Epistasia: pesquisando variantes genéticas interativas usando cruzamentos

terça-feira, junho 13, 2017

Epistasis: Searching for Interacting Genetic Variants Using Crosses

Ian M. Ehrenreich

GENETICS June 1, 2017 vol. 206 no. 2 531-535; 


Epistasis Matters in Quantitative Genetics

Source/Fonte: Study.com

Within quantitative genetics, the term “epistasis” is used to broadly describe situations in which combinations of genetic variants show nonadditive phenotypic effects (Phillips 1998, 2008; Mackay 2014). Although most work on epistasis has focused on pairs of variants that interact (Brem et al. 2005; Bloom et al. 2015), more complicated forms of epistasis can also occur (Taylor and Ehrenreich 2015a). These include higher-order interactions between three or more variants (Rowe et al. 2008; Pettersson et al. 2011; Taylor and Ehrenreich 2014) and cases in which one variant acts as a hub of interactions with a number of other variants (Carlborg et al. 2006; Forsberg et al. 2017).

Despite many reports of epistasis, its importance to quantitative genetics remains under active debate (Huang and Mackay 2016). This is in part because theory suggests that even if epistasis is present, most genetic variance will be additive (Hill et al. 2008; Maki-Tanila and Hill 2014). Consistent with this argument, purely additive models explain most of the heritability of many quantitative traits (Bloom et al. 2013) and have proven quite effective in crop and livestock breeding programs (Crow 2010). Given that epistasis can be ignored to little detriment, what do we gain by studying epistasis?

Epistasis matters for multiple reasons. A central goal of quantitative genetics is to determine the genetic architectures that underlie heritable traits (Mackay 2001). By definition, this endeavor entails identifying nearly all of the genetic effects that appreciably influence phenotypes, including epistatic effects. Achieving such a precise understanding of genotype–phenotype relationships advances our basic knowledge of genetics and may improve our ability to predict traits, such as disease risk and crop yield, from genome sequences (Forsberg et al. 2017). Because epistasis often reflects functional relationships between genes, finding interacting variants can also shed light on molecular mechanisms that give rise to trait variability (Aylor and Zeng 2008; Rowe et al. 2008; Cordell 2009; Huang et al. 2012; Taylor et al. 2016).

Furthermore, epistasis impacts our understanding of why genetically distinct individuals respond differently to new spontaneous and induced mutations (Nadeau 2001; Queitsch et al. 2002; Mackay 2014; Siegal and Leu 2014; Schell et al. 2016). Such background effects are common across species and traits, and are known to contribute to clinically relevant phenotypes (Nadeau 2001; Chandler et al. 2013). Recent work has shown that genetic background effects often reflect complex interactions between new mutations and multiple segregating variants (Dowell et al. 2010; Chari and Dworkin 2013; Chandler et al. 2014; Paaby et al. 2015; Taylor and Ehrenreich 2015b; Geiler-Samerotte et al. 2016; Lee et al. 2016; Taylor et al. 2016). Thus, predicting how individuals will respond to new mutations, including genetic changes introduced by genome editing (Cong et al. 2013; Mali et al. 2013), will likely require accounting for epistasis.