Integrating perspectives in developing mycorrhizal trait databases: a call for inclusive and collaborative continental efforts.

Global assessments of mycorrhizal symbiosis present large sampling gaps in rich biodiversity regions. Filling these gaps is necessary to build large-scale, unbiased mycorrhizal databases to obtain reliable analyses and prevent misleading generalizations. Underrepresented regions in mycorrhizal research are mainly in Africa, Asia, and South America. Despite the high biodiversity and endemism in these regions, many groups of organisms remain understudied, especially mycorrhizal fungi. In this Viewpoint, we emphasize the importance of inclusive and collaborative continental efforts in integrating perspectives for comprehensive trait database development and propose a conceptual framework that can help build large mycorrhizal databases in underrepresented regions. Based on the four Vs of big data (volume, variety, veracity, and velocity), we identify the main challenges of constructing a large mycorrhizal dataset and propose solutions for each challenge. We share our collaborative methodology, which involves employing open calls and working groups to engage all mycorrhizal researchers in the region to build a South American Mycorrhizal Database. By fostering interdisciplinary collaborations and embracing a continental-scale approach, we can create robust mycorrhizal trait databases that provide valuable insights into the evolution, ecology, and functioning of mycorrhizal associations, reducing the geographical biases that are so common in large-scale ecological studies.

Country-wide, multi-location trials of Green Super Rice lines for yield performance and stability analysis using genetic and stability parameters.

Rice (Oryza sativa L.) is an important member of the family Poaceae and more than half of world population depend for their dietary nutrition on rice. Rice cultivars with higher yield, resilience to stress and wider adaptability are essential to ensure production stability and food security. The fundamental objective of this study was to identify higher-yielding rice genotypes with stable performance and wider adaptability in a rice growing areas of Pakistan. A triplicate RCBD design experiment with 20 Green Super Rice (GSR) advanced lines was conducted at 12 rice growing ecologies in four Provinces of Pakistan. Grain yield stability performance was assessed by using different univariate and multivariate statistics. Analysis of variance revealed significant differences among genotypes, locations, and G x E interaction for mean squares (p < 0.05) of major yield contributing traits. All the studied traits except for number of tillers per plant revealed higher genotypic variance than environmental variance. Broad sense heritability was estimated in the range of 44.36% to 98.60%. Based on ASV, ASI, bi, Wi2, σ2i and WAAS statistics, the genotypes G1, G4, G5, G8, G11 and G12 revealed lowest values for parametric statistics and considered more stable genotypes based on  paddy yield. The additive main effects and multiplicative interaction (AMMI) model revealed significant variation (p < 0.05) for genotypes, non-signification for environment and highly significant for G × E interaction. The variation proportion of PC1 and PC2 from interaction revealed 67.2% variability for paddy yield. Based on 'mean verses stability analysis of GGE biplot', 'Which-won-where' GGE Biplot, 'discriminativeness vs. representativeness' pattern of stability, 'IPCA and WAASB/GY' ratio-based stability Heat-map, and ranking of genotypes, the genotypes G1, G2, G3, G5, G8, G10, G11 and G13 were observed ideal genotypes with yield potential more than 8 tons ha-1. Discriminativeness vs. representativeness' pattern of stability identifies two environments, E5 (D.I Khan, KPK) and E6 (Usta Muhammad, Baluchistan) were best suited for evaluating genotypic yield performance. Based on these findings we have concluded that the genotypes G1, G2, G3, G5, G8, G10, G11 and G13 could be included in the commercial varietal development process and future breeding program.

Mining genomic regions associated with agronomic and biochemical traits in quinoa through GWAS.

Quinoa (Chenopodium quinoa Willd.), an Andean crop, is a facultative halophyte food crop recognized globally for its high nutritional value and plasticity to adapt to harsh conditions. We conducted a genome-wide association study on a diverse set of quinoa germplasm accessions. These accessions were evaluated for the following agronomic and biochemical traits: days to 50% flowering (DTF), plant height (PH), panicle length (PL), stem diameter (SD), seed yield (SY), grain diameter (GD), and thousand-grain weight (TGW). These accessions underwent genotyping-by-sequencing using the DNBSeq-G400R platform. Among all evaluated traits, TGW represented maximum broad-sense heritability. Our study revealed average SNP density of ≈ 3.11 SNPs/10 kb for the whole genome, with the lowest and highest on chromosomes Cq1B and Cq9A, respectively. Principal component analysis clustered the quinoa population in three main clusters, one clearly representing lowland Chilean accessions, whereas the other two groups corresponded to germplasm from the highlands of Peru and Bolivia. In our germplasm set, we estimated linkage disequilibrium decay to be ≈ 118.5 kb. Marker-trait analyses revealed major and consistent effect associations for DTF on chromosomes 3A, 4B, 5B, 6A, 7A, 7B and 8B, with phenotypic variance explained (PVE) as high as 19.15%. Nine associations across eight chromosomes were also found for saponin content with 20% PVE by qSPN5A.1. More QTLs were identified for PL and TGW on multiple chromosomal locations. We identified putative candidate genes in the genomic regions associated with DTF and saponin content. The consistent and major-effect genomic associations can be used in fast-tracking quinoa breeding for wider adaptation across marginal environments.

Estimation of genetic parameters for the implementation of selective breeding in commercial insect production.

BACKGROUND: There is a burgeoning interest in using insects as a sustainable source of food and feed, particularly by capitalising on various waste materials and by-products that are typically considered of low value. Enhancing the commercial production of insects can be achieved through two main approaches: optimising environmental conditions and implementing selective breeding strategies. In order to successfully target desirable traits through selective breeding, having a thorough understanding of the genetic parameters pertaining to those traits is essential. In this study, a full-sib half-sib mating design was used to estimate variance components and heritabilities for larval size and survival at day seven of development, development time and survival from egg to adult, and to estimate correlations between these traits, within an outbred population of house flies (Musca domestica), using high-throughput phenotyping for data collection. RESULTS: The results revealed low to intermediate heritabilities and positive genetic correlations between all traits except development time and survival to day seven of development and from egg to adulthood. Surprisingly, larval size at day seven exhibited a comparatively low heritability (0.10) in contrast to development time (0.25), a trait that is believed to have a stronger association with overall fitness. A decline in family numbers resulting from low mating success and high overall mortality reduced the amount of available data which resulted in large standard errors for the estimated parameters. Environmental factors made a substantial contribution to the phenotypic variation, which was overall high for all traits. CONCLUSIONS: There is potential for genetic improvement in all studied traits and estimates of genetic correlations indicate a partly shared genetic architecture among the traits. All estimates have large standard errors. Implementing high-throughput phenotyping is imperative for the estimation of genetic parameters in fast developing insects, and facilitates age synchronisation, which is vital in a breeding population. In spite of endeavours to minimise non-genetic sources of variation, all traits demonstrated substantial influences from environmental components. This emphasises the necessity of thorough attention to the experimental design before breeding is initiated in insect populations.

Root-associated fungi and acquisitive root traits facilitate permafrost nitrogen uptake from long-term experimentally warmed tundra.

Root-associated fungi (RAF) and root traits regulate plant acquisition of nitrogen (N), which is limiting to growth in Arctic ecosystems. With anthropogenic warming, a new N source from thawing permafrost has the potential to change vegetation composition and increase productivity, influencing climate feedbacks. Yet, the impact of warming on tundra plant root traits, RAF, and access to permafrost N is uncertain. We investigated the relationships between RAF, species-specific root traits, and uptake of N from the permafrost boundary by tundra plants experimentally warmed for nearly three decades at Toolik Lake, Alaska. Warming increased acquisitive root traits of nonmycorrhizal and mycorrhizal plants. RAF community composition of ericoid (ERM) but not ectomycorrhizal (ECM) shrubs was impacted by warming and correlated with root traits. RAF taxa in the dark septate endophyte, ERM, and ECM guilds strongly correlated with permafrost N uptake for ECM and ERM shrubs. Overall, a greater proportion of variation in permafrost N uptake was related to root traits than RAF. Our findings suggest that warming Arctic ecosystems will result in interactions between roots, RAF, and newly thawed permafrost that may strongly impact feedbacks to the climate system through mechanisms of carbon and N cycling.

Pervasive under-dominance in gene expression underlying emergent growth trajectories in Arabidopsis thaliana hybrids.

BACKGROUND: Complex traits, such as growth and fitness, are typically controlled by a very large number of variants, which can interact in both additive and non-additive fashion. In an attempt to gauge the relative importance of both types of genetic interactions, we turn to hybrids, which provide a facile means for creating many novel allele combinations. RESULTS: We focus on the interaction between alleles of the same locus, i.e., dominance, and perform a transcriptomic study involving 141 random crosses between different accessions of the plant model species Arabidopsis thaliana. Additivity is rare, consistently observed for only about 300 genes enriched for roles in stress response and cell death. Regulatory rare-allele burden affects the expression level of these genes but does not correlate with F1 rosette size. Non-additive, dominant gene expression in F1 hybrids is much more common, with the vast majority of genes (over 90%) being expressed below the parental average. Unlike in the additive genes, regulatory rare-allele burden in the dominant gene set is strongly correlated with F1 rosette size, even though it only mildly covaries with the expression level of these genes. CONCLUSIONS: Our study underscores under-dominance as the predominant gene action associated with emergence of rosette growth trajectories in the A. thaliana hybrid model. Our work lays the foundation for understanding molecular mechanisms and evolutionary forces that lead to dominance complementation of rare regulatory alleles.

Sex-specific differences in the genetic and environmental effects on cardiac phenotypic variation assessed by echocardiography.

The drivers of sexual dimorphism in heart failure phenotypes are currently poorly understood. Divergent phenotypes may result from differences in heritability and genetic versus environmental influences on the interplay of cardiac structure and function. To assess sex-specific heritability and genetic versus environmental contributions to variation and inter-relations between echocardiography traits in a large community-based cohort. We studied Framingham Heart Study participants of Offspring Cohort examination 8 (2005-2008) and Third Generation Cohort examination 1 (2002-2005). Five cardiac traits and six functional traits were measured using standardized echocardiography. Sequential Oligogenic Linkage Analysis Routines (SOLAR) software was used to perform singular and bivariate quantitative trait linkage analysis. In our study of 5674 participants (age 49 ± 15 years; 54% women), heritability for all traits was significant for both men and women. There were no significant differences in traits between men and women. Within inter-trait correlations, there were two genetic, and four environmental trait pairs with sex-based differences. Within both significant genetic trait pairs, men had a positive relation, and women had no significant relation. We observed significant sex-based differences in inter-trait genetic and environmental correlations between cardiac structure and function. These findings highlight potential pathways of sex-based divergent heart failure phenotypes.

Genomic evaluation of carcass traits of Korean beef cattle Hanwoo using a single-step marker effect model.

Hanwoo beef cattle are well known for the flavor and tenderness of their meat. Genetic improvement programs have been extremely successful over the last 40 yr. Recently, genomic selection was initiated in Hanwoo to enhance genetic progress. Routine genomic evaluation based on the single-step breeding value model was implemented in 2020 for all economically important traits. In this study, we tested a single-step marker effect model for the genomic evaluation of four carcass traits, namely, carcass weight (CW), eye muscle area, backfat thickness, and marbling score. In total, 8,023,666 animals with carcass records were jointly evaluated, including 29,965 genotyped animals. To assess the prediction stability of the single-step model, carcass data from the last 4 yr were removed in a forward validation study. The estimated genomic breeding values (GEBV) of the validation animals and other animals were compared between the truncated and full evaluations. A parallel conventional best linear unbiased prediction (BLUP) evaluation with either the full or the truncated dataset was also conducted for comparison with the single-step model. The estimates of the marker effect from the truncated evaluation were highly correlated with those from the full evaluation, ranging from 0.88 to 0.92. The regression coefficients of the estimates of the marker effect for the full and truncated evaluations were close to their expected value of 1, indicating unbiased estimates for all carcass traits. Estimates of the marker effect revealed three chromosomal regions (chromosomes 4, 6, and 14) harboring the major genes for CW in Hanwoo. For validation of cows or steers, the single-step model had a much higher R2 value for the linear regression model than the conventional BLUP model. Based on the regression intercept and slope of the validation, the single-step evaluation was neither inflated nor deflated. For genotyped animals, the estimated GEBV from the full and truncated evaluations were more correlated than the estimated breeding values from the two conventional BLUP evaluations. The single-step model provided a more accurate and stable evaluation over time.

Hanwoo beef cattle are well known for the flavor and tenderness of their meat. Genetic improvement programs have been successful over the last 40 yr. Recently, genomic selection was initiated in Hanwoo to enhance genetic progress. A routine genomic evaluation based on the single-step breeding value model was implemented in 2020 for all economically important traits. In this study, we tested a single-step marker effect model for the genomic evaluation of four carcass traits. In total, 8,023,666 cows or steers with carcass records were jointly evaluated, including 29,965 genotyped animals. To assess the prediction accuracy of the single-step model, carcass data from the last 4 yr were removed in a forward validation study. Estimated genomic breeding values (GEBV) of validation animals were compared between truncated and full evaluations. A parallel conventional best linear unbiased prediction (BLUP) evaluation with either the full or truncated dataset was conducted for comparison with the single-step model. Plots of the estimates of the marker effect showed three chromosomal regions harboring the major genes for carcass weight in Hanwoo. The single-step model yielded a more accurate and stable evaluation over time than the conventional BLUP model.

Detection of CD36 gene polymorphism associated with chicken carcass traits and skin yellowness.

Investigations into the association between chicken traits and genetic variations provide helpful breeding information to improve production performance and economic benefits in chickens. The single nucleotide polymorphism technique is an important method in agricultural molecular breeding. In this study, we detected 11 SNPs in the CD36 gene, 2 SNPs (g.-1974 A>G, g.-1888 T>C) located in the 5' flanking regions, 8 SNPs (g.23496 G>A, g.23643 C>T, g.23931 T>C, g.23937 G>A, g.31256 C>A, g.31258 C>T, g.31335 C>T, g.31534 A>C) located in the intron region, 1 SNPs (g.23743 G>T) located in the exon region and it belongs to synonymous mutation. In SNPs g.23743 G>T, the abdominal fat weight and abdominal fat weight rate of the GG genotype were lower than that of the TT genotype. In SNPs g.23931 T>C, the full-bore weight rate and half-bore weight rate of the TT genotype were higher compared with the CC genotype. And the SNPs g.-1888 T>C, g.23496 G>A, g.23643 C>T, g.31335 C>T and g.31534 A>C were significantly associated with skin yellowness traits, the cloacal skin yellowness before slaughter of the TT genotype was higher than that of the TC and CC genotype in SNPs g.-1888 T>C. Furthermore, 3 haplotypes of the above eleven SNPs were calculated and they correlated with heart weight, stomach weight, wing weight, leg skin yellowness and shin skin yellowness before slaughter. Finally, the CD36 expression profile displayed the expression pattern of CD36 mRNA variation in different tissues.

Epistatic QTLs for yield heterosis in tomato.

Controlled population development and genome-wide association studies have proven powerful in uncovering genes and alleles underlying complex traits. An underexplored dimension of such studies is the phenotypic contribution of nonadditive interactions between quantitative trait loci (QTLs). Capturing of such epistasis in a genome-wide manner requires very large populations to represent replicated combinations of loci whose interactions determine phenotypic outcomes. Here, we dissect epistasis using a densely genotyped population of 1,400 backcross inbred lines (BILs) between a modern processing tomato inbred (Solanum lycopersicum) and the Lost Accession (LA5240) of a distant, green-fruited, drought-tolerant wild species, Solanum pennellii. The homozygous BILs, each harboring an average of 11 introgressions and their hybrids with the recurrent parents, were phenotyped for tomato yield components. Population-wide mean yield of the BILs was less than 50% of that of their hybrids (BILHs). All the homozygous introgressions across the genome reduced yield relative to recurrent parent, while several QTLs of the BILHs independently improved productivity. Analysis of two QTL scans showed 61 cases of less-than-additive interactions and 19 cases of more-than-additive interactions. Strikingly, a single epistatic interaction involving S. pennellii QTLs on chromosomes 1 and 7, that independently did not affect yield, increased fruit yield by 20 to 50% in the double introgression hybrid grown in irrigated and dry fields over a period of 4 y. Our work demonstrates the power of large, interspecific controlled population development to uncover hidden QTL phenotypes and how rare epistatic interactions can improve crop productivity via heterosis.

Identification of structural variations related to drought tolerance in wheat (Triticum aestivum L.).

KEY MESSAGE: Structural variations are common in plant genomes, affecting meiotic recombination and distorted segregation in wheat. And presence/absence variations can significantly affect drought tolerance in wheat. Drought is a major abiotic stress limiting wheat production. Common wheat has a complex genome with three sub-genomes, which host large numbers of structural variations (SVs). SVs play critical roles in understanding the genetic contributions of plant domestication and phenotypic plasticity, but little is known about their genomic characteristics and their effects on drought tolerance. In the present study, high-resolution karyotypes of 180 doubled haploids (DHs) were developed. Signal polymorphisms between the parents involved with 8 presence-absence variations (PAVs) of tandem repeats (TR) distributed on the 7 (2A, 4A, 5A, 7A, 3B, 7B, and 2D) of 21 chromosomes. Among them, PAV on chromosome 2D showed distorted segregation, others transmit normal conforming to a 1:1 segregation ration in the population; and a PAVs recombination occurred on chromosome 2A. Association analysis of PAV and phenotypic traits under different water regimes, we found PAVs on chromosomes 4A, 5A, and 7B showed negative effect on grain length (GL) and grain width (GW); PAV.7A had opposite effect on grain thickness (GT) and spike length (SL), with the effect on traits differing under different water regimes. PAVs on linkage group 2A, 4A, 7A, 2D, and 7B associated with the drought tolerance coefficients (DTCs), and significant negative effect on drought resistance values (D values) were detected in PAV.7B. Additionally, quantitative trait loci (QTL) associated with phenotypic traits using the 90 K SNP array showed QTL for DTCs and grain-related traits in chromosomes 4A, and 5A, 3B were co-localized in differential regions of PAVs. These PAVs can cause the differentiation of the target region of SNP and could be used for genetic improvement of agronomic traits under drought stress via marker-assisted selection (MAS) breeding.

Optimization of test day for milk yield recording and sire evaluation in Murrah buffaloes.

In the present study, random regression models (RRM) were used to estimate genetic parameters for test-day milk yield in Murrah buffaloes using Legendre polynomial function (LP), with the objective to find the best combination of "minimum test-day model," which would be essential and sufficient to evaluate the trait successfully. Data included for analysis were 10,615 first lactation monthly test-day milk yield records (5th, 35th, 65th, …, 305th) from 965 Murrah buffaloes for the period 1975-2018. Cubic to octic-order orthogonal polynomials with homogeneous residual variances were used for the estimation of genetic parameters. Random regression models with sixth-order were selected based on goodness of fit criteria like lower AIC, BIC and residual variance. Heritability estimates ranged from 0.079 (TD6) to 0.21(TD10). For both ends of lactation, the additive genetic and environmental variances were higher and ranged from 0.21 ± 0.12 (TD6) to 0.85 ± 0.35 kg2 (TD1) and 3.74 ± 0.36 (TD11) to 1.36 ± 0.14 kg2 (TD9), respectively. Between adjacent test-day records, genetic correlation estimates ranged from 0.09 ± 0.31 (TD1 and TD2) to 0.97 ± 0.03 (TD3 and TD4; TD4 and TD5), but values gradually declined as the distance between test days increased. Negative genetic correlations were also obtained between TD1 with TD3 to TD9, TD2 with TD9 and TD10, and TD3 with TD10. On the basis of genetic correlations, models with 5 and/or 6 test-days combination were able to account for 86.1%-98.7% of variation along the lactation. Models with fourth and fifth-order LP functions were considered to account for variance with combinations of 5 and/or 6 test-day milk yields. The model with 6 test-day combinations had a higher rank correlation (0.93) with model using 11 monthly test-day milk yield records. On the basis of relative efficiency, the model with 6 monthly test day combinations with fifth-order was more efficient (maximum 99%) than the model using 11 monthly test-day milk yield records. Looking into the similar accuracy with the 11TD model, and the low resources requirement, we recommend the use of the "6 test-day combination model" for sire evaluation. These models may help in reducing the cost and time for data recording of milk yield.

Simulated nonlinear genetic and environmental dynamics of complex traits.

Genetic studies of complex traits often show disparities in estimated heritability depending on the method used, whether by genomic associations or twin and family studies. We present a simulation of individual genomes with dynamic environmental conditions to consider how linear and nonlinear effects, gene-by-environment interactions, and gene-by-environment correlations may work together to govern the long-term development of complex traits and affect estimates of heritability from common methods. Our simulation studies demonstrate that the genetic effects estimated by genome wide association studies in unrelated individuals are inadequate to characterize gene-by-environment interaction, while including related individuals in genome-wide complex trait analysis (GCTA) allows gene-by-environment interactions to be recovered in the heritability. These theoretical findings provide an explanation for the "missing heritability" problem and bridge the conceptual gap between the most common findings of GCTA and twin studies. Future studies may use the simulation model to test hypotheses about phenotypic complexity either in an exploratory way or by replicating well-established observations of specific phenotypes.

Climate-Driven Adaptive Differentiation in Melia azedarach: Evidence from a Common Garden Experiment.

Studies of local adaptation in populations of chinaberry (Melia azedarach L.) are important for clarifying patterns in the population differentiation of this species across its natural range. M. azedarach is an economically important timber species, and its phenotype is highly variable across its range in China. Here, we collected M. azedarach seeds from 31 populations across its range and conducted a common garden experiment. We studied patterns of genetic differentiation among populations using molecular markers (simple sequence repeats) and data on phenotypic variation in six traits collected over five years. Our sampled populations could be subdivided into two groups based on genetic analyses, as well as patterns of isolation by distance and isolation by environment. Significant differentiation in growth traits was observed among provenances and families within provenances. Geographic distance was significantly correlated with the quantitative genetic differentiation (QST) in height (HEIT) and crown breadth. Climate factors were significantly correlated with the QST for each trait. A total of 23 climatic factors were examined. There was a significant effect of temperature on all traits, and minimum relative humidity had a significant effect on the survival rate over four years. By comparing the neutral genetic differentiation (FST) with the QST, the mode of selection acting on survival rate varied, whereas HEIT and the straightness of the main trunk were subject to the same mode of selection. The variation in survival rate was consistent with the variation in genetic differentiation among populations, which was indicative of local adaptation. Overall, our findings provide new insights into the responses of the phenological traits of M. azedarach to changes in the climate conditions of China.

A Phylogeny-aware GWAS Framework to Correct for Heritable Pathogen Effects on Infectious Disease Traits.

Infectious diseases are particularly challenging for genome-wide association studies (GWAS) because genetic effects from two organisms (pathogen and host) can influence a trait. Traditional GWAS assume individual samples are independent observations. However, pathogen effects on a trait can be heritable from donor to recipient in transmission chains. Thus, residuals in GWAS association tests for host genetic effects may not be independent due to shared pathogen ancestry. We propose a new method to estimate and remove heritable pathogen effects on a trait based on the pathogen phylogeny prior to host GWAS, thus restoring independence of samples. In simulations, we show this additional step can increase GWAS power to detect truly associated host variants when pathogen effects are highly heritable, with strong phylogenetic correlations. We applied our framework to data from two different host-pathogen systems, HIV in humans and X. arboricola in A. thaliana. In both systems, the heritability and thus phylogenetic correlations turn out to be low enough such that qualitative results of GWAS do not change when accounting for the pathogen shared ancestry through a correction step. This means that previous GWAS results applied to these two systems should not be biased due to shared pathogen ancestry. In summary, our framework provides additional information on the evolutionary dynamics of traits in pathogen populations and may improve GWAS if pathogen effects are highly phylogenetically correlated amongst individuals in a cohort.

Multivariate analysis methods improve the selection of strawberry genotypes with low cold requirement.

Methods of multivariate analysis is a powerful approach to assist the initial stages of crops genetic improvement, particularly, because it allows many traits to be evaluated simultaneously. In this study, heat-tolerant genotypes have been selected by analyzing phenotypic diversity, direct and indirect relationships among traits were identified, and four selection indices compared. Diversity was estimated using K-means clustering with the number of clusters determined by the Elbow method, and the relationship among traits was quantified by path analysis. Parametric and non-parametric indices were applied to selected genotypes using the magnitude of genotypic variance, heritability, genotypic coefficient of variance, and assigned economic weight as selection criteria. The variability among materials led to the formation of two non-overlapping clusters containing 40 and 154 genotypes. Strong to moderate correlations were found between traits with direct effect of the number of commercial fruit on the mass of commercial fruit. The Smith and Hazel index showed the greatest total gains for all criteria; however, concerning the biochemical traits, the Mulamba and Mock index showed the highest magnitudes of predicted gains. Overall, the K-means clustering, correlation analysis, and path analysis complement the use of selection indices, allowing for selection of genotypes with better balance among the assessed traits.

Improving GWAS discovery and genomic prediction accuracy in biobank data.

Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency-linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated [Formula: see text]. We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average [Formula: see text] value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies.

Inheritance of the duration of fertility in chickens and its correlation with laying performance.

BACKGROUND: Duration of fertility (DF) is an important economic trait in poultry production because it has a strong effect on chick output. Various criteria or traits to assess DF on individual hens have been reported but they are affected by many nongenetic factors. Thus, a reliable definition and associated genetic parameters are needed. Because egg production is also vital in chicken breeding, knowledge of the relationship between DF and laying performance is needed for designing selection programs. METHODS: We used five traits that consider both fertility and embryonic livability to delineate DF. Phenotypic and genetic analyses were completed for 2094 hens, with measurements of DF at 35 and 60 weeks of age and hatching egg production at 400 days of age (HEP400). The selection differentials for DF and HEP400 were evaluated. RESULTS: DF is largely independent of the number of oviposited eggs in the peak laying period but both egg production and DF naturally decline with age. The heritability of the five DF traits ranged from 0.11 to 0.13 at 35 weeks of age and increased slightly in the later laying period, ranging from 0.14 to 0.17 (except for efficient duration, time between insemination and the first unhatched egg). Estimates of the genetic correlation for a given trait measured at the two ages were moderate (0.37-0.44), except for efficient duration. However, number of viable embryos depends strongly on egg production. Estimates of genetic correlations of fertility duration day (FDD) at both ages with HEP were weak. Selection for FDD improved DF but without a significant change in laying performance. Selection for increased HEP400 did not contribute to DF improvement. CONCLUSIONS: Although estimates of heritability of the five traits related to DF were low, selection to improve DF based on any one of them is possible. Among these, FDD is an effective selection criterion when the eggs are collected for approximately two weeks after insemination. The best selection procedure for DF improvement would involve multiple measurements at various ages. FDD is independent of laying performance and can be incorporated into a breeding program with egg production to improve reproductive efficiency.

The quantitative genetics of fitness in a wild seabird.

Additive genetic variance in fitness is a prerequisite for adaptive evolution, as a trait must be genetically correlated with fitness to evolve. Despite its relevance, additive genetic variance in fitness has not often been estimated in nature. Here, we investigate additive genetic variance in lifetime and annual fitness components in common terns (Sterna hirundo). Using 28 years of data comprising approximately 6000 pedigreed individuals, we find that additive genetic variances in the zero-inflated and Poisson components of lifetime fitness were effectively zero but estimated with high uncertainty. Similarly, additive genetic variances in adult annual reproductive success and survival did not differ from zero but were again associated with high uncertainty. Simulations suggested that we would be able to detect additive genetic variances as low as 0.05 for the zero-inflated component of fitness but not for the Poisson component, for which adequate statistical power would require approximately two more decades (four tern generations) of data collection. As such, our study suggests heritable variance in common tern fitness to be rather low if not zero, shows how studying the quantitative genetics of fitness in natural populations remains challenging, and highlights the importance of maintaining long-term individual-based studies of natural populations.

How full is the evolutionary fuel tank?

A meta-analysis quantifies the heritable genetic variance in fitness-the fuel of evolution.