The mystery of missing heritability: Genetic interactions create phantom heritability

Evolutionary Action-Machine Learning Model Identifies Candidate Genes Associated With Early-Onset Coronary Artery Disease

Background Coronary artery disease is a primary cause of death around the world, with both genetic and environmental risk factors. Although genome-wide association studies have linked >100 unique loci to its genetic basis, these only explain a fraction of disease heritability. Methods and Results To find additional gene drivers of coronary artery disease, we applied machine learning to quantitative evolutionary information on the impact of coding variants in whole exomes from the Myocardial Infarction Genetics Consortium. Using ensemble-based supervised learning, the Evolutionary Action-Machine Learning framework ranked each gene's ability to classify case and control samples and identified 79 significant associations. These were connected to known risk loci; enriched in cardiovascular processes like lipid metabolism, blood clotting, and inflammation; and enriched for cardiovascular phenotypes in knockout mouse models. Among them, INPP5F and MST1R are examples of potentially novel coronary artery disease risk genes that modulate immune signaling in response to cardiac stress. Conclusions We concluded that machine learning on the functional impact of coding variants, based on a massive amount of evolutionary information, has the power to suggest novel coronary artery disease risk genes for mechanistic and therapeutic discoveries in cardiovascular biology, and should also apply in other complex polygenic diseases.

Pervasive under-dominance in gene expression underlying emergent growth trajectories in Arabidopsis thaliana hybrids

BACKGROUND

Complex traits, such as growth and fitness, are typically controlled by a very large number of variants, which can interact in both additive and non-additive fashion. In an attempt to gauge the relative importance of both types of genetic interactions, we turn to hybrids, which provide a facile means for creating many novel allele combinations.

RESULTS

We focus on the interaction between alleles of the same locus, i.e., dominance, and perform a transcriptomic study involving 141 random crosses between different accessions of the plant model species Arabidopsis thaliana. Additivity is rare, consistently observed for only about 300 genes enriched for roles in stress response and cell death. Regulatory rare-allele burden affects the expression level of these genes but does not correlate with F1 rosette size. Non-additive, dominant gene expression in F1 hybrids is much more common, with the vast majority of genes (over 90%) being expressed below the parental average. Unlike in the additive genes, regulatory rare-allele burden in the dominant gene set is strongly correlated with F1 rosette size, even though it only mildly covaries with the expression level of these genes.

CONCLUSIONS

Our study underscores under-dominance as the predominant gene action associated with emergence of rosette growth trajectories in the A. thaliana hybrid model. Our work lays the foundation for understanding molecular mechanisms and evolutionary forces that lead to dominance complementation of rare regulatory alleles.

Gene-based burden scores identify rare variant associations for 28 blood biomarkers

BACKGROUND

A relevant part of the genetic architecture of complex traits is still unknown; despite the discovery of many disease-associated common variants. Polygenic risk score (PRS) models are based on the evaluation of the additive effects attributable to common variants and have been successfully implemented to assess the genetic susceptibility for many phenotypes. In contrast, burden tests are often used to identify an enrichment of rare deleterious variants in specific genes. Both kinds of genetic contributions are typically analyzed independently. Many studies suggest that complex phenotypes are influenced by both low effect common variants and high effect rare deleterious variants. The aim of this paper is to integrate the effect of both common and rare functional variants for a more comprehensive genetic risk modeling.

METHODS

We developed a framework combining gene-based scores based on the enrichment of rare functionally relevant variants with genome-wide PRS based on common variants for association analysis and prediction models. We applied our framework on UK Biobank dataset with genotyping and exome data and considered 28 blood biomarkers levels as target phenotypes. For each biomarker, an association analysis was performed on full cohort using gene-based scores (GBS). The cohort was then split into 3 subsets for PRS construction and feature selection, predictive model training, and independent evaluation, respectively. Prediction models were generated including either PRS, GBS or both (combined).

RESULTS

Association analyses of the cohort were able to detect significant genes that were previously known to be associated with different biomarkers. Interestingly, the analyses also revealed heterogeneous effect sizes and directionality highlighting the complexity of the blood biomarkers regulation. However, the combined models for many biomarkers show little or no improvement in prediction accuracy compared to the PRS models.

CONCLUSION

This study shows that rare variants play an important role in the genetic architecture of complex multifactorial traits such as blood biomarkers. However, while rare deleterious variants play a strong role at an individual level, our results indicate that classical common variant based PRS might be more informative to predict the genetic susceptibility at the population level.

Gene network based analysis identifies a coexpression module involved in regulating plasma lipids with high-fat diet response

Plasma lipids are modulated by gene variants and many environmental factors, including diet-associated weight gain. However, understanding how these factors jointly interact to influence molecular networks that regulate plasma lipid levels is limited. Here, we took advantage of the BXD recombinant inbred family of mice to query weight gain as an environmental stressor on plasma lipids. Coexpression networks were examined in both nonobese and obese livers, and a network was identified that specifically responded to the obesogenic diet. This obesity-associated module was significantly associated with plasma lipid levels and enriched with genes known to have functions related to inflammation and lipid homeostasis. We identified key drivers of the module, including Cidec, Cidea, Pparg, Cd36, and Apoa4. The Pparg emerged as a potential master regulator of the module as it can directly target 19 of the top 30 hub genes. Importantly, activation of this module is causally linked to lipid metabolism in humans, as illustrated by correlation analysis and inverse-variance weighed Mendelian randomization. Our findings provide novel insights into gene-by-environment interactions for plasma lipid metabolism that may ultimately contribute to new biomarkers, better diagnostics, and improved approaches to prevent or treat dyslipidemia in patients.

Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the "multivariate MArginal ePIstasis Test" (mvMAPIT)-a multioutcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact-thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multitrait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogeneous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.

Role of Hydrogen Sulfide in Inflammatory Bowel Disease

Hydrogen sulfide (H2S), originally known as toxic gas, has now attracted attention as one of the gasotransmitters involved in many reactions in the human body. H2S has been assumed to play a role in the pathogenesis of many chronic diseases, of which the exact pathogenesis remains unknown. One of them is inflammatory bowel disease (IBD), a chronic intestinal disease subclassified as Crohn's disease (CD) and ulcerative colitis (UC). Any change in the amount of H2S seems to be linked to inflammation in this illness. These changes can be brought about by alterations in the microbiota, in the endogenous metabolism of H2S and in the diet. As both too little and too much H2S drive inflammation, a balanced level is needed for intestinal health. The aim of this review is to summarize the available literature published until June 2023 in order to provide an overview of the current knowledge of the connection between H2S and IBD.

Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast

Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.

Step by step: towards a better understanding of the genetic architecture of Alzheimer's disease

Alzheimer's disease (AD) is considered to have a large genetic component. Our knowledge of this component has progressed over the last 10 years, thanks notably to the advent of genome-wide association studies and the establishment of large consortia that make it possible to analyze hundreds of thousands of cases and controls. The characterization of dozens of chromosomal regions associated with the risk of developing AD and (in some loci) the causal genes responsible for the observed disease signal has confirmed the involvement of major pathophysiological pathways (such as amyloid precursor protein metabolism) and opened up new perspectives (such as the central role of microglia and inflammation). Furthermore, large-scale sequencing projects are starting to reveal the major impact of rare variants - even in genes like APOE - on the AD risk. This increasingly comprehensive knowledge is now being disseminated through translational research; in particular, the development of genetic risk/polygenic risk scores is helping to identify the subpopulations more at risk or less at risk of developing AD. Although it is difficult to assess the efforts still needed to comprehensively characterize the genetic component of AD, several lines of research can be improved or initiated. Ultimately, genetics (in combination with other biomarkers) might help to redefine the boundaries and relationships between various neurodegenerative diseases.

Higher-order genetic interaction discovery with network-based biological priors

MOTIVATION

Complex phenotypes, such as many common diseases and morphological traits, are controlled by multiple genetic factors, namely genetic mutations and genes, and are influenced by environmental conditions. Deciphering the genetics underlying such traits requires a systemic approach, where many different genetic factors and their interactions are considered simultaneously. Many association mapping techniques available nowadays follow this reasoning, but have some severe limitations. In particular, they require binary encodings for the genetic markers, forcing the user to decide beforehand whether to use, e.g. a recessive or a dominant encoding. Moreover, most methods cannot include any biological prior or are limited to testing only lower-order interactions among genes for association with the phenotype, potentially missing a large number of marker combinations.

RESULTS

We propose HOGImine, a novel algorithm that expands the class of discoverable genetic meta-markers by considering higher-order interactions of genes and by allowing multiple encodings for the genetic variants. Our experimental evaluation shows that the algorithm has a substantially higher statistical power compared to previous methods, allowing it to discover genetic mutations statistically associated with the phenotype at hand that could not be found before. Our method can exploit prior biological knowledge on gene interactions, such as protein-protein interaction networks, genetic pathways, and protein complexes, to restrict its search space. Since computing higher-order gene interactions poses a high computational burden, we also develop a more efficient search strategy and support computation to make our approach applicable in practice, leading to substantial runtime improvements compared to state-of-the-art methods.

AVAILABILITY AND IMPLEMENTATION

Code and data are available at https://github.com/BorgwardtLab/HOGImine.

Gene and pathway based burden analyses in familial lymphoid cancer cases: Rare variants in immune pathway genes

Genome-wide association studies have revealed common genetic variants with small effect sizes associated with diverse lymphoid cancers. Family studies have uncovered rare variants with high effect sizes. However, these variants explain only a portion of the heritability of these cancers. Some of the missing heritability may be attributable to rare variants with small effect sizes. We aim to identify rare germline variants associated with familial lymphoid cancers using exome sequencing. One case per family was selected from 39 lymphoid cancer families based on early onset of disease or rarity of subtype. Control data was from Non-Finnish Europeans in gnomAD exomes (N = 56,885) or ExAC (N = 33,370). Gene and pathway-based burden tests for rare variants were performed using TRAPD. Five putatively pathogenic germline variants were found in four genes: INTU, PEX7, EHHADH, and ASXL1. Pathway-based association tests identified the innate and adaptive immune systems, peroxisomal pathway and olfactory receptor pathway as associated with lymphoid cancers in familial cases. Our results suggest that rare inherited defects in the genes involved in immune system and peroxisomal pathway may predispose individuals to lymphoid cancers.

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network

Many diseases exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between disease phenotypes and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and diabetic retinopathy. Triglycerides, another blood lipid with known genetics causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities.

Transcriptome-wide gene-gene interaction associations elucidate pathways and functional enrichment of complex traits

It remains unknown to what extent gene-gene interactions contribute to complex traits. Here, we introduce a new approach using predicted gene expression to perform exhaustive transcriptome-wide interaction studies (TWISs) for multiple traits across all pairs of genes expressed in several tissue types. Using imputed transcriptomes, we simultaneously reduce the computational challenge and improve interpretability and statistical power. We discover (in the UK Biobank) and replicate (in independent cohorts) several interaction associations, and find several hub genes with numerous interactions. We also demonstrate that TWIS can identify novel associated genes because genes with many or strong interactions have smaller single-locus model effect sizes. Finally, we develop a method to test gene set enrichment of TWIS associations (E-TWIS), finding numerous pathways and networks enriched in interaction associations. Epistasis is may be widespread, and our procedure represents a tractable framework for beginning to explore gene interactions and identify novel genomic targets.

Staphylococcal diversity in atopic dermatitis from an individual to a global scale

Atopic dermatitis (AD) is a multifactorial, chronic relapsing disease associated with genetic and environmental factors. Among skin microbes, Staphylococcus aureus and Staphylococcus epidermidis are associated with AD, but how genetic variability and staphylococcal strains shape the disease remains unclear. We investigated the skin microbiome of an AD cohort (n = 54) as part of a prospective natural history study using shotgun metagenomic and whole genome sequencing, which we analyzed alongside publicly available data (n = 473). AD status and global geographical regions exhibited associations with strains and genomic loci of S. aureus and S. epidermidis. In addition, antibiotic prescribing patterns and within-household transmission between siblings shaped colonizing strains. Comparative genomics determined that S. aureus AD strains were enriched in virulence factors, whereas S. epidermidis AD strains varied in genes involved in interspecies interactions and metabolism. In both species, staphylococcal interspecies genetic transfer shaped gene content. These findings reflect the staphylococcal genomic diversity and dynamics associated with AD.

Genetic Risk Scores and Missing Heritability in Ovarian Cancer

Ovarian cancers are curable by surgical resection when discovered early. Unfortunately, most ovarian cancers are diagnosed in the later stages. One strategy to identify early ovarian tumors is to screen women who have the highest risk. This opinion article summarizes the accuracy of different methods used to assess the risk of developing ovarian cancer, including family history, BRCA genetic tests, and polygenic risk scores. The accuracy of these is compared to the maximum theoretical accuracy, revealing a substantial gap. We suggest that this gap, or missing heritability, could be caused by epistatic interactions between genes. An alternative approach to computing genetic risk scores, using chromosomal-scale length variation should incorporate epistatic interactions. Future research in this area should focus on this and other alternative methods of characterizing genomes.

The genetic architecture of human amygdala volumes and their overlap with common brain disorders

The amygdala is a crucial interconnecting structure in the brain that performs several regulatory functions, yet its genetic architectures and involvement in brain disorders remain largely unknown. We carried out the first multivariate genome-wide association study (GWAS) of amygdala subfield volumes in 27,866 UK Biobank individuals. The whole amygdala was segmented into nine nuclei groups using Bayesian amygdala segmentation. The post-GWAS analysis allowed us to identify causal genetic variants in phenotypes at the SNP, locus, and gene levels, as well as genetic overlap with brain health-related traits. We further generalized our GWAS in Adolescent Brain Cognitive Development (ABCD) cohort. The multivariate GWAS identified 98 independent significant variants within 32 genomic loci associated (P < 5 × 10^-8) with amygdala volume and its nine nuclei. The univariate GWAS identified significant hits for eight of the ten volumes, tagging 14 independent genomic loci. Overall, 13 of the 14 loci identified in the univariate GWAS were replicated in the multivariate GWAS. The generalization in ABCD cohort supported the GWAS results with the 12q23.2 (RNA gene RP11-210L7.1) being discovered. All of these imaging phenotypes are heritable, with heritability ranging from 15% to 27%. Gene-based analyses revealed pathways relating to cell differentiation/development and ion transporter/homeostasis, with the astrocytes found to be significantly enriched. Pleiotropy analyses revealed shared variants with neurological and psychiatric disorders under the conjFDR threshold of 0.05. These findings advance our understanding of the complex genetic architectures of amygdala and their relevance in neurological and psychiatric disorders.

Genetic modifiers of synucleinopathies-lessons from experimental models

α-Synuclein is a pleiotropic protein underlying a group of progressive neurodegenerative diseases, including Parkinson's disease and dementia with Lewy bodies. Together, these are known as synucleinopathies. Like all neurological diseases, understanding of disease mechanisms is hampered by the lack of access to biopsy tissues, precluding a real-time view of disease progression in the human body. This has driven researchers to devise various experimental models ranging from yeast to flies to human brain organoids, aiming to recapitulate aspects of synucleinopathies. Studies of these models have uncovered numerous genetic modifiers of α-synuclein, most of which are evolutionarily conserved. This review discusses what we have learned about disease mechanisms from these modifiers, and ways in which the study of modifiers have supported ongoing efforts to engineer disease-modifying interventions for synucleinopathies.

Predicting Cancer Risk from Germline Whole-exome Sequencing Data Using a Novel Context-based Variant Aggregation Approach

Many studies have shown that the distributions of the genomic, nucleotide, and epigenetic contexts of somatic variants in tumors are informative of cancer etiology. Recently, a new direction of research has focused on extracting signals from the contexts of germline variants and evidence has emerged that patterns defined by these factors are associated with oncogenic pathways, histologic subtypes, and prognosis. It remains an open question whether aggregating germline variants using meta-features capturing their genomic, nucleotide, and epigenetic contexts can improve cancer risk prediction. This aggregation approach can potentially increase statistical power for detecting signals from rare variants, which have been hypothesized to be a major source of the missing heritability of cancer. Using germline whole-exome sequencing data from the UK Biobank, we developed risk models for 10 cancer types using known risk variants (cancer-associated SNPs and pathogenic variants in known cancer predisposition genes) as well as models that additionally include the meta-features. The meta-features did not improve the prediction accuracy of models based on known risk variants. It is possible that expanding the approach to whole-genome sequencing can lead to gains in prediction accuracy.

Significance

There is evidence that cancer is partly caused by rare genetic variants that have not yet been identified. We investigate this issue using novel statistical methods and data from the UK Biobank.

Factors related to the development of executive functions: A cumulative dopamine genetic score and environmental factors predict performance of kindergarten children in a go/nogo task

BACKGROUND

This study aimed at investigating the interaction between genetic and environmental factors in predicting executive function in children aged four to six years.

METHODS

Response inhibition as index of EF was assessed in 197 children using a go/nogo task. A cumulative dopamine (DA) genetic score was calculated, indexing predisposition of low DA activity. Dimensions of parenting behavior and parental education were assessed.

RESULTS

Parental education was positively related to accuracy in nogo trials. An interaction between the cumulative genetic score and the parenting dimension Responsiveness predicted go RT indicating that children with a high cumulative genetic score and high parental responsiveness exhibited a careful response mode.

CONCLUSION

The development of EF in kindergarten children is related to parental education as well as to an interaction between the molecular-genetics of the DA system and parenting behavior.

A genome-wide association study (GWAS) of the personality constructs in CPAI-2 in Taiwanese Hakka populations

Here in this study we adopted genome-wide association studies (GWAS) to investigate the genetic components of the personality constructs in the Chinese Personality Assessment Inventory 2 (CPAI-2) in Taiwanese Hakka populations, who are likely the descendants of a recent admixture between a group of Chinese immigrants with high emigration intention and a group of the Taiwanese aboriginal population generally without it. A total of 279 qualified participants were examined and genotyped by an Illumina array with 547,644 SNPs to perform the GWAS. Although our sample size is small and that unavoidably limits our statistical power (Type 2 error but not Type 1 error), we still found three genomic regions showing strong association with Enterprise, Diversity, and Logical vs. Affective Orientation, respectively. Multiple genes around the identified regions were reported to be nervous system related, which suggests that genetic variants underlying the certain personalities should indeed exist in the nearby areas. It is likely that the recent immigration and admixture history of the Taiwanese Hakka people created strong linkage disequilibrium between the emigration intention-related genetic variants and their neighboring genetic markers, so that we could identify them despite with only limited statistical power.

Evidence for Epistatic Interaction between HLA-G and LILRB1 in the Pathogenesis of Nonsegmental Vitiligo

Vitiligo is the most frequent cause of depigmentation worldwide. Genetic association studies have discovered about 50 loci associated with disease, many with immunological functions. Among them is HLA-G, which modulates immunity by interacting with specific inhibitory receptors, mainly LILRB1 and LILRB2. Here we investigated the LILRB1 and LILRB2 association with vitiligo risk and evaluated the possible role of interactions between HLA-G and its receptors in this pathogenesis. We tested the association of the polymorphisms of HLA-G, LILRB1, and LILRB2 with vitiligo using logistic regression along with adjustment by ancestry. Further, methods based on the multifactor dimensionality reduction (MDR) approach (MDR v.3.0.2, GMDR v.0.9, and MB-MDR) were used to detect potential epistatic interactions between polymorphisms from the three genes. An interaction involving rs9380142 and rs2114511 polymorphisms was identified by all methods used. The polymorphism rs9380142 is an HLA-G 3'UTR variant (+3187) with a well-established role in mRNA stability. The polymorphism rs2114511 is located in the exonic region of LILRB1. Although no association involving this SNP has been reported, ChIP-Seq experiments have identified this position as an EBF1 binding site. These results highlight the role of an epistatic interaction between HLA-G and LILRB1 in vitiligo pathogenesis.

Genomic heritability and correlation between carcass traits in Japanese Black cattle evaluated under different ceilings of relatedness among individuals

The investigation of carcass traits to produce meat with high efficiency has been in focus on Japanese Black cattle since 1972. To implement a successful breeding program in carcass production, a comprehensive understanding of genetic characteristics and relationships between the traits is of paramount importance. In this study, genomic heritability and genomic correlation between carcass traits, including carcass weight (CW), rib eye area (REA), rib thickness (RT), subcutaneous fat thickness (SFT), yield rate (YI), and beef marbling score (BMS) were estimated using the genomic data of 9,850 Japanese Black cattle (4,142 heifers and 5,708 steers). In addition, we investigated the effect of genetic relatedness degree on the estimation of genetic parameters of carcass traits in sub-populations created based on different GRM-cutoff values. Genome-based restricted maximum likelihood (GREML) analysis was applied to estimate genetic parameters. Using all animal data, the heritability values for carcass traits were estimated as moderate to relatively high magnitude, ranging from 0.338 to 0.509 with standard errors, ranging from 0.014 to 0.015. The genetic correlations were obtained low and negative between SFT and REA [-0.198 (0.034)] and between SFT and BMS [-0.096 (0.033)] traits, and high and negative between SFT and YI [-0.634 (0.022)]. REA trait was genetically highly correlated with YI and BMS [0.811 (0.012) and 0.625 (0.022), respectively]. In sub-populations created based on the genetic-relatedness ceiling, the heritability estimates ranged from 0.212 (0.131) to 0.647 (0.066). At the genetic-relatedness ceiling of 0.15, the correlation values between most traits with low genomic correlation were overestimated while the correlations between the traits with relatively moderate to high correlations, ranging from 0.380 to 0.811, were underestimated. The values were steady at the ceilings of 0.30-0.95 (sample size of 5,443-9,850) for most of the highly correlated traits. The results demonstrated that there is considerable genetic variation and also favorable genomic correlations between carcass traits. Therefore, the genetic improvement for the traits can be simultaneously attained through genomic selection. In addition, we observed that depending on the degree of relationship between individuals and sample size, the genomic heritability and correlation estimates for carcass traits may be different.

MED13 and glycolysis are conserved modifiers of α-synuclein-associated neurodegeneration

α-Synuclein (α-syn) is important in synucleinopathies such as Parkinson's disease (PD). While genome-wide association studies (GWASs) of synucleinopathies have identified many risk loci, the underlying genes have not been shown for most loci. Using Drosophila, we screened 3,471 mutant chromosomes for genetic modifiers of α-synuclein and identified 12 genes. Eleven modifiers have human orthologs associated with diseases, including MED13 and CDC27, which lie within PD GWAS loci. Drosophila Skd/Med13 and glycolytic enzymes are co-upregulated by α-syn-associated neurodegeneration. While elevated α-syn compromises mitochondrial function, co-expressing skd/Med13 RNAi and α-syn synergistically increase the ratio of oxidized-to-reduced glutathione. The resulting neurodegeneration can be suppressed by overexpressing a glycolytic enzyme or treatment with deferoxamine, suggesting that compensatory glycolysis is neuroprotective. In addition, the functional relationship between α-synuclein, MED13, and glycolytic enzymes is conserved between flies and mice. We propose that hypoxia-inducible factor and MED13 are part of a druggable pathway for PD.

Increasing cassava root yield: Additive-dominant genetic models for selection of parents and clones

Genomic selection has been promising in situations where phenotypic assessments are expensive, laborious, and/or inefficient. This work evaluated the efficiency of genomic prediction methods combined with genetic models in clone and parent selection with the goal of increasing fresh root yield, dry root yield, as well as dry matter content in cassava roots. The bias and predictive ability of the combinations of prediction methods Genomic Best Linear Unbiased Prediction (G-BLUP), Bayes B, Bayes Cπ, and Reproducing Kernel Hilbert Spaces with additive and additive-dominant genetic models were estimated. Fresh and dry root yield exhibited predominantly dominant heritability, while dry matter content exhibited predominantly additive heritability. The combination of prediction methods and genetic models did not show significant differences in the predictive ability for dry matter content. On the other hand, the prediction methods with additive-dominant genetic models had significantly higher predictive ability than the additive genetic models for fresh and dry root yield, allowing higher genetic gains in clone selection. However, higher predictive ability for genotypic values did not result in differences in breeding value predictions between additive and additive-dominant genetic models. G-BLUP with the classical additive-dominant genetic model had the best predictive ability and bias estimates for fresh and dry root yield. For dry matter content, the highest predictive ability was obtained by G-BLUP with the additive genetic model. Dry matter content exhibited the highest heritability, predictive ability, and bias estimates compared with other traits. The prediction methods showed similar selection gains with approximately 67% of the phenotypic selection gain. By shortening the breeding cycle time by 40%, genomic selection may overcome phenotypic selection by 10%, 13%, and 18% for fresh root yield, dry root yield, and dry matter content, respectively, with a selection proportion of 15%. The most suitable genetic model for each trait allows for genomic selection optimization in cassava with high selection gains, thereby accelerating the release of new varieties.

Estimation of heritability using half-sib model under correlated errors

In general, statistical models for estimation of heritability follow certain assumptions, i.e. random components including the error follow a normal distribution and are identically independently distributed. But in the practical situation, sometimes these assumptions are violated. Thus, from the perspective of plant and animal breeding programs, estimating various genetic variances and inferring their inheritance based on estimations of various genetic parameters is studied. In the present study, estimation of heritability for the half-sib model is considered with correlated error, and sire and error follow a range of different distributions like normal, Cauchy, beta, and t- distribution. Two error structures AR(1) and AR(2) was considered and observations for correlated and uncorrelated cases were generated using a one-way classification model. The developed procedure was applied using the generated observations using simulation. Various heritability ranges, such as high and low (0.5, 0.1), Half-sib AR(1), varied sample sizes (100 and 500), and various correlations of errors between AR(1) and AR, were used to obtain the data (2). ρ= -1 to +1. It was noticed that correlated errors a significant effect on heritability estimation and are highly affected by the distribution it follows.

Plasticity's role in adaptive evolution depends on environmental change components

To forecast extinction risks of natural populations under climate change and direct human impacts, an integrative understanding of both phenotypic plasticity and adaptive evolution is essential. To date, the evidence for whether, when, and how much plasticity facilitates adaptive responses in changing environments is contradictory. We argue that explicitly considering three key environmental change components - rate of change, variance, and temporal autocorrelation - affords a unifying framework of the impact of plasticity on adaptive evolution. These environmental components each distinctively effect evolutionary and ecological processes underpinning population viability. Using this framework, we develop expectations regarding the interplay between plasticity and adaptive evolution in natural populations. This framework has the potential to improve predictions of population viability in a changing world.

Heritability estimates of distichiasis in Staffordshire bull terriers using pedigrees and genome-wide SNP data

BACKGROUND

Distichiasis is the most frequently recorded eye disorder in the Norwegian Staffordshire bull terrier (SBT). The condition is often mild but can, in severe cases, lead to pain and blindness. The current study's main purpose was to estimate the heritability based on pedigree information as well as single nucleotide polymorphisms (SNPs) to evaluate whether it is realistic to reduce the frequency by systematic breeding. The majority of the dogs had only one examination as a young puppy. To evaluate whether this early screening gave a reliable representation of the disease burden in the population, we compared the diagnosis in puppies and adult dogs.

RESULTS

Our material consisted of data from 4177 dogs with an overall prevalence of distichiasis of 8.38% (CI 7.56-9.26). The prevalence in puppies examined around eight weeks of age was significantly lower than in dogs examined after 52 weeks (2.87%, CI 2.29-3.54 versus 18.72%, CI 16.71-20.87). The heritability was estimated in dogs examined after 52 weeks. We used both pedigree (1391 dogs) and genotype (498 dogs) information for the estimates. The pedigree-based heritability was ~ 0.22 (on the underlying scale ~ 0.48), while the genomic-based heritability (on the underlying scale) was ~ 0.47, and ~ 0.37 when excluding close relatives with equal affection status.

CONCLUSIONS

Screening for distichiasis in puppies before eight weeks of age is not sufficient to give an accurate estimate of the prevalence, and an additional examination after one year is recommended. The heritability of distichiasis is medium to high, showing that it should be possible to reduce the prevalence by selective breeding.

Genome-wide association studies revealed complex genetic architecture and breeding perspective of maize ear traits

BACKGROUND

Maize (Zea Mays) is one of the world's most important crops. Hybrid maize lines resulted a major improvement in corn production in the previous and current centuries. Understanding the genetic mechanisms of the corn production associated traits greatly facilitate the development of superior hybrid varieties.

RESULT

In this study, four ear traits associated with corn production of Nested Association Mapping (NAM) population were analyzed using a full genetic model, and further, optimal genotype combinations and total genetic effects of current best lines, superior lines, and superior hybrids were predicted for each of the traits at four different locations. The analysis identified 21-34 highly significant SNPs (-log₁₀P > 5), with an estimated total heritability of 37.31-62.34%, while large contributions to variations was due to dominance, dominance-related epistasis, and environmental interaction effects ([Formula: see text] 14.06% ~ 49.28%), indicating these factors contributed significantly to phenotypic variations of the ear traits. Environment-specific genetic effects were also discovered to be crucial for maize ear traits. There were four SNPs found for three ear traits: two for ear length and weight, and two for ear row number and length. Using the Enumeration method and the stepwise tuning technique, optimum multi-locus genotype combinations for superior lines were identified based on the information obtained from GWAS.

CONCLUSIONS

Predictions of genetic breeding values showed that different genotype combinations in different geographical regions may be better, and hybrid-line variety breeding with homozygote and heterozygote genotype combinations may have a greater potential to improve ear traits.

Thyroid hormone elicits intergenerational epigenetic effects on adult social behavior and fetal brain expression of autism susceptibility genes

Genetic mutations identified in genome-wide association studies can only explain a small percentage of the cases of complex, highly heritable human conditions, including neurological and neurodevelopmental disorders. This suggests that intergenerational epigenetic effects, possibly triggered by environmental circumstances, may contribute to their etiology. We previously described altered DNA methylation signatures in the sperm of mice that experienced developmental overexposure to thyroid hormones as a result of a genetic defect in hormone clearance (DIO3 deficiency). Here we studied fetal brain gene expression and adult social behavior in genetically normal F2 generation descendants of overexposed mice. The brain of F2 generation E13.5 fetuses exhibited abnormal expression of genes associated with autism in humans, including Auts2, Disc1, Ldlr, Per2, Shank3, Oxtr, Igf1, Foxg1, Cd38, Grid2, Nrxn3, and Reln. These abnormal gene expression profiles differed depending on the sex of the exposed ancestor. In the three-chamber social box test, adult F2 generation males manifested significantly decreased interest in social interaction and social novelty, as revealed by decrease total time, distance traveled and time immobile in the area of interaction with novel strangers. F1 generation mice, compared to appropriate controls also exhibited altered profiles in fetal brain gene expression, although these profiles were substantially different to those in the F2 generation. Likewise adult F1 generation mice showed some abnormalities in social behavior that were sexually dimorphic and milder than those in F2 generation mice. Our results indicate that developmental overexposure to thyroid hormone causes intergenerational epigenetic effects impacting social behavior and the expression of autism-related genes during early brain development. Our results open the possibility that altered thyroid hormone states, by eliciting changes in the epigenetic information of the germ line, contribute to the susceptibility and the missing-but heriTables-etiology of complex neurodevelopmental conditions characterized by social deficits, including autism and schizophrenia.

Genetics of Primary Biliary Cholangitis

Primary biliary cholangitis (PBC) is a rare disease of the liver characterized by an autoimmune attack on the small bile ducts. PBC is a complex trait, meaning that a large list of genetic factors interacts with environmental agents to determine its onset. Genome-wide association studies have had a huge impact in fostering research in PBC, but many steps need still to be done compared with other autoimmune diseases of similar prevalence. This review presents the state-of-the-art regarding the genetic architecture of PBC and provides some thoughtful reflections about possible future lines of research, which can be helpful to fill the missing heritability gap in PBC.

Rare genetic variants explain missing heritability in smoking

Common genetic variants explain less variation in complex phenotypes than inferred from family-based studies, and there is a debate on the source of this 'missing heritability'. We investigated the contribution of rare genetic variants to tobacco use with whole-genome sequences from up to 26,257 unrelated individuals of European ancestries and 11,743 individuals of African ancestries. Across four smoking traits, single-nucleotide-polymorphism-based heritability ([Formula: see text]) was estimated from 0.13 to 0.28 (s.e., 0.10-0.13) in European ancestries, with 35-74% of it attributable to rare variants with minor allele frequencies between 0.01% and 1%. These heritability estimates are 1.5-4 times higher than past estimates based on common variants alone and accounted for 60% to 100% of our pedigree-based estimates of narrow-sense heritability ([Formula: see text], 0.18-0.34). In the African ancestry samples, [Formula: see text] was estimated from 0.03 to 0.33 (s.e., 0.09-0.14) across the four smoking traits. These results suggest that rare variants are important contributors to the heritability of smoking.

Missing Causality and Heritability of Autoimmune Hepatitis

BACKGROUND

Autoimmune hepatitis has an unknown cause and genetic associations that are not disease-specific or always present. Clarification of its missing causality and heritability could improve prevention and management strategies.

AIMS

Describe the key epigenetic and genetic mechanisms that could account for missing causality and heritability in autoimmune hepatitis; indicate the prospects of these mechanisms as pivotal factors; and encourage investigations of their pathogenic role and therapeutic potential.

METHODS

English abstracts were identified in PubMed using multiple key search phases. Several hundred abstracts and 210 full-length articles were reviewed.

RESULTS

Environmental induction of epigenetic changes is the prime candidate for explaining the missing causality of autoimmune hepatitis. Environmental factors (diet, toxic exposures) can alter chromatin structure and the production of micro-ribonucleic acids that affect gene expression. Epistatic interaction between unsuspected genes is the prime candidate for explaining the missing heritability. The non-additive, interactive effects of multiple genes could enhance their impact on the propensity and phenotype of autoimmune hepatitis. Transgenerational inheritance of acquired epigenetic marks constitutes another mechanism of transmitting parental adaptations that could affect susceptibility. Management strategies could range from lifestyle adjustments and nutritional supplements to precision editing of the epigenetic landscape.

CONCLUSIONS

Autoimmune hepatitis has a missing causality that might be explained by epigenetic changes induced by environmental factors and a missing heritability that might reflect epistatic gene interactions or transgenerational transmission of acquired epigenetic marks. These unassessed or under-evaluated areas warrant investigation.

Genome-wide association study reveals the genetic basis of brace root angle and diameter in maize

Brace roots are the main organ to support the above-ground part of maize plant. It involves in plant growth and development by water absorption and lodging resistance. The bracing root angle (BRA) and diameter (BRD) are important components of brace root traits. Illuminating the genetic basis of BRA and BRD will contribute the improvement for mechanized harvest and increasing production. A GWAS of BRA and BRD was conducted using an associated panel composed of 508 inbred lines of maize. The broad-sense heritability of BRA and BRD was estimated to be respectively 71% ± 0.19 and 52% ± 0.14. The phenotypic variation of BRA and BRD in the non-stiff stalk subgroup (NSS) and the stiff stalk subgroup (SS) subgroups are significantly higher than that in the tropical/subtropical subgroup (TST) subgroups. In addition, BRA and BRD are significantly positive with plant height (PH), ear length (EL), and kernel number per row (KNPR). GWAS revealed 27 candidate genes within the threshold of p < 1.84 × 10⁻⁶ by both MLM and BLINK models. Among them, three genes, GRMZM2G174736, GRMZM2G445169 and GRMZM2G479243 were involved in cell wall function, and GRMZM2G038073 encoded the NAC transcription factor family proteins. These results provide theoretical support for clarifying the genetic basis of brace roots traits.

The missing heritability in type 1 diabetes

Type 1 diabetes (T1D) is a complex autoimmune disease characterized by an absolute deficiency of insulin. It affects more than 20 million people worldwide and imposes an enormous financial burden on patients. The underlying pathogenic mechanisms of T1D are still obscure, but it is widely accepted that both genetics and the environment play an important role in its onset and development. Previous studies have identified more than 60 susceptible loci associated with T1D, explaining approximately 80%-85% of the heritability. However, most identified variants confer only small increases in risk, which restricts their potential clinical application. In addition, there is still a so-called 'missing heritability' phenomenon. While the gap between known heritability and true heritability in T1D is small compared with that in other complex traits and disorders, further elucidation of T1D genetics has the potential to bring novel insights into its aetiology and provide new therapeutic targets. Many hypotheses have been proposed to explain the missing heritability, including variants remaining to be found (variants with small effect sizes, rare variants and structural variants) and interactions (gene-gene and gene-environment interactions; e.g. epigenetic effects). In the following review, we introduce the possible sources of missing heritability and discuss the existing related knowledge in the context of T1D.

Interaction Of Immune Response Mediator Genes In A Predisposition To Juvenile Idiopathic Arthritis

Background/objective — The goal of our study was to investigate the role of interaction between the polymorphic loci of immune response mediator genes (TNFA rs1800629, LTA rs909253, IL1B rs16944, IL2-IL21 rs6822844, IL2RA rs2104286, IL6 rs1800795, IL10 rs1800872, MIF rs755622, CTLA4 rs3087243, NFKB1 rs28362491, PTPN22 rs2476601, and PADI4 rs2240336) in the formation of a genetic predisposition to juvenile idiopathic arthritis (JIA). Material and Methods — The study involved 330 JIA patients and 342 volunteers from the Republic of Bashkortostan. Genotyping was conducted via the real-time polymerase chain reaction. The gene-gene interactions were studied using the multifactor dimensionality reduction algorithm. Results — In general analysis, the best model of gene-gene interaction in JIA was a combination of IL1B rs16944 – IL10 rs1800872 – NFKB1 rs28362491 – PADI4 rs2240336 polymorphic loci. However, after gender-based stratification the best results were obtained when examining the combinations of IL6 rs1800795 – PADI4 rs2240336 loci in girls and of IL10 rs1800872 – IL6 rs1800795 – IL2RA rs2104286 loci in boys. Within all of these models, the genotype combinations associated with both augmented and reduced JIA risks were identified (taking into account gender-specific differences). Conclusion — The results of our study implied that an important role in the formation of a predisposition to JIA is played by gene-gene interactions of IL1B rs16944, IL2RA rs2104286, IL6 rs1800795, IL10 rs1800872, NFKB1 rs28362491, and PADI4 rs2240336 polymorphic loci (taking into account gender-specific differences).

Network approaches for omics studies of neurodegenerative diseases

The recent methodological advances in multi-omics approaches, including genomic, transcriptomic, metabolomic, lipidomic, and proteomic, have revolutionized the research field by generating “big data” which greatly enhanced our understanding of the molecular complexity of the brain and disease states. Network approaches have been routinely applied to single-omics data to provide critical insight into disease biology. Furthermore, multi-omics integration has emerged as both a vital need and a new direction to connect the different layers of information underlying disease mechanisms. In this review article, we summarize popular network analytic approaches for single-omics data and multi-omics integration and discuss how these approaches have been utilized in studying neurodegenerative diseases.

Haplotype-specific chromatin looping reveals genetic interactions of regulatory regions modulating gene expression in 8p23.1

A major goal of genetics research is to elucidate mechanisms explaining how genetic variation contributes to phenotypic variation. The genetic variants identified in genome-wide association studies (GWASs) generally explain only a small proportion of heritability of phenotypic traits, the so-called missing heritability problem. Recent evidence suggests that additional common variants beyond lead GWAS variants contribute to phenotypic variation; however, their mechanistic underpinnings generally remain unexplored. Herein, we undertake a study of haplotype-specific mechanisms of gene regulation at 8p23.1 in the human genome, a region associated with a number of complex diseases. The FAM167A-BLK locus in this region has been consistently found in the genome-wide association studies (GWASs) of systemic lupus erythematosus (SLE) in all major ancestries. Our haplotype-specific chromatin interaction (Hi-C) experiments, allele-specific enhancer activity measurements, genetic analyses, and epigenome editing experiments revealed that: 1) haplotype-specific long-range chromatin interactions are prevalent in 8p23.1; 2) BLK promoter and cis-regulatory elements cooperatively interact with haplotype-specificity; 3) genetic variants at distal regulatory elements are allele-specific modifiers of the promoter variants at FAM167A-BLK; 4) the BLK promoter interacts with and, as an enhancer-like promoter, regulates FAM167A expression and 5) local allele-specific enhancer activities are influenced by global haplotype structure due to chromatin looping. Although systemic lupus erythematosus causal variants at the FAM167A-BLK locus are thought to reside in the BLK promoter region, our results reveal that genetic variants at distal regulatory elements modulate promoter activity, changing BLK and FAM167A gene expression and disease risk. Our results suggest that global haplotype-specific 3-dimensional chromatin looping architecture has a strong influence on local allelic BLK and FAM167A gene expression, providing mechanistic details for how regional variants controlling the BLK promoter may influence disease risk.

Deep neural networks with controlled variable selection for the identification of putative causal genetic variants

Deep neural networks (DNNs) have been successfully utilized in many scientific problems for their high prediction accuracy, but their application to genetic studies remains challenging due to their poor interpretability. Here we consider the problem of scalable, robust variable selection in DNNs for the identification of putative causal genetic variants in genome sequencing studies. We identified a pronounced randomness in feature selection in DNNs due to its stochastic nature, which may hinder interpretability and give rise to misleading results. We propose an interpretable neural network model, stabilized using ensembling, with controlled variable selection for genetic studies. The merit of the proposed method includes: flexible modelling of the nonlinear effect of genetic variants to improve statistical power; multiple knockoffs in the input layer to rigorously control the false discovery rate; hierarchical layers to substantially reduce the number of weight parameters and activations, and improve computational efficiency; and stabilized feature selection to reduce the randomness in identified signals. We evaluate the proposed method in extensive simulation studies and apply it to the analysis of Alzheimer's disease genetics. We show that the proposed method, when compared with conventional linear and nonlinear methods, can lead to substantially more discoveries.

Genome-wide study of early and severe childhood asthma identifies interaction between CDHR3 and GSDMB

BACKGROUND

Asthma with severe exacerbation is one of the most common causes of hospitalization among young children. Exacerbations are typically triggered by respiratory infections, but the host factors causing recurrent infections and exacerbations in some children are poorly understood. As a result, current treatment options and preventive measures are inadequate.

OBJECTIVE

We sought to identify genetic interaction associated with the development of childhood asthma.

METHODS

We performed an exhaustive search for pairwise interaction between genetic single nucleotide polymorphisms using 1204 cases of a specific phenotype of early childhood asthma with severe exacerbations in patients aged 2 to 6 years combined with 5328 nonasthmatic controls. Replication was attempted in 3 independent populations, and potential underlying immune mechanisms were investigated in the COPSAC2010 and COPSAC2000 birth cohorts.

RESULTS

We found evidence of interaction, including replication in independent populations, between the known childhood asthma loci CDHR3 and GSDMB. The effect of CDHR3 was dependent on the GSDMB genotype, and this interaction was more pronounced for severe and early onset of disease. Blood immune analyses suggested a mechanism related to increased IL-17A production after viral stimulation.

CONCLUSIONS

We found evidence of interaction between CDHR3 and GSDMB in development of early childhood asthma, possibly related to increased IL-17A response to viral infections. This study demonstrates the importance of focusing on specific disease subtypes for understanding the genetic mechanisms of asthma.

Association of Polymorphism within the Putative miRNA Target Site in the 3'UTR Region of the DRD2 Gene with Neuroticism in Patients with Substance Use Disorder

The study aims at looking into associations between the polymorphism rs6276 that occurs in the putative miRNA target site in the 3'UTR region of the DRD2 gene in patients with substance use disorder (SUD) comorbid with a maniacal syndrome (SUD MANIA). In our study, we did not state any essential difference in DRD2 rs6276 genotype frequencies in the studied samples of SUD MANIA, SUD, and control subjects. A significant result was found for the SUD MANIA group vs. SUD vs. controls on the Neuroticism Scale of NEO FFI test, and DRD2 rs6276 (p = 0.0320) accounted for 1.7% of the variance. The G/G homozygous variants were linked with lower results on the neuroticism scale in the SUD MANIA group because G/G alleles may serve a protective role in the expression of neuroticism in patients with SUD MANIA. So far, there have been no data in the literature on the relationship between the miRSNP rs6276 region in the DRD2 gene and neuroticism (personal traits) in patients with a diagnosis of substance use disorder comorbid with the affective, maniacal type disturbances related to SUD. This is the first report on this topic.

Immunodeficiency, autoimmunity, and increased risk of B cell malignancy in humans with TRAF3 mutations

Tumor necrosis factor receptor-associated factor 3 (TRAF3) is a central regulator of immunity. TRAF3 is often somatically mutated in B cell malignancies, but its role in human immunity is not defined. Here, in five unrelated families, we describe an immune dysregulation syndrome of recurrent bacterial infections, autoimmunity, systemic inflammation, B cell lymphoproliferation, and hypergammaglobulinemia. Affected individuals each had monoallelic mutations in TRAF3 that reduced TRAF3 expression. Immunophenotyping showed that patients' B cells were dysregulated, exhibiting increased nuclear factor-κB 2 activation, elevated mitochondrial respiration, and heightened inflammatory responses. Patients had mild CD4+ T cell lymphopenia, with a reduced proportion of naïve T cells but increased regulatory T cells and circulating T follicular helper cells. Guided by this clinical phenotype, targeted analyses demonstrated that common genetic variants, which also reduce TRAF3 expression, are associated with an increased risk of B cell malignancies, systemic lupus erythematosus, higher immunoglobulin levels, and bacterial infections in the wider population. Reduced TRAF3 conveys disease risks by driving B cell hyperactivity via intrinsic activation of multiple intracellular proinflammatory pathways and increased mitochondrial respiration, with a likely contribution from dysregulated T cell help. Thus, we define monogenic TRAF3 haploinsufficiency syndrome and demonstrate how common TRAF3 variants affect a range of human diseases.

CNest: A novel copy number association discovery method uncovers 862 new associations from 200,629 whole-exome sequence datasets in the UK Biobank

Copy number variation (CNV) is known to influence human traits, having a rich history of research into common and rare genetic disease, and although CNV is accepted as an important class of genomic variation, progress on copy-number-based genome-wide association studies (GWASs) from next-generation sequencing (NGS) data has been limited. Here we present a novel method for large-scale copy number analysis from NGS data generating robust copy number estimates and allowing copy number GWASs (CN-GWASs) to be performed genome-wide in discovery mode. We provide a detailed analysis in the UK Biobank resource and a specifically designed software package. We use these methods to perform CN-GWAS analysis across 78 human traits, discovering over 800 genetic associations that are likely to contribute strongly to trait distributions. Finally, we compare CNV and SNP association signals across the same traits and samples, defining specific CNV association classes.

A cognitive neurogenetic approach to uncovering the structure of executive functions

One central mission of cognitive neuroscience is to understand the ontology of complex cognitive functions. We addressed this question with a cognitive neurogenetic approach using a large-scale dataset of executive functions (EFs), whole-brain resting-state functional connectivity, and genetic polymorphisms. We found that the bifactor model with common and shifting-specific components not only was parsimonious but also showed maximal dissociations among the EF components at behavioral, neural, and genetic levels. In particular, the genes with enhanced expression in the middle frontal gyrus (MFG) and the subcallosal cingulate gyrus (SCG) showed enrichment for the common and shifting-specific component, respectively. Finally, High-dimensional mediation models further revealed that the functional connectivity patterns significantly mediated the genetic effect on the common EF component. Our study not only reveals insights into the ontology of EFs and their neurogenetic basis, but also provides useful tools to uncover the structure of complex constructs of human cognition.

A Large-Scale Genome-Wide Gene-Gene Interaction Study of Lung Cancer Susceptibility in Europeans With a Trans-Ethnic Validation in Asians

INTRODUCTION

Although genome-wide association studies have been conducted to investigate genetic variation of lung tumorigenesis, little is known about gene-gene (G × G) interactions that may influence the risk of non-small cell lung cancer (NSCLC).

METHODS

Leveraging a total of 445,221 European-descent participants from the International Lung Cancer Consortium OncoArray project, Transdisciplinary Research in Cancer of the Lung and UK Biobank, we performed a large-scale genome-wide G × G interaction study on European NSCLC risk by a series of analyses. First, we used BiForce to evaluate and rank more than 58 billion G × G interactions from 340,958 single-nucleotide polymorphisms (SNPs). Then, the top interactions were further tested by demographically adjusted logistic regression models. Finally, we used the selected interactions to build lung cancer screening models of NSCLC, separately, for never and ever smokers.

RESULTS

With the Bonferroni correction, we identified eight statistically significant pairs of SNPs, which predominantly appeared in the 6p21.32 and 5p15.33 regions (e.g., rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.17, p = 6.57 × 10-13; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.17, p = 2.43 × 10-13; rs2858859HLA-DQA1 and rs9275572HLA-DQA2, ORinteraction = 1.15, p = 2.84 × 10-13; rs2853668TERT and rs62329694CLPTM1L, ORinteraction = 0.73, p = 2.70 × 10-13). Notably, even with much genetic heterogeneity across ethnicities, three pairs of SNPs in the 6p21.32 region identified from the European-ancestry population remained significant among an Asian population from the Nanjing Medical University Global Screening Array project (rs521828C6orf10 and rs204999PRRT1, ORinteraction = 1.13, p = 0.008; rs3135369BTNL2 and rs2858859HLA-DQA1, ORinteraction = 1.11, p = 5.23 × 10-4; rs3135369BTNL2 and rs9271300HLA-DQA1, ORinteraction = 0.89, p = 0.006). The interaction-empowered polygenetic risk score that integrated classical polygenetic risk score and G × G information score was remarkable in lung cancer risk stratification.

CONCLUSIONS

Important G × G interactions were identified and enriched in the 5p15.33 and 6p21.32 regions, which may enhance lung cancer screening models.

Identification of risk genes for Alzheimer's disease by gene embedding

Most disease-gene association methods do not account for gene-gene interactions, even though these play a crucial role in complex, polygenic diseases like Alzheimer's disease (AD). To discover new genes whose interactions may contribute to pathology, we introduce GeneEMBED. This approach compares the functional perturbations induced in gene interaction network neighborhoods by coding variants from disease versus healthy subjects. In two independent AD cohorts of 5,169 exomes and 969 genomes, GeneEMBED identified novel candidates. These genes were differentially expressed in post mortem AD brains and modulated neurological phenotypes in mice. Four that were differentially overexpressed and modified neurodegeneration in vivo are PLEC, UTRN, TP53, and POLD1. Notably, TP53 and POLD1 are involved in DNA break repair and inhibited by approved drugs. While these data show proof of concept in AD, GeneEMBED is a general approach that should be broadly applicable to identify genes relevant to risk mechanisms and therapy of other complex diseases.

Identification of FAT3 as a new candidate gene for adolescent idiopathic scoliosis

In an effort to identify rare alleles associated with adolescent idiopathic scoliosis (AIS) whole-exome sequencing was performed on a discovery cohort of 73 unrelated patients and 70 age-and sex matched controls, all of French-Canadian ancestry. A collapsing gene burden test was performed to analyze rare protein-altering variants using case–control statistics. Since no single gene achieved statistical significance, targeted exon sequencing was performed for 24 genes with the smallest p values, in an independent replication cohort of unrelated severely affected females with AIS and sex-matched controls (N = 96 each). An excess of rare, potentially protein-altering variants was noted in one particular gene, FAT3, although it did not achieve statistical significance. Independently, we sequenced the exomes of all members of a rare multiplex family of three affected sisters and unaffected parents. All three sisters were compound heterozygous for two rare protein-altering variants in FAT3. The parents were single heterozygotes for each variant. The two variants in the family were also present in our discovery cohort. A second validation step was done, using another independent replication cohort of 258 unrelated AIS patients having reach their skeletal maturity and 143 healthy controls to genotype nine FAT3 gene variants, including the two variants previously identified in the multiplex family: p.L517S (rs139595720) and p.L4544F (rs187159256). Interestingly, two FAT3 variants, rs139595720 (genotype A/G) and rs80293525 (genotype C/T), were enriched in severe scoliosis cases (4.5% and 2.7% respectively) compared to milder cases (1.4% and 0.7%) and healthy controls (1.6% and 0.8%). Our results implicate FAT3 as a new candidate gene in the etiology of AIS.

Cancer Risk Score Prediction Based on a Single-Nucleotide Polymorphism Network

Objectives

Genome-wide association studies (GWAS) are performed to study the associations between genetic variants with respect to certain phenotypic traits such as cancer. However, the method that is commonly used in GWAS assumes that certain traits are solely affected by a single mutation. We propose a network analysis method, in which we generate association networks of single-nucleotide polymorphisms (SNPs) that can differentiate case and control groups. We hypothesize that certain phenotypic traits are attributable to mutations in groups of associated SNPs.

Methods

We propose a method based on a network analysis framework to study SNP-SNP interactions related to cancer incidence. We employed logistic regression to measure the significance of all SNP pairs from GWAS for the incidence of colorectal cancer and computed a cancer risk score based on the generated SNP networks.

Results

We demonstrated our method in a dataset from a case-control study of colorectal cancer in the South Sulawesi population. From the GWAS results, 20,094 pairs of 200 SNPs were created. We obtained one cluster containing four pairs of five SNPs that passed the filtering threshold based on their p-values. A locus on chromosome 12 (12:54410007) was found to be strongly connected to the four variants on chromosome 1. A polygenic risk score was computed from the five SNPs, and a significant difference in colorectal cancer risk was obtained between the case and control groups.

Conclusions

Our results demonstrate the applicability of our method to understand SNP-SNP interactions and compute risk scores for various types of cancer.

Harnessing the Full Potential of Multi-Omic Analyses to Advance the Study and Treatment of Chronic Kidney Disease

Chronic kidney disease (CKD) was the 12th leading cause of death globally in 2017 with the prevalence of CKD estimated at ~9%. Early detection and intervention for CKD may improve patient outcomes, but standard testing approaches even in developed countries do not facilitate identification of patients at high risk of developing CKD, nor those progressing to end-stage kidney disease (ESKD). Recent advances in CKD research are moving towards a more personalised approach for CKD. Heritability for CKD ranges from 30% to 75%, yet identified genetic risk factors account for only a small proportion of the inherited contribution to CKD. More in depth analysis of genomic sequencing data in large cohorts is revealing new genetic risk factors for common diagnoses of CKD and providing novel diagnoses for rare forms of CKD. Multi-omic approaches are now being harnessed to improve our understanding of CKD and explain some of the so-called 'missing heritability'. The most common omic analyses employed for CKD are genomics, epigenomics, transcriptomics, metabolomics, proteomics and phenomics. While each of these omics have been reviewed individually, considering integrated multi-omic analysis offers considerable scope to improve our understanding and treatment of CKD. This narrative review summarises current understanding of multi-omic research alongside recent experimental and analytical approaches, discusses current challenges and future perspectives, and offers new insights for CKD.

Open problems in human trait genetics

Genetic studies of human traits have revolutionized our understanding of the variation between individuals, and yet, the genetics of most traits is still poorly understood. In this review, we highlight the major open problems that need to be solved, and by discussing these challenges provide a primer to the field. We cover general issues such as population structure, epistasis and gene-environment interactions, data-related issues such as ancestry diversity and rare genetic variants, and specific challenges related to heritability estimates, genetic association studies, and polygenic risk scores. We emphasize the interconnectedness of these problems and suggest promising avenues to address them.

Three legs of the missing heritability problem

The so-called 'missing heritability problem' is often characterized by behavior geneticists as a numerical discrepancy between alternative kinds of heritability. For example, while 'traditional heritability' derived from twin and family studies indicates that approximately ∼50% of variation in intelligence is attributable to genetics, 'SNP heritability' derived from genome-wide association studies indicates that only ∼10% of variation in intelligence is attributable to genetics. This 40% gap in variance accounted for by alternative kinds of heritability is frequently referred to as what's "missing." Philosophers have picked up on this reading, suggesting that "dissolving" the missing heritability problem is merely a matter of closing the numerical gap between traditional and molecular kinds of heritability. We argue that this framing of the problem undervalues the severity of the many challenges to scientific understanding of the "heritability" of human behavior. On our view, resolving the numerical discrepancies between alternative kinds of heritability will do little to advance scientific explanation and understanding of behavior genetics. Thus, we propose a new conceptual framework of the missing heritability problem that comprises three independent methodological and explanatory challenges: the numerical gap, the prediction gap, and the mechanism gap.