Theoretical and empirical comparisons of expected and realized relationships for the X-chromosome

The marker effects of a single-step random regression model for 4 test-day traits in German Holsteins

The single-step genomic model has become the golden standard for routine evaluation in livestock species, such as Holstein dairy cattle. The single-step genomic model with direct estimation of marker effects has been proven to be efficient in accurately accounting for millions of genotype records. For diverse applications including frequent genomic evaluation updates on a weekly basis, estimates of the marker effects from the single-step evaluations play a central role in genomic prediction. In this study we focused on exploring the marker effect estimates from the single-step evaluation. Phenotypic, genotypic, and pedigree data were taken from the official evaluation for German dairy breeds in April 2021. A multilactation random regression test-day model was applied to more than 242 million test-day records separately for 4 traits: milk, fat, and protein yields, and somatic cell scores (SCS). Approximately one million genotyped Holstein animals were considered in the single-step genomic evaluations including ∼21 million animals in pedigree. Deregressed multiple across-country breeding values of Holstein bulls having daughters outside Germany were integrated into the national test-day data to increase the reliability of genomic breeding values. To assess the stability and bias of the marker effects of the single-step model, test-day records of the last 4 yr were deleted, and the integrated bulls born in the last 4 yr were truncated from the complete phenotypic dataset. Estimates of the marker effects were shown to be highly correlated, with correlations ∼0.9, between the full and truncated evaluations. Regression slope values of the marker-effect estimates from the full on the truncated evaluations were all close to their expected value, being ∼1.03. Calculated using random regression coefficients of the marker effect estimates, drastically different shapes of the genetic lactation curve were seen for 2 markers on chromosome 14 for the 4 test-day traits. The contribution of individual chromosomes to the total additive genetic variances seemed to follow the polygenic inheritance mode for protein yield and SCS. However, chromosome 14 was found to make an exceptionally large contribution to the total additive genetic variance for milk and fat yields because of markers near the major gene DGAT1. For the first lactation test-day traits, we obtained ∼0 correlations of chromosomal direct genomic values between any pair of the chromosomes; no spurious correlations were found in our analysis, thanks to the large reference population. For trait milk yield, chromosomal direct genomic values appeared to have a large variation in the between-lactation correlations among the chromosomes, especially between first and second or third lactations. The optimal features of the random regression test-day model and the single-step marker model allowed us to track the differences in the shapes of genetic lactation curves down to the individual markers. Furthermore, the single-step random regression test-day model enabled us to better understand the inheritance mode of the yield traits and SCS (e.g., variable chromosomal contributions to the total additive genetic variance and to the genetic correlations between lactations).

X-linked genes influence various complex traits in dairy cattle

BACKGROUND

The search for quantitative trait loci (QTL) affecting traits of interest in mammals is frequently limited to autosomes, with the X chromosome excluded because of its hemizygosity in males. This study aimed to assess the importance of the X chromosome in the genetic determinism of 11 complex traits related to milk production, milk composition, mastitis resistance, fertility, and stature in 236,496 cows from three major French dairy breeds (Holstein, Montbéliarde, and Normande) and three breeds of regional importance (Abondance, Tarentaise, and Vosgienne).

RESULTS

Estimates of the proportions of heritability due to autosomes and X chromosome (h²_X) were consistent among breeds. On average over the 11 traits, h²_X=0.008 and the X chromosome explained ~ 3.5% of total genetic variance. GWAS was performed within-breed at the sequence level (~ 200,000 genetic variants) and then combined in a meta-analysis. QTL were identified for most breeds and traits analyzed, with the exception of Tarentaise and Vosgienne and two fertility traits. Overall, 3, 74, 59, and 71 QTL were identified in Abondance, Montbéliarde, Normande, and Holstein, respectively, and most were associated with the most-heritable traits (milk traits and stature). The meta-analyses, which assessed a total of 157 QTL for the different traits, highlighted new QTL and refined the positions of some QTL found in the within-breed analyses. Altogether, our analyses identified a number of functional candidate genes, with the most notable being GPC3, MBNL3, HS6ST2, and DMD for dairy traits; TMEM164, ACSL4, ENOX2, HTR2C, AMOT, and IRAK1 for udder health; MAMLD1 and COL4A6 for fertility; and NRK, ESX1, GPR50, GPC3, and GPC4 for stature.

CONCLUSIONS

This study demonstrates the importance of the X chromosome in the genetic determinism of complex traits in dairy cattle and highlights new functional candidate genes and variants for these traits. These results could potentially be extended to other species as many X-linked genes are shared among mammals.

SIMplyBee: an R package to simulate honeybee populations and breeding programs

BACKGROUND

The Western honeybee is an economically important species globally, but has been experiencing colony losses that lead to economical damage and decreased genetic variability. This situation is spurring additional interest in honeybee breeding and conservation programs. Stochastic simulators are essential tools for rapid and low-cost testing of breeding programs and methods, yet no existing simulator allows for a detailed simulation of honeybee populations. Here we describe SIMplyBee, a holistic simulator of honeybee populations and breeding programs. SIMplyBee is an R package and hence freely available for installation from CRAN http://cran.r-project.org/package=SIMplyBee .

IMPLEMENTATION

SIMplyBee builds upon the stochastic simulator AlphaSimR that simulates individuals with their corresponding genomes and quantitative genetic values. To enable honeybee-specific simulations, we extended AlphaSimR by developing classes for global simulation parameters, SimParamBee, for a honeybee colony, Colony, and multiple colonies, MultiColony. We also developed functions to address major honeybee specificities: honeybee genome, haplodiploid inheritance, social organisation, complementary sex determination, polyandry, colony events, and quantitative genetics at the individual- and colony-levels.

RESULTS

We describe its implementation for simulating a honeybee genome, creating a honeybee colony and its members, addressing haplodiploid inheritance and complementary sex determination, simulating colony events, creating and managing multiple colonies at the same time, and obtaining genomic data and honeybee quantitative genetics. Further documentation, available at http://www.SIMplyBee.info , provides details on these operations and describes additional operations related to genomics, quantitative genetics, and other functionalities.

DISCUSSION

SIMplyBee is a holistic simulator of honeybee populations and breeding programs. It simulates individual honeybees with their genomes, colonies with colony events, and individual- and colony-level genetic and breeding values. Regarding the latter, SIMplyBee takes a user-defined function to combine individual- into colony-level values and hence allows for modeling any type of interaction within a colony. SIMplyBee provides a research platform for testing breeding and conservation strategies and their effect on future genetic gain and genetic variability. Future developments of SIMplyBee will focus on improving the simulation of honeybee genomes, optimizing the simulator's performance, and including spatial awareness in mating functions and phenotype simulation. We invite the honeybee genetics and breeding community to join us in the future development of SIMplyBee.

High male specific contribution of the X-chromosome to individual global recombination rate in dairy cattle

Background

Meiotic recombination plays an important role in reproduction and evolution. The individual global recombination rate (GRR), measured as the number of crossovers (CO) per gametes, is a complex trait that has been shown to be heritable. The sex chromosomes play an important role in reproduction and fertility related traits. Therefore, variants present on the X-chromosome might have a high contribution to the genetic variation of GRR that is related to meiosis and to reproduction.

Results

We herein used genotyping data from 58,474 New Zealand dairy cattle to estimate the contribution of the X-chromosome to male and female GRR levels. Based on the pedigree-based relationships, we first estimated that the X-chromosome accounted for 30% of the total additive genetic variance for male GRR. This percentage was equal to 19.9% when the estimation relied on a SNP-BLUP approach assuming each SNP has a small contribution. We then carried out a haplotype-based association study to map X-linked QTL, and subsequently fine-mapped the identified QTL with imputed sequence variants. With this approach we identified three QTL with large effect accounting for 7.7% of the additive genetic variance of male GRR. The associated effects were equal to + 0.79, − 1.16 and + 1.18 CO for the alternate alleles. In females, the estimated contribution of the X-chromosome to GRR was null and no significant association with X-linked loci was found. Interestingly, two of the male GRR QTL were associated with candidate genes preferentially expressed in testis, in agreement with a male-specific effect. Finally, the most significant QTL was associated with PPP4R3C, further supporting the important role of protein phosphatase in double-strand break repair by homologous recombination.

Conclusions

Our study illustrates the important role the X-chromosome can have on traits such as individual recombination rate, associated with testis in males. We also show that contribution of the X-chromosome to such a trait might be sex dependent.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08328-8.

Genotype-by-environment interactions for reproduction, body composition, and growth traits in maternal-line pigs based on single-step genomic reaction norms

BACKGROUND

There is an increasing need to account for genotype-by-environment (G × E) interactions in livestock breeding programs to improve productivity and animal welfare across environmental and management conditions. This is even more relevant for pigs because selection occurs in high-health nucleus farms, while commercial pigs are raised in more challenging environments. In this study, we used single-step homoscedastic and heteroscedastic genomic reaction norm models (RNM) to evaluate G × E interactions in Large White pigs, including 8686 genotyped animals, for reproduction (total number of piglets born, TNB; total number of piglets born alive, NBA; total number of piglets weaned, NW), growth (weaning weight, WW; off-test weight, OW), and body composition (ultrasound muscle depth, MD; ultrasound backfat thickness, BF) traits. Genetic parameter estimation and single-step genome-wide association studies (ssGWAS) were performed for each trait.

RESULTS

The average performance of contemporary groups (CG) was estimated and used as environmental gradient in the reaction norm analyses. We found that the need to consider heterogeneous residual variance in RNM models was trait dependent. Based on estimates of variance components of the RNM slope and of genetic correlations across environmental gradients, G × E interactions clearly existed for TNB and NBA, existed for WW but were of smaller magnitude, and were not detected for NW, OW, MD, and BF. Based on estimates of the genetic variance explained by the markers in sliding genomic windows in ssGWAS, several genomic regions were associated with the RNM slope for TNB, NBA, and WW, indicating specific biological mechanisms underlying environmental sensitivity, and dozens of novel candidate genes were identified. Our results also provided strong evidence that the X chromosome contributed to the intercept and slope of RNM for litter size traits in pigs.

CONCLUSIONS

We provide a comprehensive description of G × E interactions in Large White pigs for economically-relevant traits and identified important genomic regions and candidate genes associated with GxE interactions on several autosomes and the X chromosome. Implementation of these findings will contribute to more accurate genomic estimates of breeding values by considering G × E interactions, in order to genetically improve the environmental robustness of maternal-line pigs.