Single-trait Analyses Research Articles

PSX-9 Comparison of single- and multiple-trait genomic predictions of cattle carcass quality traits in multibreed and purebred populations

Abstract Estimation of genetic parameters for carcass traits and their use in the evaluation of carcass quality is of considerable importance in the genetic improvement of beef cattle. The objective of this study was to compare the predictive ability of single- and multi-trait genomic prediction models for the following carcass quality traits: hot carcass weight (HCW); fat thickness (FAT); rib eye area (REA); marbling score (MARB); and peak force (PEAKF) from multibreed and purebred populations. The purebred population included 1,068 purebred Angus animals from Iowa State University whereas the multibreed population consisted of 887 animals sired by 1 of 8 purebreds (209 Angus-, 10 Brahman-, 182 Charolais-, 76 Hereford-, 44 Limousin-, 136 Maine-Anjou-, 96 Shorthorn-, and 134 South Devon- sired offspring) from the Carcass Merit Project funded by the US beef cattle industry to evaluate the genetic prediction of tenderness. After quality control of SNP markers generated with Illumina v1 50K BeadChips, 33,718 SNP markers from were used to obtain heritability and genetic correlation estimates between traits separately in either the multibreed or purebred populations. Genotyped individuals were randomly clustered into 10 groups for cross-fold validation to quantify the correlations between and predicted performance (without the individual’s data) and the realized phenotype of the individual from single- or multi-trait genomic prediction in the multibreed and purebred populations. Heritability estimates of traits from multi-trait analyses ranged from 0.42 to 0.61 in multibreed population and from 0.24 to 0.44 in the purebred population, whereas the single trait values were 0.30 to 0.53 in multibreed, and 0.23 to 0.42 in the purebred population. Estimates were greater in the multibreed population compared with the purebred population (Table 1). Compared with the single-trait analyses, multi-trait analyses resulted in higher heritability estimates for FAT, REA and PEAKF traits in the purebred population and for HCW, REA, MARB and PEAKF traits in multibreed population. Genetic correlations from multi-trait analyses indicated the genetic association between traits. In testing data sets, correlations between observed and predicted phenotypes from single-trait analyses were 4- to 7-fold greater in purebred than in the multibreed population (Table 2). These results may be due to the greater genomic relationship expected in purebred than in the multibreed population. However, the multi-trait analyses resulted in a considerable increase in correlations between observed and predicted values in multibreed population due to genetic correlations between traits.

85 Genomic regions associated with electronic measures indicative of structural soundness in pigs

Abstract The objective was to determine the genetic factors influencing mobility traits recorded from 5 mo-old gilts. Mobility was measured using a pressure-sensing mat (GAIT4) and 7 d of video recorded activity (NUtrack). Gilts (n = 3,659) were evaluated for the study, but only 2,172 gilts had data for both systems. GAIT4 system creates a series of measurements for each foot related to pressure, duration and step length of each foot generating a lameness score for each foot. Traits studied summarized values for all 4 feet: average stride length, average stance time, standard deviation of stance time, lameness score and total scaled pressure. NUtrack measurements were rotations, velocity, and distance walked as well as the time spent eating, sitting, standing, lying sternal and lying lateral. All NUtrack traits were standardized to a mean of zero and standard deviation of 1.0 based on each pen-day subgroup. Mixed model analyses were conducted in R fitting animal as a random effect and fixed effects of breed of sire and contemporary group, with day included for NUtrack data. ssGBLUP analyses were conducted in WOMBAT using animal effects from R models as phenotypes. Three generations of pedigree were included and genotypic data from the NeoGen Porcine 50K beadchip on 3,186 animals representing 60% of gilts with NUtrack data, 67% of gilts with GAIT4 data and most parents (>99%). Single trait analyses were conducted for all traits, gEBVs estimated and then SNP effects estimated by back-solving equations. Significance values were determined by 5,000 permutations and a Bonferroni adjustment factor applied. Estimates of heritability ranged from 0.08 to 0.41 and traits from the NUtrack system tended to have greater heritability than GAIT4 traits. There were 343 significant SNP effects in 39 unique chromosomal regions. There were 133 significant SNP associations located on chromosome 14 between 45-47 Mb associated with rotations, distance and times standing and lying. Pathway analysis identified proteoglycan and apelin pathways to be over-represented based on associations with SNP located at 2:45, 2:51, 13:79 and 14:40 (SSC:Mb). Proteoglycans are a subunit of cartilage and apelins affect blood flow and angiogenesis which make them candidates for the development of osteochondrosis, a leading cause of lameness in pigs. These results will facilitate selection of more robust animals capable of withstanding commercial production environments and group sow housing. USDA is an equal opportunity employer.

The goldmine of GWAS summary statistics: a systematic review of methods and tools

Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.

Single- and multiple-trait quantitative trait locus analyses for seed oil and protein contents of soybean populations with advanced breeding line background

Soybean seed oil and protein contents are negatively correlated, posing challenges to enhance both traits simultaneously. Previous studies have identified numerous oil and protein QTLs via single-trait QTL analysis. Multiple-trait QTL methods were shown to be superior but have not been applied to seed oil and protein contents. Our study aimed to evaluate the effectiveness of single- and multiple-trait multiple interval mapping (ST-MIM and MT-MIM, respectively) for these traits using three recombinant inbred line populations from advanced breeding line crosses tested in four environments. Using original and simulated data, we found that MT-MIM did not outperform ST-MIM for our traits with high heritability (H2 > 0.84). Empirically, MT-MIM confirmed only five out of the seven QTLs detected by ST-MIM, indicating single-trait analysis was sufficient for these traits. All QTLs exerted opposite effects on oil and protein contents with varying protein-to-oil additive effect ratios (-0.4 to -4.8). We calculated the economic impact of the allelic variations via estimated processed values (EPV) using the National Oilseed Processors Association (NOPA) and High Yield + Quality (HY + Q) methods. Oil-increasing alleles had positive effects on both EPVNOPA and EPVHY+Q when the protein-to-oil ratio was low (-0.4 to -0.7). However, when the ratio was high (-4.1 to -4.8), oil-increasing alleles increased EPVNOPA and decreased EPVHY+Q, which penalizes low protein meal. In conclusion, single-trait QTL analysis is adequately effective for high heritability traits like seed oil and protein contents. Additionally, the populations’ elite pedigrees and varying protein-to-oil ratios provide potential lines for further yield assessment and direct integration into breeding programs.

Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame.

Sesame (Sesamum indicum) is an important oilseed crop with rising demand owing to its nutritional and health benefits. There is an urgent need to develop and integrate new genomic-based breeding strategies to meet these future demands. While genomic resources have advanced genetic research in sesame, the implementation of high-throughput phenotyping and genetic analysis of longitudinal traits remains limited. Here, we combined high-throughput phenotyping and random regression models to investigate the dynamics of plant height, leaf area index, and five spectral vegetation indices throughout the sesame growing seasons in a diversity panel. Modeling the temporal phenotypic and additive genetic trajectories revealed distinct patterns corresponding to the sesame growth cycle. We also conducted longitudinal genomic prediction and association mapping of plant height using various models and cross-validation schemes. Moderate prediction accuracy was obtained when predicting new genotypes at each time point, and moderate to high values were obtained when forecasting future phenotypes. Association mapping revealed three genomic regions in linkage groups 6, 8, and 11, conferring trait variation over time and growth rate. Furthermore, we leveraged correlations between the temporal trait and seed-yield and applied multi-trait genomic prediction. We obtained an improvement over single-trait analysis, especially when phenotypes from earlier time points were used, highlighting the potential of using a high-throughput phenotyping platform as a selection tool. Our results shed light on the genetic control of longitudinal traits in sesame and underscore the potential of high-throughput phenotyping to detect a wide range of traits and genotypes that can inform sesame breeding efforts to enhance yield.

Hair-Derived Exposome Exploration of Cardiometabolic Health: Piloting a Bayesian Multitrait Variable Selection Approach.

Cardiometabolic health is complex and characterized by an ensemble of correlated and/or co-occurring conditions including obesity, dyslipidemia, hypertension, and diabetes mellitus. It is affected by social, lifestyle, and environmental factors, which in-turn exhibit complex correlation patterns. To account for the complexity of (i) exposure profiles and (ii) health outcomes, we propose to use a multitrait Bayesian variable selection approach and identify a sparse set of exposures jointly explanatory of the complex cardiometabolic health status. Using data from a subset (N = 941 participants) of the nutrition, environment, and cardiovascular health (NESCAV) study, we evaluated the link between measurements of the cumulative exposure to (N = 33) pollutants derived from hair and cardiometabolic health as proxied by up to nine measured traits. Our multitrait analysis showed increased statistical power, compared to single-trait analyses, to detect subtle contributions of exposures to a set of clinical phenotypes, while providing parsimonious results with improved interpretability. We identified six exposures that were jointly explanatory of cardiometabolic health as modeled by six complementary traits, of which, we identified strong associations between hexachlorobenzene and trifluralin exposure and adverse cardiometabolic health, including traits of obesity, dyslipidemia, and hypertension. This supports the use of this type of approach for the joint modeling, in an exposome context, of correlated exposures in relation to complex and multifaceted outcomes.

Genetic association between fat-to-protein ratio and traits of economic interest in early lactation Holstein cows in Brazil.

The aims of this study were to estimate the genetic parameters for fat-to-protein ratio (F:P) within the first 90days of lactation and to examine their genetic associations with daily milk yield (MY), somatic cell score (SCS), and calving interval between the first and second calving (IFSC) and between the second and third calving (ISTC) during the first three lactations of Holstein cows. We utilized 200,626 production-related data officially recorded from 77,436 cows milked two or three times a day from 2012 to 2022, sourced from the Holstein Cattle Breeders Association of Paraná State, Brazil. The (co)variance components were estimated using animal models, adopting the restricted maximum likelihood (REML) method with single-trait analysis (for heritability and repeatability) and two-trait analysis (for genetic and phenotypic correlations), per lactation. Regardless of lactation number, heritability estimates were relatively low, ranging from 0.08 ± 0.005 to 0.10 ± 0.003 for F:P; 0.08 ± 0.01 to 0.18 ± 0.005 for MY; 0.04 ± 0.01 to 0.07 ± 0.004 for SCS; and 0.03 ± 0.01 for both IFSC and ISTC. Repeatability estimates within the same lactation were low for F:P (ranging from 0.17 ± 0.002 to 0.19 ± 0.03), high for MY (between 0.50 ± 0.003 and 0.53 ± 0.002), and moderate to high for SCS (between 0.39 ± 0.003 and 0.44 ± 0.004). Genetic correlations between F:P and MY ranged from -0.26 ± 0.03 to -0.15 ± 0.02; F:P and SCS, from -0.06 ± 0.03 to -0.03 ± 0.08; F:P and IFSC, 0.31 ± 0.01; F:P and ISTC, 0.20 ± 0.01; MY and IFSC, 0.24 ± 0.05; and MY and ISTC, 0.13 ± 0.08. The fat-to-protein ratio during early lactation showed low genetic variability, regardless of lactation number. Furthermore, it was genetically correlated with MY, IFSC, and ISTC, although there is an antagonistic and unfavorable correlation between traits that can limit genetic progress.

Subset scanning for multi-trait analysis using GWAS summary statistics.

Multi-trait analysis has been shown to have greater statistical power than single-trait analysis. Most of the existing multi-trait analysis methods only work with a limited number of traits and usually prioritize high statistical power over identifying relevant traits, which heavily rely on domain knowledge. To handle diseases and traits with obscure etiology, we developed TraitScan, a powerful and fast algorithm that identifies potential pleiotropic traits from a moderate or large number of traits (e.g. dozens to thousands) and tests the association between one genetic variant and the selected traits. TraitScan can handle either individual-level or summary-level GWAS data. We evaluated TraitScan using extensive simulations and found that it outperformed existing methods in terms of both testing power and trait selection when sparsity was low or modest. We then applied it to search for traits associated with Ewing Sarcoma, a rare bone tumor with peak onset in adolescence, among 754 traits in UK Biobank. Our analysis revealed a few promising traits worthy of further investigation, highlighting the use of TraitScan for more effective multi-trait analysis as biobanks emerge. We also extended TraitScan to search and test association with a polygenic risk score and genetically imputed gene expression. Our algorithm is implemented in an R package "TraitScan" available at https://github.com/RuiCao34/TraitScan.

Drivers of functional and phylogenetic structures of mountain bird assemblages along an altitudinal gradient from the montane to alpine zones

Understanding the process underlying the spatial gradients in biodiversity is a fundamental issue in ecology. Altitudinal gradients are ideal systems for examining the community assembly process because they represent rapid changes in environmental conditions over relatively short distances. The functional and phylogenetic approaches allow a deeper mechanistic understanding of the underlying assembly process. However, whether community assembly from montane to alpine zones is driven more by ecological constraints or human settlement patterns is not fully resolved and may vary by system. Here, we examined the phylogenetic and trait-based functional structures from the montane to alpine zones and clarified the effects of natural environmental factors and human-induced landscape transformation on the breeding bird assemblage on Mount Norikura, central Japan. Breeding birds from 700 to 3026 m a.s.l. were surveyed in 2016–2017. Bird community structures were examined based on species richness, functional and phylogenetic structures. A null modelling approach was performed to examine functional and phylogenetic cluster/overdispersion structures; clustering and overdispersion imply species elimination by environmental filters and limiting similarity as a corollary of competitive exclusion of species, respectively. Furthermore, we examined the relative effect of natural environments and human disturbance. Bird species richness was high in the mountain base to mid-elevation, then decreased with elevation. We found a contrasting pattern in the phylogenetic and functional structures in the alpine zone: phylogenetic overdispersion and functional clustering. In contrast, the functional and phylogenetic structures were clustered in the upper parts of the subalpine zone. The functional and phylogenetic clusterings in the lower parts of the subalpine and montane zones were negligible with the weak effects of the environmental filtering. Single-trait analysis showed that the high abiotic tolerance and foraging or nesting capabilities in treeless environments were clustered in the alpine zone, and mobility was clustered in the subalpine zones. Natural environments strongly affected bird community structures, but human disturbance added species to assemblages in the mountain base to mid-elevation and changed the functional and phylogenetic structures. Our findings highlight that filtering by severe natural environments is a fundamental community assembly process in high mountain regions, whereas filtering effects can be weaker at lower elevations. Furthermore, the filtering effect should differ between the alpine and the upper parts of the subalpine zones because of different combinations of clustered traits. Climate change and landscape transformation pressures may strongly impact biodiversity in the alpine and subalpine zones, respectively.

New statistical selection method for pleiotropic variants associated with both quantitative and qualitative traits

BackgroundIdentification of pleiotropic variants associated with multiple phenotypic traits has received increasing attention in genetic association studies. Overlapping genetic associations from multiple traits help to detect weak genetic associations missed by single-trait analyses. Many statistical methods were developed to identify pleiotropic variants with most of them being limited to quantitative traits when pleiotropic effects on both quantitative and qualitative traits have been observed. This is a statistically challenging problem because there does not exist an appropriate multivariate distribution to model both quantitative and qualitative data together. Alternatively, meta-analysis methods can be applied, which basically integrate summary statistics of individual variants associated with either a quantitative or a qualitative trait without accounting for correlations among genetic variants.ResultsWe propose a new statistical selection method based on a unified selection score quantifying how a genetic variant, i.e., a pleiotropic variant associates with both quantitative and qualitative traits. In our extensive simulation studies where various types of pleiotropic effects on both quantitative and qualitative traits were considered, we demonstrated that the proposed method outperforms the existing meta-analysis methods in terms of true positive selection. We also applied the proposed method to a peanut dataset with 6 quantitative and 2 qualitative traits, and a cowpea dataset with 2 quantitative and 6 qualitative traits. We were able to detect some potentially pleiotropic variants missed by the existing methods in both analyses.ConclusionsThe proposed method is able to locate pleiotropic variants associated with both quantitative and qualitative traits. It has been implemented into an R package ‘UNISS’, which can be downloaded from http://github.com/statpng/uniss.

Including genomic information in the genetic evaluation of production and reproduction traits in South African Merino sheep.

Genomic selection (GS) has become common in sheep breeding programmes in Australia, New Zealand, France and Ireland but requires validation in South Africa (SA). This study aimed to compare the predictive ability, bias and dispersion of pedigree BLUP (ABLUP) and single-step genomic BLUP (ssGBLUP) for production and reproduction traits in South African Merinos. Animals in this study originated from five research and five commercial Merino flocks and included between 54,072 and 79,100 production records for weaning weight (WW), yearling weight (YW), fibre diameter (FD), clean fleece weight (CFW) and staple length (SL). For reproduction traits, the dataset included 58,744 repeated records from 17,268 ewes for the number of lambs born (NLB), number of lambs weaned (NLW) and the total weight weaned (TWW). The single-step relationship matrix, H, was calculated using PreGS90 software combining 2811 animals with medium density (~50 K) genotypes and the pedigree of 88,600 animals. Assessment was based on single-trait analysis using ASREML V4.2 software. The accuracy of prediction was evaluated according to the 'LR-method' by a cross-validation design. Validation candidates were assigned according to Scenario I: born after a certain time point; and Scenario II: born in a particular flock. In Scenario I, the genotyping rate for the reference population between 2006 and the 2013 cut-off point approached 7% of animals with phenotypes and 20% of their sires. For reproduction traits, about 20% of ewes born between 2006 and 2011 cut-off were genotyped, along with 15% of their sires. Genotyping rates in the validation set of Scenario I were 3.7% (production) and 11.4% (reproduction) for candidates and 35% of their sires. Sires were allowed to have progeny in both the reference and validation set. In Scenario I, ssGBLUP increased the accuracy of prediction for all traits except NLB, ranging between +8% (0.62-0.67) for FD and +44% (0.36-0.52) for WW. This showed a promising gain in accuracy despite a modestly sized reference population. In the 'across flock validation' (Scenario II), overall accuracy was lower, but with greater differences between ABLUP and ssGBLUP ranging between +17% (0.12-0.14) for TWW and +117% (0.18-0.39) for WW. There was little indication of severe bias, but some traits were prone to over dispersion and the use of genomic information did not improve this. These results were the first to validate the benefit of genomic information in South African Merinos. However, because production traits are moderately heritable and easy to measure at an early age, future research should be aimed at best exploiting GS methods towards genetic prediction of sex-limited and/or lowly heritable traits such as NLW. GS methods should be combined with dedicated efforts to increase genetic links between sectors and improved phenotyping by commercial flocks.

Investigating the potential for genetic selection of dairy calf disease traits using management data

Genetic selection could be a tool to help improve the health and welfare of calves, however, to date, there is limited research on the genetics of calfhood diseases. This study aimed to understand the current impact of calf diseases, by investigating incidence rates, estimating genetic parameters, and providing industry recommendations to improve calf disease recording practices on farms. Available calf disease data comprised of 69,695 Holstein calf disease records for respiratory problems (RESP) and diarrhea (DIAR), from 62,361 calves collected on 1,617 Canadian dairy herds from 2006 to 2021. Single and multiple trait analysis using both a threshold and linear animal model for each trait were evaluated. Furthermore, each trait was analyzed using 2 scenarios with respect to minimum disease incidence threshold criterion (herd-year incidence of at least 1% and 5%) to highlight the impact of different filtering thresholds on selection potential. Observed scale heritability estimates for RESP and DIAR ranged from 0.02 to 0.07 across analyses, while estimated genetic correlations between the traits ranged from 0.50 to 0.62. Sires were compared based on their estimated breeding value and their diseased daughter incidence rates. On average, calves born to the bottom 10% of sires were 1.8 times more likely to develop RESP and 1.9 times to develop DIAR compared with daughters born to the top 10% of sires. Results from the current study are promising for the inclusion of both DIAR and RESP in Canadian genetic evaluations. However, for effective genetic evaluation we require standardized approaches on data collection and industry outreach to highlight the importance of collecting and uploading this information to herd management software. In particular, it is important that the herd management software is accessible to the national milk recording system to allow for use in national genetic evaluation.

Genetic parameter estimation for pork production and litter performance traits of Landrace, Large White, and Duroc pigs in Japan.

We estimated genetic parameters for two pork production and six litter performance traits of Landrace, Large White, and Duroc pigs reared in Japan. Pork production traits were average daily gain from birth to end of performance testing and backfat thickness at end of testing (46,042 records for Landrace, 40,467 records for Large White, and 42,920 records for Duroc). Litter performance traits were number born alive, litter size at weaning (LSW), number of piglets dead during suckling (ND), survival rate of piglets during suckling (SV), total piglet weight at weaning (TWW), and average piglet weight at weaning (AWW) (27,410, 26,716, and 12,430 records for Landrace, Large White, and Duroc, respectively). ND was calculated as the difference between LSW and litter size at start of suckling (LSS). SV was calculated as LSW/LSS. AWW was calculated as TWW/LSW. Pedigree data for Landrace, Large White, and Duroc breeds contained 50,193, 44,077, and 45,336 pigs, respectively. Trait heritability was estimated via single-trait analysis and genetic correlation between two traits was estimated via two-trait analysis. When considering the linear covariate of LSS in the statistical model for LSW and TWW, for all breeds, the heritability was estimated to be 0.4-0.5 for pork production traits and below 0.2 for litter performance traits. Estimated genetic correlation between average daily gain and backfat thickness was small, ranging from 0.057 to 0.112, and those between pork production traits and litter performance traits were negligible to moderate, ranging from -0.493 to 0.487. A wide range of genetic correlation values among the litter performance traits was estimated, while that between LSW and ND could not be obtained. The results of genetic parameter estimation were affected by whether the linear covariate of LSS was included in the statistical model for LSW and TWW or not. This finding implies the necessity of carefully interpreting the results according to the choice of statistical model. Our results could give fundamental information on simultaneously improving productivity and female reproductivity for pigs.

An adaptive and robust method for multi-trait analysis of genome-wide association studies using summary statistics.

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human traits or diseases in the past decade. Nevertheless, much of the heritability of many traits is still unaccounted for. Commonly used single-trait analysis methods are conservative, while multi-trait methods improve statistical power by integrating association evidence across multiple traits. In contrast to individual-level data, GWAS summary statistics are usually publicly available, and thus methods using only summary statistics have greater usage. Although many methods have been developed for joint analysis of multiple traits using summary statistics, there are many issues, including inconsistent performance, computational inefficiency, and numerical problems when considering lots of traits. To address these challenges, we propose a multi-trait adaptive Fisher method for summary statistics (MTAFS), a computationally efficient method with robust power performance. We applied MTAFS to two sets of brain imaging derived phenotypes (IDPs) from the UK Biobank, including a set of 58 Volumetric IDPs and a set of 212 Area IDPs. Through annotation analysis, the underlying genes of the SNPs identified by MTAFS were found to exhibit higher expression and are significantly enriched in brain-related tissues. Together with results from a simulation study, MTAFS shows its advantage over existing multi-trait methods, with robust performance across a range of underlying settings. It controls type 1 error well and can efficiently handle a large number of traits.

Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes.

Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400-1,900) were measured across 8 years (2012-2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5-0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69-0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle.

Estimation of genetic parameters and inbreeding depression in Piau pig breed.

This study is aimed at estimating genetic parameters, effective population size, inbreeding, and inbreeding depression for birth weight, weaning weight, and average pre-weaning daily weight gain (ADG) in Piau pigs. We used information from 3841 Piau pigs, and four linear models were fitted in single-trait analyses, including or excluding maternal genetic effect, common litter effect, or a combination. The adjustments of the models were compared using the likelihood ratio test, in which the model that presented the best fit for each trait was used to estimate the (co)variance components. The inbreeding depression effect was evaluated using a linear model that included the fixed effects of sex, parity order, contemporary group, and inbreeding coefficient as a fixed covariate. The weights at birth and weaning showed low direct heritabilities (0.08 and 0.05, respectively), while the ADG showed moderate heritability (0.20). The weight at birth showed high genetic correlations with the weight at weaning (0.90) and the ADG (0.82). The weight at weaning and the ADG also showed a high genetic correlation (0.99). There was an inbreeding increase over the generations and a reduction in the effective population size. In the last generation evaluated, all the animals were inbred, the average inbreeding coefficient was 0.07, and the effective population size was 20.8. A significant inbreeding effect on ADG was observed, where an increase of 1% in the inbreeding coefficient resulted in a decrease of 0.005g in the ADG. Thus, increasing effective population size is mandatory for controlling inbreeding and reducing the loss of variability in this Piau pig population.

Direct and maternal genetic effects for preinflection point growth traits and humoral immunity in quail

Early growth traits in quails are considered as the growth performances before the inflection point which are genetically different from body weights (BW) at later stages. Moreover, in addition to growth performance, humoral immunity is moderately heritable and is considered in some breeding programs. However, estimating the direct genetic, particularly the maternal genetic correlations between growth and immunity in quail, are not studied sufficiently, which were the aims of the present study. The quails' BW were recorded at hatch (BW0) to 25 d of age with a 5-d interval and body weight gains (BWG) were measured as average growth performance of the birds in a 5-d period. Antibody titer against Newcastle disease virus (IgN) was measured through the hemagglutination inhibition (HI) test. For titration of anti-SRBC antibodies (IgY and IgM), a hemagglutination microtiter assay was used. In general, growth records in 4,181 birds and humoral immune responses in 1,023 birds were assigned to the study. The genetic parameters were estimated by single-trait analysis via Gibb's sampling. After finding the best model for each trait, multi-trait analysis was done to estimate the direct and maternal genetic correlations. Direct heritabilities (h2) were estimated to be moderate for BW (0.481-0.551) and BWG (0.524-0.557), while h2 for immune responses were low (0.035-0.079). Maternal environmental effect (c2) was only significant for BW0, BW5, and BWG0-5. Maternal heritabilities (m2) for BW and BWG were all lower than corresponding h2, ranging from 0.072 (BW25) to 0.098 (BW0). The m2 for IgN (0.098) was more than 2.5 times greater than h2 (0.040) for this trait. Direct (ra) and maternal (rm) genetic correlations between IgN-BW, IgY-BW, and IgY-BWG were negative, while ra and rm for IgM-BW, IgN-BWG, and IgM-BWG were positive. The ra between humoral immune responses were low to moderate and rm was significant only for IgY-IgM (0.339). Given positive genetic correlations in BWG-IgN and BWG-IgM as well as positive genetic correlations between both IgN and IgM with IgY, it is suggested that including the BWG in the breeding programs would directly result in the improvement of the birds' growth performance. It would also contribute indirectly to the improvement of the birds' humoral immune responses.

The contribution of a MOET nucleus scheme for the improvement of Guzerá (Bos indicus) cattle for milk traits in Brazil.

The Guzerá breed evolved from the introduction of breeds from India, mainly the Kankrej breed, into Brazilian livestock at the end of the 19th century. Guzerá adapted well to the climatic conditions of Brazil, where it is considered a dual-purpose breed and has been used for pasture-based beef, milk or dual-purpose production systems with the use of low-medium inputs. The importance of this genetic resource for milk production in tropical regions moved breeders to implement the National Breeding Program for the Improvement of Guzerá in 1994, based on both progeny testing and MOET nucleus schemes. We sought to evaluate the role of the MOET nucleus scheme in the phenotypic and genetic progress for milk traits in this breed. The initial database used in the present study consisted of 6,513 cows, daughters of 761 bulls. We performed genetic evaluations with different datasets using a linear mixed model in a single trait analysis, including the relationship matrix, in order to estimate breeding values. Inbreeding coefficients were also calculated using the relationship of descent between two parents. Annual phenotypic, genetic and inbreeding trends were obtained for each dataset, considering the genetic pathways of both the bull and the cow. The low genetic progress found for milk yield in the whole population (5.27 ± 0.30 kg/year) partially accounted for the dual-purpose selection goal, despite the higher genetic progress in the MOET nucleus (9.39 ± 0.79 kg/year). The inbreeding coefficient was minimized at the beginning of the breeding program based on the use of new lineages. Posteriorly, it started increasing again from 0.002 in 1991 to 0.008 in 2019. The results provided evidence of the significant contribution of the MOET nucleus scheme for the phenotypic and genetic progress of Guzerá breed for milk traits, as well as of the impact of the breeding program on the inbreeding coefficient rate in the early years. New strategies need to be designed for the Guzerá breed, to allow for greater improvement of milk traits and minimizing the rate of the inbreeding coefficient.

Factor Analysis of Genetic Parameters for Body Conformation Traits in Dual-Purpose Simmental Cattle.

Simple SummaryBody conformation traits are closely related to economically important characteristics and should be considered in cattle breeding programs. A variety of body conformation traits recorded by classifiers can complicate the analysis process. Factor analysis can reduce the number of variables by combining two or more variables into a single factor, which has biological significance. The results of this study could be used by breeders to define conformation indexes and implement genetic assessments for conformation traits in dual-purpose breeds.In this study, we estimated the genetic parameters for 6 composite traits and 27 body conformation traits of 1016 dual-purpose Simmental cattle reared in northwestern China from 2010 to 2019 using a linear animal mixed model. To integrate these traits, a variety of methods were used as follows: (1) genetic parameters estimates for composite and individual body conformation traits based on the pedigree relationship matrix (A) and combined genomic-pedigree relationship matrix (H); (2) factor analysis to explore the relationships among body conformation traits; and (3) genetic parameters of factor scores estimated using A and H, and the correlations of EBVs of the factor scores and EBVs of the composite traits. Heritability estimates of the composite traits using A and H were low to medium (0.07–0.47). The 24 common latent factors explained 96.13% of the total variance. Among factors with eigenvalues ≥ 1, F1 was mainly related to body frame, muscularity, and rump; F2 was related to feet and legs; F3, F4, F5, and F6 were related to teat placement, teat size, udder size, and udder conformation; and F7 was related to body frame. Single-trait analysis of factor scores yielded heritability estimates that were low to moderate (0.008–0.43 based on A and 0.04–0.43 based on H). Spearman and Pearson correlations, derived from the best linear unbiased prediction analysis of composite traits and factor scores, showed a similar pattern. Thus, incorporating factor analysis into the morphological evaluation to simplify the assessment of body conformation traits may improve the genetics of dual-purpose Simmental cattle.

A Bayesian random regression method using mixture priors for genome-enabled analysis of time-series high-throughput phenotyping data.

The recent advancement in image-based phenotyping platforms enables the acquisition of large-scale nondestructive crop phenotypes measured at frequent intervals. To further understand the underlying genetic basis over a physiological process and improve plant breeding programs, the question of how to efficiently utilize these time-series measurements in genome-enabled analysis including genomic prediction and genome-wide association studies (GWASs) should be considered. In this paper, a Bayesian random regression model with mixture priors is developed to introduce more meaningful biological assumptions to the analysis of longitudinal traits. The mixture prior for marker effects in Bayes Cπ is implemented in our developed model (RR-BayesC) for demonstration purpose. The estimation of single-nucleotide polymorphism-specific effects that are related to the dynamic performance of crops and the accuracy of genomic prediction by RR-BayesC were studied through both simulated and real rice (Oryza sativa L.) data. For genomic prediction, three predictive scenarios were studied. In the simulated study, RR-BayesC showed a significantly higher prediction accuracy than that obtained by single-trait analysis, especially for days when heritability were low. In real data analysis, RR-BayesC showed relatively high prediction accuracy when forecast is required for phenotypes at later period (e.g., from 0.94 to 0.98 for lines with observations at an earlier period and from 0.65 to 0.67 for lines without any observations). For GWASs, inference of single markers and inference of genomic windows were conducted. In the simulated study, RR-BayesC showed its promising ability to distinguish quantitative trait loci (QTL) that are invariant to temporal covariates and QTL that interact with time. An association study of real data was also presented to demonstrate the application of RR-BayesC in real data analysis. In this paper, we develop a Bayesian random regression model that is able to incorporate mixture priors to marker effects and show improved performance of genomic prediction and GWASs for longitudinal data analysis based on both simulated and real data. The software tool JWAS offers routines to perform our proposed random regression analysis.