Model For Genetic Effects Research Articles

Bayesian estimates of genetic relationship between calving difficulty and productive and reproductive performance in Holstein cows

The objective of present study was to estimate genetic correlations between calving difficulty and productive and reproductive traits in Iranian Holsteins. Calving records from the Animal Breeding Center of Iran, collected from 1991 to 2011 and comprising 183 203 first-calving events of Holstein cows from 1470 herds were included in the dataset. Threshold animal models included direct genetic effect (Model 1) or direct and maternal genetic effects with covariance between them (Model 2) were fitted for the genetic analysis of calving difficulty. Also, linear animal models including direct genetic effect were fitted for the genetic analysis of productive and reproductive performance traits. A set of linear-threshold bivariate models was used for obtaining genetic correlation between calving difficulty and other traits. All analyses were implemented by Bayesian approach via Gibbs sampling methodology. A single Gibbs sampling chain with 300 000 rounds was generated by the TM program. Posterior mean estimates of direct heritabilities for calving difficulty were 0.056 and 0.066, obtained from different models. Also, posterior mean estimate of maternal heritability for this trait was 0.018. Estimate of correlation between direct and maternal genetic effects for calving difficulty was negative (–0.44). Posterior mean estimates of direct heritabilities for milk yield, fat yield, protein yield, days from calving to first service, days open and first calving interval were 0.257, 0.188, 0.235, 0.034, 0.042 and 0.050 respectively. The posterior means of direct genetic correlation between calving difficulty and milk yield, fat yield, protein yield, days from calving to first service, days open and first calving interval were low and equal to –0.135, 0.030, –0.067, –0.010, –0.075 and –0.074 respectively. The results of the current study indicated that exploitable genetic variation in calving difficulty, productive and reproductive traits could be applied in designing future genetic selection plans for Iranian Holsteins.

Genetic description of growth traits in Markhoz goat using random regression models

The objective of the present study was to estimate genetic parameters and genetic trends for body weight traits in Markhoz goat using random regression (RR) models. In this study, 12,116 test-day body weight records of 3078 Markhoz goats, from birth to 365days of age, were used to compare RR models in which fixed and random effects were modeled using Legendre polynomials. The best model for direct genetic and maternal temporary environmental effects (common litter) included cubic, for direct permanent environmental effect included quadratic and for maternal permanent environmental effect included linear Legendre polynomials with a heterogeneous error structure. Direct heritability increased from 0.17 at birth to 0.32 at 120days of age then decreased suddenly to 0.27 at 240days of age and increased thereafter. Some estimates for the highest ages with the least records tended to be overestimated, in particular heritability beyond 340days. Variances due to direct permanent and maternal permanent environmental effects, respectively, were the highest and the lowest compared with the other sources of variation. This study has demonstrated the possibility of application of RR models for routine genetic evaluation of Markhoz goat in Iran. Although the results confirm that there is a scope for genetic improvement in growth pattern in Markhoz goat, but the genetic changes of body weight for selected ages (120-d and 200-d) have not shown a substantial tendency among the years.

Evolution of maternal effect senescence

Increased maternal age at reproduction is often associated with decreased offspring performance in numerous species of plants and animals (including humans). Current evolutionary theory considers such maternal effect senescence as part of a unified process of reproductive senescence, which is under identical age-specific selective pressures to fertility. We offer a novel theoretical perspective by combining William Hamilton's evolutionary model for aging with a quantitative genetic model of indirect genetic effects. We demonstrate that fertility and maternal effect senescence are likely to experience different patterns of age-specific selection and thus can evolve to take divergent forms. Applied to neonatal survival, we find that selection for maternal effects is the product of age-specific fertility and Hamilton's age-specific force of selection for fertility. Population genetic models show that senescence for these maternal effects can evolve in the absence of reproductive or actuarial senescence; this implies that maternal effect aging is a fundamentally distinct demographic manifestation of the evolution of aging. However, brief periods of increasingly beneficial maternal effects can evolve when fertility increases with age faster than cumulative survival declines. This is most likely to occur early in life. Our integration of theory provides a general framework with which to model, measure, and compare the evolutionary determinants of the social manifestations of aging. Extension of our maternal effects model to other ecological and social contexts could provide important insights into the drivers of the astonishing diversity of lifespans and aging patterns observed among species.

Genetic Worth Effect Models for Boreal Conifers and Their Utility When Integrated into Density Management Decision-Support Systems

Based on approaches deduced from previous research findings and empirical observations from density control experiments, genetic worth effect response models were developed for black spruce (Picea mariana (Mill) BSP.) and jack pine (Pinus banksiana Lamb.) plantations. The models accounted for the increased rate of stand development arising from the planting of genetically-improved stock through temporal adjustments to the species-specific site-based mean dominant height-age functions. The models utilized a relative height growth modifier based on known estimates of genetic gain. The models also incorporated a phenotypic juvenile age-mature age correlation function in order to account for the intrinsic temporal decline in the magnitude of genetic worth effects throughout the rotation. Integrating the functions into algorithmic variants of structural stand density management models produced stand development patterns that were consistent with axioms of even-aged stand dynamics.

Estimativas de parâmetros genéticos para características de crescimento de cordeiros mestiços Suffolk

Objetivou-se estimar os coeficientes de herdabilidade e correlação genética entre os pesos ao nascer (PN) e à desmama (PD) de cordeiros mestiços Suffolk, pertencentes a rebanho experimental. Assim, foram testados quatro modelos, os quais consideraram o efeito genético aditivo direto (Modelo 1), os efeitos genético direto e de ambiente permanente materno (Modelo 2), os efeitos aditivo direto, de ambiente permanente e genético materno (cov a,m=0) (Modelo 3), e no Modelo 4, além daqueles contemplados no Modelo 3, foi considerada cov a,m≠0. Após comparar os modelos por meio do teste de razão de verossimilhança (LRT), observou-se que o 3 foi o mais adequado, sendo que as estimativas de herdabilidade foram de baixa magnitude para PN e PD, iguais a 0,08±0,009 e 0,24±0,17, respectivamente, indicando pequena possibilidade de resposta à seleção direta. Da mesma maneira, o coeficiente de correlação genética entre estas características foi 0,43±0,39, resultado que implica pequena possibilidade de resposta correlacionada. A inclusão dos efeitos maternos é necessária para a estimação adequada de parâmetros genéticos para as características avaliadas.

One century later: dissecting genetic effects for looking over old paradigms.

One century later: dissecting genetic effects for looking over old paradigms.

Functional-mixed effects models for candidate genetic mapping in imaging genetic studies.

The aim of this paper is to develop a functional-mixed effects modeling (FMEM) framework for the joint analysis of high-dimensional imaging data in a large number of locations (called voxels) of a three-dimensional volume with a set of genetic markers and clinical covariates. Our FMEM is extremely useful for efficiently carrying out the candidate gene approaches in imaging genetic studies. FMEM consists of two novel components including a mixed effects model for modeling nonlinear genetic effects on imaging phenotypes by introducing the genetic random effects at each voxel and a jumping surface model for modeling the variance components of the genetic random effects and fixed effects as piecewise smooth functions of the voxels. Moreover, FMEM naturally accommodates the correlation structure of the genetic markers at each voxel, while the jumping surface model explicitly incorporates the intrinsically spatial smoothness of the imaging data. We propose a novel two-stage adaptive smoothing procedure to spatially estimate the piecewise smooth functions, particularly the irregular functional genetic variance components, while preserving their edges among different piecewise-smooth regions. We develop weighted likelihood ratio tests and derive their exact approximations to test the effect of the genetic markers across voxels. Simulation studies show that FMEM significantly outperforms voxel-wise approaches in terms of higher sensitivity and specificity to identify regions of interest for carrying out candidate genetic mapping in imaging genetic studies. Finally, FMEM is used to identify brain regions affected by three candidate genes including CR1, CD2AP, and PICALM, thereby hoping to shed light on the pathological interactions between these candidate genes and brain structure and function.

Dissecting genetic effects with imprinting

Models of genetic effects are mathematical representations of a genotype-to-phenotype (GP) map that, rather than accounting for a raw map assigning phenotypes to genotypes, rely on parameters with deliberate evolutionary meaning—additive and interaction effects. In this article, the conceptual particularities of genetic imprinting and their implications on models of genetic effects are analyzed. The molecular mechanisms by which imprinted loci affect the relationship between genotypes and phenotypes are known to be singular. Despite its epigenetic nature, the (parent-of-origin-dependent) way in which the alleles of imprinted genes are modified and segregate in each generation is precisely determined, and thus amenable to be represented through conventional models of genetic effects. The Natural and Orthogonal Interactions (NOIA) model framework is here extended to account for imprinting as a tool for a more thorough analysis of the evolutionary implications of this phenomenon. The resulting theory improves and generalizes previous proposals for modeling imprinting.

Problems and Pit-Falls in Testing for G × E and Epistasis in Candidate Gene Studies of Human Behavior

Conclusions about the genetic architecture of a phenotype relating to the contributions of genetic additivity, dominance, epistasis or genotype × environment interaction, depend upon the statistical and distributional properties of the measured trait. This dependence is frequently ignored in contemporary genetic studies and can radically change the conclusions that may be drawn from the data. The interdependence of the conclusions about genetic architecture and instruments used for behavioral measurement is explored by simulated studies of the interaction between candidate genes and measured environment in psychiatric genetics. Trait values are simulated (N = 100,000) under several commonly encountered scenarios and subjected to two simulated 20-item psychological tests each comprising items with different patterns of difficulty and sensitivity to variation (discriminating power) in the latent trait. Test scores are generated for each test by summing the binary responses across all items. The full model for digenic additive and non-additive genetic effects and G × E is fitted to the trait values and test scores under a range of different simulated genetic architectures. Untransformed test scores show complex patterns of epistasis and G × E even when the underlying effects of genes and environment are purely additive and the transformation of symptom counts does not fully recover the simulated underlying genetic architecture. Accordingly, failing to allow for the theory of measurement when analyzing details of genetic architecture may frequently lead to replicable over-reporting of interactions and mislead potential investigators and funding agencies.

Estimating directional epistasis.

Epistasis, i.e., the fact that gene effects depend on the genetic background, is a direct consequence of the complexity of genetic architectures. Despite this, most of the models used in evolutionary and quantitative genetics pay scant attention to genetic interactions. For instance, the traditional decomposition of genetic effects models epistasis as noise around the evolutionarily-relevant additive effects. Such an approach is only valid if it is assumed that there is no general pattern among interactions—a highly speculative scenario. Systematic interactions generate directional epistasis, which has major evolutionary consequences. In spite of its importance, directional epistasis is rarely measured or reported by quantitative geneticists, not only because its relevance is generally ignored, but also due to the lack of simple, operational, and accessible methods for its estimation. This paper describes conceptual and statistical tools that can be used to estimate directional epistasis from various kinds of data, including QTL mapping results, phenotype measurements in mutants, and artificial selection responses. As an illustration, I measured directional epistasis from a real-life example. I then discuss the interpretation of the estimates, showing how they can be used to draw meaningful biological inferences.

Hierarchical linear modeling of longitudinal pedigree data for genetic association analysis.

Genetic association analysis on complex phenotypes under a longitudinal design involving pedigrees encounters the problem of correlation within pedigrees, which could affect statistical assessment of the genetic effects. Approaches have been proposed to integrate kinship correlation into the mixed-effect models to explicitly model the genetic relationship. These have proved to be an efficient way of dealing with sample clustering in pedigree data. Although current algorithms implemented in popular statistical packages are useful for adjusting relatedness in the mixed modeling of genetic effects on the mean level of a phenotype, they are not sufficiently straightforward to handle the kinship correlation on the time-dependent trajectories of a phenotype. We introduce a 2-level hierarchical linear model to separately assess the genetic associations with the mean level and the rate of change of a phenotype, integrating kinship correlation in the analysis. We apply our method to the Genetic Analysis Workshop 18 genome-wide association studies data on chromosome 3 to estimate the genetic effects on systolic blood pressure measured over time in large pedigrees. Our method identifies genetic variants associated with blood pressure with estimated inflation factors of 0.99, suggesting that our modeling of random effects efficiently handles the genetic relatedness in pedigrees. Application to simulated data captures important variants specified in the simulation. Our results show that the method is useful for genetic association studies in related samples using longitudinal design.

DNA repair XPCC1 and XPD genes polymorphism as associated with the development of bladder cancer and renal cell carcinoma

We examined the correlations between the polymorphic alleles of the DNA repair genes XRCC1 (c.839G> A, rs25489; and c.1196A> G, rs25487), XPA (c.-4A> G, rs1800975), and XPD (c.2251A> C, rs13181) and the progression and severity of neoplasias in the bladder and kidney in patients of three distinct ethnic groups, Bashkir, Russians, and Tatar, residing in the Republic of Bashkorostan. The study enrolled 468 cancer patients and 351 healthy individuals. Genotyping for polymorphic alleles was carried out using the PCR-RFLP method. We identified a correlation between allele A of the c.839 G>A locus of the XRCC1 gene and the incidence of the bladder cancer (BC) and kidney cancer (KC) in the Tatar study group, using the additive genetic effects model (Odds Ratio (OR) = 5.23 and OR = 3.90). In turn, the heterozygous G/A genotype was present at a significantly higher frequency in the KC patients of Bashkir ethnic origin, compared with the control group (p = 0.0061, OR= 4.72). Additional analysis with consideration of participants' smoking status showed that the G/A genotype is significantly more frequent in smokers with BC (OR = 1.96, p = 0.05) then in healthy smokers. We also determined, using the recessive genetic model, that the genotype A/A of the c. 1196A>G locus of the XRCC1 gene was correlated with a higher risk of BC in the Russian cohort (OR = 2.29, p = 0.0082) and an increased incidence of KC in the Bashkir group (OR = 4.06, p = 0.05). A similar correlation was obtained for smokers. In contrast, the allele c.2251 A>C in the XPD gene correlated with a lower risk for BC and KC in the Tatars (p = 0.0003, OR = 0.48 and p < 0.0001, OR = 0.37) in the additive model and in the Bashkirs (p = 0.0083, OR = 0.12) and Russians (p = 0.0001, OR = 0.14) in the recessive model. Further, we uncovered that polymorphism c.839 G>A in the XRCC1 gene contributes to the progression of noninvasive and invasive BC and promotes KC at early and advanced stages of the disease. Thus, we identified similar correlations between DNA repair gene polymorphism and the incidence and progression of BC and KC. We propose that this result points to the involvement of common pathogenetic mechanisms in the initiation and progression of the urinary neoplasias.

A unifying theory for genetic epidemiological analysis of binary disease data.

BackgroundGenetic selection for host resistance offers a desirable complement to chemical treatment to control infectious disease in livestock. Quantitative genetics disease data frequently originate from field studies and are often binary. However, current methods to analyse binary disease data fail to take infection dynamics into account. Moreover, genetic analyses tend to focus on host susceptibility, ignoring potential variation in infectiousness, i.e. the ability of a host to transmit the infection. This stands in contrast to epidemiological studies, which reveal that variation in infectiousness plays an important role in the progression and severity of epidemics. In this study, we aim at filling this gap by deriving an expression for the probability of becoming infected that incorporates infection dynamics and is an explicit function of both host susceptibility and infectiousness. We then validate this expression according to epidemiological theory and by simulating epidemiological scenarios, and explore implications of integrating this expression into genetic analyses.ResultsOur simulations show that the derived expression is valid for a range of stochastic genetic-epidemiological scenarios. In the particular case of variation in susceptibility only, the expression can be incorporated into conventional quantitative genetic analyses using a complementary log-log link function (rather than probit or logit). Similarly, if there is moderate variation in both susceptibility and infectiousness, it is possible to use a logarithmic link function, combined with an indirect genetic effects model. However, in the presence of highly infectious individuals, i.e. super-spreaders, the use of any model that is linear in susceptibility and infectiousness causes biased estimates. Thus, in order to identify super-spreaders, novel analytical methods using our derived expression are required.ConclusionsWe have derived a genetic-epidemiological function for quantitative genetic analyses of binary infectious disease data, which, unlike current approaches, takes infection dynamics into account and allows for variation in host susceptibility and infectiousness.

Estimates of (co)variance components and genetic parameters for growth traits in Suffolk lambs

The study aimed to estimate the components of (co)variance and heritability for weights at birth (BW), weaning (WW) and 180 days of age (W180), as well as the average daily gains from birth to weaning (ADG1), birth to 180 days of age (ADG2) and weaning to 180 days of age (ADG3) in Suffolk sheep. Thus, three different single-trait animal models were fitted, considering the direct additive genetic effect (Model 1), the direct additive genetic and maternal permanent environmental effects (Model 2), and in Model 3, in addition to those in Model 2, the maternal additive genetic effect was included. After comparing models through the likelihood ratio test (LRT), model 3 was chosen as the most appropriate to estimate heritability for BW, WW and ADG1. Model 2 was considered as the best to estimate the coefficient of heritability for W180 and ADG2, and model 1 for ADG3. Direct heritability estimates were inflated when maternal effects were ignored. According to the most suitable models, the heritability estimates for BW, WW, W180, ADG1, ADG2 and ADG3 were 0.06, 0.08, 0.09, 0.07, 0.08 and 0.07, respectively, indicating low possibility of genetic gain through individual selection. The results show the importance of including maternal effects in the models to properly estimate genetic parameters even at post-weaning ages.

Sex-Specific Heritability of Spontaneous Lipid Levels in an Extended Pedigree of Indian-Origin Rhesus Macaques (Macaca mulatta)

The rhesus macaque is an important model for human atherosclerosis but genetic determinants of relevant phenotypes have not yet been investigated in this species. Because lipid levels are well-established and heritable risk factors for human atherosclerosis, our goal was to assess the heritability of lipoprotein cholesterol and triglyceride levels in a single, extended pedigree of 1,289 Indian-origin rhesus macaques. Additionally, because increasing evidence supports sex differences in the genetic architecture of lipid levels and lipid metabolism in humans and macaques, we also explored sex-specific heritability for all lipid measures investigated in this study. Using standard methods, we measured lipoprotein cholesterol and triglyceride levels from fasted plasma in a sample of 193 pedigreed rhesus macaques selected for membership in large, paternal half-sib cohorts, and maintained on a low-fat, low cholesterol chow diet. Employing a variance components approach, we found moderate heritability for total cholesterol (h2=0.257, P=0.032), LDL cholesterol (h2=0.252, P=0.030), and triglyceride levels (h2=0.197, P=0.034) in the full sample. However, stratification by sex (N=68 males, N=125 females) revealed substantial sex-specific heritability for total cholesterol (0.644, P=0.004, females only), HDL cholesterol (0.843, P=0.0008, females only), VLDL cholesterol (0.482, P=0.018, males only), and triglyceride levels (0.705, P=0.001, males only) that was obscured or absent when sexes were combined in the full sample. We conclude that genes contribute to spontaneous variation in circulating lipid levels in the Indian-origin rhesus macaque in a sex-specific manner, and that the rhesus macaque is likely to be a valuable model for sex-specific genetic effects on lipid risk factors for human atherosclerosis. These findings are a first-ever report of heritability for cholesterol levels in this species, and support the need for expanded analysis of these traits in this population.

Genetic and non-genetic effects on calf vigor at birth and preweaning mortality in Nellore calves

The objective of the present study was to evaluate the genetic and non-genetic effects that influence vigor at birth and preweaning mortality in Nellore calves. A total of 11,727 records of births that occurred between 1978 and 2006, offspring of 363 sires, were analyzed. Poor calf vigor at birth (VB) and preweaning mortality divided into stillbirth (SB), early mortality (EM) and total mortality (TM) were analyzed as binary variables. Generalized linear models were used for the evaluation of non-genetic effects and generalized linear mixed models for genetic effects (sire and animal models). The incidences were 4.75% for VB, 2.66% for SB, 5.28% for EM, and 7.99% for TM. Birth weight was the effect that most influenced the traits studied. Calves weighing less than 22 kg (females) and less than 24 kg (males) were at a higher risk of low vigor and preweaning mortality. Preweaning mortality was higher among calves born from cows aged <3 and >11 years at calving compared with cows aged 7 to 10 years. Male calves presented less vigor and higher preweaning mortality than female calves. Selection for postweaning weight did not influence preweaning mortality. The heritability estimates ranged between 0.01 and 0.09 for VB, 0.00 and 0.27 for SB, 0.03 and 0.17 for EM and 0.02 and 0.10 for TM. Stillbirth should be included as a selection criterion in breeding programs of Nellore cattle, alone or as part of a selection index, aiming to reduce preweaning mortality.

Fetal and Maternal Genes’ Influence on Gestational Age in a Quantitative Genetic Analysis of 244,000 Swedish Births

Although there is increasing evidence that genetic factors influence gestational age, it is unclear to what extent this is due to fetal and/or maternal genes. In this study, we apply a novel analytical model to estimate genetic and environmental contributions to pregnancy history records obtained from 165,952 Swedish families consisting of offspring of twins, full siblings, and half-siblings (1987-2008). Results indicated that fetal genetic factors explained 13.1% (95% confidence interval (CI): 6.8, 19.4) of the variation in gestational age at delivery, while maternal genetic factors accounted for 20.6% (95% CI: 18.1, 23.2). The largest contribution to differences in the timing of birth were environmental factors, of which 10.1% (95% CI: 7.0, 13.2) was due to factors shared by births of the same mother, and 56.2% (95% CI: 53.0, 59.4) was pregnancy specific. Similar models fit to the same data dichotomized at clinically meaningful thresholds (e.g., preterm birth) resulted in less stable parameter estimates, but the collective results supported a model of homogeneous genetic and environmental effects across the range of gestational age. Since environmental factors explained most differences in the timing of birth, genetic studies may benefit from understanding the specific effect of fetal and maternal genes in the context of these yet-unidentified factors.

A Network-Based Kernel Machine Test for the Identification of Risk Pathways in Genome-Wide Association Studies

Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). In this study, the kernel converts the genomic information of 2 individuals into a quantitative value reflecting their genetic similarity. With the selection of the kernel, one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for the topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case-control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms.

Genetic influences on social dominance: cow wars

Genetic influences on social dominance: cow wars

Indirect genetic effects and the spread of infectious disease: are we capturing the full heritable variation underlying disease prevalence?

Reducing disease prevalence through selection for host resistance offers a desirable alternative to chemical treatment. Selection for host resistance has proven difficult, however, due to low heritability estimates. These low estimates may be caused by a failure to capture all the relevant genetic variance in disease resistance, as genetic analysis currently is not taylored to estimate genetic variation in infectivity. Host infectivity is the propensity of transmitting infection upon contact with a susceptible individual, and can be regarded as an indirect effect to disease status. It may be caused by a combination of physiological and behavioural traits. Though genetic variation in infectivity is difficult to measure directly, Indirect Genetic Effect (IGE) models, also referred to as associative effects or social interaction models, allow the estimation of this variance from more readily available binary disease data (infected/non-infected). We therefore generated binary disease data from simulated populations with known amounts of variation in susceptibility and infectivity to test the adequacy of traditional and IGE models. Our results show that a conventional model fails to capture the genetic variation in infectivity inherent in populations with simulated infectivity. An IGE model, on the other hand, does capture some of the variation in infectivity. Comparison with expected genetic variance suggests that there is scope for further methodological improvement, and that potential responses to selection may be greater than values presented here. Nonetheless, selection using an index of estimated direct and indirect breeding values was shown to have a greater genetic selection differential and reduced future disease risk than traditional selection for resistance only. These findings suggest that if genetic variation in infectivity substantially contributes to disease transmission, then breeding designs which explicitly incorporate IGEs might help reduce disease prevalence.