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Modifier effects between regulatory and protein-coding variation.

Dimas AS, Stranger BE, Beazley C, Finn RD, Ingle CE, Forrest MS, Ritchie ME, Deloukas P, Tavaré S, Dermitzakis ET - PLoS Genet. (2008)

Bottom Line: Genome-wide associations have shown a lot of promise in dissecting the genetics of complex traits in humans with single variants, yet a large fraction of the genetic effects is still unaccounted for.We predict that about 18% (1,502 out of 8,233 nsSNPs) of protein-coding variants are differentially expressed among individuals and demonstrate that regulatory variants can modify the functional effect of a coding variant in cis.Given the abundance of both types of variants in human populations, we propose that joint consideration of regulatory and protein-coding variants may reveal additional genetic effects underlying complex traits and disease and may shed light on causes of differential penetrance of known disease variants.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK.

ABSTRACT
Genome-wide associations have shown a lot of promise in dissecting the genetics of complex traits in humans with single variants, yet a large fraction of the genetic effects is still unaccounted for. Analyzing genetic interactions between variants (epistasis) is one of the potential ways forward. We investigated the abundance and functional impact of a specific type of epistasis, namely the interaction between regulatory and protein-coding variants. Using genotype and gene expression data from the 210 unrelated individuals of the original four HapMap populations, we have explored the combined effects of regulatory and protein-coding single nucleotide polymorphisms (SNPs). We predict that about 18% (1,502 out of 8,233 nsSNPs) of protein-coding variants are differentially expressed among individuals and demonstrate that regulatory variants can modify the functional effect of a coding variant in cis. Furthermore, we show that such interactions in cis can affect the expression of downstream targets of the gene containing the protein-coding SNP. In this way, a cis interaction between regulatory and protein-coding variants has a trans impact on gene expression. Given the abundance of both types of variants in human populations, we propose that joint consideration of regulatory and protein-coding variants may reveal additional genetic effects underlying complex traits and disease and may shed light on causes of differential penetrance of known disease variants.

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Impact of rSNP-nsSNP genetic interaction on trans gene expression.(A) QQ plot of observed –log10pvalues of the interaction term in the ANOVA over expected (under the assumption of a Uniform distribution of p-values). (B) QQ plot of observed –log10pvalues of the interaction term in the ANOVA over the –log10pvalues of the interaction term in the permuted data. (C) Example 1: The interaction between rs13093220 (rSNP) and rs3009034 (nsSNP) on chromosome 3, is associated with changes in expression of NDN (probe ID GI_10800414-S) on chromosome 15 (interaction p = 4.5*10−11). (D) Example 2: The interaction between rs6776417 (rSNP) rs17040196 (nsSNP) on chromosome 3 is associated with changes in expression of RLF (probe ID GI_6912631-S) on chromosome 1 (interaction p = 2.2*10−5).
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pgen-1000244-g003: Impact of rSNP-nsSNP genetic interaction on trans gene expression.(A) QQ plot of observed –log10pvalues of the interaction term in the ANOVA over expected (under the assumption of a Uniform distribution of p-values). (B) QQ plot of observed –log10pvalues of the interaction term in the ANOVA over the –log10pvalues of the interaction term in the permuted data. (C) Example 1: The interaction between rs13093220 (rSNP) and rs3009034 (nsSNP) on chromosome 3, is associated with changes in expression of NDN (probe ID GI_10800414-S) on chromosome 15 (interaction p = 4.5*10−11). (D) Example 2: The interaction between rs6776417 (rSNP) rs17040196 (nsSNP) on chromosome 3 is associated with changes in expression of RLF (probe ID GI_6912631-S) on chromosome 1 (interaction p = 2.2*10−5).

Mentions: Thus far we have presented indirect evidence for an interaction in cis where the effect of an nsSNP is modulated by a co-segregating regulatory variant tagged by an rSNP. Under such circumstances, and if the gene containing the nsSNP has downstream targets, then it is likely that the expression of downstream genes may also be affected. In other words, apart from the modification effect observed in cis, we wanted to test for the genome-wide effects of this interaction directly, in a statistical framework. To do this we carried out ANOVA by testing the main effects of rSNPs and nsSNPs and their interaction term (rSNP×nsSNP) on genome-wide gene expression (trans effects). The rationale behind this approach is that if an rSNP-nsSNP interaction is biologically relevant, its effect may influence the expression of downstream targets of the gene harbouring the rSNP-nsSNP pair. The power to detect an interaction is maximized when all combinations of genotypes are present, each at appreciable frequencies in the population. To increase power of interaction detection, we pooled rare homozygotes with heterozygotes into a single genotypic category, creating a 2×2 table of genotypes. This does not bias our statistic as shown by permutations below. We performed this analysis in the CEU population sample as CHB and JPT population samples were small (45 individuals) and the YRI sample has generally shown low levels of trans effects in previous analyses [20]. We tested 22 rSNP-nsSNP pairs (SNP pairs) with low LD (D′≤0.5) and a MAF≥0.1 for both SNPs, against genome-wide expression. At the 0.001 nominal p-value threshold, we expect roughly 331 significant associations (assuming a uniform distribution of p-values) for the interaction term. We observe 412, which corresponds to an estimated FDR of 80%. This is overall a weak signal (see also Figure 3a), but signals at the tail of the distribution appear to be real given the limited power of this analysis (Figure 3c, d).


Modifier effects between regulatory and protein-coding variation.

Dimas AS, Stranger BE, Beazley C, Finn RD, Ingle CE, Forrest MS, Ritchie ME, Deloukas P, Tavaré S, Dermitzakis ET - PLoS Genet. (2008)

Impact of rSNP-nsSNP genetic interaction on trans gene expression.(A) QQ plot of observed –log10pvalues of the interaction term in the ANOVA over expected (under the assumption of a Uniform distribution of p-values). (B) QQ plot of observed –log10pvalues of the interaction term in the ANOVA over the –log10pvalues of the interaction term in the permuted data. (C) Example 1: The interaction between rs13093220 (rSNP) and rs3009034 (nsSNP) on chromosome 3, is associated with changes in expression of NDN (probe ID GI_10800414-S) on chromosome 15 (interaction p = 4.5*10−11). (D) Example 2: The interaction between rs6776417 (rSNP) rs17040196 (nsSNP) on chromosome 3 is associated with changes in expression of RLF (probe ID GI_6912631-S) on chromosome 1 (interaction p = 2.2*10−5).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2570624&req=5

pgen-1000244-g003: Impact of rSNP-nsSNP genetic interaction on trans gene expression.(A) QQ plot of observed –log10pvalues of the interaction term in the ANOVA over expected (under the assumption of a Uniform distribution of p-values). (B) QQ plot of observed –log10pvalues of the interaction term in the ANOVA over the –log10pvalues of the interaction term in the permuted data. (C) Example 1: The interaction between rs13093220 (rSNP) and rs3009034 (nsSNP) on chromosome 3, is associated with changes in expression of NDN (probe ID GI_10800414-S) on chromosome 15 (interaction p = 4.5*10−11). (D) Example 2: The interaction between rs6776417 (rSNP) rs17040196 (nsSNP) on chromosome 3 is associated with changes in expression of RLF (probe ID GI_6912631-S) on chromosome 1 (interaction p = 2.2*10−5).
Mentions: Thus far we have presented indirect evidence for an interaction in cis where the effect of an nsSNP is modulated by a co-segregating regulatory variant tagged by an rSNP. Under such circumstances, and if the gene containing the nsSNP has downstream targets, then it is likely that the expression of downstream genes may also be affected. In other words, apart from the modification effect observed in cis, we wanted to test for the genome-wide effects of this interaction directly, in a statistical framework. To do this we carried out ANOVA by testing the main effects of rSNPs and nsSNPs and their interaction term (rSNP×nsSNP) on genome-wide gene expression (trans effects). The rationale behind this approach is that if an rSNP-nsSNP interaction is biologically relevant, its effect may influence the expression of downstream targets of the gene harbouring the rSNP-nsSNP pair. The power to detect an interaction is maximized when all combinations of genotypes are present, each at appreciable frequencies in the population. To increase power of interaction detection, we pooled rare homozygotes with heterozygotes into a single genotypic category, creating a 2×2 table of genotypes. This does not bias our statistic as shown by permutations below. We performed this analysis in the CEU population sample as CHB and JPT population samples were small (45 individuals) and the YRI sample has generally shown low levels of trans effects in previous analyses [20]. We tested 22 rSNP-nsSNP pairs (SNP pairs) with low LD (D′≤0.5) and a MAF≥0.1 for both SNPs, against genome-wide expression. At the 0.001 nominal p-value threshold, we expect roughly 331 significant associations (assuming a uniform distribution of p-values) for the interaction term. We observe 412, which corresponds to an estimated FDR of 80%. This is overall a weak signal (see also Figure 3a), but signals at the tail of the distribution appear to be real given the limited power of this analysis (Figure 3c, d).

Bottom Line: Genome-wide associations have shown a lot of promise in dissecting the genetics of complex traits in humans with single variants, yet a large fraction of the genetic effects is still unaccounted for.We predict that about 18% (1,502 out of 8,233 nsSNPs) of protein-coding variants are differentially expressed among individuals and demonstrate that regulatory variants can modify the functional effect of a coding variant in cis.Given the abundance of both types of variants in human populations, we propose that joint consideration of regulatory and protein-coding variants may reveal additional genetic effects underlying complex traits and disease and may shed light on causes of differential penetrance of known disease variants.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK.

ABSTRACT
Genome-wide associations have shown a lot of promise in dissecting the genetics of complex traits in humans with single variants, yet a large fraction of the genetic effects is still unaccounted for. Analyzing genetic interactions between variants (epistasis) is one of the potential ways forward. We investigated the abundance and functional impact of a specific type of epistasis, namely the interaction between regulatory and protein-coding variants. Using genotype and gene expression data from the 210 unrelated individuals of the original four HapMap populations, we have explored the combined effects of regulatory and protein-coding single nucleotide polymorphisms (SNPs). We predict that about 18% (1,502 out of 8,233 nsSNPs) of protein-coding variants are differentially expressed among individuals and demonstrate that regulatory variants can modify the functional effect of a coding variant in cis. Furthermore, we show that such interactions in cis can affect the expression of downstream targets of the gene containing the protein-coding SNP. In this way, a cis interaction between regulatory and protein-coding variants has a trans impact on gene expression. Given the abundance of both types of variants in human populations, we propose that joint consideration of regulatory and protein-coding variants may reveal additional genetic effects underlying complex traits and disease and may shed light on causes of differential penetrance of known disease variants.

Show MeSH