Epistasis Blog

From the Computational Genetics Laboratory at the University of Pennsylvania (www.epistasis.org)

Tuesday, November 29, 2016

Modifiers of the Genotype–Phenotype Map: Hsp90 and Beyond

A great review on the role of HSP90 and its epistatic effects 

Schell R, Mullis M, Ehrenreich IM. Modifiers of the Genotype-Phenotype Map: Hsp90 and Beyond. PLoS Biol. 2016 Nov 10;14(11):e2001015. [PLoS]

Abstract


Disruption of certain genes alters the heritable phenotypic variation among individuals. Research on the chaperone Hsp90 has played a central role in determining the genetic basis of this phenomenon, which may be important to evolution and disease. Key studies have shown that Hsp90 perturbation modifies the effects of many genetic variants throughout the genome. These modifications collectively transform the genotype–phenotype map, often resulting in a net increase or decrease in heritable phenotypic variation. Here, we summarize some of the foundational work on Hsp90 that led to these insights, discuss a framework for interpreting this research that is centered upon the standard genetics concept of epistasis, and propose major questions that future studies in this area should address.


Wednesday, November 23, 2016

Identifying significant gene-environment interactions using a combination of screening testing and hierarchical false discovery rate control

Frost HR, Shen L, Saykin AJ, Williams SM, Moore JH; Alzheimer's Disease Neuroimaging Initiative. Identifying significant gene-environment interactions using a combination of screening testing and hierarchical false discovery rate control. Genet Epidemiol. 2016 Nov;40(7):544-557. [PubMed]

Abstract


Although gene-environment (G× E) interactions play an important role in many biological systems, detecting these interactions within genome-wide data can be challenging due to the loss in statistical power incurred by multiple hypothesis correction. To address the challenge of poor power and the limitations of existing multistage methods, we recently developed a screening-testing approach for G× E interaction detection that combines elastic net penalized regression with joint estimation to support a single omnibus test for the presence of G× E interactions. In our original work on this technique, however, we did not assess type I error control or power and evaluated the method using just a single, small bladder cancer data set. In this paper, we extend the original method in two important directions and provide a more rigorous performance evaluation. First, we introduce a hierarchical false discovery rate approach to formally assess the significance of individual G× E interactions. Second, to support the analysis of truly genome-wide data sets, we incorporate a score statistic-based prescreening step to reduce the number of single nucleotide polymorphisms prior to fitting the first stage penalized regression model. To assess the statistical properties of our method, we compare the type I error rate and statistical power of our approach with competing techniques using both simple simulation designs as well as designs based on real disease architectures. Finally, we demonstrate the ability of our approach to identify biologically plausible SNP-education interactions relative to Alzheimer's disease status using genome-wide association study data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Friday, November 04, 2016

The Genetic Architecture of Quantitative Traits Cannot Be Inferred from Variance Component Analysis

Must read!

Huang W, Mackay TF. The Genetic Architecture of Quantitative Traits Cannot Be Inferred from Variance Component Analysis. PLoS Genet. 2016 Nov 3;12(11):e1006421. [PLoS]

Abstract


Classical quantitative genetic analyses estimate additive and non-additive genetic and environmental components of variance from phenotypes of related individuals without knowing the identities of quantitative trait loci (QTLs). Many studies have found a large proportion of quantitative trait variation can be attributed to the additive genetic variance (VA), providing the basis for claims that non-additive gene actions are unimportant. In this study, we show that arbitrarily defined parameterizations of genetic effects seemingly consistent with non-additive gene actions can also capture the majority of genetic variation. This reveals a logical flaw in using the relative magnitudes of variance components to indicate the relative importance of additive and non-additive gene actions. We discuss the implications and propose that variance component analyses should not be used to infer the genetic architecture of quantitative traits.