THE CORRELATION OF SUBSTITUTION EFFECTS ACROSS POPULATIONS AND GENERATIONS IN THE PRESENCE OF NON-ADDITIVE FUNCTIONAL GENE ACTION

Allele substitution effects at quantitative trait loci (QTL) are part of the basis of quantitative genetics theory and applications such as association analysis and genomic prediction. In the presence of non-additive functional gene action, substitution effects are not constant across populations. We develop an original approach to model the difference in substitution effects across populations as a first order Taylor series expansion from a “focal” population. This expansion involves the difference in allele frequencies and second-order statistical effects (additive by additive and dominance). The change in allele frequencies is a function of relationships (or genetic distances) across populations. As a result, it is possible to estimate the correlation of substitution effects across two populations using three elements: magnitudes of additive, dominance and additive by additive variances; relationships (Nei’s minimum distances or Fst indexes); and assumed heterozygosities. Similarly, the theory applies as well to distinct generations in a population, in which case the distance across generations is a function of increase of inbreeding. Simulation results confirmed our derivations. Slight biases were observed, depending on the non-additive mechanism and the reference allele. Our derivations are useful to understand and forecast the possibility of prediction across populations and the similarity of GWAS effects.

A heterochromatic knob reducing the flowering time in maize

Maize flowering time is an important agronomic trait, which is associated with variations in the genome size and heterochromatic knobs content. We integrated three steps to show this association. Firstly, we selected inbred lines varying for heterochromatic knob composition at specific sites in the homozygous state. Then, we produced heterozygous hybrids for knobs, which allow us to carry out genetic mapping. Second, we measured the genome size and flowering time for all materials. Knob composition did not affect the genome size. Finally, we developed an association study and identified a knob marker on chromosome 9 showing the strongest association with flowering time. Indeed, modeling allele substitution and dominance effects could offer only one heterochromatic knob locus that could affect flowering time, making it earlier rather than the knob composition.

68 Genomic prediction accuracies of vulva size traits in Landrace and Yorkshire gilts

Recent results indicated that vulva size measured prior to puberty may be predictive of reproductive performance in sows. Therefore, the objective of this study was to estimate genomic prediction accuracies for vulva size traits in purebred gilts. A total of 1,185 Landrace (n = 477) and Yorkshire (n = 708) gilts originated from two different lines were used in this study. All animals had vulva size measurements taken at an average 21.5 weeks of age (SD = 5.8). Measurements included vulva width (VW), vulva height (VH), and vulva area (VA). Genotype data (Geneseek GGP-HD) was available for all animals, for ~40K SNPs. Marker allele substitution effects were estimated using Bayes-B (pi = 0.99) in a model including the fixed effects of contemporary group, line, breed (for multi-breed analysis only) and body weight (covariate), and the random effect of SNPs. Genomic prediction accuracies were estimated using three training and validation strategies: between-breed, within-breed (4 and 6 cross-validation folds for Landrace and Yorkshire, respectively), and multi-breed (10-fold cross-validation, using one-fold per breed for validation at a time). Between-breed accuracies were low and consistently negative, with -0.02, -0.10, and -0.05 in Landrace and -0.05, -0.04 and -0.03 in Yorkshire, for VW, VH, and VA, respectively. Within Landrace, these were moderate, with 0.35 (VW), 0.42 (VH), and 0.56 (VA), whereas lower accuracies were obtained for Yorkshire, with 0.07 (VW), 0.20 (VH), and 0.14 (VA). Multi-breed accuracies were low with 0.14 (VW), 0.14 (VH), and 0.24 (VA) for Landrace, and 0.03 (VW), 0.16 (VH), and 0.09 (VA) for Yorkshire. These results indicate that genomic selection for vulva size traits is possible in Landrace, but limited in Yorkshire gilts. The low between- and multi-breed results suggest that QTL for these traits are in opposite phases between breeds and/or do not segregate in both breeds. Financial support from the Iowa Pork Industry Center is appreciated.

Genomic prediction using individual-level data and summary statistics from multiple populations

ABSTRACTThis study presents a method for genomic prediction that uses individual-level data and summary statistics from multiple populations. Genome-wide markers are nowadays widely used to predict complex traits, and genomic prediction using multi-population data is an appealing approach to achieve higher prediction accuracies. However, sharing of individual-level data across populations is not always possible. We present a method that enables integration of summary statistics from separate analyses with the available individual-level data. The data can either consist of individuals with single or multiple (weighted) phenotype records per individual. We developed a method based on a hypothetical joint analysis model and absorption of population specific information. We show that population specific information is fully captured by estimated allele substitution effects and the accuracy of those estimates, i.e. the summary statistics. The method gives identical result as the joint analysis of all individual-level data when complete summary statistics are available. We provide a series of easy-to-use approximations that can be used when complete summary statistics are not available or impractical to share. Simulations show that approximations enables integration of different sources of information across a wide range of settings yielding accurate predictions. The method can be readily extended to multiple-traits. In summary, the developed method enables integration of genome-wide data in the individual-level or summary statistics form from multiple populations to obtain more accurate estimates of allele substitution effects and genomic predictions.

Required marker properties for unbiased estimates of the genetic correlation between populations

ABSTRACTPopulations generally differ in environmental and genetic factors, which can create differences in allele substitution effects between populations. Therefore, a single genotype may have different additive genetic values in different populations. The correlation between the two additive genetic values of a single genotype in both populations is known as the additive genetic correlation between populations and can differ from one. Our objective was to investigate whether differences in linkage disequilibrium (LD) and allele frequencies of markers and causal loci between populations affect bias of the estimated genetic correlation. We simulated two populations that were separated for 50 generations. Markers and causal loci were selected to either have similar or different allele frequencies in the two populations. Differences in consistency of LD between populations were obtained by using different marker density panels. Results showed that when the difference in allele frequencies of causal loci between populations was reflected by the markers, genetic correlations were only slightly underestimated using markers. This was even the case when LD patterns, measured by LD statistic r, were different between populations. When the difference in allele frequencies of causal loci between populations was not reflected by the markers, genetic correlations were severely underestimated. We conclude that for an unbiased estimate of the genetic correlation between populations, marker allele frequencies should reflect allele frequencies of causal loci so that marker-based relationships can accurately predict the relationships at causal loci, i.e. E(Gcausal loci|Gmarkers) ≠ Gmarkers. Differences in LD between populations have little effect on the estimated genetic correlation.

Association of predicted deleterious single nucleotide polymorphisms with carcass traits in meat-type chickens

ABSTRACTIn previous studies, we used genome wide association (GWAS) to identify quantitative trait loci (QTL) associated with weight and yield of abdominal fat, drumstick, thigh and breast traits in chickens. However, this methodology assumes that the studied variants are in linkage disequilibrium with the causal mutation and consequently do not identify it. In an attempt to identify causal mutations in candidate genes for carcass traits in broilers, we selected 20 predicted deleterious SNPs within QTLs for association analysis. Additive, dominance and allele substitution effects were tested. From the 20 SNPs analyzed, we identified six SNPs with significant association (p-value <0.05) with carcass traits, and three are highlighted here. The SNP rs736010549 was associated with drumstick weight and yield with significant additive and dominance effects. The SNP rs739508259 was associated with thigh weight and yield, and with significant additive and allele substitution effects. The SNP rs313532967 was associated with breast weight and yield. The three SNPs that were associated with carcass traits (rs736010549, rs739508259 and rs313532967) are respectively located in the coding regions of the WDR77, VWA8 and BARL genes. These genes are involved in biological processes such as steroid hormone signaling pathway, estrogen binding, and regulation of cell proliferation. Our strategy allowed the identification of putative casual mutations associated with muscle growth.