Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “environmental covariates”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
PubMed2026

Using high-dimensional environmental covariates to study genotype by environment interaction for reproductive traits in Duroc boars.

We investigated the potential of incorporating grid-cell-based environmental covariates (ECs) in the genetic evaluation of total sperm count (TSC), sperm motility (MOT), and sperm morphology (MOR) for Duroc boars. A total of 188,665 records derived from 3,684 genotyped boars, born between December 2018 and October 2024 and raised in three stud farms located in different U.S. states, were analyzed using multi-trait linear-threshold repeatability models. To account for genotype by environment interactions (GE), we constructed an interaction matrix as the Hadamard product of the genomic relationship matrix and an environmental (co)variance matrix. The environmental groups were defined in three ways: farm, farm-season, and farm-year-season. The (co)variance matrix was constructed based on daily ECs obtained from the NASA POWER database for each environmental group. Of all available ECs, those significantly associated with TSC, MOT, and MOR (temperature, relative humidity, atmospheric pressure, and wind speed and direction) were retained. We evaluated five models with different GE structures: M1 represented the baseline without accounting for GE, in M2 the GE included farm as environmental groups, in M3 the GE included farm-season as environmental groups, in M4 the GE included farm-year-season as environmental groups, and M5 involved M3 with an additional random effect of the farm-season. Estimates of heritability for TSC, MOT, and MOR ranged from 0.03 to 0.04, 0.05 to 0.08, and 0.04 to 0.08, respectively. Corresponding repeatability ranged from 0.15 to 0.23, 0.28 to 0.49, and 0.28 to 0.49. The proportion of phenotypic variance attributed to GE variance ranged from 0.00 to 0.32, 0.00 to 0.44, and 0.00 to 0.44. Lastly, estimates of genetic correlation, TSC-MOT, TSC-MOR, and MOT-MOR ranged from 0.27 to 0.31, 0.24 to 0.31, and 0.98 to 0.99, respectively, with minor differences across models. We assessed the predictive ability of models using the linear regression validation. Across traits and models, bias ranged from -0.05 to 0.02 standard deviations, slope varied from 0.88 to 0.99, the correlation ranged from 0.75 to 0.84, and accuracy from 0.41 to 0.53. Overall, building the GE matrix considering grid-cell-based ECs helped to account for GE, thereby reducing the proportion of phenotypic variance attributed to genetic components; however, it did not improve the validation metrics. Additional on-farm records for ECs may improve the model performance.

Animals↗
PubMed2002

Choice of stratification in Poisson process analysis of recurrent event data with environmental covariates.

The Poisson process approach for studying the association between environmental covariates and recurrent events depends on the stratification of study period into intervals within which the baseline intensities are assumed constant. In this work we investigate the problem of bias and variance due to misspecification of this stratification. We suggest a cross-validation approach to choosing a stratification model to balance the trade-off between bias and variance. We also establish a connection between the Poisson process approach and case cross-over studies.

Air Pollution↗
PubMed2005

Extension of the SIMLA package for generating pedigrees with complex inheritance patterns: environmental covariates, gene-gene and gene-environment interaction.

We have previously distributed a software package, SIMLA (SIMulation of Linkage and Association), which can be used to generate disease phenotype and marker genotype data in three-generational pedigrees of user-specified structure. To our knowledge, SIMLA is the only publicly available program that can simulate variable levels of both linkage (recombination) and linkage disequilibrium (LD) between marker and disease loci in general pedigrees. While the previous SIMLA version provided flexibility in choosing many parameters relevant for linkage and association mapping of complex human diseases, it did not allow for the segregation of more than one disease locus in a given pedigree and did not incorporate environmental covariates possibly interacting with disease susceptibility genes. Here, we present an extension of the simulation algorithm characterized by a much more general penetrance function, which allows for the joint action of up to two genes and up to two environmental covariates in the simulated pedigrees, with all possible multiplicative interaction effects between them. This makes the program even more useful for comparing the performance of different linkage and association analysis methods applied to complex human phenotypes. SIMLA can assist investigators in planning and designing a variety of linkage and association studies, and can help interpret results of real data analyses by comparing them to results obtained under a user-controlled data generation mechanism.A free download of the SIMLA package is available at http://wwwchg.duhs.duke.edu/software.

Journal Article↗
PubMed1993

Genetic and environmental covariance of serum cholesterol and blood pressure in female twins.

Blood pressure elevation is frequently associated with elevated cholesterol, triglyceride or low density lipoprotein (LDL-C) or low high density lipoprotein (HDL-C). The relative importance of genetic and environmental factors in these associations is unclear. We examined the relative contribution of genetic and environmental influences to the association between blood pressure and serum lipids in 75 pairs of female twins using path analysis and maximum-likelihood model fitting. Associations between systolic blood pressure and total cholesterol (r = 0.44, P < 0.001), and LDL-C (r = 0.38, P < 0.001), but not HDL-C (r = 0.05, N.S.), remained significant after age and body mass index adjustment. Univariate models suggested genetic effects contributed 60-70% to the variance of total cholesterol, LDL-C, HDL-C and systolic blood pressure. The remaining variance was explained by age and/or unique environmental influences. Using bivariate models, we demonstrated genetic (P = 0.017) and unique environmental covariance (P = 0.011) of cholesterol and systolic blood pressure. Significant genetic covariance (P = 0.038) was observed between LDL-C and systolic blood pressure. The association between blood pressure and total cholesterol in these twins results from shared genetic and similar unique environmental influences. The association between LDL-C and blood pressure is partly due to shared genetic influences. We conclude that both additive genetic and environmental factors unique to the individual are important determinants of the relationships between serum lipids and blood pressure.

Adolescent↗
PubMed2005

Regression analysis of longitudinal binary data with time-dependent environmental covariates: bias and efficiency.

Generalized estimating equations (Liang and Zeger, 1986) is a widely used, moment-based procedure to estimate marginal regression parameters. However, a subtle and often overlooked point is that valid inference requires the mean for the response at time t to be expressed properly as a function of the complete past, present, and future values of any time-varying covariate. For example, with environmental exposures it may be necessary to express the response as a function of multiple lagged values of the covariate series. Despite the fact that multiple lagged covariates may be predictive of outcomes, researchers often focus interest on parameters in a 'cross-sectional' model, where the response is expressed as a function of a single lag in the covariate series. Cross-sectional models yield parameters with simple interpretations and avoid issues of collinearity associated with multiple lagged values of a covariate. Pepe and Anderson (1994), showed that parameter estimates for time-varying covariates may be biased unless the mean, given all past, present, and future covariate values, is equal to the cross-sectional mean or unless independence estimating equations are used. Although working independence avoids potential bias, many authors have shown that a poor choice for the response correlation model can lead to highly inefficient parameter estimates. The purpose of this paper is to study the bias-efficiency trade-off associated with working correlation choices for application with binary response data. We investigate data characteristics or design features (e.g. cluster size, overall response association, functional form of the response association, covariate distribution, and others) that influence the small and large sample characteristics of parameter estimates obtained from several different weighting schemes or equivalently 'working' covariance models. We find that the impact of covariance model choice depends highly on the specific structure of the data features, and that key aspects should be examined before choosing a weighting scheme.

Air Pollutants↗
PubMed2004

Natural selection, evolvability and bias due to environmental covariance in the field in an annual plant.

Estimates of the form and magnitude of natural selection based on phenotypic relationships between traits and fitness measures can be biased when environmental factors influence both relative fitness and phenotypic trait values. I quantified genetic variances and covariances, and estimated linear and quadratic selection coefficients, for seven traits of an annual plant grown in the field. For replicates of 50 paternal half-sib families, coefficients of selection were calculated both for individual phenotypic values of the traits and for half-sib family mean values. The potential for evolutionary response was supported by significant heritability and phenotypic directional selection for several traits but contradicted by the absence of significant genetic variation for fitness estimates and evidence of bias in phenotypic selection coefficients due to environmental covariance for at least two of the traits analysed. Only studies of a much wider range of organisms and traits will reveal the frequency and extent of such bias.

Analysis of Variance↗
PubMed2007

A comparison of methods for the analysis of recurrent health outcome data with environmental covariates.

Recurrent events such as repeated hospital admissions for the same health outcome occur frequently in environmental health studies. Dewanji and Moolgavkar proposed a flexible parametric model and a conditional likelihood analysis for recurrent events based on a Poisson process formulation. In this paper, we examine the statistical properties of the Dewanji-Moolgavkar (DM) estimator of the risk of an adverse health outcome associated with environmental exposures based on recurrent event data using computer simulation. We also compare the DM approach with both case-crossover analysis for multiple observations and time series analysis when there are no subject-specific covariates. When using a correctly specified model, the DM method produced better estimates with respect to relative mean square error when each subject had constant or curved baseline intensity functions than it did when baseline intensities were increasing or decreasing in a linear fashion. For under-specified models, the DM method outperformed case-crossover analysis for decreasing straight line intensity functions, was outperformed by case-crossover analysis for increasing straight line intensity functions, and was roughly equivalent to case-crossover analysis for constant and curved intensity functions. Case-crossover analysis produced superior risk estimates more frequently than the other two methods in the cases considered here, especially for linear representations of the baseline intensities.

Aged↗
PubMed1999

Environmental covariates: effects on the power of sib-pair linkage methods.

The effect of inclusion of environmental risk factors on the power of sib-pair linkage methods was tested for a qualitative trait. It was found that inclusion of an environmental variable did not increase the power of the Haseman-Elston (H-E) sib-pair nonparametric linkage analysis test. However, a significant increase in power was observed for both the H-E and affected-sib-pair tests, even in small samples, when persons unexposed to the environmental risk factor were coded as unknown.

Environment↗
PubMed2001

Modeling complex disease with demographic and environmental covariates and a candidate gene marker.

We randomly chose replicates 28 and 29 of the simulated data sets of Genetic Analysis Workshop 12 to model the dependence of affection status on covariates, quantitative traits, and genes using all living pedigree members. First we explored the relationship of affection status to demographic and environmental factors using logistic regression and the Cox proportional hazards models. In the second stage of our analyses the generalized transmission disequilibrium test (GTDT) was applied to nuclear families with at least two affected siblings to select single markers and high-risk alleles, which were tested in the population association analyses including all pedigree members. Multiple logistic regression models were fitted to investigate the joint contributions of genetic and nongenetic factors and a block-recursive modeling approach was adopted to study inherent hierarchical dependence structure in the data. We found that allele 2 on marker 35 of chromosome 6 is associated with higher risk compared with the other 3 alleles of this marker. In addition to this significant genetic effect, age at exam and four of the five quantitative traits (QT1, QT2, QT4, and QT5) had a significant association with the disease. Our results were obtained without knowledge of the true disease generating models.

Adolescent↗
PubMed1998

Socioeconomic and environmental covariates of premature mortality in Ontario.

This paper contributes to debates on the broad determinants of health and on the policy shift from curative to preventive and protective interventions. It addresses empirically the relative importance of influences on health with a multiple regression analysis of ecologic data from the 49 counties of Ontario. One model achieved high predictive power (that is, Adj R2 > 75%, p < 0.0001). Educational levels were a strong predictor of population health, showing a consistent inverse relationship with premature mortality ratios for both sexes and it was the strongest predictor for females. A low income variable supplied the strongest prediction for male mortality. This variable displayed a positive association with male mortality. Municipal expenditures on environmental protection exerted a negative effect on male mortality. These findings raise questions about the current directions of health policy in Ontario where the provincial government has reduced funding to social and environmental programs, while promising to maintain health care funding.

Adolescent↗
PubMed1986

Cohabitation, convergence, and environmental covariances.

Temporal variation in traits has long been a central theme in epidemiology. However, human geneticists have largely avoided this topic. Recently, several authors have shown how temporal variation in relative-to-relative covariances can be accommodated within the framework of variance components analysis. The present paper attempts to clarify the mathematics implicit in their approach. A stochastic mechanism is discussed that causes covariances to converge or diverge exponentially fast as relatives cohabit or lead separate lives.

Environment↗
PubMed2003

Candidate-gene association studies with pedigree data: controlling for environmental covariates.

Case-control studies provide an important epidemiological tool to evaluate candidate genes. There are many different study designs available. We focus on a more recently proposed design, which we call a multiplex case-control (MCC) design. This design compares allele frequencies between related cases, each of whom are sampled from multiplex families, and unrelated controls. Since within-family genotype correlations will exist, statistical methods will need to take this into account. Moreover, there is a need to develop methods to simultaneously control for potential confounders in the analysis. Generalized estimating equations (GEE) are one approach to analyze this type of data; however, this approach can have singularity problems when estimating the correlation matrix. To allow for modeling of other covariates, we extend our previously developed method to a more general model-based approach. Our proposed methods use the score statistic, derived from a composite likelihood. We propose three different approaches to estimate the variance of this statistic. Under random ascertainment of pedigrees, score tests have correct type I error rates; however, pedigrees are not randomly ascertained. Thus, through simulations, we test the validity and power of the score tests under different ascertainment schemes, and an illustration of our methods, applied to data from a prostate cancer study, is presented. We find that our robust score statistic has estimated type I error rates within the expected range for all situations we considered whereas the other two statistics have inflated type I error rates under nonrandom ascertainment schemes. We also find GEE to fail at least 5% of the time for each simulation configuration; at times, the failure rate reaches above 80%. In summary, our robust method may be the only current regression analysis method available for MCC data.

Case-Control Studies↗
PubMedDate not supplied

Genetic and environmental covariation between verbal and nonverbal cognitive development in infancy.

Despite cognitive neuroscience's emphasis on the modularity of cognitive processes, multivariate genetic research indicates that the same genetic factors largely affect diverse cognitive abilities, at least from middle childhood onward. We explored this issue for verbal and nonverbal cognitive development in infancy in a study of 1,937 pairs of same-sex 2-year-old twins born in England and Wales in 1994. The twins were assessed by having their parents use a measure of productive vocabulary (the MacArthur Communicative Development Inventory) and a novel measure of nonverbal cognitive abilities (Parent Report of Children's Ability). Verbal and nonverbal development correlated .42. A multivariate genetic analysis indicated that genetic factors were responsible for less than half of this phenotypic correlation. Moreover, the genetic correlation between verbal and nonverbal abilities was only .30, which indicates that genetic effects on verbal and nonverbal abilities are largely independent in infancy. These multivariate genetic results suggest that genetic effects on cognitive abilities are modular early in development and then become increasingly molar. The implications of this result for theories of cognitive development are discussed.

Age Factors↗
PubMed2006

Temporal variation in prevalence and abundance of metacercariae in the pulmonate snail Lymnaea stagnalis in Chany Lake, West Siberia, Russia: long-term patterns and environmental covariates.

Infrapopulations of trematode metacercariae were monitored in the snail Lymnaea stagnalis over 17 yr (1982-1999) at Chany Lake, Novosibirskaya Oblast', Russia. Eighteen trematode species were recorded. Patterns of occurrence varied from 4 species (Echinoparyphium aconiatum, Echinoparyphium recurvatum, Moliniella anceps, and Cotylurus cornutus) that persisted at relatively high prevalence (> 60% of samples) across sites, seasons, and years, to species that were very rare and sporadic in occurrence. The stability of the 4 common species was probably because of their occurrence either in a wide range of definitive hosts or in a host adapted to the extreme abiotic changes that occurred from year to year in these wetlands. The prevalence and mean abundance of C. cornutus were negatively correlated with water level in the wetlands; its prevalence was also correlated with water temperature. The mean abundance of M. anceps was positively correlated with water level. The most probable explanation for the cyclic dynamics of infections of the common species is change in population sizes and densities of definitive and intermediate hosts, which mediated cyclic alterations in water levels.

Animals↗
PubMed1994

Restricted maximum likelihood procedures for the estimation of additive and nonadditive genetic variances and covariances in multibreed populations.

Restricted maximum-likelihood procedures were developed to estimate additive and nonadditive genetic and environmental covariances for multiple traits in multibreed populations. The computational procedure follows the expectation-maximization (EM) algorithm, where the set of equations in the maximization step is solved by successive approximations. This computational procedure does not guarantee convergence to a symmetric positive-definite covariance matrix. Thus, computer programs will need to incorporate restrictions in the maximization step to ensure positive definiteness of each covariance matrix. Additive genetic and environmental covariances were modeled in subclass form (zeros and ones in the design matrices). Nonadditive genetic covariances were modeled in regression form (any value between and including zero and one in the design matrices). Computational requirements will be larger than for intrabreed analyses. Appropriate simplifying assumptions and numerical techniques (e.g., sparse and iterative numerical techniques) will be required for the implementation of these multibreed covariance estimation procedures. Number of iterations (5 to 12) and computing times (57 to 113 min) to achieve convergence when estimating 21 genetic and environmental covariances in five small simulated multibreed data sets (two breeds, 25,200 to 50,400 calves, 120 to 135 unrelated bulls) suggest that these procedures are computationally feasible.

Algorithms↗
PubMed2003

On the relationship between time-series studies, dynamic population studies, and estimating loss of life due to short-term exposure to environmental risks.

There is a growing concern that short-term exposure to combustion-related air pollution is associated with increased risk of death. This finding is based largely on time-series studies that estimate associations between daily variations in ambient air pollution concentrations and in the number of nonaccidental deaths within a community. Because these results are not based on cohort or dynamic population designs, where individuals are followed in time, it has been suggested that estimates of effect from these time-series studies cannot be used to determine the amount of life lost because of short-term exposures. We show that results from time-series studies are equivalent to estimates obtained from a dynamic population when each individual's survival experience can be summarized as the daily number of deaths. This occurs when the following conditions are satisfied: a) the environmental covariates vary in time and not between individuals; b) on any given day, the probability of death is small; c) on any given day and after adjusting for known risk factors for mortality such age, sex, smoking habits, and environmental exposures, each subject of the at-risk population has the same probability of death; d) environmental covariates have a common effect on mortality of all members of at-risk population; and e) the averages of individual risk factors, such as smoking habits, over the at-risk population vary smoothly with time. Under these conditions, the association between temporal variation in the environmental covariates and the survival experience of members of the dynamic population can be estimated by regressing the daily number of deaths on the daily value of the environmental covariates, as is done in time-series mortality studies. Issues in extrapolating risk estimates based on time-series studies in one population to estimate the amount of life lost in another population are also discussed.

Air Pollutants↗