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

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 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.

Inferences of genetic architecture of bill morphology in house sparrow using a high‐density SNP array point to a polygenic basis

“…It is worth noting that the genetic architecture in association studies is complex, as the total historical variation in the sampled population is included. LD between a given SNP and a causal variant is determined by the history of mutation, recombination and coalescence of lineages, and SNP–trait associations may not decrease monotonically with distance (Li, Kemppainen, Rastas, & Merilä, ; Remington, ).…”

Repurposing population genetics data to discern genomic architecture: A case study of linkage cohort detection in mountain pine beetle (Dendroctonus ponderosae)

“…While useful as a means of ensuring independence of loci in classical population genetics surveys, LD analysis can also offer insights into genomic architecture and differentiation, even within non‐model species (Baird, ; Barton, ; Kemppainen et al, ). Recent work by Li et al () has explored the potential to augment genome‐wide association studies (GWAS) in model organisms by imputing loci of interest using PCA to reduce complexity in large datasets, followed by linkage network analysis. Here, we demonstrate an independently developed version of such a method as a tool to detect genomic islands of differentiation in wild populations.…”

“…Recent work by Li et al (2018) has explored the potential to augment genome-wide association studies (GWAS) in model organisms by imputing loci of interest using PCA to reduce complexity in large datasets, followed by linkage network analysis. Here, we demonstrate an independently developed version of such a method as a tool to detect genomic islands of differentiation in wild populations.…”

“…Kemppainen et al () recently released a tool for calculating linkage disequilibrium (LDna) that uses network analytical tools to visualize groups of linked loci across a genome. LDna has been used to reduce data dimensionality while searching for QTLs in model organisms (Li, Kemppainen, Rastas, & Merila, ) and can provide evidence of inversions and islands of genomic differentiation (Benestan et al, ; Lindtke et al, ; Ravinet et al, ). In this paper, we employ a similar approach to reduce dimensionality in our data while looking for cohorts of linked markers undergoing divergence or directional selection.…”

Genome sequencing of the nine-spined stickleback (Pungitius pungitius) provides insights into chromosome evolution

“…While genomic resources for the three-spined stickleback, including an annotated reference genome (Jones et al 2012), are well-developed, those for the nine-spined stickleback are rather less developed, typically relying on the three-spined stickleback reference genome. The recently published ultra-high density linkage map (Rastas et al 2015;Li et al 2018) and a draft version of the nine-spined stickleback genome based on short-read sequencing technology (Nelson and Cresko 2018) are important developments in this regard. However, short-read technology based draft assemblies are often of limited utility when dealing with fairly large and complex vertebrate genomes.…”