Genetic Covariance Research Articles

Multienvironment genomic prediction in tetraploid potato.

Multienvironment genomic prediction was applied to tetraploid potato using 147 potato varieties, tested for 2 years, in 3 locations representative of 3 distinct regions in Europe. Different prediction scenarios were investigated to help breeders predict genotypic performance in the regions from one year to the next, for genotypes that were tested this year (scenario 1), as well as new genotypes (scenario 3). In scenario 2, we predicted new genotypes for any one of the 6 trials, using all the information that is available. The choice of prediction model required assessment of the variance-covariance matrix in a mixed model that takes into account heterogeneity of genetic variances and correlations. This was done for each analyzed trait (tuber weight, tuber length, and dry matter) where examples of both limited and higher degrees of heterogeneity was observed. This explains why dry matter did not need complex multienvironment modeling to combine environments and increase prediction ability, while prediction in tuber weight, improved only when models were flexible enough to capture the heterogeneous variances and covariances between environments. We also found that the prediction abilities in a target trial condition decreased, if trials with a low genetic correlation to the target were included when training the model. Genomic prediction in tetraploid potato can work once there is clarity about the prediction scenario, a suitable training set is created, and a multienvironment prediction model is chosen based on the patterns of G×E indicated by the genetic variances and covariances.

Incorporation of emergent symptoms and genetic covariates improves prediction of aromatase inhibitor therapy discontinuation.

Early discontinuation is common among breast cancer patients taking aromatase inhibitors (AIs). Although several predictors have been identified, it is unclear how to simultaneously consider multiple risk factors for an individual. We sought to develop a tool for prediction of AI discontinuation and to explore how predictive value of risk factors changes with time. Survival machine learning was used to predict time-to-discontinuation of AIs in 181 women who enrolled in a prospective cohort. Models were evaluated via time-dependent area under the curve (AUC), c-index, and integrated Brier score. Feature importance was analysis was conducted via Shapley Additive Explanations (SHAP) and time-dependence of their predictive value was analyzed by time-dependent AUC. Personalized survival curves were constructed for risk communication. The best-performing model incorporated genetic risk factors and changes in patient-reported outcomes, achieving mean time-dependent AUC of 0.66, and AUC of 0.72 and 0.67 at 6- and 12-month cutoffs, respectively. The most significant features included variants in ESR1 and emergent symptoms. Predictive value of genetic risk factors was highest in the first year of treatment. Decrease in physical function was the strongest independent predictor at follow-up. Incorporation of genomic and 3-month follow-up data improved the ability of the models to identify the individuals at risk of AI discontinuation. Genetic risk factors were particularly important for predicting early discontinuers. This study provides insight into the complex nature of AI discontinuation and highlights the importance of incorporating genetic risk factors and emergent symptoms into prediction models.

Geographic variation in individual face learning based on plasticity rather than local genetic adaptation in Polistes wasps

Abstract Signals and receiver responses often vary across a species’ geographic range. Effective communication requires a match between signal and receiver response, so there is much interest in the developmental mechanisms that maintain this link. Two potential mechanisms are genetic covariance between signal and receiver response and plasticity where individuals adjust their phenotype based on their partner’s phenotype. Here, we test how plasticity contributes to geographic variation in individual face recognition in Polistes fuscatus wasps. Previous work has shown that P. fuscatus from Michigan, USA (MI) have variable facial patterns used for individual recognition, while P. fuscatus from central Pennsylvania, USA (PA) lack variable facial patterns and are unable to learn individual conspecifics. We experimentally altered rearing environment, so wasps were either reared with their own population or in a common garden with wasps from both populations. Then, we tested the wasps’ capacity to learn and remember individual conspecific faces. Consistent with previous work, MI wasps reared with MI wasps were adept at learning conspecific faces, while PA wasps reared with PA wasps were unable to learn conspecific faces. However, MI and PA wasps reared in a common garden developed similar, intermediate capacity for individual face learning. These results indicate that individual face learning in Polistes wasps is highly plastic and responsive to the social environment. Plasticity in receiver responses may be a common mechanism mediating geographic differences in non-sexual signaling systems and may play a role in maintaining links between signals and receiver responses in geographically variable communication systems.

Genetic and environmental influences on alpha amylase stress reactivity and shared genetic covariation with cortisol

Salivary alpha amylase (sAA) is a common measure of stress reactivity, primarily reflecting sympathetic nervous system activity. Salivary cortisol is also a reliable, frequently used biomarker of stress and reflects the hypothalamic-pituitary-adrenal (HPA) axis response. This study examined heritability across varying metrics of sAA in response to a social evaluative stressor, the Trier Social Stress Test (TSST). The goal of this study was to estimate genetic and environmental influences on measurements of sAA stress reactivity. Moreover, we evaluated the shared genetic covariation between sAA and cortisol. Participants included twins aged 15–20 years (54% female). We measured alpha amylase and cortisol reactivity to the TSST via serial salivary cortisol samples collected pre- and post-TSST. Modest to moderate heritability estimates (11–64%) were observed across measures purported to capture alpha amylase stress reactivity (peak, area under the curve, baseline-to-peak change). Findings also indicate that sAA baseline and peak are primarily influenced by a shared genetic factor. There was no evidence of shared genetic influences between sAA and cortisol. These findings suggest the genetic control of the HPA and Sympathetic Adreno-Medullar axis are genetically independent of one another despite both playing a role in response to stressors.

Polygenic Analyses Show Important Differences Between Major Depressive Disorder Symptoms Measured Using Various Instruments

BackgroundSymptoms of major depressive disorder (MDD) are commonly assessed using self-rating instruments like the Patient Health Questionnaire-9 (PHQ-9) (current symptoms) and the Composite International Diagnostic Interview Short-Form (CIDI-SF) (worst-episode symptoms). We performed a systematic comparison between them for their genetic architecture and utility in investigating MDD heterogeneity. MethodsUsing data from the UK Biobank (n = 41,948–109,417), we assessed the single nucleotide polymorphism heritability and genetic correlation (rg) of both sets of MDD symptoms. We further compared their rg with non-MDD traits and used Mendelian randomization to assess whether either set of symptoms has more genetic sharing with non-MDD traits. We also assessed how specific each set of symptoms is to MDD using the metric polygenic risk score pleiotropy. Finally, we used genomic structural equation modeling to identify factors that explain the genetic covariance between each set of symptoms. ResultsCorresponding symptoms reported through the PHQ-9 and CIDI-SF have low to moderate genetic correlations (rg = 0.43–0.87), and this cannot be fully attributed to different severity thresholds or the use of a skip structure in the CIDI-SF. Both Mendelian randomization and polygenic risk score pleiotropy analyses showed that PHQ-9 symptoms are more associated with traits that reflect general dysphoria, whereas the skip structure in the CIDI-SF allows for the identification of heterogeneity among likely MDD cases. Finally, the 2 sets of symptoms showed different factor structures in genomic structural equation modeling, reflective of their genetic differences. ConclusionsMDD symptoms assessed using the PHQ-9 and CIDI-SF are not interchangeable; the former better indexes general dysphoria, while the latter is more informative about within-MDD heterogeneity.

A mathematical framework for evo-devo dynamics

Natural selection acts on phenotypes constructed over development, which raises the question of how development affects evolution. Classic evolutionary theory indicates that development affects evolution by modulating the genetic covariation upon which selection acts, thus affecting genetic constraints. However, whether genetic constraints are relative, thus diverting adaptation from the direction of steepest fitness ascent, or absolute, thus blocking adaptation in certain directions, remains uncertain. This limits understanding of long-term evolution of developmentally constructed phenotypes. Here we formulate a general, tractable mathematical framework that integrates age progression, explicit development (i.e., the construction of the phenotype across life subject to developmental constraints), and evolutionary dynamics, thus describing the evolutionary and developmental (evo-devo) dynamics. The framework yields simple equations that can be arranged in a layered structure that we call the evo-devo process, whereby five core elementary components generate all equations including those mechanistically describing genetic covariation and the evo-devo dynamics. The framework recovers evolutionary dynamic equations in gradient form and describes the evolution of genetic covariation from the evolution of genotype, phenotype, environment, and mutational covariation. This shows that genotypic and phenotypic evolution must be followed simultaneously to yield a dynamically sufficient description of long-term phenotypic evolution in gradient form, such that evolution described as the climbing of a fitness landscape occurs in “geno-phenotype” space. Genetic constraints in geno-phenotype space are necessarily absolute because the phenotype is related to the genotype by development. Thus, the long-term evolutionary dynamics of developed phenotypes is strongly non-standard: (1) evolutionary equilibria are either absent or infinite in number and depend on genetic covariation and hence on development; (2) developmental constraints determine the admissible evolutionary path and hence which evolutionary equilibria are admissible; and (3) evolutionary outcomes occur at admissible evolutionary equilibria, which do not generally occur at fitness landscape peaks in geno-phenotype space, but at peaks in the admissible evolutionary path where “total genotypic selection” vanishes if exogenous plastic response vanishes and mutational variation exists in all directions of genotype space. Hence, selection and development jointly define the evolutionary outcomes if absolute mutational constraints and exogenous plastic response are absent, rather than the outcomes being defined only by selection. Moreover, our framework provides formulas for the sensitivities of a recurrence and an alternative method to dynamic optimization (i.e., dynamic programming or optimal control) to identify evolutionary outcomes in models with developmentally dynamic traits. These results show that development has major evolutionary effects.

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation.

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi-kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution; (B) incorporating demographics & clinical covariates; (C) the impact of the size of the training data set; (D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of-sample predictions. The highest performance for Aβ42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status.

The association between change and stability in subjective health ratings and dementia risk: a multi‐cohort study in Swedish and Danish twins

AbstractBackgroundPoorer self‐rated health (SRH) is associated with increased dementia risk (Stephan et al., 2021). However, it is unclear whether genetic and/or environmental variance accounts for this risk. Although SRH declines with age (Finkel et al. 2020), it is unknown whether rates of change in SRH are independently associated with dementia outcomes. Using a validated latent dementia index (LDI) (Beam et al., 2022), we tested whether stability and change components of SRH predicted LDI via genetic and environmental pathways in two international cohorts of twins.MethodOur data consisted of two samples of monozygotic (MZ) and dizygotic (DZ) twins. We used data from the Swedish Adoption Twin Study of Aging (SATSA; Ntwins = 548, Npairs = 339) and the Longitudinal Study of Aging Danish Twins (LSADT; Ntwins = 3238, Npairs = 2561). Bivariate Cholesky decompositions and three‐level growth‐curve models with predictive intercepts and slopes were used for our cross‐sectional and longitudinal analyses respectively. All models were estimated in Mplus (Muthen & Muthen, 2017) using maximum likelihood with robust standard errors.ResultsCross‐sectional model results indicate that those with poorer SRH have lower LDI scores (b = ‐0.01, p = .008), even after adjusting for shared genetic covariance, age, sex, education, and depressive symptoms. Predictive growth models indicate that rates of change in SRH are negatively associated with LDI after adjusting for age, sex, education, and depressive symptoms (b = ‐0.17, p = .032). The degree of genetic confounding in the association between rates of change and LDI was inconclusive.ConclusionOur study provides novel insights into the relationship between individual ratings of health and dementia risk. SRH predicted dementia likelihood after controlling for genetic confounding, opening up the possibility that subjective ratings of health account for unique variance in dementia risk. Additionally, changes in SRH also predicted dementia likelihood beyond their mean score. Taken together these results highlight the importance of subjective health measures in understanding dementia risk.

Impact of Drought Stress on Yield-Related Agronomic Traits of Different Genotypes in Spring Wheat

Drought stress is one of the major abiotic stresses to wheat worldwide, with negative effects on wheat growth and yield. Assessing genetic variation and drought stress tolerance of key agronomic and physiological traits of spring wheat and screening germplasm resources for higher drought tolerance and yield stability are a prerequisite for developing new, better-adapted spring wheat varieties. This study evaluated nine important agronomic and physiological traits in 152 spring wheat cultivars under non-stress (NS) and drought-stress (DS) conditions. Under DS conditions, grain yield per plot (GYP) and grain weight per spike (GWE) were significantly reduced by 33.8% and 31.7%, and their drought-tolerance indexes (DIs) were only 0.66 and 0.69, respectively, indicating that GYP and GWE are the most susceptible traits to drought stress. The SPAD value of flag leave at flowering stage decreased by 13.9% under DS conditions, and the DI of SPAD was 0.86. In addition, DI-SPAD was significantly positively correlated with DIs of plant height (PH), grain number per spikelet (GPS), grain number per spike (GNS), GWE and GYP, indicating that the drought tolerance and yield of wheat are closely related to chlorophyll retention. Six wheat germplasm accessions were identified for their ability to sustain grain yield and improve drought tolerance simultaneously. These results provide insights into the genetic co-variation between grain yield and drought stress tolerance and provide a theoretical basis for the development of new wheat cultivars with excellent drought tolerance and high yields in the presence and absence of drought.

The genetic relationships between brain structure and schizophrenia

Genetic risks for schizophrenia are theoretically mediated by genetic effects on brain structure but it has been unclear which genes are associated with both schizophrenia and cortical phenotypes. We accessed genome-wide association studies (GWAS) of schizophrenia (N = 69,369 cases; 236,642 controls), and of three magnetic resonance imaging (MRI) metrics (surface area, cortical thickness, neurite density index) measured at 180 cortical areas (N = 36,843, UK Biobank). Using Hi-C-coupled MAGMA, 61 genes were significantly associated with both schizophrenia and one or more MRI metrics. Whole genome analysis with partial least squares demonstrated significant genetic covariation between schizophrenia and area or thickness of most cortical regions. Genetic similarity between cortical areas was strongly coupled to their phenotypic covariance, and genetic covariation between schizophrenia and brain phenotypes was strongest in the hubs of structural covariance networks. Pleiotropically associated genes were enriched for neurodevelopmental processes and positionally concentrated in chromosomes 3p21, 17q21 and 11p11. Mendelian randomization analysis indicated that genetically determined variation in a posterior cingulate cortical area could be causal for schizophrenia. Parallel analyses of GWAS on bipolar disorder, Alzheimer’s disease and height showed that pleiotropic association with MRI metrics was stronger for schizophrenia compared to other disorders.

Genomic-enhanced Markov causal models for predicting breeding values in forest trees

Traditionally, in forest genetic evaluations, the pedigree-based individual-tree mixed model (ABLUP) has been utilized to identify individuals with the highest breeding value (BV). The ABLUP is considered a Markov causal model as the BV of an individual is independent of all its non-descendants, conditional to including the BV of its parents. Under this model, an individual's BV is expressed as a linear regression on parental BVs, along with a Mendelian sampling term. The availability of genome-wide marker panels has led to the development of a technique known as genomic selection (GS). In forest tree breeding, the most widespread GS method is the GBLUP model, which replaces the pedigree-based genetic covariance matrix with a marker-based relatedness matrix (G-matrix) derived from genotyped individuals. The GBLUP model has demonstrated improved BV accuracy compared to the ABLUP model. However, the use of Markov causal models incorporating genomic information remains unexplored in forest tree breeding. Therefore, the objective of this study was to develop and apply two new Markov causal models that substitute the “path coefficient” of the ABLUP model (which is 0.5) with an alternative coefficient, calculated using genomic information. Subsequently, we compared the performance of these new two causal models with the classical causal ABLUP and non-causal GBLUP models in terms of their predictive ability (PA) for BVs. Our investigation focused on a fourth-generation population of Eucalyptus grandis (Hill ex Maiden), comprising 3,082 genotyped trees with 14,033 single-nucleotide polymorphism markers. The study assessed six growth and wood quality traits in 1,219 trees spanning the first three generations. Progeny validation was conducted to evaluate PA, where individuals from generations 1 and 2 comprised the training set, and individuals from generation 3 constituted the validation set. Overall, the GBLUP model exhibited slightly superior predictive performance compared to the Markov causal models for most analyzed traits (with an average PA of 0.24 for causal models compared to 0.27 for GBLUP). However, when analyzing BVs of individuals from generation 4 in the greenhouse, without phenotypic data, the GBLUP model displayed greater dispersion in predicted BVs compared to those obtained through the genomic Markov causal models. The Markov causal models developed in this study appear to be more “stable”, and, due to their lower computational burden, represent a potential alternative to classical non-causal GBLUP GS model employed in forest breeding programs.

Genetic covariance components for measures of nitrogen utilization in grazing dairy cows

Improved nitrogen utilization of dairy production systems should improve not only the economic output of the systems but also the environmental metrics. One strategy to improve efficiency is through breeding programs. Improving a trait through breeding is conditional on the presence of exploitable genetic variability. Using a database of 1,291 deeply phenotyped grazing dairy cows, the genetic variability for 2 definitions of nitrogen utilization was studied: nitrogen use efficiency (i.e., nitrogen output in milk and meat divided by nitrogen available) and nitrogen balance (i.e., nitrogen available less nitrogen output in milk and meat). Variance components for both variables were estimated using animal repeatability linear mixed models. Genetic variability was detected for both nitrogen utilization metrics, even though their heritability estimates were low (<0.10). Validation of genetic evaluations revealed that animals divergent for nitrogen use efficiency or nitrogen balance indeed differed phenotypically, further demonstrating that breeding for improved nitrogen efficiency should result in a shift in the population mean toward better efficiency. Nitrogen use efficiency and nitrogen balance were not genetically correlated with each other (<|0.28|), and neither metric was correlated with milk urea nitrogen (<|0.12|). Nitrogen balance was unfavorably correlated with milk yield, showing the importance of including the nitrogen utilization metrics in a breeding index to improve nitrogen utilization without negatively impacting milk yield. In conclusion, improvement of nitrogen utilization through breeding is possible, even if more nitrogen utilization phenotypic data need to be collected to improve the selection accuracy considering the low heritability estimates.

A Physiologically Based Pharmacokinetic Approach to Recommend an Individual Dose of Tacrolimus in Adult Heart Transplant Recipients.

Tacrolimus is the principal immunosuppressive drug which is administered after heart transplantation. Managing tacrolimus therapy is challenging due to a narrow therapeutic index and wide pharmacokinetic (PK) variability. We aimed to establish a physiologically based pharmacokinetic (PBPK) model of tacrolimus in adult heart transplant recipients to optimize dose regimens in clinical practice. A 15-compartment full-PBPK model (Simbiology® Simulator, version 5.8.2) was developed using clinical observations from 115 heart transplant recipients. This study detected 20 genotypes associated with tacrolimus metabolism. CYP3A5*3 (rs776746), CYP3A4*18B (rs2242480), and IL-10 G-1082A (rs1800896) were identified as significant genetic covariates in tacrolimus pharmacokinetics. The PBPK model was evaluated using goodness-of-fit (GOF) and external evaluation. The predicted peak blood concentration (Cmax) and area under the drug concentration-time curve (AUC) were all within a two-fold value of the observations (fold error of 0.68-1.22 for Cmax and 0.72-1.16 for AUC). The patients with the CYP3A5*3/*3 genotype had a 1.60-fold increase in predicted AUC compared to the patients with the CYP3A5*1 allele, and the ratio of the AUC with voriconazole to alone was 5.80 when using the PBPK model. Based on the simulation results, the tacrolimus dosing regimen after heart transplantation was optimized. This is the first PBPK model used to predict the PK of tacrolimus in adult heart transplant recipients, and it can serve as a starting point for research on immunosuppressive drug therapy in heart transplant patients.

DNA methylation is not associated with sensitization to or dietary introduction of highly allergenic foods in a subset of the CHILD cohort at age 1 year.

In the first year of life, DNA methylation (DNAm) patterns are established and are particularly susceptible to exposure-induced changes. Some of these changes may leave lasting effects by persistently altering gene expression or cell type composition or function, contributing to disease. In this discovery study, we investigated DNAm associations with sensitization to peanut, egg, or cow's milk and hypothesized that genes demonstrating DNAm differences in immune cells may play a role in the development of food sensitization. Infant sensitization (a skin prick test wheal size that is at least 2 mm greater than the negative control) was measured to peanut, egg, and cow's milk at age 1 year, and ages of food introduction were reported prospectively. PBMC DNAm was measured in blood samples at 1 year in 144 infants, oversampled for atopy or wheeze. Statistical analysis of Illumina 450k array DNAm data was conducted in R with adjustment for clinical and genetic covariables and a minimum effect size of 1%, false discovery rate of 5%, and medium-confidence false discovery rate threshold of 20%. There were no DNAm differences between infants with and without peanut, egg, or cow's milk sensitization. Borderline significant sites with high effect sizes were enriched for methylation quantitative trait loci, hinting at genetic factors influencing DNAm at these sites. DNAm patterns did not differ by peanut or egg introduction before or after 12 months. This small pilot study did not show differences inmethylation by food sensitization or introduction, but it diddemonstrate DNAm patterns linked to genetic variants.

The evolution of genetic covariance and modularity as a result of multigenerational environmental fluctuation.

The genetic covariance between traits can affect the evolution of a population through selection, drift, and migration. Conversely, research has demonstrated the reciprocal effect of evolutionary processes on changing genetic covariances, in part through mutational covariance, correlational selection, and plasticity. In this article, we propose that correlated changes in selective optima over generations can cause the evolution of genetic covariance and the G-matrix in such a way that the population can, in the future, evolve faster. We use individual-based simulations of populations exposed to three types of changing environments that differ in the correlation of the change between selective pressures. Our simulation experiments demonstrate that selection pressures for different traits changing in a correlated pattern over generations can lead to stronger trait correlations compared to the case with independently changing selective optima. Our findings show that correlated selective pressures result in significantly higher genetic trait covariance and that pleiotropy accounts for the majority of the difference in covariance between treatments. We also observe that the mutational variance evolves according to the environment that the populations were exposed to. Moreover, we show that clustered patterns of changes in selection can allow the evolution of genetic modularity. We show that the pattern of change in the selective environment affects the pace at which fitness evolves, with populations experiencing correlated change in optima having on average higher mean fitness than those experiencing uncorrelated environment change.

GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors.

Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures. This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses. Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values <5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors. This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.

The effect of environment on depressive symptoms in late adolescence and early adulthood: an exposome-wide association study and twin modeling

The exposome represents the totality of environmental effects, but systematic evaluation between it and depressive symptoms is scant. Here we sought to comprehensively identify the association of the exposome with depressive symptoms in late adolescence and early adulthood and determine genetic and environmental covariances between them. Based on the FinnTwin12 cohort (3,025 participants in young adulthood and 4,127 at age 17), the exposome-wide association study (ExWAS) design was used to identify significant exposures from 12 domains. Bivariate Cholesky twin models were fitted to an exposome score and depressive symptoms. In ExWASes, 29 and 46 exposures were significantly associated with depressive symptoms in young adulthood and at age 17, respectively, and familial exposures were the most influential. Twin models indicated considerable genetic and environmental covariances between the exposome score and depressive symptoms with sex differences. The findings underscore the systematic approach of the exposome and the consideration of relevant genetic effects.

A Regression-based Approach to Robust Estimation and Inference for Genetic Covariance

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits, and some variants are shown to be associated with multiple complex traits. Genetic covariance between two traits is defined as the underlying covariance of genetic effects and can be used to measure the shared genetic architecture. The data used to estimate such a genetic covariance can be from the same group or different groups of individuals, and the traits can be of different types or collected based on different study designs. This paper proposes a unified regression-based approach to robust estimation and inference for genetic covariance of general traits that may be associated with genetic variants nonlinearly. The asymptotic properties of the proposed estimator are provided and are shown to be robust under certain model mis-specification. Our method under linear working models provides a robust inference for the narrow-sense genetic covariance, even when both linear models are mis-specified. Numerical experiments are performed to support the theoretical results. Our method is applied to an outbred mice GWAS data set to study the overlapping genetic effects between the behavioral and physiological phenotypes. The real data results reveal interesting genetic covariance among different mice developmental traits.

Selection and the direction of phenotypic evolution.

Predicting adaptive phenotypic evolution depends on invariable selection gradients and on the stability of the genetic covariances between the component traits of the multivariate phenotype. We describe the evolution of six traits of locomotion behavior and body size in the nematode Caenorhabditis elegans for 50 generations of adaptation to a novel environment. We show that the direction of adaptive multivariate phenotypic evolution can be predicted from the ancestral selection differentials, particularly when the traits were measured in the new environment. Interestingly, the evolution of individual traits does not always occur in the direction of selection, nor are trait responses to selection always homogeneous among replicate populations. These observations are explained because the phenotypic dimension with most of the ancestral standing genetic variation only partially aligns with the phenotypic dimension under directional selection. These findings validate selection theory and suggest that the direction of multivariate adaptive phenotypic evolution is predictable for tens of generations.

Host genomic variation shapes gut microbiome diversity in threespine stickleback fish.

A major focus of host-microbe research is to understand how genetic differences, of various magnitudes, among hosts translate to differences in their microbiomes. This has been challenging for animal hosts, including humans, because it is difficult to control environmental variables tightly enough to isolate direct genetic effects on the microbiome. Our work in stickleback fish is a significant contribution because our experimental approach allowed strict control over environmental factors, including standardization of the microbiome from the earliest stage of development and unrestricted co-housing of fish in a truly common environment. Furthermore, we measured host genetic variation over 2,000 regions of the stickleback genome, comparing this information and microbiome composition data among fish from very similar and very different genetic backgrounds. Our findings highlight how differences in the host genome influence microbiome diversity and make a case for future manipulative microbiome experiments that use host systems with naturally occurring genetic variation.