Inferring the Nature of Missing Heritability in Human Traits Using Data from the GWAS Catalog

Assessing the Clinical Utility of Published Prostate Cancer Polygenic Risk Scores in a Large Biobank Data Set

BACKGROUND AND OBJECTIVE

Polygenic risk scores (PRSs) have been developed to identify men with the highest risk of prostate cancer. Our aim was to compare the performance of 16 PRSs in identifying men at risk of developing prostate cancer and then to evaluate the performance of the top-performing PRSs in differentiating individuals at risk of aggressive prostate cancer.

METHODS

For this case-control study we downloaded 16 published PRSs from the Polygenic Score Catalog on May 28, 2021 and applied them to Michigan Genomics Initiative (MGI) patients. Cases were matched to the Michigan Urological Surgery Improvement Collaborative (MUSIC) registry to obtain granular clinical and pathological data. MGI prospectively enrolls patients undergoing surgery at the University of Michigan, and MUSIC is a multi-institutional registry that prospectively tracks demographic, treatment, and clinical variables. The predictive performance of each PRS was evaluated using the area under the covariate-adjusted receiver operating characteristic curve (aAUC), and the association between PRS and disease aggressiveness according to prostate biopsy data was measured using logistic regression.

KEY FINDINGS AND LIMITATIONS

We included 18 050 patients in the analysis, of whom 15 310 were control subjects and 2740 were prostate cancer cases. The median age was 66.1 yr (interquartile range 59.9-71.6) for cases and 56.6 yr (interquartile range 42.6-66.7) for control subjects. The PRS performance in predicting the risk of developing prostate cancer according to aAUC ranged from 0.51 (95% confidence interval 0.51-0.53) to 0.67 (95% confidence interval 0.66-0.68). By contrast, there was no association between PRS and disease aggressiveness.

CONCLUSIONS AND CLINICAL IMPLICATIONS

Prostate cancer PRSs have modest real-world performance in identifying patients at higher risk of developing prostate cancer; however, they are limited in distinguishing patients with indolent versus aggressive disease.

PATIENT SUMMARY

Risk scores using data for multiple genes (called polygenic risk scores) can identify men at higher risk of developing prostate cancer. However, these scores need to be refined to be able to identify men with the highest risk for clinically significant prostate cancer.

Identifying latent genetic interactions in genome-wide association studies using multiple traits

The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit ).

Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding

Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.

GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa

Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping. To enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time. We found that the resulting statistics were of highly non-normal distributions, and thus employed transformations or permutations to avoid violating assumptions of linear models used in GWAS. We report over 200 statistically supported quantitative trait loci (QTLs), with genes encompassing or near to top QTLs including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. Our results encourage models of hormonal signaling during plant regeneration to consider keystone roles of stress-related signaling (e.g. involving jasmonates and salicylic acid), in addition to the auxin and cytokinin pathways commonly considered. The putative regulatory genes and biological processes we identified provide new insights into the biological complexity of plant regeneration, and may serve as new reagents for improving regeneration and transformation of recalcitrant genotypes and species.

The value of genetic data from 665,460 individuals in managing iron deficiency anaemia and suitability to donate blood

BACKGROUND AND OBJECTIVES

Although the genetic determinants of haemoglobin and ferritin have been widely studied, those of the clinically and globally relevant iron deficiency anaemia (IDA) and deferral due to hypohaemoglobinemia (Hb-deferral) are unclear. In this investigation, we aimed to quantify the value of genetic information in predicting IDA and Hb-deferral.

MATERIALS AND METHODS

We analysed genetic data from up to 665,460 participants of the FinnGen, Blood Service Biobank and UK Biobank, and used INTERVAL (N = 39,979) for validation. We performed genome-wide association studies (GWASs) of IDA and Hb-deferral and utilized publicly available genetic associations to compute polygenic scores for IDA, ferritin and Hb. We fitted models to estimate the effect sizes of these polygenic risk scores (PRSs) on IDA and Hb-deferral risk while accounting for the individual's age, sex, weight, height, smoking status and blood donation history.

RESULTS

Significant variants in GWASs of IDA and Hb-deferral appear to be a small subset of variants associated with ferritin and Hb. Effect sizes of genetic predictors of IDA and Hb-deferral are similar to those of age and weight which are typically used in blood donor management. A total genetic score for Hb-deferral was estimated for each individual. The odds ratio estimate between first decile against that at ninth decile of total genetic score distribution ranged from 1.4 to 2.2.

CONCLUSION

The value of genetic data in predicting IDA or suitability to donate blood appears to be on a practically useful level.

Leveraging Single-Cell Populations to Uncover the Genetic Basis of Complex Traits

The ease and throughput of single-cell genomics have steadily improved, and its current trajectory suggests that surveying single-cell populations will become routine. We discuss the merger of quantitative genetics with single-cell genomics and emphasize how this synergizes with advantages intrinsic to plants. Single-cell population genomics provides increased detection resolution when mapping variants that control molecular traits, including gene expression or chromatin accessibility. Additionally, single-cell population genomics reveals the cell types in which variants act and, when combined with organism-level phenotype measurements, unveils which cellular contexts impact higher-order traits. Emerging technologies, notably multiomics, can facilitate the measurement of both genetic changes and genomic traits in single cells, enabling single-cell genetic experiments. The implementation of single-cell genetics will advance the investigation of the genetic architecture of complex molecular traits and provide new experimental paradigms to study eukaryotic genetics.

Identifying latent genetic interactions in genome-wide association studies using multiple traits

Genome-wide association studies of complex traits frequently find that SNP-based estimates of heritability are considerably smaller than estimates from classic family-based studies. This 'missing' heritability may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. To circumvent these challenges, we propose a new method to detect genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Our approach, Latent Interaction Testing (LIT), uses the observation that correlated traits with shared latent genetic interactions have trait variance and covariance patterns that differ by genotype. LIT examines the relationship between trait variance/covariance patterns and genotype using a flexible kernel-based framework that is computationally scalable for biobank-sized datasets with a large number of traits. We first use simulated data to demonstrate that LIT substantially increases power to detect latent genetic interactions compared to a trait-by-trait univariate method. We then apply LIT to four obesity-related traits in the UK Biobank and detect genetic variants with interactive effects near known obesity-related genes. Overall, we show that LIT, implemented in the R package lit, uses shared information across traits to improve detection of latent genetic interactions compared to standard approaches.

A review of genetic risk in systemic lupus erythematosus

INTRODUCTION

Systemic Lupus Erythematosus (SLE) is a complex multisystem autoimmune disease with a wide range of signs and symptoms in affected individuals. The utilization of genome-wide association study (GWAS) technology has led to an explosion in the number of genetic risk factors mapped for autoimmune diseases, including SLE.

AREAS COVERED

In this review, we summarize the more recent genetic risk loci mapped in SLE, which bring the total number of loci mapped to approximately 200. We review prioritization analyses of the associated variants and experimental validation of the putative causal variants. This includes the implementation of new bioinformatic techniques to align genomic and functional data and the use of transcriptomics with single-cell RNA-sequencing, CRISPR genome editing, and Massive Parallel Reporter Assays to analyze non-coding regulatory genetics.

EXPERT OPINION

Despite progress in identifying more genetic risk loci and variant-gene pairs for SLE, understanding its pathogenesis and applying findings clinically remains challenging. The polygenic risk score (PRS) has been used as an application of SLE genetics, but with limited performance in non-EUR populations. In the next few years, advancements in proteomics, post-translational modification estimation, and whole-genome sequencing will enhance disease understanding.

An eco-evo-devo genetic network model of stress response

The capacity of plants to resist abiotic stresses is of great importance to agricultural, ecological and environmental sustainability, but little is known about its genetic underpinnings. Existing genetic tools can identify individual genetic variants mediating biochemical, physiological, and cellular defenses, but fail to chart an overall genetic atlas behind stress resistance. We view stress response as an eco-evo-devo process by which plants adaptively respond to stress through complex interactions of developmental canalization, phenotypic plasticity, and phenotypic integration. As such, we define and quantify stress response as the developmental change of adaptive traits from stress-free to stress-exposed environments. We integrate composite functional mapping and evolutionary game theory to reconstruct omnigenic, information-flow interaction networks for stress response. Using desert-adapted Euphrates poplar as an example, we infer salt resistance-related genome-wide interactome networks and trace the roadmap of how each SNP acts and interacts with any other possible SNPs to mediate salt resistance. We characterize the previously unknown regulatory mechanisms driving trait variation; i.e. the significance of a SNP may be due to the promotion of positive regulators, whereas the insignificance of a SNP may result from the inhibition of negative regulators. The regulator-regulatee interactions detected are not only experimentally validated by two complementary experiments, but also biologically interpreted by their encoded protein-protein interactions. Our eco-evo-devo model of genetic interactome networks provides an approach to interrogate the genetic architecture of stress response and informs precise gene editing for improving plants' capacity to live in stress environments.

Proportionality of single nucleotide causation

Assessing the proportionality of causal relationships help us pick out the most relevant causes of an effect. In this paper, I nuance and apply the concept of proportionality from the philosophy of causation debate to the mapping of associations between variations in single nucleotides in the DNA and complex phenotypic traits, such as cancer, bipolar disorder, and happiness. I discuss under what circumstances single nucleotides, as far as these can be understood as causes, may satisfy the criterion of proportionality in an interventionist understanding of causality. I suggest that the overall relatively low stability and explanatory power of such variants may indicate that, in the causation of complex phenotypic traits, there are alternative causal levels that are more proportional than the level of nucleotides. I suggest network modules as candidates for more proportional organism-internal causes of complex phenotypes. Additionally, I address the broadness of many phenotypic traits investigated in GWAS, as well as the selection between several different proportional causes of an effect.

Evaluation of Existing Methods for High-Order Epistasis Detection

Finding epistatic interactions among loci when expressing a phenotype is a widely employed strategy to understand the genetic architecture of complex traits in GWAS. The abundance of methods dedicated to the same purpose, however, makes it increasingly difficult for scientists to decide which method is more suitable for their studies. This work compares the different epistasis detection methods published during the last decade in terms of runtime, detection power and type I error rate, with a special emphasis on high-order interactions. Results show that in terms of detection power, the only methods that perform well across all experiments are the exhaustive methods, although their computational cost may be prohibitive in large-scale studies. Regarding non-exhaustive methods, not one could consistently find epistasis interactions when marginal effects are absent. If marginal effects are present, there are methods that perform well for high-order interactions, such as BADTrees, FDHE-IW, SingleMI or SNPHarvester. As for false-positive control, only SNPHarvester, FDHE-IW and DCHE show good results. The study concludes that there is no single epistasis detection method to recommend in all scenarios. Authors should prioritize exhaustive methods when sufficient computational resources are available considering the data set size, and resort to non-exhaustive methods when the analysis time is prohibitive.

Atopic eczema: How genetic studies can contribute to understanding this complex trait

Atopic eczema is an itchy inflammatory skin disease. This complex trait results from multiple genetic and environmental factors, but atopic eczema also shows great complexity in its heterogenous presentation, clinical signs and longitudinal trajectory, with or without co-morbid conditions. The past 50 years have produced substantial improvements in the management of atopic eczema, but many patients still suffer a burden of disease affecting personal, social and family life. Genetic research refocused interest on skin barrier function, but effective targeting of this central pathomechanism remains elusive. This Perspective highlights progress in understanding molecular mechanisms and translational opportunities for the future.

Kernel-based gene-environment interaction tests for rare variants with multiple quantitative phenotypes

Previous studies have suggested that gene-environment interactions (GEIs) between a common variant and an environmental factor can influence multiple correlated phenotypes simultaneously, that is, GEI pleiotropy, and that analyzing multiple phenotypes jointly is more powerful than analyzing phenotypes separately by using single-phenotype GEI tests. Methods to test the GEI for rare variants with multiple phenotypes are, however, lacking. In our work, we model the correlation among the GEI effects of a variant on multiple quantitative phenotypes through four kernels and propose four multiphenotype GEI tests for rare variants, which are a test with a homogeneous kernel (Hom-GEI), a test with a heterogeneous kernel (Het-GEI), a test with a projection phenotype kernel (PPK-GEI) and a test with a linear phenotype kernel (LPK-GEI). Through numerical simulations, we show that correlation among phenotypes can enhance the statistical power except for LPK-GEI, which simply combines statistics from single-phenotype GEI tests and ignores the phenotypic correlations. Among almost all considered scenarios, Het-GEI and PPK-GEI are more powerful than Hom-GEI and LPK-GEI. We apply Het-GEI and PPK-GEI in the genome-wide GEI analysis of systolic blood pressure (SBP) and diastolic blood pressure (DBP) in the UK Biobank. We analyze 18,101 genes and find that LEUTX is associated with SBP and DBP (p = 2.20×10-6) through its interaction with hemoglobin. The single-phenotype GEI test and our multiphenotype GEI tests Het-GEI and PPK-GEI are also used to evaluate the gene-hemoglobin interactions for 22 genes that were previously reported to be associated with SBP or DBP in a meta-analysis of genetic main effects. MYO1C shows nominal significance (p < 0.05) by the Het-GEI test. NOS3 shows nominal significance in DBP and MYO1C in both SBP and DBP by the single-phenotype GEI test.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Decoding 'Unnecessary Complexity': A Law of Complexity and a Concept of Hidden Variation Behind "Missing Heritability" in Precision Medicine

The high hopes for the Human Genome Project and personalized medicine were not met because the relationship between genotypes and phenotypes turned out to be more complex than expected. In a previous study we laid the foundation of a theory of complexity and showed that because of the blind nature of evolution, and molecular and historical contingency, cells have accumulated unnecessary complexity, complexity beyond what is necessary and sufficient to describe an organism. Here we provide empirical evidence and show that unnecessary complexity has become integrated into the genome in the form of redundancy and is relevant to molecular evolution of phenotypic complexity. Unnecessary complexity creates uncertainty between molecular and phenotypic complexity, such that phenotypic complexity (CP) is higher than molecular complexity (CM), which is higher than DNA complexity (CD). The qualitative inequality in complexity is based on the following hierarchy: CP > CM > CD. This law-like relationship holds true for all complex traits, including complex diseases. We present a hypothesis of two types of variation, namely open and closed (hidden) systems, show that hidden variation provides a hitherto undiscovered "third source" of phenotypic variation, beside genotype and environment, and argue that "missing heritability" for some complex diseases is likely to be a case of "diluted heritability". There is a need for radically new ways of thinking about the principles of genotype-phenotype relationship. Understanding how cells use hidden, pathway variation to respond to stress can shed light on why two individuals who share the same risk factors may not develop the same disease, or how cancer cells escape death.

Pharmacogenomics: A road ahead for precision medicine in psychiatry

Psychiatric genomics is providing insights into the nature of psychiatric conditions that in time should identify new drug targets and improve patient care. Less attention has been paid to psychiatric pharmacogenomics research, despite its potential to deliver more rapid change in clinical practice and patient outcomes. The pharmacogenomics of treatment response encapsulates both pharmacokinetic ("what the body does to a drug") and pharmacodynamic ("what the drug does to the body") effects. Despite early optimism and substantial research in both these areas, they have to date made little impact on clinical management in psychiatry. A number of bottlenecks have hampered progress, including a lack of large-scale replication studies, inconsistencies in defining valid treatment outcomes across experiments, a failure to routinely incorporate adverse drug reactions and serum metabolite monitoring in study designs, and inadequate investment in the longitudinal data collections required to demonstrate clinical utility. Nonetheless, advances in genomics and health informatics present distinct opportunities for psychiatric pharmacogenomics to enter a new and productive phase of research discovery and translation.

Meta-analysis: Congruence of genomic and phenotypic differentiation across diverse natural study systems

Linking genotype to phenotype is a primary goal for understanding the genomic underpinnings of evolution. However, little work has explored whether patterns of linked genomic and phenotypic differentiation are congruent across natural study systems and traits. Here, we investigate such patterns with a meta-analysis of studies examining population-level differentiation at subsets of loci and traits putatively responding to divergent selection. We show that across the 31 studies (88 natural population-level comparisons) we examined, there was a moderate (R 2 = 0.39) relationship between genomic differentiation (F ST ) and phenotypic differentiation (P ST ) for loci and traits putatively under selection. This quantitative relationship between P ST and F ST for loci under selection in diverse taxa provides broad context and cross-system predictions for genomic and phenotypic adaptation by natural selection in natural populations. This context may eventually allow for more precise ideas of what constitutes "strong" differentiation, predictions about the effect size of loci, comparisons of taxa evolving in nonparallel ways, and more. On the other hand, links between P ST and F ST within studies were very weak, suggesting that much work remains in linking genomic differentiation to phenotypic differentiation at specific phenotypes. We suggest that linking genotypes to specific phenotypes can be improved by correlating genomic and phenotypic differentiation across a spectrum of diverging populations within a taxon and including wide coverage of both genomes and phenomes.

Genetic risk factors of ME/CFS: a critical review

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex multisystem illness that lacks effective therapy and a biomedical understanding of its causes. Despite a prevalence of ∼0.2-0.4% and its high public health burden, and evidence that it has a heritable component, ME/CFS has not yet benefited from the advances in technology and analytical tools that have improved our understanding of many other complex diseases. Here we critically review existing evidence that genetic factors alter ME/CFS risk before concluding that most ME/CFS candidate gene associations are not replicated by the larger CFS cohort within the UK Biobank. Multiple genome-wide association studies of this cohort also have not yielded consistently significant associations. Ahead of upcoming larger genome-wide association studies, we discuss how these could generate new lines of enquiry into the DNA variants, genes and cell types that are causally involved in ME/CFS disease.

Highly pleiotropic variants of human traits are enriched in genomic regions with strong background selection

Recent studies have shown the ubiquity of pleiotropy for variants affecting human complex traits. These studies also show that rare variants tend to be less pleiotropic than common ones, suggesting that purifying natural selection acts against highly pleiotropic variants of large effect. Here, we investigate the mean frequency, effect size and recombination rate associated with pleiotropic variants, and focus particularly on whether highly pleiotropic variants are enriched in regions with putative strong background selection. We evaluate variants for 41 human traits using data from the NHGRI-EBI GWAS Catalog, as well as data from other three studies. Our results show that variants involving a higher degree of pleiotropy tend to be more common, have larger mean effect sizes, and contribute more to heritability than variants with a lower degree of pleiotropy. This is consistent with the fact that variants of large effect and frequency are more likely detected by GWAS. Using data from four different studies, we also show that more pleiotropic variants are enriched in genome regions with stronger background selection than less pleiotropic variants, suggesting that highly pleiotropic variants are subjected to strong purifying selection. From the above results, we hypothesized that a number of highly pleiotropic variants of low effect/frequency may pass undetected by GWAS.

Lifestyle Risk Score: handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions

Recent studies consider lifestyle risk score (LRS), an aggregation of multiple lifestyle exposures, in identifying association of gene-lifestyle interaction with disease traits. However, not all cohorts have data on all lifestyle factors, leading to increased heterogeneity in the environmental exposure in collaborative meta-analyses. We compared and evaluated four approaches (Naïve, Safe, Complete and Moderator Approaches) to handle the missingness in LRS-stratified meta-analyses under various scenarios. Compared to "benchmark" results with all lifestyle factors available for all cohorts, the Complete Approach, which included only cohorts with all lifestyle components, was underpowered due to lower sample size, and the Naïve Approach, which utilized all available data and ignored the missingness, was slightly inflated. The Safe Approach, which used all data in LRS-exposed group and only included cohorts with all lifestyle factors available in the LRS-unexposed group, and the Moderator Approach, which handled missingness via moderator meta-regression, were both slightly conservative and yielded almost identical p values. We also evaluated the performance of the Safe Approach under different scenarios. We observed that the larger the proportion of cohorts without missingness included, the more accurate the results compared to "benchmark" results. In conclusion, we generally recommend the Safe Approach, a straightforward and non-inflated approach, to handle heterogeneity among cohorts in the LRS based genome-wide interaction meta-analyses.

Neighbor GWAS: incorporating neighbor genotypic identity into genome-wide association studies of field herbivory

An increasing number of field studies have shown that the phenotype of an individual plant depends not only on its genotype but also on those of neighboring plants; however, this fact is not taken into consideration in genome-wide association studies (GWAS). Based on the Ising model of ferromagnetism, we incorporated neighbor genotypic identity into a regression model, named "Neighbor GWAS". Our simulations showed that the effective range of neighbor effects could be estimated using an observed phenotype when the proportion of phenotypic variation explained (PVE) by neighbor effects peaked. The spatial scale of the first nearest neighbors gave the maximum power to detect the causal variants responsible for neighbor effects, unless their effective range was too broad. However, if the effective range of the neighbor effects was broad and minor allele frequencies were low, there was collinearity between the self and neighbor effects. To suppress the false positive detection of neighbor effects, the fixed effect and variance components involved in the neighbor effects should be tested in comparison with a standard GWAS model. We applied neighbor GWAS to field herbivory data from 199 accessions of Arabidopsis thaliana and found that neighbor effects explained 8% more of the PVE of the observed damage than standard GWAS. The neighbor GWAS method provides a novel tool that could facilitate the analysis of complex traits in spatially structured environments and is available as an R package at CRAN ( https://cran.rproject.org/package=rNeighborGWAS ).

An epigenetic, transgenerational model of increased mental health disorders in children, adolescents and young adults

Prevalence rates of mental health disorders in children and adolescents have increased two to threefold from the 1990s to 2016. Some increase in prevalence may stem from changing environmental conditions in the current generation which interact with genes and inherited genetic variants. Current measured genetic variant effects do not explain fully the familial clustering and high heritability estimates in the population. Another model considers environmental conditions shifting in the previous generation, which altered brain circuits epigenetically and were transmitted to offspring via non-DNA-based mechanisms (intergenerational and transgenerational effects). Parental substance use, poor diet and obesity are environmental factors with known epigenetic intergenerational and transgenerational effects, that regulate set points in brain pathways integrating sensory-motor, reward and feeding behaviors. Using summary statistics for eleven neuropsychiatric and three metabolic disorders from 128,989 families, an epigenetic effect explains more of the estimated heritability when a portion of parental environmental effects are transmitted to offspring alongside additive genetic variance.

Genetic control of non-genetic inheritance in mammals: state-of-the-art and perspectives

Thought to be directly and uniquely dependent from genotypes, the ontogeny of individual phenotypes is much more complicated. Individual genetics, environmental exposures, and their interaction are the three main determinants of individual's phenotype. This picture has been further complicated a decade ago when the Lamarckian theory of acquired inheritance has been rekindled with the discovery of epigenetic inheritance, according to which acquired phenotypes can be transmitted through fertilization and affect phenotypes across generations. The results of Genome-Wide Association Studies have also highlighted a big degree of missing heritability in genetics and have provided hints that not only acquired phenotypes, but also individual's genotypes affect phenotypes intergenerationally through indirect genetic effects. Here, we review available examples of indirect genetic effects in mammals, what is known of the underlying molecular mechanisms and their potential impact for our understanding of missing heritability, phenotypic variation. and individual disease risk.

Accelerated inbreeding depression suggests synergistic epistasis for deleterious mutations in Drosophila melanogaster

Epistasis may have important consequences for a number of issues in quantitative genetics and evolutionary biology. In particular, synergistic epistasis for deleterious alleles is relevant to the mutation load paradox and the evolution of sex and recombination. Some studies have shown evidence of synergistic epistasis for spontaneous or induced deleterious mutations appearing in mutation-accumulation experiments. However, many newly arising mutations may not actually be segregating in natural populations because of the erasing action of natural selection. A demonstration of synergistic epistasis for naturally segregating alleles can be achieved by means of inbreeding depression studies, as deleterious recessive allelic effects are exposed in inbred lines. Nevertheless, evidence of epistasis from these studies is scarce and controversial. In this paper, we report the results of two independent inbreeding experiments carried out with two different populations of Drosophila melanogaster. The results show a consistent accelerated inbreeding depression for fitness, suggesting synergistic epistasis among deleterious alleles. We also performed computer simulations assuming different possible models of epistasis and mutational parameters for fitness, finding some of them to be compatible with the results observed. Our results suggest that synergistic epistasis for deleterious mutations not only occurs among newly arisen spontaneous or induced mutations, but also among segregating alleles in natural populations.

GWEHS: A Genome-Wide Effect Sizes and Heritability Screener

During the last decade, there has been a huge development of Genome-Wide Association Studies (GWAS), and thousands of loci associated to complex traits have been detected. These efforts have led to the creation of public databases of GWAS results, making a huge source of information available on the genetic background of many diverse traits. Here we present GWEHS (Genome-Wide Effect size and Heritability Screener), an open-source online application to screen loci associated to human complex traits and diseases from the NHGRI-EBI GWAS Catalog. This application provides a way to explore the distribution of effect sizes of loci affecting these traits, as well as their contribution to heritability. Furthermore, it allows for making predictions on the change in the expected mean effect size, as well as in the heritability as new loci are found. The application enables inferences on whether the additive contribution of loci expected to be discovered in the future will be able to explain the estimates of familial heritability for the different traits. We illustrate the use of this tool, compare some of the results obtained with those from a previous meta-analysis, and discuss its uses and limitations.