Multitrait genome association analysis identifies new susceptibility genes for human anthropometric variation in the GCAT cohort. Issue 11 (30th August 2018)

• Record Type: Journal Article
• Authors: Galván-Femenía, Iván; Obón-Santacana, Mireia; Piñeyro, David; Guindo-Martinez, Marta; Duran, Xavier; Carreras, Anna; Pluvinet, Raquel; Velasco, Juan; Ramos, Laia; Aussó, Susanna; Mercader, J M; Puig, Lluis; Perucho, Manuel; Torrents, David; Moreno, Victor; Sumoy, Lauro; de Cid, Rafael
• Is Part Of: Journal of medical genetics. Volume 55:Issue 11(2018)
• Page Start: 765
• Page End: 778
• Publication Date: 2018-08-30
• Subjects: gwas -- cohort -- complex traits -- multitrait -- phenome; Medical genetics -- Periodicals; 616.042
• DOI: 10.1136/jmedgenet-2018-105437
• Languages: English
• ISSNs: 1468-6244

Abstract

Abstract : Background: Heritability estimates have revealed an important contribution of SNP variants for most common traits; however, SNP analysis by single-trait genome-wide association studies (GWAS) has failed to uncover their impact. In this study, we applied a multitrait GWAS approach to discover additional factor of the missing heritability of human anthropometric variation. Methods: We analysed 205 traits, including diseases identified at baseline in the GCAT cohort (Genomes For Life- Cohort study of the Genomes of Catalonia) (n=4988), a Mediterranean adult population-based cohort study from the south of Europe. We estimated SNP heritability contribution and single-trait GWAS for all traits from 15 million SNP variants. Then, we applied a multitrait-related approach to study genome-wide association to anthropometric measures in a two-stage meta-analysis with the UK Biobank cohort (n=336 107). Results: Heritability estimates (eg, skin colour, alcohol consumption, smoking habit, body mass index, educational level or height) revealed an important contribution of SNP variants, ranging from 18% to 77%. Single-trait analysis identified 1785 SNPs with genome-wide significance threshold. From these, several previously reported single-trait hits were confirmed in our sample with LINC01432 (p=1.9×10 −9 ) variants associated with male baldness, LDLR variants with hyperlipidaemia (ICD-9:272) (p=9.4×10 −10 ) and variants in IRF4 (p=2.8×10 −57 ), SLC45A2 (p=2.2×10 −130 ), HERC2 (p=2.8×10 −176 ), OCA2 (p=2.4×10 −121 ) and MC1R (p=7.7×10 −22 ) associated with hair, eye and skin colour, freckling, tanning capacity and sun burning sensitivity and the Fitzpatrick phototype score, all highly correlated cross-phenotypes. Multitrait meta-analysis of anthropometric variation validated 27 loci in a two-stage meta-analysis with a large British ancestry cohort, six of which are newly reported here (p value threshold <5×10 −9 ) at ZRANB2-AS2, PIK3R1, EPHA7, MAD1L1, CACUL1 and MAP3K9 . Conclusion: Considering multiple-related genetic phenotypes improve associated genome signal detection. These results indicate the potential value of data-driven multivariate phenotyping for genetic studies in large population-based cohorts to contribute to knowledge of complex traits.