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Meta-Analysis
. 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

Iván Galván-Femenía  1 , Mireia Obón-Santacana  1   2 , David Piñeyro  3 , Marta Guindo-Martinez  4 , Xavier Duran  1 , Anna Carreras  1 , Raquel Pluvinet  3 , Juan Velasco  1 , Laia Ramos  3 , Susanna Aussó  3 , J M Mercader  5   6 , Lluis Puig  7 , Manuel Perucho  8 , David Torrents  4   9 , Victor Moreno  2   10 , Lauro Sumoy  3 , Rafael de Cid  1
Affiliations
Meta-Analysis

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

Iván Galván-Femenía et al. J Med Genet. 2018 Nov.

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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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Flow chart of the methods and criteria used in this study. GCAT, Genomes For Life- Cohort Study of the Genomes of Catalonia; GWAS, genome-wide association studies; MAF, minor allele frequency; QC, quality control.
Figure 2
Figure 2
Manhattan plot of the anthropometric traits (BMI, height, weight and hip and waist circumference) from the GCAT. BMI, body mass index.
See this image and copyright information in PMC

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