Polygenic Effects Research Articles

A computationally efficient method for approximating reliabilities in large-scale single-step genomic prediction

BackgroundIn this study, computationally efficient methods to approximate the reliabilities of genomic estimated breeding values (GEBV) in a single-step genomic prediction model including a residual polygenic (RPG) effect are described. In order to calculate the reliabilities of the genotyped animals, a single nucleotide polymorphism best linear unbiased prediction (SNPBLUP) or a genomic BLUP (GBLUP), was used, where two alternatives to account for the RPG effect were tested. In the direct approach, the genomic model included the RPG effect, while in the blended method, it did not but an index was used to weight the genomic and pedigree-based BLUP (PBLUP) reliabilities. In order to calculate the single-step GBLUP reliabilities for the breeding values for the non-genotyped animals, a simplified weighted-PBLUP model that included a general mean and additive genetic effects with weights accounting for the non-genomic and genomic information was used. We compared five schemes for the weights. Two datasets, i.e., a small (Data 1) one and a large (Data 2) one were used.ResultsFor the genotyped animals in Data 1, correlations between approximate reliabilities using the blended method and exact reliabilities ranged from 0.993 to 0.996 across three lactations. The slopes observed by regressing the reliabilities of GEBV from the exact method on those from the blended method were 1.0 for all three lactations. For Data 2, the correlations and slopes ranged, respectively, from 0.980 to 0.986 and from 0.91 to 0.96, and for the non-genotyped animals in Data 1, they ranged, respectively, from 0.987 to 0.994 and from 0.987 to 1, which indicate that the approximations were in line with the exact results. The best approach achieved correlations of 0.992 to 0.994 across lactations.ConclusionsOur results demonstrate that the approximated reliabilities calculated using our proposed approach are in good agreement with the exact reliabilities. The blended method for the genotyped animals is computationally more feasible than the direct method when RPG effects are included, particularly for large-scale datasets. The approach can serve as an effective strategy to estimate the reliabilities of GEBV in large-scale single-step genomic predictions.

Polygenic Effect on Tau Pathology Progression in Alzheimer's Disease.

Polygenic variation accounts for a substantial portion of the risk of Alzheimer's disease (AD), but its effect on the rate of fibrillar-tau accumulation as a key driver of dementia symptoms is unclear. We combined the to-date largest number of genetic risk variants of AD (n=85 lead single-nucleotide polymorphisms [SNPs]) from recent genome-wide association studies (GWAS) to generate a polygenic score (PGS). We assessed longitudinal tau-positron emission tomography (PET), amyloid-PET, and cognition in 231 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Using the PGS, together with global amyloid-PET, we predicted the rate of tau-PET increases in Braak-stage regions-of-interest and cognitive decline. We also assessed PGS-risk enrichment effects on the required sample size in clinical trials targeting tau pathology. We found that a higher PGS was associated with higher rates of tau-PET accumulation, in particular at elevated amyloid-PET levels. The tau-PET increases mediated the association between PGS and faster cognitive decline. Risk enrichment through high PGS afforded sample size savings by 34%. Our results demonstrate that the PGS predicts faster tau progression and thus cognitive decline, showing utility to enhance statistical power in clinical trials. ANN NEUROL 2023;93:819-829.

Monogenic and Polygenic Contributions to Venous Thromboembolism (VTE) and Splanchnic Thrombosis in Patients (pts) with Pancreatic Cancer (PC)

BACKGROUND Cancer-associated VTE is a significant cause of morbidity and mortality. The Khorana score (KS), which includes cancer type, body mass index, and prechemotherapy laboratory studies, is commonly used to stratify the risk of cancer-associated VTE (1). KS has low discriminative ability in some clinical situations (2) and does not include genomic information. We investigated a cohort of 753 pts with PC for germline and somatic predictors of VTE and splanchnic thrombosis and developed a PC-specific polygenic risk score (PC-PRS) to predict VTE. METHODS We manually abstracted clinical risk factors for VTE in pts of European ancestry with PC, sequenced at Dana-Farber Cancer Institute (DFCI). VTE was defined as 1) thrombosis in deep veins of the lower extremities/upper extremities or 2) pulmonary embolism. Splanchnic thrombosis was separately analyzed and defined as thrombosis of the portal, hepatic, splenic, or superior/inferior mesenteric veins. Incident VTE/splanchnic thrombosis was defined as the first occurrence of thrombosis or thrombosis-related death. KS was calculated using pretreatment laboratory values obtained prior to cancer treatment. A genome-wide association study (GWAS) was conducted for time to VTE across 5.35 million common imputed germline variants. A germline PRS was computed by cross-validation: 1) the cohort was split into 5 subsets; 2) GWAS was conducted within 4/5 subsets and PRS weights calculated using pruning and thresholding; 3) each score was predicted into the held out 1/5 subset; 4) this was repeated five times; 5) significance of the association between held-out PRS and VTE was evaluated by permutation. Associations between both PC-PRS and KS and time to VTE were determined. To assess PRS and GWAS specificity, we conducted an association analysis of 5760 solid tumors sequenced at DFCI and treated with anti-neoplastic therapy for which VTE information was available. Pathogenic somatic variants in tumor suppressor genes included truncating variants, splice-site variants affecting consensus nucleotides, or homozygous deletions. For proto-oncogenes, pathogenic calls included gene amplifications and missense variants with functional impact determined using SIFT(3) and Polyphen-2(4). False discovery rate was applied using Benjamini Hochberg (FDR<10%) RESULTS Of 753 pts with PC, 305 (40.5%) underwent PC surgery and 661 (87.8%) received chemotherapy within 6 months (mo) of diagnosis. During a median follow-up of 4.4 years, 223 (29.6%) incident VTEs and 94 (12.5%) splanchnic thromboses were identified. For 529 pts with available KS, 131 (24.8%) were intermediate-risk while the remaining were high-risk. GWAS across 753 samples identified 1 locus that was genome-wide significant for VTE (p<5x10-8). The lead SNP was rs13327332, with the minor allele associated with a significantly lower risk of VTE (HR=0.13, p=2.8x10-8, fig 1) in the PC cohort. The variant was not associated with VTE in other solid tumors. To investigate genome-wide polygenic effects, a risk score was estimated using all GWAS SNPs and evaluated by cross-validation (PC-PRS). Individuals in the top 20% of PC-PRS were at 5-fold higher risk for developing a VTE in the first 6 mo relative to the lowest 20% of PC-PRS, after accounting for the competing risk of death. Cumulative incidence of VTE in the first 6 mo was similar between pts with intermediate and high KS (fold-change=1). After adjusting for sex, age, chemotherapy within 6 months, history of prior VTE, surgery for pancreatic cancer, metastatic disease at time of VTE, and tumor purity, PC-PRS was significantly associated with time to incident VTE (HR=1.9, p=0.02) while KS was not (HR=1, p=0.96, fig 2). The PC-PRS was specific to PC and was not associated with other solid tumors. Analyzing 445 genes for associations between pathogenic somatic alterations and time-to-VTE, CNV amplifications in CIC were associated with significantly higher risk of VTE (HR=2.5, p=0.0001, FDR<10%). MAP3K1 gene alterations (n=12) were associated with 9.3x higher risk of splanchnic thrombosis (p=0.0001, FDR<10%). CONCLUSION We suggest a role for PC-PRS in VTE risk stratification and identify multiple somatic alterations with high effects on VTE incidence. Implementation of PC-PRS and integration with somatic data may provide a more refined risk estimation of VTE risk in PC than current clinical tools permit. Further validation in independent cohorts is warranted. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Polygenic coronary artery disease association with brain atrophy in the cognitively impaired.

While a number of low-frequency genetic variants of large effect size have been shown to underlie both cardiovascular disease and dementia, recent studies have highlighted the importance of common genetic variants of small effect size, which, in aggregate, are embodied by a polygenic risk score. We investigate the effect of polygenic risk for coronary artery disease on brain atrophy in Alzheimer's disease using whole-brain volume and put our findings in context with the polygenic risk for Alzheimer's disease and presumed small vessel disease as quantified by white-matter hyperintensities. We use 730 subjects from the Alzheimer's disease neuroimaging initiative database to investigate polygenic risk score effects (beyond APOE) on whole-brain volumes, total and regional white-matter hyperintensities and amyloid beta across diagnostic groups. In a subset of these subjects (N = 602), we utilized longitudinal changes in whole-brain volume over 24 months using the boundary shift integral approach. Linear regression and linear mixed-effects models were used to investigate the effect of white-matter hyperintensities at baseline as well as Alzheimer's disease-polygenic risk score and coronary artery disease-polygenic risk score on whole-brain atrophy and whole-brain atrophy acceleration, respectively. All genetic associations were examined under the oligogenic (P = 1e-5) and the more variant-inclusive polygenic (P = 0.5) scenarios. Results suggest no evidence for a link between the polygenic risk score and markers of Alzheimer's disease pathology at baseline (when stratified by diagnostic group). However, both Alzheimer's disease-polygenic risk score and coronary artery disease-polygenic risk score were associated with longitudinal decline in whole-brain volume (Alzheimer's disease-polygenic risk score t = 3.3, P FDR = 0.007 over 24 months in healthy controls) and surprisingly, under certain conditions, whole-brain volume atrophy is statistically more correlated with cardiac polygenic risk score than Alzheimer's disease-polygenic risk score (coronary artery disease-polygenic risk score t = 2.1, P FDR = 0.04 over 24 months in the mild cognitive impairment group). Further, in our regional analysis of white-matter hyperintensities, Alzheimer's disease-polygenic risk score beyond APOE is predictive of white-matter volume in the occipital lobe in Alzheimer's disease subjects in the polygenic regime. Finally, the rate of change of brain volume (or atrophy acceleration) may be sensitive to Alzheimer's disease-polygenic risk beyond APOE in healthy individuals (t = 2, P = 0.04). For subjects with mild cognitive impairment, beyond APOE, a more inclusive polygenic risk score including more variants, shows coronary artery disease-polygenic risk score to be more predictive of whole-brain volume atrophy, than an oligogenic approach including fewer larger effect size variants.

Associations of polygenic risks, depression, and obesity-related traits in Taiwan Biobank

BackgroundThe comorbidity of obesity and major depressive disorder (MDD) may be attributable to a bidirectional relationship and shared genetic influence. We aimed to examine the polygenic associations between obesity and MDD and to characterize their corresponding impacts on the obesity mechanism. MethodsGenome-wide genotyping was available in 106,604 unrelated individuals from Taiwan Biobank. Polygenic risk score (PRS) for body mass index (BMI) and MDD was derived to evaluate their effects on obesity-related traits. Stratified analyses were performed for the modified effect of depression on the polygenic associations. ResultsThe MDD PRS was positively associated with waistline (beta in per SD increase in PRS = 0.12), hipline (beta = 0.08), waist-hip ratio (WHR) (beta = 0.05), body fat rate (beta = 0.08), BMI (beta = 0.05), overweight (OR = 1.02 for BMI ≥ 25), and obesity (OR = 1.05 for BMI ≥ 30). For the synergism between depression and BMI PRS, the presence of active depression symptoms defined by the PHQ-4 (p for interaction < 0.05 for waistline, WHR, and BMI) was more salient than lifetime MDD. LimitationsLimitations include recall bias for MDD due to a retrospective self-reporting questionnaire, a low response rate of the PHQ-4 for evaluating active psychological symptoms, and limited generalizability to non-Taiwanese ancestries. ConclusionsThe shared genetic etiology of obesity and depression was demonstrated. The amplified effect of BMI polygenic effect on obesity for individuals with active depressive symptoms was also characterized. The study may be helpful for designing public health interventions to reduce the disease burden caused by obesity and depression.

Evaluation of GENESIS, SAIGE, REGENIE and fastGWA-GLMM for genome-wide association studies of binary traits in correlated data.

Performing a genome-wide association study (GWAS) with a binary phenotype using family data is a challenging task. Using linear mixed effects models is typically unsuitable for binary traits, and numerical approximations of the likelihood function may not work well with rare genetic variants with small counts. Additionally, imbalance in the case-control ratios poses challenges as traditional statistical methods such as the Score test or Wald test perform poorly in this setting. In the last couple of years, several methods have been proposed to better approximate the likelihood function of a mixed effects logistic regression model that uses Saddle Point Approximation (SPA). SPA adjustment has recently been implemented in multiple software, including GENESIS, SAIGE, REGENIE and fastGWA-GLMM: four increasingly popular tools to perform GWAS of binary traits. We compare Score and SPA tests using real family data to evaluate computational efficiency and the agreement of the results. Additionally, we compare various ways to adjust for family relatedness, such as sparse and full genetic relationship matrices (GRM) and polygenic effect estimates. We use the New England Centenarian Study imputed genotype data and the Long Life Family Study whole-genome sequencing data and the binary phenotype of human extreme longevity to compare the agreement of the results and tools’ computational performance. The evaluation suggests that REGENIE might not be a good choice when analyzing correlated data of a small size. fastGWA-GLMM is the most computationally efficient compared to the other three tools, but it appears to be overly conservative when applied to family-based data. GENESIS, SAIGE and fastGWA-GLMM produced similar, although not identical, results, with SPA adjustment performing better than Score tests. Our evaluation also demonstrates the importance of adjusting by full GRM in highly correlated datasets when using GENESIS or SAIGE.

35 Young Scholar Award Talk: Computing Strategies for National Beef Cattle Evaluations

Abstract The constantly growing size of the national beef cattle evaluations datasets creates computational challenges to estimate breeding values and their accuracies under the single-step methodology timely and in a robust way. This study aims to present strategies for making weekly national single-step beef cattle evaluations computationally feasible. The process for estimating breeding values in single-step can be divided into two parts. In the first part, hereafter called genomic setup, the genotype file is read, and the inverse of the genomic relationship matrix is calculated using the Algorithm for Proven and Young (APY). The genotype file is read in a stream-unformatted format, and the markers are stored bit-wise. A residual polygenic effect can be fitted with an algorithm whose computing cost is constant with the number of genotyped animals. The calculation of is done using parallel computing and optimized matrix algebra procedures. The second part estimates breeding values by iteration on data with a block diagonal preconditioner. Finally, the accuracy of the estimated breeding values is calculated by combining the pedigree and genomic information in terms of effective daughter contributions. For an Angus dataset with 10 million animals in the pedigree and 500,000 genotypes, the genomic setup took 4 hours. With the same dataset, solutions for a three-trait model with maternal effects including birth weight, weaning weight, and post-weaning gain were obtained after 12 hours. The approximation of the accuracies of breeding values took less than one hour. Our results show that weekly national beef cattle evaluations are computationally feasible.

Genetic architecture of repeated phenotypic divergence in Littorina saxatilis ecotype evolution.

Chromosomal inversions have been shown to play a major role in a local adaptation by suppressing recombination between alternative arrangements and maintaining beneficial allele combinations. However, so far, their importance relative to the remaining genome remains largely unknown. Understanding the genetic architecture of adaptation requires better estimates of how loci of different effect sizes contribute to phenotypic variation. Here, we used three Swedish islands where the marine snail Littorina saxatilis has repeatedly evolved into two distinct ecotypes along a habitat transition. We estimated the contribution of inversion polymorphisms to phenotypic divergence while controlling for polygenic effects in the remaining genome using a quantitative genetics framework. We confirmed the importance of inversions but showed that contributions of loci outside inversions are of similar magnitude, with variable proportions dependent on the trait and the population. Some inversions showed consistent effects across all sites, whereas others exhibited site-specific effects, indicating that the genomic basis for replicated phenotypic divergence is only partly shared. The contributions of sexual dimorphism as well as environmental factors to phenotypic variation were significant but minor compared to inversions and polygenic background. Overall, this integrated approach provides insight into the multiple mechanisms contributing to parallel phenotypic divergence.

Relative importance of genotype, gene expression, and DNA methylation on complex traits in perennial ryegrass.

The growing demand for food and feed crops in the world because of growing population and more extreme weather events requires high-yielding and resilient crops. Many agriculturally important traits are polygenic, controlled by multiple regulatory layers, and with a strong interaction with the environment. In this study, 120 F2 families of perennial ryegrass (Lolium perenne L.) were grown across a water gradient in a semifield facility with subsoil irrigation. Genomic (single-nucleotide polymorphism [SNP]), transcriptomic (gene expression [GE]), and DNA methylomic (MET) data were integrated with feed quality trait data collected from control and drought sections in the semifield facility, providing a treatment effect. Deep root length (DRL) below 110 cm was assessed with convolutional neural network image analysis. Bayesian prediction models were used to partition phenotypic variance into its components and evaluated the proportion of phenotypic variance in all traits captured by different regulatory layers (SNP, GE, and MET). The spatial effects and effects of SNP, GE, MET, the interaction between GE and MET (GE × MET) and GE× treatment (GEControl and GEDrought ) interaction were investigated. Gene expression explained a substantial part of the genetic and spatial variance for all the investigated phenotypes, whereas MET explained residual variance not accounted for by SNPs or GE. For DRL, MET also contributed to explaining spatial variance. The study provides a statistically elegant analytical paradigm that integrates genomic, transcriptomic, and MET information to understand the regulatory mechanisms of polygenic effects for complex traits.

Novel genetic associations with five aesthetic facial traits: A genome-wide association study in the Chinese population.

Background: The aesthetic facial traits are closely related to life quality and strongly influenced by genetic factors, but the genetic predispositions in the Chinese population remain poorly understood. Methods: A genome-wide association studies (GWAS) and subsequent validations were performed in 26,806 Chinese on five facial traits: widow’s peak, unibrow, double eyelid, earlobe attachment, and freckles. Functional annotation was performed based on the expression quantitative trait loci (eQTL) variants, genome-wide polygenic scores (GPSs) were developed to represent the combined polygenic effects, and single nucleotide polymorphism (SNP) heritability was presented to evaluate the contributions of the variants. Results: In total, 21 genetic associations were identified, of which ten were novel: GMDS-AS1 (rs4959669, p = 1.29 × 10−49) and SPRED2 (rs13423753, p = 2.99 × 10−14) for widow’s peak, a previously unreported trait; FARSB (rs36015125, p = 1.96 × 10−21) for unibrow; KIF26B (rs7549180, p = 2.41 × 10−15), CASC2 (rs79852633, p = 4.78 × 10−11), RPGRIP1L (rs6499632, p = 9.15 × 10−11), and PAX1 (rs147581439, p = 3.07 × 10−8) for double eyelid; ZFHX3 (rs74030209, p = 9.77 × 10−14) and LINC01107 (rs10211400, p = 6.25 × 10−10) for earlobe attachment; and SPATA33 (rs35415928, p = 1.08 × 10−8) for freckles. Functionally, seven identified SNPs tag the missense variants and six may function as eQTLs. The combined polygenic effect of the associations was represented by GPSs and contributions of the variants were evaluated using SNP heritability. Conclusion: These identifications may facilitate a better understanding of the genetic basis of features in the Chinese population and hopefully inspire further genetic research on facial development.

The Role of Polygenic Susceptibility on Air Pollution-Associated Asthma between German and Japanese Elderly Women.

Polygenic susceptibility likely influences individual responses to air pollutants and the risk of asthma. We compared the role of polygenic susceptibility on air pollution-associated asthma between German and Japanese women. We investigated women that were enrolled in the German SALIA cohort (n = 771, mean age = 73 years) and the Japanese Shika cohort (n = 847, mean age = 67 years) with known asthma status. Adjusted logistic regression models were used to assess the associations between (1) particulate matter with a median aerodynamic diameter ≤ 2.5μm (PM2.5) and nitrogen dioxide (NO2), (2) polygenic risk scores (PRS), and (3) gene-environment interactions (G × E) with asthma. We found an increased risk of asthma in Japanese women after exposure to low pollutant levels (PM2.5: median = 12.7µg/m3, p-value < 0.001, NO2: median = 8.5µg/m3, p-value < 0.001) and in German women protective polygenic effects (p-value = 0.008). While we found no significant G × E effects, the direction in both groups was that the PRS increased the effect of PM2.5 and decreased the effect of NO2 on asthma. Our study confirms that exposure to low air pollution levels increases the risk of asthma in Japanese women and indicates polygenic effects in German women; however, there was no evidence of G × E effects. Future genome-wide G × E studies should further explore the role of ethnic-specific polygenic susceptibility to asthma.

Impact of blending the genomic relationship matrix with different levels of pedigree relationships or the identity matrix on genetic evaluations

Evaluations using single-step genomic BLUP require blending the genomic relationship matrix (G) with a positive definite matrix to ensure nonsingularity for solving the mixed model equations. Many organizations blend G with a proportion of the numerator relationship matrix for genotyped animals (A22) to improve stability and possibly add a residual polygenic effect. However, when nearly all the polygenic variance is explained by G, blending with A22 may cause inflation and add excess computing time; thus, blending with an identity matrix (I) multiplied by a small value may be a better solution. The objective of this study was to evaluate changes in reliability and inflation of genomic estimated breeding values, convergence rate, elapsed wall-clock time for blending G with different levels of A22 or I, and develop a more time-efficient blending method. A US Holstein cattle data set was used with 9.7 million animals in the pedigree, 569,404 animals with genotypes, and 10.1 million stature phenotypes. Blending G by adding a small value to the diagonal elements had comparable performance to A22 with fewer rounds to convergence required to solve the system of equations. Reliability and inflation of genomic estimated breeding values ranged from 0.63 to 0.68 and 0.86 to 0.89 for all blending scenarios tested. The current blending default in the BLUPF90 software is to replace G with (1 – β)G + βA22, where β equals 0.05. In this study, β values of 0.30, 0.20, 0.05, 0.01, 0.005, and 0.001 were evaluated with A22 and I. Negligible differences in elapsed computing time between the blending types and levels were observed. Subsequently, the current blending algorithm used in the BLUPF90 family of programs was optimized, reducing the blending time from approximately 2 h to 5 min for A22 and less than 1 s for I. The new time difference between blending with A22 or I is negligible and not computationally critical. The results indicate that blending G with A22 does not have clear advantages over blending with a small proportion of I.

On the equivalence between marker effect models and breeding value models and direct genomic values with the Algorithm for Proven and Young

BackgroundSingle-step genomic predictions obtained from a breeding value model require calculating the inverse of the genomic relationship matrix ({mathbf{G}}^{-1}). The Algorithm for Proven and Young (APY) creates a sparse representation of {mathbf{G}}^{-1} with a low computational cost. APY consists of selecting a group of core animals and expressing the breeding values of the remaining animals as a linear combination of those from the core animals plus an error term. The objectives of this study were to: (1) extend APY to marker effects models; (2) derive equations for marker effect estimates when APY is used for breeding value models, and (3) show the implication of selecting a specific group of core animals in terms of a marker effects model.ResultsWe derived a family of marker effects models called APY-SNP-BLUP. It differs from the classic marker effects model in that the row space of the genotype matrix is reduced and an error term is fitted for non-core animals. We derived formulas for marker effect estimates that take this error term in account. The prediction error variance (PEV) of the marker effect estimates depends on the PEV for core animals but not directly on the PEV of the non-core animals. We extended the APY-SNP-BLUP to include a residual polygenic effect and accommodate non-genotyped animals. We show that selecting a specific group of core animals is equivalent to select a subspace of the row space of the genotype matrix. As the number of core animals increases, subspaces corresponding to different sets of core animals tend to overlap, showing that random selection of core animals is algebraically justified.ConclusionsThe APY-(ss)GBLUP models can be expressed in terms of marker effect models. When the number of core animals is equal to the rank of the genotype matrix, APY-SNP-BLUP is identical to the classic marker effects model. If the number of core animals is less than the rank of the genotype matrix, genotypes for non-core animals are imputed as a linear combination of the genotypes of the core animals. For estimating SNP effects, only relationships and estimated breeding values for core animals are needed.

Airway inflammation in adolescents and elderly women: Chronic air pollution exposure and polygenic susceptibility

Background and aimThe fractional exhaled nitric oxide (FeNO) concentration in the exhaled breath is a biomarker for eosinophilic airway inflammation. We explored the interplay between chronic air pollution exposure and polygenic susceptibility to airway inflammation at different critical age stages. MethodsAdolescents (15 yr) enrolled in the GINIplus/LISA birth cohorts (n = 2434) and 220 elderly women (75 yr on average) enrolled in the SALIA cohort with FeNO measurements available were investigated. Environmental main effects of the mean of ESCAPE land-use regression air pollutant concentrations within a time window of 15 years and main effects of the polygenic risk scores (PRS) using internal weights from elastic net regression of genome-wide derived single nucleotide polymorphisms were investigated. Furthermore, we examined gene-environment interaction (GxE) effects on natural log-transformed FeNO levels by adjusted linear regression models. ResultsWhile we observed no significant environmental and polygenic main effects on airway inflammation in either age group, we found robust harmful effects of chronic nitrogen dioxide (NO2) exposure in the GxE models for elderly women (16.2 % increase in FeNO, p-value = 0.027). Stratified analyses found GxE effects between the PRS and chronic NO2 exposure in never-smoker elderly women and in adolescents without any inflammatory respiratory conditions. ConclusionsFeNO measurement is a useful biomarker to detect higher risk of NO2-induced eosinophilic airway inflammation in the elderly. There was limited evidence for GxE effects on airway inflammation in adolescents or the elderly. Further GxE studies in subpopulations should be conducted to investigate the assumption that susceptibility to airway inflammation differs between age stages.

Mixed model-based eQTL analysis reveals lncRNAs associated with regulation of genes involved in sex determination and spermatogenesis: The key to understanding human gender imbalance

BackgroundAn imbalance in the prenatal sex ratio in humans may be due to several factors affecting sperm physiology, including genetic features. In this study, we conducted a transcriptome-wide analysis of expression quantitative trait loci (eQTLs) to identify target genes associated with previously described QTLs associated with gender imbalance. MethodsA mixed model explaining polygenic effects by genomic covariance among individuals was used to identify the eQTLs using gene expression and genotype data from 462 European/African individuals. ResultsEight eGenes were associated with four QTLs (P < 4.00 × 10-5), with strong associations found (P < 4.00 × 10-8) between rs2485007 and eGenes ANKRD26P3 (P = 3.40 × 10-9) and LINC00421 (P = 1.35 × 10-9). ANKRD26P3 and LINC00421 are both lncRNAs associated with the control of testis-dominant genes PELP1, TAF15, NANOG, TEX14, TCF3, ZNF433, ZNF555, TEX37, FATE1, TCP11, and CYLC2 and Y-linked genes SRY and ZFY, as well as several genes with roles in spermatogenesis (ODF1, SPATC1, SPATA3, SPATA31E1, SPERT, SPATA16, MOSPD1, SPATA24, and SPO11) and sex determination (SOX family genes). ConclusionsThe above eGenes contribute directly or indirectly to gene regulation for sex determination and spermatogenesis, thereby serving as important functional clues for gender-biased selection.

Metabolic polygenic risk scores effect on antipsychotic-induced metabolic dysregulation: A longitudinal study in a first episode psychosis cohort

ObjectiveMetabolic syndrome is a health-threatening condition suffered by approximately one third of schizophrenia patients and largely attributed to antipsychotic medication. Previous evidence reports a common genetic background of psychotic and metabolic disorders. In this study, we aimed to assess the role of polygenic risk scores (PRSs) on the progression of the metabolic profile in a first-episode psychosis (FEP) cohort. MethodOf the 231 FEP individuals included in the study, 192–220 participants were included in basal analysis and 118–179 in longitudinal 6-month models. Eleven psychopathologic and metabolic PRSs were constructed. Basal and longitudinal PRSs association with metabolic measurements was assessed by statistical analyses. ResultsNo major association of psychopathological PRSs with the metabolic progression was found. However, high risk individuals for depression and cholesterol-related PRSs reported a higher increase of cholesterol levels during the follow-up (FDR ≤ 0.023 for all analyses). Their effect was comparable to other well-established pharmacological and environmental risk factors (explaining at least 1.2% of total variance). ConclusionOur findings provide new evidence of the effects of metabolic genetic risk on the development of metabolic dysregulation. The future establishment of genetic profiling tools in clinical procedures could enable practitioners to better personalize antipsychotic treatment selection and dosage.

Characterizing the polygenic overlaps of bipolar disorder subtypes with schizophrenia and major depressive disorder

BackgroundLarge-scale studies have shown that bipolar I disorder (BD-I) and bipolar II disorder (BD-II) have differences in genetic association with schizophrenia (SCZ) and major depressive disorder (MDD). However, the underlying shared genetic architectures between BD subtypes and both SCZ and MDD remain largely unknown. MethodsWe applied univariate and bivariate causal mixture models (MiXeR) to estimate the polygenicity and polygenic overlaps on large GWASs summary statistics of BD-I (n = 25,060), BD-II (n = 6781), SCZ (n = 69,369) and MDD (n = 170,756). Then, conjunctional false discovery rate approach was used to identify specific shared genetic loci between BD subtypes and both SCZ and MDD. ResultsUnivariate MiXeR revealed that BD-II was substantially more polygenic (22.37 K causal variants) as compared to BD-I, SCZ and MDD (7.87–12.43 K causal variants). Bivariate MiXeR revealed substantial polygenic overlaps between BD-I and SCZ (Dice-coefficient = 0.83) and between BD-I and MDD (Dice-coefficient = 0.76), which are beyond the genetic correlation (rg = 0.71 and 0.36). Conjunctional FDR analysis identified 236 distinct shared loci between BD-I and BD-II (2 loci), BD-I and SCZ (227 loci), BD-I and MDD (19 loci), BD-II and SCZ (1 locus), and BD-II and MDD (3 loci). Most of these shared loci have concordant effect directions among BD subtypes, SCZ and MDD. LimitationsThe bivariate MiXeR model was not applied for the BD-II because of insufficient power and inadequate model fit. ConclusionsThese findings provide evidence for extensive polygenic effects across BD subtypes, SCZ and MDD, which further our understanding of the potential genetic basis for the comorbid symptoms across these disorders.

Genome-wide association study reveals candidate genes relevant to body weight in female turkeys (Meleagris gallopavo).

The underlying genetic mechanisms affecting turkey growth traits have not been widely investigated. Genome-wide association studies (GWAS) is a powerful approach to identify candidate regions associated with complex phenotypes and diseases in livestock. In the present study, we performed GWAS to identify regions associated with 18-week body weight in a turkey population. The data included body weight observations for 24,989 female turkeys genotyped based on a 65K SNP panel. The analysis was carried out using a univariate mixed linear model with hatch-week-year and the 2 top principal components fitted as fixed effects and the accumulated polygenic effect of all markers captured by the genomic relationship matrix as random. Thirty-three significant markers were observed on 1, 2, 3, 4, 7 and 12 chromosomes, while 26 showed strong linkage disequilibrium extending up to 410 kb. These significant markers were mapped to 37 genes, of which 13 were novel. Interestingly, many of the investigated genes are known to be involved in growth and body weight. For instance, genes AKR1D1, PARP12, BOC, NCOA1, ADCY3 and CHCHD7 regulate growth, body weight, metabolism, digestion, bile acid biosynthetic and development of muscle cells. In summary, the results of our study revealed novel candidate genomic regions and candidate genes that could be managed within a turkey breeding program and adapted in fine mapping of quantitative trait loci to enhance genetic improvement in this species.

Robo2 and Gen1 Coregulate Ureteric Budding by Activating the MAPK/ERK Signaling Pathway in Mice.

Congenital anomalies of the kidney and urinary tract (CAKUT) are some of the most common developmental defects and have a complicated etiology, indicating an interaction of (epi-) genetic and environmental factors. Single gene mutations and copy number variations (CNVs) do not explain most cases of CAKUT, and simultaneous contributions of more than one gene (di-, oligo-, or polygenic effects; i.e., complex genetics) may lead to the pathogenesis of CAKUT. Robo2 plays a key role in regulating ureteric bud (UB) formation in the embryo, with mutations leading to supernumerary kidneys. Gen1 is a candidate gene associated with CAKUT because of its important role in early metanephric development in mice. We established a mouse model with double disruption of Robo2 and Gen1 using a piggyBac transposon and found that double gene mutation led to significantly increased CAKUT phenotypes in Robo2PB/+Gen1PB/+ mouse offspring, especially a duplicated collecting system. Increased ectopic UB formation was observed in the Robo2PB/+Gen1PB/+ mice during the embryonic period. Robo2 and Gen1 exert synergistic effects on mouse kidney development, promoting cell proliferation by activating the GDNF/RET pathway and downstream MAPK/ERK signaling. Our findings provide a disease model for CAKUT as an oligogenic disorder.

Familial Segregation of Venous Thromboembolism in Sweden: A Nationwide Family Study of Heritability and Complex Segregation Analysis.

BackgroundThis is the first nationwide segregation analysis that aimed to determine whether familial venous thromboembolism (VTE) is attributable to inheritance and/or shared environment, and the possible mode of inheritance.Methods and ResultsThe Swedish Multi‐Generation Register was linked to the Swedish patient register for the period 1964 to 2015. Three generational families of Swedish‐born individuals were identified. Heritability was examined using Falconer regression. Complex segregation analysis was conducted using the Statistical Analysis for Genetic Epidemiology software (version 6.4, 64‐bit Linux). Among the 4 301 174 relatives from 450 558 pedigrees, 177 865 (52% women) individuals were affected with VTE. VTE occurred in 2 or more affected relatives in 61 217 (13.6%) of the pedigrees. Heritability showed age and sex dependence with higher heritability for men and young individuals. In 18 933 pedigrees, VTE occurred only in the first generation and was not inherited. Segregation analysis was performed in the remaining 42 284 pedigrees with inherited VTE and included 939 192 individuals. Prevalence constraints were imposed in the models to allow for the selection of the pedigrees analyzed. The sporadic nongenetic model could be discarded. The major‐type‐only model, with a correlation structure compatible with some polygenic effects, was the preferred model. Among the Mendelian models, the mixed codominant (plus polygenic) model was preferred.ConclusionsThis nationwide segregation analysis of VTE supports a genetic cause of the familial aggregation of VTE. Heritability was higher for men and younger individuals, suggesting a Carter effect, in agreement with a multifactorial threshold inheritance.