Re-analysis of a Genome-Wide Gene-By-Environment Interaction Study of Case Parent Trios, Adjusted for Population Stratification

Ratnasekera, Pulindu; McNeney, Brad

doi:10.3389/fgene.2020.600232

Cited by 2 publications

(4 citation statements)

References 6 publications

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“…This lack of compelling biological plausibility, coupled with the striking differences in exposure distributions between the self-reported European and East Asian strata, motivated our G × E analysis that adjusts for population structure. However, our results (Table 6) and those of (Ratnasekera and McNeney, 2021) do not contradict the hypothesis of G × E, but rather suggest that any G × E signal is due to the self-reported European trios. Further data collection aimed at self-reported European trios may provide stronger conclusions regarding the presence of G × E.…”

Section: Discussioncontrasting

confidence: 77%

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Inference of gene-environment interaction from heterogeneous case-parent trios

2023

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Introduction: In genetic epidemiology, log-linear models of population risk may be used to study the effect of genotypes and exposures on the relative risk of a disease. Such models may also include gene-environment interaction terms that allow the genotypes to modify the effect of the exposure, or equivalently, the exposure to modify the effect of genotypes on the relative risk. When a measured test locus is in linkage disequilibrium with an unmeasured causal locus, exposure-related genetic structure in the population can lead to spurious gene-environment interaction; that is, to apparent gene-environment interaction at the test locus in the absence of true gene-environment interaction at the causal locus. Exposure-related genetic structure occurs when the distributions of exposures and of haplotypes at the test and causal locus both differ across population strata. A case-parent trio design can protect inference of genetic main effects from confounding bias due to genetic structure in the population. Unfortunately, when the genetic structure is exposure-related, the protection against confounding bias for the genetic main effect does not extend to the gene-environment interaction term.Methods: We show that current methods to reduce the bias in estimated gene-environment interactions from case-parent trio data can only account for simple population structure involving two strata. To fill this gap, we propose to directly accommodate multiple population strata by adjusting for genetic principal components (PCs).Results and Discussion: Through simulations, we show that our PC adjustment maintains the nominal type-1 error rate and has nearly identical power to detect gene-environment interaction as an oracle approach based directly on population strata. We also apply the PC-adjustment approach to data from a study of genetic modifiers of cleft palate comprised primarily of case-parent trios of European and East Asian ancestry. Consistent with earlier analyses, our results suggest that the gene-environment interaction signal in these data is due to the self-reported European trios.

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Section: Discussioncontrasting

confidence: 77%

“…If the frequencies of haplotypes spanning causal SNPs also vary by ancestral groups, exposure-related genetic structure may lead to spurious gene-environment interaction. ( Ratnasekera and McNeney, 2021 ). focused on the self-reported Europeans and East Asians in the GENEVA Oral Cleft Study data.…”

Section: Introductionmentioning

confidence: 99%

Inference of gene-environment interaction from heterogeneous case-parent trios

2023

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“…This lack of compelling biological plausibility, coupled with the striking differences in exposure distributions between the self-reported European and East Asian strata, motivated our G × E analysis that adjusts for population structure. However, our results (Table 6) and those of [13] do not contradict the hypothesis of G × E, but rather suggest that any G × E signal is due to the self-reported European trios. Further data collection aimed at self-reported European trios may provide stronger conclusions regarding the presence of G × E.…”

Section: Discussioncontrasting

confidence: 77%

“…If the frequencies of haplotypes spanning causal SNPs also vary by ancestral groups, exposure-related population stratification may lead to spurious gene-environment interaction. [11] focused on the self-reported Europeans and East Asians in the GENEVA Oral Cleft Study data. Applying the approach of [17], they confirmed the gene-environment interaction found by [2], and concluded that the evidence for gene-environment interaction is predominantly from the data of self-reported Europeans.…”

Section: Introductionmentioning

confidence: 99%

Inference of gene-environment interaction from heterogeneous case-parent trios

Ratnasekera

Graham

McNeney

2022

Preprint

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In genetic epidemiology, log-linear models of population risk may be used to study the effect of genotypes and exposures on the relative risk of a disease. Such models may also include gene environment interaction terms that allow the genotypes to modify the effect of the exposure, or equivalently, the exposure to modify the effect of genotypes on the relative risk. When a measured test locus is in linkage disequilibrium with a causal locus, exposure-related genetic population structure can lead to spurious gene-environment interaction; that is, to apparent gene-environment interaction at the test locus in the absence of true gene-environment interaction at the causal locus. Exposure-related genetic population structure occurs when the distributions of exposures and of haplotypes at the test and causal locus both differ across population strata (i.e., population substructure). In a case-parent trio design researchers collect genotypes and exposures on affected children and genotypes on their parents. The design permits inference of genetic main effects while avoiding confounding bias due to population stratification. Unfortunately, when there is exposure-related genetic population structure, the protection against confounding bias for the main effect does not extend to the interaction term. We show that current methods to reduce the bias in estimated gene-environment interactions can only account for simple population structure involving two strata. To fill this gap, we propose to directly accommodate multiple population strata by adjusting for genetic principal components. We evaluate our approach through simulation and illustrate it on data from a study of genetic modifiers of cleft palate.

show abstract

Re-analysis of a Genome-Wide Gene-By-Environment Interaction Study of Case Parent Trios, Adjusted for Population Stratification

Cited by 2 publications

References 6 publications

Inference of gene-environment interaction from heterogeneous case-parent trios

Inference of gene-environment interaction from heterogeneous case-parent trios

Inference of gene-environment interaction from heterogeneous case-parent trios

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