Epistasis Blog

From the Artificial Intelligence Innovation Lab at Cedars-Sinai Medical Center (www.epistasis.org)

Tuesday, April 26, 2005

Epistasis and Hirschsprung disease

Hirschsprung disease or aganglionic megacolon is a congenital disorder characterized by absence of enteric ganglia along a variable length of the intestine [OMIM]. A nice study by Owens et al. published in Human Molecular Genetics documents epistasis in Hirschsprung disease using genome-wide SNP analysis in mice.

Owens SE, Broman KW, Wiltshire T, Elmore JB, Bradley KM, Smith JR, Southard-Smith EM. Genome-wide Linkage Identifies Novel Modifier Loci of Aganglionosis in the Sox10Dom Model of Hirschsprung Disease. Hum Mol Genet. 2005 Apr 20 [PubMed]


Hirschsprung disease (HSCR) is a complex disorder that exhibits incomplete penetrance and variable expressivity due to interactions among multiple susceptibility genes. Studies in HSCR families have identified RET-dependent modifiers for short-segment HSCR (S-HSCR), but epistatic effects in long-segment (L-HSCR) and syndromic cases have not been fully explained. SOX10 mutations contribute to syndromic HSCR cases and Sox10 alleles in mice exhibit aganglionosis and pigmentary anomalies typical of a subset of HSCR patients categorized as Waardenburg-Shah Syndrome (WS4, OMIM 277580). Sox10 mutant alleles in mice exhibit strain dependent variation in penetrance and expressivity of aganglionic megacolon analogous to the variation observed in patients with aganglionosis. In this study we focused on enteric ganglia deficits in Sox10(Dom) mice and defined aganglionosis as a quantitative trait in Sox10(Dom) intercross progeny to investigate the contribution of strain background to variation in enteric nervous system deficits. We observe that the phenotype of Sox10(Dom/+) mutants ranges over a continuum from severe aganglionosis to no detectable phenotype in the gut. To systematically identify genes that modulate Sox10-dependent aganglionosis we performed a SNP-based genome scan in Sox10(Dom/+) F1 intercross progeny. Our analysis reveals modifier loci on mouse chromosomes 3, 5, 8, 11 and 14 with distinct effects on penetrance and severity of aganglionosis. Three of these loci on chromosomes 3, 8, and 11 do not coincide with previously known aganglionosis susceptibility genes or modifier loci and offer new avenues for elucidating the genetic network that modulates this complex neurocristopathy.

Sunday, April 24, 2005

Epistatic control of genome-wide gene expression

Timothy York and colleagues at VCU have published an interesting paper in Twin Research and Human Genetics on epistatic control of genome-wide gene expression as assessed by correlation studies in monozygotic (MZ) and dizygotic (DZ) twins:

York TP, Miles MF, Kendler KS, Jackson-Cook C, Bowman ML, Eaves LJ. Epistatic and environmental control of genome-wide gene expression. Twin Res Hum Genet. 2005 Feb;8(1):5-15. [PubMed]


All etiological studies of complex human traits focus on analyzing the causes of variation. Given this complexity, there is a premium on studying those processes that mediate between gene products and cellular or organismal phenotypes. Studies of levels of gene expression could offer insight into these processes and are likely to be especially useful to the extent that the major sources of their variation are known in normal tissues. The classical study of monozygotic (MZ) and dizygotic (DZ) twins was employed to partition the genetic and environmental influences in gene expression for over 6500 human genes measured using microarrays from lymphoblastoid cell lines. Our results indicate that mean expression levels are correlated about .3 in monozygotic (MZ) and .0 in dizygotic (DZ) twins suggesting an overall epistatic regulation of gene expression. Furthermore, the functions of several of the genes whose expression was most affected by environmental effects, after correction for measurement error, were consistent with their known role in mediating sensitivity to environmental influences.

Other recent studies on the genetics of gene expression in humans include:

Morley M, Molony CM, Weber TM, Devlin JL, Ewens KG, Spielman RS, Cheung VG. Genetic analysis of genome-wide variation in human gene expression. Nature. 2004 Aug 12;430(7001):743-7. [PubMed]

Monks SA, Leonardson A, Zhu H, Cundiff P, Pietrusiak P, Edwards S, Phillips JW, Sachs A, Schadt EE. Genetic inheritance of gene expression in human cell lines. Am J Hum Genet. 2004 Dec;75(6):1094-105. [PubMed]

Saturday, April 23, 2005

Do we need genomic research for the prevention of common diseases with environmental causes?

A new paper by Khoury et al. in the American Journal of Epidemiology discusses the role of genomics and gene-environment interactions in epidemiology.

Khoury MJ, Davis R, Gwinn M, Lindegren ML, Yoon P. Do we need genomic research for the prevention of common diseases with environmental causes? Am J Epidemiol. 2005 May 1;161(9):799-805. [PubMed]


Concerns have been raised about the value of genomic research for prevention and public health, especially for complex diseases with risk factors that are amenable to environmental modification. Given that gene-environment interactions underlie almost all human diseases, the public health significance of genomic research on common diseases with modifiable environmental risks is based not necessarily on finding new genetic "causes" but on improving existing approaches to identifying and modifying environmental risk factors to better prevent and treat disease. Such applied genomic research for environmentally caused diseases is important, because 1) it could help stratify disease risks and differentiate interventions for achieving population health benefits; 2) it could help identify new environmental risk factors for disease or help confirm suspected environmental risk factors; and 3) it could aid our understanding of disease occurrence in terms of transmission, natural history, severity, etiologic heterogeneity, and targets for intervention at the population level. While genomics is still in its infancy, opportunities exist for developing, testing, and applying the tools of genomics to clinical and public health research, especially for conditions with known or suspected environmental causes. This research is likely to lead to population-wide health promotion and disease prevention efforts, not only to interventions targeted according to genetic susceptibility.

See also:

Khoury MJ, Millikan R, Little J, Gwinn M. The emergence of epidemiology in the genomics age. Int J Epidemiol. 2004 Oct;33(5):936-44. [PubMed]

Two-level Haseman-Elston regression for general pedigree data analysis

A new paper by Wang and Elston published in Genetic Epidemiology reports an extension of the Haseman-Elston linkage analysis method that allows direct modeling of gene-gene and gene-environment interactions in general pedigrees.

Wang T, Elston RC. Two-level Haseman-Elston regression for general pedigree data analysis. Genet Epidemiol. 2005 Apr 18; [PubMed]


The Haseman-Elston (HE) (Haseman and Elston [1972] Behav Genet 2:3-19) method is widely used in genetic linkage studies for quantitative traits. We propose a new version of the HE regression model, a two-level HE regression model (tHE) in which the variance-covariance structure of family data is modeled under the framework of multiple-level regression. An iterative generalized least squares (IGLS) algorithm is adopted to handle the varying variance-covariance structures across families in a simple fashion. In this way, the tHE can compete favorably with any current version of HE in that it can naturally make use of all the trait information available in any general pedigree, simultaneously incorporate individual-level and pedigree-level covariates, marker genotypes for linkage (i.e., the number of allele shared identically by descent [IBD]), and marker alleles for association. Under the assumption of normality, the method is asymptotically equivalent to the usual variance component model for detecting linkage. For the situation where the assumption of normality is critical, a robust globally consistent estimator of the quantitative trait locus (QTL) variance is available. Complex genetic mechanisms, including gene-gene interaction, gene-environmental interaction, and imprinting, can be directly modeled in this version of HE regression.

Other recent paper on the HE method include:

Wang T, Elston RC. A modified revisited Haseman-Elston method to further improve power. Hum Hered. 2004;57(2):109-16. [PubMed]

Chen WM, Broman KW, Liang KY. Quantitative trait linkage analysis by generalized estimating equations: unification of variance components and Haseman-Elston regression. Genet Epidemiol. 2004 May;26(4):265-72. [PubMed]

Barber MJ, Cordell HJ, MacGregor AJ, Andrew T. Gamma regression improves Haseman-Elston and variance components linkage analysis for sib-pairs. Genet Epidemiol. 2004 Feb;26(2):97-107. [PubMed]

Schaid DJ, Olson JM, Gauderman WJ, Elston RC. Regression models for linkage: issues of traits, covariates, heterogeneity, and interaction. Hum Hered. 2003;55(2-3):86-96. [PubMed]

Wednesday, April 20, 2005

Application of Logistic Regression to Case-Control Association Studies Involving Two Causative Loci

A new paper by North et al. explores the use of logistic regression for detecting two-locus interactions.

North BV, Curtis D, Sham PC. Application of Logistic Regression to Case-Control Association Studies Involving Two Causative Loci. Hum Hered. 2005 Apr 18;59(2):79-87 [PubMed]


Models in which two susceptibility loci jointly influence the risk of developing disease can be explored using logistic regression analysis. Comparison of likelihoods of models incorporating different sets of disease model parameters allows inferences to be drawn regarding the nature of the joint effect of the loci.We have simulated case-control samples generated assuming different two-locus models and then analysed them using logistic regression. We show that this method is practicable and that, for the models we have used, it can be expected to allow useful inferences to be drawn from sample sizes consisting of hundreds of subjects. Interactions between loci can be explored, but interactive effects do not exactly correspond with classical definitions of epistasis. We have particularly examined the issue of the extent to which it is helpful to utilise information from a previously identified locus when investigating a second, unknown locus. We show that for some models conditional analysis can have substantially greater power while for others unconditional analysis can be more powerful. Hence we conclude that in general both conditional and unconditional analyses should be performed when searching for additional loci.

Wednesday, April 06, 2005

Gene-Environment Interactions

A new review paper by David Hunter focuses on gene-environment interactions in human disease:

Hunter DJ. Gene-environment interactions in human diseases. Nat Rev Genet. 2005 Apr;6(4):287-298. [PubMed]

No discussion about gene-environment interactions is complete without covering reaction norms. For more information on reaction norms and phenotypic plasticity, I highly recommend the following books:

Schlichting CD, Pigliucci M. Phenotypic Evolution: A Rection Norm Perspective. Sinauer (1998). [Amazon.com]

Pigliucci M. Phenotypic Plasticity: Beyond Nature and Nurture. Johns Hopkins University Press(2001). [Amazon.com]

Pigliucci M. Phenotypic Integration: Studying the Ecology and Evolution of Complex Phenotypes. Oxford University Press(2004). [Amazon.com]

See also: http://www.genotype-environment.org/

Tuesday, April 05, 2005

Genome-wide strategies for detecting epistasis

A wonderful new paper in Nature Genetics explores the plausibility of detecting epistasis in genome-wide assocation studies. Here is the citation and the abstract:

Marchini J, Donnelly P, Cardon LR. Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet. 2005 Apr;37(4):413-7. [PubMed]

After nearly 10 years of intense academic and commercial research effort, large genome-wide association studies for common complex diseases are now imminent. Although these conditions involve a complex relationship between genotype and phenotype, including interactions between unlinked loci, the prevailing strategies for analysis of such studies focus on the locus-by-locus paradigm. Here we consider analytical methods that explicitly look for statistical interactions between loci. We show first that they are computationally feasible, even for studies of hundreds of thousands of loci, and second that even with a conservative correction for multiple testing, they can be more powerful than traditional analyses under a range of models for interlocus interactions. We also show that plausible variations across populations in allele frequencies among interacting loci can markedly affect the power to detect their marginal effects, which may account in part for the well-known difficulties in replicating association results. These results suggest that searching for interactions among genetic loci can be fruitfully incorporated into analysis strategies for genome-wide association studies.

See also the News and Views piece about the paper:

Daly MJ, Altshuler D. Partners in crime. Nat Genet. 2005 Apr;37(4):337-8. [PubMed]

The genetic culprits that contribute to common diseases remain at large, despite dedicated sleuthing by many laboratories. A new study evaluates the power of genome-wide searches for variants acting in combination, with results that are both unexpected and encouraging.