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J Med Genet. 2018 Nov;55(11):765-778. doi: 10.1136/jmedgenet-2018-105437. Epub 2018 Aug 30.

Multitrait genome association analysis identifies new susceptibility genes for human anthropometric variation in the GCAT cohort.

Author information

1
GenomesForLife-GCAT Lab Group, Program of Predictive and Personalized Medicine of Cancer (PMPPC), Germans Trias i Pujol Research Institute (IGTP), Crta. de Can Ruti, Badalona, Catalunya, Spain.
2
Unit of Biomarkers and Susceptibility, Cancer Prevention and Control Program, Catalan Institute of Oncology (ICO), IDIBELL and CIBERESP, Barcelona, Spain.
3
High Content Genomics and Bioinformatics Unit, Program of Predictive and Personalized Medicine of Cancer (PMPPC), Germans Trias i Pujol Research Institute (IGTP), Badalona, Catalunya, Spain.
4
Life Sciences - Computational Genomics, Barcelona Supercomputing Center (BSC-CNS), Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona, Spain.
5
Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, US.
6
Diabetes Unit and Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, US.
7
Blood Division, Banc de Sang i Teixits, Barcelona, Spain.
8
Cancer Genetics and Epigenetics Group, Program of Predictive and Personalized Medicine of Cancer (PMPPC), Germans Trias i Pujol Research Institute (IGTP), Badalona, Catalunya, Spain.
9
ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, Catalunya, Spain.
10
Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain.

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.

KEYWORDS:

cohort; complex traits; gwas; multitrait; phenome

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