Evolution of pleiotropy: epistatic interaction pattern supports a mechanistic model underlying variation in genotype–phenotype map

Pavličev, Mihaela; Norgard, Elizabeth A.; Fawcett, Gloria L.; Cheverud, James M.

doi:10.1002/jez.b.21410

Cited by 50 publications

(62 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This is consistent with other rQTL studies (Pavlicev et al 2011b). APOE is the only locus to have a direct association with any of the traits (TC, LDL, ln TG, and HDL), which is already well established in the literature (Templeton et al 2005).…”

Section: Resultssupporting

confidence: 92%

“…Sign epistasis occurs when the allelic effects change direction across genetic backgrounds. Pavlicev et al (2011b) found that rQTL have a higher proportion of sign epistatic interactions than non-rQTL. Sign epistasis sometimes involve compensatory mutations, which occur when the deleterious effect of a mutation at one locus is alleviated with a context dependency at another locus.…”

Section: Genetic Analysesmentioning

confidence: 95%

“…Differential epistasis can cause the pattern observed in an rQTL (Pavlicev et al 2011b). The CHD and TC loci found to interact with APOE exhibit differential epistasis by interacting differently among the pairs of traits (TC/ln TG and CHD/TC), which in turn create different relationships between the traits across APOE genotypes (i.e., the APOE rQTL pattern).…”

Section: Apoe Changes Chd Risk For Lipids 1401mentioning

confidence: 99%

“…This approach to detecting G 3 G greatly increases statistical power by reducing the multiple testing burden, and it connects multiple loci to each other and to multiple interrelated traits. A theoretical basis for and methodologies to detect relationship loci has been established in animal QTL work (Ehrich et al 2003;Cheverud et al 2004;Pavlicev et al 2008Pavlicev et al , 2011b). An early example comes from a French population (Boerwinkle et al 1987), where the correlation between total cholesterol and triglycerides varied among the most common genotypes of the three isoforms of apolipoprotein E (APOE) (MIM 107741).…”

mentioning

confidence: 99%

“…For the other three, rs912618, the MAF in EA is 0.01 and the other two are ,0.01, giving little or no power for replication. However, the remaining three interacting SNPs all had at least one nominally significant SNP interacting with APOE for their respective trait within a 40-kb region.Differential epistasis can cause the pattern observed in an rQTL (Pavlicev et al 2011b). The CHD and TC loci found to interact with APOE exhibit differential epistasis by interacting differently among the pairs of traits (TC/ln TG and CHD/TC), which in turn create different relationships between the traits across APOE genotypes (i.e., the APOE rQTL pattern).…”

mentioning

confidence: 99%

See 4 more Smart Citations

APOEModulates the Correlation Between Triglycerides, Cholesterol, and CHD Through Pleiotropy, and Gene-by-Gene Interactions

et al. 2013

View full text Add to dashboard Cite

Relationship loci (rQTL) exist when the correlation between multiple traits varies by genotype. rQTL often occur due to gene-by-gene (G 3 G) or gene-by-environmental interactions, making them a powerful tool for detecting G 3 G. Here we present an empirical analysis of apolipoprotein E (APOE) with respect to lipid traits and incident CHD leading to the discovery of loci that interact with APOE to affect these traits. We found that the relationship between total cholesterol (TC) and triglycerides (ln TG) varies by APOE isoform genotype in African-American (AA) and European-American (EA) populations. The e2 allele is associated with strong correlation between ln TG and TC while the e4 allele leads to little or no correlation. This led to a priori hypotheses that APOE genotypes affect the relationship of TC and/or ln TG with incident CHD. We found that APOE*TC was significant (P = 0.016) for AA but not EA while APOE*ln TG was significant for EA (P = 0.027) but not AA. In both cases, e2e2 and e2e3 had strong relationships between TC and ln TG with CHD while e2e4 and e4e4 results in little or no relationship between TC and ln TG with CHD. Using ARIC GWAS data, scans for loci that significantly interact with APOE produced four loci for African Americans (one CHD, one TC, and two HDL). These interactions contribute to the rQTL pattern. rQTL are a powerful tool to identify loci that modify the relationship between risk factors and disease and substantially increase statistical power for detecting G 3 G. CORONARY heart disease [CHD (MIM 608901)] is challenging because it is a complex trait with a complicated genetic architecture. The MIM number is a reference in the Online Mendelian Inheritance in Man (OMIM) database. Recent genome-wide association studies (GWAS) have been successful in identifying regions of the genome with significant marginal effects. However, the combined effect of these loci explains only a small portion of the estimated total heritability. The traditional approach in association studies has been to test one phenotype at a time, even when multiple interrelated phenotypes are available for each individual.Because biological systems are organized in highly interactive pathways, changes at one level are likely to affect multiple traits throughout the system. Pleiotropy occurs when a single gene influences the variation of multiple phenotypes. Pleiotropic loci are common in complex biological systems (Stearns 2010) and tend to interact with other loci affecting traits within the same modular units (Wagner et al. 2007;Kenney-Hunt and Cheverud 2009). Pleiotropy is thought to play a primary role in the evolution of complex structures and systems (Wagner and Zhang 2011).A conundrum in evolutionary biology is how a complex multitrait system with pleiotropy can evolve when a beneficial mutation for one trait may have detrimental consequences for another. Recent work (Pavlicev et al. 2011a;Pavlicev and Wagner 2012) has shown that relationship loci (rQTL) creates variation in pleiotropy that can be sel...

show abstract

Section: Resultssupporting

confidence: 92%

Section: Genetic Analysesmentioning

confidence: 95%

Section: Apoe Changes Chd Risk For Lipids 1401mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

APOEModulates the Correlation Between Triglycerides, Cholesterol, and CHD Through Pleiotropy, and Gene-by-Gene Interactions

et al. 2013

View full text Add to dashboard Cite

show abstract

A Versatile Omnibus Test for Detecting Mean and Variance Heterogeneity

Cao

Wei

Bailey

et al. 2013

Genetic Epidemiology

140

View full text Add to dashboard Cite

Recent research has revealed loci that display variance heterogeneity through various means such as biological disruption, linkage disequilibrium (LD), gene-by-gene (GxG), or gene-by-environment (GxE) interaction. We propose a versatile likelihood ratio test that allows joint testing for mean and variance heterogeneity (LRTMV) or either effect alone (LRTM or LRTV) in the presence of covariates. Using extensive simulations for our method and others we found that all parametric tests were sensitive to non-normality regardless of any trait transformations. Coupling our test with the parametric bootstrap solves this issue. Using simulations and empirical data from a known mean-only functional variant we demonstrate how linkage disequilibrium (LD) can produce variance-heterogeneity loci (vQTL) in a predictable fashion based on differential allele frequencies, high D’ and relatively low r2 values. We propose that a joint test for mean and variance heterogeneity is more powerful than a variance only test for detecting vQTL. This takes advantage of loci that also have mean effects without sacrificing much power to detect variance only effects. We discuss using vQTL as an approach to detect gene-by-gene interactions and also how vQTL are related to relationship loci (rQTL) and how both can create prior hypothesis for each other and reveal the relationships between traits and possibly between components of a composite trait.

show abstract

Toward a mechanistic explanation of phenotypic evolution: The need for a theory of theory integration

2018

View full text Add to dashboard Cite

Reconciling different underlying ontologies and explanatory contexts has been one of the main challenges and impediments for theory integration in biology. Here, we analyze the challenge of developing an inclusive and integrative theory of phenotypic evolution as an example for the broader challenge of developing a theory of theory integration within the life sciences and suggest a number of necessary formal steps toward the resolution of often incompatible (hidden) assumptions. Theory integration in biology requires a better formal understanding of the structure of biological theories The strategy for integrating theories crucially depends on the relationships of the underlying ontologies.

show abstract

Evolution of pleiotropy: epistatic interaction pattern supports a mechanistic model underlying variation in genotype–phenotype map

Cited by 50 publications

References 83 publications

APOEModulates the Correlation Between Triglycerides, Cholesterol, and CHD Through Pleiotropy, and Gene-by-Gene Interactions

APOEModulates the Correlation Between Triglycerides, Cholesterol, and CHD Through Pleiotropy, and Gene-by-Gene Interactions

A Versatile Omnibus Test for Detecting Mean and Variance Heterogeneity

Toward a mechanistic explanation of phenotypic evolution: The need for a theory of theory integration

Contact Info

Product

Resources

About