Model For Genetic Effects Research Articles

Estimation of genetic parameters for growth traits and kleiber ratio in dorper sheep breed

The study aimed to estimate genetic and phenotypic parameters for growth traits and Kleiber ratio in Dorper sheep breed. Data used in this study were collected over 12 years (2012–2023) at Debre Birhan Agricultural Research Center sheep research station in Ethiopia. Studied traits were body weights at birth (WT0), weaning (WT3), six month (WT6), and yearling (WT12) age; average daily gains from birth to weaning (ADG0-3), from weaning to six months (ADG3-6), from six months to yearling (ADG6-12); and Kleiber ratios from birth to weaning (KR1) and from weaning to six months (KR2). The (co)variance components were estimated with different animal models using Average Information Restricted Maximum Likelihood (AI-REML) procedure. The best-fitted model for each trait was determined using likelihood ratio tests. Phenotypic performance for WT3, WT6, WT12, ADG0-3 and ADG3-6 showed a decline trend at a rate of 0.216 kg, 0.794 kg, 0.671 kg, 2.601 g and 4.865 g over years respectively. However, WT3, WT6, WT12, ADG0-3 showed a positive genetic improvement trend at a rate of 0.029 kg, 0.043 kg, 0.049 kg and 0.257 g over years respectively. Year of birth had a significant effect (P < 0.001) on all studied traits. Model including direct genetic as well as permanent environmental effect (Model 2) was chosen as the most appropriate model for WT0. Model which included only direct genetic effect (Model 1) was the best-fit model for all other studied traits. Direct heritability estimates based on suitable models were 0.07 ± 0.06, 0.11 ± 0.06, 0.09 ± 0.07, 0.11 ± 0.09 0.11 ± 0.06, 0.00 ± 0.04, 0.15 ± 0.07 and 0.00 ± 0.04 for WT0, WT3, WT6, WT12, ADG0-3, ADG3-6, KR1 and KR2 respectively. The variance ratio for the permanent environmental effect was 0.13 ± 0.04 for WT0. Genetic correlations among the traits ranged from negative (−0.39) for WT0-KR1 to high (0.99) for WT3-ADG0-3 and phenotypic correlations ranged from negative (−0.31) for WT0-KR1 to high (0.98) for WT3-ADG0-3. The low direct heritability estimates for the studied traits indicated that genetic improvement by direct selection might be difficult. Further investigation for the unexpected declined trend of phenotypic performance over years need to be required.

Delineating maternal influence in regulation of variance in major economic traits of White Leghorns: Bayesian insights.

Proper variance partitioning and estimation of genetic parameters at appropriate time interval is crucial for understanding the dynamics of trait variance and genetic correlations and for deciding the future breeding strategy of the population. This study was conducted on the same premise to estimate genetic parameters of major economic traits in a White Leghorn strain IWH using Bayesian approach and to identify the role of maternal effects in the regulation of trait variance. Three different models incorporating the direct additive effect (Model 1), direct additive and maternal genetic effect (Model 2) and direct additive, maternal genetic and maternal permanent environmental effects (Model 3) were tried to estimate the genetic parameters for body weight traits (birth weight, body weight at 16, 20, 40 and 52 weeks), Age at sexual maturity (ASM), egg production traits (egg production up to 24, 28, 40, 52, 64 and 72 weeks) and egg weight traits (egg weight at 28, 40 and 52 weeks). Model 2 and Model 3 with maternal effects were found to be the best having the highest accuracy for almost all the traits. The direct additive genetic heritability was moderate for ASM, moderate to high for body weight traits and egg weight traits and low to moderate for egg production traits. Though the maternal heritability (h2mat) and permanent environmental effect (c2mpe) was low (<0.1) for most of the traits, they formed an important component of trait variance. Traits like egg weight at 28 weeks (0.14±0.06) and egg production at 72 weeks (0.13±0.07) reported comparatively higher values for c2mpe and h2mat respectively. Additive genetic correlation was high and positive between body weight traits, between egg weight traits, between consecutive egg production traits and between body weight and egg weight traits. However, a negative genetic correlation existed between egg production and egg weight traits, egg production and body weight traits, ASM and early egg production traits. Overall, a moderate positive genetic correlation was estimated between ASM and body weight traits and ASM and egg weight traits. Based on our findings, we can deduce that maternal effects constitute an important source of variation for all the major economic traits in White Leghorn and should be necessarily considered in genetic evaluation programs.

431 Awardee Talk: Phenomics and Phenotyping in Animal Breeding

Abstract Genomic prediction is now a mature technology for animal breeding and genetics and many breeders’ associations and livestock, and poultry genetics companies have adopted high-through genotyping for routine breeding value prediction and for assisting in selection decisions. In this context, more research efforts in breeding and genetics are focusing more on genome annotation and phenotyping. In animal breeding, the term phenomics is almost exchangeable with high-throughput phenotyping and automated phenotyping, i.e., the recording of high dimensional phenotypic data on an organism-wide scale. High throughput phenotyping is achieved through using a variety of sensors. The sensors generate raw data that must be captured, processed, cleaned, and modeled to produce meaningful phenotypes. Animal breeders are used to working with large amounts of data. However, high throughput phenomics usually produces data that may quickly overwhelm storage and processing capabilities of even the most prepared data analysis teams. Moreover, the black-box nature of many algorithms used to process raw sensor data into actionable phenotypes usually require thorough validation and checking. Animal breeders may build on prior lessons learned processing and modeling high-throughput genotypes and traditional phenotypes and we also need to adopt new practices commonly used in the field of machine learning and computer science. Finally, high throughput phenomics will produce novel phenotypes that will require different modeling or revisiting traditional models. In this talk I will share my views on all these points, and I will illustrate them using literature data and data from my own research related to computer vision, modeling of social genetic effects and modeling of social interactions.

Association of the rs1990760, rs3747517, and rs10930046 polymorphisms in the IFIH1 gene with susceptibility to autoimmune diseases: a meta-analysis.

Interferon induced with helicase C domain 1 (IFIH1) single-nucleotide polymorphisms (SNP) rs1990760, rs3747517, and rs10930046 have been shown to be closely related to the risk of autoimmune diseases. The aim of this study was firstly to examine the association of the rs1990760 with type 1 diabetes (T1D) in a Chinese population. Secondly, to assess the association of SNP rs1990760, rs3747517, and rs10930046 with autoimmune diseases susceptibility. A total of 1,273 T1D patients and 1,010 healthy control subjects in a Chinese population were enrolled in this case-control study. Subsequently, we performed a meta-analysis on the association of the SNP rs1990760, rs3747517, and rs10930046 in the IFIH1 gene with susceptibility to autoimmune diseases. The random and fixed genetic effects models were used to evaluate the association and the effect sizes, including odds ratios (OR) and 95% confidence intervals (CI). Stratification analyses based on ethnicity and the type of autoimmune diseases were performed. IFIH1 SNP rs1990760 was not associated with a significant risk of T1D in the Chinese population in the case-control study. A total of 35 studies including 70,966 patients and 124,509 controls were identified and included in the meta-analysis. The results displayed significant associations between IFIH1 rs1990760 A allele and rs3747517 C allele and autoimmune diseases risk (OR=1.09, 95% CI: 1.01~1.17; OR=1.24, 95% CI: 1.15~1.25, respectively). Stratified analysis indicated a significant association rs1990760 and rs3747517 with autoimmune diseases risk in the Caucasian population (OR=1.11, 95% CI: 1.02~1.20, OR=1.29, 95% CI: 1.18~1.41, respectively). This study revealed no association between IFIH1 SNP rs1990760 and T1D in Chinese. Furthermore, the meta-analysis indicated that rs1990760 and rs3747517 polymorphisms, confer susceptibility to autoimmune diseases, especially in the Caucasian population.

Genetic Evaluation of Gir Cattle for Success Rate to Conception using GLIMMIX Model

Background: The objective of this study was to assess the genetic and phenotypic parameters associated with the success rate to conception using 90-days non-return rate (NRR90F), conception Rate (CR) and number of insemination to conception (INS) traits in service bull. Methods: A total of 2590 insemination including 1776 first time insemination was carried out during the period of 2018-2020 in the Gir cattle distributed in the semi-arid region of RajasthanNRR90F and CR were analysed as binary variable and INS as multi-nominal trait. Generalized linear models were used to for the evaluation of non-genetic factors and generalized linear mixed models for genetic effects. Result: The average success rate of artificial insemination was estimated as 0.35 for NRR90F and 0.41 for CR. Further, cumulative logit link function was used to analyse the number of successful insemination to conception (INS) and cumulative estimates was observed as 0.95 for two number of insemination to conception. Period and season were the non-genetic factors that influenced the traits studied. Decreasing trend was observed over the period for success of insemination for all studied traits. Summer season showed highest odd ratio and estimated as 1.213 for NRR90F, 1.388 for CR and 1.440 for INS with reference to autumn season. Heritability was estimated as 0.015 for NRR90F, 0.028 for CR and 0.086 for INS.

Estimation of genetic parameters for important traits using a multi-trait model in late-feathering Qingyuan partridge hens in China.

Qingyuan partridge chicken is one of the most well-known Chinese indigenous yellow broilers. In breeding programmes, five traits are usually selected when the chickens are 105 days old, namely body weight (BW), comb height (CH), shank length (SL), shank girth (SG) and feather maturity (FM). The objective of this study was to estimate the genetic parameters of these five traits, especially direct additive genetic correlations, to lay the foundation for balanced selection of Qingyuan partridge chickens. Approximately 9600 records were used for estimation. Variance components for these five traits were estimated using three multi-trait models incorporating different effects via Gibbs sampling. Based on model 1 in which the random effects included direct additive genetic effects and residuals, the estimated direct heritabilities for BW, CH, SL, SG and FM were 0.29 ± 0.04, 0.53 ± 0.04, 0.47 ± 0.04, 0.43 ± 0.05 and 0.18 ± 0.03, respectively. The direct genetic correlations ranged from -0.08 to 0.46. When additionally considering maternal additive genetic effects (model 2), the estimates of direct heritabilities and absolute values of direct additive genetic correlations were smaller. The heritabilities were 0.14 ± 0.04, 0.40 ± 0.02, 0.34 ± 0.05, 0.27 ± 0.05 and 0.12 ± 0.03 for BW, CH, SL, SG and FM, respectively. The direct additive genetic correlations ranged from -0.33 to 0.36. More specifically, the direct additive genetic correlations between BW and CH, SL, SG and FM were 0.19 ± 0.13, 0.15 ± 0.15, 0.36 ± 0.15 and - 0.33 ± 0.21, respectively. The genetic correlations of FM with SL, SG and CH were - 0.15 ± 0.15, -0.08 ± 0.17 and 0.18 ± 0.15, respectively. The direct genetic correlations between CH and SG and SL were - 0.02 ± 0.11 and - 0.20 ± 0.11, respectively, and that between SL and SG was 0.19 ± 0.11. The total heritabilities and maternal additive genetic correlations ranged from 0.16 to 0.44 and from -0.13 to 0.61, respectively. The third model also included the maternal permanent environmental effect for BW. The estimates of direct heritability, direct additive genetic correlation, total heritability and maternal additive genetic correlation were only slightly different from those based on the second model. Therefore, the maternal additive genetic effect has a large effect on the estimation of genetic parameters, and it is better to consider this effect in the genetic evaluation of these five traits. Relatively high direct and maternal additive genetic correlations for most trait pairs suggested that it is better to jointly evaluate these five traits in breeding programmes.

Age-dependent effects of schizophrenia genetic risk on cortical thickness and cortical surface area: Evaluating evidence for neurodevelopmental and neurodegenerative models of schizophrenia.

Risk for schizophrenia peaks during early adulthood, a critical period for brain development. Although several influential theoretical models have been proposed for the developmental relationship between brain pathology and clinical onset, to our knowledge, no study has directly evaluated the predictions of these models for schizophrenia developmental genetic effects on brain structure. To address this question, we introduce a framework to estimate the effects of schizophrenia genetic variation on brain structure phenotypes across the life span. Five-hundred and six participants, including 30 schizophrenia probands, 200 of their relatives (aged 12-85 years) from 32 families with at least two first-degree schizophrenia relatives, and 276 unrelated controls, underwent MRI to assess regional cortical thickness (CT) and cortical surface area (CSA). Genetic variance decomposition analyses were conducted to distinguish among schizophrenia neurogenetic effects that are most salient before schizophrenia peak age-of-risk (i.e., early neurodevelopmental effects), after peak age-of-risk (late neurodevelopmental effects), and during the later plateau of age-of-risk (neurodegenerative effects). Genetic correlations between schizophrenia and cortical traits suggested early neurodevelopmental effects for frontal and insula CSA, late neurodevelopmental effects for overall CSA and frontal, parietal, and occipital CSA, and possible neurodegenerative effects for temporal CT and parietal CSA. Importantly, these developmental neurogenetic effects were specific to schizophrenia and not found with nonpsychotic depression. Our findings highlight the potentially dynamic nature of schizophrenia genetic effects across the lifespan and emphasize the utility of integrating neuroimaging methods with developmental behavior genetic approaches to elucidate the nature and timing of risk-conferring processes in psychopathology. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Genetic variation and parameters for growth and feed efficiency traits in Boran beef cattle in Kenya

A major drawback of improving feed efficiency in beef cattle enterprises is to identify measures of feed efficiency which do not require feed intake information on an individual animal basis. Kleiber Index, (KI) and relative growth rate (RGR) are alternative measures which do not need individual measurement of intake and can be used select for improved feed and growth efficiency in relation to their body size. The aim of this study was to estimate genetic parameters for measures of feed efficiency and growth traits for the Boran beef cattle in Kenya. A total of 1348, 2209, 2183 and 2184 weight records were available for birth, weaning, yearling and 550-day weight records, respectively, were available for animals born between 1973 and 2019. The traits derived were growth rates (ADG), adjusted weights (WA), KI and RGR at weaning (205 days), yearling (365 days) and 550 days. Genetic parameters were estimated using a maternal genetic effects model. Direct heritability estimates for direct heritability estimates for ADG ranged from 0.12±0.04 for ADG205-365 to 0.27±0.02 for ADGB-205. Estimates for WA were in range of 0.05±0.05 at to 0.10±0.05 at 550 days. Estimates for KI were 0.13±0.05 at 205 to 0.29±0.05 at 550 days. For RGR estimate direct heritability at 205 was 0.21±0.05, 0.19±0.04 at 365 and 0.33±0.05 at 550 days. The direct-maternal genetic correlations for WA, ADG, KI and RGR at 205 days were -0.30±0.05, -0.30±0.04, 0.90±0.05 and -0.72±0.04, respectively. Alongside growth traits, Kleiber Index and residual growth rate had substantial heritability estimates, implying potential for improvement through selection.

A Synthesis of Game Theory and Quantitative Genetic Models of Social Evolution.

Two popular approaches for modeling social evolution, evolutionary game theory and quantitative genetics, ask complementary questions but are rarely integrated. Game theory focuses on evolutionary outcomes, with models solving for evolutionarily stable equilibria, whereas quantitative genetics provides insight into evolutionary processes, with models predicting short-term responses to selection. Here we draw parallels between evolutionary game theory and interacting phenotypes theory, which is a quantitative genetic framework for understanding social evolution. First, we show how any evolutionary game may be translated into two quantitative genetic selection gradients, nonsocial and social selection, which may be used to predict evolutionary change from a single round of the game. We show that synergistic fitness effects may alter predicted selection gradients, causing changes in magnitude and sign as the population mean evolves. Second, we show how evolutionary games involving plastic behavioral responses to partners can be modeled using indirect genetic effects, which describe how trait expression changes in response to genes in the social environment. We demonstrate that repeated social interactions in models of reciprocity generate indirect effects and conversely, that estimates of parameters from indirect genetic effect models may be used to predict the evolution of reciprocity. We argue that a pluralistic view incorporating both theoretical approaches will benefit empiricists and theorists studying social evolution. We advocate the measurement of social selection and indirect genetic effects in natural populations to test the predictions from game theory and, in turn, the use of game theory models to aid in the interpretation of quantitative genetic estimates.

A compressed variance component mixed model framework for detecting small and linked QTL-by-environment interactions.

Detecting small and linked quantitative trait loci (QTLs) and QTL-by-environment interactions (QEIs) for complex traits is a difficult issue in immortalized F2 and F2:3 design, especially in the era of global climate change and environmental plasticity research. Here we proposed a compressed variance component mixed model. In this model, a parametric vector of QTL genotype and environment combination effects replaced QTL effects, environmental effects and their interaction effects, whereas the combination effect polygenic background replaced the QTL and QEI polygenic backgrounds. Thus, the number of variance components in the mixed model was greatly reduced. The model was incorporated into our genome-wide composite interval mapping (GCIM) to propose GCIM-QEI-random and GCIM-QEI-fixed, respectively, under random and fixed models of genetic effects. First, potentially associated QTLs and QEIs were selected from genome-wide scanning. Then, significant QTLs and QEIs were identified using empirical Bayes and likelihood ratio test. Finally, known and candidate genes around these significant loci were mined. The new methods were validated by a series of simulation studies and real data analyses. Compared with ICIM, GCIM-QEI-random had 29.77 ± 18.20% and 24.33 ± 10.15% higher average power, respectively, in 0.5-3.0% QTL and QEI detection, 43.44 ± 9.53% and 51.47 ± 15.70% higher average power, respectively, in linked QTL and QEI detection, and identified 30 more known genes for four rice yield traits, because GCIM-QEI-random identified more small genes/loci, being 2.69 ± 2.37% for additional genes. GCIM-QEI-random was slightly better than GCIM-QEI-fixed. In addition, the new methods may be extended into backcross and genome-wide association studies. This study provides effective methods for detecting small-effect and linked QTLs and QEIs.

The Indirect Genetic Effect Interaction Coefficient ψ: Theoretically Essential and Empirically Neglected.

The interaction effect coefficient ψ has been a much-discussed, fundamental parameter of indirect genetic effect (IGE) models since its formal mathematical description in 1997. The coefficient simultaneously describes the form of changes in trait expression caused by genes in the social environment and predicts the evolutionary consequences of those IGEs. Here, we report a striking mismatch between theoretical emphasis on ψ and its usage in empirical studies. Surveying all IGE research, we find that the coefficient ψ has not been equivalently conceptualized across studies. Several issues related to its proper empirical measurement have recently been raised, and these may severely distort interpretations about the evolutionary consequences of IGEs. We provide practical advice on avoiding such pitfalls. The majority of empirical IGE studies use an alternative variance-partitioning approach rooted in well-established statistical quantitative genetics, but several hundred estimates of ψ (from 15 studies) have been published. A significant majority are positive. In addition, IGEs with feedback, that is, involving the same trait in both interacting partners, are far more likely to be positive and of greater magnitude. Although potentially challenging to measure without bias, ψ has critically-developed theoretical underpinnings that provide unique advantages for empirical work. We advocate for a shift in perspective for empirical work, from ψ as a description of IGEs, to ψ as a robust predictor of evolutionary change. Approaches that “run evolution forward” can take advantage of ψ to provide falsifiable predictions about specific trait interactions, providing much-needed insight into the evolutionary consequences of IGEs.

The estimation of (co)variance components and genetic parameters for growth and wool traits in Harnali sheep

The present study was undertaken to estimate (co)variance components and genetic parameters of important growth and wool traits in Harnali sheep using data records of 1862 lambs born to 144 sires and 591 dams for the period from 1998 to 2018. Six univariate animal models including or ignoring maternal genetic and permanent environmental effects were fitted to birth weight (BWT), weaning weight (WWT), 6 months weight (6WT), 12 months weight (YWT), first clip greasy fleece weight (FGFW) and annual greasy fleece weight (AGFW) and further compared using likelihood ratio test. It was observed that the model including direct additive genetic and maternal additive genetic effect without taking covariance between them into account was the most suitable for BWT and WWT. For remaining traits, only direct additive genetic effect model was found to be most appropriate. The estimates of heritability under the best model for BWT, WWT, 6WT, YWT, FGFW and AGFW were 0.23, 0.10, 0.18, 0.11, 0.08 and 0.31, respectively. The maternal influence accounted for 8 % of variation for BWT and WWT which indicated importance of maternal effect during preweaning period. The genetic and phenotypic correlations of 6WT with other traits were positive and ranged medium to moderate. Therefore, it was concluded that medium genetic variability of 6WT and its potential ability for improving other traits should be exploited to achieve genetic improvement in Harnali sheep.

Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation

Potassium (K) plays a significant role in the formation of crop quality and yield. Accurate estimation of plant potassium content using remote sensing (RS) techniques is therefore of great interest to better manage crop K nutrition. To improve RS of crop K, meteorological information might prove useful, as it is well established that weather conditions affect crop K uptake. We aimed to determine whether including meteorological data into RS-based models can improve K estimation accuracy in rice (Oryza sativa L.). We conducted field experiments throughout three growing seasons (2017–2019). During each year, different treatments (i.e., nitrogen, potassium levels and plant varieties) were applied and spectra were taken at different growth stages throughout the growing season. Firstly, we conducted a correlation analysis between rice plant potassium content and transformed spectra (reflectance spectra (R), first derivative spectra (FD) and reciprocal logarithm-transformed spectra (log [1/R])) to select correlation bands. Then, we performed the genetic algorithms partial least-squares and linear mixed effects model to select important bands (IBs) and important meteorological factors (IFs) from correlation bands and meteorological data (daily average temperature, humidity, etc.), respectively. Finally, we used the spectral index and machine learning methods (partial least-squares regression (PLSR) and random forest (RF)) to construct rice plant potassium content estimation models based on transformed spectra, transformed spectra + IFs and IBs, and IBs + IFs, respectively. Results showed that normalized difference spectral index (NDSI (R1210, R1105)) had a moderate estimation accuracy for rice plant potassium content (R2 = 0.51; RMSE = 0.49%) and PLSR (FD-IBs) (R2 = 0.69; RMSE = 0.37%) and RF (FD-IBs) (R2 = 0.71; RMSE = 0.40%) models based on FD could improve the prediction accuracy. Among the meteorological factors, daily average temperature contributed the most to estimating rice plant potassium content, followed by daily average humidity. The estimation accuracy of the optimal rice plant potassium content models was improved by adding meteorological factors into the three RS models, with model R2 increasing to 0.65, 0.74, and 0.76, and RMSEs decreasing to 0.42%, 0.35%, and 0.37%, respectively, suggesting that including meteorological data can improve our ability to remotely sense plant potassium content in rice.

Genomic Predictions With Nonadditive Effects Improved Estimates of Additive Effects and Predictions of Total Genetic Values in Pinus sylvestris.

Genomic selection study (GS) focusing on nonadditive genetic effects of dominance and the first order of epistatic effects, in a full-sib family population of 695 Scots pine (Pinus sylvestris L.) trees, was undertaken for growth and wood quality traits, using 6,344 single nucleotide polymorphism markers (SNPs) generated by genotyping-by-sequencing (GBS). Genomic marker-based relationship matrices offer more effective modeling of nonadditive genetic effects than pedigree-based models, thus increasing the knowledge on the relevance of dominance and epistatic variation in forest tree breeding. Genomic marker-based models were compared with pedigree-based models showing a considerable dominance and epistatic variation for growth traits. Nonadditive genetic variation of epistatic nature (additive × additive) was detected for growth traits, wood density (DEN), and modulus of elasticity (MOEd) representing between 2.27 and 34.5% of the total phenotypic variance. Including dominance variance in pedigree-based Best Linear Unbiased Prediction (PBLUP) and epistatic variance in genomic-based Best Linear Unbiased Prediction (GBLUP) resulted in decreased narrow-sense heritability and increased broad-sense heritability for growth traits, DEN and MOEd. Higher genetic gains were reached with early GS based on total genetic values, than with conventional pedigree selection for a selection intensity of 1%. This study indicates that nonadditive genetic variance may have a significant role in the variation of selection traits of Scots pine, thus clonal deployment could be an attractive alternative for the species. Additionally, confidence in the role of nonadditive genetic effects in this breeding program should be pursued in the future, using GS.

Holobiont effect accounts for more methane emission variance than the additive and microbiome effects on dairy cattle

Rumen microbiota has been previously related to phenotypic complex traits of relevance in dairy cattle. The joint analysis of the host's genetic background and its microbiota can be statistically modelled using similarity matrices between microorganism communities in the different hosts. Microbiota relationship matrices (K) enable considering the whole microbiota and the cumbersome interrelations between taxa, rather than analyzing single taxa one at the time. Several methods have been proposed to ordinate these matrices. The aim of this study was to compare the performance of twelve K built from different microbiome distance metrics, within a variance component estimation framework for methane concentration in dairy cattle. Phenotypic, genomic and rumen microbiome information from simulations (n = 1000) and real data (cows = 437) were analyzed. Four models were considered: an additive genomic model (GBLUP), a microbiome model (MBLUP), a genetic and microbiome effects model (HBLUP) and a genetic, microbiome and genetic × microbiome interaction effects model (HiBLUP). Results from simulation were obtained from 25 replicates. Results from simulated data suggested that Ks with flattened off-diagonal elements were more accurate in variance components estimation for all compared models that included Ks information (MBLUP, HBLUP and HiBLUP). Multidimensional scaling (MDS), redundancy analysis (RDA) and constrained correspondence analysis (CCA) performed better in simulation to estimate heritability and microbiability. The models including Ks from the MDS, RDA and CCA methods were also between the most plausible models in the real data set, according to the deviance information criteria (DIC). Real data was analyzed under the same framework as in the simulation. The most plausible model in real data was HiBLUP. Estimates variated depending on K; methane heritability (0.15–0.17) and microbiability (0.15–0.21) were lower than the proportion of the phenotypic variance attributable to the host-microbiome holobiont effect (0.42–0.59), which we have defined here as “holobiability”. The holobiability including the genomic × microbiome interaction from the HiBLUP was between 0.01 and 0.15 larger than the holobiability explained from the sum of the genetic and microbiome effects without interaction between them, from the HBLUP, depending on K. The findings in this study support the potential of the joint analysis of genomic and microbiome information. Accounting for the hologenome effect (genomic and microbiome) could improve the accuracy in variance component estimation of complex traits relevant in livestock science.

Comparing Evidence for Neurodevelopmental and Neurodegenerative Models of Schizophrenia: Do Effects of Schizophrenia Genetic Risk on Cortical Thickness and Cortical Surface Area Vary by Age?

Risk for schizophrenia peaks during late adolescence and early adulthood, a critical period for brain development (Häfner, 2003). Although several influential theoretical models have been proposed for the developmental relationship between brain pathology and clinical onset, to our knowledge, no study has directly evaluated the predictions of these models for schizophrenia developmental genetic effects on brain structure.

Estimating heritability of social phenotypes from social networks

Abstract For understanding how social behaviour evolves and responds to selection, we need to be able to accurately estimate heritability with quantitative genetic models. More recently, this has moved into using node‐specific statistics from social networks as social phenotypes. However, parameter estimation can be problematic because social phenotypes are not independent observations and standard models tend to ignore the uncertainties around their estimates. Here I present a framework using latent variable modelling to account for these dependencies and uncertainties. I use edge weights, rather than node‐specific network statistics, as dependent variables. From these edge weights, two types of latent (i.e. unobserved) phenotypes are estimated: the individual tendency to be social (i.e. social tendency) and the relative contribution to associations (i.e. social governance). Effects of the social environment and indirect genetic effects are accounted for in the model and can be estimated post hoc. If edge weights are a proportion (e.g. simple ratio index) their uncertainty can be accounted for by a binomial sampling process. I illustrate this method in Stan, a flexible Bayesian inference library, using a publicly available dataset on bottlenose dolphin networks. This method not only accounts for dependencies and uncertainties, it also illuminates aspects of social evolution which are not observed with standard quantitative genetic models. For instance, indirect genetic effects models predict heritable variation in sociality (21.9%), while latent variable modelling shows heritability of social tendency (28.7%), but not for social governance (0.0%). Covariates at different levels in the model (edge and node level) highlight differences in sociality between different foraging strategies and the sexes. This example shows that not properly accounting for the assumptions underlying the use of social network statistics can have misleading effects on conclusions. Although some model assumption violations are less common, others are inherit to the study of (semi)wild populations. The presented framework offers solutions for some critical assumptions and is a flexible tool to further develop and tailor to the needs of specific studies, to ensure the proper fit to the study system.

Gene-Environment Interaction in the Era of Precision Medicine - Filling the Potholes Rather Than Starting to Build a New Road.

Gene–environment interaction is a key part of evolutionary biology, animal, and plant breeding, and a number of health sciences, like epidemiology and precision medicine. However, bottlenecks in models of gene–environment interaction have recently been made manifest, particularly in the field of medicine and, consequently, specific improvements have been explicitly requested—namely, an implementation of gene–environment interaction satisfactorily disentangled from gene–environment correlation. The present paper meets those demands by providing mathematical developments that implement classical models of genetic effects and bring them up to date with the prospects current available data bestow. These developments are shown to overcome the limitations of previous proposals through the analysis of illustrative examples on disease susceptibility, with special attention paid to precision medicine. Indeed, a number of misconceptions about the application of models of genetic/environmental effects to precision medicine are here identified and clarified. The theory here provided is argued to strengthen, in particular, the methodology required for high-precision characterization of strain virulence in the study of the COVID-19 pandemic.

Inferring Gene-by-Environment Interactions with a Bayesian Whole-Genome Regression Model

SummaryThe contribution of gene-by-environment (GxE) interactions for many human traits and diseases is poorly characterized. We propose a Bayesian whole-genome regression model for joint modeling of main genetic effects and GxE interactions in large-scale datasets, such as the UK Biobank, where many environmental variables have been measured. The method is called LEMMA (Linear Environment Mixed Model Analysis) and estimates a linear combination of environmental variables, called an environmental score (ES), that interacts with genetic markers throughout the genome. The ES provides a readily interpretable way to examine the combined effect of many environmental variables. The ES can be used both to estimate the proportion of phenotypic variance attributable to GxE effects and to test for GxE effects at genetic variants across the genome. GxE effects can induce heteroskedasticity in quantitative traits, and LEMMA accounts for this by using robust standard error estimates when testing for GxE effects. When applied to body mass index, systolic blood pressure, diastolic blood pressure, and pulse pressure in the UK Biobank, we estimate that , , , and , respectively, of phenotypic variance is explained by GxE interactions and that low-frequency variants explain most of this variance. We also identify three loci that interact with the estimated environmental scores ().

Canalization and Robustness in Human Genetics and Disease.

Canalization refers to the evolution of populations such that the number of individuals who deviate from the optimum trait, or experience disease, is minimized. In the presence of rapid cultural, environmental, or genetic change, the reverse process of decanalization may contribute to observed increases in disease prevalence. This review starts by defining relevant concepts, drawing distinctions between the canalization of populations and robustness of individuals. It then considers evidence pertaining to three continuous traits and six domains of disease. In each case, existing genetic evidence for genotype-by-environment interactions is insufficient to support a strong inference of decanalization, but we argue that the advent of genome-wide polygenic risk assessment now makes an empirical evaluation of the role of canalization in preventing disease possible. Finally, the contributions of both rare and common variants to congenital abnormality and adult onset disease are considered in light of a new kerplunk model of genetic effects.