ArticlePLoS genetics2007
Linkage disequilibrium in wild mice.
Article in PLoS genetics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers.
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Who cites it
77 citing papers in PubMed.
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- Tissue-specific and cis-regulatory changes underlie parallel, adaptive gene expression evolution in house mice.PLoS genetics · 2024Article
- Into the Wild: A novel wild-derived inbred strain resource expands the genomic and phenotypic diversity of laboratory mouse models.bioRxiv : the preprint server for biology · 2023Article
- Population genomics and conservation management of the threatened black-footed tree-rat (Mesembriomys gouldii) in northern Australia.Heredity · 2023Article
- Dynamic changes to signal allocation rules in response to variable social environments in house mice.Communications biology · 2023Article
- Identification of a Chromosome 1 Substitution Line B6-Chr1BLD as a Novel Hyperlipidemia ModelMetabolites · 2022Article
- Population structure and inbreeding in wild house mice (Mus musculus) at different geographic scales.Heredity · 2022Article
- Genomic characterization of the world's longest selection experiment in mouse reveals the complexity of polygenic traits.BMC biology · 2022Article
- The genomic basis of high-elevation adaptation in wild house mice (Mus musculus domesticus) from South America.Genetics · 2022Article
- Host genetic factors associated with the range limit of a European hantavirus.Molecular ecology · 2022Article
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- How imputation can mitigate SNP ascertainment Bias.BMC genomics · 2021Article
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- The genomics of rapid climatic adaptation and parallel evolution in North American house mice.PLoS genetics · 2021Article
- How array design creates SNP ascertainment bias.PloS one · 2021Article
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- An evaluation of sequencing coverage and genotyping strategies to assess neutral and adaptive diversity.Molecular ecology resources · 2019Article
17 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Crosses between laboratory strains of mice provide a powerful way of detecting quantitative trait loci for complex traits related to human disease. Hundreds of these loci have been detected, but only a small number of the underlying causative genes have been identified. The main difficulty is the extensive linkage disequilibrium (LD) in intercross progeny and the slow process of fine-scale mapping by traditional methods. Recently, new approaches have been introduced, such as association studies with inbred lines and multigenerational crosses. These approaches are very useful for interval reduction, but generally do not provide single-gene resolution because of strong LD extending over one to several megabases. Here, we investigate the genetic structure of a natural population of mice in Arizona to determine its suitability for fine-scale LD mapping and association studies. There are three main findings: (1) Arizona mice have a high level of genetic variation, which includes a large fraction of the sequence variation present in classical strains of laboratory mice; (2) they show clear evidence of local inbreeding but appear to lack stable population structure across the study area; and (3) LD decays with distance at a rate similar to human populations, which is considerably more rapid than in laboratory populations of mice. Strong associations in Arizona mice are limited primarily to markers less than 100 kb apart, which provides the possibility of fine-scale association mapping at the level of one or a few genes. Although other considerations, such as sample size requirements and marker discovery, are serious issues in the implementation of association studies, the genetic variation and LD results indicate that wild mice could provide a useful tool for identifying genes that cause variation in complex traits.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.