Additive Genetic Variance Components Research Articles

Combining ability and multiple parent‐based approach to derive simultaneous transgressive segregants for multiple traits in cotton (Gossypium hirsutum L.)

AbstractPlant breeders are always in pursuit of desirable genes and gene complexes, leading to identification of potential and promising individuals/segregants often exhibiting traits with higher intensity. Identifying segregants with desirable combination of multiple traits would enable a breeder to develop superior cultivars. In this study, combining ability and multiple parent‐based approach was employed to derive segregants simultaneously superior for multiple traits. Single and double crosses were developed by crossing 11 parental lines in half diallel fashion and were evaluated under seven environments. To create crosses, the parental lines with the highest positive gca effects were selected in addition to desirable yield and fibre quality traits (SCYP, NOBP, BW, GOT, UHML and FS). The positive and significantly higher gca effects in these parents is an evidence of prominence of additive and additive × additive component of genetic variance. To identify desirable segregants, six F2 populations derived from four single and their two double crosses were evaluated by raising an optimal population size of 910–935 and 3040–3055 plants for single and double crosses, respectively. In each population, proportion of desirable and simultaneous transgressive segregants was estimated. A total of 19 (2.87%), 22 (3.32%) and 24 (3.60%) plants were transgressive in nature simultaneously for multiple traits in single crosses, namely, RAHH‐1755 and RAHH‐455, and double cross, namely, RHDC‐1933, of Group I, respectively. While in Group II, 22 (3.32%), 22 (3.32%) and 24 (3.60%) plants showed simultaneous transgressive segregation in RAHH‐1702, SHH‐818 and RHDC‐1940, respectively. The per cent simultaneous transgressive segregants were found to be higher in the double crosses compared with their respective single crosses, possibly for the reason that double crosses involve more number of parents owing to increased recombination. Hence, breeding for desirable segregating recombinants using diallel and multiple parent‐based approach paves the way towards directional selection based on combining ability making it more efficient compared with conventional means of hybridization followed by selection.

The Garden of Forking Paths: Reinterpreting Haseman-Elston Regression for a Genotype-by-Environment Model.

Haseman-Elston regression (HE-reg) has been known as a classic tool for detecting an additive genetic variance component. However, in this study we find that HE-reg can capture GxE under certain conditions, so we derive and reinterpret the analytical solution of HE-reg. In the presence of GxE, it leads to a natural discrepancy between linkage and association results, the latter of which is not able to capture GxE if the environment is unknown. Considering linkage and association as symmetric designs, we investigate how the symmetry can and cannot hold in the absence and presence of GxE, and consequently we propose a pair of statistical tests, Symmetry Test I and Symmetry Test II, both of which can be tested using summary statistics. Test statistics, and their statistical power issues are also investigated for Symmetry Tests I and II. Increasing the number of sib pairs is important to improve statistical power for detecting GxE.

The effects of host quantitative genetic architecture on the gut microbiota composition of Chinook salmon (Oncorhynchus tshawytscha).

The microbiota consists of microbes living in or on an organism and has been implicated in host health and function. Environmental and host-related factors were shown to shape host microbiota composition and diversity in many fish species, but the role of host quantitative architecture across populations and among families within a population is not fully characterized. Here, Chinook salmon were used to determine if inter-population differences and additive genetic variation within populations influenced the gut microbiota diversity and composition. Specifically, hybrid stocks of Chinook salmon were created by crossing males from eight populations with eggs from an inbred line created from self-fertilized hermaphrodite salmon. Based on high-throughput sequencing of the 16S rRNA gene, significant gut microbial community diversity and composition differences were found among the hybrid stocks. Furthermore, additive genetic variance components varied among hybrid stocks, indicative of population-specific heritability patterns, suggesting the potential to select for specific gut microbiota composition for aquaculture purposes. Determining the role of host genetics in shaping their gut microbiota has important implications for predicting population responses to environmental changes and will thus impact conservation efforts for declining populations of Chinook salmon.

Longitudinal genetic analysis for Pacific abalone growth-related traits in sea-based culture system

Pedigreed family-based selective breeding is an appealing approach in genetic improvement of aquaculture species as the benefits can be cumulative and permanent. To determine the longitudinal growth characteristics of Pacific abalone (Haliotis discus hannai), shell colour rings were manipulated by feeding alternative algae, and shell length (SL) identification was performed at different ages. Eight SL records were obtained from 3147 individuals in 192 full-sib families at 6, 7, 9, 13, 18, 24, 28, and 32 months after hatching. To estimate the additive genetic and environmental variance components, longitudinal genetic analysis of repeated SL measurements was conducted using multivariate, auto-regressive, antedependence and random regression models with restricted maximum likelihood method. The results showed that the multivariate model with common full-sib family variance and residual heterogeneity of variance was the best model for the given dataset. Heritability estimates showed a decreasing trend with age, which is similar to the results obtained from the quadratic polynomial random regression model, and ranged from 0.629 at 9 months to 0.259 at 32 months. The separate rearing and tagging of single abalone at 6 months may have led to significant common environment family bias and overestimation of heritability. The antedependence model did not achieve a better fit for the obtained dataset. Estimates of genetic correlation between contiguous ages were high, ranging from 0.622 between 6 and 32 months to 0.987 between 28 and 32 months. The results demonstrated that genetic gain can be obtained in selection for Pacific abalone growth and outlined information regarding the age at which selection should be performed. This study can provide data and guidelines to a Pacific abalone selection program for growth related-traits.

Heritability Estimation of Multiple Sclerosis Related Plasma Protein Levels in Sardinian Families with Immunochip Genotyping Data.

This work aimed at estimating narrow-sense heritability, defined as the proportion of the phenotypic variance explained by the sum of additive genetic effects, via Haseman–Elston regression for a subset of 56 plasma protein levels related to Multiple Sclerosis (MS). These were measured in 212 related individuals (with 69 MS cases and 143 healthy controls) obtained from 20 Sardinian families with MS history. Using pedigree information, we found seven statistically significant heritable plasma protein levels (after multiple testing correction), i.e., Gc ( = 0.77; 95%CI: 0.36, 1.00), Plat ( = 0.70; 95%CI: 0.27, 0.95), Anxa1 ( = 0.68; 95%CI: 0.27, 1.00), Sod1 ( = 0.58; 95%CI: 0.18, 0.96), Irf8 ( = 0.56; 95%CI: 0.19, 0.99), Ptger4 ( = 0.45; 95%CI: 0.10, 0.96), and Fadd ( = 0.41; 95%CI: 0.06, 0.84). A subsequent analysis was performed on these statistically significant heritable plasma protein levels employing Immunochip genotyping data obtained in 155 healthy controls (92 related and 63 unrelated); we found a meaningful proportion of heritable plasma protein levels’ variability explained by a small set of SNPs. Overall, the results obtained, for these seven MS-related proteins, emphasized a high additive genetic variance component explaining plasma levels’ variability.

On the Genetic and Environmental Relationship Between Suicide Attempt and Death by Suicide.

The authors examined the extent to which the genetic and environmental etiology of suicide attempt and suicide death is shared or unique. The authors used Swedish national registry data for a large cohort of twins, full siblings, and half siblings (N=1,314,990) born between 1960 and 1990 and followed through 2015. They conducted twin-family modeling of suicide attempt and suicide death to estimate heritability for each outcome, along with genetic and environmental correlations between them. They further assessed the relationship between suicide attempt by young people compared with adults. In bivariate models, suicide attempt and death were moderately heritable among both women (attempt: additive genetic variance component [A]=0.52, 95% CI=0.44, 0.56; death: A=0.45, 95% CI=0.39, 0.59) and men (attempt: A=0.41, 95% CI=0.38, 0.49; death: A=0.44, 95% CI=0.43, 0.44). The outcomes were substantially, but incompletely, genetically correlated (women: rA=0.67, 95% CI=0.55, 0.67; men: rA=0.74, 95% CI=0.63, 0.87). Environmental correlations were weaker (women: rE=0.36, 95% CI=0.29, 0.45; men: rE=0.21, 95% CI=0.19, 0.27). Heritability of suicide attempt was stronger among people ages 10-24 (A=0.55-0.62) than among those age 25 and older (A=0.36-0.38), and the genetic correlation between attempt during youth and during adulthood was stronger for women (rA=0.79, 95% CI=0.72, 0.79) than for men (rA=0.39, 95% CI=0.26, 0.47). The genetic and environmental etiologies of suicide attempt and death are partially overlapping, exhibit modest sex differences, and shift across the life course. These differences must be considered when developing prevention efforts and risk prediction algorithms. Where feasible, suicide attempt and death should be considered separately rather than collapsed, including in the context of gene identification efforts.

The Heritability of Type D Personality by an Extended Twin-Pedigree Analysis in the Netherlands Twin Register

Type D (Distressed) personality combines negative affectivity (NA) and social inhibition (SI) and is associated with an increased risk of cardiovascular disease. We aimed to (1) validate a new proxy based on the Achenbach System of Empirically Based Assessment (ASEBA) for Type D personality and its NA and SI subcomponents and (2) estimate the heritability of the Type D proxy in an extended twin-pedigree design in the Netherlands Twin Register (NTR). Proxies for the dichotomous Type D classification, and continuous NA, SI, and NAxSI (the continuous measure of Type D) scales were created based on 12 ASEBA items for 30,433 NTR participants (16,449 twins and 13,984 relatives from 11,106 pedigrees) and sources of variation were analyzed in the ‘Mendel’ software package. We estimated additive and non-additive genetic variance components, shared household and unique environmental variance components and ran bivariate models to estimate the genetic and non-genetic covariance between NA and SI. The Type D proxy showed good reliability and construct validity. The best fitting genetic model included additive and non-additive genetic effects with broad-sense heritabilities for NA, SI and NAxSI estimated at 49%, 50% and 49%, respectively. Household effects showed small contributions (4–9%) to the total phenotypic variation. The genetic correlation between NA and SI was .66 (reflecting both additive and non-additive genetic components). Thus, Type D personality and its NA and SI subcomponents are heritable, with a shared genetic basis for the two subcomponents.

Genetic parameters for growth and survival traits in a base population of Pacific white shrimp (Litopenaeus vannamei) developed from domesticated strains in China

A base population containing broad genetic variation provides a fundamental resource for a successful genetic improvement program. Here, we assess quantitative data on growth traits taken from the base population of Pacific white shrimp. Body weight was scored at two ages (BW1 and BW2) from 2752 and 2452 individuals, representing 89 full-sib families. Estimated heritabilities (h2 ± SE) for BW1, BW2 and survival (S) were 0.52 ± 0.09, 0.44 ± 0.07, and 0.01 ± 0.02, respectively. The genetic correlation between growth traits (BW1 and BW2) was 0.95, a result significantly different from zero. Genetic correlations between survival and body weight were low however, 0.26 (S vs BW1) and 0.18 (S vs BW2), respectively. The SA_1 strain from South Asia showed the best performance for both growth rate and survival in the test tank systems. High heritability estimates for growth traits confirm that a substantial component of additive genetic variance is available for growth in our L. vannamei culture line families in China prior to imposing genetic selection to improve relative productivity.

ADDO: a comprehensive toolkit to detect, classify and visualize additive and non-additive quantitative trait loci.

During the past decade, genome-wide association studies (GWAS) have been used to map quantitative trait loci (QTLs) underlying complex traits. However, most GWAS focus on additive genetic effects while ignoring non-additive effects, on the assumption that most QTL act additively. Consequently, QTLs driven by dominance and other non-additive effects could be overlooked. We developed ADDO, a highly efficient tool to detect, classify and visualize QTLs with additive and non-additive effects. ADDO implements a mixed-model transformation to control for population structure and unequal relatedness that accounts for both additive and dominant genetic covariance among individuals, and decomposes single-nucleotide polymorphism effects as either additive, partial dominant, dominant or over-dominant. A matrix multiplication approach is used to accelerate the computation: a genome scan on 13 million markers from 900 individuals takes about 5 h with 10 CPUs. Analysis of simulated data confirms ADDO's performance on traits with different additive and dominance genetic variance components. We showed two real examples in outbred rat where ADDO identified significant dominant QTL that were not detectable by an additive model. ADDO provides a systematic pipeline to characterize additive and non-additive QTL in whole genome sequence data, which complements current mainstream GWAS software for additive genetic effects. ADDO is customizable and convenient to install and provides extensive analytics and visualizations. The package is freely available online at https://github.com/LeileiCui/ADDO. Supplementary data are available at Bioinformatics online.

Estimation of additive and non-additive genetic variance component for growth traits in Adani goats.

Non-additive genetic effects are important to increase the accuracy of estimating genetic parameters for growth traits. The aim of this study was to estimate genetic parameters and variance components, specially dominance and epistasis genetic effects, for growth traits (birth weight (BW), weaning weight (WW), 3 (W3), 6 (W6), 9 (W9), and 12 (W12) month weight) in Adani goats. Analyses were carried out using Bayesian method via Gibbs sampler animal model by fitting of 18 different models. All fixed effects (sex, type of birth, age of dam, and year) showed significant effects on BW, WW, W3, and W6, whereas the type of birth and age of dam were not significant on W9 and W12. With the best model, direct heritability estimates were 0.347, 0.178, 0.158, 0.359, 0.278, and 0.281 for BW, WW, W3, W6, W9, and W12 traits, respectively. Maternal permanent environmental effect was significant for BW and WW, but maternal genetic effect was significant only for W3. Dominance and epitasis effects were significant almost for all traits and as a proportion of phenotypic variance were ranged from 0.115 to 0.258 and 0.107 to 0.218, respectively. The range of accuracy of breeding values estimated for growth traits with appropriate evaluation models was from 0.521 to 0.652, 0.616 to 0.694, and 0.548 to 0.684 for the all animals, 10% of the best males and 50% of the best females, respectively. When dominance and epistasis effects added to models, the error variance was reduced and the accuracy of estimated breeding values increased. The accuracy of the best model showed a significant difference with the accuracy of other models (p < 0.01). The result of the present study suggests that non-additive genetic effects should be in genetic evaluation models for goat growth traits because of its effect on accuracy of estimated breeding values.

Accuracy of Genomic-Polygenic and Polygenic Breeding Values for Age at First Calving and Milk Yield in Thai Multibreed Dairy Cattle

Abstract Single-nucleotide polymorphisms (SNPs) have been used in genomic prediction and shown to increase prediction accuracy and selection responses for economic traits in dairy cattle. The successful report in genomic prediction for improving age at first calving (AFC) and 305-d milk yield (MY) in multibreed dairy population is limited. Therefore, the objective of this research was to compare estimates of variance components, genetic parameters, and prediction accuracies for AFC and MY using a genomic-polygenic model (GPM) and a polygenic model (PM). The AFC and MY records of 9,106 first-lactating multibreed dairy cows, calved between 1991 and 2014, were collected from 1,012 Thai dairy farms. The SNP genotyped individuals were selected from cows that had completed pedigree and phenotypes information. The total genomic DNA samples of 2,661 dairy cattle were genotyped using various GeneSeek Genomic Profiler low-density bead chips (9K, 20K, and 80K). The 2-trait GPM and PM contained herd-year-season and heterosis as fixed effects, and animal additive genetic and residual as random effects. Variance components and genetic parameters were estimated using the procedure of average information-restricted maximum likelihood (AI-REML). Estimates of additive genetic variance components and heritabilities from GPM were higher than PM for AFC and MY. Correlations between AFC and MY were near zero for both models. Mean EBV accuracies were higher for GPM (32.95% for AFC and 38.24% for MY) than for PM (32.65% for AFC, and 32.99% for MY). Mean sire EBV accuracies were higher for GPM (31.35% for AFC and 36.25% for MY) than for PM (28.37% for AFC and 28.80% for MY). Thus, the GPM should be considered the model of choice to increase accuracy of genetic predictions for AFC and MY in the Thai multibreed dairy population.

Genome-wide study to detect single nucleotide polymorphisms associated with visceral and subcutaneous fat deposition in Holstein dairy cows

Excessive abdominal fat might be associated with more severe metabolic disorders in Holstein cows. Our hypothesis was that there are genetic differences between cows with low and high abdominal fat deposition and a normal cover of subcutaneous adipose tissue. The objective of this study was to assess the genetic basis for variation in visceral adiposity in US Holstein cows. The study included adult Holstein cows sampled from a slaughterhouse (Green Bay, WI, USA) during September 2016. Only animals with a body condition score between 2.75 and 3.25 were considered. The extent of omental fat at the level of the insertion of the lesser omentum over the pylorus area was assessed. A group of 100 Holstein cows with an omental fold <5 mm in thickness and minimum fat deposition throughout the entire omentum, and the second group of 100 cows with an omental fold ⩾20 mm in thickness and with a marked fat deposition observed throughout the entire omentum were sampled. A small piece of muscle from the neck was collected from each cow into a sterile container for DNA extraction. Samples were submitted to a commercial laboratory for interrogation of genome-wide genomic variation using the Illumina BovineHD Beadchip. Genome-Wide association analysis was performed to test potential associations between fat deposition and genomic variation. A univariate mixed linear model analysis was performed using genome-wide efficient mixed model association to identify single nucleotide polymorphisms (SNPs) significantly associated with variation in a visceral fat deposition. The chip heritability was 0.686 and the estimated additive genetic and residual variance components were 0.427 and 0.074, respectively. In total, 11 SNPs defining four quantitative trait locus (QTL) regions were found to be significantly associated with visceral fat deposition (P<0.00001). Among them, two of the QTL were detected with four and five significantly associated SNPs, respectively; whereas, the QTLs detected on BTA12 and BTA19 were each detected with only one significantly associated SNP. No enriched gene ontology terms were found within the gene networks harboring these genes when supplied to DAVID using either theBos taurus or human gene ontology databases. We conclude that excessive omental fat in Holstein cows with similar body condition scores is not caused by a single Mendelian locus and that the trait appears to be at least moderately heritable; consequently, selection to reduce excessive omental fat is potentially possible, but would require the generation of predicted transmitting abilities from larger and random samples of Holstein cattle.

Estimation of additive and dominance genetic variance components for female fertility traits in Iranian Holstein cows

AbstractThe aim of the current study was to estimate additive and dominance genetic variance components for days from calving to first service (DFS), a number of services to conception (NSC) and days open (DO). Data consisted of 25 518 fertility records from first parity dairy cows collected from 15 large Holstein herds of Iran. To estimate the variance components, two models, one including only additive genetic effects and another fitting both additive and dominance genetic effects together, were used. The additive and dominance relationship matrices were constructed using pedigree data. The estimated heritability for DFS, NSC and DO were 0.068, 0.035 and 0.067, respectively. The differences between estimated heritability using the additive genetic and additive-dominance genetic models were negligible regardless of the trait under study. The estimated dominance variance was larger than the estimated additive genetic variance. The ratio of dominance variance to phenotypic variance was 0.260, 0.231 and 0.196 for DFS, NSC and DO, respectively. Akaike's information criteria indicated that the model fitting both additive and dominance genetic effects is the best model for analysing DFS, NSC and DO. Spearman's rank correlations between the predicted breeding values (BV) from additive and additive-dominance models were high (0.99). Therefore, ranking of the animals based on predicted BVs was the same in both models. The results of the current study confirmed the importance of taking dominance variance into account in the genetic evaluation of dairy cows.

Age-moderation of genetic and environmental contributions to cognitive functioning in mid- and late-life for specific cognitive abilities

Age moderation of genetic and environmental contributions to Digits Forward, Digits Backward, Block Design, Symbol Digit, Vocabulary, and Synonyms was investigated in a sample of 14,534 twins aged 26 to 98years. The Interplay of Genes and Environment across Multiple Studies (IGEMS) consortium contributed the sample, which represents nine studies from three countries (USA, Denmark, and Sweden). Average test performance was lower in successively older age groups for all tests. Significant age moderation of additive genetic, shared environmental, and non-shared environmental variance components was observed, but the pattern varied by test. The genetic contribution to phenotypic variance across age was smaller for both Digit Span tests, greater for Synonyms, and stable for Block Design and Symbol Digit. The non-shared environmental contribution was greater with age for the Digit Span tests and Block Design, while the shared environmental component was small for all tests, often more so with age. Vocabulary showed similar age-moderation patterns as Synonyms, but these effects were nonsignificant. Findings are discussed in the context of theories of cognitive aging.

SOCIOECONOMIC ADVERSITY AND ALLOSTATIC LOAD: A TWIN ANALYSIS

Childhood socioeconomic status (SES) is related to health concurrently and into adulthood, and is associated with adulthood SES. Given these far-reaching relationships, some question whether adulthood SES associates with health beyond childhood SES. Biometric twin (ACE) models partition the predictor variables into additive genetic (A), shared environmental (C), and nonshared (unique) environmental (E) variance components. Researchers can control for latent genetic and shared environmental factors that may confound conclusions about the causal effects of adulthood SES on health. We used family data from the second wave of the national Midlife in the United States (MIDUS) Study (128 MZ pairs, 99 DZ pairs, 119 Sibling pairs) to examine this question. Health was assessed with a measure of multi-system physiological dysregulation posited to confer risk for the development of physical health problems. Adult SES was calculated with education, family-adjusted poverty to income ratio, current financial situation, enough money to meet needs, and difficulty paying bills. Results of ACE models suggested that male twins with lower adult SES had higher allostatic load, indicating greater physiological risk, than their co-twins with higher SES (b = 0.19), adjusting for genetic and shared environmental correlations between allostatic load and SES, and observed childhood SES, health status, and perceived support from family and friends. This relationship was not observed among female twins. These results add to a growing literature indicating that lower adult SES is associated with poorer multi-system physiological health, even after accounting for shared genetic and early environmental influences.

Personality traits below facets: The consensual validity, longitudinal stability, heritability, and utility of personality nuances.

It has been argued that facets do not represent the bottom of the personality hierarchy-even more specific personality characteristics, nuances, could be useful for describing and understanding individuals and their differences. Combining 2 samples of German twins, we assessed the consensual validity (correlations across different observers), rank-order stability, and heritability of nuances. Personality nuances were operationalized as the 240 items of the Revised NEO Personality Inventory (NEO-PI-R). Their attributes were examined by analyzing item residuals, controlling for the variance of the facet the item had been assigned to and all other facets. Most nuances demonstrated significant (p < .0002) cross-method agreement and rank-order stability. A substantial proportion of them (48% in self-reports, 20% in informant ratings, and 50% in combined ratings) demonstrated a significant (p < .0002) component of additive genetic variance, whereas evidence for environmental influences shared by twins was modest. Applying a procedure to estimate stability and heritability of true scores of item residuals yielded estimates comparable with those of higher-order personality traits, with median estimates of rank-order stability and heritability being .77 and .52, respectively. Few nuances demonstrated robust associations with age and gender, but many showed incremental, conceptually meaningful, and replicable (across methods and/or samples) predictive validity for a range of interest domains and body mass index. We argue that these narrow personality characteristics constitute a valid level of the personality hierarchy. They may be especially useful for providing a deep and contextualized description of the individual, but also for the prediction of specific outcomes. (PsycINFO Database Record

Genetic architecture of biparental progenies for yi eld in Eggplant (Solanum melongena L.)

The type of gene action for yield and its components was determined using biparental progenies devel-oped from the F2 generation of an intervarietal cross Swarna Pratibha × Hisar Shyamal (SP × H-8) of eggplant (Solanum melongena L.) using North Carolina Design - 1. The experiment was conducted during the Kharif (April- November) 2012 and 2013. The biparental and F3 progenies differed. Biparental progenies were superior in mean performance than were F3’s generated by selfing. Dominance variances were greater than additive variance for most characters. For fruit diameter, plant height, branches per plant and total soluble solid, the additive component of genetic variance was of higher magnitude. The average degree of dominance was in over-dominance range for most traits. Plant height, branches per plant, fruit diameter and total soluble solids was in the partial dominance range. Heritability estimates were generally low to medium. Fruit weight exhibited moderate to high heritability. The pre-ponderance of additive and non-additive genetic components of variance for most traits indicated role for addi-tive and non-additive gene action for inheritance of marketable fruit yield and its component traits. These could be utilized through reciprocal recurrent selection and heterosis breeding for the development of high yielding and quality cultivars in eggplant.

Fullfact: an R package for the analysis of genetic and maternal variance components from full factorial mating designs

Full factorial breeding designs are useful for quantifying the amount of additive genetic, nonadditive genetic, and maternal variance that explain phenotypic traits. Such variance estimates are important for examining evolutionary potential. Traditionally, full factorial mating designs have been analyzed using a two‐way analysis of variance, which may produce negative variance values and is not suited for unbalanced designs. Mixed‐effects models do not produce negative variance values and are suited for unbalanced designs. However, extracting the variance components, calculating significance values, and estimating confidence intervals and/or power values for the components are not straightforward using traditional analytic methods. We introduce fullfact – an R package that addresses these issues and facilitates the analysis of full factorial mating designs with mixed‐effects models. Here, we summarize the functions of the fullfact package. The observed data functions extract the variance explained by random and fixed effects and provide their significance. We then calculate the additive genetic, nonadditive genetic, and maternal variance components explaining the phenotype. In particular, we integrate nonnormal error structures for estimating these components for nonnormal data types. The resampled data functions are used to produce bootstrap‐t confidence intervals, which can then be plotted using a simple function. We explore the fullfact package through a worked example. This package will facilitate the analyses of full factorial mating designs in R, especially for the analysis of binary, proportion, and/or count data types and for the ability to incorporate additional random and fixed effects and power analyses.

Estimating Modifying Effect of Age on Genetic and Environmental Variance Components in Twin Models.

Twin studies have been adopted for decades to disentangle the relative genetic and environmental contributions for a wide range of traits. However, heritability estimation based on the classical twin models does not take into account dynamic behavior of the variance components over age. Varying variance of the genetic component over age can imply the existence of gene-environment (G×E) interactions that general genome-wide association studies (GWAS) fail to capture, which may lead to the inconsistency of heritability estimates between twin design and GWAS. Existing parametricG×Einteraction models for twin studies are limited by assuming a linear or quadratic form of the variance curves with respect to a moderator that can, however, be overly restricted in reality. Here we propose spline-based approaches to explore the variance curves of the genetic and environmental components. We choose the additive genetic, common, and unique environmental variance components (ACE) model as the starting point. We treat the component variances as variance functions with respect to age modeled by B-splines or P-splines. We develop an empirical Bayes method to estimate the variance curves together with their confidence bands and provide an R package for public use. Our simulations demonstrate that the proposed methods accurately capture dynamic behavior of the component variances in terms of mean square errors with a data set of >10,000 twin pairs. Using the proposed methods as an alternative and major extension to the classical twin models, our analyses with a large-scale Finnish twin data set (19,510 MZ twins and 27,312 DZ same-sex twins) discover that the variances of the A, C, and E components for body mass index (BMI) change substantially across life span in different patterns and the heritability of BMI drops to ∼50% after middle age. The results further indicate that the decline of heritability is due to increasing unique environmental variance, which provides more insights into age-specific heritability of BMI and evidence ofG×Einteractions. These findings highlight the fundamental importance and implication of the proposed models in facilitating twin studies to investigate the heritability specific to age and other modifying factors.

Genetic variation within and between subpopulations of the Australian Merino breed

Genetic variation within and between Australian Merino subpopulations was estimated from a large breeding nucleus in which up to 8500 progeny from over 300 sires were recorded at eight sites across Australia. Subpopulations were defined as genetic groups using the Westell–Quaas model in which base animals with unknown pedigree were allocated to groups based on their flock of origin if there were sufficient ‘expressions’ for the flock, or to one of four broad sheep-type groups otherwise (Ultra/Superfine, Fine/Fine-medium, Medium/Strong, or unknown). Linear models including genetic groups and additive genetic breeding values as random effects were used to estimate variance components for 12 traits: yearling greasy and clean fleece weight (ygfw and ycfw), yearling mean and coefficient of variation of fibre diameter (yfd and ydcv), yearling staple length and staple strength (ysl and yss), yearling fibre curvature (ycuv), yearling body wrinkle (ybdwr), post-weaning weight (pwt), muscle (pemd) and fat depth (pfat), and post-weaning worm egg count (pwec). For the majority of traits, the genetic group variance ranged from approximately equal to two times larger than the additive genetic (within group) variance. The exceptions were pfat and ydcv where the genetic group to additive variance ratios were 0.58 and 0.22, respectively, and pwec and yss where there was no variation between genetic groups. Genetic group correlations between traits were generally the same sign as corresponding additive genetic correlations, but were stronger in magnitude (either more positive or more negative). These large differences between genetic groups have long been exploited by Merino ram breeders, to the extent that the animals in the present study represent a significantly admixed population of the founding groups. The relativities observed between genetic group and additive genetic variance components in this study can be used to refine the models used to estimate breeding values for the Australian Merino industry.