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

Korte A, Farlow A - Plant Methods (2013)

Bottom Line: 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.GWAS can offer a valuable first insight into trait architecture or candidate loci for subsequent validation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Gregor Mendel Institute of Molecular Plant Biology, Vienna, Austria.

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.

No MeSH data available.


Related in: MedlinePlus

Taking genetic background into account improves the performance of GWAS. Manhattan plots for a simulated trait, in which each data point represents a genotyped SNP, ordered across the five chromosomes of Arabidopsis. Five SNPs (indicated by vertical dashed lines) were randomly chosen to be ‘causative’ and account for up to 10% of the phenotypic variance each. GWAS using a) a linear model, and b) a mixed model that accounts for population structure and other background genomic factors. The simple linear model leads to heavily inflated p-values and the five causative markers are not the strongest associations. The mixed model is superior, but still leads to one false negative and one false positive. A dashed horizontal line denotes the 5% Bonferroni threshold.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3750305&req=5

Figure 3: Taking genetic background into account improves the performance of GWAS. Manhattan plots for a simulated trait, in which each data point represents a genotyped SNP, ordered across the five chromosomes of Arabidopsis. Five SNPs (indicated by vertical dashed lines) were randomly chosen to be ‘causative’ and account for up to 10% of the phenotypic variance each. GWAS using a) a linear model, and b) a mixed model that accounts for population structure and other background genomic factors. The simple linear model leads to heavily inflated p-values and the five causative markers are not the strongest associations. The mixed model is superior, but still leads to one false negative and one false positive. A dashed horizontal line denotes the 5% Bonferroni threshold.

Mentions: Two major issues discussed above: that related individuals share both causal and non-causal alleles, and that LD between these sites can lead to synthetic associations, are actually a single problem, that of confounding due to genetic background [50]. A powerful method to account for this artifact was first developed in the field of animal breeding: mixed models that handle population structure by accounting for the amount of phenotypic covariance that is due to genetic relatedness (i.e. including relationship or kinship as a random term within the model). Since then, mixed models have been applied to GWAS [11,51-53], and can markedly reduce the number of false positive associations (Figure 3).


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

Korte A, Farlow A - Plant Methods (2013)

Taking genetic background into account improves the performance of GWAS. Manhattan plots for a simulated trait, in which each data point represents a genotyped SNP, ordered across the five chromosomes of Arabidopsis. Five SNPs (indicated by vertical dashed lines) were randomly chosen to be ‘causative’ and account for up to 10% of the phenotypic variance each. GWAS using a) a linear model, and b) a mixed model that accounts for population structure and other background genomic factors. The simple linear model leads to heavily inflated p-values and the five causative markers are not the strongest associations. The mixed model is superior, but still leads to one false negative and one false positive. A dashed horizontal line denotes the 5% Bonferroni threshold.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750305&req=5

Figure 3: Taking genetic background into account improves the performance of GWAS. Manhattan plots for a simulated trait, in which each data point represents a genotyped SNP, ordered across the five chromosomes of Arabidopsis. Five SNPs (indicated by vertical dashed lines) were randomly chosen to be ‘causative’ and account for up to 10% of the phenotypic variance each. GWAS using a) a linear model, and b) a mixed model that accounts for population structure and other background genomic factors. The simple linear model leads to heavily inflated p-values and the five causative markers are not the strongest associations. The mixed model is superior, but still leads to one false negative and one false positive. A dashed horizontal line denotes the 5% Bonferroni threshold.
Mentions: Two major issues discussed above: that related individuals share both causal and non-causal alleles, and that LD between these sites can lead to synthetic associations, are actually a single problem, that of confounding due to genetic background [50]. A powerful method to account for this artifact was first developed in the field of animal breeding: mixed models that handle population structure by accounting for the amount of phenotypic covariance that is due to genetic relatedness (i.e. including relationship or kinship as a random term within the model). Since then, mixed models have been applied to GWAS [11,51-53], and can markedly reduce the number of false positive associations (Figure 3).

Bottom Line: 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.GWAS can offer a valuable first insight into trait architecture or candidate loci for subsequent validation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Gregor Mendel Institute of Molecular Plant Biology, Vienna, Austria.

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.

No MeSH data available.


Related in: MedlinePlus