Analysis of genetic-statistical models for Genomic Selection and QTL-Mapping

基因组选择和 QTL 定位的遗传统计模型分析

• 批准号: 29487966
• 负责人: Dr. David Habier
• 金额: --
• 依托单位: Institut für Tierzucht und Tierhaltung
• 依托单位国家: 德国
• 项目类别: Research Fellowships
• 财政年份: 2006
• 资助国家: 德国
• 起止时间: 2005-12-31 至 2008-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/29487966?language=en
• 关键词: Analysis; genetic; statistical; models; Genomic

Due to advances in molecular genetics, animals can be genotyped for thousands of maker loci at one time, providing dense marker maps. It has been proposed to use these to obtain genome-assisted breeding values (GABV). Results from simulation studies were promising with high correlations between true and estimated breeding values of around 0.8 from a single generation of data. This is comparable to the accuracy of BLUP-EBV for progeny tested selection candidates. Since GABV are available as soon as genotyping is feasible, generation intervals in breeding programs can be reduced and thus genetic improvement can be increased enormously. The models that have been proposed so far assume independent effects of marker loci, which is not realistic. Furthermore, these models capture linkage disequilibrium (LD) while neglecting information from cosegregation (CS). Even though the precision of GABVs is expected to be high, models combining LD and CS should be able to reduce both the cost for genotyping and the number of test animals. These models might make GABV more attractive for pigs and poultry breeding in particular. The objective of the proposed research project is first to develop a statistical model which includes a dependence structure for effects of marker loci. Then, models that combine LD and CS will be considered. These models will be evaluated with simulated data and real data provided by a poultry breeding company.

由于分子遗传学的进步，动物可以一次对数千个标记基因座进行基因分型，提供密集的标记图谱。有人建议利用这些来获得基因组辅助育种价值(GABV)。模拟研究的结果很有希望，单代数据的真实繁殖值与估计的育种值之间有很高的相关性，约为0.8。这与BLUP-EBV对子代测试的选择候选者的准确性相当。由于GABV在基因分型可行后立即可用，育种计划中的世代间隔可以缩短，从而可以极大地提高遗传改良。到目前为止，已提出的模型假设了标记基因座的独立效应，这是不现实的。此外，这些模型捕捉到了连锁不平衡(LD)，而忽略了来自共分离(CS)的信息。尽管GABV的精确度预计会很高，但结合LD和CS的模型应该能够降低基因分型的成本和实验动物的数量。这些模型可能会使GABV对猪和家禽养殖业更具吸引力。提出的研究项目的目标是首先开发一个统计模型，该模型包括标记基因座效应的依赖结构。然后，将考虑将LD和CS相结合的模型。这些模型将用一家家禽养殖公司提供的模拟数据和真实数据进行评估。