Integrating genomics technologies and breeding methods to tweak core parameters of the breeder’s equation could accelerate delivery of climate-resilient and nutrient rich crops for future food security.σ2g) is the identification or creation of favorable alleles/haplotypes and their deployment for improving key traits. Novel alleles for new and existing target traits need to be accessed and added to the breeding population while maintaining genetic diversity. Selection intensity (i) in the breeding program can be improved by testing a larger population size, enabled by the statistical designs with minimal replications and high-throughput phenotyping. Selection priorities and criteria to select appropriate portion of the population too assume an important role. The most important component of breeder′s equation is heritability (h2). Heritability estimates depend on several factors including the size and the type of population and the statistical methods. The present article starts with a brief discussion on the potential ways to enhance σ2g in the population. We highlight statistical methods and experimental designs that could improve trait heritability estimation. We also offer a perspective on reducing the breeding cycle time (t), which could be achieved through the selection of appropriate parents, optimizing the breeding scheme, rapid fixation of target alleles, and combining speed breeding with breeding programs to optimize trials for release. Finally, we summarize knowledge from multiple disciplines for enhancing genetic gains for climate resilience and nutritional traits.Accelerating genetic gain in crop improvement programs with respect to climate resilience and nutrition traits, and the realization of the improved gain in farmers’ fields require integration of several approaches. This article focuses on innovative approaches to address core components of the breeder’s equation. A prerequisite to enhancing genetic variance ( Extreme weather and precipitation events, decreasing soil fertility and plant productivity, and rising disease and pest pressure and crop failures resulting from climate change threaten global agricultural production (Dhankher and Foyer t) and depends on the genetic variation (σ2g), the intensity of selection (i), and heritability of the trait (h2). Enhancing genetic gain in breeding programs and its realization in farmers’ fields calls for an integration of multiple aspects including germplasm resources, genomics, breeding, and agronomic practices in concern with improved seed delivery systems  can be reduced by harvesting more generations per year, instead of the usual one or two cycles per year, and making selection on a single plant basis, using visual selection or molecular markers.Innovations in high-throughput genotyping, phenotyping and informatics tools present an enormous opportunity to revisit the breeder’s equations in terms of genetic gain over time. Growing phenotyping capacities such as disease sick plots, artificial screening in laboratories or greenhouse can help to screen a large number of plants for the trait of interest in a short period of time. Molecular markers can serve as proxies for target phenotypes allowing selection to be performed on young plants and/or in early generations. Gene-based/linked markers allow selection in “off-target” years and at any place and in any given situation. The availability of cost-effective and high-throughput genotyping platforms can allow the assaying of thousands of plants in a relatively short time. Breeding cycle time  through the identification and creation of favorable alleles and their deployment in the breeding population. This is followed by a discussion on selection intensity (i), and in this section, we have covered various aspects including population size, proportion of selected individuals, selection criteria (single/multi-trait), selection methods , selection priorities , and selection targets . In the third section, we have discussed how to estimate and increase the heritability (h2) of the component traits. We have considered several methods of statistical analyses, and experimental designs to estimate the heritability. And in the last, we proposed reducing the breeding cycle time (t) through the selection of appropriate parents, optimizing the breeding scheme, rapid fixation of target alleles, and integrating speed breeding approach into the main breeding programs.We begin by highlighting the potential ways to enhance genetic variance  and multi-parent advanced generation inter-cross (MAGIC) are new experimental designs that combine allele richness, high mapping resolution, and high statistical power of both linkage analysis and association mapping  locus in rice following the MutMap approach, and the study demonstrated its immediate utility in rice genetic improvement through breeding salt-tolerant rice Kaijin carrying the recessive hst1 allele in just two years.Advances in genomics have facilitated a variety of NGS-based rapid trait mapping approaches like QTL-seq, MutMap, Indel-Seq, or BSR-Seq  trait mapping through pooled sequencing of populations and b) trait mapping through sequencing of the entire population. Examples of NGS-based trait mapping have been reported in many crop species  for improving elite varieties. This approach has been used extensively to develop a large number of improved varieties for commercial release in the public and private sectors. Compared to MABC, MARS is better equipped to address complex traits and has been proven useful to introgress as many as 40 loci through intercrossing elite × elite parents  × environment interaction (G × E) for specific environments or G × E interactions for multi-traits into statistical models in predicting unobserved individuals. Understanding the complexity of traits requires a theoretical framework that accounts for often cryptic interactions. Usually, additive genetic effects can be predicted by Bayesian inference , a linear combination of GEBVs, combines phenotypic and GEBV information to predict the net genetic merit of the unobserved individuals  , i.e., the difference of means of selected individuals over the mean of the non-selected population. Selection intensity (i) defined as standardized selection differential can be increased through i) enhancing experimental scales and populations size, ii) reducing the proportion selected for the next generation, and iii) improving accuracy in selection targets, selection methods, and with better-defined selection priorities   Fig. . Optimizts) Fig. .Fig. 2Eni.e., the shift of allele frequencies that occurs due to the sampling of alleles that are contributed to progeny. Genetic drift causes non-independence of alleles at different loci . This LD allows markers to predict the phenotype. The number of marker effects to estimate should be proportional to the effective population size. Therefore, to maintain a constant ratio of the training population size to the number of effects estimated, the training and effective population sizes need to scale together  traits because the basic traits had contributed to wider acceptance of the established product, a popular existing variety in this case. Value-added/ game-changing traits can also be listed in selection priorities, which are not yet available to the elite market-oriented breeding program. These traits have enough potential for market or value-chain transformation to warrant additional investment in their development. Recent high-throughput phenotyping (HTP) platforms that facilitate accurate measurements on large populations for an array of morphological and physiological traits conferring climate resilience are noteworthy  and their respective underlying true genotypic values (g) with genotypic variance σ2g. Further, g and σ2g can be dissected into additive, dominance, and epistasis components to extract the average effects of alleles and breeding values (a), with variance σ2a. Depending on whether total genotypic variance  or additive genetic variance  are considered, we refer to broad-sense heritability (H2) or narrow-sense heritability (h2), respectively  sets a prerequisite priority to commence any breeding programs. This can help design the prototype required for the market and breeder-preferred traits while developing new varieties. This step will ensure not only focus breeding activities on developing varieties/lines for specific regions but also rapid adoption after the release for commercial cultivation in any crop. The selection of the appropriate parents to be used in artificial crosses is one of the key breeding decisions that will facilitate the exploitation of maximum genetic variability and recovery of superior recombinants. Several techniques have been used to identify genotypes with promising and desirable agronomic traits for hybridization.1s. The true F1 plants confirmed by haplotype-specific SNPs are self-pollinated to generate large numbers of F2 seeds. Single seed descent (SSD) methods coupled with accelerated generation turnover under field or controlled conditions could lead to rapid fixing of the advanced lines for homozygosity. Selected fixed lines exhibiting phenotypic superiority can be further tested with trait-specific SNP panels for the selection of lines carrying maximum alleles of interest. The entire process from crossing to the development of the final set of lines for genotyping will demand a short period of < 2 years.Speed-GS can accelerate the development of new lines based on product profiles by establishing haplotype-based breeding panels. Based on the region-based needs, which include donors for targeted traits , high-yielding varieties for different ecologies and elite lines possessing superior haplotypes for the traits of interest could enhance genetic gains. These assembled panels can be grown in the TPEs for a minimum of two years for generating high-quality phenotypic data for the targeted traits and need to be genotyped at high-density. Based on the haplotypic and breeding values (gBLUP) and the targeted market-oriented demands, parental lines shall be selected for the generation of FLines with superior alleles selected using genotypic and phenotypic scores can be further subjected to GS analysis. Homozygous lines can be assayed on fixed arrays (SNP arrays) or low-cost sequencing-based genotyping platforms such as rAMPSeq  including targeted phenotypic environments at farmer fields with their best performing checks. The best performing lines evaluated and identified in such ways will ensure high genetic gains . Elite/favorable alleles can be introduced into the breeding pipeline through the development of new parental lines with the allele of interest. The development of new breeding lines required optimized breeding programs that maintain a balance between the size of the population and the selection intensity (i). Better estimation of trait heritability (h2) remains crucial to improve the rate of genetic gain. In this context, advances in the statistical methods and experimental designs could contribute to improving heritability estimates. Likewise, accelerating the breeding cycle time (t) significantly influences selection response. Thus, informed choice of appropriate parents, optimized breeding pipelines, rapid fixation of target alleles, and combining SB/RGA into breeding programs present potential ways to enhance breeding efficiency. Deployment of these approaches in the modernized breeding programs will help realizing the higher genetic gains in fields for the small-holder farmers in the developing world.Enhancing genetic gain amid climate change necessitates breeding innovations to deliver the productivity gain not only in the research plots but in farmers fields’ as well. An integrated approach is required to realize the genetic gain through the modernization of the breeding programs. As discussed above, creation and use of favorable effect alleles into the breeding programs is required to enhance the genetic variance (


A non‐metric multidimensional scaling showed line differences between genera. The α‐diversity indexes were higher in the LRFI line than in the HRFI line (p < .01), with h2 estimates of 0.19 ± 0.08 (Shannon) and 0.12 ± 0.06 (Simpson). Forty‐eight genera had a significant h2 (>0.125). The rg of the α‐diversities indexes with production traits were negative. Some rg of genera belonging to the Lachnospiraceae, Ruminococcaceae, Prevotellaceae, Lactobacillaceae, Streptococcaceae, Rikenellaceae and Desulfovibrionaceae families significantly differed from zero (p < .05) with FE traits, RFI (3), DFI (7) and BFT (11). These results suggest that a sizable part of the variability of the gut microbial community is under genetic control and has genetic relationships with FE, including diversity indicators. It offers promising perspectives for selection for feed efficiency using gut microbiome composition in pigs.This study aimed to evaluate the genetic relationship between faecal microbial composition and five feed efficiency (FE) and production traits, residual feed intake (RFI), feed conversion ratio (FCR), daily feed intake (DFI), average daily gain (ADG) and backfat thickness (BFT). A total of 588 samples from two experimental pig lines developed by divergent selection for RFI were sequenced for the 16 rRNA hypervariable V3‐V4 region. The 75 genera with less than 20% zero values (97% of the counts) and two α‐diversity indexes were analysed. Line comparison of the microbiota traits and estimations of heritability (h Heritability is a population‐specific parameter that estimates the proportion of additive genetic variance to the phenotypic variance of the trait. Besides the heritability, another essential genetic parameter is the additive genetic correlation (rg). These two parameters are crucial to predict direct and correlated responses to selection, which are other parameters to evaluate if and how a trait would be affected by selection  traits, because of their key economic and environmental importance, have a high impact on the sustainability of this industry . The selection process and structure of the data from the two divergent lines have been described in Gilbert et al.  and average daily gain (ADG) between 35 and 95 kg BW, and backfat thickness (BFT) at 95 kg BW  and maintenance requirements (average metabolic BW (AMBW)), and the fixed effects of sex, pen size, CG, and the covariate of BW at the beginning of the test for response animals  gene, a housekeeping gene in all bacteria Woese, . Sequenc™ Faecal Microbe Miniprep Kit™ (Zymo Research) and a 15 min bead‐beating step at 30 Hz was applied. The V3‐V4 region was then amplified from diluted genomic DNA with the primers F343 (CTTTCCCTACACGACGCTCTTCCGATCTTACGGRAGGCAGCAG) and R784 (GGAGTTCAGACGTGTGCTCTTCCGATCTTACCAGGGTATCTAATCCT) using 30 amplification cycles with an annealing temperature of 65°C. This V3‐V4 region has proved useful to study the variability of the pig microbiota in previous studies  and reverse primer (CAAGCAGAAGACGGCATACGAGAT‐index‐GTGACTGGAGTTCAGACGTGT). The resulting PCR products were purified and loaded to the Illumina MiSeq cartridge following the manufacturer's instructions. Run quality was internally checked using PhiX, and each pair‐end sequence was assigned to its sample using the integrated index, with the bcl2fastq Illumina software. The sequences were submitted to the Short‐Read Archive with accession number PRJNA701065. Filtering and trimming of sequences of high quality was applied to the reads with the DADA2 package in the R software , the genera table was filtered for a maximum proportion of 20% zero abundancy for each genus, and the resulting abundancies were log‐transformed after adding a constant value of 1 to all counts. After this filtration step, 75 genera remained for the downstream analyses. Finally, to better understand how the genera are distributed, two α‐diversity metrics, the Shannon Shannon,  and Simp2.3p < .05 for the corresponding F‐test.The beta‐diversity is usually used to demonstrate the community differentiation between cohorts variance components. The best linear unbiased prediction (BLUP) method was applied to the filtered genera and the two α‐diversity metrics to obtain the (co)variance components. To follow the assumption of the BLUP method, which should be applied to a non‐selected base population, all analyses were done in bivariate models including the selection index as the first trait. The second trait was the microbiota observation vector (abundance of each genus or α‐diversity metric). To compute genetic correlations between the performance traits and microbiota observations, each of the production traits was added in three‐variate analyses.p < .05) on all response variables was tested in preliminary GLM analyses. Significant fixed factors, including pen size (5 levels), herd of birth (two levels), sex (three levels), and contemporary group  for performance traits, microbiota data and α‐diversity metrics, were systematically fitted. The fitted covariates were slaughter body weight (BW) for BFT and BW at test for genera abundancies and α‐diversity metrics. The significance of all fitted fixed factors on the 75 genera are given in Table p < .05 for a χ2 test applied to the likelihood ratio test comparing the models with and without this term).The significance of fixed environmental factors , b is the vector of fixed effects (described above), a is the vector of additive genetic effects, l is the vector of litter effects, and e is the vector of random residuals. X, Z1 and Z2 are the incidence matrices for b, a and l. The distributions assumed for the random terms were a ~ N , l ~ N , and e ~ N , where 0G is a 2 × 2 or 3 × 3 symmetrical direct additive genetic effect (co)variance matrix, and lR and eR are 2 × 2 or 3 × 3 symmetrical litter effect and residual effect (co)variance matrices, respectively. I denoted the identity matrix of adequate dimension. The pedigree relationship matrix (A) included 10 generations of pedigree information plus ancestors, and contained 7,293 animals. The analyses were performed using AIREMLF90 software  for the BLUP method.The following bivariate and three‐variate animal models were used to estimate the variance components:2 of the 75 genera, an empirical significance threshold equal to 0.125 was considered. The threshold was obtained after running 10,000 univariate analyses using the above described genetic model applied to microbiota abundancies, based on a null hypothesis of no genetic control on the abundancies. The null hypothesis was obtained by shuffling the abundances across individuals for two arbitrary genera. The minimum value of the top 5% of the estimated h2 was considered as the threshold to decide that a genus was heritable. Thereafter, the three‐variate analyses were conducted for genera with h2 significantly different from zero. The deviation from zero of the additive rg of genera and α‐diversity metrics with the production traits were tested using a Z‐test.To test the significance of h33.1p < .05 for a Student test applied to the log‐transformed abundances), the genera Lactobacillus , Prevotella_9 , and Streptococcus  were the more abundant genera in both lines, and they were all more abundant in the HRFI line. The three genera Clostridium_sensu_stricto_1 (p < .0001), Prevotella_7 (p < .004), and Terrisporobacter (p < .0001) were more abundant in the LRFI line  than in the HRFI line . The four genera Dialister (p < .05), NA_Prevotellaceae (p < .0001), NA_Lachnospiraceae , and Blautia  represented on average 2.2% of the counts. The other 42 differentially abundant genera had abundances lower than 2% in the two lines, and represented a total of 25.9% and 18.6% of the abundances in the LRFI and HRFI lines, respectively. The remaining 23 genera that were not significantly different (p > .05) between the lines had total abundance of 16.2% in the LRFI and 14.6% in the HRFI lines.The 75 filtered genera represented on average 97% of the sample counts of the rarefied table. Among these genera, 42 had significantly higher abundances in the LRFI line than in the HRFI line, and 10 were more abundant in the HRFI line Figure . Of the p < .01) dimension. Among the 75 genera included in the NMDS, the genus Rikenellaceae_RC9_gut_group had the highest (2.9%) contribution to the plan defined by dimensions 1 and 2, and the genus Succinivibrio had the lowest (0.03%) contribution (Table Prevotella_7 (5.2%), Syntrophococcus (5.0%), NA_Family_XIII (5.0%), Lachnospiraceae_NK3A20_group (4.7%), Olsenella (4.6%), Dialister (4.5%), Mitsuokella (4.5%), and Shuttleworthia (4.3%) in one direction, and the genera Lachnospiraceae_ND3007_group (4.0%), Ruminococcaceae_UCG‐008 (3.9%), and Marvinbryantia (3.4%) in the other direction. On the second axis, 25 genera had contributions larger than 1.33%, including 22 genera differentially abundant between the lines. The genera Prevotella_9 (4.3%) drove the direction towards more HRFI samples, whereas the genera Ruminococcaceae_NK4A214_group (5.7%), Rikenellaceae_RC9_gut_group (5.6%), Ruminococcaceae_UCG‐002 (5.1%), Family_XIII_AD3011_group (4.5%), NA_Ruminococcaceae (4.4%), Christensenellaceae_R‐7_group (4.2%), NA_Muribaculaceae (4.0%), Ruminococcaceae_UCG‐005 (3.6%), Prevotellaceae_UCG‐001 (3.3%) and Ruminococcaceae_UCG‐010 (3.2%) were the main contributors to the opposite direction, towards the LRFI line (contributions higher than 3.2%).The NMDS showed differences between the genera communities of the LRFI and HRFI lines Figure . The twoowest 0.0% contribp < .01, Figure The Shannon and Simpson α‐diversities indexes showed significantly higher microbial diversity in the LRFI line than in the HRFI line . Out of the 23 genera that did not differ between lines, 15 had significant h2. For the 48 heritable genera, the abundances per line are shown in Table The gut microbiota composition can be highly heritable in pigs, but not for all genera. The hp < .05 for a Student test applied to the average of log‐transformed abundances). A Spearman correlation of 0.26 (p < .05) was estimated between the h2 estimates and the average of log‐transformed abundances, while a correlation of 0.10 (p > .05) was obtained with the raw averages.Heritable genera were also more abundant genera, while non‐heritable genera tended to be at lower abundance (p < .05) of contribution to the first axis between the two groups of genera: the average contribution of the heritable genera to axis 1 was 1.8%, whereas the non‐heritable genera had an average contribution of 0.5%. The two groups of genera similarly contributed to the second axis (p = .08): the average contribution of the heritable and non‐heritable genera to the second axis were 1.1% and 1.8%, respectively.Comparison of the contributions of the heritable and non‐heritable genera to the axes of NMDS showed a significant difference  and FCR (rg < −0.61 ± 0.52).The two α‐diversities indexes and 48 genera with significant hs Table  . With ADrg ranged from −0.36 ± 0.24 (Romboutsia) to 0.32 ± 0.12 (Streptococcus) with RFI, from −0.38 ± 0.55 (Ruminococcaceae_UCG‐005) to 0.51 ± 0.31 (Fusicatenibacter) with FCR, from −0.63 ± 0.45 (Desulfovibrio) to 0.60 ± 0.12  with DFI, −0.98 ± NE (NA_Ruminococcaceae) to 0.86 ± 0.05 (Lactobacillus) with BFT, and from −0.48 ± 0.56 (NA_Ruminococcaceae) to 0.73 ± 0.76 (Lachnospiraceae_UCG‐001) with ADG. In Table 5, the rg of the 22 genera that had at least one significant genetic correlation with the performance traits are presented. The production trait with the highest number of significant rg with genera was BFT (11 significant correlations with genera). In addition, three genera had rg estimates close to −1 with this trait , but Z‐tests could not be applied for these cases, as standard errors were not estimable at the borders of the parameter space. The DFI and RFI showed significant rg with 7 and 3 genera, respectively, and there were no genera with significant rg with ADG and FCR. The genus Shuttleworthia had significant genetic correlations with two traits (DFI and BFT), and the genus Desulfovibrio had a significant rg with RFI and close to −1 with BFT.With the genera, rg with at least one production trait, and out of the 42 genera more abundant in the LRFI line, only 7 had significant correlations with the production traits. The other 9 genera with significant genetic correlations with at least one trait were from the 23 genera that had similar abundances between the lines. Distribution between the LRFI and HRFI lines of the abundance of the 22 genera with significant rg are presented in Figure rg with RFI  had significant line abundance differences that were consistent with the sign of the rg. The genera Streptococcus and Prevotella_2 were more abundant in the HRFI line and had a positive rg with RFI, whereas the genus Desulfovibrio was more abundant in the LRFI line, and had a negative rg with RFI. Out of the 14 genera with significant or very negative genetic correlations with BFT, genera Blautia, Lactobacillus, and Dorea were significantly more abundant in the HRFI line, and had positive rg with BFT, and the 5 genera Prevotella_7, Rikenellaceae_RC9_gut_group, Desulfovibrio, NA_Ruminococcaceae, and Lachnospira were more abundant in the LRFI line, and had negative rg with BFT. Of the 7 genera that had significant rg with DFI, only the genus Roseburia (more abundant in the LRFI line) had significant abundance difference between the two lines, and the sign of the rg was not consistent with the line differences.From the 10 genera more abundant in the HRFI line, 6 had significant Box plots showing genera abundances between the LRFI and HRFI lines for the other 53 genera are given in Figure 4The objective of the present study was to clarify if some components of pig faecal microbiota have genetic relationships with production and FE traits, taking advantage of data collected in two experimental pig lines divergent for RFI. The approach combined a comparison of the microbiota composition between the genetic lines, and quantitative genetic models to quantify the genetic control on the microbiota components and estimate genetic correlations with traits of interest. These approaches were applied to the subset of genera counts that presented reasonably good properties (number of zeros and Gaussian distribution) to be submitted to linear mixed models. A substantial genetic control for these genera abundances was evidenced with the two approaches, and interesting genetic relationships with the traits of interest were pointed out.4.1Most studies that compared microbiome data of pigs between low and high RFI groups are based on a phenotypic selection of extreme pigs in a population, so most of the reported differences would be driven by phenotypic relationships. In our study differences between animals were established by at least 9 generations of selection, therefore a large proportion of the line differences would result from genetic differences between pigs. Because of the limited size of the lines, the differences can result from an association with the selected trait, or from genetic differences arising by chance . The quLactobacillus was one of the more abundant ones, with higher abundance associated with high RFI. This genus is well described for its commonness and its important functions in gut health in animals  compared to less feed efficient (17.85%) pigs. The prevalence of members of the Prevotella genera is related to their enhancer role in the digestion ability and nutrients extraction from high‐fibre plants , the extent of the contributions was not related to the abundance in the two lines. For instance, genera from the Ruminococcaceae family had an abundance lower than 2% in the LRFI line, but they were among the highest positive contributors to the second axis.The results of NMDS confirmed the hypothesis of changes in the intestinal microbial community as a result of selection for feed efficiency. Even though the genera contributions were consistent with their prevalence in the lines  and consequently missing heritable genera with low abundances. Limited sequencing depth of the microbiota data would cause less precise quantification and high proportion of zeros, that result in imperfect analyses of genera with low abundancies.Our results showed significant additive genetic variance for 61% of the analysed genera. Overall, observing significant heritabilities for more than half of the analysed genera, which represented about 97% of the gut microbial communities, suggests that a considerable part of variability of the gut microbial community is under genetic control. However, some heritable genera were shown to differ between lines, but some differentially abundant genera were not heritable, and some heritable genera did not differ between the lines. This last situation could correspond to genera with limited genetic relationship with the selection criterion that would thus not respond to selection and be differentially abundant. The situation of genera that were differentially abundant between lines and not heritable in our study can be related to a limited power of our experimental design to estimate accurately the variance components: only h2 estimates were in the range of previously published values for these genera . Among the genera that now have sub‐types , we obtained different h2 values for the different types. For Prevotella, h2 ranged from 0.44 ± 0.11 for Prevotella_1 to 0.01 ± NE (Prevotella_9). Chen et al. (2 of 0.23 for the genus Prevotella and 0.22 for the genus Coprococcus that are in agreement with our estimations for the Prevotella_7 (0.28 ± 0.10) and Coprococcus_1 (0.32 ± 0.10). The estimated h2 for genus Lactobacillus (0.24 ± 0.09) was higher than the reported value (0.08) by Chen et al. (2 for the genus Blautia (0.39 ± 0.11) as Camarinha‐Silva et al. (2 for the genus Alloprevotella (0.22 ± 0.09) than their report (0.34 ± 0.16). Some discrepancies with previously reported estimates could indicate that the genetic determinism of some genera is affected by the study conditions, either animal dependent  or related to external conditions , and would need validation in larger and more diverse conditions.Except in few cases, our hn et al.  have been et al.  and lowen et al. . We obtaa et al.  , that would deserve further analyses.The negative n et al. . Similarn et al. . Finally Gibson, . The gen, et al. . Identife et al.  also reprg between genera and BFT could be partly due to the higher h2 of BFT, in comparison to the other traits, that could give more power to these estimations. The general composition of backfat in pigs includes water, collagen, and lipids (mainly triacylglycerols)  belonged to the Lachnospiraceae family. Biddle et al.  in the gut. Compounds resulting from such decomposition would be fermented and converted into the acetate, butyrate, and propionate (short‐chain fatty acids—SCFAs) that are absorbable and useable as energy sources by the host  will result in increased intestinal microbial community diversity. In the literature, genetic parameters for α‐diversity metrics are rarely reported. Lu et al. , but given the standard errors in both studies, our estimates are not statistically different from theirs. Given the genetic properties found in our study and the links reported with gut health and immunity, those synthetic descriptors of gut microbiota composition could be promising traits for selection.Higher microbial diversity is often considered as an attribute of gut health, as animals with the more diverse microbial community are potentially more capable to better deal with pathogenic microbes  and body composition traits (Lachnospiraceae family). However, selection to change single microbiota components in order to improve performance traits seems contradictory with the beneficial relationships found between performance traits and microbiota diversity. In that respect, selecting for indicators of microbiota diversity, such as the Shannon index, could be a more generic option. This could also be less dependent on the microbiota specificities due to breeding conditions and sampling characteristics. Indeed, in addition to the genetic, multiple factors can affect the relative abundance of microbiota components and their relationships with traits, including breed and age at sampling  the genetic relationships seemed to be also depend on other factors that could not be accounted for in the present analysis. Deciphering the role of these different factors  would clarify the potential for use of these microbiota components to orientate pig performances via different levers of management, including the use of pro‐ and prebiotics, as proposed by Maltecca et al. (Our results clearly indicate a genetic basis for part of the gut microbiota composition involved in the variation of feed efficiency (a et al. .5Lachnospiraceae family had more significant correlations with the studied traits than genera from other families. Functional analyses will be needed to validate the underlying mechanisms. The robustness of these findings requires further validations in different breeding conditions. However, they offer promising perspectives for selection for feed efficiency using gut microbiome composition in pigs.Our results showed substantial effects of genetics on the variability of gut genera community and their relationship with the feed efficiency in pigs. Both analyses of line effect and genetic correlations with production traits revealed a substantial genetic basis for the links between feed efficiency traits and genera and individual diversity of the gut microbial community. The higher diversity in more feed efficient pigs might be related to better gut health and resilience to feed changes. Genera annotated to the The authors declare that they have no competing interests.Fig S1Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.


SERPINA6/SERPINA1. This locus contains genes encoding corticosteroid binding globulin (CBG) and α1-antitrypsin. Expression quantitative trait loci (eQTL) analyses undertaken in the STARNET cohort of 600 individuals showed that specific genetic variants within the SERPINA6/SERPINA1 locus influence expression of SERPINA6 rather than SERPINA1 in the liver. Moreover, trans-eQTL analysis demonstrated effects on adipose tissue gene expression, suggesting that variations in CBG levels have an effect on delivery of cortisol to peripheral tissues. Two-sample Mendelian randomisation analyses provided evidence that each genetically-determined standard deviation (SD) increase in morning plasma cortisol was associated with increased odds of chronic ischaemic heart disease  and myocardial infarction  in UK Biobank and similarly in CARDIoGRAMplusC4D. These findings reveal a causative pathway for CBG in determining cortisol action in peripheral tissues and thereby contributing to the aetiology of cardiovascular disease.The stress hormone cortisol modulates fuel metabolism, cardiovascular homoeostasis, mood, inflammation and cognition. The CORtisol NETwork (CORNET) consortium previously identified a single locus associated with morning plasma cortisol. Identifying additional genetic variants that explain more of the variance in cortisol could provide new insights into cortisol biology and provide statistical power to test the causative role of cortisol in common diseases. The CORNET consortium extended its genome-wide association meta-analysis for morning plasma cortisol from 12,597 to 25,314 subjects and from ~2.2 M to ~7 M SNPs, in 17 population-based cohorts of European ancestries. We confirmed the genetic association with  Cortisol plays a vital role in adaptation to environmental stress, modulating fuel metabolism, cardiovascular homoeostasis, mood, memory and inflammation . CortisoSERPINA6 and SERPINA1 and influences function of corticosteroid-binding globulin , a protein that binds cortisol in the blood. However, it was unclear if the effect is mediated directly through SERPINA6 or indirectly through the product of SERPINA1, α1-antitrypsin, which is involved in regulating cleavage and inactivation of CBG [SERPINA6 are associated with plasma cortisol and with coronary artery disease [We established the CORtisol NETwork (CORNET) consortium with the initial aim of identifying genetic determinants of inter-individual variation in HPA axis function. A genome-wide association meta-analysis (GWAMA), investigating ~2.2 M SNPs in 12,597 individuals from 11 European cohorts, identified a single locus on chromosome 14 associated with morning plasma cortisol at genome-wide significance [n of CBG . An indeSERPINA6/A1 locus explain only ~0.5% of the variance in morning plasma cortisol. Moreover, attempts to identify genetic variants associated with an alternative phenotype of salivary cortisol have not been successful [SERPINA6/A1 locus there is an influence on cortisol; to establish whether SERPINA6/A1 variation influences tissue-specific expression of CBG and α1-antitrypsin; and to confirm whether high cortisol is causal in ischaemic heart disease and test if it is causal in other common diseases. To achieve these aims, we undertook an extended GWAMA analysis, with more subjects and more SNPs than the original CORNET GWAMA [The genetic variants in the ccessful . IdentifET GWAMA , and usen  = 886), CROATIA-Korcula (n  = 897), CROATIA-Split (n  = 493), ORCADES (n  = 1974), Rotterdam Study (n  = 2870), NFBC1966 (n  = 1324), Helsinki Birth Cohort Study 1934–44 (n  = 399), ALSPAC (n  = 1487), PREVEND (n  = 1151), PIVUS (n  = 919), Raine Study (n = 860), ET2DS (n  = 847), MrOS-Sweden (n  = 969), KORA (n = 1651), TwinsUK (n = 5654), SHIP (n  = 910) and VIKING (n = 2073). Characteristics of the study populations are presented in Table We performed a meta-analysis of genome-wide association studies of morning plasma cortisol in 25,314 subjects from 17 European population-based cohorts: CROATIA-Vis (z-scores of log-transformed morning plasma cortisol (additive genetic effects), adjusted for sex, age and cohort-specific genetic ancestry. Additional models also adjusted for smoking and body mass index. Imputation of the gene-chip results used the 1000 Genomes European population reference panel. Details of the genotyping, imputation and cohort-specific adjustment for genetic ancestry are provided in Table Each study performed linear regression on Quality control was carried out on the imputed genome-wide data for all 17 studies prior to meta-analysis; this excluded all SNPs with a minor allele frequency (MAF) <0.5%, call rate <95%, and poor imputation quality . Furthermore, only SNPs with estimates from at least four studies were included, resulting in a final number of 8,452,427 SNPs. Quantile–quantile (QQ) plots and genomic control (lambda) were used to assess evidence for population structure that was not accounted for in association analyses. Sex chromosomes were not analysed. Quality control at the study-level and meta-level was performed using EasyQC software .Z-scores proportional to the square-root of the number of individuals in each sample, using METAL software [The results from all cohorts were combined into a fixed-effects meta-analysis using Stouffer’s method with weighting of software . Manhattsoftware .Linkage disequilibrium (LD) score regression exploits the relationship between SNP-phenotype association strengths and LD patterns . AssuminGenetic correlations between morning plasma cortisol and selected diseases and traits from UK Biobank were estimated using bivariate LD score regression. This technique examines the correlation structure of genetic effects of SNPs across the genome. The data processing pipeline devised by Bulik-Sullivan et al.  was follα = 0.05/18 062; P < 2.768 × 10−6).Gene- and pathway- based associations, which assign SNPs to genes and biological pathways respectively, were performed using MAGMA and FUMA software , 24. SNPeQTL analyses were undertaken in the STARNET study which is composed of Caucasian individuals of Eastern European origin , with a confirmed diagnosis of coronary artery disease. Of these individuals 27% had diabetes, 77% had hypertension and 37% had suffered a myocardial infarction before the age of 60 years. Genotyping and RNA sequencing of seven vascular and metabolic tissue samples from 600 patients undergoing coronary artery bypass surgery was performed . The tisSERPINA6/SERPINA1 locus on chromosome 14 identified from the CORNET plasma cortisol GWAMA, defined as within 100 Kb of SERPINA6 (±of the transcription start/end point), with no missing data, and MAF >5%. All transcripts were annotated using the Human Genome Reference Consortium Human Build 37 (GRCh37).For the analysis, we included 580 SNPs within p values were computed using the kruX algorithm [q values [SERPINA6 were visualised using the GWAS visualisation tool LocusZoom [SERPINA6 was visualised by constructing boxplots for each allele.Transcriptome-wide eQTL associations between selected SNPs and all genes expressed in the STARNET tissues were tested using the Kruskal–Wallis test, a non-parametric ANOVA used to determine whether sample groups originate from the same distribution that has been used previously in genetic association studies . Kruskallgorithm , using algorithm  as inputq values  using thocusZoom . The allp value for each SNP.The global tissue-specific effect on gene expression for SNPs associated with plasma cortisol at a genome-wide level of significance was depicted using Q–Q plots showing the observed transcriptome-wide SNP-gene associations against the expected uniform distribution. Deviation from the uniform distribution was tested using the Kolmogorov–Smirnov test statistic and p values were consistent  genetic variants that reached a genome-wide level of significance (P < 5 × 10−8). To detect independent top SNPs we used the clumping function as implemented in PLINK. The European samples from the 1000 Genomes Project were used to estimate LD between SNPs. Amongst those SNPs within 1000 kb and r2 < 0.3, only the SNP with the lowest P value was retained.The instrument for morning plasma cortisol consisted of independent . Details of the selected disease and traits, the population, consortia details and genetic instruments are provided in Table p < 5 × 10−8) , and the SERPINA1 gene, encoding α1-antitrypsin, an inhibitor of neutrophil elastase which cleaves and inactivates CBG. In an additive genetic model, the top SNP rs9989237 reported a per minor allele effect of 0.11 cortisol z-score (p  = 2.2 × 10−19). The effect allele frequency was 0.22 and this variation explained 0.13% of the morning plasma cortisol variance. Within this locus we identified four blocks of SNPs in low LD (r2 < 0.3) visualised in Figs. The GWAMA of morning plasma cortisol levels in 25,314 identified a single locus on chromosome 14 reaching a genome-wide level of significance  of the phenotypic variation of morning plasma cortisol. There was no evidence of enrichment in a particular cell type, and particularly not in the adrenal/pancreas or liver cell type groups of SNPs, when partitioning the heritability into functional cell types  and a negative genetic correlation with BMI .LD score regression was used to test whether genetic variants associated with morning plasma cortisol also contribute to health-related traits. Estimated genetic correlations are presented in Fig. SERPINA6, SERPINA1 and SERPINA10, all located on chromosome 14, that attained genome-wide significance following correction for multiple comparisons   Table .SERPINA6 region on gene expression were obtained using individual-level genotype and RNA-sequencing data from seven vascular and metabolic tissues from the STARNET study [q ≤ 0.05), 32 cis-eQTLs for SERPINA6 were identified in the liver, the only tissue where SERPINA6 is highly expressed , and therefore also associated with variation for plasma cortisol   Fig.  and show15) Fig. . In all NET Fig. .SERPINA6 cis-eQTLs in liver and SNPs associated with plasma cortisol are driven by the same causal variant, Bayes factor colocalisation analysis was performed while accounting for allelic heterogeneity [SERPINA6. However, when examining each LD block individually, the block represented by rs2736898 returns a 99.2% probability of shared causal variant in this region SERPINA6 on Table .Fig. 3ScThe clumping procedure identified four SNPs  as markers representing genome-wide significant signals in this region. Two-sample Mendelian randomisation analyses provided evidence that each genetically-determined standard deviation (SD) increase in morning plasma cortisol was associated with an increased risk of chronic ischaemic heart disease  and myocardial infarction  in UK Biobank Fig. . SimilarThe bidirectional Mendelian Randomisation analyses, estimating the genetically predicted effect of various traits or disease on plasma cortisol, did not support any causal associations Fig. .Fig. 5BiSERPINA6/A1 locus on chromosome 14 is associated with morning plasma cortisol. Despite doubling the sample size and trebling the SNP density from the previous genome-wide association study [SERPINA6/A1 locus and strengthened the instrument used in two-sample Mendelian randomisation analyses. This additional information coupled with tissue-specific gene expression data suggests that genetic variation within this locus influences expression of SERPINA6 rather than SERPINA1 in the liver. Furthermore, it appears that the eQTL and GWAMA signals colocalise within the block of SNPs in LD, represented by rs2736898, suggesting this block is primarily responsible for driving CBG-mediated variation for cortisol. Moreover, it demonstrates effects on adipose tissue gene expression, suggesting that resulting variations in CBG levels in turn influence the delivery of cortisol to peripheral tissues.These results confirm that genetic variation in the on study  no new gSERPINA6/SERPINA1 locus demonstrates important insights.Given previous evidence of heritability of plasma cortisol it is surprising that the increase in sample size and SNP coverage from our earlier GWAMA did not identify any new loci associated with morning plasma cortisol. Twin studies have estimated the heritability of plasma cortisol ranging from 14 to 45% –38. HoweSERPINA6/A1 variants we used eQTL analyses in the STARNET cohort. A hypothesis-free approach that investigated the effect of all 580 SNPs within the SERPINA6/SERPINA1 locus on expression of all STARNET genes in each of the seven tissue types identified cis-eQTLs for SERPINA6 expression in the liver, the only tissue where SERPINA6 is highly expressed. This effect was refined to one of the LD blocks, represented by rs2736898. Importantly, genetic variation in SERPINA6/SERPINA1 was also associated in trans-eQTL analyses with gene expression in visceral abdominal fat and subcutaneous fat as well as liver. These results suggest that not only does cortisol-associated genetic variation influence CBG expression in the liver, but it also influences cortisol signalling in peripheral tissues, an effect that is likely to be mediated by CBG.To test the functional significance of SERPINA6 [SERPINA6/SERPINA1 might mediate their effect through altered inhibition of neutrophil elastase by α1-antitrypsin. However, the more detailed analyses facilitated by the expanded GWAMA presented here do not support this interpretation since we did not identify eQTLs for α1-antitrypsin expression. Moreover, the reliability of immunoassays to determine CBG cleavage has since been called into question [CBG binds ~90% of cortisol in plasma but it is usually thought that only the free cortisol can access tissues and have biological effects. Patients with mutations in SERPINA6  and animSERPINA6 , 12, andquestion .SERPINA6/A1 locus and strengthened the instrument used in the two-sample Mendelian randomisation analyses. We tested whether genetically-elevated cortisol is causally associated with cortisol-related outcomes selected from the features of Cushing’s syndrome—a rare condition caused by tumours secreting ACTH or cortisol—that are highly prevalent in the general population. Consistent with previous reports based on a less refined genetic instrument [Epidemiological analysis of cortisol has demonstrated associations with a large number of diseases and traits –9, but tstrument , 14 we fIt is arguable that morning plasma cortisol or salivary cortisol are poor surrogates for overall cortisol exposure given their diurnal and ultradian fluctuations and variation with acute stress. A more robust assessment of cortisol phenotype, perhaps from hair cortisol  or ambulSERPINA6/SERPINA1 locus through variation in liver SERPINA6 expression to variation in CBG-mediated gene transcription including in adipose tissue that is causally associated with cardiovascular disease.In summary, this large GWAMA has revealed the limited magnitude and range of genetic effects on plasma cortisol, but has identified a pathway from variation in the Table S1Table S2Table S3Table S4Table S5Table S6Table S7Figure S1Figure S2Figure S3Figure S4Figure S5Figure S6Figure S7full supp list


Pyrenophoratritici-repentis (Ptr), is a severe foliar disease of wheat (Triticumaestivum L.). Improving genetic resistance is a durable strategy to reduce Ptr-related losses. Here, we dissected Ptr-infection’s genetic basis in 372 European wheat varieties via simple sequence repeats (SSRs) plus 35k and 90k single nucleotide polymorphism (SNP) marker platforms. In our phenotypic data analyses, Ptr infection showed a significant genotypic variance and a significant negative correlation with plant height. Genome-wide association studies revealed a highly quantitative nature of Ptr infection and identified two quantitative trait loci (QTL), viz., QTs.ipk-7A and QTs.ipk-7B, which imparted 21.23 and 5.84% of the genotypic variance, respectively. Besides, the Rht-D1 gene showed a strong allelic influence on the infection scores. Due to the complex genetic nature of the Ptr infection, the potential of genome-wide prediction (GP) was assessed via three different genetic models on individual and combined marker platforms. The GP results indicated that the marker density and marker platforms do not considerably impact prediction accuracy (~40–42%) and that higher-order epistatic interactions may not be highly pervasive. Our results provide a further understanding of Ptr-infection’s genetic nature, serve as a resource for marker-assisted breeding, and highlight the potential of genome-wide selection for improved Ptr resistance.Tan spot, caused by the fungus  Pyrenophora tritici-repentis,  anamorph Drechslera tritici-repentis  Shoem. (syn. Helminthosporium tritici-repentis), the Ptr infection is mainly diagnosed by tan-colored necrotic lesions with yellow margins that are often surrounded by chlorotic haloes on susceptible wheat leaves. Mature lesions have a dark area in the center. With time, the lesions become larger and often fuse, resulting in the decrease of leaves’ photosynthetic surface area. Consequently, dead leaf tissue areas translate to plant stress and eventually yield loss ), (3) correction of familial relatedness via genomic relationship matrix PC[1–3] and PCs and Genome-wide association studies (GWAS) were performed on data taken from the individual environment and markers (both SSRs and SNPs) passing the quality criteria and the functional-gene markers. Let u et al. , a standVanRaden . Since pP-values in a multiple linear regression model; https://wheat.pw.usda.gov/ggpages/wgc/98/Intro.htm, accessed on 18 January 2021).To declare the MTA, a liberal false discovery rate (FDR) to account for multiple testing was applied at α=0.20 . As descα=0.20 , the perGenome-wide prediction (GP) studies were performed by using three different models with different assumptions, viz., genomic best linear unbiased predictions (GBLUP), Bayesian alphabet B (BayesB), and reproducing kernel Hilbert space regression (RKHSR). GBLUP is a standard robust parametric procedure to predict the total genetic value of the trait under consideration by exploiting additive effects of the markers assuming equal variances ,42. It it-distribution for marker effects by using both shrinkage and variable selection methods. Here, Since the distribution of marker variances across loci is not always equal, the BayesB model, which is of the form:s Campos , the pri package .The RKHSR is a semiparametric method that accounts for the additive as well as epistatic interactions among loci . It is oWe evaluated the genome-wide prediction accuracy software  mainly bsoftware  and rrBLsoftware  packagesPtr) infection assessment on 372 wheat varieties registered primarily for European markets was performed in replicated field trials. The phenotypic data from the individual environment was square-root transformed (The tan spot (nsformed . We obseed means . The ANOhan zero . The besectively . The broPtr infection and that shorter plants are more susceptible to the disease infestation.We retrieved data for plant height and heading date from previously published studies to observe their influence on the tan spot infection. We observed a highly significant negative Pearson’s product-moment correlation of tan spot infection with PH while a moderate negative correlation with HD . This inQTs.ipk-7A) and 1 on chromosome 7B (QTs.ipk-7B). The remaining eight MTA were unmapped and, therefore, no chromosomal and physical position was assigned to them  . In this to them . Since QPtr infection was observed with the plant height, suggesting that taller plants escape the disease infestation. Nevertheless, our GWAS—albeit setting a liberal MTA detection threshold (FDR) of 0.20—did not identify the Rht genes. The FDR value for Rht-D1 was, however, 0.25 and, therefore, being close to the threshold and frequent  in the European wheat germplasm, we investigated its genetic/allelic influence on the tan spot. The findings concurred with the phenotypic analyses where the impact of Rht-D1a  was significantly greater than Rht-D1b  in terms of reducing the Ptr infection , it is difficult to identify a single causative gene. Nevertheless, the large effect of 7A-QTL explaining >20% genotypic variance merits its future use for gene cloning and downstream molecular and functional analyses.As observed in the phenotypic data analyses, a highly significant negative correlation of nfection a. SimilaPC analyses based on singular value decomposition, as described previously [PCs accounted for 29.2% of the total variation —the minor allele of which was present in only 6.7% of the varieties—showed no clear pattern (The extent of linkage disequilibrium (LD), the non-random association between different loci, plays a vital role in GWAS. The panel under investigation has been previously examined for the LD via different marker platforms ,49. In aariation a. A two-ral axis b. Howeve pattern c.Ptr infection, we tested three different models making different assumptions in this study, thus creating four scenarios as (1) SSR alleles, (2) 35k SNP array, (3) 90k SNP array, and (4) the full set of markers altogether. In every scenario, we incorporated the functional candidate-gene markers as well. The mean prediction accuracies resulting from the five-fold cross-validation scenario of Ptr infection generally produced similar results (~40%) across all three tested model scenarios, i.e., the GBLUP model that accounted for the main additive effects of markers assuming equal variances, BayesB by assuming unequal marker variances, and RKHSR that accounted for both additive and the epistatic interactions among the loci  infection. However, due to large and significant genotype-by-environment interaction, we observed a moderate broad-sense heritability that amounted to 0.33. Recently, based on multiple environment trials, Juliana et al. [A significant genetic variation for the traits under selection provides a substantial impetus in improving breeding programs’ genetic gains. However, especially for disease traits, besides the genotypic variation, the presence of a large and significant genotype-by-environment interaction is virtually a norm mainly because (1) the disease pressures are uneven across environments, and (2) the environmental effects are very unpredictable. We evaluated 372 registered wheat varieties in replicated field trials and observed significant genotypic variation for tan spot  or photoperiodism (Ppd) may show a pleiotropic effect on the disease traits.Coupled with significant genetic variation, certain easy-to-score morphological traits have been purposed to escape disease infestations not only for the tan spot but also for other diseases, e.g., i blotch ,33,48. WWe identified two significant QTL associated with tan spot on chromosomes 7A and 7B at the 263.18 and 709.08-Mb positions. Although previous studies on both bi-parental and diverse populations have reported tan spot-associated loci on chromosomes 7A and 7B, none of them resulted in identifying QTL imparting >20% of the genotypic variance ,26,27,28Rht-D1—although it did not pass the significance threshold—showed a relatively large effect on the tan spot infection score. This indicates that the MAS based on the Rht-D1a, i.e., the wild-type or tall allele for plant height, may help select for tan spot resistance. Since the tan spot infection is more lethal at later plant growth stages  [Rht-D1a were more resistant than those bearing Rht-D1b  may be used to fine-tune the plant height for improved lodging resistance in breeding programs [Besides, as shown in H-45–65) ,4, the r Rht-D1b a. Semi-darieties , which wprograms ,51.Improving qualitative disease resistance by selecting for or against major genes or QTL is a resource and time-efficient measure. However, most disease genes are responsive only against one or a few pathogen races and lack a broad-spectrum application . MoreoveRht-D1, are usually not used for MAS. Therefore, instead of concentrating only on large-effect loci, genome-wide prediction (GP) of the total genetic value of tan spot based on small- and large-effect markers is a holistic tactic to improve the broad-spectrum resistance. We evaluated the GP accuracy for tan spot resistance by modeling the loci’s additive effects assuming equal variances, unequal variances, and epistatic interaction. In line with a previous study, the mean GP accuracies calculated across 100 cycles and five-fold cross-validation scenarios amounted to ~40%, with virtually no statistically significant difference between the models [In our study, we observed a highly quantitative genetic nature of the tan spot where the significantly associated markers—albeit considerably improving marker density—in total, explained only 25.79% of the genotypic variance. Markers that do not cross significance thresholds in GWAS, e.g., e models . Althouge models , we obseBased on the hypothesis that marker platform and thus marker density influences the outcome of GP accuracy, we evaluated all three models on all marker platforms, viz., SSR, 35k, and 90k SNP arrays individually and marker loci combined from all platforms. However, increasing the marker density did not result in any significant increase in GP accuracy. This finding is in line with previous reports where GP accuracy was not influenced above a certain number of markers ,32, unde


Triticum aestivum L.) grain quality is often costly and time-consuming since it requires extensive phenotyping in the last phases of development of new lines and cultivars. The development of high-throughput genotyping in the last decade enabled reliable and rapid predictions of breeding values based only on marker information. Genomic selection (GS) is a method that enables the prediction of breeding values of individuals by simultaneously incorporating all available marker information into a model. The success of GS depends on the obtained prediction accuracy, which is influenced by various molecular, genetic, and phenotypic factors, as well as the factors of the selected statistical model. The objectives of this article are to review research on GS for wheat quality done so far and to highlight the key factors affecting prediction accuracy, in order to suggest the most applicable approach in GS for wheat quality traits.Selection for wheat ( Triticum aestivum L.) and its products could increase by at least 50% until 2050 [According to estimates of the International Maize and Wheat Improvement Center (CIMMYT), the need for wheat  ,12. The Fusarium head blight, stripe and brown rust resistance, plant height, days to heading, and preharvest sprouting (PHS) tolerance is given by Rutkoski et al. [Currently, the majority of researchers of GS in wheat consider grain yield and disease resistance as key traits for successful wheat production ,19,20,21i et al. . DespiteGenomic selection is one of the newly developed methods that enables the prediction of breeding values of individuals by simultaneously incorporating all available marker information into a model . Unlike High-throughput genotyping generates a large amount of marker data, which are then used in GS. When the number of predictor variables (markers) is much greater than the number of observations , the result is an infinite number of marker effect estimates. In order to reduce the problem of highly dimensioned data, different parametric and nonparametric models have been developed and used in GS .The prediction models differ mainly in the assumptions of the distribution of the marker effects, i.e., the assumptions of how the marker effects contribute to the overall variance . DetaileThe prediction accuracy of GS is commonly estimated using cross-validation, in which a set of individuals that are both genotyped and phenotyped is divided into a training set (training population) and validation set , with marker effects estimated in the training set used to predict GEBVs for the validation set . The acce × L), while the minimum size of the population was estimated to be 2 × Ne × L. In the case of wheat (the L of which is approximately 30 Morgans) and assumption of Ne = 50, that would amount to 15,000 markers and 3000 individuals in an unrelated population. However, those estimations were obtained using simulations, while empirical studies on wheat have reported acceptable accuracies for a much lower number of markers and smaller populations, depending on the population structure [Three major factors that affect the GS accuracy are population structure, TP size, and marker density, the effects of which are highly interrelated ,47. Poputructure ,21,30. Atructure . The inttructure .The first GS study for wheat quality traits was published in 2011 . The stuA study by Heffner et al.  based onTP) significantly impacts the GS accuracy. The average accuracy for nine wheat quality traits was reported to be roughly 1.6-fold higher for NTP = 96 compared to NTP = 24 when applied to a biparental population [TP from 96 to 288 resulted in an overall increase in accuracy by approximately 30%. It is interesting to note that in order to achieve approximately the same GS prediction accuracy, a TP that is three times greater should be used in multifamily populations compared to biparental populations . The positive influence of an increased number of lines in TP was observed for GPC and protein yield (PY) traits [TP = 240) and amounted to 0.51 and 0.16 for GPC and PY, respectively. When investigating the influence of using different proportions of the entire population as TP (20–80%), Hu et al. [k-fold cross-validation proved that the use of smaller TP resulted in slightly lower GS prediction abilities. Similar results for flour yield (FY) and alveograph traits were reported by Kristensen et al. [As early as with the first studies of GS for wheat quality traits, it was demonstrated that TP size (Npulation . A similpulation  in which) traits , where mu et al.  concludeu et al.  reportedn et al. .Overall, the size of the TP depends on the genetic relatedness between TP and VP. The more related the two populations are, the smaller the size of the TP will be needed to obtain satisfying GS prediction accuracies for wheat quality traits . BattenfAs for the other wheat traits , it has Prediction accuracies for GPC and PY traits showed a strong bias when predicting within the breeding cycle (lower relatedness) compared to predicting between-cycle (higher relatedness). According to Michel et al. , the higM) all led to the same conclusion that the accuracy of the prediction enhances with increasing marker density until it reaches a plateau, after which a further increase in marker density has no effect on accuracy [M = 256, after which a slight drop in accuracy was observed (NM = 384), while in a multifamily approach [M from 192 to 1158 increased GS accuracy by approximately 10%, after which response reached a plateau. Huang et al. [M = 13,198) and a subset of 3919 markers, implying that lower NM is already sufficient for predicting quality traits in wheat elite lines and varieties. Juliana et al. [M = 16,072), moderate coverage (NM = 9285), and low marker coverage (NM = 2253). They concluded that marker density had a minimal impact on GS accuracy, suggesting that when a genomic resolution is reached in a high LD species , marker density no longer represents a limiting factor.Studies of GS for wheat quality traits investigating the effect of marker density , moderate (0.4–0.7), and high (>0.7) ,30. Gene2O-SRC), and lactic-acid solvent retention capacity (LA-SRC)), mean GS accuracy across the four models used was 0.6 and higher, for FY and KH, accuracies were 0.45 and 0.38, respectively, despite their heritability values being > 0.9 [Studies on wheat quality traits showed that heritability was the main factor that affected the accuracy of GS . Interesng > 0.9 . Low herng > 0.9 . AccordiA broad range of models can be used to predict the phenotypic performance of wheat, but the performance of each model is interrelated with the genetic architecture of the examined trait and relatedness of TP and VP. As it is presented in Little or no difference in prediction accuracy was detected between RRBLUP and Bayesian models in a study by Heffner et al. , which sBayesian models usually require longer computation time compared to GBLUP or RRBLUP ,58,62 buWheat quality traits can often be hard to improve, since they usually require a large amount of flour and/or labor to be invested, thus limiting the size of the TP that can be phenotyped which leads to insufficient GS accuracy. Incorporating additional phenotypic information in the multitrait approach for GS could help to overcome the problem of potentially low GS accuracy obtained for wheat quality traits. Multitrait GS data obtained utilizing rapid quality tests are used for predicting parameters of more laborious wheat quality tests. Rapid tests such as near-infrared (NIR) and nuclear magnetic resonance (NMR) methods are less labor-intensive and require a small amount of flour. It has been proved that incorporating NIR and NMR data into the multitrait approach increases the accuracy of GS for some wheat quality traits  . IncorpoDue to the complex nature of inheritance for the majority of wheat quality traits, GS seems to be the method of choice because it simultaneously accounts for small and medium effect loci as well as for major QTLs. Numerous studies in the last decade proved that GS has sufficient accuracy for implementation in the breeding programs targeting wheat quality. Genomic selection can be helpful in predicting the performance of lines in early generations and preselecting high-performing lines, boosting trait stability, and efficiently selecting superior genotypes for wheat quality traits. There is some evidence that GS could also be used to address one of the biggest problems in wheat breeding—how to simultaneously select for grain yield and quality traits since the existence of a strong negative correlation between those traits is well known and documented. Nevertheless, before implementing GS in the breeding for wheat quality traits, some limitations considering trait heritability, genetic relationship between TP and VP, and size of the TP must be taken into account.


P < 0.01) differences among varieties in all agronomic and yield traits. Phenotypic coefficient of variation values ranged from 0.75% (specific gravity) to 32.22%  while the genotypic coefficient of variation values ranged between 0.70% (specific gravity) to 30.22% . Maximum difference between phenotypic and genotypic coefficient of variation values were noted for stem number, average tuber number, average tuber weight, number of leaves per plant and tuber yield. Hence, these traits are substantially influenced by the physiological status of the seed tuber at planting and by the environment, post emergence. Range of variability for most of the traits was high, indicating ample scope for selection and improvement in these traits. The estimated values for broad sense heritability and genetic advance, as percent of mean, ranged from 33.52% to 98.66% and 1.35% to 58.26%, respectively. All the traits had high heritability values, except average tuber number per hill, days to physiological maturity, average tuber weight and number of leaves per plant with moderate heritability values.Information on the extent of genetic variability and association among quantitative traits are vital for any crop improvement program and the development of suitable selection strategies. Limited research has been carried out thus far on potato genetic variability and trait association. This study on genetic variability and association among quantitative traits was conducted to assess the extent of genetic variability among yield and agronomic traits to identify superior varieties for the breeding program. To this effect, 20 improved varieties and a local cultivar were planted at two locations in central Ethiopia during the main cropping season of 2017/18 in a randomized complete block design using three replications. Analysis of variance of tuber yield and yield traits at each location and over locations, revealed the existence of highly significant ( Solanum tuberosum) is an important staple food and cash crop domesticated around 8,000 years ago in the Andes Mountains of South America , genetic coefficient of variation as well as genetic advance would be helpful to plant breeders to implement selection in breeding programs  were under taken based on the procedures indicated by For each quantitative attribute, phenotypic and genotypic variability were estimated using variances and coefficient of variations using the procedure suggested by 2g = genotypic varianceg = mean square of genotypeMe = mean square of error; r = number of replications; Phenotypic Variance (σ2p) = σ2g + σ2eM2g = Genotypic variance; σ2e = Environmental variance; σ2p = phenotypic variancewhere, σGCV = Genotypic coefficient of variation= population mean of the character being evaluated.PCV and GCV values were classified as low, moderate, and high according to Broad sense heritability values were estimated for each location using the formula adopted by 2 is broad-sense heritability, σ2p-phenotypic variance2g-genotypic varianceσThe heritability percentage was categorized as low, moderate, and high, as suggested by Genetic advance in absolute units (GA) and percent of the mean (GAM), assuming selection of superior 5% of the genotypes, were estimated for each location based on the methods illustrated by SDp = Phenotypic standard deviation on mean basis;2 = Heritability in the broad sense.HK = the standardized selection differential at 5% selection intensity (K = 2.063).Genetic advance as percent of mean was estimated as follows:The GAM values were grouped in to low, moderate, and high categories, as suggested by 2P, σ2G and H2 = phenotypic variance, genotypic variance and heritability in broad sense were determined as indicated in The computation of genotypic and phenotypic variance across locations considered the expected mean squares from combined analysis of variance and the σ−1). The maximum mean values of tuber yield, specific gravity, dry matter content, starch content and total starch yield were recorded from the variety Belete while the lowest mean value of all these traits was recorded from the variety Menagesha  for each location are presented in At Holetta, the estimated phenotypic (PCV) and genotypic (GCV) coefficient of variations of specific gravity and total starch yield of the different potato genotypes studied ranged between (0.71% to 35.23% and 0.62% to 34.51%), respectively. At Adaberga the PCV and GCV values of specific gravity and total starch yield ranged between (0.91% to 32.75% and 0.83% to 31.97%), respectively . The oveThe difference between PCV and GCV was the most pronounced for the traits stem number, average tuber number, average tuber weight, number of leaves per plant, tuber yield. Therefore, we speculated that these traits are substantially influenced by the growing environments.2 and GAM ranged from 33.52% (average tuber number per hill) to 98.66% (days to 50% flowering) and 1.35% (specific gravity) to 58.26% , respectively  analysis was used to combine the observed traits into four main components that have eigenvalues above 1.0 and was able to explain the tested variability at 87.53% . PC1 wit−1 without significant differences from the highest yielding variety (Belete) but significantly different from the other varieties . −1 among potato varieties in northwestern Ethiopia. −1 in Haramaya, Arbarakete and Hirna, respectively in a study conducted in eastern Ethiopia. Based on yield superiority and important traits identified, the breeding program could use those prospective varieties for future breeding programs as a parent for future potato improvement works.The observed genetic variability among the evaluated varieties in the present study indicated the presence of breeding lines that can be used as parental materials for the potato improvement program . Four vaarieties . Two var−1. Similarly, maximum PCV and GCV values were reported by viz., specific gravity and dry matter content which is due to the fact that most tuber quality traits like tuber specific gravity and dry matter content are less influenced by environments  by 2 and GAM indicated the existence of genetic variation in potato varieties which could be an opportunity for further improvement of potato. Similarly, −1, single tuber weight, and tuber breadth. Similar reports for total tuber yield (kg per plot) were reported by 2 and GA values were reported by High value of HThe results of principal component analysis (PCA) for the studied traits are presented in In general, this study found diverse genetic variability estimates among potato varieties studied. The agronomic characters of potato varieties showed high genotypic coefficient of variation and phenotypic coefficient of variation values, high estimation of broad-sense heritability values for most traits some coupled with genetic advance as percent of mean. Hence, this information and the identified varieties and traits could be used for future potato breeding programs in the country.10.7717/peerj.12860/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.12860/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.12860/supp-3Supplemental Information 3Click here for additional data file.


It is well understood that variation in relatedness among individuals, or kinship, can lead to false genetic associations. Multiple methods have been developed to adjust for kinship while maintaining power to detect true associations. However, relatively unstudied are the effects of kinship on genetic interaction test statistics. Here, we performed a survey of kinship effects on studies of six commonly used mouse populations. We measured inflation of main effect test statistics, genetic interaction test statistics, and interaction test statistics reparametrized by the Combined Analysis of Pleiotropy and Epistasis (CAPE). We also performed linear mixed model (LMM) kinship corrections using two types of kinship matrix: an overall kinship matrix calculated from the full set of genotyped markers, and a reduced kinship matrix, which left out markers on the chromosome(s) being tested. We found that test statistic inflation varied across populations and was driven largely by linkage disequilibrium. In contrast, there was no observable inflation in the genetic interaction test statistics. CAPE statistics were inflated at a level in between that of the main effects and the interaction effects. The overall kinship matrix overcorrected the inflation of main effect statistics relative to the reduced kinship matrix. The two types of kinship matrices had similar effects on the interaction statistics and CAPE statistics, although the overall kinship matrix trended toward a more severe correction. In conclusion, we recommend using an LMM kinship correction for both main effects and genetic interactions and further recommend that the kinship matrix be calculated from a reduced set of markers in which the chromosomes being tested are omitted from the calculation. This is particularly important in populations with substantial population structure, such as recombinant inbred lines in which genomic replicates are used. Variation in relatedness, or kinship, among individuals in genetic association studies can lead to artificial inflation of association statistics leading to false positives and loss of power to detect true positive associations  that canThere are a number of statistical strategies to remove this bulk effect of relatedness . One popGenetic interactions, or epistasis, are an important aspect of describing complex traits. Statistical models of complex traits are improved when epistasis is taken into account . KinshipPrevious work suggests that kinship inflates interaction test statistics, and that adjusting specifically for epistatatic kinship effects effectively reduces FPR in interaction test statistics and improves modeling of complex traits through genetic interaction networks . We previously developed CAPE to combine information across multiple traits to infer directed genetic interactions  mouse with a small (SM/J) mouse (This population was generated to investigate gene–environment interactions influencing diabetes and obesity (lit mutation in growth hormone-releasing hormone receptor (GHRHR), which arose naturally on the C57Bl6/J (B6) background, and was transferred to the C3H/HeJ (C3H) background. C3H mice with the lit mutation have the same body weight as B6 mice with the lit mutation but have higher bone density. The purpose of this cross was to identify genetic factors that increase bone density in the absences of GHRHR. We used 1095 female mice from this cross. They were genotyped at 100 microsatellite markers. We analyzed percent body fat (pctFat) and trabecular bone thickness (Tb.Th) here.This large F2 intercross was generated to investigate genetic influences on bone density traits in mice  mouse and the DBA/2J (D) mouse. We downloaded data from the MousePhenomeDatabase . Two of our populations here, the backcross and the outbred populations, include NZO genomes, and using body weight traits in these populations could lead to increased test statistic inflation due to high levels of polygenicity. To reduce this effect, we selected traits from each population that minimized the correlation with the first principal component of the kinship matrix .G is the genotype matrix, and n is the number of genotyped markers. For calculating main effects, we use the LOCO method  using the sum of the heterozygosity across all loci π  and a kinship matrix, K, which is simply the variance-covariance matrix of the genotypes among individuals. Fitting this model requires identifying the maximum likelihood parameters for the genetic (fixed) effects and the two mixing parameters defining the correlated residual errors. As shown by K into its spectral decomposition and adjusting the genotypes and phenotypes to align with the residual error structure.To account for population structure in our association tests, we used an LMM correction as described in The mathematical form of the model allows the fixed effects and the genetic variance to be solved for explicitly as a function of a mixing parameter, which can be optimized using a one-dimensional grid search. We have reimplemented this procedure within CAPE for use with mouse model populations using the code from the R/qtl2 implementation .In each trial, we assessed the inflation of each set of test statistics using λ . This inhttps://figshare.com/articles/journal_contribution/Epistasis_and_Kinship_in_Mouse_Populations/13863101.All data used in this study and the code used to analyze it are available as part of a reproducible workflow on Figshare. The workflow is called Epistasis_and_Kinship_in_Mouse_Populations and can be found here: https://github.com/TheJacksonLaboratory/cape.CAPE is available at CRAN and on Github at Supplementary File S1 contains descriptions of each of the supplemental files. Supplementary Figure S1 shows box plots of the correlations between traits and the first PC of the kinship matrix for each population. Supplementary Figure S2 is identical to STF values more similar to human populations.We observed varying degrees of relatedness across the populations . The heaIndependent of the population structure, the populations also had varying degrees of relatedness. On average, the Outbred mice were related to each other at a level equivalent to first cousins, which is by design .Before running CAPE, we investigated overall trends in test statistic inflation by scanning all traits for main effects using marker regression. This revealed wide variation in test statistic inflation by population . Across Linkage disequilibrium (LD) influences test statistic inflation because a single causal SNP within an LD block can inflate the test statistics of all SNPs linked to it. If there are relatively few recombinations in the population, such as in an F2 or backcross, large portions of the genome may be significantly associated with a trait due to linkage alone.r) between markers on the same chromosome across all chromosomes and all populations. These distributions are shown in the inset in To investigate whether LD may be related to the inflation of test statistics in the populations used here, we calculated pairwise Pearson correlations .However, although the F2 had lower LD than the backcross, it had substantially greater inflation of test statistics. The F2 also had many more individuals than the backcross, and thus greater power to detect effects. This increase in power combined with high LD could lead to the high levels of inflation seen in the F2. To test this, we subsampled the F2 to the same number of individuals in the backcross and recalculated P-values. The more the points rise above the line y = x, the stronger the inflation factor λ. In the absence of a kinship correction, the F2 and RIL showed strong inflation, the AIL and RIL without replicates showed moderate inflation, and the backcross and Outbred populations showed very minor inflation if any at all .Numeric values are shown in the legends of The overall kinship correction had a strong effect on inflation across all populations . This inflation was reduced by both the LTCO and overall kinship corrections.The nflation . The onlλ values .The LTCO correction appeared to slightly improve power to detect interaction effects in the Outbred population, although this was not evident in the λ = 1.7), F2 (λ = 1.4), and backcross populations (λ = 1.1). The overall and LTCO kinship corrections had remarkably similar effects across all populations.The CAPE coefficients influenced by inflation at a level in between that of the main effect statistics and the pairwise statistics . The bulIn this study, we examined inflation of main effect and genetic interaction statistics in six mouse mapping populations. We also investigated the effect of kinship corrections on this inflation.We found large variation in test statistic inflation across populations and across traits. Across populations, the primary driving factors of inflation were LD and population size. Populations with high LD, like the F2 and backcross, had the highest inflation, which is consistent with previous observations , which did not. It has been shown previously that including genetic replicates increases power to detect genetic effects  mapping populations is not really a fair representation, since in practice the markers in these populations would not be treated as independent measurements. However, this exercise illustrates important, albeit dramatic, aspects of test statistic inflation, and how kinship corrections affect test statistics in different situations.β coefficients did not show any inflation in any population except possibly in the RIL, despite these populations being well powered to detect epistasis . When there was inflation of CAPE coefficients, both corrections controlled the inflation well. The similarity in effects of the two corrections was somewhat surprising. We predicted that as with LOCO, the LTCO correction would have been less stringent than the overall correction, but this was not what we observed. Extrapolating from the main effect results, test statistic in the RIL should be most subject to inflation derived from kinship. In this population, both kinship matrices controlled inflation well, but the overall correction did trend toward the more severe correction. Although more work needs to be done, these results suggest that using the LTCO kinship matrix for interaction effects may maintain power to detect effects better than the overall matrix.P-value distributions across all traits. In conjunction, relatedness and the degree to which low P-values are enriched can reveal important features of the population and traits, including the degree to which LD and polygenicity may be influencing significance testing. In all populations, but particularly in those with greater structure, applying a kinship correction reduces FPR and increases power to detect true effects. However, our empirical analysis also found that pairwise interactions were generally avoided inflation in most study designs. The one potential exception was recombinant inbred designs (RIL), suggesting that traits with an expectation of epistasis may be more effectively analyzed with the other experimental approaches. For integrative analyses such as CAPE, an intermediate level of inflation may be best avoided with balanced outbred populations.We conclude that although these association mapping populations are created in such a way as to minimize population structure, cryptic relatedness and population structure may still increase FPR and decrease power to detect both main effects and genetic interactions. This is particularly true in populations with unusual relatedness patterns, such as RILs with genomic replicates. These findings highlight the importance of examining the kinship matrix of a study population as well as We recommend applying the reduced kinship matrix in which the chromosomes containing the tested markers are left out. These kinship matrices reduce FPR related to population structure with minimal effect on power. The kinship matrices calculated from the full genome reduced power to detect effects, particularly in the RIL. Simulations were beyond the scope of this project, but could potentially further delineate guidelines for when kinship corrections are necessary, and which types of kinship matrices to use. Such simulations should take LD, polygenicity, and multiple types of population structure into account.