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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

The mixed model dramatically reduces inflation of p-values. Quantile-Quantile plot showing strong p-values inflation for a marginal GWAS that does not consider population structure (red line). Accounting for population structure with the mixed model dramatically reduces inflation (blue line). The grey line indicates the expected p-value distribution under the  hypothesis of no causative markers in the data. Note, that after correction for population structure, only the most significant markers deviate from the  expectation.
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Figure 4: The mixed model dramatically reduces inflation of p-values. Quantile-Quantile plot showing strong p-values inflation for a marginal GWAS that does not consider population structure (red line). Accounting for population structure with the mixed model dramatically reduces inflation (blue line). The grey line indicates the expected p-value distribution under the hypothesis of no causative markers in the data. Note, that after correction for population structure, only the most significant markers deviate from the expectation.

Mentions: On what criteria can one judge the most appropriate GWAS method for a particular trait? The most basic and often informative approach is a correction for multiple testing (usually a 5% Bonferroni threshold is used) and inspection of Q-Q plots and Manhattan plots for evidence of P value inflation (Figures 3 and 4). Both approaches give a general impression of the data, i.e. are there too many, or too few significant SNPs relative to ones prior expectation? The main limitation of these corrections is the assumption that every SNP tested is independent. Structure in the Arabidopsis population clearly violates this assumption and thus many spurious associations survive a multiple testing correction due to LD in the data.


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

Korte A, Farlow A - Plant Methods (2013)

The mixed model dramatically reduces inflation of p-values. Quantile-Quantile plot showing strong p-values inflation for a marginal GWAS that does not consider population structure (red line). Accounting for population structure with the mixed model dramatically reduces inflation (blue line). The grey line indicates the expected p-value distribution under the  hypothesis of no causative markers in the data. Note, that after correction for population structure, only the most significant markers deviate from the  expectation.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: The mixed model dramatically reduces inflation of p-values. Quantile-Quantile plot showing strong p-values inflation for a marginal GWAS that does not consider population structure (red line). Accounting for population structure with the mixed model dramatically reduces inflation (blue line). The grey line indicates the expected p-value distribution under the hypothesis of no causative markers in the data. Note, that after correction for population structure, only the most significant markers deviate from the expectation.
Mentions: On what criteria can one judge the most appropriate GWAS method for a particular trait? The most basic and often informative approach is a correction for multiple testing (usually a 5% Bonferroni threshold is used) and inspection of Q-Q plots and Manhattan plots for evidence of P value inflation (Figures 3 and 4). Both approaches give a general impression of the data, i.e. are there too many, or too few significant SNPs relative to ones prior expectation? The main limitation of these corrections is the assumption that every SNP tested is independent. Structure in the Arabidopsis population clearly violates this assumption and thus many spurious associations survive a multiple testing correction due to LD in the data.

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