SYSTEMS OF MATING. I. THE BIOMETRIC RELATIONS BETWEEN PARENT AND OFFSPRING

Understanding indirect assortative mating and its intergenerational consequences for educational attainment

We develop a framework for understanding indirect assortative mating and provide updated definitions of key terms. We then develop family models that use partners of twins and siblings to freely estimate the degree of genetic and social homogamy, and account for it when investigating sources of parent-offspring similarity. We applied the models to educational attainment using 1,545,444 individuals in 212,070 extended families in the Norwegian population and Norwegian Twin Registry. Partner similarity in education was better explained by indirect assortment than direct assortment on observed educational attainment, with social homogamy being particularly important. The implied genotypic partner correlation (r = 0.34) was comparable to earlier studies, and higher than expected under direct assortment. About 38% of the parent-offspring correlation (r = 0.34) was attributable to various forms of environmental transmission. Alternative models that assumed direct assortment estimated environmental transmission to be lower, but these did not fit the data well.

Animal genetics 100 years ago

One hundred years ago, the first book with the phrase "Animal Genetics" in its title was published. It was written by F.A.E. Crew, then Lecturer in Genetics and foundation Director of the Department of Research in Animal Breeding at the University of Edinburgh. The 352 pages of text provide a most interesting summary of the knowledge of animal genetics at that time. It is impressive to see the extent to which the understanding of genetics had developed in just a couple of decades since the rediscovery of Mendelism. There was, for example, recognition that genes are borne on chromosomes; that XX/XY sex determination provides a very satisfactory explanation for most of the relevant evidence; that sex-linked inheritance has a practical application; that variation in quantitative traits is determined by the combined action of many genes and many non-genetic factors; that inbreeding results in substantial decreases in fecundity and fertility due to homozygosity for undesirable alleles; that crossing between lines or breeds gives rise to hybrid vigour (heterosis); and that many disorders are inherited in a Mendelian fashion, and hence can be controlled by informed breeding. There is, however, no mention of Fisher's 1918 paper nor of Wright's recently published inbreeding coefficient and coefficient of relationship. Crew's book inspired the next generation of geneticists, such as Fred Hutt, who travelled from Canada to Edinburgh to do a PhD with Crew, and who later published his own very influential book with the same title, which was dedicated to Crew.

Performance of homozygosity by descent in two mice lines divergently selected for birth weight environmental variability

Inbreeding can have negative effects, such as increasing the expression of deleterious alleles or reducing fitness. A method based on Hidden Markov Models (HMM) was developed to determine the probability of an individual genome in a homozygous-by-descent state (HBD). As a result of an experiment of divergent selection for birth weight environmental variability two lines were created: high variability line (H-Line) and low variability line (L-Line). The L-Line demonstrated a better performance in traits related with robustness than the H-Line. From a selection period of 20 generations, a total of 655 individuals from the H-Line and 675 individuals from the L-Line were genotyped with a high-density SNP array. We used a predefined multiclass HMM with a total of 9 age related HBD classes and 1 non HBD class. The sum of the probabilities of each HBD class was defined as the total HBD inbreeding (FHBD). In addition, FHBD was divided into age related groups as recent and ancient. Moreover, recent pedigree inbreeding (FPEDR) was defined using different generation thresholds (4 to 14). The evolution of FHBD across generations was similar in both selected lines. However, the distribution in each age-related class was different between lines in more recent generations. The H-Line presented twice as much FHBD by ancestors from 8 generations ago than the L-Line. Moreover, the correlations between recent FHBD and FPEDR obtained with different generation thresholds were greater in the H-Line when very recent FHBD was calculated from classes related with ancestors from 1 to 8 generations ago. However, in the L-Line, considering more than 4 generations ago to define very recent inbreeding did not affect the correlations with FPEDR. The HBD was the first methodology that could detect differences in the inbreeding pattern between the selected lines that could be related with the divergent selection, despite being under the identical mating policy and similar intensity of selection.

Genomic prediction of heterosis, inbreeding control, and mate allocation in outbred diploid and tetraploid populations

Breeders have long appreciated the need to balance selection for short-term genetic gain with maintaining genetic variance for long-term gain. For outbred populations, the method called optimum contribution selection (OCS) chooses parental contributions to maximize the average breeding value at a prescribed inbreeding rate. With optimum mate allocation (OMA), the contribution of each mating is optimized, which allows for specific combining ability due to dominance. To enable OCS and OMA in polyploid species, new theoretical results were derived to (1) predict midparent heterosis due to dominance and (2) control inbreeding in a population of arbitrary ploidy. A new convex optimization framework for OMA, named COMA, was developed and released as public software. Under stochastic simulation of a genomic selection program, COMA maintained a target inbreeding rate of 0.5% using either pedigree or genomic IBD (identity-by-descent) kinship. Significantly more genetic gain was realized with pedigree kinship, which is consistent with previous studies showing the selective advantage of an individual under OCS is dominated by its Mendelian sampling term. Despite the higher accuracy (+0.2-0.3) when predicting mate performance with OMA compared with OCS, there was little long-term gain advantage. The sparsity of the COMA mating design and flexibility to incorporate mating constraints offer practical incentives over OCS. In a potato breeding case study with 170 candidates, the optimal solution at 0.5% inbreeding involved 43 parents but only 43 of the 903 possible matings.

Calculating Relatedness: A Pedigree of Definitions

Biology can be viewed from both an organismal and a genic perspective. A good example is W.D. Hamilton's work on inclusive fitness and kin selection, which puts relatedness at the heart of our understanding of social behavior. Relatedness mediates how much an actor should value a specific behavior's effect on a relative compared to the cost incurred to itself. Despite its key explanatory role, relatedness is also a concept marred with misunderstanding. Part of the problem has been that the term has been used in different ways by different people. To help address this, we survey the history of how relatedness has been formally modeled, paying particular attention to how it is conceptualized from both a gene-centric and an organism-centric point of view.

Systematic assessment of COVID-19 host genetics using whole genome sequencing data

Courses of SARS-CoV-2 infections are highly variable, ranging from asymptomatic to lethal COVID-19. Though research has shown that host genetic factors contribute to this variability, cohort-based joint analyses of variants from the entire allelic spectrum in individuals with confirmed SARS-CoV-2 infections are still lacking. Here, we present the results of whole genome sequencing in 1,220 mainly vaccine-naïve individuals with confirmed SARS-CoV-2 infection, including 827 hospitalized COVID-19 cases. We observed the presence of autosomal-recessive or likely compound heterozygous monogenic disorders in six individuals, all of which were hospitalized and significantly younger than the rest of the cohort. We did not observe any suggestive causal variants in or around the established risk gene TLR7. Burden testing in the largest population subgroup (i.e., Europeans) suggested nominal enrichments of rare variants in coding and non-coding regions of interferon immune response genes in the overall analysis and male subgroup. Case-control analyses of more common variants confirmed associations with previously reported risk loci, with the key locus at 3p21 reaching genome-wide significance. Polygenic scores accurately captured risk in an age-dependent manner. By enabling joint analyses of different types of variation across the entire frequency spectrum, this data will continue to contribute to the elucidation of COVID-19 etiology.

Genetics and Genomics 101

Genetic mutations, both favorable and unfavorable, are the raw material for improvement in livestock populations. The random inheritance of these mutations is essential for generating progenies with genetic potential greater than their parents. These mutations can act either in a simple manner, such that a single alteration disrupts phenotype, or in a complex manner where hundreds or thousands of mutations of small effect create a continuous distribution of phenotypes. Selection tools leverage phenotypic records, pedigrees, and genomics to estimate the genetic potential of individual animals. This more accurate accounting of genetic potential has generated enormous gains in livestock populations.

Genome-Wide Genetic Diversity and Population Structure of Charybdis feriata (Crustacea, Decapoda, and Portunidae) Along the Southeast Coast of China Inferred from Genotyping-by-Sequencing (GBS) Approach

BACKGROUND/OBJECTIVES

The swimming crab Charybdis feriata is an important commercial fishery species and a major economic contributor to the southeast coastal fishing communities in China. Under the scenario of resource decline and shortage in the market over recent years, it has become more urgent and necessary to explore the fine-scale population genetic characteristics of C. feriata.

METHODS

In this study, the genotyping-by-sequencing (GBS) method was used to estimate the genome-wide genetic variation in and population differentiation pattern of C. feriata collected from four geographical locations (Zhoushan, Quanzhou, Yangjiang, and Qinzhou) along the southeast coast of China.

RESULTS

A total of 18,815 high-quality single-nucleotide polymorphisms (SNPs) were identified and the results revealed the existence of high genetic diversity and low genetic divergence among the populations of C. feriata. Floating eggs and larvae transported by alongshore currents during the reproductive season might enhance the interpopulation genetic exchange. Principal component analysis (PCA) and a phylogenetic tree showed a high genetic connectivity of C. feriata across the southeast coast of China, but C. feriata distributed in the Zhoushan Archipelago might possess some genetic distinctiveness and diversification.

CONCLUSIONS

The results supplemented basic genetic information of C. feriata at the genome level and also provided specific knowledge that could lead to the improved spatial management of fishery resources.

Joint identity among loci under mutation and regular inbreeding

This study describes a compact method for determining joint probabilities of identity-by-state (IBS) within and between loci in populations evolving under genetic drift, crossing-over, mutation, and regular inbreeding (partial self-fertilization). Analogues of classical indices of associations among loci arise as functions of these joint identities. This coalescence-based analysis indicates that multi-locus associations reflect simultaneous coalescence events across loci. Measures of association depend on genetic diversity rather than allelic frequencies, as do linkage disequilibrium and its relatives. Scaled indices designed to show monotonic dependence on rates of crossing-over, inbreeding, and mutation may prove useful for interpreting patterns of genome-scale variation.

Genomic and Conventional Inbreeding Coefficient Estimation Using Different Estimator Models in Korean Duroc, Landrace, and Yorkshire Breeds Using 70K Porcine SNP BeadChip

The purpose of this study was to estimate the homozygosity distribution and compute genomic and conventional inbreeding coefficients in three genetically diverse pig breed populations. The genomic and pedigree data of Duroc (1586), Landrace (2256), and Yorkshire (3646) were analyzed. We estimated and compared various genomic and pedigree inbreeding coefficients using different models and approaches. A total of 709,384 ROH segments in Duroc, 816,898 in Landrace, and 1,401,781 in Yorkshire, with average lengths of 53.59 Mb, 56.21 Mb, and 53.46 Mb, respectively, were identified. Relatively, the Yorkshire breed had the shortest ROH segments, whereas the Landrace breed had the longest mean ROH segments. Sus scrofa chromosome 1 (SSC1) had the highest chromosomal coverage by ROH across all breeds. Across breeds, an absolute correlation (1.0) was seen between FROH total and FROH1-2Mb, showing that short ROH were the primary contributors to overall FROH values. The overall association between genomic and conventional inbreeding was weak, with values ranging from 0.058 to 0.140. In contrast, total genomic inbreeding (FROH) and ROH classes showed a strong association, ranging from 0.663 to 1.00, across the genotypes. The results of genomic and conventional inbreeding estimates improve our understanding of the genetic diversity among genotypes.

The genetic basis of the kākāpō structural color polymorphism suggests balancing selection by an extinct apex predator

The information contained in population genomic data can tell us much about the past ecology and evolution of species. We leveraged detailed phenotypic and genomic data of nearly all living kākāpō to understand the evolution of its feather color polymorphism. The kākāpō is an endangered and culturally significant parrot endemic to Aotearoa New Zealand, and the green and olive feather colorations are present at similar frequencies in the population. The presence of such a neatly balanced color polymorphism is remarkable because the entire population currently numbers less than 250 birds, which means it has been exposed to severe genetic drift. We dissected the color phenotype, demonstrating that the two colors differ in their light reflectance patterns due to differential feather structure. We used quantitative genomics methods to identify two genetic variants whose epistatic interaction can fully explain the species' color phenotype. Our genomic forward simulations show that balancing selection might have been pivotal to establish the polymorphism in the ancestrally large population, and to maintain it during population declines that involved a severe bottleneck. We hypothesize that an extinct apex predator was the likely agent of balancing selection, making the color polymorphism in the kākāpō a "ghost of selection past."

Genomic inbreeding coefficients using imputation genotypes: Assessing the effect of ancestral genotyping in Holstein-Friesian dairy cows

The objective of this study was to assess the effect of using or not using the genotypes of the parents of a cow for imputing SNPs on the estimation of genomic inbreeding coefficients of cows. Imputation (i.e., genotyped plus imputed) genotypes from 68,127 Italian Holstein dairy cows registered in the Italian National Association of Holstein, Brown, and Jersey Breeders were analyzed. Cows were genotyped with the high-density (HD) Illumina Infinium BovineHD BeadChip and GeneSeek Genomic Profiler HD-150K, and the medium-density (MD) GeneSeek Genomic Profiler 3, GeneSeek Genomic Profiler 4, GeneSeek MD, and the Labogena MD. To assess differences among estimators, genomic inbreeding coefficients were estimated with 4 PLINK v1.9 estimators (F, Fhat1,Fhat2, andFhat3), 2 genomic relationship matrix- (grm) based estimators (Fgrm and Fgrm2, with the latter including also pedigree information), and one estimator of runs of homozygosity (ROH; FROH). Assuming that the correct genomic inbreeding coefficients should be those estimated from genotyped SNPs, a comparison of the genomic inbreeding coefficients estimated either with the genotyped SNPs or the SNPs after imputation was made. Information on the presence or absence of genotypic information from sire, dam, and maternal grandsire during the imputation was investigated. Genomic inbreeding coefficients estimated with genotyped SNPs or SNPs after imputation were consistent for F, Fhat3, Fgrm2, and FROH, when at least one of the parents was genotyped. Biased (mainly higher) genomic inbreeding coefficients of imputation SNPs were observed in cows that were genotyped with MD SNP panels whose SNPs were poorly represented in the selected imputation SNP dataset and also did not have their parents genotyped, when compared with what would be expected based on actual genotype data. For cows genotyped with MD the estimators Fhat1, Fhat2, and Fgrm provided higher genomic inbreeding coefficients of imputation SNPs even with both parents and the maternal grandsire genotyped. Overall, FROH was the most robust estimator, followed by F and Fhat3. Our findings suggest that SNPs selection, parental genotyping and estimator should be considered for designing imputation strategies in dairy cattle for estimating genomic inbreeding with imputation SNPs. For computing genomic inbreeding coefficients, it is recommendable to have at least one parent genotyped and use an ROH-based estimator.

Allele frequency dynamics under sex-biased demography and sex-specific inheritance in a pedigreed jay population

Sex-biased demography, including sex-biased survival or migration, can alter allele frequency changes across the genome. In particular, we can expect different patterns of genetic variation on autosomes and sex chromosomes due to sex-specific differences in life histories, as well as differences in effective population size, transmission modes, and the strength and mode of selection. Here, we demonstrate the role that sex differences in life history played in shaping short-term evolutionary dynamics across the genome. We used a 25-year pedigree and genomic dataset from a long-studied population of Florida Scrub-Jays (Aphelocoma coerulescens) to directly characterize the relative roles of sex-biased demography and inheritance in shaping genome-wide allele frequency trajectories. We used gene dropping simulations to estimate individual genetic contributions to future generations and to model drift and immigration on the known pedigree. We quantified differential expected genetic contributions of males and females over time, showing the impact of sex-biased dispersal in a monogamous system. Due to female-biased dispersal, more autosomal variation is introduced by female immigrants. However, due to male-biased transmission, more Z variation is introduced by male immigrants. Finally, we partitioned the proportion of variance in allele frequency change through time due to male and female contributions. Overall, most allele frequency change is due to variance in survival and births. Males and females make similar contributions to autosomal allele frequency change, but males make higher contributions to allele frequency change on the Z chromosome. Our work shows the importance of understanding sex-specific demographic processes in characterizing genome-wide allele frequency change in wild populations.

Bursts of coalescence within population pedigrees whenever big families occur

We consider a simple diploid population-genetic model with potentially high variability of offspring numbers among individuals. Specifically, against a backdrop of Wright-Fisher reproduction and no selection, there is an additional probability that a big family occurs, meaning that a pair of individuals has a number of offspring on the order of the population size. We study how the pedigree of the population generated under this model affects the ancestral genetic process of a sample of size two at a single autosomal locus without recombination. Our population model is of the type for which multiple-merger coalescent processes have been described. We prove that the conditional distribution of the pairwise coalescence time given the random pedigree converges to a limit law as the population size tends to infinity. This limit law may or may not be the usual exponential distribution of the Kingman coalescent, depending on the frequency of big families. But because it includes the number and times of big families, it differs from the usual multiple-merger coalescent models. The usual multiple-merger coalescent models are seen as describing the ancestral process marginal to, or averaging over, the pedigree. In the limiting ancestral process conditional on the pedigree, the intervals between big families can be modeled using the Kingman coalescent but each big family causes a discrete jump in the probability of coalescence. Analogous results should hold for larger samples and other population models. We illustrate these results with simulations and additional analysis, highlighting their implications for inference and understanding of multilocus data.

Wright's Hierarchical F-Statistics

This perspective article offers a meditation on FST and other quantities developed by Sewall Wright to describe the population structure, defined as any departure from reproduction through random union of gametes. Concepts related to the F-statistics draw from studies of the partitioning of variation, identity coefficients, and diversity measures. Relationships between the first two approaches have recently been clarified and unified. This essay addresses the third pillar of the discussion: Nei's GST and related measures. A hierarchy of probabilities of identity-by-state provides a description of the relationships among levels of a structured population with respect to genetic diversity. Explicit expressions for the identity-by-state probabilities are determined for models of structured populations undergoing regular inbreeding and recurrent mutation. Levels of genetic diversity within and between subpopulations reflect mutation as well as migration. Accordingly, indices of the population structure are inherently locus-specific, contrary to the intentions of Wright. Some implications of this locus-specificity are explored.

PyPop: a mature open-source software pipeline for population genomics

Python for Population Genomics (PyPop) is a software package that processes genotype and allele data and performs large-scale population genetic analyses on highly polymorphic multi-locus genotype data. In particular, PyPop tests data conformity to Hardy-Weinberg equilibrium expectations, performs Ewens-Watterson tests for selection, estimates haplotype frequencies, measures linkage disequilibrium, and tests significance. Standardized means of performing these tests is key for contemporary studies of evolutionary biology and population genetics, and these tests are central to genetic studies of disease association as well. Here, we present PyPop 1.0.0, a new major release of the package, which implements new features using the more robust infrastructure of GitHub, and is distributed via the industry-standard Python Package Index. New features include implementation of the asymmetric linkage disequilibrium measures and, of particular interest to the immunogenetics research communities, support for modern nomenclature, including colon-delimited allele names, and improvements to meta-analysis features for aggregating outputs for multiple populations. Code available at: https://zenodo.org/records/10080668 and https://github.com/alexlancaster/pypop.

Risk scores, label bias, and everything but the kitchen sink

In designing risk assessment algorithms, many scholars promote a "kitchen sink" approach, reasoning that more information yields more accurate predictions. We show, however, that this rationale often fails when algorithms are trained to predict a proxy of the true outcome, for instance, predicting arrest as a proxy for criminal behavior. With this "label bias," one should exclude a feature if its correlation with the proxy and its correlation with the true outcome have opposite signs, conditional on the other model features. This criterion is often satisfied when a feature is weakly correlated with the true outcome, and, additionally, that feature and the true outcome are both direct causes of the proxy outcome. For example, criminal behavior and geography may be weakly correlated and, due to patterns of police deployment, direct causes of one's arrest record-suggesting that excluding geography in criminal risk assessment will weaken an algorithm's performance in predicting arrest but will improve its capacity to predict actual crime.

The Impossible Dream - Panmixia

This study starts with a simple model by which Hardy-Weinberg proportions are attained in a single generation while maintaining gene frequencies. The question of differentiating between random and non-random mating is explored by simulation. Sample mating proportions are generated using the model as base. The difficulty of differentiating between random and non-random mating is illustrated.

Estimation of microsatellite-based autozygosity and its correlation with pedigree inbreeding coefficient in crossbred cattle

In countries where farming is largely subsistence, no pedigree records of farm animals are maintained at farmers' herd and scientific mating plans are not observed which leads to the accumulation of inbreeding and loss of production potential. Microsatellites have been widely used as reliable molecular markers to measure inbreeding. We attempted to correlate autozygosity estimated from microsatellite data with the inbreeding coefficient (F) calculated from pedigree data in Vrindavani crossbred cattle developed in India. The inbreeding coefficient was calculated from the pedigree of ninety-six Vrindavani cattle. Animals were further grouped into three groups viz. acceptable/low (F: 0-5%), moderate (F: 5-10%) and high (F: ≥10%), based on their inbreeding coefficients. The overall mean of the inbreeding coefficient was found to be 0.070 ± 0.007. A panel of twenty-five bovine-specific loci were chosen for the study according to ISAG/FAO. The mean FIS, FST, and FIT values were 0.0548 ± 0.025, 0.012 ± 0.001 and 0.0417 ± 0.025, respectively. There was no significant correlation between the FIS values obtained and the pedigree F values. The locus-wise individual autozygosity was estimated using the method-of-moments estimator (MME) formula for locus-specific autozygosity. The autozygosities ascribing to CSSM66 and TGLA53 were found to be significantly (p < .01 and p < .05, respectively) correlated with pedigree F values.

Symmetry in models of natural selection

Symmetry arguments are frequently used-often implicitly-in mathematical modelling of natural selection. Symmetry simplifies the analysis of models and reduces the number of distinct population states to be considered. Here, I introduce a formal definition of symmetry in mathematical models of natural selection. This definition applies to a broad class of models that satisfy a minimal set of assumptions, using a framework developed in previous works. In this framework, population structure is represented by a set of sites at which alleles can live, and transitions occur via replacement of some alleles by copies of others. A symmetry is defined as a permutation of sites that preserves probabilities of replacement and mutation. The symmetries of a given selection process form a group, which acts on population states in a way that preserves the Markov chain representing selection. Applying classical results on group actions, I formally characterize the use of symmetry to reduce the states of this Markov chain, and obtain bounds on the number of states in the reduced chain.

Iterative Level-0: A new and fast algorithm to traverse mating networks calculating the inbreeding and relationship coefficients

In population medical genetics, the study of autosomal recessive disorders in highly endogamous populations is a major topic where calculating the inbreeding and relationship coefficients on mating networks is crucial. However, a challenge arises when dealing with large and complex mating networks, making their traversal difficult during the calculation process. For this calculation, we propose using Iterative Level-0 (IL0) as a new and faster algorithm that traverses mating networks more efficiently. The purpose of this work is to explain in detail the IL0 algorithm and prove its superiority by comparing it with two algorithms based on the best-known algorithms in the area: Depth First Search (DFS) and Breadth First Search (BFS). A Cytoscape application has been developed to calculate the inbreeding and relationship coefficients of individuals composing any mating network. In this application, the IL0 proposal together with DFS-based and BFS-based algorithms have been implemented. Any user can access this freely available Cytoscape application (https://apps.cytoscape.org/apps/inbreeding) that allows the comparison between the IL0 proposal and the best-known algorithms (based on DFS and BFS). In addition, a diverse set of mating networks has been collected in terms of complexity (number of edges) and species (humans, primates, and dogs) for the experiments. The runtime obtained by the IL0, DFS-based, and BFS-based algorithms when calculating the inbreeding and relationship coefficients proved the improvement of IL0. In fact, a speedup study reflected that the IL0 algorithm is 7.60 to 127.50 times faster than DFS-based and BFS-based algorithms. Moreover, a scalability study found that the growth of the IL0 runtime has a linear dependence on the number of edges of the mating network, while the DFS-based and BFS-based runtimes have a quadratic dependence. Therefore, the IL0 algorithm can solve the problem of calculating the inbreeding and relationship coefficients many times faster (up to 127.50) than the two algorithms based on the famous DFS and BFS. Furthermore, our results demonstrate that IL0 scales much better as the complexity of mating networks increases.

Potentials of genotypes, morpho-physio-biochemical traits, and growing media on shelf life and future prospects of gene editing in tomatoes

Background: To study the genetic basis of the impact of genotypes and morpho-physio-biochemical traits under different organic and inorganic fertilizer doses on the shelf life attribute of tomatoes, field experiments were conducted in randomized block designs during the rabi seasons of 2018-2019 and 2019-2020. The experiment comprised three diverse nutrient environments [T1-organic; T2-inorganic; T3-control (without any fertilizers)] and five tomato genotypes with variable growth habits, specifically Angoorlata (Indeterminate), Avinash-3 (semi-determinate), Swaraksha (semi-determinate), Pusa Sheetal (semi-determinate), and Pusa Rohini (determinate). Results: The different tomato genotypes behaved apparently differently from each other in terms of shelf life. All the genotypes had maximum shelf life when grown in organic environments. However, the Pusa Sheetal had a maximum shelf life of 8.35 days when grown in an organic environment and showed an increase of 12% over the control. The genotype Pusa Sheetal, organic environment and biochemical trait Anthocyanin provides a promise as potential contributor to improve the keeping quality of tomatoes. Conclusion: The genotype Pusa Sheetal a novel source for shelf life, organic environment, and anthocyanin have shown promises for extended shelf life in tomatoes. Thus, the identified trait and genotype can be utilized in tomato improvement programs. Furthermore, this identified trait can also be targeted for its quantitative enhancement in order to increase tomato shelf life through a genome editing approach. A generalized genome editing mechanism is consequently suggested.

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

BACKGROUND

In breeding programmes, the observed genetic change is a sum of the contributions of different selection paths represented by groups of individuals. Quantifying these sources of genetic change is essential for identifying the key breeding actions and optimizing breeding programmes. However, it is difficult to disentangle the contribution of individual paths due to the inherent complexity of breeding programmes. Here we extend the previously developed method for partitioning genetic mean by paths of selection to work both with the mean and variance of breeding values.

METHODS

First, we extended the partitioning method to quantify the contribution of different paths to genetic variance assuming that the breeding values are known. Second, we combined the partitioning method with the Markov Chain Monte Carlo approach to draw samples from the posterior distribution of breeding values and use these samples for computing the point and interval estimates of partitions for the genetic mean and variance. We implemented the method in the R package AlphaPart. We demonstrated the method with a simulated cattle breeding programme.

RESULTS

We show how to quantify the contribution of different groups of individuals to genetic mean and variance and that the contributions of different selection paths to genetic variance are not necessarily independent. Finally, we observed that the partitioning method under the pedigree-based model has some limitations, which suggests the need for a genomic extension.

CONCLUSIONS

We presented a partitioning method to quantify sources of change in genetic mean and variance in breeding programmes. The method can help breeders and researchers understand the dynamics in genetic mean and variance in a breeding programme. The developed method for partitioning genetic mean and variance is a powerful method for understanding how different selection paths interact within a breeding programme and how they can be optimised.

Heritability Estimation Approaches Utilizing Genome-Wide Data

Prior to the development of genome-wide arrays and whole genome sequencing technologies, heritability estimation mainly relied on the study of related individuals. Over the past decade, various approaches have been developed to estimate SNP-based narrow-sense heritability (h SNP 2 ${\rm{h}}_{{\rm{SNP}}}^2$ ) in unrelated individuals. These latter approaches use either individual-level genetic variations or summary results from genome-wide association studies (GWAS). Recently, several studies compared these approaches using extensive simulations and empirical datasets. However, sparse information on hands-on training necessitates revisiting these approaches from the perspective of a stepwise guide for practical applications. Here, we provide an overview of the commonly used SNP-heritability estimation approaches utilizing genome-wide array, imputed or whole genome data from unrelated individuals, or summary results. We not only discuss these approaches based on their statistical concepts, utility, advantages, and limitations, but also provide step-by-step protocols to apply these approaches. For illustration purposes, we estimateh SNP 2 ${\rm{h}}_{{\rm{SNP}}}^2$ of height and BMI utilizing individual-level data from The Northern Finland Birth Cohort (NFBC) and summary results from the Genetic Investigation of ANthropometric Traits (GIANT;) consortium. We present this review as a template for the researchers who estimate and use heritability in their studies and as a reference for geneticists who develop or extend heritability estimation approaches. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: GREML (GCTA) Alternate Protocol 1: Stratified GREML Basic Protocol 2: LDAK Alternate Protocol 2: Stratified LDAK Basic Protocol 3: Threshold GREML Basic Protocol 4: LD score (LDSC) regression Basic Protocol 5: SumHer.

Genomic selection for agronomic traits in a winter wheat breeding program

rAMP-seq based genomic selection for agronomic traits has been shown to be a useful tool for winter wheat breeding programs by increasing the rate of genetic gain. Genomic selection (GS) is an effective strategy to employ in a breeding program that focuses on optimizing quantitative traits, which results in the ability for breeders to select the best genotypes. GS was incorporated into a breeding program to determine the potential for implementation on an annual basis, with emphasis on selecting optimal parents and decreasing the time and costs associated with phenotyping large numbers of genotypes. The design options for applying repeat amplification sequencing (rAMP-seq) in bread wheat were explored, and a low-cost single primer pair strategy was implemented. A total of 1870 winter wheat genotypes were phenotyped and genotyped using rAMP-seq. The optimization of training to testing population size showed that the 70:30 ratio provided the most consistent prediction accuracy. Three GS models were tested, rrBLUP, RKHS and feed-forward neural networks using the University of Guelph Winter Wheat Breeding Program (UGWWBP) and Elite-UGWWBP populations. The models performed equally well for both populations and did not differ in prediction accuracy (r) for most agronomic traits, with the exception of yield, where RKHS performed the best with an r = 0.34 and 0.39 for each population, respectively. The ability to operate a breeding program where multiple selection strategies, including GS, are utilized will lead to higher efficiency in the program and ultimately lead to a higher rate of genetic gain.

Assortativity in cognition

In pairwise interactions, where two individuals meet and play a social game with each other, assortativity in cognition means that pairs where both decision-makers use the same cognitive process are more likely to occur than what happens under random matching. In this paper, we show theoretically that assortativity in cognition may arise as a consequence of assortativity in other dimensions. Moreover, we analyze an applied model where we investigate the effects of assortativity in cognition on the emergence of cooperation and on the degree of prosociality of intuition and deliberation, which are the typical cognitive processes postulated by the dual process theory in psychology. In particular, with assortativity in cognition, deliberation is able to shape the intuitive heuristic toward cooperation, increasing the degree of prosociality of intuition, and ultimately promoting the overall cooperation. Our findings rely on agent-based simulations, but analytical results are also obtained in a special case. We conclude with examples involving different payoff matrices of the underlying social games, showing that assortativity in cognition can have non-trivial implications in terms of its societal desirability.

Genomic inbreeding coefficients using imputed genotypes: assessing differences among SNP panels in Holstein-Friesian dairy cows

The objective of this study was to evaluate the effect of imputation of single nucleotide polymorphisms (SNP) on the estimation of genomic inbreeding coefficients. Imputed genotypes of 68,127 Italian Holstein dairy cows were analyzed. Cows were initially genotyped with two high density (HD) SNP panels, namely the Illumina Infinium BovineHD BeadChip (678 cows; 777,962 SNP) and the Genomic Profiler HD-150K (641 cows; 139,914 SNP), and four medium density (MD): GeneSeek Genomic Profiler 3 (10,679 cows; 26,151 SNP), GeneSeek Genomic Profiler 4 (33,394 cows; 30,113 SNP), GeneSeek MD (12,030 cows; 47,850 SNP) and the Labogena MD (10,705 cows; 41,911 SNP). After imputation, all cows had genomic information on 84,445 SNP. Seven genomic inbreeding estimators were tested: (i) four PLINK v1.9 estimators (F, F_hat1,2,3), (ii) two genomic relationship matrix (grm) estimators [VanRaden's 1^st method, but with observed allele frequencies (F_grm) and VanRaden's 3^rd method that is allelic free and pedigree dependent (F_grm2)], and (iii) a runs of homozygosity (roh) - based estimator (F_roh). Genomic inbreeding coefficients of each SNP panel were compared with genomic inbreeding coefficients derived from the 84,445 imputation SNP. Coefficients of the HD SNP panels were consistent between genotyped-imputed SNP (Pearson correlations ~99%), while variability across SNP panels and estimators was observed in the MD SNP panels, with Labogena MD providing, on average, more consistent estimates. The robustness of Labogena MD, can be partly explained by the fact that 97.85% of the SNP of this panel is included in the 84,445 SNP selected by ANAFIBJ for routine genomic imputations, while this percentage for the other MD SNP panels varied between 55 and 60%. Runs of homozygosity was the most robust estimator. Genomic inbreeding estimates using imputation SNP are influenced by the SNP number of the SNP panel that are included in the imputed SNP, and performance of genomic inbreeding estimators depends on the imputation.

Integrating plant and fungal quantitative genetics to improve the ecological and agricultural applications of mycorrhizal symbioses

Finding and targeting genes that quantitatively contribute to agricultural and ecological processes progresses food production and conservation efforts. Typically, quantitative genetic approaches link variants in a single organism's genome with a trait of interest. Recently, genome-to-genome mapping has found genome variants interacting between species to produce the result of a multiorganism (including multikingdom) interaction. These were plant and bacterial pathogen genome interactions; plant-fungal coquantitative genetics have not yet been applied. Plant-mycorrhizae symbioses exist across most biomes, for a majority of land plants, including crop plants, and manipulate many traits from single organisms to ecosystems for which knowing the genetic basis would be useful. The availability of Rhizophagus irregularis mycorrhizal isolates, with genomic information, makes dual-genome methods with beneficial mutualists accessible and imminent.

Heritability of dispersal-related larval traits in the clown anemonefish Amphiprion percula

A major goal of marine ecology is to identify the drivers of variation in larval dispersal. Larval traits are emerging as an important potential source of variation in dispersal outcomes, but little is known about how the evolution of these traits might shape dispersal patterns. Here, we consider the potential for adaptive evolution in two possibly dispersal-related traits by quantifying the heritability of larval size and swimming speed in the clown anemonefish (Amphiprion percula). Using a laboratory population of wild-caught A. percula, we measured the size and swimming speed of larvae from 24 half-sibling families. Phenotypic variance was partitioned into genetic and environmental components using a linear mixed-effects model. Importantly, by including half-siblings in the breeding design, we ensured that our estimates of genetic variance do not include nonheritable effects shared by clutches of full-siblings, which could lead to significant overestimates of heritability. We find unequivocal evidence for the heritability of larval body size (estimated between 0.21 and 0.34) and equivocal evidence for the heritability of swimming speed (between 0.05 and 0.19 depending on the choice of prior). From a methodological perspective, this work demonstrates the importance of evaluating sensitivity to prior distribution in Bayesian analysis. From a biological perspective, it advances our understanding of potential dispersal-related larval traits by quantifying the extent to which they can be inherited and thus have the potential for adaptive evolution.

Fisher's historic 1922 paper On the dominance ratio

R.A. Fisher's 1922 paper On the dominance ratio has a strong claim to be the foundation paper for modern population genetics. It greatly influenced subsequent work by Haldane and Wright, and contributed 3 major innovations to the study of evolution at the genetic level. First, the introduction of a general model of selection at a single locus, which showed how variability could be maintained by heterozygote advantage. Second, the use of the branching process approach to show that a beneficial mutation has a substantial chance of loss from the population, even when the population size is extremely large. Third, the invention of the concept of a probability distribution of allele frequency, caused by random sampling of allele frequencies due to finite population size, and the first use of a diffusion equation to investigate the properties of such a distribution. Although Fisher was motivated by an inference that later turned out to lack strong empirical support (a substantial contribution of dominance to quantitative trait variability), and his use of a diffusion equation was marred by a technical mistake, the paper introduced concepts and methods that pervade much subsequent work in population genetics.

Genomic Prediction Methods Accounting for Nonadditive Genetic Effects

The use of genomic information for prediction of future phenotypes or breeding values for the candidates to selection has become a standard over the last decade. However, most procedures for genomic prediction only consider the additive (or substitution) effects associated with polymorphic markers. Nevertheless, the implementation of models that consider nonadditive genetic variation may be interesting because they (1) may increase the ability of prediction, (2) can be used to define mate allocation procedures in plant and animal breeding schemes, and (3) can be used to benefit from nonadditive genetic variation in crossbreeding or purebred breeding schemes. This study reviews the available methods for incorporating nonadditive effects into genomic prediction procedures and their potential applications in predicting future phenotypic performance, mate allocation, and crossbred and purebred selection. Finally, a brief outline of some future research lines is also proposed.

Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction

Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E.

Estimating heritability of glycaemic response to metformin using nationwide electronic health records and population-sized pedigree

Background

Variability of response to medication is a well-known phenomenon, determined by both environmental and genetic factors. Understanding the heritable component of the response to medication is of great interest but challenging due to several reasons, including small study cohorts and computational limitations.

Methods

Here, we study the heritability of variation in the glycaemic response to metformin, first-line therapeutic agent for type 2 diabetes (T2D), by leveraging 18 years of electronic health records (EHR) data from Israel’s largest healthcare service provider, consisting of over five million patients of diverse ethnicities and socio-economic background. Our cohort consists of 80,788 T2D patients treated with metformin, with an accumulated number of 1,611,591 HbA1C measurements and 4,581,097 metformin prescriptions. We estimate the explained variance of glycated hemoglobin (HbA1c%) reduction due to inheritance by constructing a six-generation population-size pedigree from national registries and linking it to medical health records.

Results

Using Linear Mixed Model-based framework, a common-practice method for heritability estimation, we calculate a heritability measure of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}^{2}=12.6 \%$$\end{document}h2=12.6% (95% CI, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$6.1 \%\! -\!19.1 \%$$\end{document}6.1%−19.1%) for absolute reduction of HbA1c% after metformin treatment in the entire cohort, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}^{2}=21.0 \%$$\end{document}h2=21.0% (95% CI, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7.8 \%\! -\!34.4 \%$$\end{document}7.8%−34.4%) for males and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}^{2}=22.9 \%$$\end{document}h2=22.9% (95% CI, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10.0 \%\! -\!35.7 \%$$\end{document}10.0%−35.7%) in females. Results remain unchanged after adjusting for pre-treatment HbA1c%, and in proportional reduction of HbA1c%.

Conclusions

To the best of our knowledge, our work is the first to estimate heritability of drug response using solely EHR data combining a pedigree-based kinship matrix. We demonstrate that while response to metformin treatment has a heritable component, most of the variation is likely due to other factors, further motivating non-genetic analyses aimed at unraveling metformin’s action mechanism.

Individuals in a population might respond differently to the same medication and this phenomenon is commonly attributed to either genes or the environment. Here, we studied the familial aspects of the response to metformin, a medication used in the treatment of type 2 diabetes. We combined information from 18 years of medical records identifying newly treated patients with type 2 diabetes with information about how the trait was inherited within their families. We calculated a metric that tells us how well differences in people’s genes account for differences in their traits, and demonstrate that although the difference in response to metformin is in part explained by the genes people with type 2 diabetes inherit, most of it is not explained by genes. This finding contributes to a better understanding of differences in metformin response and might help inform treatment in future.

Kalka and Gavrieli et al. assessed the heritability of variation in the glycaemic response to metformin by leveraging electronic health records data gathered from a large cohort of patients with diabetes and combining it with pedigree information. The authors show that although the variability in this response has a heritable component, most of it is likely non-genetic.

Why don't all animals avoid inbreeding?

Individuals are expected to avoid mating with relatives as inbreeding can reduce offspring fitness, a phenomenon known as inbreeding depression. This has led to the widespread assumption that selection will favour individuals that avoid mating with relatives. However, the strength of inbreeding avoidance is variable across species and there are numerous cases where related mates are not avoided. Here we test if the frequency that related males and females encounter each other explains variation in inbreeding avoidance using phylogenetic meta-analysis of 41 different species from six classes across the animal kingdom. In species reported to mate randomly with respect to relatedness, individuals were either unlikely to encounter relatives, or inbreeding had negligible effects on offspring fitness. Mechanisms for avoiding inbreeding, including active mate choice, post-copulatory processes and sex-biased dispersal, were only found in species with inbreeding depression. These results help explain why some species seem to care more about inbreeding than others: inbreeding avoidance through mate choice only evolves when there is both a risk of inbreeding depression and related sexual partners frequently encounter each other.

Dynamic landscapes in northwestern North America structured populations of wolverines (Gulo gulo)

Cyclic climatic and glacial fluctuations of the Late Quaternary produced a dynamic biogeographic history for high latitudes. To refine our understanding of this history in northwestern North America, we explored geographic structure in a wide-ranging carnivore, the wolverine (Gulo gulo). We examined genetic variation in populations across mainland Alaska, coastal Southeast Alaska, and mainland western Canada using nuclear microsatellite genotypes and sequence data from the mitochondrial DNA (mtDNA) control region and Cytochrome b (Cytb) gene. Data from maternally inherited mtDNA reflect stable populations in Northwest Alaska, suggesting the region harbored wolverine populations since at least the Last Glacial Maximum (LGM; 21 Kya), consistent with their persistence in the fossil record of Beringia. Populations in Southeast Alaska are characterized by minimal divergence, with no genetic signature of long-term refugial persistence (consistent with the lack of pre-Holocene fossil records there). The Kenai Peninsula population exhibits mixed signatures depending on marker type: mtDNA data indicate stability (i.e., historical persistence) and include a private haplotype, whereas biparentally inherited microsatellites exhibit relatively low variation and a lack of private alleles consistent with a more recent Holocene colonization of the peninsula. Our genetic work is largely consistent with the early 20th century taxonomic hypothesis that wolverines on the Kenai Peninsula belong to a distinct subspecies. Our finding of significant genetic differentiation of wolverines inhabiting the Kenai Peninsula, coupled with the peninsula’s burgeoning human population and the wolverine’s known sensitivity to anthropogenic impacts, provides valuable foundational data that can be used to inform conservation and management prescriptions for wolverines inhabiting these landscapes.

The value of genomic relationship matrices to estimate levels of inbreeding

BACKGROUND

Genomic relationship matrices are used to obtain genomic inbreeding coefficients. However, there are several methodologies to compute these matrices and there is still an unresolved debate on which one provides the best estimate of inbreeding. In this study, we investigated measures of inbreeding obtained from five genomic matrices, including the Nejati-Javaremi allelic relationship matrix (FNEJ), the Li and Horvitz matrix based on excess of homozygosity (FL&H), and the VanRaden (methods 1, FVR1, and 2, FVR2) and Yang (FYAN) genomic relationship matrices. We derived expectations for each inbreeding coefficient, assuming a single locus model, and used these expectations to explain the patterns of the coefficients that were computed from thousands of single nucleotide polymorphism genotypes in a population of Iberian pigs.

RESULTS

Except for FNEJ, the evaluated measures of inbreeding do not match with the original definitions of inbreeding coefficient of Wright (correlation) or Malécot (probability). When inbreeding coefficients are interpreted as indicators of variability (heterozygosity) that was gained or lost relative to a base population, both FNEJ and FL&H led to sensible results but this was not the case for FVR1, FVR2 and FYAN. When variability has increased relative to the base, FVR1, FVR2 and FYAN can indicate that it decreased. In fact, based on FYAN, variability is not expected to increase. When variability has decreased, FVR1 and FVR2 can indicate that it has increased. Finally, these three coefficients can indicate that more variability than that present in the base population can be lost, which is also unreasonable. The patterns for these coefficients observed in the pig population were very different, following the derived expectations. As a consequence, the rate of inbreeding depression estimated based on these inbreeding coefficients differed not only in magnitude but also in sign.

CONCLUSIONS

Genomic inbreeding coefficients obtained from the diagonal elements of genomic matrices can lead to inconsistent results in terms of gain and loss of genetic variability and inbreeding depression estimates, and thus to misleading interpretations. Although these matrices have proven to be very efficient in increasing the accuracy of genomic predictions, they do not always provide a useful measure of inbreeding.

Estimating Soil Water Content and Evapotranspiration of Winter Wheat under Deficit Irrigation Based on SWAP Model

Deficit irrigation strategy is essential for sustainable agricultural development in arid regions. A two−year deficit irrigation field experiment was conducted to study the water dynamics of winter wheat under deficit irrigation in Guanzhong Plain in Northwest China. Three irrigation levels were implemented during four growth stages of winter wheat: 100%, 80% and 60% of actual evapotranspiration (ET) measured by the lysimeter with sufficient irrigation treatment (CK). The agro−hydrological model soil−water−atmosphere−plant (SWAP) was used to simulate the components of the farmland water budget. Sensitivity analysis for parameters of SWAP indicated that the saturated water content and water content shape factor n were more sensitive than the other parameters. The verification results showed that the SWAP model accurately simulated soil water content (average relative error (MRE) < 21.66%, root mean square error (RMSE) < 0.07 cm3 cm−3) and ET (R2 = 0.975, p < 0.01). Irrigation had an important impact on actual plant transpiration, but the actual soil evaporation had little change among different treatments. The average deep percolation was 14.54 mm and positively correlated with the total irrigation amount. The model established using path analysis and regression methods for estimating ET performed well (R2 = 0.727, p < 0.01). This study provided effective guidance for SWAP model parameter calibration and a convenient way to accurately estimate ET with fewer variables.

Evaluation of Genotypes and Association of Traits in Watermelon Across Two Southern Texas Locations

Watermelon is the most important horticultural crop in Texas and is grown across the state under diverse environments. Our study was conducted in the southern region of Texas to understand genotype-by-environment interactions and the contribution of yield components to yield. To accomplish this, twenty genotypes were evaluated for important traits and characteristics at two locations, Uvalde and Weslaco TX, for two years, 2018 and 2019. The genotypes were evaluated for total yield, total fruit count, total soluble solids, rind thickness, fruit length, diameter and weight. Genotype-by-environment (G x E) interaction was not significant, possibly due to similarity in climatic conditions and nutrient management practices. In the grouped analysis, cultivars Crimson Diamond, Sunshade and the breeding line TAM 2 had a higher total yield. Path analysis showed a high direct effect for total fruit count and fruit diameter of 0.89 and 0.85, respectively. However, total fruit count had a high indirect effect of −0.44. Fruit weight was the only trait that showed a significant (p < 0.01) correlation towards total yield at r = 0.58. Neither of the high direct effects, total fruit count and fruit diameter, had a significant correlation. The study inferred that breeding resources could be optimized by reducing the testing location to only one representative location for measured traits in southern Texas. The indirect selection of total fruit or fruit diameter could result in better yield. The study suggested selecting for optimum total fruit and fruit diameter for higher yield.

Selection criteria for high-yielding and early-flowering bread wheat hybrids under heat stress

Hybrid performance during wheat breeding can be improved by analyzing genetic distance (GD) among wheat genotypes and determining its correlation with heterosis. This study evaluated the GD between 16 wheat genotypes by using 60 simple sequence repeat (SSR) markers to classify them according to their relationships and select those with greater genetic diversity, evaluate the correlation of the SSR marker distance with heterotic performance and specific combining ability (SCA) for heat stress tolerance, and identify traits that most influence grain yield (GY). Eight parental genotypes with greater genetic diversity and their 28 F1 hybrids generated using diallel crossing were evaluated for 12 measured traits in two seasons. The GD varied from 0.235 to 0.911 across the 16 genotypes. Cluster analysis based on the GD estimated using SSRs classified the genotypes into three major groups and six sub-groups, almost consistent with the results of principal coordinate analysis. The combined data indicated that five hybrids showed 20% greater yield than mid-parent or better-parent. Two hybrids (P2 × P4) and (P2 × P5), which showed the highest performance of days to heading (DH), grain filling duration (GFD), and GY, and had large genetic diversity among themselves (0.883 and 0.911, respectively), were deemed as promising heat-tolerant hybrids. They showed the best mid-parent heterosis and better-parent heterosis (BPH) for DH (-11.57 and -7.65%; -13.39 and -8.36%, respectively), GFD (12.74 and 12.17%; 12.09 and 10.59%, respectively), and GY (36.04 and 20.04%; 44.06 and 37.73%, respectively). Correlation between GD and each of BPH and SCA effects based on SSR markers was significantly positive for GFD, hundred kernel weight, number of kernels per spike, harvest index, GY, and grain filling rate and was significantly negative for DH. These correlations indicate that the performance of wheat hybrids with high GY and earliness could be predicted by determining the GD of the parents by using SSR markers. Multivariate analysis (stepwise regression and path coefficient) suggested that GFD, hundred kernel weight, days to maturity, and number of kernels per spike had the highest influence on GY.

Creating outbred and inbred populations in haplodiploids to measure adaptive responses in the laboratory

Laboratory studies are often criticized for not being representative of processes occurring in natural populations. One reason for this is the fact that laboratory populations generally do not capture enough of the genetic variation of natural populations. This can be mitigated by mixing the genetic background of several field populations when creating laboratory populations. From these outbred populations, it is possible to generate inbred lines, thereby freezing and partitioning part of their variability, allowing each genotype to be characterized independently. Many studies addressing adaptation of organisms to their environment, such as those involving quantitative genetics or experimental evolution, rely on inbred or outbred populations, but the methodology underlying the generation of such biological resources is usually not explicitly documented. Here, we developed different procedures to circumvent common pitfalls of laboratory studies, and illustrate their application using two haplodiploid species, the spider mites Tetranychus urticae and Tetranychus evansi. First, we present a method that increases the chance of capturing high amounts of variability when creating outbred populations, by performing controlled crosses between individuals from different field-collected populations. Second, we depict the creation of inbred lines derived from such outbred populations, by performing several generations of sib-mating. Third, we outline an experimental evolution protocol that allows the maintenance of a constant population size at the beginning of each generation, thereby preventing bottlenecks and diminishing extinction risks. Finally, we discuss the advantages of these procedures and emphasize that sharing such biological resources and combining them with available genetic tools will allow consistent and comparable studies that greatly contribute to our understanding of ecological and evolutionary processes.

Toward a "Green Revolution" for Soybean

Soybean (Glycine max), as an economically important food and oilseedcrop, is a major source of plant proteins and oils. Although considerable progress has been made in increasing the yields of rice, wheat, and maize through the "Green Revolution", little improvements have been made for soybean. With the increasing demand of soybean production and the rapid development of crop breeding technologies, time has come for this important crop to undergo a Green Revolution. Here, we briefly summarize the history of crop breeding and Green Revolution in other crops. We then discuss the possible directions and potential approaches toward achieving a Green Revolution for soybean. We provide our views and perspectives on how to breed new soybean varieties with improved yield.

Estimating narrow-sense heritability using family data from admixed populations

Estimating total narrow-sense heritability in admixed populations remains an open question. In this work, we used extensive simulations to evaluate existing linear mixed-model frameworks for estimating total narrow-sense heritability in two population-based cohorts from Greenland, and compared the results with data from unadmixed individuals from Denmark. When our analysis focused on Greenlandic sib pairs, and under the assumption that shared environment among siblings has a negligible effect, the model with two relationship matrices, one capturing identity by descent and one capturing identity by state, returned heritability estimates close to the true simulated value, while using each of the two matrices alone led to downward biases. When phenotypes correlated with ancestry, heritability estimates were inflated. Based on these observations, we propose a PCA-based adjustment that recovers the true simulated heritability. We use this knowledge to estimate the heritability of ten quantitative traits from the two Greenlandic cohorts, and report differences such as lower heritability for height in Greenlanders compared with Europeans. In conclusion, narrow-sense heritability in admixed populations is best estimated when using a mixture of genetic relationship matrices on individuals with at least one first-degree relative included in the sample.

Gene-mating dynamic evolution theory: fundamental assumptions, exactly solvable models and analytic solutions

Fundamental properties of macroscopic gene-mating dynamic evolutionary systems are investigated. A model is studied to describe a large class of systems within population genetics. We focus on a single locus, any number of alleles in a two-gender dioecious population. Our governing equations are time-dependent continuous differential equations labeled by a set of parameters, where each parameter stands for a population percentage carrying certain common genotypes. The full parameter space consists of all allowed parameters of these genotype frequencies. Our equations are uniquely derived from four fundamental assumptions within any population: (1) a closed system; (2) average-and-random mating process (mean-field behavior); (3) Mendelian inheritance; and (4) exponential growth and exponential death. Even though our equations are nonlinear with time-evolutionary dynamics, we have obtained an exact analytic time-dependent solution and an exactly solvable model. Our findings are summarized from phenomenological and mathematical viewpoints. From the phenomenological viewpoint, any initial parameter of genotype frequencies of a closed system will eventually approach a stable fixed point. Under time evolution, we show (1) the monotonic behavior of genotype frequencies, (2) any genotype or allele that appears in the population will never become extinct, (3) the Hardy-Weinberg law and (4) the global stability without chaos in the parameter space. To demonstrate the experimental evidence for our theory, as an example, we show a mapping from the data of blood type genotype frequencies of world ethnic groups to our stable fixed-point solutions. From the mathematical viewpoint, our highly symmetric governing equations result in continuous global stable equilibrium solutions: these solutions altogether consist of a continuous curved manifold as a subspace of the whole parameter space of genotype frequencies. This fixed-point manifold is a global stable attractor known as the Hardy-Weinberg manifold, attracting any initial point in any Euclidean fiber bounded within the genotype frequency space to the fixed point where this fiber is attached. The stable base manifold and its attached fibers form a fiber bundle, which fills in the whole genotype frequency space completely. We can define the genetic distance of two populations as their geodesic distance on the equilibrium manifold. In addition, the modification of our theory under the process of natural selection and mutation is addressed.

Quantitative Trait Loci (QTL) Mapping

Quantitative trait loci (QTL) are genetic regions that influence phenotypic variation of a complex trait, often through genetic interactions with each other and the environment. These are commonly identified through a statistical genetic analysis known as QTL mapping. Here, I present a step-by-step, practical approach to QTL mapping along with a sample data file. I focus on methods commonly used and discoveries that have been made in fishes, and utilize a multiple QTL mapping (MQM) approach in the free software package R/qtl.

bAIcis: A Novel Bayesian Network Structural Learning Algorithm and Its Comprehensive Performance Evaluation Against Open-Source Software

Structural learning of Bayesian networks (BNs) from observational data has gained increasing applied use and attention from various scientific and industrial areas. The mathematical theory of BNs and their optimization is well developed. Although there are several open-source BN learners in the public domain, none of them are able to handle both small and large feature space data and recover network structures with acceptable accuracy. bAIcis^® is a novel BN learning and simulation software from BERG. It was developed with the goal of learning BNs from “Big Data” in health care, often exceeding hundreds of thousands features when research is conducted in genomics or multi-omics. This article provides a comprehensive performance evaluation of bAIcis and its comparison with the open-source BN learners. The study investigated synthetic datasets of discrete, continuous, and mixed data in small and large feature space, respectively. The results demonstrated that bAIcis outperformed the publicly available algorithms in structure recovery precision in almost all of the evaluated settings, achieving the true positive rates of 0.9 and precision of 0.8. In addition, bAIcis supports all data types, including continuous, discrete, and mixed variables. It is effectively parallelized on a distributed system and can work with datasets of thousands of features that are infeasible for any of the publicly available tools with a desired level of recovery accuracy.

Accelerated inbreeding depression suggests synergistic epistasis for deleterious mutations in Drosophila melanogaster

Epistasis may have important consequences for a number of issues in quantitative genetics and evolutionary biology. In particular, synergistic epistasis for deleterious alleles is relevant to the mutation load paradox and the evolution of sex and recombination. Some studies have shown evidence of synergistic epistasis for spontaneous or induced deleterious mutations appearing in mutation-accumulation experiments. However, many newly arising mutations may not actually be segregating in natural populations because of the erasing action of natural selection. A demonstration of synergistic epistasis for naturally segregating alleles can be achieved by means of inbreeding depression studies, as deleterious recessive allelic effects are exposed in inbred lines. Nevertheless, evidence of epistasis from these studies is scarce and controversial. In this paper, we report the results of two independent inbreeding experiments carried out with two different populations of Drosophila melanogaster. The results show a consistent accelerated inbreeding depression for fitness, suggesting synergistic epistasis among deleterious alleles. We also performed computer simulations assuming different possible models of epistasis and mutational parameters for fitness, finding some of them to be compatible with the results observed. Our results suggest that synergistic epistasis for deleterious mutations not only occurs among newly arisen spontaneous or induced mutations, but also among segregating alleles in natural populations.