Department of Animal Science, Texas A&M University, College Station, TX.
USDA-ARS, National Animal Germplasm Program, Fort Collins, CO.
J Anim Sci. 2020 Jan 1;98(1). doi: 10.1093/jas/skz359.
Accounting for genotype-environment interactions may improve genetic prediction and parameter estimation. The objective was to use random regression analyses to estimate variances and thereby heritability for intramuscular fat (IMF) across longitude and latitude coordinates within the continental United States. Records from the American Hereford Association (n = 169,440) were used. Analyses were first conducted using the continental United States in its entirety, and then as subdivided into two or four regions. Data were analyzed with an animal model, and linear and quadratic random regressions of additive genetic merit on longitude or latitude as covariate (separately). Subdivided data were analyzed with linear random regressions unique to regions. Regions were North and South separated at 40°N latitude, or West and East separated at 99°W longitude using longitude or latitude as covariate, respectively. Further subdivision to four regions included additional boundaries of 44.46° and 36.46°N latitude and 104.55° and 92.22°W longitude. The estimated heritability of IMF from the traditional model was 0.19 ± 0.004. Without regional subdivision of data, quadratic random regression had the best fit for the data based on likelihood ratio tests using longitude or latitude as covariate (P < 0.01). Estimates of heritability from quadratic random regression on latitude ranged from 0.12 in the South to a high of 0.27 at the extreme Northern latitude. Estimates of heritability from quadratic random regression on longitude ranged from 0.17 in the middle of the parameter space (corresponding to the central United States) to 0.37; higher estimates were noted at the extremes, that is, the far West and East longitudes. Random regression analyses of data divided into regions were conducted with a linear coefficient, as increasing to a quadratic polynomial was never accomplished. Results from random regression on latitude in the East region were similar to results from analyses without regions (h2 ranged from 0.09 to 0.32); however, estimates of heritability in the West region had a lower range from South to North (0.14 to 0.27). Estimates of heritability from random regression on longitude with data divided into two regions were similar to those from analyses that did not include region. Estimates in the South region were somewhat lower and had a lower range (0.15 to 0.31) than those from the North region (0.19 to 0.47). When data were further subdivided, estimation of only a subset of covariances among random regression coefficients was possible, that is, within-region covariances of intercept and linear terms (latitude); those and covariances between all linear random regression coefficients were estimated when longitude was the covariate. Results from random regression analyses of data with four regions modeled produced very high estimates of heritability in low latitudes in the furthest West and high latitudes in the furthest East region, with approximate difference of 0.3 and 0.2 between estimates in the two West regions and the two East regions, respectively. Results from random regression on longitude indicated higher estimates of heritability in North region, especially at the furthest East longitudes of the most Northern region. There appeared to be substantial additive genetic variance differences, as well as estimates of heritability, that correspond to different geographical environments as modeled by random regressions on within-region latitude or longitude coordinates.
考虑基因型-环境互作可能会提高遗传预测和参数估计的准确性。本研究旨在利用随机回归分析,估计整个美国大陆的经度和纬度范围内肌内脂肪(IMF)的方差和遗传力。使用美国海勒姆协会(n=169440)的数据进行分析。首先,对整个美国大陆进行分析,然后将其细分为两个或四个区域。采用动物模型进行数据分析,并将加性遗传优势与经度或纬度的线性和二次随机回归作为协变量(分别)。细分数据采用特定区域的线性随机回归进行分析。以 40°N 纬度将区域分为南北两部分,或以 99°W 经度将区域分为东西两部分,分别作为经度或纬度的协变量。进一步将数据细分为四个区域,包括另外的 44.46°和 36.46°N 纬度以及 104.55°和 92.22°W 经度的边界。传统模型估计的 IMF 遗传力为 0.19±0.004。不进行数据区域细分,基于使用经度或纬度作为协变量的似然比检验(P<0.01),二次随机回归对数据的拟合最好。基于纬度的二次随机回归的遗传力估计值从南部的 0.12 到最北部的 0.27 不等。基于经度的二次随机回归的遗传力估计值从参数空间中部(对应美国中部)的 0.17 到 0.37 不等,在极远的西部和东部经度处,估计值更高。对分为区域的数据进行线性系数的随机回归分析,因为增加到二次多项式从未完成过。东部地区基于纬度的随机回归分析的结果与无区域分析的结果相似(h2 范围为 0.09 至 0.32);然而,西部地区从南到北的遗传力估计值范围较低(0.14 至 0.27)。将数据分为两个区域的基于经度的随机回归分析的遗传力估计值与不包括区域的分析相似。南部地区的估计值略低,范围也较小(0.15 至 0.31),而北部地区的估计值(0.19 至 0.47)较大。当进一步细分数据时,只能估计随机回归系数之间的一部分协方差,即区域内截距和线性项(纬度)的随机回归系数之间的协方差;当经度为协变量时,还会估计所有线性随机回归系数之间的协方差。使用四个区域建模的随机回归分析结果产生了在最西部和最东部的低纬度地区非常高的遗传力估计值,在最西部的两个地区和最东部的两个地区之间,估计值相差约 0.3 和 0.2。基于经度的随机回归分析结果表明,北部地区的遗传力估计值较高,尤其是在最北部地区最东部的经度。似乎存在大量的加性遗传方差差异,以及与随机回归分析中不同地理环境相对应的遗传力估计值,这些环境是通过对区域内的纬度或经度坐标进行随机回归来建模的。