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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
biochemistry, genetics and molecular biology
Complimentary methods for multivariate genome-wide association study identify new susceptibility genes for blood cell traits
Frontiers in Genetics, Volume 10, No. APR, Article 334, Year 2019
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Description
Genome-wide association studies (GWAS) have found hundreds of novel loci associated with full blood count (FBC) phenotypes. However, most of these studies were performed in a single phenotype framework without putting into consideration the clinical relatedness among traits. In this work, in addition to the standard univariate GWAS, we also use two different multivariate methods to perform the first multiple traits GWAS of FBC traits in ∼7000 individuals from the Ugandan General Population Cohort (GPC). We started by performing the standard univariate GWAS approach. We then performed our first multivariate method, in this approach, we tested for marker associations with 15 FBC traits simultaneously in a multivariate mixed model implemented in GEMMA while accounting for the relatedness of individuals and pedigree structures, as well as population substructure. In this analysis, we provide a framework for the combination of multiple phenotypes in multivariate GWAS analysis and show evidence of multi-collinearity whenever the correlation between traits exceeds the correlation coefficient threshold of r2 >=0.75. This approach identifies two known and one novel loci. In the second multivariate method, we applied principal component analysis (PCA) to the same 15 correlated FBC traits. We then tested for marker associations with each PC in univariate linear mixed models implemented in GEMMA. We show that the FBC composite phenotype as assessed by each PC expresses information that is not completely encapsulated by the individual FBC traits, as this approach identifies three known and five novel loci that were not identified using both the standard univariate and multivariate GWAS methods. Across both multivariate methods, we identified six novel loci. As a proof of concept, both multivariate methods also identified known loci, HBB and ITFG3. The two multivariate methods show that multivariate genotype-phenotype methods increase power and identify novel genotype-phenotype associations not found with the standard univariate GWAS in the same dataset. Copyright © 2019 Fatumo, Carstensen, Nashiru, Gurdasani, Sandhu and Kaleebu.
Available Materials
https://efashare.b-cdn.net/share/pmc/articles/PMC6497788/bin/Data_Sheet_1.docx
Authors & Co-Authors
Fatumo, Segun A.
United Kingdom, London
London School of Hygiene & Tropical Medicine
Carstensen, Tommy
United Kingdom, Hinxton
Wellcome Sanger Institute
Nashiru, Oyekanmi
Unknown Affiliation
Gurdasani, Deepti
Unknown Affiliation
Sandhu, Manjinder Singh
United Kingdom, Hinxton
Wellcome Sanger Institute
United Kingdom, Cambridge
University of Cambridge
Kaleebu, Pontiano P.
United Kingdom, London
London School of Hygiene & Tropical Medicine
Statistics
Citations: 20
Authors: 6
Affiliations: 3
Identifiers
Doi:
10.3389/fgene.2019.00334
ISSN:
16648021
Research Areas
Genetics And Genomics
Study Design
Cross Sectional Study
Cohort Study