Genetic Architecture of Highly Complex Chemical Resistance Traits across Four Yeast Strains

Ehrenreich, Ian M.; Bloom, Joshua S.; Torabi, Noorossadat; Wang, Xin Mei; Jia, Yue; Kruglyak, Leonid

doi:10.1371/journal.pgen.1002570

Cited by 92 publications

(109 citation statements)

References 29 publications

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“…This means that while the small-effect alleles are present in both crosses, they are detected only as QTL in the cross containing the interacting alleles of the large-effect QTN. The degree of complexity found in this trait is similar to what has been found elsewhere (Ehrenreich et al 2010(Ehrenreich et al , 2012Parts et al 2011), but the level of epistasis we observe has not previously been established. Our data suggest that the allelic status of large-effect QTL influence the effect sizes of additional genetic variants present in the genome of an individual.…”

Section: Discussionsupporting

confidence: 87%

Small- and Large-Effect Quantitative Trait Locus Interactions Underlie Variation in Yeast Sporulation Efficiency

Lorenz

Cohen

2012

Genetics

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Quantitative trait loci (QTL) with small effects on phenotypic variation can be difficult to detect and analyze. Because of this a large fraction of the genetic architecture of many complex traits is not well understood. Here we use sporulation efficiency in Saccharomyces cerevisiae as a model complex trait to identify and study small-effect QTL. In crosses where the large-effect quantitative trait nucleotides (QTN) have been genetically fixed we identify small-effect QTL that explain approximately half of the remaining variation not explained by the major effects. We find that small-effect QTL are often physically linked to large-effect QTL and that there are extensive genetic interactions between small-and large-effect QTL. A more complete understanding of quantitative traits will require a better understanding of the numbers, effect sizes, and genetic interactions of small-effect QTL. COMPLEX traits exhibit non-Mendelian inheritance patterns, which arise from the segregation of multiple quantitative trait loci (QTL) (Lander and Schork 1994). A QTL is a region of the genome containing an allelic difference that causes a change in phenotype. Many medically and agriculturally important traits exhibit complex genetic architecture, including phenotypes ranging from diabetes and cancer penetrance to meat quality and frost tolerance in crops (Glazier et al. 2002;Heuven et al. 2009;Dumont et al. 2009;Gaudet et al. 2010). QTL with relatively large effects are the easiest to identify and analyze, yet most QTL have small average effects on complex traits (Mackay 2001). Thus, while theory and experiment suggest that a large fraction of the variation of many phenotypes will be explained by QTL with smaller effect sizes (Fisher 1930;Lango Allen et al. 2010;Yang et al. 2011), our current understanding of complex traits is based primarily on analyses of QTL with the largest effect sizes. Because small-effect QTL are necessarily more difficult to detect and analyze, a large fraction of the genetic architecture of most complex traits is not well understood. A more complete model of complex traits should include an understanding of the numbers, effect sizes, and interactions of small-effect QTL.To identify and study small-effect QTL, we used sporulation efficiency in the yeast Saccharomyces cerevisiae as a model complex trait (Gerke et al. 2006). This system offers several advantages for the study of QTL with relatively small-effect sizes. Sporulation efficiency is a highly heritable trait in yeast (Gerke et al. 2006). The measurements of sporulation efficiency can be performed in controlled environments that provide the statistical power to detect QTL with small-effect sizes. With this system, we previously identified four quantitative trait nucleotides (QTN) that have large effects on sporulation efficiency (Gerke et al. 2009). Here we describe crosses designed to uncover additional QTL that account for phenotypic variation not explained by the major-effect QTN. For the purposes of this study we define these addition...

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Section: Discussionsupporting

confidence: 87%

Small- and Large-Effect Quantitative Trait Locus Interactions Underlie Variation in Yeast Sporulation Efficiency

Lorenz

Cohen

2012

Genetics

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“…However, more complex allelic series for QTL in selection pools were also evident. Our results are consistent with previous findings of excess biallelic QTL compared to other allele distributions in F 1 crosses Ehrenreich et al 2012), although this could be affected by the choice of founder strains, crossing design, and mapping approach.Narrow mapping intervals allow rapid causal gene identification QTL mapping in SGRP-4X resulted in narrow regions ripe for following up with single-gene studies. First, we used reciprocal hemizygosity (Steinmetz et al 2002) to test the effects of different IRA1 and IRA2 alleles on growth under heat stress.…”

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confidence: 92%

Section: -Segregant F 1 Populations (Cubillosmentioning

confidence: 67%

“…Nineteen of these overlap between ploidies, suggesting that heat-resistance QTL operate largely independently of ploidy, indicating dominant or additive effects and giving additional evidence for the reproducibility of our calls. The mapped regions have a median size of 4.8 kb, a resolution comparable to that expected under linkage mapping and finer than that of bulk segregant analyses in F 1 crosses (Ehrenreich et al 2012).The number of QTL found is a substantial increase compared to the 21 mapped using the same methodology in a F 12 WA 3 NA cross (Parts et al 2011). Seven and 10 of these previously identified loci were detected in the haploid and diploid outbred populations at 1% FDR, respectively ( Figure 4B), which increased to 13 and 17 at a more lenient 5% FDR (Table S7).…”

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confidence: 87%

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High-Resolution Mapping of Complex Traits with a Four-Parent Advanced Intercross Yeast Population

et al. 2013

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A large fraction of human complex trait heritability is due to a high number of variants with small marginal effects and their interactions with genotype and environment. Such alleles are more easily studied in model organisms, where environment, genetic makeup, and allele frequencies can be controlled. Here, we examine the effect of natural genetic variation on heritable traits in a very large pool of baker's yeast from a multiparent 12th generation intercross. We selected four representative founder strains to produce the Saccharomyces Genome Resequencing Project (SGRP)-4X mapping population and sequenced 192 segregants to generate an accurate genetic map. Using these individuals, we mapped 25 loci linked to growth traits under heat stress, arsenite, and paraquat, the majority of which were best explained by a diverging phenotype caused by a single allele in one condition. By sequencing pooled DNA from millions of segregants grown under heat stress, we further identified 34 and 39 regions selected in haploid and diploid pools, respectively, with most of the selection against a single allele. While the most parsimonious model for the majority of loci mapped using either approach was the effect of an allele private to one founder, we could validate examples of pleiotropic effects and complex allelic series at a locus. SGRP-4X is a deeply characterized resource that provides a framework for powerful and high-resolution genetic analysis of yeast phenotypes and serves as a test bed for testing avenues to attack human complex traits.T HE strong tendency for progeny to closely resemble their parents has turned out to be difficult to understand in detail. Nearly all traits, including lifetime risk for many common diseases, have a complex genetic basis that is determined by multiple quantitative trait loci (QTL) (Donnelly 2008;Manolio et al. 2009). The first step toward accurate models of trait variability, and a prerequisite for predicting and modulating them, is characterization of the underlying genetic factors in the context of rest of the genome and their external environment. Research in model systems has led the way in this effort and produced powerful experimental and computational approaches for genetic mapping (Nordborg and Weigel 2008;Flint and Mackay 2009;Mackay et al. 2009).A traditional, well-controlled approach for finding the QTL underlying natural phenotypic variation is to analyze a large number of progenies from two-parent crosses (Brem et al. 2002;Simon et al. 2008). Studies using this design have improved our understanding of complex traits and provided concrete evidence of natural segregating variants ), but have been limited in their scope with regard to the extent of genetic variation between the two parents. Mapping populations of popular model organisms ranging from fruit flies (King et al. 2012) (Churchill et al. 2004;Durrant et al. 2011) to plants (Kover et al. 2009;Gan et al. 2011;Huang et al. 2011) has recently expanded the genetic and phenotypic diversity available to study by ...

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“…Genetic heterogeneity can reduce the statistical power of mapping studies (Manchia et al 2013;Wray and Maier 2014) and may involve multiple variants segregating in the same gene (allelic heterogeneity) or different genes (nonallelic heterogeneity) (Risch 2000). Work to date has shown that allelic heterogeneity is widespread (e.g., McClellan and King 2010;Ehrenreich et al 2012;Long et al 2014) and often involves two or more null or partial loss-of-function variants segregating in a single phenotypically important gene (e.g., Nogee et al 2000;Sutcliffe et al 2005;Will et al 2010). However, the prominence and underlying mechanisms of nonallelic heterogeneity are less understood.…”

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confidence: 99%

Regulatory Rewiring in a Cross Causes Extensive Genetic Heterogeneity

et al. 2015

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Genetic heterogeneity occurs when individuals express similar phenotypes as a result of different underlying mechanisms. Although such heterogeneity is known to be a potential source of unexplained heritability in genetic mapping studies, its prevalence and molecular basis are not fully understood. Here we show that substantial genetic heterogeneity underlies a model phenotype-the ability to grow invasively-in a cross of two Saccharomyces cerevisiae strains. The heterogeneous basis of this trait across genotypes and environments makes it difficult to detect causal loci with standard genetic mapping techniques. However, using selective genotyping in the original cross, as well as in targeted backcrosses, we detected four loci that contribute to differences in the ability to grow invasively. Identification of causal genes at these loci suggests that they act by changing the underlying regulatory architecture of invasion. We verified this point by deleting many of the known transcriptional activators of invasion, as well as the gene encoding the cell surface protein Flo11 from five relevant segregants and showing that these individuals differ in the genes they require for invasion. Our work illustrates the extensive genetic heterogeneity that can underlie a trait and suggests that regulatory rewiring is a basic mechanism that gives rise to this heterogeneity.KEYWORDS complex traits; genetic mapping; invasive growth; regulatory networks; yeast G ENETIC studies in humans and model organisms have reported unexplained heritability for many traits (Manolio et al. 2009). A possible contributor to this "missing" heritability is genetic heterogeneity-individuals exhibiting similar phenotypes owing to different genetic and molecular mechanisms (Risch 2000;McClellan and King 2010;Wray and Maier 2014). Genetic heterogeneity can reduce the statistical power of mapping studies (Manchia et al. 2013;Wray and Maier 2014) and may involve multiple variants segregating in the same gene (allelic heterogeneity) or different genes (nonallelic heterogeneity) (Risch 2000). Work to date has shown that allelic heterogeneity is widespread (e.g., McClellan and King 2010;Ehrenreich et al. 2012;Long et al. 2014) and often involves two or more null or partial loss-of-function variants segregating in a single phenotypically important gene (e.g., Nogee et al. 2000;Sutcliffe et al. 2005;Will et al. 2010). However, the prominence and underlying mechanisms of nonallelic heterogeneity are less understood.In this paper we describe an example of nonallelic heterogeneity using heritable variation in the ability of Saccharomyces cerevisiae strains to undergo haploid invasive growth as our model. Invasive growth is a phenotype that is triggered by low carbon or nitrogen availability and is thought to be an adaptive response that allows yeast cells to adhere to and penetrate surfaces (Cullen and Sprague 2000). Invasion typically requires expression of FLO11, which encodes a cell surface glycoprotein that facilitates cell-cell and cell-surface adhes...

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Genetic Architecture of Highly Complex Chemical Resistance Traits across Four Yeast Strains

Cited by 92 publications

References 29 publications

Small- and Large-Effect Quantitative Trait Locus Interactions Underlie Variation in Yeast Sporulation Efficiency

Small- and Large-Effect Quantitative Trait Locus Interactions Underlie Variation in Yeast Sporulation Efficiency

High-Resolution Mapping of Complex Traits with a Four-Parent Advanced Intercross Yeast Population

Regulatory Rewiring in a Cross Causes Extensive Genetic Heterogeneity

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