Linkage disequilibrium clustering-based approach for association mapping with tightly linked genomewide data

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http://hdl.handle.net/10138/321979

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Li , Z , Kemppainen , P , Rastas , P & Merilä , J 2018 , ' Linkage disequilibrium clustering-based approach for association mapping with tightly linked genomewide data ' , Molecular Ecology Resources , vol. 18 , no. 4 , pp. 809-824 . https://doi.org/10.1111/1755-0998.12893

Title: Linkage disequilibrium clustering-based approach for association mapping with tightly linked genomewide data
Author: Li, Zitong; Kemppainen, Petri; Rastas, Pasi; Merilä, Juha
Contributor: University of Helsinki, Biosciences
University of Helsinki, Ecological Genetics Research Unit
University of Helsinki, Faculty of Biological and Environmental Sciences
University of Helsinki, Organismal and Evolutionary Biology Research Programme
Date: 2018-07
Language: eng
Number of pages: 16
Belongs to series: Molecular Ecology Resources
ISSN: 1755-098X
URI: http://hdl.handle.net/10138/321979
Abstract: Genomewide association studies (GWAS) aim to identify genetic markers strongly associated with quantitative traits by utilizing linkage disequilibrium (LD) between candidate genes and markers. However, because of LD between nearby genetic markers, the standard GWAS approaches typically detect a number of correlated SNPs covering long genomic regions, making corrections for multiple testing overly conservative. Additionally, the high dimensionality of modern GWAS data poses considerable challenges for GWAS procedures such as permutation tests, which are computationally intensive. We propose a cluster-based GWAS approach that first divides the genome into many large nonoverlapping windows and uses linkage disequilibrium network analysis in combination with principal component (PC) analysis as dimensional reduction tools to summarize the SNP data to independent PCs within clusters of loci connected by high LD. We then introduce single- and multilocus models that can efficiently conduct the association tests on such high-dimensional data. The methods can be adapted to different model structures and used to analyse samples collected from the wild or from biparental F-2 populations, which are commonly used in ecological genetics mapping studies. We demonstrate the performance of our approaches with two publicly available data sets from a plant (Arabidopsis thaliana) and a fish (Pungitius pungitius), as well as with simulated data.
Subject: four-way cross
GWAS
multilocus method
principal component regression
quantitative trait loci
QUANTITATIVE TRAIT LOCI
WIDE ASSOCIATION
POPULATION-STRUCTURE
LINEAR-MODELS
REGRESSION
SELECTION
RELATEDNESS
GENETICS
CROSSES
FORMAT
1181 Ecology, evolutionary biology
1172 Environmental sciences
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