Sequential Elimination of Major-Effect Contributors Identifies Additional Quantitative Trait Loci Conditioning High-Temperature Growth in Yeast

Sinha, Himanshu; David, Lior; Pascon, Renata C.; Clauder‐Münster, Sandra; Krishnakumar, Sujatha; Nguyen, Michelle; Shi, Getao; Dean, Jed; Davis, Ronald W.; Oefner, Peter J.; McCusker, John H.; Steinmetz, Lars M.

doi:10.1534/genetics.108.092932

Cited by 144 publications

(163 citation statements)

References 55 publications

(73 reference statements)

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“…Our results show that the nonchromosomal contribution to heritability can be large and, in some cases, can completely mask the effect of a chromosomal mutation. Nonchromosomal elements may have affected previous yeast studies ( [28][29][30][31][32] that crossed a strain carrying a dsRNA virus, as many feral yeast strains do (33), with a virus-free strain such as the reference strain S288c (34) (Supporting Information, Note 1).…”

Section: Discussionmentioning

confidence: 99%

Interactions between chromosomal and nonchromosomal elements reveal missing heritability

Edwards

Symbor-Nagrabska

Dollard

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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The measurement of any nonchromosomal genetic contribution to the heritability of a trait is often confounded by the inability to control both the chromosomal and nonchromosomal information in a population. We have designed a unique system in yeast where we can control both sources of information so that the phenotype of a single chromosomal polymorphism can be measured in the presence of different cytoplasmic elements. With this system, we have shown that both the source of the mitochondrial genome and the presence or absence of a dsRNA virus influence the phenotype of chromosomal variants that affect the growth of yeast. Moreover, by considering this nonchromosomal information that is passed from parent to offspring and by allowing chromosomal and nonchromosomal information to exhibit nonadditive interactions, we are able to account for much of the heritability of growth traits. Taken together, our results highlight the importance of including all sources of heritable information in genetic studies and suggest a possible avenue of attack for finding additional missing heritability.A fundamental problem in genetics is unraveling the link between genotype and phenotype. Ascertaining the heritability of a trait is a key step toward harnessing the predictive capacity of genetic information for human disease risk assessment and therapy (1). Knowledge of all of the elements contributing to heritability would facilitate the establishment of a causal relationship between the information that is passed down from generation to generation and the resulting phenotype. Genomewide association studies (GWASs) have successfully identified many human polymorphisms that are associated with traits such as height, eye color, or susceptibility to common diseases, but these variants typically explain only a small proportion of the observed heritability of a trait (2, 3).A number of explanations for missing heritability have been suggested (2), including the existence of many weak variants with effects too small to achieve statistical significance (4), interactions between variants that cannot be identified with current studies (5), rare variants that were not identified by GWAS, and epigenetic effects (6-8). The contribution of nonchromosomal information to the missing heritability is rarely considered, despite the fact that there is a long history documenting the effect in many organisms of diverse cytoplasmic elements on phenotype. Recent work on a mouse model of Crohn disease supports a combinatorial model of complex disease traits in which the pathology requires the interaction between a specific mutation in the mouse and a specific strain of virus (9). Another recent study showed strong effects on the plant metabolome stemming from variation in mitochondrial and chloroplast genomes (10). In humans, the importance of nonchromosomal information has been supported by targeted analyses, but these studies have not analyzed its impact on heritability in a well-controlled context (11-13). Such nonchromosomal interactions might help e...

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Section: Discussionmentioning

confidence: 99%

Interactions between chromosomal and nonchromosomal elements reveal missing heritability

Edwards

Symbor-Nagrabska

Dollard

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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“…High-temperature growth (HTG) has been investigated in S. cerevisiae as a model for complex quantitative inheritance (Steinmetz et al 2002;Sinha et al 2008), and three major contributing loci have been identified: NCS2, MKT1, and (Carver et al 2005). Red lines connect regions of sequence similarity higher than 85%; gaps in white lower or absent similarity; green indicates S288c Chr6 sequences; thick areas indicate regions conserved in JAY291; thin areas indicate nonconserved regions.…”

Section: Overview Of Specific Gene Polymorphisms Important For Bioethmentioning

confidence: 99%

“…In addition to the shared genes discussed above, it is possible that the genes present in JAY291 but absent in S288c also contribute to the cerevisiae strains based on a 49-kb region from Chr14 containing the three HTG QTLs (Sinha et al 2008). (B) HTG QTL allele distribution in various S. cerevisiae strains.…”

Section: Overview Of Specific Gene Polymorphisms Important For Bioethmentioning

confidence: 99%

Genome structure of a Saccharomyces cerevisiae strain widely used in bioethanol production

Argueso

Carazzolle

Mieczkowski³

et al. 2009

Genome Res.

238

236

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Bioethanol is a biofuel produced mainly from the fermentation of carbohydrates derived from agricultural feedstocks by the yeast Saccharomyces cerevisiae. One of the most widely adopted strains is PE-2, a heterothallic diploid naturally adapted to the sugar cane fermentation process used in Brazil. Here we report the molecular genetic analysis of a PE-2 derived diploid (JAY270), and the complete genome sequence of a haploid derivative (JAY291). The JAY270 genome is highly heterozygous (;2 SNPs/kb) and has several structural polymorphisms between homologous chromosomes. These chromosomal rearrangements are confined to the peripheral regions of the chromosomes, with breakpoints within repetitive DNA sequences. Despite its complex karyotype, this diploid, when sporulated, had a high frequency of viable spores. Hybrid diploids formed by outcrossing with the laboratory strain S288c also displayed good spore viability. Thus, the rearrangements that exist near the ends of chromosomes do not impair meiosis, as they do not span regions that contain essential genes. This observation is consistent with a model in which the peripheral regions of chromosomes represent plastic domains of the genome that are free to recombine ectopically and experiment with alternative structures. We also explored features of the JAY270 and JAY291 genomes that help explain their high adaptation to industrial environments, exhibiting desirable phenotypes such as high ethanol and cell mass production and high temperature and oxidative stress tolerance. The genomic manipulation of such strains could enable the creation of a new generation of industrial organisms, ideally suited for use as delivery vehicles for future bioenergy technologies.

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“…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 additional QTL as small-effect QTL.One previous effort in yeast uncovered additional smalleffect QTL using a single targeted backcross to fix a single large-effect QTL (Sinha et al 2008). While this method was effective at identifying some small-effect QTL, as a backcross it could only assay the subset of the variation remaining in the F2 parent.…”

mentioning

confidence: 99%

“…One previous effort in yeast uncovered additional smalleffect QTL using a single targeted backcross to fix a single large-effect QTL (Sinha et al 2008). While this method was effective at identifying some small-effect QTL, as a backcross it could only assay the subset of the variation remaining in the F2 parent.…”

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confidence: 99%

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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Sequential Elimination of Major-Effect Contributors Identifies Additional Quantitative Trait Loci Conditioning High-Temperature Growth in Yeast

Cited by 144 publications

References 55 publications

Interactions between chromosomal and nonchromosomal elements reveal missing heritability

Interactions between chromosomal and nonchromosomal elements reveal missing heritability

Genome structure of a Saccharomyces cerevisiae strain widely used in bioethanol production

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

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