Single-step genomic evaluation in forest genetics

森林遗传学中的单步基因组评估

• 批准号: RGPIN-2017-03772
• 负责人: ElKassaby, Yousry
• 金额: $ 1.82万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710530
• 关键词: Single; step; genomic; evaluation; forest

The availability of genomic marker (SNPs) for non-model organisms with complex and large genome such as conifers, made it possible to infuse genomics in forest quantitative genetics. Traditional evaluation of breeding populations relied on the expected pedigree-based relationship (A-matrix) for estimating the genetic parameters. With SNPs, the genomic-based relationship (G-matrix) between individuals can be estimated and used as a substitute to the A-matrix in the individual-tree mixed model to predict breeding values by the Best Linear Unbiased Prediction (BLUP). Recent plant and animal genetic evaluations have demonstrated the superiority of G-matrix for its ability to account for the Mendelian segregation and hidden pedigree as well as historical pedigree that cannot be estimated with the A-matrix. Fitting the G-matrix in the mixed model equations is known as the genomic BLUP (GBLUP) which has proven to be effective in tree breeding; however, the tree breeding populations are exceedingly large, with 1000s of progenies from 100s parents, planted over multiple sites. Thus, GBLUP implementation is hampered by genotyping costs (many individuals) and logistical issues (sampling across sites). Recently, a novel approach combines both the A- and G-matrices in a single-step was proposed, where the traditional A-matrix is concurrently utilized with the G-matrix, thus forming a blended relationship H-matrix combines the A-matrix of many non-genotyped individuals and G-matrix of a subset of genotyped individuals. The H-matrix can be seen as a projection of genetic merit from genotyped to non-genotyped individuals using pedigree relationships. Thus, additional information is generated in the blended approach as the A-matrix acts like bridges across individuals and parents, thus ultimately facilitating better information utilization during the BLUP analysis, resulting in the generation of more reliable and accurate genetic parameters. The H-matrix is common in animal breeding as genotype x environment interaction (GxE) is minimal, a situation does not apply to tree testing as GxE is common. Since the implementation of the H-matrix requires genotyped (subset) and non-genotyped (the entire progeny test), then several questions must be answered, including; 1) what is the number of SNPs needed for constructing the G-matrix?, 2) what is the balance between genotyped and non-genotyped?, 3) can genotyping be limited to specific sites?, and 4) what is the impact of GxE on the estimated genetic parameters? Here, I plan to implement the H-matrix on the British Columbia's white spruce 40 year-old testing program which is ideal for providing reliable growth and wood attributes.

针叶树等基因组复杂、庞大的非模式生物的基因组标记（SNPs）的出现，使基因组学在森林数量遗传学中的应用成为可能。传统的育种群体的评价依赖于预期的基于系谱的关系（A矩阵）估计的遗传参数。利用SNPs，可以估计个体之间基于基因组的关系（G矩阵），并将其用作个体-树混合模型中A矩阵的替代，以通过最佳线性无偏预测（BLUP）来预测育种值。最近的植物和动物遗传评估表明，G-矩阵的优越性，它能够解释孟德尔分离和隐藏的谱系，以及历史谱系，不能估计与A-矩阵。在混合模型方程中拟合G矩阵被称为基因组BLUP（GBLUP），其已被证明在树木育种中是有效的;然而，树木育种种群非常大，来自100个亲本的1000个后代，种植在多个地点。因此，GBLUP的实施受到基因分型成本（许多个体）和后勤问题（跨站点采样）的阻碍。最近，一种新的方法将A-矩阵和G-矩阵在一个步骤中结合起来，其中传统的A-矩阵与G-矩阵同时使用，从而形成混合关系H-矩阵结合了许多非基因型个体的A-矩阵和基因型个体子集的G-矩阵。H矩阵可以被看作是使用系谱关系从基因分型个体到非基因分型个体的遗传价值的投影。因此，在混合方法中产生额外的信息，因为A矩阵就像跨越个体和亲本的桥梁，从而最终促进BLUP分析期间更好的信息利用，从而产生更可靠和准确的遗传参数。H矩阵在动物育种中很常见，因为基因型x环境相互作用（GxE）很小，这种情况不适用于树测试，因为GxE很常见。由于H矩阵的实施需要基因分型（子集）和非基因分型（整个后代测试），因此必须回答几个问题，包括：1）构建G矩阵所需的SNP数量是多少？2)基因型和非基因型之间的平衡是什么？3)基因分型能被限制在特定位点吗？4）GxE对估计的遗传参数有何影响？在这里，我计划在不列颠哥伦比亚省的白色云杉40年的测试程序，这是理想的提供可靠的增长和木材属性的H矩阵。