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Mapping of quantitative trait loci for life history traits segregating within common frog populations.

ABSTRACT: The evolution of complex traits is often shaped by adaptive divergence. However, very little is known about the number, effect size, and location of the genomic regions influencing the variation of these traits in natural populations. Based on a dense linkage map of the common frog, Rana temporaria, we have localized, for the first time in amphibians, three significant and nine suggestive quantitative trait loci (QTLs) for metabolic rate, growth rate, development time, and weight at metamorphosis, explaining 5.6-18.9% of the overall phenotypic variation in each trait. We also found a potential pleiotropic QTL between development time and size at metamorphosis that, if confirmed, might underlie the previously reported genetic correlation between these traits. Furthermore, we demonstrate that the genetic variation linked to fitness-related larval traits segregates within Rana temporaria populations. This study provides the first insight into the genomic regions that affect larval life history traits in anurans, providing a valuable resource to delve further into the genomic basis of evolutionary change in amphibians.

SUBMITTER: Palomar G

PROVIDER: S-EPMC6781108 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Mapping of quantitative trait loci for life history traits segregating within common frog populations.

Palomar Gemma G Vasemägi Anti A Ahmad Freed F Nicieza Alfredo G AG Cano José Manuel JM

Heredity 20190110 6

The evolution of complex traits is often shaped by adaptive divergence. However, very little is known about the number, effect size, and location of the genomic regions influencing the variation of these traits in natural populations. Based on a dense linkage map of the common frog, Rana temporaria, we have localized, for the first time in amphibians, three significant and nine suggestive quantitative trait loci (QTLs) for metabolic rate, growth rate, development time, and weight at metamorphosi ...[more]

PMID: 30631147

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Project description:BackgroundSingle nucleotide polymorphisms (SNPs) which capture a significant impact on a trait can be identified with genome-wide association studies. High linkage disequilibrium (LD) among SNPs makes it difficult to identify causative variants correctly. Thus, often target regions instead of single SNPs are reported. Sample size has not only a crucial impact on the precision of parameter estimates, it also ensures that a desired level of statistical power can be reached. We study the design of experiments for fine-mapping of signals of a quantitative trait locus in such a target region.MethodsA multi-locus model allows to identify causative variants simultaneously, to state their positions more precisely and to account for existing dependencies. Based on the commonly applied SNP-BLUP approach, we determine the z-score statistic for locally testing non-zero SNP effects and investigate its distribution under the alternative hypothesis. This quantity employs the theoretical instead of observed dependence between SNPs; it can be set up as a function of paternal and maternal LD for any given population structure.ResultsWe simulated multiple paternal half-sib families and considered a target region of 1 Mbp. A bimodal distribution of estimated sample size was observed, particularly if more than two causative variants were assumed. The median of estimates constituted the final proposal of optimal sample size; it was consistently less than sample size estimated from single-SNP investigation which was used as a baseline approach. The second mode pointed to inflated sample sizes and could be explained by blocks of varying linkage phases leading to negative correlations between SNPs. Optimal sample size increased almost linearly with number of signals to be identified but depended much stronger on the assumption on heritability. For instance, three times as many samples were required if heritability was 0.1 compared to 0.3. An R package is provided that comprises all required tools.ConclusionsOur approach incorporates information about the population structure into the design of experiments. Compared to a conventional method, this leads to a reduced estimate of sample size enabling the resource-saving design of future experiments for fine-mapping of candidate variants.

Project description:BACKGROUND: Modern watermelon (Citrullus lanatus L.) cultivars share a narrow genetic base due to many years of selection for desirable horticultural qualities. Wild subspecies within C. lanatus are important potential sources of novel alleles for watermelon breeding, but successful trait introgression into elite cultivars has had limited success. The application of marker assisted selection (MAS) in watermelon is yet to be realized, mainly due to the past lack of high quality genetic maps. Recently, a number of useful maps have become available, however these maps have few common markers, and were constructed using different marker sets, thus, making integration and comparative analysis among maps difficult. The objective of this research was to use single-nucleotide polymorphism (SNP) anchor markers to construct an integrated genetic map for C. lanatus. RESULTS: Under the framework of the high density genetic map, an integrated genetic map was constructed by merging data from four independent mapping experiments using a genetically diverse array of parental lines, which included three subspecies of watermelon. The 698 simple sequence repeat (SSR), 219 insertion-deletion (InDel), 36 structure variation (SV) and 386 SNP markers from the four maps were used to construct an integrated map. This integrated map contained 1339 markers, spanning 798 cM with an average marker interval of 0.6 cM. Fifty-eight previously reported quantitative trait loci (QTL) for 12 traits in these populations were also integrated into the map. In addition, new QTL identified for brix, fructose, glucose and sucrose were added. Some QTL associated with economically important traits detected in different genetic backgrounds mapped to similar genomic regions of the integrated map, suggesting that such QTL are responsible for the phenotypic variability observed in a broad array of watermelon germplasm. CONCLUSIONS: The integrated map described herein enhances the utility of genomic tools over previous watermelon genetic maps. A large proportion of the markers in the integrated map are SSRs, InDels and SNPs, which are easily transferable across laboratories. Moreover, the populations used to construct the integrated map include all three watermelon subspecies, making this integrated map useful for the selection of breeding traits, identification of QTL, MAS, analysis of germplasm and commercial hybrid seed detection.

Dataset Information

Mapping of quantitative trait loci for life history traits segregating within common frog populations.

Publications

Mapping of quantitative trait loci for life history traits segregating within common frog populations.

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