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Summary The proportion of variation in complex traits that can be attributed to non-additive genetic effects has been a topic of intense debate. The availability of biobank-scale datasets of genotype and trait data from unrelated individuals opens up the possibility of obtaining precise estimates of the contribution of non-additive genetic effects. We present an efficient method to estimate the variation in a complex trait that can be attributed to additive (additive heritability) and dominance deviation (dominance heritability) effects across all genotyped SNPs in a large collection of unrelated individuals. Over a wide range of genetic architectures, our method yields unbiased estimates of additive and dominance heritability. We applied our method, in turn, to array genotypes as well as imputed genotypes (at common SNPs with minor allele frequency [MAF] > 1%) and 50 quantitative traits measured in 291,273 unrelated white British individuals in the UK Biobank. Averaged across these 50 traits, we find that additive heritability on array SNPs is 21.86% while dominance heritability is 0.13% (about 0.48% of the additive heritability) with qualitatively similar results for imputed genotypes. We find no statistically significant evidence for dominance heritability (p

Hypothesis-free Mendelian randomization studies provide a way to assess the causal relevance of a trait across the human phenome but can be limited by statistical power, sample overlap or complicated by horizontal pleiotropy. The recently described latent causal variable (LCV) approach provides an alternative method for causal inference which might be useful in hypothesis-free experiments across human phenome. We developed an automated pipeline for phenome-wide tests using the LCV approach including steps to estimate partial genetic causality, filter to a meaningful set of estimates, apply correction for multiple testing and then present the findings in a graphical summary termed causal architecture plot. We apply this pipeline to body mass index (BMI) and lipid traits as exemplars of traits where there is strong prior expectation for causal effects, and to dental caries and periodontitis as exemplars of traits where there is a need for causal inference. The results for lipids and BMI suggest that these traits are best viewed as contributing factors on a multitude of traits and conditions, thus providing additional evidence that supports viewing these traits as targets for interventions to improve health. On the other hand, caries and periodontitis are best viewed as a downstream consequence of other traits and diseases rather than a cause of ill health. The automated pipeline is implemented in the Complex-Traits Genetics Virtual Lab ( https://vl.genoma.io ) and results are available in https://view.genoma.io . We propose causal architecture plots based on phenome-wide partial genetic causality estimates as a new way visualizing the overall causal map of the human phenome.

Summary To facilitate scientific collaboration on polygenic risk scores (PRSs) research, we created an extensive PRS online repository for 35 common cancer traits integrating freely available genome-wide association studies (GWASs) summary statistics from three sources: published GWASs, the NHGRI-EBI GWAS Catalog, and UK Biobank-based GWASs. Our framework condenses these summary statistics into PRSs using various approaches such as linkage disequilibrium pruning/p value thresholding (fixed or data-adaptively optimized thresholds) and penalized, genome-wide effect size weighting. We evaluated the PRSs in two biobanks: the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and the population-based UK Biobank (UKB). For each PRS construct, we provide measures on predictive performance and discrimination. Besides PRS evaluation, the Cancer-PRSweb platform features construct downloads and phenome-wide PRS association study results (PRS-PheWAS) for predictive PRSs. We expect this integrated platform to accelerate PRS-related cancer research.

Understanding the consequences of local adaptation at the genomic diversity is a central goal in evolutionary genetics of natural populations. In species with large continuous geographical distributions the phenotypic signal of local adaptation is frequently clear, but the genetic background often remains elusive. We examined the patterns of genetic diversity inPinus sylvestris, a keystone species in many Eurasian ecosystems with a huge distribution range and decades of forestry research showing that it is locally adapted to the vast range of environmental conditions. MakingP. sylvestrisan even more attractive subject of local adaptation study, population structure has been shown to be weak previously and in this study. However, little is known about the molecular genetic basis of adaptation, as the massive size of gymnosperm genomes has prevented large scale genomic surveys. We generated a both geographically and genomically extensive dataset using a targeted sequencing approach. By applying divergence-based and landscape genomics methods we found that several coding loci contribute to local adaptation. We also discovered a very large (ca. 300 Mbp) putative inversion with a signal of local adaptation, which to our knowledge is the first such discovery in conifers. Our results call for more detailed analysis of structural variation in relation to genomic basis of local adaptation, emphasize the lack of large effect loci contributing to local adaptation in the coding regions and thus point out to the need for more attention towards multi-locus analysis of polygenic adaptation.

Positive selection may be the main factor of the between‐population divergence in gene expression. Expression profiles of two Drosophila melanogaster laboratory strains of different geographical origin and long‐term laboratory maintenance were analyzed using microchip arrays encompassing probes for 18,500 transcripts. The Russian strain D18 and the North American strain Canton‐S were compared. A set of 223 known or putative genes demonstrated significant changes in expression levels between these strains. Differentially expressed genes (DEG) were enriched in response to DDT (p = .0014), proteolysis (p = 2.285E−5), transmembrane transport (p = 1.03E−4), carbohydrate metabolic process (p = .0317), protein homotetramerization (p = .0444), and antibacterial humoral response (p = 425E−4). The expression in subset of genes from different categories was verified by qRT‐PCR. Analysis of transcript abundance between Canton‐S and D18 strains allowed to select several genes to estimate their participation in latitude adaptation. Expression of selected genes was analyzed in five D. melanogaster lines of different geographic origins by qRT‐PCR, and we found two candidate genes that may be associated with latitude adaptation in adult flies—smp‐30 and Cda9. Quite possible that several alleles of these genes may be important for insect survival in the environments of global warming. It is interesting that the number of genes involved in local adaptation demonstrates expression level appropriate to their geographical origin even after decades of laboratory maintenance., It is interesting that the number of genes involved in local adaptation demonstrates expression level appropriate to their geographical origin even after decades of laboratory maintenance.

BackgroundAdolescent idiopathic scoliosis (AIS) is a genetically heterogeneous disease characterised by three-dimensional deformity of the spine in the absence of a congenital spinal anomaly or neurological musculoskeletal disorder. The clinical variability and incomplete penetrance of some genes linked with AIS indicate that this disease constitutes an oligogenic trait.ObjectiveWe aimed to explore the oligogenic nature of this disease and identify novel AIS genes.MethodsWe analysed rare damaging variants within AIS-associated genes by using exome sequencing in 40 AIS trios and 183 sporadic patients.ResultsMultiple variants within AIS-associated genes were identified in eight AIS trios, and five individuals harboured rare damaging variants in theFLNBgene. The patients showed more frequent oligogenicity than the controls. In the gene-based burden test, the top signal resided inFLNB. In functional studies, we found that the AIS-associatedFLNBvariants altered the protein’s conformation and subcellular localisation and its interaction with other proteins (TTC26 and OFD1) involved in AIS. The most compelling evidence of an oligogenic basis was that the number of rare damaging variants was recognised as an independent prognostic factor for curve progression in Cox regression analysis.ConclusionOur data indicate that AIS is an oligogenic disease and identifyFLNBas a susceptibility gene for AIS.

Mouse models have been instrumental in understanding human disease biology and proposing possible new treatments. The precise control of the environment and genetic composition of mice allows more rigorous observations, but limits the generalizability and translatability of the results into human applications. In the era of precision medicine, strategies using mouse models have to be revisited to effectively emulate human populations. Systems genetics is one promising paradigm that may promote the transition to novel precision medicine strategies. Here, we review the state-of-the-art resources and discuss how mouse systems genetics helps to understand human diseases and to advance the development of precision medicine, with an emphasis on the existing resources and strategies.

In transcriptome‐wide association studies (TWAS), gene expression values are predicted using genotype data and tested for association with a phenotype. The power of this approach to detect associations relies, at least in part, on the accuracy of the prediction. Here we compare the prediction accuracy of six different methods—LASSO, Ridge regression, Elastic net, Best Linear Unbiased Predictor, Bayesian Sparse Linear Mixed Model, and Random Forests—by performing cross‐validation using data from the Geuvadis Project. We also examine prediction accuracy (a) at different sample sizes, (b) when ancestry of the prediction model training and testing populations is different, and (c) when the tissue used to train the model is different from the tissue to be predicted. We find that, for most genes, the expression cannot be accurately predicted, but in general sparse statistical models tend to outperform polygenic models at prediction. Average prediction accuracy is reduced when the model training set size is reduced or when predicting across ancestries and is marginally reduced when predicting across tissues. We conclude that using sparse statistical models and the development of large reference panels across multiple ethnicities and tissues will lead to better prediction of gene expression, and thus may improve TWAS power.

BackgroundParkinson’s disease (PD) is a neurodegenerative disorder with complex genetic architecture. Besides rare mutations in high-risk genes related to monogenic familial forms of PD, multiple variants associated with sporadic PD were discovered via association studies.MethodsWe studied the whole-exome sequencing data of 340 PD cases and 146 ethnically matched controls from the Parkinson’s Progression Markers Initiative (PPMI) and performed burden analysis for different rare variant classes. Disease prediction models were built based on clinical, non-clinical and genetic features, including both common and rare variants, and two machine learning methods.ResultsWe observed a significant exome-wide burden of singleton loss-of-function variants (corrected p=0.037). Overall, no exome-wide burden of rare amino acid changing variants was detected. Finally, we built a disease prediction model combining singleton loss-of-function variants, a polygenic risk score based on common variants, and family history of PD as features and reached an area under the curve of 0.703 (95% CI 0.698 to 0.708). By incorporating a rare variant feature, our model increased the performance of the state-of-the-art classification model for the PPMI dataset, which reached an area under the curve of 0.639 based on common variants alone.ConclusionThe main finding of this study is to highlight the contribution of singleton loss-of-function variants to the complex genetics of PD and that disease risk prediction models combining singleton and common variants can improve models built solely on common variants.

Hereditary diseases and complex traits often manifest in specific tissues, whereas their causal genes are expressed in many tissues that remain unaffected. Among the mechanisms that have been suggested for this enigmatic phenomenon is dosage-sensitive compensation by paralogs of causal genes. Accordingly, tissue-selectivity stems from dosage imbalance between causal genes and paralogs that occurs particularly in disease-susceptible tissues. Here, we used a large-scale dataset of thousands of tissue transcriptomes and applied a linear mixed model (LMM) framework to assess this and other dosage-sensitive mechanisms. LMM analysis of 382 hereditary diseases consistently showed evidence for dosage-sensitive compensation by paralogs across diseases subsets and susceptible tissues. LMM analysis of 135 candidate genes that are strongly associated with 16 tissue-selective complex traits revealed a similar tendency among half of the trait-associated genes. This suggests that dosage-sensitive compensation by paralogs affects the tissue-selectivity of complex traits, and can be used to illuminate candidate genes' modes of action. Next, we applied LMM to analyze dosage imbalance between causal genes and three classes of genetic modifiers, including regulatory micro-RNAs, pseudogenes, and genetic interactors. Our results propose modifiers as a fundamental axis in tissue-selectivity of diseases and traits, and demonstrates the power of LMM as a statistical framework for discovering treatment avenues.

Realistic mappings of genes to morphology are inherently multivariate on both sides of the equation. The importance of coordinated gene effects on morphological phenotypes is clear from the intertwining of gene actions in signaling pathways, gene regulatory networks, and developmental processes underlying the development of shape and size. Yet, current approaches tend to focus on identifying and localizing the effects of individual genes and rarely leverage the information content of high-dimensional phenotypes. Here, we explicitly model the joint effects of biologically coherent collections of genes on a multivariate trait – craniofacial shape – in a sample of n = 1145 mice from the Diversity Outbred (DO) experimental line. We use biological process Gene Ontology (GO) annotations to select skeletal and facial development gene sets and solve for the axis of shape variation that maximally covaries with gene set marker variation. We use our process-centered, multivariate genotype-phenotype (process MGP) approach to determine the overall contributions to craniofacial variation of genes involved in relevant processes and how variation in different processes corresponds to multivariate axes of shape variation. Further, we compare the directions of effect in phenotype space of mutations to the primary axis of shape variation associated with broader pathways within which they are thought to function. Finally, we leverage the relationship between mutational and pathway-level effects to predict phenotypic effects beyond craniofacial shape in specific mutants. We also introduce an online application that provides users the means to customize their own process-centered craniofacial shape analyses in the DO. The process-centered approach is generally applicable to any continuously varying phenotype and thus has wide-reaching implications for complex trait genetics.

Comprehensive maps of genetic interactions in mammalian cells are daunting to construct because of the large number of potential interactions, ~ 2 × 108 for protein coding genes. We previously used co-inheritance of distant genes from published radiation hybrid (RH) datasets to identify genetic interactions. However, it was necessary to combine six legacy datasets from four species to obtain adequate statistical power. Mapping resolution was also limited by the low density PCR genotyping. Here, we employ shallow sequencing of nascent human RH clones as an economical approach to constructing interaction maps. In this initial study, 15 clones were analyzed, enabling construction of a network with 225 genes and 2359 interactions (FDR < 0.05). Despite its small size, the network showed significant overlap with the previous RH network and with a protein-protein interaction network. Consumables were ≲ $50 per clone, showing that affordable, high quality genetic interaction maps are feasible in mammalian cells.

It is of great interest and potential to discover causal relationships between pairs of exposures and outcomes using genetic variants as instrumental variables (IVs) to deal with hidden confounding in observational studies. Two most popular approaches are Mendelian randomization (MR), which usually use independent genetic variants/SNPs across the genome, and transcriptome-wide association studies (TWAS) (or their generalizations) using cis-SNPs local to a gene (or some genome-wide and likely dependent SNPs), as IVs. In spite of their many promising applications, both approaches face a major challenge: the validity of their causal conclusions depends on three critical assumptions on valid IVs, and more generally on other modeling assumptions, which however may not hold in practice. The most likely as well as challenging situation is due to the wide-spread horizontal pleiotropy, leading to two of the three IV assumptions being violated and thus to biased statistical inference. More generally, we’d like to conduct a goodness-of-fit (GOF) test to check the model being used. Although some methods have been proposed as being robust to various degrees to the violation of some modeling assumptions, they often give different and even conflicting results due to their own modeling assumptions and possibly lower statistical efficiency, imposing difficulties to the practitioner in choosing and interpreting varying results across different methods. Hence, it would help to directly test whether any assumption is violated or not. In particular, there is a lack of such tests for TWAS. We propose a new and general GOF test, called TEDE (TEsting Direct Effects), applicable to both correlated and independent SNPs/IVs (as commonly used in TWAS and MR respectively). Through simulation studies and real data examples, we demonstrate high statistical power and advantages of our new method, while confirming the frequent violation of modeling (including valid IV) assumptions in practice and thus the importance of model checking by applying such a test in MR/TWAS analysis., Author summary With the increasing availability of large-scale GWAS summary data of various complex traits/diseases and software packages, it has become convenient and popular to apply Mendelian randomization (MR) and transcriptome-wide association studies (TWAS), using genetic variants as instrumental variables (IVs), to address fundamental and significant questions by unraveling causal relationships between complex or molecular traits such as gene expression and other complex traits. However, the validity of such causal conclusions critically depends on the validity of the model being used, including three key IV assumptions. In particular, with the wide-spread horizontal pleiotropy of genetic variants, two of the three IV assumptions may be violated, leading to biased inference from MR and TWAS. This issue may become more severe as more trait-associated genetic variants are used as IVs to increase the power of MR and TWAS. Although there are some methods to check the modeling assumptions for MR with independent genetic variants as IVs, there is barely any powerful one for TWAS (or more generally for MR and similar methods) with correlated SNPs as IVs. We propose such a powerful method applicable to both MR and TWAS with local or genome-wide, possibly correlated/dependent, SNPs as IVs, demonstrating its higher statistical power than several commonly used methods, while confirming the frequent violation of modeling/IV assumptions in TWAS with our example GWAS data of schizophrenia, Alzheimer’s disease and blood lipids. An important conclusion is that in practice it is necessary to conduct model checking in MR and TWAS, and our proposed method is expected to be useful for such a task.

Background Migraine is a complex neurological disorder that is considered the most common disabling brain disorder affecting 14 % of people worldwide. The present study sought to infer potential causal relationships between self-reported migraine and other complex traits, using genetic data and a hypothesis-free approach. Methods We leveraged available summary statistics from genome-wide association studies (GWAS) of 1,504 phenotypes and self-reported migraine and inferred pair-wise causal relationships using the latent causal variable (LCV) method. Results We identify 18 potential causal relationships between self-reported migraine and other complex traits. Hypertension and blood clot formations were causally associated with an increased migraine risk, possibly through vasoconstriction and platelet clumping. We observed that sources of abdominal pain and discomfort might influence a higher risk for migraine. Moreover, occupational and environmental factors such as working with paints, thinner or glues, and being exposed to diesel exhaust were causally associated with higher migraine risk. Psychiatric-related phenotypes, including stressful life events, increased migraine risk. In contrast, ever feeling unenthusiastic / disinterested for a whole week, a phenotype related to the psychological well-being of individuals, was a potential outcome of migraine. Conclusions Overall, our results suggest a potential vascular component to migraine, highlighting the role of vasoconstriction and platelet clumping. Stressful life events and occupational variables potentially influence a higher migraine risk. Additionally, a migraine could impact the psychological well-being of individuals. Our findings provide novel testable hypotheses for future studies that may inform the design of new interventions to prevent or reduce migraine risk and recurrence.

Genetic prediction of complex traits has great promise for disease prevention, monitoring, and treatment. The development of accurate risk prediction models is hindered by the wide diversity of genetic architecture across different traits, limited access to individual level data for training and parameter tuning, and the demand for computational resources. To overcome the limitations of the most existing methods that make explicit assumptions on the underlying genetic architecture and need a separate validation data set for parameter tuning, we develop a summary statistics-based nonparametric method that does not rely on validation datasets to tune parameters. In our implementation, we refine the commonly used likelihood assumption to deal with the discrepancy between summary statistics and external reference panel. We also leverage the block structure of the reference linkage disequilibrium matrix for implementation of a parallel algorithm. Through simulations and applications to twelve traits, we show that our method is adaptive to different genetic architectures, statistically robust, and computationally efficient. Our method is available at https://github.com/eldronzhou/SDPR., Author summary Recently there has been much interest in predicting an individual’s phenotype from genetic information, which has great promise for disease prevention, monitoring, and treatment. It has been found that there is great variation in the genetic architecture underlying different complex traits, including the number of genetic variants involved and the distribution of the effect sizes of genetic variants. How to model such genetic contribution is a key aspect for accurate prediction of complex traits. So far, most existing methods make specific assumptions about the shape of the genetic contribution. If these assumptions are not correct, the prediction accuracy might be compromised. Here we propose a method that learns the shape of the genetic contribution without making any explicit assumptions. We found that our method achieved robust performance when compared with other recently developed methods through simulation and real data analysis. Our method is also practically more feasible, since it supports the use of public summary statistics and consumes only small amount of computational resources.

The accuracy of polygenic risk scores (PRSs) to predict complex diseases increases with the training sample size. PRSs are generally derived based on summary statistics from large meta-analyses of multiple genome-wide association studies (GWAS). However, it is now common for researchers to have access to large individual-level data as well, such as the UK biobank data. To the best of our knowledge, it has not yet been explored how to best combine both types of data (summary statistics and individual-level data) to optimize polygenic prediction. The most widely used approach to combine data is the meta-analysis of GWAS summary statistics (Meta-GWAS), but we show that it does not always provide the most accurate PRS. Through simulations and using twelve real case-control and quantitative traits from both iPSYCH and UK Biobank along with external GWAS summary statistics, we compare Meta-GWAS with two alternative data-combining approaches, stacked clumping and thresholding (SCT) and Meta-PRS. We find that, when large individual-level data is available, the linear combination of PRSs (Meta-PRS) is both a simple alternative to Meta-GWAS and often more accurate.

Physiological, fitness and disease phenotypes are complex traits exhibiting continuous variation in natural populations. To understand complex trait gene functions transgenic lines of undefined genetic background are often combined to assess quantitative phenotypes ignoring the impact of genetic polymorphisms. Here, we used inbred wild-type strains of theDrosophilaGenetics Reference Panel to assess the phenotypic variation of six physiological and fitness traits, namely, female fecundity, survival and intestinal mitosis upon oral infection, defecation rate and fecal pH upon oral infection, and terminal tracheal cell branching in hypoxia. We found continuous variation in the approximately 150 strains tested for each trait, with extreme values differing by more than four standard deviations for all traits. In addition, we assessed the effects of commonly usedDrosophila UAS-RNAitransgenic strains and their backcrossed isogenized counterparts, in the same traits plus baseline intestinal mitosis and tracheal branching in normoxia, in heterozygous conditions, when only half of the genetic background was different among strains. We tested 20 non-isogenic strains (10 KK and 10 GD) from the ViennaDrosophilaResource Center and their isogenized counterparts without Gal4 induction. Survival upon infection and female fecundity exhibited differences in 50% and 40% of the tested isogenic vs. non-isogenic pairs, respectively, whereas all other traits were affected in only 10-25% of the cases. Upon Gal4-inducedUAS-RNAiexpression, 4 out of 11 isogenic vs. non-isogenic pairs tested exhibited differences in survival to infection. Andvice versa, crossing a singleUAS-RNAiline with a Gal4 transgene inserted in different genetic backgrounds exhibited quantitative variations that were unpredictable on the basis of pure line performance. Thus, irrespective of the trait of interest, the genetic background of commonly used transgenic strains needs to be considered carefully during experimentation.

[EN] Complex traits include, among many others, the evolution of eyes, wings, body forms, reproductive modes, human intelligence, social behaviour, diseases, and chromosome morphology. Dollo¿s law states that the evolution of complex traits is irreversible. However, potential exceptions have been proposed. Here, we investigated whether reticulation, a simple and elegant means by which complex characters may be reacquired, could account for suggested reversals in the evolution of complex characters using two datasets with sufficient genetic coverage and a total of five potential reversals. Our analyses uncovered a potential reversal in the evolution of parity mode and a potential reversal in the evolution of placentotrophy of fish (Cyprinodontiformes) as reticulation events. Moreover, in a reptile that exhibits a potential reversal from viviparity to oviparity (Zootoca vivipara), reticulation provided the most parsimonious explanation for sex chromosome evolution. Therefore, three of the five studied potential reversals were unravelled as reticulation events. This constitutes the first evidence that accounting for reticulation can fundamentally influence the interpretation of the evolution of complex traits, that testing for reticulation is crucial for obtaining robust phylogenies, and that complex ancestral characters may be reacquired through hybridization with a lineage that still exhibits the trait. Hybridization, rather than re-appearance of ancestral traits by means of small evolutionary steps, may thus account for suggested exceptions to Dollo¿s law. Consequently, ruling out reticulation is required to claim evolutionary reversal of complex characters and potential exceptions to Dollo's rule, EU Marie Curie‐Clarín CoFund, Grant/Award Number: ACA14–26; Swiss National Science Foundation, Grant/Award Numbers: PP00P3_152929/1, PPOOP3_128375; Spanish MINECO postdoc, Grant/Award Number: FPDI‐2013‐16116; Spanish Ministry of Education and Science, Grant/Award Number: CGL2008‐01522 and CGL2012‐32459

Recent work has provided strong empirical support for the classic polygenic model for trait variation. Population-based findings suggest that most regions of genome harbor variation affecting most traits. Here, we use the approach of experimental genetics to show that, indeed, most genomic regions carry variants with detectable effects on growth and reproduction in Caenorhabditis elegans populations sensitized by nickel stress. Nine of 15 adjacent intervals on the X chromosome, each encompassing ∼0.001 of the genome, have significant effects when tested individually in near-isogenic lines (NILs). These intervals have effects that are similar in magnitude to those of genome-wide significant loci that we mapped in a panel of recombinant inbred advanced intercross lines (RIAILs). If NIL-like effects were randomly distributed across the genome, the RIAILs would exhibit phenotypic variance that far exceeds the observed variance. However, the NIL intervals are arranged in a pattern that significantly reduces phenotypic variance relative to a random arrangement; adjacent intervals antagonize one another, cancelling each other's effects. Contrary to the expectation of small additive effects, our findings point to large-effect variants whose effects are masked by epistasis or linkage disequilibrium between alleles of opposing effect.

Summary The horse reference genome from the Thoroughbred mare Twilight has been available for a decade and, together with advances in genomics technologies, has led to unparalleled developments in equine genomics. At the core of this progress is the continuing improvement of the quality, contiguity and completeness of the reference genome, and its functional annotation. Recent achievements include the release of the next version of the reference genome (EquCab3.0) and generation of a reference sequence for the Y chromosome. Horse satellite‐free centromeres provide unique models for mammalian centromere research. Despite extremely low genetic diversity of the Y chromosome, it has been possible to trace patrilines of breeds and pedigrees and show that Y variation was lost in the past approximately 2300 years owing to selective breeding. The high‐quality reference genome has led to the development of three different SNP arrays and WGSs of almost 2000 modern individual horses. The collection of WGS of hundreds of ancient horses is unique and not available for any other domestic species. These tools and resources have led to global population studies dissecting the natural history of the species and genetic makeup and ancestry of modern breeds. Most importantly, the available tools and resources, together with the discovery of functional elements, are dissecting molecular causes of a growing number of Mendelian and complex traits. The improved understanding of molecular underpinnings of various traits continues to benefit the health and performance of the horse whereas also serving as a model for complex disease across species.

Fillet yield (FY) and harvest weight (HW) are economically important traits in Nile tilapia production. Genetic improvement of these traits, especially for FY, are lacking, due to the absence of efficient methods to measure the traits without sacrificing fish and the use of information from relatives to selection. However, genomic information could be used by genomic selection to improve traits that are difficult to measure directly in selection candidates, as in the case of FY. The objectives of this study were: (i) to perform genome-wide association studies (GWAS) to dissect the genetic architecture of FY and HW, (ii) to evaluate the accuracy of genotype imputation and (iii) to assess the accuracy of genomic selection using true and imputed low-density (LD) single nucleotide polymorphism (SNP) panels to determine a cost-effective strategy for practical implementation of genomic information in tilapia breeding programs. The data set consisted of 5,866 phenotyped animals and 1,238 genotyped animals (108 parents and 1,130 offspring) using a 50K SNP panel. The GWAS were performed using all genotyped and phenotyped animals. The genotyped imputation was performed from LD panels (LD0.5K, LD1K and LD3K) to high-density panel (HD), using information from parents and 20% of offspring in the reference set and the remaining 80% in the validation set. In addition, we tested the accuracy of genomic selection using true and imputed genotypes comparing the accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions. The results from GWAS supports evidence of the polygenic nature of FY and HW. The accuracy of imputation ranged from 0.90 to 0.98 for LD0.5K and LD3K, respectively. The accuracy of genomic prediction outperformed the estimated breeding value from PBLUP. The use of imputation for genomic selection resulted in an increased relative accuracy independent of the trait and LD panel analyzed. The present results suggest that genotype imputation could be a cost-effective strategy for genomic selection in Nile tilapia breeding programs.

The Collaborative Cross (CC) mouse model is a next‐generation mouse genetic reference population (GRP) designated for a high‐resolution quantitative trait loci (QTL) mapping of complex traits during health and disease. The CC lines were generated from reciprocal crosses of eight divergent mouse founder strains composed of five classical and three wild‐derived strains. Complex traits are defined to be controlled by variations within multiple genes and the gene/environment interactions. In this article, we introduce and present variety of protocols and results of studying the host response to infectious and chronic diseases, including type 2 diabetes and metabolic diseases, body composition, immune response, colorectal cancer, susceptibility to Aspergillus fumigatus, Klebsiella pneumoniae, Pseudomonas aeruginosa, sepsis, and mixed infections of Porphyromonas gingivalis and Fusobacterium nucleatum, which were conducted at our laboratory using the CC mouse population. These traits are observed at multiple levels of the body systems, including metabolism, body weight, immune profile, susceptibility or resistance to the development and progress of infectious or chronic diseases. Herein, we present full protocols and step‐by‐step methods, implemented in our laboratory for the phenotypic and genotypic characterization of the different CC lines, mapping the gene underlying the host response to these infections and chronic diseases. The CC mouse model is a unique and powerful GRP for dissecting the host genetic architectures underlying complex traits, including chronic and infectious diseases.

Background Copy number variations (CNVs) are structural variants consisting of large-scale insertions and deletions of genomic fragments. Exploring CNVs and estimating their effects on phenotypes are useful for genome selection but remain challenging in the livestock. Results We identified 1043 CNV regions (CNVRs) from array comparative genomic hybridization (CGH) data of 47 Holstein bulls. Using a probe-based CNV association approach, we detected 87 CNVRs significantly (Bonferroni-corrected P value

To better understand cryptic genetic variation, Lee et al. comprehensively map the genetic basis of a trait that is typically suppressed in a yeast cross. By determining how three different genetic perturbations give rise..., Cryptic genetic variation may be an important contributor to heritable traits, but its extent and regulation are not fully understood. Here, we investigate the cryptic genetic variation underlying a Saccharomyces cerevisiae colony phenotype that is typically suppressed in a cross of the laboratory strain BY4716 (BY) and a derivative of the clinical isolate 322134S (3S). To do this, we comprehensively dissect the trait’s genetic basis in the BYx3S cross in the presence of three different genetic perturbations that enable its expression. This allows us to detect and compare the specific loci that interact with each perturbation to produce the trait. In total, we identify 21 loci, all but one of which interact with just a subset of the perturbations. Beyond impacting which loci contribute to the trait, the genetic perturbations also alter the extent of additivity, epistasis, and genotype–environment interaction among the detected loci. Additionally, we show that the single locus interacting with all three perturbations corresponds to the coding region of the cell surface gene FLO11. While nearly all of the other remaining loci influence FLO11 transcription in cis or trans, the perturbations tend to interact with loci in different pathways and subpathways. Our work shows how layers of cryptic genetic variation can influence complex traits. Here, these layers mainly represent different regulatory inputs into the transcription of a single key gene.

Forward genetics determines the function of genes underlying trait variation by identifying the change in DNA responsible for changes in phenotype. Detecting phenotypically-relevant variation outside protein coding sequences and distinguishing this from neutral variants is not trivial; partly because the mechanisms by which DNA polymorphisms in the intergenic regions affect gene regulation are poorly understood. Here we utilized a dominant genetic marker with a convenient phenotype to investigate the effect of cis and trans-acting regulatory variation. We performed a forward genetic screen for natural variation that suppress or enhance the semi-dominant mutant alleleOy1-N1989,encoding the magnesium chelatase subunit I of maize. This mutant permits rapid phenotyping of leaf color as a reporter for chlorophyll accumulation, and mapping of natural variation in maize affecting chlorophyll metabolism. We identified a single modifier locus segregating between B73 and Mo17 that was linked to the reporter gene itself, which we callvery oil yellow1. Based on the variation in OY1 transcript abundance and genome-wide association data,vey1is predicted to consist of multiple cis-acting regulatory sequence polymorphisms encoded at the wild-typeoy1alleles. Thevey1allele appears to be a common polymorphism in the maize germplasm that alters the expression level of a key gene in chlorophyll biosynthesis. Thesevey1alleles have no discernable impact on leaf chlorophyll in the absence of theOy1-N1989reporter. Thus, use of a mutant as a simple and efficient reporter for magnesium chelatase activity resulted in the detection of expression-level polymorphisms not readily visible in the laboratory.

Made available in DSpace on 2021-06-25T11:13:19Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-05-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) We conducted analysis to estimate genetic parameters and to identify genomic regions and candidate genes affecting direct and maternal effects of preweaning calf mortality (PWM) in Nellore cattle. Phenotypic records of 67,196 animals, and 8443 genotypes for 410,936 SNPs were used. Analysis were performed through the weighted single-step GBLUP approach and considering a threshold animal model via Bayesian Inference. Direct and maternal heritability estimates were of 0.2143 ± 0.0348 and 0.0137 ± 0.0066, respectively. The top 10 genomic regions accounted for 13.61 and 14.23% of the direct and maternal additive genetic variances and harbored a total of 63 and 91 positional candidate genes, respectively. Two overlapping regions on BTA2 were identified for both direct and maternal effects. Candidate genes are involved in biological mechanisms i.e. embryogenesis, immune response, feto-maternal communication, circadian rhythm, hormone alterations, myometrium adaptation, and milk secretion, which are critical for the successful calf growth and survival during preweaning period. São Paulo State University (Unesp) College of Agricultural and Veterinarian Sciences, Via de Acesso Professor Paulo Donato Castelane Castellane S/N - Vila Industrial National Council for Science and Technological Development (CNPq) São Paulo State University (Unesp) College of Agricultural and Veterinarian Sciences, Via de Acesso Professor Paulo Donato Castelane Castellane S/N - Vila Industrial FAPESP: #2017/10630-2 FAPESP: #2018/20026-8 CAPES: 001

BackgroundHeight and other anthropometric measures are consistently found to associate with differential cancer risk. However, both genetic and mechanistic insights into these epidemiological associations are notably lacking. Conversely, inherited genetic variants in tumour suppressors and oncogenes increase cancer risk, but little is known about their influence on anthropometric traits.MethodsBy integrating inherited and somatic cancer genetic data from the Genome-Wide Association Study Catalog, expression Quantitative Trait Loci databases and the Cancer Gene Census, we identify SNPs that associate with different cancer types and differential gene expression in at least one tissue type, and explore the potential pleiotropic associations of these SNPs with anthropometric traits through SNP-wise association in a cohort of 500,000 individuals.ResultsWe identify three regulatory SNPs for three important cancer genes, FANCA, MAP3K1 and TP53 that associate with both anthropometric traits and cancer risk. Of particular interest, we identify a previously unrecognised strong association between the rs78378222[C] SNP in the 3' untranslated region (3'-UTR) of TP53 and both increased risk for developing non-melanomatous skin cancer (OR=1.36 (95% 1.31 to 1.41), adjusted p=7.62E−63), brain malignancy (OR=3.12 (2.22 to 4.37), adjusted p=1.43E−12) and increased standing height (adjusted p=2.18E−24, beta=0.073±0.007), lean body mass (adjusted p=8.34E−37, beta=0.073±0.005) and basal metabolic rate (adjusted p=1.13E−31, beta=0.076±0.006), thus offering a novel genetic link between these anthropometric traits and cancer risk.ConclusionOur results clearly demonstrate that heritable variants in key cancer genes can associate with both differential cancer risk and anthropometric traits in the general population, thereby lending support for a genetic basis for linking these human phenotypes.

Genetic association data from national biobanks and large-scale association studies have provided new prospects for understanding the genetic evolution of complex traits and diseases in humans. In turn, genomes from ancient human archaeological remains are now easier than ever to obtain, and provide a direct window into changes in frequencies of trait-associated alleles in the past. This has generated a new wave of studies aiming to analyse the genetic component of traits in historic and prehistoric times using ancient DNA, and to determine whether any such traits were subject to natural selection. In humans, however, issues about the portability and robustness of complex trait inference across different populations are particularly concerning when predictions are extended to individuals that died thousands of years ago, and for which little, if any, phenotypic validation is possible. In this review, we discuss the advantages of incorporating ancient genomes into studies of trait-associated variants, the need for models that can better accommodate ancient genomes into quantitative genetic frameworks, and the existing limits to inferences about complex trait evolution, particularly with respect to past populations.

People in the Americas represent a diverse continuum of populations with varying degrees of admixture among African, European, and Amerindigenous ancestries. In the United States, populations with non-European ancestry remain understudied, and thus little is known about the genetic architecture of phenotypic variation in these populations. Using genotype data from the Hispanic Community Health Study/Study of Latinos, we find that Amerindigenous ancestry increased by an average of ~20% spanning 1940s-1990s in Mexican Americans. These patterns result from complex interactions between several population and cultural factors which shaped patterns of genetic variation and influenced the genetic architecture of complex traits in Mexican Americans. We show for height how polygenic risk scores based on summary statistics from a European-based genome-wide association study perform poorly in Mexican Americans. Our findings reveal temporal changes in population structure within Hispanics/Latinos that may influence biomedical traits, demonstrating a need to improve our understanding of admixed populations.

The use of genomic information to better understand and prevent common complex diseases has been an ongoing goal of genetic research. Over the past few years, research in this area has proliferated with several proposed methods of generating polygenic scores. This has been driven by the availability of larger data sets, primarily from genome-wide association studies and concomitant developments in statistical methodologies. Here we provide an overview of the methodological aspects of polygenic model construction. In addition, we consider the state of the field and implications for potential applications of polygenic scores for risk estimation within healthcare.

Phenotype prediction is a key goal for medical genetics. Unfortunately, most genome-wide association studies are done in European populations, which reduces the accuracy of predictions via polygenic scores in non-European populations. Here, we use population genetic models to show that human demographic history and negative selection on complex traits can result in population-specific genetic architectures. For traits where alleles with the largest effect on the trait are under the strongest negative selection, approximately half of the heritability can be accounted for by variants in Europe that are absent from Africa, leading to poor performance in phenotype prediction across these populations. Further, under such a model, individuals in the tails of the genetic risk distribution may not be identified via polygenic scores generated in another population. We empirically test these predictions by building a model to stratify heritability between European-specific and shared variants and applied it to 37 traits and diseases in the UK Biobank. Across these phenotypes, ∼30% of the heritability comes from European-specific variants. We conclude that genetic association studies need to include more diverse populations to enable the utility of phenotype prediction in all populations.

Despite more than two decades of extensive research focusing on nonalcoholic fatty liver disease (NAFLD), no approved therapy for steatohepatitis—the severe histological form of the disease—presently exists. More importantly, new drugs and small molecules with diverse molecular targets on the pathways of hepatocyte injury, inflammation, and fibrosis cannot achieve the primary efficacy endpoints. Precision medicine can potentially overcome this issue, as it is founded on extensive knowledge of the druggable genome/proteome. Hence, this review summarizes significant trends and developments in precision medicine with a particular focus on new potential therapeutic discoveries modeled via systems biology approaches. In addition, we computed and simulated the potential utility of the NAFLD polygenic risk score, which could be conceptually very advantageous not only for early disease detection but also for implementing actionable measures. Incomplete knowledge of the druggable NAFLD genome severely impedes the drug discovery process and limits the likelihood of identifying robust and safe drug candidates. Thus, we close this article with some insights into emerging disciplines, such as chemical genetics, that may accelerate accurate identification of the druggable NAFLD genome/proteome. Fil: Sookoian, Silvia Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Médicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina Fil: Pirola, Carlos José. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Médicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; Argentina

The Jiangquhai (JQ) pig breed is one of the most widely recognized pig populations in China due to its unique and dominant characteristics. In this study, we examined the extent of Linkage disequilibrium (LD) and haplotype block structure of the JQ pig breed, and scanned the blocks for possible genes underlying important QTLs that could either be responsible for some adaptive features in these pigs or might have undergone some selection pressure. We compared some of our results with other Chinese and Western pig breeds. The results show that the JQ breed had the highest total block length (349.73 Mb ≈ 15% of its genome), and the coverage rate of blocks in most of its chromosomes was larger than those of other breeds except for Sus scrofa chromosome 4 (SSC4), SSC6, SSC7, SSC8, SSC10, SSC12, SSC13, SSC14, SSC17, SSC18, and SSCX. Moreover, the JQ breed had more SNPs that were clustered into haplotype blocks than the other breeds examined in this study. Our shared and unique haplotype block analysis revealed that the Hongdenglong (HD) breed had the lowest percentage of shared haplotype blocks while the Shanzhu (SZ) breed had the highest. We found that the JQ breed had an average r2 > 0.2 at SNPs distances 10–20 kb and concluded that about 120,000–240,000 SNPs would be needed for a successful GWAS in the breed. Finally, we detected a total of 88 genes harbored by selected haplotype blocks in the JQ breed, of which only 4 were significantly enriched (p-value ≤ 0.05). These genes were significantly enriched in 2 GO terms (p-value < 0.01), and 2 KEGG pathways (p-value < 0.02). Most of these enriched genes were related to health. Also, most of the overlapping QTLs detected in the haplotype blocks were related to meat and carcass quality, as well as health, with a few of them relating to reproduction and production. These results provide insights into the genetic architecture of some adaptive and meat quality traits observed in the JQ pig breed and also revealed the pattern of LD in the genome of the pig. Our result provides significant guidance for improving the statistical power of GWAS and optimizing the conservation strategy for this JQ pig breed.

Genome-Wide Association Studies (GWAS) in large human cohorts have identified thousands of loci associated with complex traits and diseases. For identifying the genes and gene-associated variants that underlie complex traits in livestock, especially where sample sizes are limiting, it may help to integrate the results of GWAS for equivalent traits in humans as prior information. In this study, we sought to investigate the usefulness of results from a GWAS on human height as prior information for identifying the genes and gene-associated variants that affect stature in cattle, using GWAS summary data on samples sizes of 700,000 and 58,265 for humans and cattle, respectively. Using Fisher’s exact test, we observed a significant proportion of cattle stature-associated genes (30/77) that are also associated with human height (odds ratio = 5.1, p = 3.1e-10). Result of randomized sampling tests showed that cattle orthologs of human height-associated genes, hereafter referred to as candidate genes (C-genes), were more enriched for cattle stature GWAS signals than random samples of genes in the cattle genome (p = 0.01). Randomly sampled SNPs within the C-genes also tend to explain more genetic variance for cattle stature (up to 13.2%) than randomly sampled SNPs within random cattle genes (p = 0.09). The most significant SNPs from a cattle GWAS for stature within the C-genes did not explain more genetic variance for cattle stature than the most significant SNPs within random cattle genes (p = 0.87). Altogether, our findings support previous studies that suggest a similarity in the genetic regulation of height across mammalian species. However, with the availability of a powerful GWAS for stature that combined data from 8 cattle breeds, prior information from human-height GWAS does not seem to provide any additional benefit with respect to the identification of genes and gene-associated variants that affect stature in cattle., Author summary Provided that there is a similarity in the biological control of complex traits in different species, the results of genome-wide association study for a given trait in a model species can be utilized as prior information to identify genes and gene-associated variants that affect the same trait in a non-model species. Here we report that the number of genes found to be affecting height in both humans and cattle is more than can be expected by chance, suggesting a similarity in the biology of height in both species. In addition, genes that are associated with height in humans were found to be highly enriched with variants that are strongly associated with height in cattle. Using height as a model trait, we demonstrated that across-species prior information cannot compete with a within-species prior information, with respect to the identification of genes and the gene-associated variants that affect a complex trait. Across-species prior information can be useful, however, when within-species prior information is either unavailable or poor.

PurposeMolecular diagnosis based on singleton exome sequencing (sES) is particularly challenging in fetuses with multiple congenital abnormalities (MCA). Indeed, some studies reveal a diagnostic yield of about 20%, far lower than in live birth individuals showing developmental abnormalities (30%), suggesting that standard analyses, based on the correlation between clinical hallmarks described in postnatal syndromic presentations and genotype, may underestimate the impact of the genetic variants identified in fetal analyses.MethodsWe performed sES in 95 fetuses with MCA. Blind to phenotype, we applied a genotype-first approach consisting of combined analyses based on variants annotation and bioinformatics predictions followed by reverse phenotyping. Initially applied to OMIM-morbid genes, analyses were then extended to all genes. We complemented our approach by using reverse phenotyping, variant segregation analysis, bibliographic search and data sharing in order to establish the clinical significance of the prioritised variants.ResultssES rapidly identified causal variant in 24/95 fetuses (25%), variants of unknown significance in OMIM genes in 8/95 fetuses (8%) and six novel candidate genes in 6/95 fetuses (6%).ConclusionsThis method, based on a genotype-first approach followed by reverse phenotyping, shed light on unexpected fetal phenotype-genotype correlations, emphasising the relevance of prenatal studies to reveal extreme clinical presentations associated with well-known Mendelian disorders.

Resolving the molecular processes that mediate genetic risk remains a challenge as most disease-associated variants are non-coding and functional and bioinformatic characterization of these signals requires knowledge of the specific tissues and cell-types in which they operate. To address this challenge, we developed a framework for integrating tissue-specific gene expression and epigenomic maps (primarily from tissues involved in insulin secretion and action) to obtain tissue-of-action (TOA) scores for each association signal by systematically partitioning posterior probabilities from Bayesian fine-mapping. We applied this scheme to credible set variants for 380 association signals from a recent GWAS meta-analysis of type 2 diabetes (T2D) in Europeans. The resulting tissue profiles underscored a predominant role for pancreatic islets and, to a lesser extent, subcutaneous adipose and liver, that was largely attributable to enhancer elements and transcribed regions, particularly among signals with greater fine-mapping resolution. We incorporated resulting TOA scores into a rule-based classifier, and validated the tissue assignments through comparison with data from cis-eQTL enrichment, functional fine-mapping, RNA co-expression, and patterns of physiological association. In addition to implicating signals with a single tissue-of-action, we also found evidence for signals with shared effects in multiple tissues as well as distinct tissue profiles between independent signals within heterogeneous loci. Lastly, we demonstrated that TOA scores can be directly coupled with eQTL colocalization to further resolve effector transcripts at T2D signals. This framework guides mechanistic inference by directing functional validation studies to the most relevant tissues and can gain power as fine-mapping resolution and cell-specific annotations become richer. This method is generalizable to all complex traits with relevant annotation data and is made available as an R package.

We tackle modelling and inference for variable selection in regression problems with many predictors and many responses. We focus on detecting hotspots, that is, predictors associated with several responses. Such a task is critical in statistical genetics, as hotspot genetic variants shape the architecture of the genome by controlling the expression of many genes and may ini-tiate decisive functional mechanisms underlying disease endpoints. Existing hierarchical regression approaches designed to model hotspots suffer from two limitations: their discrimination of hotspots is sensitive to the choice of top-level scale parameters for the propensity of predictors to be hotspots, and they do not scale to large predictor and response vectors, for example, of dimensions 103 –105 in genetic applications. We address these shortcomings by introducing a flexible hierarchical regression framework that is tailored to the detection of hotspots and scalable to the above dimensions. Our pro-posal implements a fully Bayesian model for hotspots based on the horseshoe shrinkage prior. Its global-local formulation shrinks noise globally and, hence, accommodates the highly sparse nature of genetic analyses while be-ing robust to individual signals, thus leaving the effects of hotspots unshrunk. Inference is carried out using a fast variational algorithm coupled with a novel simulated annealing procedure that allows efficient exploration of multimodal distributions.

Traditional univariate genome-wide association studies generate false positives and negatives due to difficulties distinguishing associated variants from variants with spurious nonzero effects that do not directly influence the trait. Recent efforts have been directed at identifying genes or signaling pathways enriched for mutations in quantitative traits or case-control studies, but these can be computationally costly and hampered by strict model assumptions. Here, we present gene-ε, a new approach for identifying statistical associations between sets of variants and quantitative traits. Our key insight is that enrichment studies on the gene-level are improved when we reformulate the genome-wide SNP-level null hypothesis to identify spurious small-to-intermediate SNP effects and classify them as non-causal. gene-ε efficiently identifies enriched genes under a variety of simulated genetic architectures, achieving greater than a 90% true positive rate at 1% false positive rate for polygenic traits. Lastly, we apply gene-ε to summary statistics derived from six quantitative traits using European-ancestry individuals in the UK Biobank, and identify enriched genes that are in biologically relevant pathways., Author summary Enrichment tests augment the standard univariate genome-wide association (GWA) framework by identifying groups of biologically interacting mutations that are enriched for associations with a trait of interest, beyond what is expected by chance. These analyses model local linkage disequilibrium (LD), allow many different mutations to be disease-causing across patients, and generate biologically interpretable hypotheses for disease mechanisms. However, existing enrichment analyses are hampered by high computational costs, and rely on GWA summary statistics despite the high false positive rate of the standard univariate GWA framework. Here, we present the gene-level association framework gene-ε (pronounced “genie”), an empirical Bayesian approach for identifying statistical associations between sets of mutations and quantitative traits. The central innovation of gene-ε is reformulating the GWA null model to distinguish between (i) mutations that are statistically associated with the disease but are unlikely to directly influence it, and (ii) mutations that are most strongly associated with a disease of interest. We find that, with our reformulated SNP-level null hypothesis, our gene-level enrichment model outperforms existing enrichment methods in simulation studies and scales well for application to emerging biobank datasets. We apply gene-ε to six quantitative traits in the UK Biobank and recover novel and functionally validated gene-level associations.

The spontaneously hypertensive rat (SHR) is a genetic model of primary hypertension with an etiology that includes sympathetic overdrive. To elucidate the neurogenic mechanisms underlying the pathophysiology of this model, we analyzed the dynamic baroreflex response to spontaneous fluctuations in arterial pressure in conscious SHRs, as well as in the Wistar-Kyoto (WKY), the Dahl salt-sensitive, the Dahl salt-resistant, and the Sprague-Dawley rat. Observations revealed the existence of long intermittent periods (lasting up to several minutes) of engagement and disengagement of baroreflex control of heart rate. Analysis of these intermittent periods revealed a predictive relationship between increased mean arterial pressure and progressive baroreflex disengagement that was present in the SHR and WKY strains but absent in others. This relationship yielded the hypothesis that a lower proportion of engagement versus disengagement of the baroreflex in SHR compared with WKY contributes to the hypertension (or increased blood pressure) in SHR compared with WKY. Results of experiments using sinoaortic baroreceptor denervation were consistent with the hypothesis that dysfunction of the baroreflex contributes to the etiology of hypertension in the SHR. Thus, this study provides experimental evidence for the roles of the baroreflex in long-term arterial pressure regulation and in the etiology of primary hypertension in this animal model., Baroreflex dysfunction contributes to the etiology of hypertension in a genetic model of primary hypertension.

Most of our current knowledge on plant molecular biology is based on experiments in controlled laboratory environments. However, translating this knowledge from the laboratory to the field is often not straightforward, in part because field growth conditions are very different from laboratory conditions. Here, we test a new experimental design to unravel the molecular wiring of plants and study gene–phenotype relationships directly in the field. We molecularly profiled a set of individual maize plants of the same inbred background grown in the same field and used the resulting data to predict the phenotypes of individual plants and the function of maize genes. We show that the field transcriptomes of individual plants contain as much information on maize gene function as traditional laboratory‐generated transcriptomes of pooled plant samples subject to controlled perturbations. Moreover, we show that field‐generated transcriptome and metabolome data can be used to quantitatively predict individual plant phenotypes. Our results show that profiling individual plants in the field is a promising experimental design that could help narrow the lab‐field gap., A new experimental design based on profiling individual plants of the same inbred line under uncontrolled field conditions produces gene function and phenotype predictions that complement predictions gathered from traditional lab‐based experiments.

Summary Genome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. However, for most traits it remains difficult to interpret what genes and biological processes are impacted by the top hits. Here, as a contrast, we describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—that are biologically simpler than most diseases, and for which we know a great deal in advance about the core genes and pathways. Unlike most GWAS of complex traits, for all three traits we find that most top hits are readily interpretable. We observe huge enrichment of significant signals near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of variation in each trait, including insights into differences in testosterone regulation between females and males. Meanwhile, in other respects the results are reminiscent of GWAS for more-complex traits. In particular, even these molecular traits are highly polygenic, with most of the variance coming not from core genes, but from thousands to tens of thousands of variants spread across most of the genome. Given that diseases are often impacted by many distinct biological processes, including these three, our results help to illustrate why so many variants can affect risk for any given disease.

Heterogeneity in the phenotypic mean and variance across populations is often observed for complex traits. One way to understand heterogeneous phenotypes lies in uncovering heterogeneity in genetic effects. Previous studies on genetic heterogeneity across populations were typically based on discrete groups of population stratified by different countries or cohorts, which ignored the difference of population characteristics for the individuals within each group and resulted in loss of information. Here we introduce a novel concept of genotype-by-population (G×P) interaction where population is defined by the first and second ancestry principal components (PCs), which are less likely to be confounded with country/cohort-specific factors. We applied a reaction norm model fitting each of 70 complex traits with significant SNP-heritability and the PCs as covariates to examine G×P interactions across diverse populations including white British and other white Europeans from the UK Biobank (N = 22,229). Our results demonstrated a significant population genetic heterogeneity for behavioural traits such as age first had sexual intercourse and qualifications. Our approach may shed light on the latent genetic architecture of complex traits that underlies the modulation of genetic effects across different populations.

The development of explanatory models of protein sequence evolution has broad implications for our understanding of cellular biology, population history, and disease etiology. Here we analyze the GTEx transcriptome resource to quantify the effect of the transcriptome on protein sequence evolution in a multi-tissue framework. We find substantial variation among the central nervous system tissues in the effect of expression variance on evolutionary rate, with highly variable genes in the cortex showing significantly greater purifying selection than highly variable genes in subcortical regions (Mann–Whitney Up = 1.4 × 10−4). The remaining tissues cluster in observed expression correlation with evolutionary rate, enabling evolutionary analysis of genes in diverse physiological systems, including digestive, reproductive, and immune systems. Importantly, the tissue in which a gene attains its maximum expression variance significantly varies (p = 5.55 × 10−284) with evolutionary rate, suggesting a tissue-anchored model of protein sequence evolution. Using a large-scale reference resource, we show that the tissue-anchored model provides a transcriptome-based approach to predicting the primary affected tissue of developmental disorders. Using gradient boosted regression trees to model evolutionary rate under a range of model parameters, selected features explain up to 62% of the variation in evolutionary rate and provide additional support for the tissue model. Finally, we investigate several methodological implications, including the importance of evolutionary-rate-aware gene expression imputation models using genetic data for improved search for disease-associated genes in transcriptome-wide association studies. Collectively, this study presents a comprehensive transcriptome-based analysis of a range of factors that may constrain molecular evolution and proposes a novel framework for the study of gene function and disease mechanism.

Genomic prediction (GP) is the procedure whereby the genetic merits of untested candidates are predicted using genome wide marker information. Although numerous examples of GP exist in plants and animals, applications to polyploid organisms are still scarce, partly due to limited genome resources and the complexity of this system. Deep learning (DL) techniques comprise a heterogeneous collection of machine learning algorithms that have excelled at many prediction tasks. A potential advantage of DL for GP over standard linear model methods is that DL can potentially take into account all genetic interactions, including dominance and epistasis, which are expected to be of special relevance in most polyploids. In this study, we evaluated the predictive accuracy of linear and DL techniques in two important small fruits or berries: strawberry and blueberry. The two datasets contained a total of 1,358 allopolyploid strawberry (2n=8x=112) and 1,802 autopolyploid blueberry (2n=4x=48) individuals, genotyped for 9,908 and 73,045 single nucleotide polymorphism (SNP) markers, respectively, and phenotyped for five agronomic traits each. DL depends on numerous parameters that influence performance and optimizing hyperparameter values can be a critical step. Here we show that interactions between hyperparameter combinations should be expected and that the number of convolutional filters and regularization in the first layers can have an important effect on model performance. In terms of genomic prediction, we did not find an advantage of DL over linear model methods, except when the epistasis component was important. Linear Bayesian models were better than convolutional neural networks for the full additive architecture, whereas the opposite was observed under strong epistasis. However, by using a parameterization capable of taking into account these non-linear effects, Bayesian linear models can match or exceed the predictive accuracy of DL. A semiautomatic implementation of the DL pipeline is available at https://github.com/lauzingaretti/deepGP/., LZ was supported by a PhD grant from the Ministry of Economy and Science (MINECO, Spain), by the MINECO grant AGL2016-78709-R to MP-E and from the EU through the BFU2016-77236-P (MINECO/AEI/FEDER, EU) and the “Centro de Excelencia Severo Ochoa 2016-2019” award SEV-2015-0533. VW and LO were supported by the US Department of Agriculture/National Institute of Food and Agriculture Specialty Crop Research Initiative (SCRI) project ‘RosBREED: Combining disease resistance with horticultural quality in new rosaceous cultivars' under Award Number 2014-51181-22378.

International audience; Functional characterisation of cell-type-specific regulatory networks is key to establish a causal link between genetic variation and phenotype. The osteoclast offers a unique model for interrogating the contribution of co-regulated genes to in vivo phenotype as its multinucleation and resorption activities determine quantifiable skeletal traits. Here we took advantage of a trans-regulated gene network (MMnet, macrophage multinucleation network) which we found to be significantly enriched for GWAS variants associated with bone-related phenotypes. We found that the network hub gene Bcat1 and seven other co-regulated MMnet genes out of 13, regulate bone function. Specifically, global (Pik3cb-/-, Atp8b2+/-, Igsf8-/-, Eml1-/-, Appl2-/-, Deptor-/-) and myeloid-specific Slc40a1 knockout mice displayed abnormal bone phenotypes. We report opposing effects of MMnet genes on bone mass in mice and osteoclast multinucleation/resorption in humans with strong correlation between the two. These results identify MMnet as a functionally conserved network that regulates osteoclast multinucleation and bone mass.

Motivation Understanding the biological mechanism of complex phenotypes is challenging due to the lack of efficient approaches that can associate the vast majority of the genome-wide association studies (GWAS) loci in the non-coding regions of the human genome with relevant genes and ultimately the downstream pathways. Transcriptome-wide association studies (TWAS) provides a way to associate genes with phenotype of interest by correlating GWAS summary statistics with expression quantitative trait loci (eQTL) summary statistics obtained from a reference panel. However, genes that are correlated by the predicted gene expression may exhibit high TWAS statistic even though they are the causal genes for the trait. Existing gene set enrichment analysis assume independence of genes and may therefore lead to false discoveries. Results We propose a novel statistical method called Gene Score Regression (GSR). The rationale of GSR is based on the insight that genes that are highly correlated with the causal genes in the causal gene set or pathways will exhibit high marginal TWAS statistic. Consequently, by regressing on the genes’ marginal statistic using the sum of the gene-gene correlation scores in each gene set, we can assess the amount of phenotypic variance explained by the predicted expression of the genes in that gene set. Our approach does not only operates on summary statistics without requiring individual genotype and phenotype but can also work with observed gene expression and phenotype data without the need of genotype. Based on simulation, GSR demonstrates superior power and better controlled Type I Error rate over the existing methods. We applied GSR to investigate 28 complex traits using diverse genome-wide and knowledge-based gene sets such as REACTOME and KEGG from MSigDB and also 205 cell-type-specific gene sets derived from observed gene expression. The significant gene sets detected by GSR are by and large consistent with the known biology of the traits. We also demonstrated the utility of using GSR on cancer data where only gene expression and tumor/normal tissue labels are available. Overall, GSR is not only accurate but efficient method usually taking less than 5 minutes to perform the full analysis on one phenotype, thereby presenting as a significantly useful novel contribution to the analytical bioinformatic pipeline. Availability The GSR software is available at GitHub at https://github.com/li-lab-mcgill/GSR .

Over the past 15 years, genome-wide association studies (GWASs) have enabled the systematic identification of genetic loci associated with traits and diseases. However, due to resolution issues and methodological limitations, the true causal variants and genes associated with traits remain difficult to identify. In this post-GWAS era, many biological and computational fine-mapping approaches now aim to solve these issues. Here, we review fine-mapping and gene prioritization approaches that, when combined, will improve the understanding of the underlying mechanisms of complex traits and diseases. Fine-mapping of genetic variants has become increasingly sophisticated: initially, variants were simply overlapped with functional elements, but now the impact of variants on regulatory activity and direct variant-gene 3D interactions can be identified. Moreover, gene manipulation by CRISPR/Cas9, the identification of expression quantitative trait loci and the use of co-expression networks have all increased our understanding of the genes and pathways affected by GWAS loci. However, despite this progress, limitations including the lack of cell-type- and disease-specific data and the ever-increasing complexity of polygenic models of traits pose serious challenges. Indeed, the combination of fine-mapping and gene prioritization by statistical, functional and population-based strategies will be necessary to truly understand how GWAS loci contribute to complex traits and diseases.

Background Understanding genetic architecture is essential for determining how traits will change in response to evolutionary processes such as selection, genetic drift and/or gene flow. In Atlantic salmon, age at maturity is an important life history trait that affects factors such as survival, reproductive success, and growth. Furthermore, age at maturity can seriously impact aquaculture production. Therefore, characterizing the genetic architecture that underlies variation in age at maturity is of key interest. Results Here, we refine our understanding of the genetic architecture for age at maturity of male Atlantic salmon using a genome-wide association study of 11,166 males from a single aquaculture strain, using imputed genotypes at 512,397 single nucleotide polymorphisms (SNPs). All individuals were genotyped with a 50K SNP array and imputed to higher density using parents genotyped with a 930K SNP array and pedigree information. We found significant association signals on 28 of 29 chromosomes (P-values: 8.7 × 10−133–9.8 × 10−8), including two very strong signals spanning the six6 and vgll3 gene regions on chromosomes 9 and 25, respectively. Furthermore, we identified 116 independent signals that tagged 120 candidate genes with varying effect sizes. Five of the candidate genes found here were previously associated with age at maturity in other vertebrates, including humans. Discussion These results reveal a mixed architecture of large-effect loci and a polygenic component that consists of multiple smaller-effect loci, suggesting a more complex genetic architecture of Atlantic salmon age at maturity than previously thought. This more complex architecture will have implications for selection on this key trait in aquaculture and for management of wild salmon populations.

Although recent studies provide evidence for a common genetic basis between complex traits and Mendelian disorders, a thorough quantification of their overlap in a phenotype-specific manner remains elusive. Here, we have quantified the overlap of genes identified through large-scale genome-wide association studies (GWASs) for 62 complex traits and diseases with genes containing mutations known to cause 20 broad categories of Mendelian disorders. We identified a significant enrichment of genes linked to phenotypically matched Mendelian disorders in GWAS gene sets; of the total 1,240 comparisons, a higher proportion of phenotypically matched or related pairs (n = 50 of 92 [54%]) than phenotypically unmatched pairs (n = 27 of 1,148 [2%]) demonstrated significant overlap, confirming a phenotype-specific enrichment pattern. Further, we observed elevated GWAS effect sizes near genes linked to phenotypically matched Mendelian disorders. Finally, we report examples of GWAS variants localized at the transcription start site or physically interacting with the promoters of genes linked to phenotypically matched Mendelian disorders. Our results are consistent with the hypothesis that genes that are disrupted in Mendelian disorders are dysregulated by non-coding variants in complex traits and demonstrate how leveraging findings from related Mendelian disorders and functional genomic datasets can prioritize genes that are putatively dysregulated by local and distal non-coding GWAS variants.

Rare genetic variants in yeast explain a large amount of phenotypic variation in a complex trait like growth.