A method for partitioning trends in genetic mean and variance to understand breeding practices

Quantifying the sources of genetic change is essential for optimising breeding programmes. However, breeding programmes are often complex because many breeding groups are subject to different breeding actions. Understanding the contribution of these groups to changes in genetic mean and variance is essential to understanding genetic change in breeding programmes. Here we extend the previously developed method for analysing the contribution of groups to changes in genetic mean to analysing changes in genetic variance. We, expectedly, show that the contribution of females and males to change in genetic variance can differ and are not independent, indicating we should not look at the contributions in isolation.

Genetic variance and heritability estimation of hybridized pepper plants (Capsicum annuum L.) F2 progeny for begomovirus resistance in growth stage

Pepper is widely cultivated as a condiment and cash crop in Indonesia. However, Pepper yellow leaf curl disease (PepYLCD) caused by begomovirus is currently seriously affect the domestic pepper production. Breeding for begomovirus resistance material by crossing is currently necessary to overcome the constraint. The present study is aimed to determine the resistance of pepper (C. annuum) plants F2 progenies to begomovirus infection in the growth stage. Two local C. annuum accessions, BaPep-5 as a resistance donor for pepy-1 begomovirus resistance gene (locally called Perintis) and BaPep-4 as a susceptible parent (locally called Kencana) were crossed to generate F2 progenies. The research was conducted in Agricultural Extension Training Centre (BLPP) Saree and Horticulture Laboratory of Syiah Kuala University from February to July 2020. 500 F2 progenies were transplanted to the field along with 15 plants of each parent as control. The result suggested that plant height and crown width had the highest broad sense heritability value, whereas the dichotomous height, stem diameter, secondary branch, and tertiary branch had the lowest broad sense heritability value. Coefficient of genetic variance and coefficient of phenotypic variance from overall characteristics were relatively low which suggest the narrow sense to slightly narrow sense heritability.

Estimation of Genetic Parameters of Biomass Production and Composition Traits in Miscanthus Sinensis Using a Staggered-start Design

Traits for biomass production and composition make Miscanthus a promising bioenergy crop for different bioconversion routes. They need to be considered in miscanthus breeding programs as they are subjected to genetic and genetic x environment factors. The objective was to estimate the genetic parameters of an M. sinensis population grown during four years in two French locations. In each location, the experiment was established according to a staggered-start design in order to decompose the year effect into age and climate effects. Linear Mixed Models were used to estimate genetic variance, genotype x age, genotype x climate interaction variances and residual variances. Individual plant broad-sense heritability means ranged from 0.42 to 0.62 for biomass production traits, and were more heritable than biomass composition traits with means ranging from 0.26 to 0.47. Heritability increased through time for most of the biomass production and composition traits. Low genetic variance along with large genotype x age and genotype x climate interaction variances tended to decrease the heritability of biomass production traits for young plant ages. Most of the production traits showed large interaction variances for age and climate in both locations, while biomass composition traits highlighted large interaction variances due to climate in Orléans. The genetic and phenotypic correlations between biomass production and composition traits were moderate and positive, while hemicelluloses were negatively correlated with all traits. Efficient genetic progress is achievable for miscanthus breeding when plants get older. The joint improvement of biomass production and composition traits would help provide a better response of miscanthus to selection.

ESTİMATİON OF GENETİC AND ENVİRONMENTAL PARAMETERS AFFECTİNG PRODUCTİVİTY İN MORKARAMAN SHEEP AND ECONOMİC EVALUATİON OF PARAMETERS

This study was carried out to protect the continuity of productivity in morkaraman sheep raised in Turkey and determine their economic importance. Morkaraman sheep are concentrated in the Eastern Regions of the country. The province of Bingöl, where the study was conducted, is located in this region and has an important morkaraman population. The study was carried out between 2008-2018. Sixty-eight morkaraman sheep were used during the study period out of 317 lambing lambs. In the study, the total number of lambs born per sheep (TNLBS), the number of weaned lambs (NWL), the weights of the lambs weaned per sheep (WLWS) and the total weight of the lambs weaned in the first period (TWLWFP) were determined. In addition, Additive genetic variance, Error variance, Phenotypic variance, Heritability and Ratio of error variation were determined for these variables. As a result, the correlation between the examined variables was significant and positive, except for the relationship between TNLBS and TWLWFP. The relationship between these two variables was significant but negative. Significant changes were also observed in terms of genetic parameters. It was concluded that the economic aspects of the examined variables should not be ignored in terms of sustainability. Keywords: Sheep, morkaraman, sustainability, genotypic and phenotypic variance.

Genome-wide association study of disease resilience traits from a natural polymicrobial disease challenge model in pigs identifies the importance of the major histocompatibility complex region

Infectious diseases cause tremendous financial losses in the pork industry, emphasizing the importance of disease resilience, which is the ability of an animal to maintain performance under disease. Previously, a natural polymicrobial disease challenge model was established, in which pigs were challenged in the late nursery phase by multiple pathogens to maximize expression of genetic differences in disease resilience. Genetic analysis found that performance traits in this model, including growth rate, feed and water intake, and carcass traits, as well as clinical disease phenotypes, were heritable and could be selected for to increase disease resilience of pigs. The objectives of the current study were to identify genomic regions that are associated with disease resilience in this model, using genome-wide association studies and fine mapping methods, and to use gene set enrichment analyses to determine whether genomic regions associated with disease resilience are enriched for previously published quantitative trait loci (QTL), functional pathways, and differentially expressed genes subject to physiological states. Multiple QTL were detected for all recorded performance and clinical disease traits. The major histocompatibility complex (MHC) region was found to explain substantial genetic variance for multiple traits, including for growth rate in the late nursery (12.8%) and finisher (2.7%), for several clinical disease traits (up to 2.7%), and for several feeding and drinking traits (up to 4%). Further fine mapping identified four QTL in the MHC region for growth rate in the late nursery that spanned the subregions for class I, II, and III, with one SNP in the MHC Class I subregion capturing the largest effects, explaining 0.8 to 27.1% of genetic variance for growth rate and for multiple clinical disease traits. This SNP was located in the enhancer of TRIM39 gene, which is involved in innate immune response. The MHC region was pleiotropic for growth rate in the late nursery and finisher, and for treatment and mortality rates. Growth rate in the late nursery showed strong negative genetic correlations in the MHC region with treatment or mortality rates (-0.62 to -0.85) and a strong positive genetic correlation with growth rate in the finisher (0.79). Gene set enrichment analyses found genomic regions associated with resilience phenotypes to be enriched for previously identified disease susceptibility and immune capacity QTL, for genes that were differentially expressed following bacterial or virus infection and immune response, and for gene ontology terms related to immune and inflammatory response. In conclusion, the MHC and other QTL that harbor immune related genes were identified to be associated with disease resilience traits in a large-scale natural polymicrobial disease challenge. The MHC region was pleiotropic for growth rate under challenge and for clinical disease traits. Four QTL were identified across the class I, II, and III subregions of the MHC for nursery growth rate under challenge, with one SNP in the MHC Class I subregion capturing the largest effects. The MHC and other QTL identified play an important role in host response to infectious diseases and can be incorporated in selection to improve disease resilience, in particular the identified SNP in the MHC Class I subregion.

Additive and Dominance Genomic Analysis for Litter Size in Purebred and Crossbred Iberian Pigs

INGA FOOD S. A., as a Spanish company that produces and commercializes fattened pigs, has produced a hybrid Iberian sow called CASTÚA by crossing the Retinto and Entrepelado varieties. The selection of the parental populations is based on selection criteria calculated from purebred information, under the assumption that the genetic correlation between purebred and crossbred performance is high; however, these correlations can be less than one because of a GxE interaction or the presence of non-additive genetic effects. This study estimated the additive and dominance variances of the purebred and crossbred populations for litter size, and calculated the additive genetic correlations between the purebred and crossbred performances. The dataset consisted of 2030 litters from the Entrepelado population, 1977 litters from the Retinto population, and 1958 litters from the crossbred population. The individuals were genotyped with a GeneSeek® GGP Porcine70K HDchip. The model of analysis was a ‘biological’ multivariate mixed model that included additive and dominance SNP effects. The estimates of the additive genotypic variance for the total number born (TNB) were 0.248, 0.282 and 0.546 for the Entrepelado, Retinto and Crossbred populations, respectively. The estimates of the dominance genotypic variances were 0.177, 0.172 and 0.262 for the Entrepelado, Retinto and Crossbred populations. The results for the number born alive (NBA) were similar. The genetic correlations between the purebred and crossbred performance for TNB and NBA—between the brackets—were 0.663 in the Entrepelado and 0.881 in Retinto poplulations. After backsolving to obtain estimates of the SNP effects, the additive genetic variance associated with genomic regions containing 30 SNPs was estimated, and we identified four genomic regions that each explained > 2% of the additive genetic variance in chromosomes (SSC) 6, 8 and 12: one region in SSC6, two regions in SSC8, and one region in SSC12.

Selection on early survival does not explain germination rate clines in Mimulus cardinalis (Phrymaceae)

Premise Many traits covary with environmental gradients to form phenotypic clines. While local adaptation to the environment can generate phenotypic clines, other nonadaptive processes may also. If local adaptation causes phenotypic clines, then the direction of genotypic selection on traits should shift from one end of the cline to the other. Traditionally genotypic selection on non-Gaussian traits like germination rate have been hampered because it is challenging to measure their genetic variance. Methods Here we used quantitative genetics and reciprocal transplants to test whether a previously discovered cline in germination rate showed additional signatures of adaptation in the scarlet monkeyflower (Mimulus cardinalis). We measured genotypic and population level covariation between germination rate and early survival, a component of fitness. We developed a novel discrete log-normal model to estimate genetic variance in germination rate. Results Contrary to our adaptive hypothesis, we found no evidence that genetic variation in germination rate contributed to variation in early survival. Across populations, southern populations in both gardens germinated earlier and survived more. Conclusions Southern populations have higher early survival but this is not caused by faster germination. This pattern is consistent with nonadaptive forces driving the phenotypic cline in germination rate, but future work will need to assess whether there is selection at other life stages. The statistical framework should help expand quantitative genetic analyses for other waiting-time traits.

A novel framework for analysis of the shared genetic background of correlated traits

We propose a novel effective framework for analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be to effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHER is versatile and applicable to summary statistics from GWASs with arbitrary sample sizes and sample overlaps, allows incorporation of different GWAS models (Cox, linear and logistic) and is computationally fast.

Temporal and genomic analysis of additive genetic variance in breeding programmes

Genetic variance is a central parameter in quantitative genetics and breeding. Assessing changes in genetic variance over time as well as the genome is therefore of high interest. Here, we extend a previously proposed framework for temporal analysis of genetic variance using the pedigree-based model, to a new framework for temporal and genomic analysis of genetic variance using marker-based models. To this end, we describe the theory of partitioning genetic variance into genic variance and within-chromosome and between-chromosome linkage-disequilibrium, and how to estimate these variance components from a marker-based model fitted to observed phenotype and marker data. The new framework involves three steps: (i) fitting a marker-based model to data, (ii) sampling realisations of marker effects from the fitted model and for each sample calculating realisations of genetic values and (iii) calculating the variance of sampled genetic values by time and genome partitions. Analysing time partitions indicates breeding programme sustainability, while analysing genome partitions indicates contributions from chromosomes and chromosome pairs and linkage-disequilibrium. We demonstrate the framework with a simulated breeding programme involving a complex trait. Results show good concordance between simulated and estimated variances, provided that the fitted model is capturing genetic complexity of a trait. We observe a reduction of genetic variance due to selection and drift changing allele frequencies, and due to selection inducing negative linkage-disequilibrium.