The complex genetic and molecular basis of a model quantitative trait

Linder, Robert A.; Seidl, Fabian; Ha, Kimberly; Ehrenreich, Ian M.

doi:10.1091/mbc.e15-06-0408

Cited by 18 publications

(26 citation statements)

References 43 publications

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“…Based on the X‐QTL mapping results, we used an approach previously applied to yeast (Linder et al . ) and plotted the allele count for each SNP across the genomic regions associated with germination speed QTLs on chromosomes 1, 3 and 4 (Fig. ).…”

Section: Resultsmentioning

confidence: 99%

“…Even the nonsignificant region on chromosome 3 displayed a 10% allele frequency shift towards the early-germination Bs-2 allele. Based on the X-QTL mapping results, we used an approach previously applied to yeast (Linder et al 2016) and plotted the allele count for each SNP across the genomic regions associated with germination speed QTLs on chromosomes 1, 3 and 4 (Fig. 6).…”

Section: Germination Speed X-qtls Confirmed By Sequencing Individual mentioning

confidence: 99%

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ExtremeQTLmapping of germination speed inArabidopsis thaliana

et al. 2016

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Seed germination is a key life history transition for annual plants and partly determines lifetime performance and fitness. Germination speed, the elapsed time for a nondormant seed to germinate, is a poorly understood trait important for plants' competitiveness and fitness in fluctuating environments. Germination speed varied by 30% among 18 Arabidopsis thaliana populations measured, and exhibited weak negative correlation with flowering time and seed weight, with significant genotype effect (P < 0.005). To dissect the genetic architecture of germination speed, we developed the extreme QTL (X-QTL) mapping method in A. thaliana. The method has been shown in yeast to increase QTL mapping power by integrating selective screening and bulk-segregant analysis in a very large mapping population. By pooled genotyping of top 5% of rapid germinants from ~100 000 F3 individuals, three X-QTL regions were identified on chromosomes 1, 3 and 4. All regions were confirmed as QTL regions by sequencing 192 rapid germinants from an independent F3 selection experiment. Positional overlaps were found between X-QTLs and previously identified seed, life history and fitness QTLs. Our method provides a rapid mapping platform in A. thaliana with potentially greater power. One can also relate identified X-QTLs to the A. thaliana physical map, facilitating candidate gene identification.

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Section: Resultsmentioning

confidence: 99%

Section: Germination Speed X-qtls Confirmed By Sequencing Individual mentioning

confidence: 99%

ExtremeQTLmapping of germination speed inArabidopsis thaliana

et al. 2016

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“…In model organisms, the hunt for causative, naturally-segregating variation commonly begins with linkage-based QTL (quantitative trait locus) mapping. Whether initiated with two parental strains (LANDER AND BOTSTEIN 1989), or more recently with several founders (KOVER et al 2009;CHURCHILL et al 2012;KING et al 2012b; THREADGILL AND CHURCHILL 2012), such mapping designs have tremendous power to find QTL, and in some cases have led to the identification of specific polymorphisms contributing to complex trait variation (e.g., LONG et al 6 2000; DEUTSCHBAUER AND DAVIS 2005;BENDESKY et al 2011;COOK et al 2016;LINDER et al 2016). These variants facilitate a deeper understanding of specific biomedically-relevant traits, and collectively add to a fundamental appreciation of complex trait variation and its maintenance in populations.…”

Section: Introductionmentioning

confidence: 99%

Naturally-segregating Variation atUgt86DdContributes to Nicotine Resistance inDrosophila melanogaster

Highfill

Tran

Nguyen

et al. 2017

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Identifying the sequence polymorphisms underlying complex trait variation is a key goal of genetics research, since knowing the precise causative molecular events allows insight into the pathways governing trait variation. Genetic analysis of complex traits in model systems regularly starts by constructing QTL maps, but generally fails to identify causative sequence polymorphisms. Previously we mapped a series of QTL contributing to resistance to nicotine in a Drosophila melanogaster multiparental mapping resource and here use a battery of functional tests to resolve QTL to the molecular level. One large-effect QTL resided over a cluster of UDP-glucuronosyltransferases, and quantitative complementation tests using deficiencies eliminating subsets of these detoxification genes revealed allelic variation impacting resistance. RNAseq showed that Ugt86Dd had significantly higher expression in genotypes that are more resistant to nicotine, and anterior midgut-specific RNA interference (RNAi) of this gene reduced resistance. We discovered a segregating 22-bp frameshift deletion in Ugt86Dd, and accounting for the InDel during mapping largely eliminates the QTL, implying the event explains the bulk of the effect of the mapped locus. CRISPR/Cas9 editing of a relatively resistant genotype to generate lesions in Ugt86Dd that recapitulate the naturally occurring putative loss-of-function allele, leads to a large reduction in resistance. Despite this major effect of the deletion, the allele appears to be very rare in wild-caught populations and likely explains only a small fraction of the natural variation for the trait. Nonetheless, this putatively causative coding InDel can be a launchpad for future mechanistic exploration of xenobiotic detoxification.

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“…While we found no significant QTLs for basal H 2 O 2 resistance, we did find a significant QTL peak on chromosome XII for cross protection (Fig 2). It is unlikely that our failure to detect a chromosome XII QTL for basal H 2 O 2 resistance was due to a lack of statistical power, because two independent basal H 2 O 2 resistance QTL studies using millions of S288c x YPS163 F 2 segregants also found no significant associations at this locus [69,70]. Additionally, we estimated the heritability of phenotypic variation in basal resistance to be 0.79, which is slightly above the median value estimated by Bloom and colleagues for 46 yeast traits [71], and is only moderately lower than the heritability for cross protection (0.92).…”

Section: The Genetic Basis Of Natural Variation In Yeast Cross Protecmentioning

confidence: 91%

Linkage mapping of yeast cross protection connects gene expression variation to a higher-order organismal trait

Stuecker

Scholes

Lewis

2017

Preprint

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Gene expression variation is extensive in nature, and is hypothesized to play a major role in shaping phenotypic diversity. However, connecting differences in gene expression across individuals to higher-order organismal traits is not trivial. In many cases, gene expression variation may be evolutionarily neutral, and in other cases expression variation may only affect phenotype under specific conditions. To understand connections between gene expression variation and stress defense phenotypes, we have been leveraging extensive natural variation in the gene expression response to acute ethanol in laboratory and wild Saccharomyces cerevisiae strains. Previous work found that the genetic architecture underlying these expression differences included dozens of "hotspot" loci that affected many transcripts in trans.In the present study, we provide new evidence that one of these expression QTL hotspot loci is responsible for natural variation in one particular stress defense phenotype-ethanol-induced cross protection against severe doses of H 2 O 2 . The causative polymorphism is in the hemeactivated transcription factor Hap1p, which we show directly impacts cross protection, but not the basal H 2 O 2 resistance of unstressed cells. This provides further support that distinct cellular mechanisms underlie basal and acquired stress resistance. We also show that the Hap1p-dependent cross protection relies on novel regulation of cytosolic catalase T (Ctt1p) during ethanol stress in wild strains. Because ethanol accumulation precedes aerobic respiration and accompanying reactive oxygen species formation, wild strains with the ability to anticipate impending oxidative stress would likely be at an advantage. This study highlights how strategically chosen traits that better correlate with gene expression changes can improve our power to identify novel connections between gene expression variation and higher-order organismal phenotypes.

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The complex genetic and molecular basis of a model quantitative trait

Cited by 18 publications

References 43 publications

ExtremeQTLmapping of germination speed inArabidopsis thaliana

ExtremeQTLmapping of germination speed inArabidopsis thaliana

Naturally-segregating Variation atUgt86DdContributes to Nicotine Resistance inDrosophila melanogaster

Linkage mapping of yeast cross protection connects gene expression variation to a higher-order organismal trait

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