The advantages and limitations of trait analysis with GWAS: a review

Abstract

Over the last 10 years, high-density SNP arrays and DNA re-sequencing have illuminated the majority of the genotypic space for a number of organisms, including humans, maize, rice and Arabidopsis. For any researcher willing to define and score a phenotype across many individuals, Genome Wide Association Studies (GWAS) present a powerful tool to reconnect this trait back to its underlying genetics. In this review we discuss the biological and statistical considerations that underpin a successful analysis or otherwise. The relevance of biological factors including effect size, sample size, genetic heterogeneity, genomic confounding, linkage disequilibrium and spurious association, and statistical tools to account for these are presented. GWAS can offer a valuable first insight into trait architecture or candidate loci for subsequent validation.

Common garden experiments in the genomic era: new perspectives and opportunities

“…Hence, the most useful, but most expensive, genotyping method for this kind of analysis is wholegenome sequencing. Note also that heterogeneity in recombination/ mutation rates along the genome can generate false positives during such analyses (Korte and Farlow, 2013). Here, the number of populations is also of importance, as it will determine the power to detect significance for the parameter β B .…”

“…Accounting for population structure should help in distinguishing between neutral and selective scenarios for markers associated with between-population variability. As always (Korte and Farlow, 2013), the power of a genome-wide association study to actually detect loci linked to the phenotypic variability strongly depends on the extent of linkage disequilibrium and the density of markers along the genome, in addition to the sample size. Hence, the most useful, but most expensive, genotyping method for this kind of analysis is wholegenome sequencing.…”

Genome-Wide Association in Tomato Reveals 44 Candidate Loci for Fruit Metabolic Traits

“…This approach is based on the analysis of the segregation of polymorphism between the parental lines and their progeny. However, one of the limitations of this approach is the reduced number of recombination events that occur per generation (for review, see Korte and Farlow, 2013). This leads to extended linkage blocks that reduce the accuracy of the linkage mapping.…”

Genetics paired with CT angiography in the setting of atherosclerosis

“…Table 1 summarizes the significant findings based on large, population-based cohort through GWAS. Notably, despite achievements, various phenotypes of interest so far have failed to be uncovered by GWAS [35]. Despite the high number of genetic loci identified through GWAS, these do not encompass any protein-coding genes, thus failing to explain the heritability patterns of CAD phenotypes [36].…”