao C, Toma C, Tondo L, Turecki G, Uher R, Ursano RJ, Van der Auwera S, Vawter MP, Paska AV, Vilella E, Vincent JB, Viswanath B, Vladimirov V, Wasserman DE, Weickert TW, Whiteman DC, Willour VL, Wiström ED, Witt SH, Won H, Wootton RE, Zai CC, Zhang J, Zillich L, CVEDA and MG.   +235 more
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Genome-wide association studies in pharmacogenomics

Nature Reviews Genetics, 2010
Genome-wide association (GWA) studies for pharmacogenomics-related traits are increasingly being performed to identify loci that affect either drug response or susceptibility to adverse drug reactions. Until now, only the largest effects have been detected, partly because of the challenges of obtaining large numbers of cases for pharmacogenomic studies.
Ann Daly, Daly Ann K
exaly   +3 more sources

Genome-Wide Association Studies

2012
A host of data on genetic variation from the Human Genome and International HapMap projects, and advances in high-throughput genotyping technologies, have made genome-wide association (GWA) studies technically feasible. GWA studies help in the discovery and quantification of the genetic components of disease risks, many of which have not been unveiled ...
Tun-Hsiang, Yang, Mark, Kon, Charles, DeLisi   +2 more
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Genome-Wide Association Studies (GWAS)

2023
Most of the breeding targets are quantitative traits. In exploring the quantitative trait locus (QTL) system of a trait, linkage mapping was established using sparse polymerase chain reaction (PCR) markers. With the genome-wide sequencing technology advanced, genome-wide association study (GWAS) was developed for natural (germplasm) populations using ...
Jianbo, He, Junyi, Gai
openaire   +2 more sources

Genome-Wide Association Studies of Cancer

Future Oncology, 2007
Genome-wide association studies provide a new and powerful approach to investigate the effect of inherited genetic variation on the risk of human disease. These studies rely on high throughput DNA microarray technology to genotype hundreds of thousands of genetic variants across the human genome.
Eric, Jorgenson, John S, Witte
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Genome-Wide Association Studies

Cold Spring Harbor Protocols, 2009
INTRODUCTIONThe goal of association studies is to discover genetic variation that differs in frequency between cases and controls or between individuals with different phenotypic values. Until a few years ago, the only method available for such studies was low-throughput analysis in which a single gene was selected and either genotyped for known ...
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R for Genome-Wide Association Studies

2013
In recent years R has become de facto statistical programming language of choice for statisticians and it is also arguably the most widely used generic environment for analysis of high-throughput genomic data. In this chapter we discuss some approaches to improve performance of R when working with large SNP datasets.
Gondro, Cedric, Porto-Neto, Laercio R., Lee, Seung Hwan   +2 more
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Genome-Wide Association Studies

2010
Genome-wide association (GWA) studies are best understood as an extension of candidate gene association studies, scaled up to cover hundreds of thousands of markers across the genome in samples usually of several thousand cases and controls. The GWA approach allows the detection of much smaller effect sizes than with previous linkage-based genome-wide ...
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Genome-Wide Association Studies

2018
Genetic association studies have made a major contribution to our understanding of the genetics of complex disorders over the last 10 years through genome-wide association studies (GWAS). In this chapter, we review the key concepts that underlie the GWAS approach.
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