DEVELOPMENT, MANAGEMENT AND USE OF A SNP MAP FOR RHESUS MACAQUES

恒河猴 SNP 图谱的开发、管理和使用

• 批准号: 8357326
• 负责人: DAVID Glenn SMITH
• 金额: $ 5.04万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-05-01 至 2012-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8357326
• 关键词: Admixture; Animals; Bioinformatics; Biological Assay; California; Chinese People; Country; DNA; Development; Family; Frequencies; Funding; Gene Frequency; Genetic; Genome; Genome Mappings; Genomics; Genotype; Grant; Health; Hybrids; Libraries; Location; Macaca; Macaca mulatta; Maps; Methods; Minor; Modeling; National Center for Research Resources; Population; Primates; Principal Investigator; Quantitative Trait Loci; Recovery; Research; Research Infrastructure; Research Personnel; Resources; Sampling; Single Nucleotide Polymorphism; Single Nucleotide Polymorphism Map; Source; Structure; Temperament; United States National Institutes of Health; cost; disease phenotype; genetic resource; genome wide association study; human disease; segregation; tool

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. We will use the recently completed rhesus macaque genome map, to identify approximately 83,333 single nucleotide polymorphisms (SNPs) randomly distributed throughout the rhesus macaque (Macaca mulatta) genome. At least ten copies of each DNA fragments in reduced representation libraries (RRLs) constructed from pooled (n=25)DNA samples from 7 different regional populations will be sequenced, then filtered, parsed and compared to the rhesus macaque draft sequence and to each other using bioinformatics tools we previously developed to identify the locations, flanking sequences and minor allele frequencies (MAFs) of 83,333 SNPs. Genotyping assays on lliumina iSelect chips will be constructed for the 7,600 SNPs whose frequencies differ most between Indian and Chinese rhesus macaques and genotyped in geographically representative samples of 192 Indian rhesus macaques, and192 Chinese rhesus macaques and in 678 Indian/Chines rhesus macaques with varying proportions of Indian and Chinese ancestry. Genotypes for the 7,600 SNPs will be used to 1) confirm Mendelian segregation of SNPs in multigeneration families, 2) assess the ability of the SNPs to accurately estimate proportions of Indian and Chinese ancestry in Indian/Chinese hybrid, pedigreed animals and 3) identify ancestry informative markers for admixture mapping of QTLs and to conduct admixture mapping of a QTL for "temperament.". Our SNP map will be made publically available through several outlets including dbSNP. The 384 most potentially informative of the 83,333 SNPs for longtail macaques will be genotyped in 96 samples from six different longtail macaque populations to identify minimum panels of 96 SNPs each for identifying country of origin and for parentage analysis and estiinating parameters useful for genetic management. Assays will be constructed of these two panels and made available to NIH investigators, providing cost recovery. The genotypes of SNPs described above will be used for genomic and population structure analyses of regional populations of rhesus and longtail macaques. PUBLIC HEALTH RELEVANCE (provided by applicant): Rhesus and longtail macaques are the most and second most used models for the biomedical study of human diseases. Genetic resources developed for rhesus macaques are not available for longtail macaques. Mapping disease phenotypes and QTLs is possible with the recent sequencing of the rhesus genome but requires the identification of many known polymorphic loci (e.g., SNPs) throughout the macaque genome. The most practical method for conducting whole genome association studies is admixture mapping.

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