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An evolutionary perspective on epistasis and the missing heritability.

Hemani G, Knott S, Haley C - PLoS Genet. (2013)

Bottom Line: We propose that one reason that the problem of the "missing heritability" arises is because the additive genetic variation that is estimated to be contributing to the variance of a trait will most likely be an artefact of the non-additive variance that can be maintained over evolutionary time.We demonstrate that the perception of independent additive effects comprising the majority of the genetic architecture of complex traits is biased upwards and that the search for causal variants in complex traits under selection is potentially underpowered by parameterising for additive effects alone.Given dense SNP panels the detection of causal variants through genome-wide association studies may be improved by searching for epistatic effects explicitly.

View Article: PubMed Central - PubMed

Affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Science, University of Edinburgh, Edinburgh, United Kingdom.

ABSTRACT
The relative importance between additive and non-additive genetic variance has been widely argued in quantitative genetics. By approaching this question from an evolutionary perspective we show that, while additive variance can be maintained under selection at a low level for some patterns of epistasis, the majority of the genetic variance that will persist is actually non-additive. We propose that one reason that the problem of the "missing heritability" arises is because the additive genetic variation that is estimated to be contributing to the variance of a trait will most likely be an artefact of the non-additive variance that can be maintained over evolutionary time. In addition, it can be shown that even a small reduction in linkage disequilibrium between causal variants and observed SNPs rapidly erodes estimates of epistatic variance, leading to an inflation in the perceived importance of additive effects. We demonstrate that the perception of independent additive effects comprising the majority of the genetic architecture of complex traits is biased upwards and that the search for causal variants in complex traits under selection is potentially underpowered by parameterising for additive effects alone. Given dense SNP panels the detection of causal variants through genome-wide association studies may be improved by searching for epistatic effects explicitly.

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Related in: MedlinePlus

The percentage of total additive variance detected by each of 5 different methods.Columns of graphs refer to G-P maps (Figure 1), rows refer to  between causal variants and observed SNPs. (a) Deterministic calculations were performed 25 times, each with different initial allele frequencies. The percentage of additive variance explained is summed across all runs and generations. (b) The summed  detected at each generation as a percentage of the summed  simultaneously present in 50 populations. For clarity, only the most powerful 1D test (A+D) is compared against the most powerful 2D test (full parameterisation). A Bonferroni threshold was used,  for 1D strategies and  for 2D strategies.
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pgen-1003295-g004: The percentage of total additive variance detected by each of 5 different methods.Columns of graphs refer to G-P maps (Figure 1), rows refer to between causal variants and observed SNPs. (a) Deterministic calculations were performed 25 times, each with different initial allele frequencies. The percentage of additive variance explained is summed across all runs and generations. (b) The summed detected at each generation as a percentage of the summed simultaneously present in 50 populations. For clarity, only the most powerful 1D test (A+D) is compared against the most powerful 2D test (full parameterisation). A Bonferroni threshold was used, for 1D strategies and for 2D strategies.

Mentions: Broadly, the results show two important points. Firstly, there is no single method that is always superior under the conditions that were tested. Secondly, it is very rare that parameterising for additive effects is the most powerful method (Figure 4a and Figure S7). Rather, if LD is no higher than, for example , between causal variants and observed SNPs then one dimensional scans, although not particularly powerful in absolute terms, are most effective provided that both additive and dominance effects are modelled (2 d.f. test). In the case of very high LD (e.g. dense marker panels, imputed data, sequence data), a strong advantage in power to detect variants at evolutionarily likely frequencies can be conferred by using exhaustive two-dimensional scans and modelling whole genotype effects (8 d.f. test).


An evolutionary perspective on epistasis and the missing heritability.

Hemani G, Knott S, Haley C - PLoS Genet. (2013)

The percentage of total additive variance detected by each of 5 different methods.Columns of graphs refer to G-P maps (Figure 1), rows refer to  between causal variants and observed SNPs. (a) Deterministic calculations were performed 25 times, each with different initial allele frequencies. The percentage of additive variance explained is summed across all runs and generations. (b) The summed  detected at each generation as a percentage of the summed  simultaneously present in 50 populations. For clarity, only the most powerful 1D test (A+D) is compared against the most powerful 2D test (full parameterisation). A Bonferroni threshold was used,  for 1D strategies and  for 2D strategies.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3585114&req=5

pgen-1003295-g004: The percentage of total additive variance detected by each of 5 different methods.Columns of graphs refer to G-P maps (Figure 1), rows refer to between causal variants and observed SNPs. (a) Deterministic calculations were performed 25 times, each with different initial allele frequencies. The percentage of additive variance explained is summed across all runs and generations. (b) The summed detected at each generation as a percentage of the summed simultaneously present in 50 populations. For clarity, only the most powerful 1D test (A+D) is compared against the most powerful 2D test (full parameterisation). A Bonferroni threshold was used, for 1D strategies and for 2D strategies.
Mentions: Broadly, the results show two important points. Firstly, there is no single method that is always superior under the conditions that were tested. Secondly, it is very rare that parameterising for additive effects is the most powerful method (Figure 4a and Figure S7). Rather, if LD is no higher than, for example , between causal variants and observed SNPs then one dimensional scans, although not particularly powerful in absolute terms, are most effective provided that both additive and dominance effects are modelled (2 d.f. test). In the case of very high LD (e.g. dense marker panels, imputed data, sequence data), a strong advantage in power to detect variants at evolutionarily likely frequencies can be conferred by using exhaustive two-dimensional scans and modelling whole genotype effects (8 d.f. test).

Bottom Line: We propose that one reason that the problem of the "missing heritability" arises is because the additive genetic variation that is estimated to be contributing to the variance of a trait will most likely be an artefact of the non-additive variance that can be maintained over evolutionary time.We demonstrate that the perception of independent additive effects comprising the majority of the genetic architecture of complex traits is biased upwards and that the search for causal variants in complex traits under selection is potentially underpowered by parameterising for additive effects alone.Given dense SNP panels the detection of causal variants through genome-wide association studies may be improved by searching for epistatic effects explicitly.

View Article: PubMed Central - PubMed

Affiliation: The Roslin Institute and Royal (Dick) School of Veterinary Science, University of Edinburgh, Edinburgh, United Kingdom.

ABSTRACT
The relative importance between additive and non-additive genetic variance has been widely argued in quantitative genetics. By approaching this question from an evolutionary perspective we show that, while additive variance can be maintained under selection at a low level for some patterns of epistasis, the majority of the genetic variance that will persist is actually non-additive. We propose that one reason that the problem of the "missing heritability" arises is because the additive genetic variation that is estimated to be contributing to the variance of a trait will most likely be an artefact of the non-additive variance that can be maintained over evolutionary time. In addition, it can be shown that even a small reduction in linkage disequilibrium between causal variants and observed SNPs rapidly erodes estimates of epistatic variance, leading to an inflation in the perceived importance of additive effects. We demonstrate that the perception of independent additive effects comprising the majority of the genetic architecture of complex traits is biased upwards and that the search for causal variants in complex traits under selection is potentially underpowered by parameterising for additive effects alone. Given dense SNP panels the detection of causal variants through genome-wide association studies may be improved by searching for epistatic effects explicitly.

Show MeSH
Related in: MedlinePlus