A perspective on epistasis: limits of models displaying no main effect - PubMed (original) (raw)
A perspective on epistasis: limits of models displaying no main effect
Robert Culverhouse et al. Am J Hum Genet. 2002 Feb.
Abstract
The completion of a draft sequence of the human genome and the promise of rapid single-nucleotide-polymorphism-genotyping technologies have resulted in a call for the abandonment of linkage studies in favor of genome scans for association. However, there exists a large class of genetic models for which this approach will fail: purely epistatic models with no additive or dominance variation at any of the susceptibility loci. As a result, traditional association methods (such as case/control, measured genotype, and transmission/disequilibrium test [TDT]) will have no power if the loci are examined individually. In this article, we examine this class of models, delimiting the range of genetic determination and recurrence risks for two-, three-, and four-locus purely epistatic models. Our study reveals that these models, although giving rise to no additive or dominance variation, do give rise to increased allele sharing between affected sibs. Thus, a genome scan for linkage could detect genomic subregions harboring susceptibility loci. We also discuss some simple multilocus extensions of single-locus analysis methods, including a conditional form of the TDT.
Figures
Figure 1
Limits of two-locus, biallelic, purely epistatic (i.e.,
V _A_=V _D_=0
at each locus) models, with all alleles equally frequent. The bottom curve represents the maximum variance due to genotype (i.e., V I), the middle curve represents the total variance as a function of disease prevalence (i.e.,
V _T_=K(1-K
)), and the top curve represents the maximum proportion of variance attributable to genotype (i.e.,
_h_2=V I/V T
).
Figure 2
Limits of three-locus, biallelic, purely epistatic (i.e.,
V _A_=V _D_=0
at each locus) models, with all alleles equally frequent. The bottom curve represents the approximate maximum variance due to genotype (i.e., V I), estimated by an iterative maximization algorithm from the SAS Institute (1995), the middle curve represents the total variance (i.e.,
V _T_=K(1-K
)) as a function of disease prevalence, and the top curve represents the values for _h_2 that we have found for particular models; the dots on the top curve are the maximum proportion of variance attributable to genotype (i.e.,
_h_2=V I/V T
), estimated by the iterative maximization method.
Figure 3
Limits of three-locus, biallelic, purely epistatic (i.e.,
V _A_=V _D_=0
at each locus) models. The bottom curve represents the estimated maximum variance due to genotype (i.e., V I), the middle curve represents the total variance (i.e.,
V _T_=K(1-K
)) as a function of disease prevalence, and the top curve represents the estimated maximum proportion of variance attributable to genotype (i.e.,
_h_2=V I/V T
).
Figure 4
Limits of four-locus, biallelic, purely epistatic (i.e.,
V _A_=V _D_=0
at each locus) models, with all alleles equally frequent. The bottom curve represents the estimated maximum variance due to genotype (i.e., V I), the middle curve represents the total variance (i.e.,
V _T_=K(1-K
) as a function of disease prevalence, and the top curve represents the estimated maximum proportion of variance attributable to genotype (i.e.,
_h_2=V I/V T
).
Figure 5
Limits of four-locus, biallelic, purely epistatic (i.e.,
V _A_=V _D_=0
at each locus) models. The bottom curve represents the estimated maximum variance due to genotype (i.e., V I), the middle curve represents the total variance (i.e.,
V _T_=K(1-K
)) as a function of disease prevalence, and the top curve represents the estimated maximum proportion of variance attributable to genotype (i.e.,
_h_2=V I/V T
).
Figure 6
Comparison of maximum heritabilities for three-locus, purely epistatic models with (top curve) and without (bottom curve) two-locus interactions. The maximum heritabilities for two-locus, purely epistatic models (middle curve) are included as a reference.
Similar articles
- Multilocus linkage tests based on affected relative pairs.
Cordell HJ, Wedig GC, Jacobs KB, Elston RC. Cordell HJ, et al. Am J Hum Genet. 2000 Apr;66(4):1273-86. doi: 10.1086/302847. Epub 2000 Mar 21. Am J Hum Genet. 2000. PMID: 10729111 Free PMC article. - A complete classification of epistatic two-locus models.
Hallgrímsdóttir IB, Yuster DS. Hallgrímsdóttir IB, et al. BMC Genet. 2008 Feb 19;9:17. doi: 10.1186/1471-2156-9-17. BMC Genet. 2008. PMID: 18284682 Free PMC article. - Deriving components of genetic variance for multilocus models.
Tiwari HK, Elston RC. Tiwari HK, et al. Genet Epidemiol. 1997;14(6):1131-6. doi: 10.1002/(SICI)1098-2272(1997)14:6<1131::AID-GEPI95>3.0.CO;2-H. Genet Epidemiol. 1997. PMID: 9433636 - Adaptation of the extended transmission/disequilibrium test to distinguish disease associations of multiple loci: the Conditional Extended Transmission/Disequilibrium Test.
Koeleman BP, Dudbridge F, Cordell HJ, Todd JA. Koeleman BP, et al. Ann Hum Genet. 2000 May;64(Pt 3):207-13. doi: 10.1017/S0003480000008095. Ann Hum Genet. 2000. PMID: 11246472 Review. - Combining the case-control methodology with the small size transmission/disequilibrium test for multiallelic markers.
Guo W, Fung WK. Guo W, et al. Eur J Hum Genet. 2005 Sep;13(9):1007-12. doi: 10.1038/sj.ejhg.5201453. Eur J Hum Genet. 2005. PMID: 15957000 Review.
Cited by
- The Spherical Evolutionary Multi-Objective (SEMO) Algorithm for Identifying Disease Multi-Locus SNP Interactions.
Ren F, Li S, Wen Z, Liu Y, Tang D. Ren F, et al. Genes (Basel). 2023 Dec 20;15(1):11. doi: 10.3390/genes15010011. Genes (Basel). 2023. PMID: 38275593 Free PMC article. - Inflammation and endothelial function relevant genetic polymorphisms in carotid stenosis in southwestern China.
Liu L, Yi X, Luo H, Yu M. Liu L, et al. Front Neurol. 2023 Jan 4;13:1076898. doi: 10.3389/fneur.2022.1076898. eCollection 2022. Front Neurol. 2023. PMID: 36686520 Free PMC article. - Overview of frequent pattern mining.
Ott J, Park T. Ott J, et al. Genomics Inform. 2022 Dec;20(4):e39. doi: 10.5808/gi.22074. Epub 2022 Dec 30. Genomics Inform. 2022. PMID: 36617647 Free PMC article. - EpiReSIM: A Resampling Method of Epistatic Model without Marginal Effects Using Under-Determined System of Equations.
Shang J, Cai X, Zhang T, Sun Y, Zhang Y, Liu J, Guan B. Shang J, et al. Genes (Basel). 2022 Dec 4;13(12):2286. doi: 10.3390/genes13122286. Genes (Basel). 2022. PMID: 36553553 Free PMC article. - Missing Causality and Heritability of Autoimmune Hepatitis.
Czaja AJ. Czaja AJ. Dig Dis Sci. 2023 Apr;68(4):1585-1604. doi: 10.1007/s10620-022-07728-w. Epub 2022 Oct 19. Dig Dis Sci. 2023. PMID: 36261672
References
Electronic-Database Information
- “cdd and cddplus Homepage,” http://www.ifor.math.ethz.ch/~fukuda/cdd_home/cdd.html
References
- Boerwinkle E, Chakraborty R, Sing CF (1986) The use of measured genotype information in the analysis of quantitative phenotypes in man. I. Models and analytical methods. Ann Hum Genet 50:181–194 - PubMed
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources
Research Materials
Miscellaneous