Estimates Of Genetic Effects Research Articles

Estimation of dominance genetic effects on pre-weaning efficiency-related traits in Baluchi sheep

Abstract This study was conducted to estimate the relative contribution of dominance genetic effects to efficiency-related traits including Kleiber ratio (KR), efficiency of growth (EF) and relative growth rate (RGR) in Baluchi sheep. To this end, each trait was analysed with a series of 12 animal models which were identical for fixed and additive genetic effects but differed for combinations of dominance genetic and maternal effects. The Akaike's information criterion (AIC) was used to rank models. (Co)variances between traits were estimated using bivariate analyses. For all traits studied, according to AIC values, models containing the dominance genetic effects provided a better data fit than otherwise identical models. By including dominance genetic effects in the model, additive genetic variance did not change, but a significant decrease was observed in the residual variance (24, 19 and 25% for KR, EF and RGR, respectively). Estimates of dominance heritability $( {\boldsymbol h}_{\boldsymbol d}^ 2 )$ were 0.20 ± 0.05, 0.17 ± 0.05 and 0.19 ± 0.07 for KR, EF and RGR, respectively, more than corresponding estimates of additive heritability ${\bf ( }{\boldsymbol h}_{\boldsymbol a}^{\bf 2} {\bf ) }$ as 0.14 ± 0.02, 0.09 ± 0.03 and 0.13 ± 0.02, respectively. Dominance genetic correlations between traits were 0.89 ± 0.17 (KR-EF), 0.86 ± 0.20 (KR-RGR) and 0.93 ± 0.21 (EF-RGR). Additive genetic correlations between traits were 0.84 ± 0.05 (KR-EF), 0.78 ± 0.04 (KR-RGR) and 0.83 ± 0.04 (EF-RGR). The Spearman correlation between additive breeding values including and excluding dominance genetic effects were close to unity either for all animals or top ranked animals. Since presence of dominance genetic effects increased the model power to fit the data, inclusion of these effects in the genetic evaluation models for Baluchi sheep was recommended.

The importance of family-based sampling for biobanks.

Biobanks aim to improve our understanding of health and disease by collecting and analysing diverse biological and phenotypic information in large samples. So far, biobanks have largely pursued a population-based sampling strategy, where the individual is the unit of sampling, and familial relatedness occurs sporadically and by chance. This strategy has been remarkably efficient and successful, leading to thousands of scientific discoveries across multiple research domains, and plans for the next wave of biobanks are underway. In this Perspective, we discuss the strengths and limitations of a complementary sampling strategy for future biobanks based on oversampling of close genetic relatives. Such family-based samples facilitate research that clarifies causal relationships between putative risk factors and outcomes, particularly in estimates of genetic effects, because they enable analyses that reduce or eliminate confounding due to familial and demographic factors. Family-based biobank samples would also shed new light on fundamental questions across multiple fields that are often difficult to explore in population-based samples. Despite the potential for higher costs and greater analytical complexity, the many advantages of family-based samples should often outweigh their potential challenges.

Protein catabolites as blood-based biomarkers of aging physiology: Findings from the Dog Aging Project.

Our understanding of age-related physiology and metabolism has grown through the study of systems biology, including transcriptomics, single-cell analysis, proteomics and metabolomics. Studies in lab organisms in controlled environments, while powerful and complex, fall short of capturing the breadth of genetic and environmental variation in nature. Thus, there is now a major effort in geroscience to identify aging biomarkers and to develop aging interventions that might be applied across the diversity of humans and other free-living species. To meet this challenge, the Dog Aging Project (DAP) is designed to identify cross-sectional and longitudinal patterns of aging in complex systems, and how these are shaped by the diversity of genetic and environmental variation among companion dogs. Here we surveyed the plasma metabolome from the first year of sampling of the Precision Cohort of the DAP. By incorporating extensive metadata and whole genome sequencing information, we were able to overcome the limitations inherent in breed-based estimates of genetic and physiological effects, and to probe the physiological and dietary basis of the age-related metabolome. We identified a significant effect of age on approximately 40% of measured metabolites. Among other insights, we discovered a potentially novel biomarker of age in the post-translationally modified amino acids (ptmAAs). The ptmAAs, which can only be generated by protein hydrolysis, covaried both with age and with other biomarkers of amino acid metabolism, and in a way that was robust to diet. Clinical measures of kidney function mediated about half of the higher ptmAA levels in older dogs. This work identifies ptmAAs as robust indicators of age in dogs, and points to kidney function as a physiological mediator of age-associated variation in the plasma metabolome.

Exploring the Germline Genetics of In Situ and Invasive Cutaneous Melanoma

It is unknown whether germline genetic factors influence in situ melanoma risk differently than invasive melanoma risk. To determine whether differences in risk of in situ melanoma and invasive melanoma are heritable. Three genome-wide association study meta-analyses were conducted of in situ melanoma vs controls, invasive melanoma vs controls, and in situ vs invasive melanoma (case-case) using 4 population-based genetic cohorts: the UK Biobank, the FinnGen cohort, the QSkin Sun and Health Study, and the Queensland Study of Melanoma: Environmental and Genetic Associations (Q-MEGA). Melanoma status was determined using International Statistical Classification of Diseases and Related Health Problems codes from cancer registry data. Data were collected from 1987 to 2022, and data were analyzed from September 2022 to June 2023. In situ and invasive cutaneous melanoma. To test whether in situ and invasive melanoma have independent heritable components, genetic effect estimates were calculated for single-nucleotide variants (SNV; formerly single-nucleotide polymorphisms) throughout the genome for each melanoma. Then, SNV-based heritability was estimated, the genetic correlation between melanoma subtypes was assessed, and polygenic risk scores (PRS) were generated for in situ vs invasive status in Q-MEGA participants. A total of 6 genome-wide significant loci associated with in situ melanoma and 18 loci with invasive melanoma were identified. A strong genetic correlation (genetic r = 0.96; 95% CI, 0.76-1.15) was observed between the 2 classifications. Notably, loci near IRF4, KLF4, and HULC had significantly larger effects for in situ melanoma compared with invasive melanoma, while MC1R had a significantly larger effect on invasive melanoma compared with in situ melanoma. Heritability estimates were consistent for both, with in situ melanoma heritability of 6.7% (95% CI, 4.1-9.3) and invasive melanoma heritability of 4.9% (95% CI, 2.8-7.2). Finally, a PRS, derived from comparing invasive melanoma with in situ melanoma genetic risk, was on average significantly higher in participants with invasive melanoma (odds ratio per 1-SD increase in PRS, 1.43; 95% CI, 1.16-1.77). There is much shared genetic architecture between in situ melanoma and invasive melanoma. Despite indistinguishable heritability estimates between the melanoma classifications, PRS suggest germline genetics may influence whether a person gets in situ melanoma or invasive melanoma. PRS could potentially help stratify populations based on invasive melanoma risk, informing future screening programs without exacerbating the current burden of melanoma overdiagnosis.

A novel framework with automated horizontal pleiotropy adjustment in mendelian randomization

The presence of horizontal pleiotropy in Mendelian randomization (MR) analysis has long been a concern due to its potential to induce substantial bias. In recent years, many robust MR methods have been proposed to address this by relaxing the ‘no horizontal pleiotropy’ assumption. Here, we propose a novel two-stage framework called CMR, which integrates a conditional analysis of multiple genetic variants to remove pleiotropy induced by linkage disequilibrium, followed by the application of robust MR methods to model the conditional genetic effect estimates. We demonstrate how the conditional analysis can reduce horizontal pleiotropy and improve the performance of existing MR methods. Extensive simulation studies covering a wide range of scenarios of horizontal pleiotropy showcased the superior performance of the proposed CMR framework over the standard MR framework in which marginal genetic effects are modeled. Moreover, the application of CMR in a negative control outcome analysis and investigation into the causal role of body mass index across various diseases highlighted its potential to deliver more reliable results in real-world applications.

Estimates of Genetic Parameters, Growth Curve, and Environmental Effects for Nellore Cattle in the Pantanal.

The objective was to estimate the growth curves and genetic parameters using random regression methodology for Nellore cattle raised in Pantanal, MS, Brazil (6974 calves; n = 53,233 weights), with at least four weighings per individual. The model considered direct and maternal genetic additives and maternal permanent environmental effects at random. Orthogonal Legendre polynomials of cubic order were used to fit the growth curve. Analyses of variance were performed using the GLM procedure. The model used contained the fixed effects of sex, year of birth, farm, and the covariates calf birth month (linear and quadratic) and cow age at calving (linear and quadratic). The adjusted mean weight at 120 days of age was 93.43 ± 19.78 kg, and for 205 days of age, it was 180.42 ± 26.58 kg. Animals born in the dry season had a higher average weight [kg] (219.57 vs. 211.78, 3.7% higher) and, consequently, had higher weights at 646 days of age. Estimates of direct heritabilities (h2a) ranged from 0.35 to 0.75 (high magnitudes), and maternal heritabilities (h2m) along the trajectory of low magnitudes ranged from 0.03 to 0.08, respectively. The use of random regression to evaluate beef animals allows for adjusting the growth curve and selecting the best animals to be the parents of future generations.

Metabolic liability for weight gain in early adulthood

While weight gain is associated with a host of chronic illnesses, efforts in obesity have relied on single "snapshots" of body mass index (BMI) to guide genetic and molecular discovery. Here, we study >2,000 young adults with metabolomics and proteomics to identify a metabolic liability to weight gain in early adulthood. Using longitudinal regression and penalized regression, we identify a metabolic signature for weight liability, associated with a 2.6% (2.0%-3.2%, p= 7.5×10-19) gain in BMI over≈20 years per SD higher score, after comprehensive adjustment. Identified molecules specified mechanisms of weight gain, including hunger and appetite regulation, energy expenditure, gut microbial metabolism, and host interaction with external exposure. Integration of longitudinal and concurrent measures in regression with Mendelian randomization highlights the complexity of metabolic regulation of weight gain, suggesting caution in interpretation of epidemiologic or genetic effect estimates traditionally used in metabolic research.

Interpreting population- and family-based genome-wide association studies in the presence of confounding.

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding and can also absorb the "indirect" genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect-size estimates are used in polygenic scores (PGSs). We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding.

Estimation of genetic and non-genetic effects on productive life of Iranian Holstein dairy cows.

Understanding the factors that influence the lifespan of dairy cows is crucial for enhancing their productive life (PL). This study aimed to investigate the impact of genetic and environmental effects on the PL of Holstein cows. Data included 82,505 cows from 1952 sires that calved for the first time between 2001 and 2016. PL was defined from first calving to culling. Proportional hazard models, assuming a piecewise Weibull distribution of the baseline hazard function, were utilized to account for time-dependent effects, such as herd size variations, year-season, milk yield, fat and protein contents, and the time-independent fixed effect of age at first calving. Herd-year and sire effects were considered as random effects. All effects showed significant associations with PL (p< 0.001). The relative risk of culling was higher in heifers that calved at an older age and cows that calved during the cold season. Moreover, cows with lower production had a significantly shorter PL compared with high-producing cows. The effective heritability in the absence of censored data was estimated at 0.15. These findings suggest that greater attention should be paid to regular and accurate breeding programs, which are essential for enhancing profitability and the PL of Iranian dairy cows.

Estimates of Genetic Effects of Yield, Yield Components, Yellow and Stem Rust Resistance through Generation Mean Analysis in Three Bread Wheat Crosses

Estimates of Genetic Effects of Yield, Yield Components, Yellow and Stem Rust Resistance through Generation Mean Analysis in Three Bread Wheat Crosses

Developing flexible models for genetic evaluations in smallholder crossbred dairy farms

The productivity of smallholder dairy farms is very low in developing countries. Important genetic gains could be realized using genomic selection, but genetic evaluations need to be tailored for lack of pedigree information and very small farm sizes. To accommodate this situation, we propose a flexible Bayesian model for the genetic evaluation of milk yield, which allows us to simultaneously account for nongenetic random effects for farms and varying SNP variance (BayesR model). First, we used simulations based on real genotype data from Indian crossbred dairy cattle to demonstrate that the proposed model can separate the true genetic and nongenetic parameters even for small farm sizes (2 cows on average) although with high standard errors in scenarios with low heritability. The accuracy of genomic genetic evaluation increased until farm size was approximately 5. We then applied the model to real data from 4,655 crossbred cows with 106,109 monthly test day milk records and 689,750 autosomal SNPs. We estimated a heritability of 0.16 (0.04) for milk yield and using cross-validation, a genomic estimated breeding value (GEBV) accuracy of 0.45 and bias (regression of phenotype on GEBV) of 1.04 (0.26). Estimated genetic parameters were very similar using BayesR, BayesC, and genomic BLUP approaches. Candidate genes near the top variants, IMMP2L and ARHGEF2, have been previously associated with milk protein composition, mastitis resistance, and milk cholesterol content. The estimated heritability and GEBV accuracy for milk yield are much lower than those from intensive or pasture-based systems in many countries. Further increases in the number of phenotyped and genotyped animals in farms with at least 2 cows (preferably 3-5, to allow for dropout of cows) are needed to improve the estimation of genetic effects in these smallholder dairy farms.

Bayesian estimates of genetic effects on calf survival in Hardhenu (Bos taurus × Bos indicus) cattle.

The aim of the present study is to carry out a risk analysis and Bayesian estimates of genetic effects on calf survival in Hardhenu cattle using data records of 2593 calves born to 102 sires and 790 dams over 25 years. The Bayesian analysis using Gibbs sampling was employed towards threshold animal models to estimate direct and maternal effects on animal survival of studied population. The results showed that mortality from birth to 3 months of age (S1), birth to 6 months of age (S2) and birth to 12 months of age (S3) was 10.22, 12.88 and 14.65%, respectively. It was revealed from the results of logistic regression analysis that the male animals had greater risk (1.41-1.61 times) of mortality during S1, S2 and S3 as compared to female animals. However, calves born during rainy season had higher risk (1.36 to 1.44 times) of mortality than calves born during winter season. Among died animals, the simultaneous infection of respiratory and digestive diseases had leading contribution (26.84%-30.19%) to deaths while alone of them contributed to 18%-20% only. On evaluation of six threshold animal models, model 1 was found to be most appropriate model and the Bayesian estimates (95% highest posterior density confidence intervals) of direct additive heritability for S1, S2 and S3 under model 1 were 0.15 ± 0.07 (0.04-0.23), 0.23 ± 0.12, (0.02-0.44) and 0.26 ± 0.06 (0.08-0.41), respectively. It was concluded that the inclusion of survival traits in existing selection criteria may be helpful to increase calf survival and ultimately economic gain in the dairy herd.

Polygenic score: An anchor holding the whole life course.

Polygenic score: An anchor holding the whole life course.

Estimation of nonadditive and additive genetic effects on growth traits of Horro cattle.

The estimation of nonadditive genetic effects plays a significant role in the accuracy of estimated breeding values for growth traits. Data for estimation of individual and maternal heterosis of crossbreed cattle in Ethiopia are limited to research institutions and universities. This paper aimed to estimate the crossbreeding effects for growth traits of Holstein Friesian × Horro and Jersey × Horro crossbreds in Ethiopia. The data included pedigree and weight data of animals born between 1980 and 2008. Heritability of growth traits was estimated using restricted maximum likelihood (ASREML). However, the additive genetic, maternal, and heterosis effects for growth traits of crossbred animals were estimated using the crossbreeding effects (CBE3) package by fitting Kinghorn's Model one. The direct and maternal heritability estimates for 1-year weight with the model that included the direct maternal covariance were 0.77 ± 0.12 and 0.26 ± 0.09, respectively. Individual heteroses estimated for Holstein Friesian × Horro and Jersey × Horro were significantly high and positive for 1-year weight (21.6 ± 6.7 and 26.0 ± 3.9kg), preweaning average daily gain (27.4 ± 26 and 28.9 ± 15g), and postweaning average daily gain (68.8 ± 16.6 and 61.8 ± 9.9g), respectively. The maternal additive genetic effect for growth traits was mostly positive; therefore, it may be preferable to use crossbred cows from purebred dams instead of crossbred cows from crossbred dams. The significantly higher heterosis and additive genetic parameters in Holstein Friesian × Horro crossbreds indicated that these crossbreds may be preferable to Jersey × Horro crossbreds in areas occupied by Horro cattle.

Using adopted individuals to partition indirect maternal genetic effects into prenatal and postnatal effects on offspring phenotypes.

Maternal genetic effects can be defined as the effect of a mother's genotype on the phenotype of her offspring, independent of the offspring's genotype. Maternal genetic effects can act via the intrauterine environment during pregnancy and/or via the postnatal environment. In this manuscript, we present a simple extension to the basic adoption design that uses structural equation modelling (SEM) to partition maternal genetic effects into prenatal and postnatal effects. We examine the power, utility and type I error rate of our model using simulations and asymptotic power calculations. We apply our model to polygenic scores of educational attainment and birth weight associated variants, in up to 5,178 adopted singletons, 943 trios, 2687 mother-offspring pairs, 712 father-offspring pairs and 347,980 singletons from the UK Biobank. Our results show the expected pattern of maternal genetic effects on offspring birth weight, but unexpectedly large prenatal maternal genetic effects on offspring educational attainment. Sensitivity and simulation analyses suggest this result may be at least partially due to adopted individuals in the UK Biobank being raised by their biological relatives. We show that accurate modelling of these sorts of cryptic relationships is sufficient to bring type I error rate under control and produce asymptotically unbiased estimates of prenatal and postnatal maternal genetic effects. We conclude that there would be considerable value in following up adopted individuals in the UK Biobank to determine whether they were raised by their biological relatives, and if so, to precisely ascertain the nature of these relationships. These adopted individuals could then be incorporated into informative statistical genetics models like the one described in our manuscript to further elucidate the genetic architecture of complex traits and diseases.

Estimates of direct and maternal genetic effects on birth and weaning weights in Egyptian buffalo calves

A total of 1173 first calves of Egyptian buffalo kept at Mehalet Mousa Farm, belonging to Animal Production Research, Ministry of Agriculture, Dokki, Cairo, Egypt during the period from 2000 to 2015 were used to estimate variances of direct and maternal genetic effects for birth weight (BW) and weaning weight (WW). Two animal models are used. Model 1 (full model) includes month and year of birth and sex as fixed effects and direct genetic, maternal genetic, covariance between direct and maternal genetic and residual as random effects. Model 2 is similar to model 1 while additive maternal genetic and covariance between direct and maternal effects were omitted from the analysis. Year of birth and sex had highly significant effects on BW and WW, while month of birth had no significant effects on both traits studied. Bulls of the buffalo had highly significant effects on BW and WW. Direct heritability estimates for BW are 0.30 and 0.33, for Model 1 and Model 2, respectively. Direct heritability estimates for WW are 0.34 and 0.39 for Model 1 and Model 2, respectively. Maternal heritability estimates are 0.10 and 0.03 for BW and WW, respectively. Phenotypic and genetic correlations among BW and WW are positive and highly significant. These results indicate the important of maternal genetic effect on birth weight.

Mendelian imputation of parental genotypes improves estimates of direct genetic effects

Effects estimated by genome-wide association studies (GWASs) include effects of alleles in an individual on that individual (direct genetic effects), indirect genetic effects (for example, effects of alleles in parents on offspring through the environment) and bias from confounding. Within-family genetic variation is random, enabling unbiased estimation of direct genetic effects when parents are genotyped. However, parental genotypes are often missing. We introduce a method that imputes missing parental genotypes and estimates direct genetic effects. Our method, implemented in the software package snipar (single-nucleotide imputation of parents), gives more precise estimates of direct genetic effects than existing approaches. Using 39,614 individuals from the UK Biobank with at least one genotyped sibling/parent, we estimate the correlation between direct genetic effects and effects from standard GWASs for nine phenotypes, including educational attainment (r = 0.739, standard error (s.e.) = 0.086) and cognitive ability (r = 0.490, s.e. = 0.086). Our results demonstrate substantial confounding bias in standard GWASs for some phenotypes.

Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.

10 Deep Learning for Multi-Behavioral Video Classification of Interactive Behaviors of Pigs in Single-Spaced Automatic Feeders

Abstract Automatic pig feeders record individual feeding behavior traits that can be used in genetic selection. However, these feeders cannot measure how feeding traits are affected by behavioral interactions between animals. Moreover, recording behavioral interactions can improve the estimation of social genetic effects. This motivated our assessment of computer vision to classify 4 types of interactions at the feeder: head-to-body contact (HB) including head knocking, tail biting, pushing, gentle nosing and casual contact between head/ears of a pig with a feeding pig; levering (L) where the feeding pig was lifted from behind by another pig; mounting (M) where the feeding pig was mounted by another pig; and no-contact (NC) when a second pig entered the feeder without touching the feeding pig. Behavior at the feeder was filmed twice, 3 weeks apart, for 2 consecutive days each week in 6 groups of grow-finish pigs (10 per group) housed in pens equipped with FIRE feeders. Video segments that involved 2 pigs in the feeder were selected, and labeled as HB (n=10,114), L (n=925), M (n=1,242) and NC (n=3,398). Due to the sparse data available for training and the complex nature of the behavioral interactions, we utilized pretrained convolutional neural networks for automatic spatial feature extraction followed by long short-term memory for temporal feature extraction from sequences of frames. Focal loss, a loss function that assigns different weights to hard/easily misclassified examples to handle class imbalance problem, was used in this study. Accuracy, recall, and precision of behavior classification were obtained from 5 random cross-validations. The overall accuracy was 0.967±0.002. The average recalls for HB, L, M, and NC were 0.977±0.004, 0.866±0.047, 0.969±0.011, and 0.964±0.009 respectively, while average precisions were 0.976±0.002, 0.901±0.029, 0.956±0.018, and 0.963±0.012. The proposed algorithm accurately classified multiple interactive behaviors in an automated feeding stall from digital videos.

EQTL mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects.

Using information from allele-specific gene expression (ASE) can improve the power to map gene expression quantitative trait loci (eQTLs). However, such practice has been limited, partly due to computational challenges and lack of clarification on the size of power gain or new findings besides improved power. We have developed geoP, a computationally efficient method to estimate permutation p-values, which makes it computationally feasible to perform eQTL mapping with ASE counts for large cohorts. We have applied geoP to map eQTLs in 28 human tissues using the data from the Genotype-Tissue Expression (GTEx) project. We demonstrate that using ASE data not only substantially improve the power to detect eQTLs, but also allow us to quantify individual-specific genetic effects, which can be used to study the variation of eQTL effect sizes with respect to other covariates. We also compared two popular methods for eQTL mapping with ASE: TReCASE and RASQUAL. TReCASE is ten times or more faster than RASQUAL and it provides more robust type I error control.