Polygenic Effects Research Articles

Abstract PR05: Does a comprehensive family history of colorectal cancer improve risk prediction?

Abstract Background: Family history of colorectal cancer (CRC) is a strong and well-established risk factor for CRC. To date, however, family history (FH) of the disease is generally only broadly categorized (usually as present or absent) in most risk prediction models (Freedman et al. 2009). These approaches fail to fully utilize information on family history and may lead to suboptimal predictive performance of CRC risk. We investigated the utility of a CRC risk model that incorporates a comprehensive family history of CRC as well as information on known genetic and environmental risk factors and personal characteristics. Methods: We used data from the Colon Cancer Family Registry (CCFR), a large, international consortium of six study centers. Prediction models were developed based on incident invasive CRC cases (N = 4445) and population-based controls (N = 3967) that were recruited from three study sites (Seattle, USA; Ontario, Canada; and Melbourne, Australia). A familial risk profile (FRP) score, a probability index of absolute risk for lifetime CRC was estimated based on family structure, age of onset for affected relatives and the polygenic effect of MLH1, MSH2, MSH6, PMS2 and MUTYH using modified segregation analysis, an approach adapted from Antoniou et al (2002)). Two sets of gender-specific logistic regression models were built: (I) the FRP models, which included FRP and other known risk factors (e.g., BMI, consumption of red meat, calcium and NSAID use duration, smoking amount (pack-years), a history of polyps, and history of FOBT, sigmoidoscopy, colonoscopy, fruit intake, and use of hormone replacement therapy for female); and (II) binary FH models, which replaced FRP with a binary indicator (yes/no) for any self-reported first-degree family member with CRC. 5-year absolute risks were calculated based on the estimated odds ratios (OR), country-, sex- and age-specific CRC incidence rate and mortality due to causes other than CRC. Model validation was conducted with unaffected relatives (N=12,120) and population-based controls (N=1,899) from five study sites based on the follow-up information on incident CRC and death status. The primary endpoint was CRC diagnosis within 5-year after baseline. We used calibration plots to compare the predicted 5-year absolute risks with the observed cumulative incidence rates. Receiver Operating Characteristic (ROC) curve analyses were conducted and areas under the ROC curve (AUC) were used to assess the discriminatory capacity for separating subjects with and without a CRC diagnosis within 5 years, accounting for censoring and competing risk. Results: The ORs (95% confidence interval [CI]) using the FRP per 10% increase were 1.16 (1.11-1.20) for males, and 1.09 (1.06-1.12) for females in the FRP models, while the ORs for the binary FH model were 2.32 (1.88- 2.85) for men and 1.70 (1.38-2.09) for women. The FRP models provided slightly better calibration, with average predicted risks falling within the 95% CIs of the empirical cumulative rates. The binary FH models, by comparison, tended to yield higher estimated CRC risks compared with the observed risks among individuals whose risks were above the top10% of the risk distribution. Both models yielded comparable AUCs using the full validation set. Among individuals with at least one first-degree family member affected with CRC, the FRP model performed significantly better (AUC = 0.71) than the FH model (AUC = 0.63) for male participants; difference equaled 0.09 (95% CI: 0.02, 0.16). The models were comparable for females. Conclusion: Our CRC prediction model that incorporates more comprehensive family history of CRC can provide improved calibration and discrimination of risks compared with the simple FH model, especially in populations with higher underlying risk. The models developed may potentially further improve screening decision making among subgroups with elevated CRC risk.

Genomic Selection for Drought Tolerance Using Genome-Wide SNPs in Maize.

Traditional breeding strategies for selecting superior genotypes depending on phenotypic traits have proven to be of limited success, as this direct selection is hindered by low heritability, genetic interactions such as epistasis, environmental-genotype interactions, and polygenic effects. With the advent of new genomic tools, breeders have paved a way for selecting superior breeds. Genomic selection (GS) has emerged as one of the most important approaches for predicting genotype performance. Here, we tested the breeding values of 240 maize subtropical lines phenotyped for drought at different environments using 29,619 cured SNPs. Prediction accuracies of seven genomic selection models (ridge regression, LASSO, elastic net, random forest, reproducing kernel Hilbert space, Bayes A and Bayes B) were tested for their agronomic traits. Though prediction accuracies of Bayes B, Bayes A and RKHS were comparable, Bayes B outperformed the other models by predicting highest Pearson correlation coefficient in all three environments. From Bayes B, a set of the top 1053 significant SNPs with higher marker effects was selected across all datasets to validate the genes and QTLs. Out of these 1053 SNPs, 77 SNPs associated with 10 drought-responsive transcription factors. These transcription factors were associated with different physiological and molecular functions (stomatal closure, root development, hormonal signaling and photosynthesis). Of several models, Bayes B has been shown to have the highest level of prediction accuracy for our data sets. Our experiments also highlighted several SNPs based on their performance and relative importance to drought tolerance. The result of our experiments is important for the selection of superior genotypes and candidate genes for breeding drought-tolerant maize hybrids.

Bayesian analysis of pig growth curves combining pedigree and genomic information

We proposed a genome association study for pig growth curves based on Bayesian hierarchical framework considering different sets of SNP markers and pedigree. Additionally, we aimed also to identify possible chromosome regions affecting the growth curve parameters using empirical weight-age data from an outbred F2 (Brazilian Piau vs commercial) pig population. Under the proposed hierarchical approach, individual growth trajectories were modeled by the nonlinear Gompertz function, so that the parameter estimates were considered to be affected by additive polygenic, systematic and SNP markers effects. The model assuming jointly pedigree and SNP markers presented the best fit based on Deviance Information Criterion. Heritability estimates ranged from 0.53 to 0.56 and from 0.55 to 0.57, respectively for the parameters mature weight (a) and maturing rate (k). Additionally, we found high and positive genetic correlation (0.78) between “a” and "k". The percentages of the genetic variances explained by each SNP allowed identifying the most relevant chromosome regions for each phenotype (growth curve parameters). The majority of these regions were closed to QTL regions previously reported for growth traits. However, we identified three relevant SNPs (55840514 bp at SSC17, 55814469 at SSC17 and 76475804 at SSC X) affecting "a" and "k" simultaneously, and three SNPs affecting only "a" (292758 bp at SSC1, 67319 bp at SSC8 and 50290193 bp at SSC17), that are located in regions not previously described as QTL for growth traits in pigs.

Single-step SNP-BLUP with on-the-fly imputed genotypes and residual polygenic effects

BackgroundSingle-step genomic best linear unbiased prediction (BLUP) evaluation combines relationship information from pedigree and genomic marker data. The inclusion of the genomic information into mixed model equations requires the inverse of the combined relationship matrix {mathbf{H}}, which has a dense matrix block for genotyped animals.MethodsTo avoid inversion of dense matrices, single-step genomic BLUP can be transformed to single-step single nucleotide polymorphism BLUP (SNP-BLUP) which have observed and imputed marker coefficients. Simple block LDL type decompositions of the single-step relationship matrix {mathbf{H}} were derived to obtain different types of linearly equivalent single-step genomic mixed model equations with different sets of reparametrized random effects. For non-genotyped animals, the imputed marker coefficient terms in the single-step SNP-BLUP were calculated on-the-fly during the iterative solution using sparse matrix decompositions without storing the imputed genotypes. Residual polygenic effects were added to genotyped animals and transmitted to non-genotyped animals using relationship coefficients that are similar to imputed genotypes. The relationships were further orthogonalized to improve convergence of iterative methods.ResultsAll presented single-step SNP-BLUP models can be solved efficiently using iterative methods that rely on iteration on data and sparse matrix approaches. The efficiency, accuracy and iteration convergence of the derived mixed model equations were tested with a small dataset that included 73,579 animals of which 2885 were genotyped with 37,526 SNPs.ConclusionsInversion of the large and dense genomic relationship matrix was avoided in single-step evaluation by using fully orthogonalized single-step SNP-BLUP formulations. The number of iterations until convergence was smaller in single-step SNP-BLUP formulations than in the original single-step GBLUP when heritability was low, but increased above that of the original single-step when heritability was high.

Genome-wide random regression analysis for parent-of-origin effects of body composition allometries in mouse

Genomic imprinting underlying growth and development traits has been recognized, with a focus on the form of absolute or pure growth. However, little is known about the effect of genomic imprinting on relative growth. In this study, we proposed a random regression model to estimate genome-wide imprinting effects on the relative growth of multiple tissues and organs to body weight in mice. Joint static allometry scaling equation as sub-model is nested within the genetic effects of markers and polygenic effects caused by a pedigree. Both chromosome-wide and genome-wide statistical tests were conducted to identify imprinted quantitative trait nucleotides (QTNs) associated with relative growth of individual tissues and organs to body weight. Real data analysis showed that three of six analysed tissues and organs are significantly associated with body weight in terms of phenotypic relative growth. At the chromosome-wide level, a total 122 QTNs were associated with allometries of kidney, spleen and liver weights to body weight, 36 of which were imprinted with different imprinting fashions. Further, only two imprinted QTNs responsible for relative growth of spleen and liver were verified by genome-wide test. Our approach provides a general framework for statistical inference of genomic imprinting underlying allometry scaling in animals.

Abstract P109: Detection of Haplotypes Underlying a Coronary Artery Calcification Linkage Peak in the Family Heart Study

Coronary artery calcification (CAC) is the buildup of calcium deposits in the arteries leading to the heart, and is an indicator of atherosclerosis. CAC is known to have a genetic component, and a genome-wide linkage screen in the Family Heart Study identified significant evidence of linkage on chromosome 9 in the area near rs13293430, but no common variant in the linkage region showed significant evidence of association. Linkage screens attempt to identify covariance due to common ancestry at a genomic locus. This is robust to the presence of rare variation or multiple variants in a region, but is unable to identify the genomic feature causing the signal and additional research is needed to identify potential causative variants. Pairs of individuals who have inherited a locus from a common ancestor will share an identical sequence of alleles (a haplotype) within the region. To identify haplotypes influencing the trait in the region, a mixed effects model was used. CAC was modeled as a trait influenced by polygenic effects and locus-specific haplotype effects. This model has the benefit of jointly estimating the effect of each haplotype while separating the haplotype effect from the background polygenic effect. From the study, 2,687 individuals of European descent from 508 pedigrees with CAC scores were genotyped on the Illumina Human 1M-DuoV3 single nucleotide polymorphisms (SNP) array. Of these, 180 individuals from 23 pedigrees were for subsequent analysis due to membership in a pedigree contributing to the linkage signal. CAC scores were determined by cardiac CT scan and adjusted for age, sex, and principal components before analysis. Phased haplotypes were generated for 906,856 common SNPs across the genome using SHAPEIT. The region between 25Mb and 35Mb (3,500 SNPs) was divided into 250kb sections, and within each section haplotypes were created based on 24 evenly spaced SNPs. Statistical significance for each region was determined by likelihood ratio test. Haplotype analysis identified two regions showing strong evidence of association (p<10 -7 ) with CAC. The region 28-28.25Mb is located completely within the gene LINGO2, which has been associated with BMI and cholesterol. The region 27.5-27.75Mb contains MOBKL2B, IFNK and C9orf72. Additional work will be needed to validate haplotypes used in the model and identify the particular haplotype influencing the trait.

The interaction of GSK3B and FXR1 genotypes may influence the mania and depression dimensions in mood disorders

BackgroundPrevious evidence in healthy subjects suggested that functional polymorphisms GSK3B rs12630592 and FXR1 rs496250 interact in regulating mood and emotional processing. We attempted to replicate this interaction primarily on manic and depressive dimensions in mood disorder patients, and secondarily on schizophrenia patients, diagnosis itself and age of onset. MethodsSymptom dimensions were derived from the Comprehensive Assessment of Symptoms and History 82 items rated lifetime in acute episodes and stabilized interepisode intervals in 384 patients from the Schizophrenia and Bipolar Disorder Eastern Quebec Kindred Study. Linear mixed effect models of symptom dimensions included rs12630592-rs496250 main and interaction fixed effects (obtained from TaqMan genotypes), and a polygenic random effect. The distribution of lifetime best-estimate DSM-IV diagnosis of 855 kindred members was studied versus genotype under a polytomous logistic model. ResultsIn mood disorder patients, the level of mania (in both acute and stabilized periods) and depression in stabilized periods was positively associated with GSK3B rs12630592 T only in FXR1 rs496250 A-allele carriers (Bonferroni-corrected interaction p=0.024, 0.052 and 0.017 respectively). The two polymorphisms explained 11% of mania variance and 5% of interepisode depression variance. The association was observed neither in schizophrenia patients nor with the psychotic dimension in mood disorder patients. Interaction with the diagnosis distribution (p=0.03) was driven by the decreasing prevalence of recurrent major depression with rs12630592 T also only in carriers of rs496250 A. LimitationsSample size was limited, but power was sufficient to detect the tested interaction effect in this replication sample. ConclusionsWe replicate in affective patients an interaction between the FXR1 rs496250 and GSK3B rs12630592 polymorphisms in regulating mood dimensions.

Genome-wide mapping of 10 calving and fertility traits in Holstein dairy cattle with special regard to chromosome 18

Over the last decades, a dramatic decrease in reproductive performance has been observed in Holstein cattle and fertility problems have become the most common reason for a cow to leave the herd. The premature removal of animals with high breeding values results in both economic and breeding losses. For efficient future Holstein breeding, the identification of loci associated with low fertility is of major interest and thus constitutes the aim of this study. To reach this aim, a genome-wide combined linkage disequilibrium and linkage analysis (cLDLA) was conducted using data on the following 10 calving and fertility traits in the form of estimated breeding values: days from first service to conception of heifers and cows, nonreturn rate on d 56 of heifers and cows, days from calving to first insemination, days open, paternal and maternal calving ease, paternal and maternal stillbirth. The animal data set contained 2,527 daughter-proven Holstein bulls from Germany that were genotyped with Illumina's BovineSNP50 BeadChip (Illumina Inc., San Diego, CA). For the cLDLA, 41,635 sliding windows of 40 adjacent single nucleotide polymorphisms (SNP) were used. At each window midpoint, a variance component analysis was executed using ASReml. The underlying mixed linear model included random quantitative trait locus (QTL) and polygenic effects. We identified 50 genome-wide significant QTL. The most significant peak was detected for direct calving ease at 59,179,424 bp on chromosome 18 (BTA18). Next, a mixed-linear model association (MLMA) analysis was conducted. A comparison of the cLDLA and MLMA results with special regard to BTA18 showed that the genome-wide most significant SNP from the MLMA was associated with the same trait and located on the same chromosome at 57,589,121 bp (i.e., about 1.5 Mb apart from the cLDLA peak). The results of 5 different cLDLA and 2 MLMA models, which included the fixed effects of either SNP or haplotypes, suggested that the cLDLA method outperformed the MLMA in accuracy and precision. The haplotype-based cLDLA method allowed for a more precise mapping and the definition of ancestral and derived QTL alleles, both of which are essential for the detection of underlying quantitative trait nucleotides.

ASD and schizophrenia show distinct developmental profiles in common genetic overlap with population-based social communication difficulties

Difficulties in social communication are part of the phenotypic overlap between autism spectrum disorders (ASD) and schizophrenia. Both conditions follow, however, distinct developmental patterns. Symptoms of ASD typically occur during early childhood, whereas most symptoms characteristic of schizophrenia do not appear before early adulthood. We investigated whether overlap in common genetic influences between these clinical conditions and impairments in social communication depends on the developmental stage of the assessed trait. Social communication difficulties were measured in typically-developing youth (Avon Longitudinal Study of Parents and Children, N⩽5553, longitudinal assessments at 8, 11, 14 and 17 years) using the Social Communication Disorder Checklist. Data on clinical ASD (PGC-ASD: 5305 cases, 5305 pseudo-controls; iPSYCH-ASD: 7783 cases, 11 359 controls) and schizophrenia (PGC-SCZ2: 34 241 cases, 45 604 controls, 1235 trios) were either obtained through the Psychiatric Genomics Consortium (PGC) or the Danish iPSYCH project. Overlap in genetic influences between ASD and social communication difficulties during development decreased with age, both in the PGC-ASD and the iPSYCH-ASD sample. Genetic overlap between schizophrenia and social communication difficulties, by contrast, persisted across age, as observed within two independent PGC-SCZ2 subsamples, and showed an increase in magnitude for traits assessed during later adolescence. ASD- and schizophrenia-related polygenic effects were unrelated to each other and changes in trait-disorder links reflect the heterogeneity of genetic factors influencing social communication difficulties during childhood versus later adolescence. Thus, both clinical ASD and schizophrenia share some genetic influences with impairments in social communication, but reveal distinct developmental profiles in their genetic links, consistent with the onset of clinical symptoms.

Single Marker Family-Based Association Analysis Not Conditional on Parental Information.

Family-based association analysis unconditional on parental genotypes models the effects of observed genotypes. This approach has been shown to have greater power than conditional methods. In this chapter, we review popular association analysis methods accounting for familial correlations: the marginal model using generalized estimating equations (GEE), the mixed model with a polygenic random component, and genome-wide association analyses. The marginal approach does not explicitly model familial correlations but uses the information to improve the efficiency of parameter estimates. This model, using GEE, is useful when the correlation structure is not of interest; the correlations are treated as nuisance parameters. In the mixed model, familial correlations are modeled as random effects, e.g., the polygenic inheritance model accounts for correlations originating from shared genomic components within a family. These unconditional methods provide a flexible modeling framework for general pedigree data to accommodate traits with various distributions and many types of covariate effects. Genome-wide association studies usually test more than 10,000 SNPs and thus traditional statistical methods accounting for the familial correlations often suffer from a computational burden. Multiple approaches that have been recently proposed to avoid this computational issue are reviewed. The single-marker analysis procedures are demonstrated using the R package gee and the ASSOC program in the S.A.G.E. package, including how to prepare input data, conduct the analysis, and interpret the output. ASSOC allows models to include random components of additional familial correlations that may be not sufficiently explained by a polygenic effect and addresses nonnormality of response variables by transformation methods. With its ease of use, ASSOC provides a useful tool for association analysis of large pedigree data.

Association between single nucleotide polymorphisms and sexual precocity in Nellore heifers

The aim of this study was to determine the extent (r2) of linkage disequilibrium (LD) in the genome of Nellore cattle, and to examine associations between single nucleotide polymorphisms (SNP) and age at first calving (AFC) and early pregnancy (EP) using a panel of high-density SNPs and data from 1182 Nellore females. A total of 13 contemporary groups (CG) were used consisting of farm, season, and year of birth. For genome-wide association analysis, SNPs with a minor allele frequency (MAF)<0.05 and animals with a call rate<0.90 were excluded, totaling 431,885 SNPs. For statistical analysis, a linear model was used for AFC and a threshold model for EP. To estimate the significance of the associations for the two traits, the model included the categorical fixed effects of CG, SNPs, and sire. In addition, the polygenic effect was included in the analysis. The additive effects and dominance deviations of Bonferroni-adjusted significant SNPs for AFC and EP were estimated using orthogonal contrasts. The average estimate of r2 for all autosomes was 0.18 at a distance of 4.8kb and the mean MAF was 0.25±0.13. The LD decreased as the distance between markers increased: 0.35 (1kb) to 0.12 (100kb). Eleven significant associations were detected in seven different chromosomes. Seven SNPs were associated with AFC and four were associated with EP. Three SNPs were significant for both traits. The identification of SNPs associated with AFC and EP may contribute for selecting sexually precocious animals.

Expression QTLs Mapping and Analysis: A Bayesian Perspective.

The aim of expression Quantitative Trait Locus (eQTL) mapping is the identification of DNA sequence variants that explain variation in gene expression. Given the recent yield of trait-associated genetic variants identified by large-scale genome-wide association analyses (GWAS), eQTL mapping has become a useful tool to understand the functional context where these variants operate and eventually narrow down functional gene targets for disease. Despite its extensive application to complex (polygenic) traits and disease, the majority of eQTL studies still rely on univariate data modeling strategies, i.e., testing for association of all transcript-marker pairs. However these "one at-a-time" strategies are (1) unable to control the number of false-positives when an intricate Linkage Disequilibrium structure is present and (2) are often underpowered to detect the full spectrum of trans-acting regulatory effects. Here we present our viewpoint on the most recent advances on eQTL mapping approaches, with a focus on Bayesian methodology. We review the advantages of the Bayesian approach over frequentist methods and provide an empirical example of polygenic eQTL mapping to illustrate the different properties of frequentist and Bayesian methods. Finally, we discuss how multivariate eQTL mapping approaches have distinctive features with respect to detection of polygenic effects, accuracy, and interpretability of the results.

Computational strategies for alternative single-step Bayesian regression models with large numbers of genotyped and non-genotyped animals.

BackgroundTwo types of models have been used for single-step genomic prediction and genome-wide association studies that include phenotypes from both genotyped animals and their non-genotyped relatives. The two types are breeding value models (BVM) that fit breeding values explicitly and marker effects models (MEM) that express the breeding values in terms of the effects of observed or imputed genotypes. MEM can accommodate a wider class of analyses, including variable selection or mixture model analyses. The order of the equations that need to be solved and the inverses required in their construction vary widely, and thus the computational effort required depends upon the size of the pedigree, the number of genotyped animals and the number of loci.TheoryWe present computational strategies to avoid storing large, dense blocks of the MME that involve imputed genotypes. Furthermore, we present a hybrid model that fits a MEM for animals with observed genotypes and a BVM for those without genotypes. The hybrid model is computationally attractive for pedigree files containing millions of animals with a large proportion of those being genotyped.ApplicationWe demonstrate the practicality on both the original MEM and the hybrid model using real data with 6,179,960 animals in the pedigree with 4,934,101 phenotypes and 31,453 animals genotyped at 40,214 informative loci. To complete a single-trait analysis on a desk-top computer with four graphics cards required about 3 h using the hybrid model to obtain both preconditioned conjugate gradient solutions and 42,000 Markov chain Monte-Carlo (MCMC) samples of breeding values, which allowed making inferences from posterior means, variances and covariances. The MCMC sampling required one quarter of the effort when the hybrid model was used compared to the published MEM.ConclusionsWe present a hybrid model that fits a MEM for animals with genotypes and a BVM for those without genotypes. Its practicality and considerable reduction in computing effort was demonstrated. This model can readily be extended to accommodate multiple traits, multiple breeds, maternal effects, and additional random effects such as polygenic residual effects.

Polygenic Score × Intervention Moderation: an Application of Discrete-Time Survival Analysis to Model the Timing of First Marijuana Use Among Urban Youth.

The present study examines the interaction between a polygenic score and an elementary school-based universal preventive intervention trial and its effects on a discrete-time survival analysis of time to first smoking marijuana. Research has suggested that initiation of substances is both genetically and environmentally driven (Rhee et al., Archives of general psychiatry 60:1256-1264, 2003; Verweij et al., Addiction 105:417-430, 2010). A previous work has found a significant interaction between the polygenic score and the same elementary school-based intervention with tobacco smoking (Musci et al., in press). The polygenic score reflects the contribution of multiple genes and has been shown in prior research to be predictive of smoking cessation, tobacco use, and marijuana use (Uhl et al., Molecular Psychiatry 19:50-54, 2014). Using data from a longitudinal preventive intervention study (N = 678), we examined age of first marijuana use from sixth grade to age 18. Genetic data were collected during emerging adulthood and were genotyped using the Affymetrix 6.0 microarray (N = 545). The polygenic score was computed using these data. Discrete-time survival analysis was employed to test for intervention main and interaction effects with the polygenic score. We found main effect of the polygenic score approaching significance, with the participants with higher polygenic scores reporting their first smoking marijuana at an age significantly later than controls (p = .050). We also found a significant intervention × polygenic score interaction effect at p = .003, with participants at the higher end of the polygenic score benefiting the most from the intervention in terms of delayed age of first use. These results suggest that genetics may play an important role in the age of first use of marijuana and that differences in genetics may account for the differential effectiveness of classroom-based interventions in delaying substance use experimentation.

Familial resemblances in blood leukocyte DNA methylation levels

ABSTRACTEpigenetic factors such as DNA methylation are DNA alterations affecting gene expression that can convey environmental information through generations. Only a few studies have demonstrated epigenetic inheritance in humans. Our objective is to quantify genetic and common environmental determinants of familial resemblances in DNA methylation levels, using a family based sample. DNA methylation was measured in 48 French Canadians from 16 families as part of the GENERATION Study. We used the Illumina HumanMethylation450 BeadChip array to measure DNA methylation levels in blood leukocytes on 485,577 CpG sites. Heritability was assessed using the variance components method implemented in the QTDT software, which partitions the variance into polygenic (G), common environmental (C), and non-shared environmental (E) effects. We computed maximal heritability, genetic heritability, and common environmental effect for all probes (12.7%, 8.2%, and 4.5%, respectively) and for statistically significant probes (81.8%, 26.9%, and 54.9%, respectively). Higher maximal heritability was observed in the Major Histocompatibility Complex region on chromosome 6. In conclusion, familial resemblances in DNA methylation levels are mainly attributable to genetic factors when considering the average across the genome, but common environmental effect plays an important role when considering statistically significant probes. Further epigenome-wide studies on larger samples combined with genome-wide genotyping studies are needed to better understand the underlying mechanisms of DNA methylation heritability.

Genome-wide association analysis of forage quality in maize mature stalk.

BackgroundPlant digestibility of silage maize (Zea mays L.) has a large influence on nutrition intake for animal feeding. Improving forage quality will enhance the utilization efficiency and feeding value of forage maize. Dissecting the genetic basis of forage quality will improve our understanding of the complex nature of cell wall biosynthesis and degradation, which is also helpful for breeding good quality silage maize.ResultsAcid detergent fiber (ADF), neutral detergent fiber (NDF) and in vitro dry matter digestibility (IVDMD) of stalk were evaluated in a diverse maize population, which is comprised of 368 inbred lines and planted across seven environments. Using a mixed model accounting for population structure and polygenic background effects, a genome-wide association study was conducted to identify single nucleotide polymorphisms (SNPs) significantly associated with forage quality. Scanning 559,285 SNPs across the whole genome, 73, 41 and 82 SNPs were found to be associated with ADF, NDF, and IVDMD, respectively. Each significant SNP explained 4.2 %–6.2 % of the phenotypic variation. Underlying these associated loci, 56 genes were proposed as candidate genes for forage quality.ConclusionsOf all the candidate genes proposed by GWAS, we only found a C3H gene (ZmC3H2) that is directly involved in cell wall component biosynthesis. The candidate genes found in this study are mainly involved in signal transduction, stress resistance, and transcriptional regulation of cell wall biosynthetic gene expression. Adding high digestibility maize into the association panel would be helpful for increasing genetic variability and identifying more genes associated with forage quality traits. Cloning and functional validation of these genes would be helpful for understanding the molecular mechanism of the fiber content and digestibility. These findings provide us new insights into cell wall formation and deposition.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-016-0919-9) contains supplementary material, which is available to authorized users.

A pathway-centric approach to rare variant association analysis

Current endeavours in rare variant analysis are typically underpowered when investigating association signals from individual genes. We undertook an approach to rare variant analysis which utilises biological pathway information to analyse functionally relevant genes together. Conventional filtering approaches for rare variant analysis are based on variant consequence and are therefore confined to coding regions of the genome. Therefore, we undertook a novel approach to this process by obtaining functional annotations from the Combined Annotation Dependent Depletion (CADD) tool, which allowed potentially deleterious variants from intronic regions of genes to be incorporated into analyses. This work was undertaken using whole-genome sequencing data from the UK10K project. Rare variants from the KEGG pathway for arginine and proline metabolism were collectively associated with systolic blood pressure (P=3.32x10−5) based on analyses using the optimal sequence kernel association test. Variants along this pathway also showed evidence of replication using imputed data from the Avon Longitudinal Study of Parents and Children cohort (P=0.02). Subsequent analyses found that the strength of evidence diminished when analysing genes in this pathway individually, suggesting that they would have been overlooked in a conventional gene-based analysis. Future studies that adopt similar approaches to investigate polygenic effects should yield value in better understanding the genetic architecture of complex disease.

Genetic and Environmental Risk for Chronic Pain and the Contribution of Risk Variants for Major Depressive Disorder: A Family-Based Mixed-Model Analysis.

BackgroundChronic pain is highly prevalent and a significant source of disability, yet its genetic and environmental risk factors are poorly understood. Its relationship with major depressive disorder (MDD) is of particular importance. We sought to test the contribution of genetic factors and shared and unique environment to risk of chronic pain and its correlation with MDD in Generation Scotland: Scottish Family Health Study (GS:SFHS). We then sought to replicate any significant findings in the United Kingdom Biobank study.Methods and FindingsUsing family-based mixed-model analyses, we examined the contribution of genetics and shared family environment to chronic pain by spouse, sibling, and household relationships. These analyses were conducted in GS:SFHS (n = 23,960), a family- and population-based study of individuals recruited from the Scottish population through their general practitioners. We then examined and partitioned the correlation between chronic pain and MDD and estimated the contribution of genetic factors and shared environment in GS:SFHS. Finally, we used data from two independent genome-wide association studies to test whether chronic pain has a polygenic architecture and examine whether genomic risk of psychiatric disorder predicted chronic pain and whether genomic risk of chronic pain predicted MDD. These analyses were conducted in GS:SFHS and repeated in UK Biobank, a study of 500,000 from the UK population, of whom 112,151 had genotyping and phenotypic data. Chronic pain is a moderately heritable trait (heritability = 38.4%, 95% CI 33.6% to 43.9%) that is significantly concordant in spouses (variance explained 18.7%, 95% CI 9.5% to 25.1%). Chronic pain is positively correlated with depression (ρ = 0.13, 95% CI 0.11 to 0.15, p = 2.72x10-68) and shows a tendency to cluster within families for genetic reasons (genetic correlation = 0.51, 95%CI 0.40 to 0.62, p = 8.24x10-19). Polygenic risk profiles for pain, generated using independent GWAS data, were associated with chronic pain in both GS:SFHS (maximum β = 6.18x10-2, 95% CI 2.84 x10-2 to 9.35 x10-2, p = 4.3x10-4) and UK Biobank (maximum β = 5.68 x 10−2, 95% CI 4.70x10-2 to 6.65x10-2 , p < 3x10-4). Genomic risk of MDD is also significantly associated with chronic pain in both GS:SFHS (maximum β = 6.62x10-2, 95% CI 2.82 x10-2 to 9.76 x10-2 , p = 4.3x10-4) and UK Biobank (maximum β = 2.56x10-2, 95% CI 1.62x10-2 to 3.63x10-2 , p < 3x10-4). Limitations of the current study include the possibility that spouse effects may be due to assortative mating and the relatively small polygenic risk score effect sizes.ConclusionsGenetic factors, as well as chronic pain in a partner or spouse, contribute substantially to the risk of chronic pain for an individual. Chronic pain is genetically correlated with MDD, has a polygenic architecture, and is associated with polygenic risk of MDD.

Shared Genetic Risk Factors of Intracranial, Abdominal, and Thoracic Aneurysms.

BackgroundIntracranial aneurysms (IAs), abdominal aortic aneurysms (AAAs), and thoracic aortic aneurysms (TAAs) all have a familial predisposition. Given that aneurysm types are known to co‐occur, we hypothesized that there may be shared genetic risk factors for IAs, AAAs, and TAAs.Methods and ResultsWe performed a mega‐analysis of 1000 Genomes Project‐imputed genome‐wide association study (GWAS) data of 4 previously published aneurysm cohorts: 2 IA cohorts (in total 1516 cases, 4305 controls), 1 AAA cohort (818 cases, 3004 controls), and 1 TAA cohort (760 cases, 2212 controls), and observed associations of 4 known IA, AAA, and/or TAA risk loci (9p21, 18q11, 15q21, and 2q33) with consistent effect directions in all 4 cohorts. We calculated polygenic scores based on IA‐, AAA‐, and TAA‐associated SNPs and tested these scores for association to case‐control status in the other aneurysm cohorts; this revealed no shared polygenic effects. Similarly, linkage disequilibrium–score regression analyses did not show significant correlations between any pair of aneurysm subtypes. Last, we evaluated the evidence for 14 previously published aneurysm risk single‐nucleotide polymorphisms through collaboration in extended aneurysm cohorts, with a total of 6548 cases and 16 843 controls (IA) and 4391 cases and 37 904 controls (AAA), and found nominally significant associations for IA risk locus 18q11 near RBBP8 to AAA (odds ratio [OR]=1.11; P=4.1×10−5) and for TAA risk locus 15q21 near FBN1 to AAA (OR=1.07; P=1.1×10−3).ConclusionsAlthough there was no evidence for polygenic overlap between IAs, AAAs, and TAAs, we found nominally significant effects of two established risk loci for IAs and TAAs in AAAs. These two loci will require further replication.

The Role of Metabotropic Glutamate Receptor Genes in Schizophrenia.

Genomic studies revealed two main components in the genetic architecture of schizophrenia, one constituted by common variants determining a distributed polygenic effect and one represented by a large number of heterogeneous rare and highly disruptive mutations. These gene modifications often affect neural transmission and different studies proved an involvement of metabotropic glutamate receptors in schizophrenia phenotype. Through the combination of literature information with genomic data from public repositories, we analyzed the current knowledge on the involvement of genetic variations of the human metabotropic glutamate receptors in schizophrenia and related endophenotypes. Despite the analysis did not reveal a definitive connection, different suggestive associations have been identified and in particular a relevant role has emerged for GRM3 in affecting specific schizophrenia endophenotypes. This supports the hypothesis that these receptors are directly involved in schizophrenia disorder.