Details

Autor(en) / Beteiligte

• Jones, Chris G
• Peltonen, Leena
• Ripatti, Samuli
• Sovio, Ulla
• Gabriel, Stacey
• Kaakinen, Marika
• Varilo, Teppo
• Pouta, Anneli
• Freimer, Nelson B
• Brodsky, Jae
• Turunen, Hannu
• Hartikainen, Anna-Liisa
• Hoggart, Clive
• Ruokonen, Aimo
• Coin, Lachlan
• Service, Susan K
• Elliot, Paul
• Collins, Andrew
• McCarthy, Mark I
• Daly, Mark J
• Zaitlen, Noah A
• Jakkula, Eveliina
• Sabatti, Chiara
• Järvelin, Marjo-Riitta
• Laitinen, Jaana

Titel

Genome-wide association analysis of metabolic traits in a birth cohort from a founder population

Ist Teil von

• Nature genetics, 2009-01, Vol.41 (1), p.35-46

Ort / Verlag

New York, NY: Nature Publishing Group

Erscheinungsjahr

2009

Quelle

MEDLINE

Beschreibungen/Notizen

• Genome-wide association studies (GWAS) of longitudinal birth cohorts enable joint investigation of environmental and genetic influences on complex traits. We report GWAS results for nine quantitative metabolic traits (triglycerides, high-density lipoprotein, low-density lipoprotein, glucose, insulin, C-reactive protein, body mass index, and systolic and diastolic blood pressure) in the Northern Finland Birth Cohort 1966 (NFBC1966), drawn from the most genetically isolated Finnish regions. We replicate most previously reported associations for these traits and identify nine new associations, several of which highlight genes with metabolic functions: high-density lipoprotein with NR1H3 (LXRA), low-density lipoprotein with AR and FADS1-FADS2, glucose with MTNR1B, and insulin with PANK1. Two of these new associations emerged after adjustment of results for body mass index. Gene-environment interaction analyses suggested additional associations, which will require validation in larger samples. The currently identified loci, together with quantified environmental exposures, explain little of the trait variation in NFBC1966. The association observed between low-density lipoprotein and an infrequent variant in AR suggests the potential of such a cohort for identifying associations with both common, low-impact and rarer, high-impact quantitative trait loci.