Parallel selection mapping using artificially selected mice reveals body weight control loci
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE36452 | GEO | 2012/06/30
SECONDARY ACCESSION(S): PRJNA153545
REPOSITORIES: GEO
Similar Datasets
Project description:Understanding how polygenic traits evolve and respond to selection is a major unsolved problem, because challenges exist for identifying genes underlying a complex trait and understanding how multi-locus selection operates in the genome. Here we used artificial selection experiments to study polygenic response to selection. Inbred strains from seven independent long-term selection experiments in mice for extreme bodyweight (“High” lines weigh 77-42g vs. 40-16g in “Controls” lines), were genotyped at 527,572 SNPs to identify genetic variants controlling bodyweight. We identified 67 high-resolution parallel selected regions (PSRs) where multiple High lines share variants rarely found among the Controls. By comparing allele frequencies in one selection experiment against its unselected control, we found classical selective sweep signatures centered on the PSRs. Multiple lines of evidence support two G protein-coupled receptors GPR133 and Prlhr, as positional candidate genes controlling bodyweight. Artificial selection may mimic natural selection in the wild: compared to control loci, we detected reduced heterozygosity in PSRs in wild populations of unusually large mice on islands. Many PSRs overlap loci associated with human height variation, possibly through evolutionary conservation of functional pathways. Our data suggest that parallel selection on complex traits may evoke parallel responses at many genes involved in diverse but relevant pathways. These samples were used to test the enrichment of certain gene functional categories.
2012-06-30 | GSE36409 | GEO
Project description:Understanding how polygenic traits evolve and respond to selection is a major unsolved problem, because challenges exist for identifying genes underlying a complex trait and understanding how multi-locus selection operates in the genome. Here we used artificial selection experiments to study polygenic response to selection. Inbred strains from seven independent long-term selection experiments in mice for extreme bodyweight (“High” lines weigh 77-42g vs. 40-16g in “Controls” lines), were genotyped at 527,572 SNPs to identify genetic variants controlling bodyweight. We identified 67 high-resolution parallel selected regions (PSRs) where multiple High lines share variants rarely found among the Controls. By comparing allele frequencies in one selection experiment against its unselected control, we found classical selective sweep signatures centered on the PSRs. Multiple lines of evidence support two G protein-coupled receptors GPR133 and Prlhr, as positional candidate genes controlling bodyweight. Artificial selection may mimic natural selection in the wild: compared to control loci, we detected reduced heterozygosity in PSRs in wild populations of unusually large mice on islands. Many PSRs overlap loci associated with human height variation, possibly through evolutionary conservation of functional pathways. Our data suggest that parallel selection on complex traits may evoke parallel responses at many genes involved in diverse but relevant pathways. These samples were used to test the enrichment of certain gene functional categories. Genomic DNA SNP comparison between artificially selected high lines (BEH, DAHi, DUH, MUH, EDH, RAHi, Du6/G154 and Du6i/G80) and unselected control lines.
2012-06-29 | E-GEOD-36409 | biostudies-arrayexpress
Project description:Understanding how polygenic traits evolve and respond to selection is a major unsolved problem, because challenges exist for identifying genes underlying a complex trait and understanding how multi-locus selection operates in the genome. Here we used artificial selection experiments to study polygenic response to selection. Inbred strains from seven independent long-term selection experiments in mice for extreme bodyweight (“High” lines weigh 77-42g vs. 40-16g in “Controls” lines), were genotyped at 527,572 SNPs to identify genetic variants controlling bodyweight. We identified 67 high-resolution parallel selected regions (PSRs) where multiple High lines share variants rarely found among the Controls. By comparing allele frequencies in one selection experiment against its unselected control, we found classical selective sweep signatures centered on the PSRs. Multiple lines of evidence support two G protein-coupled receptors GPR133 and Prlhr, as positional candidate genes controlling bodyweight. Artificial selection may mimic natural selection in the wild: compared to control loci, we detected reduced heterozygosity in PSRs in wild populations of unusually large mice on islands. Many PSRs overlap loci associated with human height variation, possibly through evolutionary conservation of functional pathways. Our data suggest that parallel selection on complex traits may evoke parallel responses at many genes involved in diverse but relevant pathways. These samples were used to test the enrichment of certain gene functional categories.
2012-06-30 | GSE36443 | GEO
Project description:Understanding how polygenic traits evolve and respond to selection is a major unsolved problem, because challenges exist for identifying genes underlying a complex trait and understanding how multi-locus selection operates in the genome. Here we used artificial selection experiments to study polygenic response to selection. Inbred strains from seven independent long-term selection experiments in mice for extreme bodyweight (M-bM-^@M-^\HighM-bM-^@M-^] lines weigh 77-42g vs. 40-16g in M-bM-^@M-^\ControlsM-bM-^@M-^] lines), were genotyped at 527,572 SNPs to identify genetic variants controlling bodyweight. We identified 67 high-resolution parallel selected regions (PSRs) where multiple High lines share variants rarely found among the Controls. By comparing allele frequencies in one selection experiment against its unselected control, we found classical selective sweep signatures centered on the PSRs. Multiple lines of evidence support two G protein-coupled receptors GPR133 and Prlhr, as positional candidate genes controlling bodyweight. Artificial selection may mimic natural selection in the wild: compared to control loci, we detected reduced heterozygosity in PSRs in wild populations of unusually large mice on islands. Many PSRs overlap loci associated with human height variation, possibly through evolutionary conservation of functional pathways. Our data suggest that parallel selection on complex traits may evoke parallel responses at many genes involved in diverse but relevant pathways. These samples were used to test the enrichment of certain gene functional categories. Mouse tissue samples (muscle and liver) were collected from 44 and 45 days old males from each of the nine out of thirteen lines (5 High lines: BEHi, DUHi, EDHi, MUHi, ROHi and 4 Control lines: BELi, EDLi, MULi and ROLi; n=23 total) at the University of Edinburgh. Frozen tissue samples were stored at -80M-BM-:C until use. Tissues were dissociated using TissueLyser II with steel beads (both Qiagen) following manufacturersM-bM-^@M-^Y instructions. mRNA was extracted and purified using TRIzol Reagent and the PureLink RNA Mini Kit (Life Technologies GmbH, Darmstadt, Germany). cDNA was synthesized using Maxima First Strand cDNA synthesis kit (Thermo Fisher Scientific, St. Leon-Rot, Germany). Muscle and liver cDNA samples were hybridized on a Mouse GE 4x44k Microarray (Agilent Technologies, Inc., Waldbronn, Germany) according to manufacturerM-bM-^@M-^Ys recommendations. Three technical replicate each was done on pooled cDNA samples from each mouse line.
2012-06-29 | E-GEOD-36443 | biostudies-arrayexpress
Project description:Background:Liver weight is a complex trait, controlled by polygenic factors and differs within populations. Dissecting the genetic architecture underlying these variations will facilitate the search for key role candidate genes involved directly in the hepatomegaly process and indirectly involved in related diseases etiology. Methods:Liver weight of 506 mice generated from 39 different Collaborative Cross (CC) lines with both sexes at age 20 weeks old was determined using an electronic balance. Genomic DNA of the CC lines was genotyped with high-density single nucleotide polymorphic markers. Results:Statistical analysis revealed a significant (P < 0.05) variation of liver weight between the CC lines, with broad sense heritability (H 2) of 0.32 and genetic coefficient of variation (CVG) of 0.28. Subsequently, quantitative trait locus (QTL) mapping was performed, and results showed a significant QTL only for females on chromosome 8 at genomic interval 88.61-93.38 Mb (4.77 Mb). Three suggestive QTL were mapped at chromosomes 4, 12 and 13. The four QTL were designated as LWL1-LWL4 referring to liver weight loci 1-4 on chromosomes 8, 4, 12 and 13, respectively. Conclusion:To our knowledge, this report presents, for the first time, the utilization of the CC for mapping QTL associated with baseline liver weight in mice. Our findings demonstrate that liver weight is a complex trait controlled by multiple genetic factors that differ significantly between sexes.
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Project description:Despite recent flourish of proposals on variable selection, genome-wide multiple loci mapping remains to be challenging. The majority of existing variable selection methods impose a model, and often the homoscedastic linear model, prior to selection. However, the true association between the phenotypical trait and the genetic markers is rarely known a priori, and the presence of epistatic interactions makes the association more complex than a linear relation. Model-free variable selection offers a useful alternative in this context, but the fact that the number of markers p often far exceeds the number of experimental units n renders all the existing model-free solutions that require n > p inapplicable. In this article, we examine a number of model-free variable selection methods for small-n-large-p regressions in the context of genome-wide multiple loci mapping. We propose and advocate a multivariate group-wise adaptive penalization solution, which requires no model prespecification and thus works for complex trait-marker association, and handles one variable at a time so that works for n < p. Effectiveness of the new method is demonstrated through both intensive simulations and a comprehensive real data analysis across 6100 gene expression traits.
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Project description:Artificially selected lines are widely used to investigate the genetic basis of quantitative traits and make inferences about evolutionary trajectories. Yet, the relevance of selected traits to field fitness is rarely tested. Here, we assess the relevance of thermal stress resistance artificially selected in the laboratory to one component of field fitness by investigating the likelihood of adult Drosophila melanogaster reaching food bait under different temperatures. Lines resistant to heat reached the bait more often than controls under hot and cold conditions, but less often at intermediate temperatures, suggesting a fitness cost of increased heat resistance but not at temperature extremes. Cold-resistant lines were more common at baits than controls under cold as well as hot field conditions, and there was no cost at intermediate temperatures. One of the replicate heat-resistant lines was caught less often than the others under hot conditions. Direct and correlated patterns of responses in laboratory tests did not fully predict the low performance of the heat selected lines at intermediate temperatures, nor the high performance of the cold selected lines under hot conditions. Therefore, lines selected artificially not only behaved partly as expected based on laboratory assays but also evolved patterns only evident in the field releases.
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Project description:Quantitative trait locus (QTL) mapping is often conducted in line-crossing experiments where a sample of individuals is randomly selected from a pool of all potential progeny. QTLs detected from such an experiment are important for us to understand the genetic mechanisms governing a complex trait, but may not be directly relevant to plant breeding if they are not detected from the breeding population where selection is targeting for. QTLs segregating in one population may not necessarily segregate in another population. To facilitate marker-assisted selection, QTLs must be detected from the very population which the selection is targeting. However, selected breeding populations often have depleted genetic variation with small population sizes, resulting in low power in detecting useful QTLs. On the other hand, if selection is effective, loci controlling the selected trait will deviate from the expected Mendelian segregation ratio. In this study, we proposed to detect QTLs in selected breeding populations via the detection of marker segregation distortion in either a single population or multiple populations using the same selection scheme. Simulation studies showed that QTL can be detected in strong selected populations with selected population sizes as small as 25 plants. We applied the new method to detect QTLs in two breeding populations of rice selected for high grain yield. Seven QTLs were identified, four of which have been validated in advanced generations in a follow-up study. Cloned genes in the vicinity of the four QTLs were also reported in the literatures. This mapping-by-selection approach provides a new avenue for breeders to improve breeding progress. The new method can be applied to breeding programs not only in rice but also in other agricultural species including crops, trees and animals.
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Project description:The genetic architecture underlying nematode resistance and body weight in Blackface lambs was evaluated comparing genome-wide association (GWA) and regional heritability mapping (RHM) approaches. The traits analysed were faecal egg count (FEC) and immunoglobulin A activity against third-stage larvae from Teladorsagia circumcincta, as indicators of nematode resistance, and body weight in a population of 752 Scottish Blackface lambs, genotyped with the 50k single-nucleotide polymorphism (SNP) chip. FEC for both Nematodirus and Strongyles nematodes (excluding Nematodirus), as well as body weight were collected at approximately 16, 20 and 24 weeks of age. In addition, a weighted average animal effect was estimated for both FEC and body weight traits. After quality control, 44?388 SNPs were available for the GWA analysis and 42?841 for the RHM, which utilises only mapped SNPs. The same fixed effects were used in both analyses: sex, year, management group, litter size and age of dam, with day of birth as covariate. Some genomic regions of interest for both nematode resistance and body weight traits were identified, using both GWA and RHM approaches. For both methods, strong evidence for association was found on chromosome 14 for Nematodirus average animal effect, chromosome 6 for Strongyles FEC at 16 weeks and chromosome 6 for body weight at 16 weeks. Across the entire data set, RHM identified more regions reaching the suggestive level than GWA, suggesting that RHM is capable of capturing some of the variation not detected by GWA analyses.
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Project description:Several genome-wide association studies (GWAS) have identified genetic variants associated with birth weight. To date, however, most GWAS of birth weight have focused primarily on European ancestry samples even though prevalence of low birth weight is higher among African-Americans. We conducted admixture mapping using 2918 ancestral informative markers in 2596 participants of the Black Women's Health Study, with the goal of identifying novel genomic regions where local African ancestry is associated with birth weight. In addition, we performed a replication analysis of 11 previously identified index single nucleotide polymorphisms (SNPs), and fine-mapped those genetic loci to identify better or new genetic variants associated with birth weight in African-Americans. We found that high African ancestry at 12q14 was associated with low birth weight, and we identified multiple independent birth weight-lowering variants in this genomic region. We replicated the association of a previous GWAS SNP in ADRB1 and our fine-mapping efforts suggested the presence of new birth weight-associated variants in ADRB1, HMGA2, and SLC2A4. Further studies are needed to determine whether birth weight-associated loci can in part explain race-associated birth weight disparities.
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