Removing data and using metafounders alleviates biases for all traits in Lacaune dairy sheep predictions

Abstract

Bias in dairy genetic evaluations, when it exists, has to be understood and properly addressed. The origin of biases is not always clear. We analyzed 40 yr of records from the Lacaune dairy sheep breeding program to evaluate the extent of bias, assess possible corrections, and emit hypotheses on its origin. The data set included 7 traits (milk yield, fat and protein contents, somatic cell score, teat angle, udder cleft, and udder depth) with records from 600,000 to 5 million depending on the trait, ~1,900,000 animals, and ~5,900 genotyped elite artificial insemination rams. For the ~8% animals with missing sire, we fit 25 unknown parent groups. We used the linear regression method to compare "partial" and "whole" predictions of young rams before and after progeny testing, with 7 cut-off points, and we obtained estimates of their bias, (over)dispersion, and accuracy in early proofs. We tried (1) several scenarios as follows: multiple or single trait, the "official" (routine) evaluation, which is a mixture of both single and multiple trait, and "deletion" of data before 1990; and (2) several models as follows: BLUP and single-step genomic (SSG)BLUP with fixed unknown parent groups or metafounders, where, for metafounders, their relationship matrix gamma was estimated using either a model for inbreeding trend, or base allele frequencies estimated by peeling. The estimate of gamma obtained by modeling the inbreeding trend resulted in an estimated increase of inbreeding, based on markers, faster than the pedigree-based one. The estimated genetic trends were similar for most models and scenarios across all traits, but were shrunken when gamma was estimated by peeling. This was due to shrinking of the estimates of metafounders in the latter case. Across scenarios, all traits showed bias, generally as an overestimate of genetic trend for milk yield and an underestimate for the other traits. As for the slope, it showed overdispersion of estimated breeding values for all traits. Using multiple-trait models slightly reduced the overestimate of genetic trend and the overdispersion, as did including genomic information (i.e., SSGBLUP) when the gamma matrix was estimated by the model for inbreeding trend. However, only deletion of historical data before 1990 resulted in elimination of both kind of biases. The SSGBLUP resulted in more accurate early proofs than BLUP for all traits. We considered that a snowball effect of small errors in each genetic evaluation, combined with selection, may have resulted in biased evaluations. Improving statistical methods reduced some bias but not all, and a simple solution for this data set was to remove historical records.

Correcting for base-population differences and unknown parent groups in single-step genomic predictions of Norwegian Red cattle

“…In setting parents of the validation animals to missing, the relationship between genotyped and non-genotyped animals might become zero (i.e., A 12 = 0) and in such situation, H −1 will not contribute to the estimation of group effects ( Tsuruta et al, 2019 ). Truncation of the data set, e.g., only considering data after the year 2000, may also reduce the number of genetic groups, and has been found to reduce prediction biases ( Cesarani et al, 2021 ; Hidalgo et al, 2021 ; Hollifield et al, 2021 ; Macedo et al, 2022 ).…”

Accounting for population structure in genomic predictions of Eucalyptus globulus

“…In contrast, Granado-Tajada et al (2020) found that bias in dairy sheep evaluations was greater for HBLUP than ABLUP, although the difference was not statistically significant. Macedo, Christensen, et al (2020) reported the least bias for HBLUP with MF, as did Macedo et al (2022) using a method of construction based on inbreeding trend over time.…”

International genetic and genomic evaluations of beef cattle

“…Recent studies suggested that truncating datasets that trace many generations of pedigrees for the estimation of breeding values may help to reduce level and dispersion bias more than fitting genetic groups. In a dairy sheep population, Macedo et al (2022) compared different strategies to model genetic groups and studied the effects on level and dispersion bias. The best strategy to reduce level bias and over-dispersion in their dataset was to truncate old pedigree and phenotypic data.…”

“…Truncating data yielded better results compared to modelling genetic groups while using the full dataset in either pedigree or single-step genomic evaluations. Macedo et al (2022) suggested that the accumulation across generations of small noises or approximations during the estimation of breeding values could lead to a snowball effect, resulting in bias and dispersion of younger animals' EBV. Truncating the data was an effective solution to alleviate this problem in their study.…”

“…Indeed, most of the publishable sires with available frozen semen to exchange across countries are probably born in the late 20 th century or more recent generations. Then, truncating these deep pedigrees could standardize the base population in the pedigree across animals and could possibly contribute to reduce level and dispersion bias without affecting the accuracies of pedigree and genomic EBV, similarly to Cesarani et al (2021b) and Macedo et al (2022). Thus, I recommend that next to examining the optimal definition of genetic groups, future investigations should also consider the simple, yet possibly effective, strategy of truncating old data.…”