Efficient association mapping of quantitative trait loci with selective genotyping.
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ABSTRACT:
SUBMITTER: Huang BE
PROVIDER: S-EPMC1821103 | biostudies-other | 2007 Mar
REPOSITORIES: biostudies-other
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Project description:Statistical power calculations are a critical part of any study design for gene mapping. Most calculations assume that the locus of interest is biallelic. However, there are common situations in human genetics such as X-linked loci in males where the locus is haploid. The purpose of this work is to mathematically derive the biometric model for haploid loci, and to compute power for QTL mapping when the loci are haploid.We have derived the biometric model for power calculations for haploid loci and have developed software to perform these calculations. We have verified our calculations with independent mathematical methods.Our results fill a need in power calculations for QTL mapping studies. Furthermore, failure to appropriately model haploid loci may cause underestimation of power.
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Project description:Although mapping quantitative traits in inbred strains is simpler than mapping the analogous traits in humans, classical inbred crosses suffer from reduced genetic diversity compared to experimental designs involving outbred animal populations. Multiple crosses, for example the Complex Trait Consortium's eight-way cross, circumvent these difficulties. However, complex mating schemes and systematic inbreeding raise substantial computational difficulties. Here we present a method for locally imputing the strain origins of each genotyped animal along its genome. Imputed origins then serve as mean effects in a multivariate Gaussian model for testing association between trait levels and local genomic variation. Imputation is a combinatorial process that assigns the maternal and paternal strain origin of each animal on the basis of observed genotypes and prior pedigree information. Without smoothing, imputation is likely to be ill-defined or jump erratically from one strain to another as an animal's genome is traversed. In practice, one expects to see long stretches where strain origins are invariant. Smoothing can be achieved by penalizing strain changes from one marker to the next. A dynamic programming algorithm then solves the strain imputation process in one quick pass through the genome of an animal. Imputation accuracy exceeds 99% in practical examples and leads to high-resolution mapping in simulated and real data. The previous fastest quantitative trait loci (QTL) mapping software for dense genome scans reduced compute times to hours. Our implementation further reduces compute times from hours to minutes with no loss in statistical power. Indeed, power is enhanced for full pedigree data.
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Project description:BACKGROUND: Cucurbita pepo is a member of the Cucurbitaceae family, the second- most important horticultural family in terms of economic importance after Solanaceae. The "summer squash" types, including Zucchini and Scallop, rank among the highest-valued vegetables worldwide. There are few genomic tools available for this species.The first Cucurbita transcriptome, along with a large collection of Single Nucleotide Polymorphisms (SNP), was recently generated using massive sequencing. A set of 384 SNP was selected to generate an Illumina GoldenGate assay in order to construct the first SNP-based genetic map of Cucurbita and map quantitative trait loci (QTL). RESULTS: We herein present the construction of the first SNP-based genetic map of Cucurbita pepo using a population derived from the cross of two varieties with contrasting phenotypes, representing the main cultivar groups of the species' two subspecies: Zucchini (subsp. pepo) × Scallop (subsp. ovifera). The mapping population was genotyped with 384 SNP, a set of selected EST-SNP identified in silico after massive sequencing of the transcriptomes of both parents, using the Illumina GoldenGate platform. The global success rate of the assay was higher than 85%. In total, 304 SNP were mapped, along with 11 SSR from a previous map, giving a map density of 5.56 cM/marker. This map was used to infer syntenic relationships between C. pepo and cucumber and to successfully map QTL that control plant, flowering and fruit traits that are of benefit to squash breeding. The QTL effects were validated in backcross populations. CONCLUSION: Our results show that massive sequencing in different genotypes is an excellent tool for SNP discovery, and that the Illumina GoldenGate platform can be successfully applied to constructing genetic maps and performing QTL analysis in Cucurbita. This is the first SNP-based genetic map in the Cucurbita genus and is an invaluable new tool for biological research, especially considering that most of these markers are located in the coding regions of genes involved in different physiological processes. The platform will also be useful for future mapping and diversity studies, and will be essential in order to accelerate the process of breeding new and better-adapted squash varieties.
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Project description:BackgroundThe conversion of lignocellulosic biomass from agricultural waste into biofuels and chemicals is considered a promising way to provide sustainable low carbon products without compromising food security. However, the use of lignocellulosic biomass for biofuel and chemical production is limited by the cost-effectiveness of the production process due to its recalcitrance to enzymatic hydrolysis and fermentable sugar release (i.e., saccharification). Rice straw is a particularly attractive feedstock because millions of tons are currently burned in the field each year for disposal. The aim of this study was to explore the underlying natural genetic variation that impacts the recalcitrance of rice (Oryza sativa) straw to enzymatic saccharification. Ultimately, we wanted to investigate whether we could identify genetic markers that could be used in rice breeding to improve commercial cultivars for this trait. Here, we describe the development and characterization of a Vietnamese rice genome-wide association panel, high-throughput analysis of rice straw saccharification and lignin content, and the results from preliminary genome-wide association studies (GWAS) of the combined data sets. We identify both QTL and plausible candidate genes that may have an impact on the saccharification of rice straw.ResultsWe assembled a diversity panel comprising 151 rice genotypes (Indica and Japonica types) from commercial, historical elite cultivars, and traditional landraces grown in Vietnam. The diversity panel was genotyped using genotype by sequencing (GBS) methods yielding a total of 328,915 single nucleotide polymorphisms (SNPs). We collected phenotypic data from stems of these 151 genotypes for biomass saccharification and lignin content. Using GWAS on the indica genotypes over 2 years we identified ten significant QTL for saccharification (digestibility) and seven significant QTL for lignin. One QTL on chromosome 11 occurred in both GWAS for digestibility and for lignin. Seven QTL for digestibility, on CH2, CH6, CH7, CH8, and CH11, were observed in both years of the study. The QTL regions for saccharification include three potential candidate genes that have been previously reported to influence digestibility: OsAT10; OsIRX9; and OsMYB58/63-L.ConclusionsDespite the difficulties associated with multi-phasic analysis of complex traits in novel germplasm, a moderate resolution GWAS successfully identified genetic associations encompassing both known and/or novel genes involved in determining the saccharification potential and lignin content of rice straw. Plausible candidates within QTL regions, in particular those with roles in cell wall biosynthesis, were identified but will require validation to confirm their value for application in rice breeding.
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