Single-step genomic evaluation in forest genetics

基因组标记（snp）在针叶树等基因组大而复杂的非模式生物中的应用，使基因组学在森林定量遗传学中的应用成为可能。传统的育种群体评估依赖于预期的基于家系的关系（a -矩阵）来估计遗传参数。利用snp，可以估计个体间基于基因组的关系（g矩阵），并用最佳线性无偏预测（BLUP）代替个体-树混合模型中的a矩阵来预测育种值。最近的植物和动物遗传评估已经证明了g -矩阵的优势，因为它能够解释孟德尔分离和隐藏谱系以及无法用a -矩阵估计的历史谱系。混合模型方程中g矩阵的拟合被称为基因组BLUP (GBLUP)，已被证明在树木育种中是有效的；然而，树木繁殖种群非常大，有1000个后代来自100个父母，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，提出了一种将a -矩阵和g -矩阵单步结合的新方法，将传统的a -矩阵与g -矩阵同时使用，从而形成一个将许多非基因型个体的a矩阵与基因型个体子集的g矩阵结合的混合关系h矩阵。h矩阵可以看作是利用系谱关系从基因型到非基因型个体的遗传优点的投影。因此，在混合方法中，由于a矩阵在个体和父母之间起着桥梁的作用，因此产生了额外的信息，从而最终促进了在BLUP分析过程中更好地利用信息，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），那么必须回答几个问题，包括；1)构建g矩阵需要多少个snp ？2)基因型和非基因型之间的平衡是什么？3)基因分型能否局限于特定位点？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40年的白云杉测试项目中实施h矩阵，这对于提供可靠的生长和木材属性是理想的

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.**