Genomic prediction and selection response for grain yield in safflower

In plant breeding programs, multiple traits are recorded in each trial, and the traits are often correlated. Correlated traits can be incorporated into genomic selection models, especially for traits with low heritability, to improve prediction accuracy. In this study, we investigated the genetic correlation between important agronomic traits in safflower. We observed the moderate genetic correlations between grain yield (GY) and plant height (PH, 0.272–0.531), and low correlations between grain yield and days to flowering (DF, −0.157–0.201). A 4%–20% prediction accuracy improvement for grain yield was achieved when plant height was included in both training and validation sets with multivariate models. We further explored the selection responses for grain yield by selecting the top 20% of lines based on different selection indices. Selection responses for grain yield varied across sites. Simultaneous selection for grain yield and seed oil content (OL) showed positive gains across all sites with equal weights for both grain yield and oil content. Combining g×E interaction into genomic selection (GS) led to more balanced selection responses across sites. In conclusion, genomic selection is a valuable breeding tool for breeding high grain yield, oil content, and highly adaptable safflower varieties

The repeatability and heritability of traits derived from accelerometer sensors associated with grazing and rumination time in an extensive sheep farming system

IntroductionThe automated collection of phenotypic measurements in livestock is of interest to both researchers and farmers. Real-time, low-cost, and accurate phenotyping can enhance precision livestock management and could lead to the optimized utilization of pasture and breeding of efficient animals. Wearable sensors provide the tools for researchers to develop novel phenotypes across all production systems, which is especially valuable for grazing conditions. The objectives of this study were to estimate the repeatability and heritability of traits related to grazing and rumination activities and their correlations with other traits.MethodsThis study was conducted on a commercial Merino farm in the west of Victoria, Australia, from 4 May 2020 to 29 May 2020. A total of 160 ActiGraph sensors embedded in halters were attached to the left side of the muzzles of Merino sheep (M = 74, F = 86) aged 10–11 months while the sheep were grazing on pasture. Support vector machine (SVM) algorithms classified the sensor output into the categories of grazing, rumination, walking, idle, and other activities. These activities were further classified into daily grazing time (GT), number of grazing events (NGE), grazing length (GL), rumination time (RT), number of rumination events (NRE), rumination length (RL), walking time (WT), and idle time (IT). The data were analyzed using univariate and bivariate models in ASReml-SA to estimate the repeatability, heritability, and phenotypic correlations among traits.ResultsThe heritability of GT was estimated to be 0.44 ± 0.23, whereas the other traits had heritability estimates close to zero. The estimated repeatability for all traits was moderate to high, with the highest estimate being for GT (0.70 ± 0.03) and the lowest for RT (0.44 ± 0.03). The intraclass correlation or repeatability at a 1-day interval (i.e., 2 consecutive days) was high for all traits, and steadily reduced when the interval between measurements was longer than 1 week.DiscussionThe estimated repeatability for the grazing traits showed that wearable sensors and SVM methods are reliable methods for recording sheep activities on pasture, and have a potential application in the ranking of animals for selective breeding

Effects of Surface Geology on Seismic Ground Motion Deduced from Ambient-Noise Measurements in the Town of Avellino, Irpinia Region (Italy)

The effects of surface geology on ground motion provide an important tool in seismic hazard studies. It is well known that the presence of soft sediments can cause amplification of the ground motion at the surface, particularly when there is a sharp impedance contrast at shallow depth. The town of Avellino is located in an area characterised by high seismicity in Italy, about 30 km from the epicentre of the 23 November 1980, Irpinia earthquake (M = 6.9). No earthquake recordings are available in the area. The local geology is characterised by strong heterogeneity, with impedance contrasts at depth. We present the results from seismic noise measurements carried out in the urban area of Avellino to evaluate the effects of local geology on the seismic ground motion. We computed the horizontal-to-vertical (H/V) noise spectral ratios at 16 selected sites in this urban area for which drilling data are available within the first 40 m of depth. A Rayleigh wave inversion technique using the peak frequencies of the noise H/V spectral ratios is then presented for estimating Vs models, assuming that the thicknesses of the shallow soil layers are known. The results show a good correspondence between experimental and theoretical peak frequencies, which are interpreted in terms of sediment resonance. For one site, which is characterised by a broad peak in the horizontal-to-vertical spectral-ratio curve, simple one-dimensional modelling is not representative of the resonance effects. Consistent variations in peak amplitudes are seen among the sites. A site classification based on shear-wave velocity characteristics, in terms of Vs30, cannot explain these data. The differences observed are better correlated to the impedance contrast between the sediments and basement. A more detailed investigation of the physical parameters of the subsoil structure, together with earthquake data, are desirable for future research, to confirm these data in terms of site response

Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency.

BACKGROUND Sharing individual phenotype and genotype data between countries is complex and fraught with potential errors, while sharing summary statistics of genome-wide association studies (GWAS) is relatively straightforward, and thus would be especially useful for traits that are expensive or difficult-to-measure, such as feed efficiency. Here we examined: (1) the sharing of individual cow data from international partners; and (2) the use of sequence variants selected from GWAS of international cow data to evaluate the accuracy of genomic estimated breeding values (GEBV) for residual feed intake (RFI) in Australian cows. RESULTS GEBV for RFI were estimated using genomic best linear unbiased prediction (GBLUP) with 50k or high-density single nucleotide polymorphisms (SNPs), from a training population of 3797 individuals in univariate to trivariate analyses where the three traits were RFI phenotypes calculated using 584 Australian lactating cows (AUSc), 824 growing heifers (AUSh), and 2526 international lactating cows (OVE). Accuracies of GEBV in AUSc were evaluated by either cohort-by-birth-year or fourfold random cross-validations. GEBV of AUSc were also predicted using only the AUS training population with a weighted genomic relationship matrix constructed with SNPs from the 50k array and sequence variants selected from a meta-GWAS that included only international datasets. The genomic heritabilities estimated using the AUSc, OVE and AUSh datasets were moderate, ranging from 0.20 to 0.36. The genetic correlations (rg) of traits between heifers and cows ranged from 0.30 to 0.95 but were associated with large standard errors. The mean accuracies of GEBV in Australian cows were up to 0.32 and almost doubled when either overseas cows, or both overseas cows and AUS heifers were included in the training population. They also increased when selected sequence variants were combined with 50k SNPs, but with a smaller relative increase. CONCLUSIONS The accuracy of RFI GEBV increased when international data were used or when selected sequence variants were combined with 50k SNP array data. This suggests that if direct sharing of data is not feasible, a meta-analysis of summary GWAS statistics could provide selected SNPs for custom panels to use in genomic selection programs. However, since this finding is based on a small cross-validation study, confirmation through a larger study is recommended

Genomic selection for residual feed intake

© 2016 Dr. Majid KhansefidThe efficiency of animals in converting feed to products is one of the main factors influencing the profitability of beef and dairy production. However, inclusion of feed efficiency as a new trait into the breeding objective requires re-estimation of all economic weights for other traits unless it is independent of them. A convenient measure of efficiency is residual feed intake (RFI) which is the difference between actual intake and predicted feed intake and hence is independent of other traits used in the prediction. RFI and other measures of feed efficiency are expensive to measure because they requires precise measurements of individual feed consumption and this has limited direct selection for feed efficiency in cattle. Genomic selection, using single nucleotide polymorphisms (SNP) genotypes to estimate breeding values without measuring feed intake on selection candidates, could overcome this limitation. However, genomic selection requires a reference population that has been measured for RFI and genotyped. The limited size of reference populations, due to the high costs for RFI measures, currently results in modest prediction accuracies. The aim of the research in this thesis is to increase the accuracies of genomic estimated breeding values (GEBVs) for RFI and to find biological processes and genes associated with RFI variation using genomic data (RNA and DNA). Chapter 1 reviews methods of selecting for RFI and concludes that genomic selection offers the best opportunity. Chapter 2 combines data from multiple breeds to increase the size of the reference population. We show that there is substantial SNP × breed interaction variance but, despite this, the combined reference increases the accuracy of GEBV especially for breeds with a small number of animals in the reference. An alternative analysis used the beef cattle data to identify SNPs with a larger than average effect on RFI and gave these SNPs extra weight in calculating GEBVs for Holstein cattle. This increased the accuracy of GEBV in Holsteins. The results showed the highly polygenic nature of RFI. However, to get most benefit from a multiple breed reference population, it is necessary to use methods of analysis that concentrate on SNPs near causal variants. Therefore, an effort was made to find genes and SNPs that have an effect on RFI using gene expression measured by RNA sequencing data (RNA-Seq) from Angus bulls (muscle and liver samples) and Holstein cows (liver and blood samples). Chapter 3 reports many genes whose expression was correlated with RFI. These genes were represented in wide range of biological processes such as energy metabolism and proteolysis. However, correlations do not prove causation and there is no guarantee that SNPs in these genes influence RFI. SNPs might influence RFI by changing the expression of the genes, so in Chapter 4, RNA-Seq data was analysed to find cis acting expression quantitative trait loci (eQTL). A traditional local eQTL analysis with allele specific expression (ASE) was compared and both methods identified the same cis eQTL in many cases although there were systematic differences, due for instance to parent of origin effects. There was a significant overlap between SNPs associated with gene expression and SNPs associated with traditional phenotypes including RFI. This shows that some QTL are actually eQTL. In chapter 5, by combining the results from other chapters, genes were found where a SNP was associated with both RFI and expression of a gene whose expression was correlated with RFI. These genes and SNPs are good candidates for use in genomic selection but the effects are mostly small and need to be verified in future research to discover their actual biological function in RFI variation before they are used with high confidence in genomic selection

A practical approach for optimised partitioning of genomic relationship across chromosomes

In genomic best linear unbiased prediction (GBLUP) models, genotyped markers are used to make a single genomic relationship matrix (GRM) and consequently each marker contributes similarly in explaining the genetic variance of traits. Some new methods incorporate markers effects in genomic prediction by applying different weights to markers in the GRM. These models often show small improvements in accuracy, but sometimes an increase in the bias of prediction. Alternatively, multiple GRMs made from markers located on each chromosome can be fitted in a GBLUP model. So, the chromosomes containing mutations with large effects on a trait can be used to explain more of the genetic variance. However, fitting many GRMs in a model is not always practical. In this study, for analysing final weight in Hereford cattle (2n=60), initially, we ran 30 models with 2 GRMs made from markers located on each chromosome (GRM_chr) and the markers from the remaining chromosomes (GRM_remaining-chrs). We found GRM_chr for chromosome 6 and 20 explained 20% and 23% of the total genetic variance, respectively, but the rest of GRM_chr failed to absorb any variance. Finally, the prediction model with 3 GRMs, GRM_chr for chromosome 6 and 20 and GRM_remaining-chrs, explained 22%, 26% and 52% of genetic variance, respectively, and compared to the model with a GRM made from all markers, log-likelihood was improved significantly (

Increasing the goodness-of-fit of genomic prediction model with addition of maternal genomic relationship matrix

Genomic prediction models use a genomic relationship matrix (GRM) to quantify relationships. However, the GRM relationships do not distinguish between the maternal and paternal origins, thereby ignoring parent-of-origin effects such as imprinting. Genomic imprinting is a phenomenon whereby gene expressions within progeny are varied based on the parental origin of haplotypes or alleles, generally due to epigenetic effects such as DNA methylation. Genomic imprinting has been reported for many economically important traits in livestock such as weight. In this study, we explored the effect of fitting a maternal and/or paternal genomic relationship matrix (GRM), in addition to a regular GRM, on the goodness-of-fit of a genomic prediction model for 600 day weight by measuring the log-likelihood of restricted maximum likelihood (REML). The results showed the log-likelihood of the model was improved significantly when using the combination of regular GRM and maternal GRM simultaneously suggesting a better model. This result could be due to maternal imprinting, however further research is required to differentiate the maternal effects from parent-of-origin-dependent effects

Meta‐analysis of genetic parameters for growth traits in meat, wool and dual‐purpose sheep breeds in the world using a random‐effects model

Abstract Background There is large variation in genetic parameters in literature for growth traits in sheep. Reliable estimation of genetic parameters is required for developing breeding programmes. Objectives The aim of this study was to aggregate results of different studies by meta‐analysis to improve reliability of estimated parameters. Methods In the current study, 221 papers that have been published between 1995 and 2021 were reviewed. Using a random‐effects model in the Comprehensive Meta‐Analysis software, direct and maternal heritabilities, as well as, genetic and phenotypic correlations between growth traits were estimated in meat (M), wool (W) and dual‐purpose (D) sheep breeds. The growth traits in this study were birth weight, 3‐month weight, 6‐month weight, 9‐month weight and yearling weight. Results The combined direct heritability was the lowest for birth weight (0.190 ± 0.004, 0.198 ± 0.003 and 0.196 ± 0.004 for M, W and D breeds, respectively) and the highest for yearling weight (0.264 ± 0.010, 0.304 ± 0.005 and 0.285 ± 0.020 for M, W and D breeds, respectively). The maternal heritability was the lowest for yearling weight (0.085 ± 0.003, 0.055 ± 0.002 and 0.052 ± 0.005 for M, W and D breeds, respectively) and the highest for 6‐month weight (0.240 ± 0.088, 0.164 ± 0.001 and 0.162 ± 0.006 for M, W and D breeds, respectively). The phenotypic and genetic correlations were lower between the weights measured at more distant intervals. The lowest genetic correlation was observed between birth weight and yearling weight (0.290 ± 0.051 for W breeds). Conclusions The small standard errors could indicate that the aggregation of results from different studies improved the reliability of estimated parameters and reduced range of 95% confidence intervals. Hence, the results could be used with greater level of confidence in sheep breeding programmes

Signatures of selection reveal candidate genes involved in economic traits and cold acclimation in five Swedish cattle breeds

Background Thousands of years of natural and artificial selection have resulted in indigenous cattle breeds that are well-adapted to the environmental challenges of their local habitat and thereby are considered as valuable genetic resources. Understanding the genetic background of such adaptation processes can help us design effective breeding objectives to preserve local breeds and improve commercial cattle. To identify regions under putative selection, GGP HD 150 K single nucleotide polymorphism (SNP) arrays were used to genotype 106 individuals representing five Swedish breeds i.e. native to different regions and covering areas with a subarctic cold climate in the north and mountainous west, to those with a continental climate in the more densely populated south regions. Results Five statistics were incorporated within a framework, known as de-correlated composite of multiple signals (DCMS) to detect signatures of selection. The obtained p-values were adjusted for multiple testing (FDR < 5%), and significant genomic regions were identified. Annotation of genes in these regions revealed various verified and novel candidate genes that are associated with a diverse range of traits, including e.g. high altitude adaptation and response to hypoxia (DCAF8, PPP1R12A, SLC16A3, UCP2, UCP3, TIGAR), cold acclimation (AQP3, AQP7, HSPB8), body size and stature (PLAG1, KCNA6, NDUFA9, AKAP3, C5H12orf4, RAD51AP1, FGF6, TIGAR, CCND2, CSMD3), resistance to disease and bacterial infection (CHI3L2, GBP6, PPFIBP1, REP15, CYP4F2, TIGD2, PYURF, SLC10A2, FCHSD2, ARHGEF17, RELT, PRDM2, KDM5B), reproduction (PPP1R12A, ZFP36L2, CSPP1), milk yield and components (NPC1L1, NUDCD3, ACSS1, FCHSD2), growth and feed efficiency (TMEM68, TGS1, LYN, XKR4, FOXA2, GBP2, GBP5, FGD6), and polled phenotype (URB1, EVA1C). Conclusions We identified genomic regions that may provide background knowledge to understand the mechanisms that are involved in economic traits and adaptation to cold climate in cattle. Incorporating p-values of different statistics in a single DCMS framework may help select and prioritize candidate genes for further analyses

Genome-wide assessment and mapping of inbreeding depression identifies candidate genes associated with semen traits in Holstein bulls

Abstract Background The reduction in phenotypic performance of a population due to mating between close relatives is called inbreeding depression. The genetic background of inbreeding depression for semen traits is poorly understood. Thus, the objectives were to estimate the effect of inbreeding and to identify genomic regions underlying inbreeding depression of semen traits including ejaculate volume (EV), sperm concentration (SC), and sperm motility (SM). The dataset comprised ~ 330 K semen records from ~ 1.5 K Holstein bulls genotyped with 50 K single nucleotide polymorphism (SNP) BeadChip. Genomic inbreeding coefficients were estimated using runs of homozygosity (i.e., F ROH > 1 Mb) and excess of SNP homozygosity (F SNP). The effect of inbreeding was estimated by regressing phenotypes of semen traits on inbreeding coefficients. Associated variants with inbreeding depression were also detected by regressing phenotypes on ROH state of the variants. Results Significant inbreeding depression was observed for SC and SM (p < 0.01). A 1% increase in F ROH reduced SM and SC by 0.28% and 0.42% of the population mean, respectively. By splitting F ROH into different lengths, we found significant reduction in SC and SM due to longer ROH, which is indicative of more recent inbreeding. A genome-wide association study revealed two signals positioned on BTA 8 associated with inbreeding depression of SC (p < 0.00001; FDR < 0.02). Three candidate genes of GALNTL6, HMGB2, and ADAM29, located in these regions, have established and conserved connections with reproduction and/or male fertility. Moreover, six genomic regions on BTA 3, 9, 21 and 28 were associated with SM (p < 0.0001; FDR < 0.08). These genomic regions contained genes including PRMT6, SCAPER, EDC3, and LIN28B with established connections to spermatogenesis or fertility. Conclusions Inbreeding depression adversely affects SC and SM, with evidence that longer ROH, or more recent inbreeding, being especially detrimental. There are genomic regions associated with semen traits that seems to be especially sensitive to homozygosity, and evidence to support some from other studies. Breeding companies may wish to consider avoiding homozygosity in these regions for potential artificial insemination sires