Speed-mapping quantitative trait loci using microarrays

Lai, Chao‐Qiang; Leips, Jeff; Zou, Wei; Roberts, Jessica F.; Wollenberg, Kurt; Parnell, Laurence D.; Zeng, Zhao‐Bang; Ordovas, José M.; Mackay, Trudy F. C.

doi:10.1038/nmeth1084

Cited by 43 publications

(45 citation statements)

References 12 publications

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“…We used extreme QTL mapping (17)(18)(19)(20), which maps traitassociated loci by contrasting allele frequencies among individuals with extreme phenotypes, to identify SNPs associated with the three traits. Briefly, we phenotyped samples of 2,000 females from the Flyland population for starvation resistance, startle response, and chill coma recovery.…”

Section: Resultsmentioning

confidence: 99%

Epistasis dominates the genetic architecture of Drosophila quantitative traits

Huang¹,

Richards

Carbone³

et al. 2012

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

353

390

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Epistasis-nonlinear genetic interactions between polymorphic loci-is the genetic basis of canalization and speciation, and epistatic interactions can be used to infer genetic networks affecting quantitative traits. However, the role that epistasis plays in the genetic architecture of quantitative traits is controversial. Here, we compared the genetic architecture of three Drosophila life history traits in the sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) and a large outbred, advanced intercross population derived from 40 DGRP lines (Flyland). We assessed allele frequency changes between pools of individuals at the extremes of the distribution for each trait in the Flyland population by deep DNA sequencing. The genetic architecture of all traits was highly polygenic in both analyses. Surprisingly, none of the SNPs associated with the traits in Flyland replicated in the DGRP and vice versa. However, the majority of these SNPs participated in at least one epistatic interaction in the DGRP. Despite apparent additive effects at largely distinct loci in the two populations, the epistatic interactions perturbed common, biologically plausible, and highly connected genetic networks. Our analysis underscores the importance of epistasis as a principal factor that determines variation for quantitative traits and provides a means to uncover genetic networks affecting these traits. Knowledge of epistatic networks will contribute to our understanding of the genetic basis of evolutionarily and clinically important traits and enhance predictive ability at an individualized level in medicine and agriculture.chill coma recovery | genetic interaction networks | genome-wide association studies | startle response | starvation resistance

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Section: Resultsmentioning

confidence: 99%

Epistasis dominates the genetic architecture of Drosophila quantitative traits

Huang¹,

Richards

Carbone³

et al. 2012

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

353

390

View full text Add to dashboard Cite

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“…Because a plethora of new genome assemblies is available and various kinds of mapping populations have been constructed in many species, including plants (23,24) and animals (25)(26)(27), the method developed here will be generally applicable and cost-effective for genotyping of mapping populations.…”

Section: Discussionmentioning

confidence: 99%

Parent-independent genotyping for constructing an ultrahigh-density linkage map based on population sequencing

Xie

Feng²,

Yu³

et al. 2010

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

294

258

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Bar-coded multiplexed sequencing approaches based on newgeneration sequencing technologies provide capacity to sequence a mapping population in a single sequencing run. However, such approaches usually generate low-coverage and error-prone sequences for each line in a population. Thus, it is a significant challenge to genotype individual lines in a population for linkage map construction based on low-coverage sequences without the availability of high-quality genotype data of the parental lines. In this paper, we report a method for constructing ultrahigh-density linkage maps composed of high-quality single-nucleotide polymorphisms (SNPs) based on low-coverage sequences of recombinant inbred lines. First, all potential SNPs were identified to obtain drafts of parental genotypes using a maximum parsimonious inference of recombination, making maximum use of SNP information found in the entire population. Second, high-quality SNPs were identified by filtering out low-quality ones by permutations involving resampling of windows of SNPs followed by Bayesian inference. Third, lines in the mapping population were genotyped using the high-quality SNPs assisted by a hidden Markov model. With 0.05× genome sequence per line, an ultrahigh-density linkage map composed of bins of high-quality SNPs using 238 recombinant inbred lines derived from a cross between two rice varieties was constructed. Using this map, a quantitative trait locus for grain width (GW5) was localized to its presumed genomic region in a bin of 200 kb, confirming the accuracy and quality of the map. This method is generally applicable in genetic map construction with low-coverage sequence data.genomics | maximum parsimony of recombination | Bayesian inference | hidden Markov model | rice G enetic maps provide the bases for a wide range of genetic and genomic studies and are pivotal for mapping and identifying genes associated with phenotypic performance, referred to as traits. The resolution of a genetic linkage map depends on the number of recombination events in the mapping population and the density of molecular markers. The number of recombination events depends on how the population is created, whereas the density of the markers can be improved continually with advances in molecular techniques. Traditional molecular markers that have been widely used in genotyping assays of populations, although laborious and time-consuming, have limitations in throughput and can provide information only for low-density maps, and thus are of low efficiency.Oligonucleotide microarrays, composed of millions of probes based on genome sequences, can capture large numbers of sequence variations between different samples by comparative genomic hybridization, which can be used for high-throughput marker discovery and genotyping (1-3). However, restrictions in microarray design and the number of probes on the microarrays limit the applications of this technology. In addition, it is cost-prohibitive for genotyping especially if the mapping population is large.New sequencing t...

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“…For instance, doubling the number of mice in the mapping population from 800 to 1600 more than doubled the number of QTLs found to influence a measure of fear-related behavior (Turri et al 2001a, b). A study in Drosophila genotyped over 2000 markers on pools of flies selected from an initial sample of over 10,000 F 2 individuals (Lai et al 2007) and substantially increased the number of QTLs known to influence life span in the fly. Furthermore, high-resolution mapping of QTLs revealed more complex genetic architectures than implicated from the initial genome scans.…”

Section: Genetic Architecture: Many Loci Of Small Effectmentioning

confidence: 99%

Genetic architecture of quantitative traits in mice, flies, and humans

Flint¹,

Mackay²

2009

Genome Res.

409

364

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We compare and contrast the genetic architecture of quantitative phenotypes in two genetically well-characterized model organisms, the laboratory mouse, Mus musculus, and the fruit fly, Drosophila melanogaster, with that found in our own species from recent successes in genome-wide association studies. We show that the current model of large numbers of loci, each of small effect, is true for all species examined, and that discrepancies can be largely explained by differences in the experimental designs used. We argue that the distribution of effect size of common variants is the same for all phenotypes regardless of species, and we discuss the importance of epistasis, pleiotropy, and gene by environment interactions. Despite substantial advances in mapping quantitative trait loci, the identification of the quantitative trait genes and ultimately the sequence variants has proved more difficult, so that our information on the molecular basis of quantitative variation remains limited. Nevertheless, available data indicate that many variants lie outside genes, presumably in regulatory regions of the genome, where they act by altering gene expression. As yet there are very few instances where homologous quantitative trait loci, or quantitative trait genes, have been identified in multiple species, but the availability of high-resolution mapping data will soon make it possible to test the degree of overlap between species.Recent successes in mapping quantitative trait loci (QTLs) that contribute to phenotypic variation in humans and model organisms make it possible to address important questions about the genetic architecture of quantitative phenotypes, such as the likely number of loci, their mode of genetic action, and interaction with each other and with the environment. An understanding of the genetic architecture of quantitative traits is not only important in its own right, it will also inform studies that seek to proceed to the identification of the quantitative trait genes (QTGs) and the quantitative trait nucleotide (QTN) variants, the functional changes in DNA sequence that contribute to trait variation.In this work we use examples from two genetically wellcharacterized model organisms, the laboratory mouse, Mus musculus, and the fruit fly, Drosophila melanogaster, to discuss how an understanding of the genetic architecture of quantitative phenotypes in model organisms informs mapping experiments in human genetics. In our discussion of the latter, we draw upon the recent successful application of genome-wide association studies (GWAS) to both quantitative phenotypes (such as height and weight) and disease phenotypes (assuming that the genetic architecture of common disease is similar, if not identical, to that of quantitative phenotypes).Genetic architecture has been the subject of a number of recent reviews, most of which deal with information from a single model organism (Mackay 2004) or review a small number of phenotypes (Kendler and Greenspan 2006). Here, our purpose is different. We tackle three relate...

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Speed-mapping quantitative trait loci using microarrays

Cited by 43 publications

References 12 publications

Epistasis dominates the genetic architecture of Drosophila quantitative traits

Epistasis dominates the genetic architecture of Drosophila quantitative traits

Parent-independent genotyping for constructing an ultrahigh-density linkage map based on population sequencing

Genetic architecture of quantitative traits in mice, flies, and humans

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