Search PubMedSearch

PubMed · 3425632

Predicting recurrence risks under epistatic models.

Abstract

We present the expected recurrence risks to a sib of an affected proband under 4 simple 2-locus epistatic models for various allele frequencies at both the disease locus and the epistatic locus. Four obvious epistatic models are considered: an autosomal recessive disease with both 1) dominant and 2) recessive masking by the epistatic locus, and an autosomal dominant disease again with both 3) dominant and 4) recessive masking. Expected recurrence risks to a sib of an affected proband and to a sib of an affected proband with another normal sib are presented in the absence of information on parental status. Similar risks are presented for the case where both parents are known to be phenotypically normal. These recurrence risks were calculated using a convenient matrix notation which allows sequential calculation of genotypic probabilities. In general, 2-locus epistatic models can give surprisingly low recurrence risks, and often these risks, especially for models of recessive diseases, fall into the range associated with a more general multifactorial model for liability.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

T H Beaty, N E Maestri, D A Meyers, E A Murphy. 1987. Predicting recurrence risks under epistatic models.. https://doi.org/10.1002/ajmg.1320280311

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Evaluation of epistasis detection methods for quantitative phenotypes.

MOTIVATION: Epistasis, or genetic interaction, plays a crucial role in shaping complex traits and has been increasingly recognized for its widespread influence in genetic architectures. While epistasis detection has been extensively evaluated in case-control studies, its performance with quantitative phenotypes remains comparatively understudied. RESULTS: We identified and evaluated six epistasis detection methods applicable to quantitative trait analysis: EpiSNP, Matrix Epistasis, MIDESP, PLINK Epistasis, QMDR, and REMMA. Using the EpiGEN simulator, we generated synthetic datasets modeling four classes of pairwise SNP interactions-dominant, multiplicative, recessive, and XOR. We also assessed BOOST and MDR algorithms using discretized (case-control) versions of the same datasets. Performance varied notably by interaction type: REMMA achieved the highest overall detection rate (55%), particularly excelling with dominant interactions (100%). MDR excelled with multiplicative (57%) and XOR (69%) interactions. Meanwhile, EpiSNP attained the best performance for recessive interactions (67%). All methods except BOOST produced F1 scores below 0.05 for most interaction types. We further evaluated the methods using a real-world dataset. When applied to the Adolescent Brain Cognitive Development dataset to analyse the externalizing behavior phenotype, both PLINK Epistasis and PLINK BOOST identified SNPs within the DRD2 and DRD4 genes, consistent with previously reported genetic associations. Given the variability in tool performance across interaction types, no single method provides optimal detection across all scenarios. Leveraging multiple detection algorithms may therefore yield more comprehensive insights into epistatic effects in quantitative trait analyses. AVAILABILITY AND IMPLEMENTATION: All relevant code and simulated datasets can be found at github.com/staslist/Epistasis_Review repository.

Epistasis, Genetic

Testing for Genetic Interactions in Complex Disease With Distance Correlation.

Understanding epistasis (genetic interaction) may shed some light on the genomic basis of common diseases, including disorders of maximum interest due to their high socioeconomic burden, like schizophrenia. Distance correlation is an association measure that characterizes general statistical independence between random variables, not only the linear one. Here, we propose distance correlation as a novel tool for the detection of epistasis from case-control data of single-nucleotide polymorphisms. On the methodological side, we highlight the derivation of the explicit asymptotic null distribution of the test statistic. We show that this is the only way to obtain enough computational speed for the method to be used in practice, in a scenario where the resampling techniques found in the literature are impractical. Our simulations show satisfactory calibration of significance, as well as comparable or better power than existing methodology. We conclude with the application of our technique to a schizophrenia genetics dataset, obtaining biologically sound insights.

Epistasis, Genetic