Gene-level Tests Research Articles

Decomposed interaction testing improves detection of genetic modifiers of the relationship of dietary omega-3 fatty acid intake and its plasma biomarkers with hsCRP in the UK Biobank.

Discovery and translation of gene-environment interactions (GxEs) influencing clinical outcomes is limited by low statistical power and poor mechanistic understanding. Molecular omics data may help address these limitations, but their incorporation into GxE testing requires principled analytic approaches. We focused on genetic modification of the established mechanistic link between dietary long-chain omega-3 fatty acid (dN3FA) intake, plasma N3FA (pN3FA), and chronic inflammation as measured by high sensitivity CRP (hsCRP). We considered an approach that decomposes the overall genetic effect modification into components upstream and downstream of a molecular mediator to increase the potential to discover gene-N3FA interactions. Simulations demonstrated improved power of the upstream and downstream tests compared to the standard approach when the molecular mediator for many biologically plausible scenarios. The approach was applied in the UK Biobank (N = 188,700) with regression models that used measures of dN3FA (based on fish and fish oil intake), pN3FA (% of total fatty acids measured by nuclear magnetic resonance), and hsCRP. Mediation analysis showed that pN3FA fully mediated the dN3FA-hsCRP main effect relationship. Next, we separately tested modification of the dN3FA-hsCRP ("standard"), dN3FA-pN3FA ("upstream"), and pN3FA-hsCRP ("downstream") associations. The known FADS1-3 locus variant rs174535 reached p = 1.6×10-12 in the upstream discovery analysis, with no signal in the downstream analysis (p = 0.94). It would not have been prioritized based on a naïve analysis with dN3FA exposure and hsCRP outcome (p = 0.097), indicating the value of the decomposition approach. Gene-level enrichment testing of the genome-wide results further prioritized two genes from the downstream analysis, CBLL1 and MICA, with links to immune cell counts and function. In summary, a molecular mediator-focused interaction testing approach enhanced statistical power to identify GxEs while homing in on relevant sub-components of the dN3FA-hsCRP pathway.

Contribution of copy number variations to education, socioeconomic status and cognition from a genome-wide study of 305,401 subjects.

Educational attainment (EA), socioeconomic status (SES) and cognition are phenotypically and genetically linked to health outcomes. However, the role of copy number variations (CNVs) in influencing EA/SES/cognition remains unclear. Using a large-scale (n = 305,401) genome-wide CNV-level association analysis, we discovered 33 CNV loci significantly associated with EA/SES/cognition, 20 of which were novel (deletions at 2p22.2, 2p16.2, 2p12, 3p25.3, 4p15.2, 5p15.33, 5q21.1, 8p21.3, 9p21.1, 11p14.3, 13q12.13, 17q21.31, and 20q13.33, as well as duplications at 3q12.2, 3q23, 7p22.3, 8p23.1, 8p23.2, 17q12 (105 kb), and 19q13.32). The genes identified in gene-level tests were enriched in biological pathways such as neurodegeneration, telomere maintenance and axon guidance. Phenome-wide association studies further identified novel associations of EA/SES/cognition-associated CNVs with mental and physical diseases, such as 6q27 duplication with upper respiratory disease and 17q12 (105 kb) duplication with mood disorders. Our findings provide a genome-wide CNV profile for EA/SES/cognition and bridge their connections to health. The expanded candidate CNVs database and the residing genes would be a valuable resource for future studies aimed at uncovering the biological mechanisms underlying cognitive function and related clinical phenotypes.

Identifying pleiotropic genes via the composite test amidst the complexity of polygenic traits.

Identifying the causal relationship between genotype and phenotype is essential to expanding our understanding of the gene regulatory network spanning the molecular level to perceptible traits. A pleiotropic gene can act as a central hub in the network, influencing multiple outcomes. Identifying such a gene involves testing under a composite null hypothesis where the gene is associated with, at most, one trait. Traditional methods such as meta-analyses of top-hit $P$-values and sequential testing of multiple traits have been proposed, but these methods fail to consider the background of genome-wide signals. Since Huang's composite test produces uniformly distributed $P$-values for genome-wide variants under the composite null, we propose a gene-level pleiotropy test that entails combining the aforementioned method with the aggregated Cauchy association test. A polygenic trait involves multiple genes with different functions to co-regulate mechanisms. We show that polygenicity should be considered when identifying pleiotropic genes; otherwise, the associations polygenic traits initiate will give rise to false positives. In this study, we constructed gene-trait functional modules using the results of the proposed pleiotropy tests. Our analysis suite was implemented as an R package PGCtest. We demonstrated the proposed method with an application study of the Taiwan Biobank database and identified functional modules comprising specific genes and their co-regulated traits.

Control of false discoveries in grouped hypothesis testing for eQTL data

BackgroundExpression quantitative trait locus (eQTL) analysis aims to detect the genetic variants that influence the expression of one or more genes. Gene-level eQTL testing forms a natural grouped-hypothesis testing strategy with clear biological importance. Methods to control family-wise error rate or false discovery rate for group testing have been proposed earlier, but may not be powerful or easily apply to eQTL data, for which certain structured alternatives may be defensible and may enable the researcher to avoid overly conservative approaches.ResultsIn an empirical Bayesian setting, we propose a new method to control the false discovery rate (FDR) for grouped hypotheses. Here, each gene forms a group, with SNPs annotated to the gene corresponding to individual hypotheses. The heterogeneity of effect sizes in different groups is considered by the introduction of a random effects component. Our method, entitled Random Effects model and testing procedure for Group-level FDR control (REG-FDR), assumes a model for alternative hypotheses for the eQTL data and controls the FDR by adaptive thresholding. As a convenient alternate approach, we also propose Z-REG-FDR, an approximate version of REG-FDR, that uses only Z-statistics of association between genotype and expression for each gene-SNP pair. The performance of Z-REG-FDR is evaluated using both simulated and real data. Simulations demonstrate that Z-REG-FDR performs similarly to REG-FDR, but with much improved computational speed.ConclusionOur results demonstrate that the Z-REG-FDR method performs favorably compared to other methods in terms of statistical power and control of FDR. It can be of great practical use for grouped hypothesis testing for eQTL analysis or similar problems in statistical genomics due to its fast computation and ability to be fit using only summary data.

Abstract P494: Gene-Level Gene-Environment Interaction Testing Reveals Rare Variants With Large Impacts on the Omega-3 Fatty Acid-Inflammation Relationship

Dietary omega-3 fatty acid (dN3FA) intake affects plasma N3FA (pN3FA) concentrations, circulating biomarkers of chronic inflammation such as high-sensitivity C-reactive protein (hsCRP), and ultimately cardiometabolic disease risk. Genetic variation may modify the N3FA-inflammation relationship, facilitating applications in precision medicine and possibly explaining variable intervention trial responses. Here, we explored the dual hypothesis that pN3FA mediates the dN3FA-hsCRP relationship and that genetic variants across the frequency spectrum modify this association. Regression models in the UK Biobank (N = 342,120) tested main effects of oily fish intake (a primary dietary source of N3FA; measured by food frequency questionnaire) on hsCRP and its mediation by pN3FA (measured by nuclear magnetic resonance; N = 188,701). Three genome-wide interaction studies tested modification of the dN3FA-hsCRP (“full”), dN3FA-pN3FA (“upstream”), and pN3FA-hsCRP (“downstream”) associations. Regression covariates included demographics, lifestyle indicators, socioeconomic measures, and genetic principal components. Variant-specific GWIS were followed by gene-level enrichment testing using the MAGMA tool. Replication of gene-based signals was conducted in the Women’s Genome Health Study (WGHS; N = 23,294) by testing the interaction of the top variant in each gene with total dN3FA impacting hsCRP. Oily fish intake associated most strongly with hsCRP compared to other questionnaire-based estimates of fish or fish oil intake, with statistical evidence that plasma N3FA mediated approximately 100% of this diet-inflammation relationship. While the “full” genetic interaction analysis did not produce any FDR-significant gene-level interactions, the “upstream” analysis uncovered nine genes, including a cluster containing the biologically-relevant fatty acid desaturases FADS1 and FADS2 , and the “downstream” analysis uncovered four genes. Primary variant interaction effect sizes were small (e.g., -0.02 std. dev. bN3FA / std. dev. fish / allele for the top FADS1 variant rs174560), but in-depth follow-up at these genes revealed allelic spectra including rare variants with substantially larger effects (e.g., -0.11 for a FADS1 missense variant with allele frequency of 0.14%, after conditioning on rs174560). WGHS results supported the overall robustness of the findings (consistent interaction effect signs at 8/10 loci), replicated specific signals (p = 0.04 for FADS1 rs174560), and implicated additional inflammatory biomarkers (e.g., fibrinogen and ICAM-1 for the top GLTSCR1 variant). In conclusion, we report (1) the novel epidemiological finding that pN3FA fully mediates the dN3FA-hsCRP relationship, and (2) specific genes with large-effect rare variants that modify the upstream (absorption) and downstream (inflammatory pathways) components of this relationship.

RAVAR: a curated repository for rare variant-trait associations.

Rare variants contribute significantly to the genetic causes of complex traits, as they can have much larger effects than common variants and account for much of the missing heritability in genome-wide association studies. The emergence of UK Biobank scale datasets and accurate gene-level rare variant-trait association testing methods have dramatically increased the number of rare variant associations that have been detected. However, no systematic collection of these associations has been carried out to date, especially at the gene level. To address the issue, we present the Rare Variant Association Repository (RAVAR), a comprehensive collection of rare variant associations. RAVAR includes 95047 high-quality rare variant associations (76186 gene-level and 18861 variant-level associations) for 4429 reported traits which are manually curated from 245 publications. RAVAR is the first resource to collect and curate published rare variant associations in an interactive web interface with integrated visualization, search, and download features. Detailed gene and SNP information are provided for each association, and users can conveniently search for related studies by exploring the EFO tree structure and interactive Manhattan plots. RAVAR could vastly improve the accessibility of rare variant studies. RAVAR is freely available for all users without login requirement at http://www.ravar.bio.

A Large-Scale Exome-Wide Association Study Identifies Novel Germline Mutations in Lung Cancer.

Rationale: Genome-wide association studies have identified common variants of lung cancer. However, the contribution of rare exome-wide variants, especially protein-coding variants, to cancers remains largely unexplored. Objectives: To evaluate the role of human exomes in genetic predisposition to lung cancer. Methods: We performed exome-wide association studies to detect the association of exomes with lung cancer in 30,312 patients and 652,902 control subjects. A scalable and accurate implementation of a generalized mixed model was used to detect the association signals for loss-of-function, missense, and synonymous variants and gene-level sets. Furthermore, we performed association and Bayesian colocalization analyses to evaluate their relationships with intermediate exposures. Measurements and Main Results: We systematically analyzed 216,739 single-nucleotide variants in the human exome. The loss-of-function variants exhibited the most notable effects on lung cancer risk. We identified four novel variants, including two missense variants (rs202197044TET3 [Pmeta (P values of meta-analysis) = 3.60 × 10-8] and rs202187871POT1 [Pmeta = 2.21 × 10-8]) and two synonymous variants (rs7447927TMEM173 [Pmeta = 1.32 × 10-9] and rs140624366ATRN [Pmeta = 2.97 × 10-9]). rs202197044TET3 was significantly associated with emphysema (odds ratio, 3.55; Pfdr = 0.015), whereas rs7447927POT1 was strongly associated with telomere length (β = 1.08; Pfdr (FDR corrected P value) = 3.76 × 10-53). Functional evidence of expression of quantitative trait loci, splicing quantitative trait loci, and isoform expression was found for the four novel genes. Gene-level association tests identified several novel genes, including POT1 (protection of telomeres 1), RTEL1, BSG, and ZNF232. Conclusions: Our findings provide insights into the genetic architecture of human exomes and their role in lung cancer predisposition.

A gene-level test for directional selection on gene expression.

Most variants identified in human genome-wide association studies and scans for selection are noncoding. Interpretation of their effects and the way in which they contribute to phenotypic variation and adaptation in human populations is therefore limited by our understanding of gene regulation and the difficulty of confidently linking noncoding variants to genes. To overcome this, we developed a gene-wise test for population-specific selection based on combinations of regulatory variants. Specifically, we use the QX statistic to test for polygenic selection on cis-regulatory variants based on whether the variance across populations in the predicted expression of a particular gene is higher than expected under neutrality. We then applied this approach to human data, testing for selection on 17,388 protein-coding genes in 26 populations from the Thousand Genomes Project. We identified 45 genes with significant evidence (FDR<0.1) for selection, including FADS1, KHK, SULT1A2, ITGAM, and several genes in the HLA region. We further confirm that these signals correspond to plausible population-level differences in predicted expression. While the small number of significant genes (0.2%) is consistent with most cis-regulatory variation evolving under genetic drift or stabilizing selection, it remains possible that there are effects not captured in this study. Our gene-level QX score is independent of standard genomic tests for selection, and may therefore be useful in combination with traditional selection scans to specifically identify selection on regulatory variation. Overall, our results demonstrate the utility of combining population-level genomic data with functional data to understand the evolution of gene expression.

Rare genetic variants correlate with better processing speed

We conducted a genome-wide association study of Digit Symbol Substitution Test scores administered in 4207 family members of the Long Life Family Study (LLFS). Genotype data were imputed to the HRC panel of 64,940 haplotypes resulting in ∼15M genetic variants with a quality score > 0.7. The results were replicated using genetic data imputed to the 1000 Genomes phase 3 reference panel from 2 Danish twin cohorts: the study of Middle Aged Danish Twins and the Longitudinal Study of Aging Danish Twins. The genome-wide association study in LLFS discovered 18 rare genetic variants (minor allele frequency (MAF) < 1.0%) that reached genome-wide significance (p-value < 5 × 10−8). Among these, 17 rare variants in chromosome 3 had large protective effects on the processing speed, including rs7623455, rs9821776, rs9821587, rs78704059, which were replicated in the combined Danish twin cohort. These SNPs are located in/near 2 genes, THRB and RARB, that belonged to the thyroid hormone receptors family that may influence the speed of metabolism and cognitive aging. The gene-level tests in LLFS confirmed that these 2 genes are associated with processing speed.

Integrative analysis of multiomics data identifies selenium-related gene ALAD associating with keshan disease.

Keshan disease is an endemic fatal dilated cardiomyopathy that can cause heart enlargement, heart failure, and cardiogenic death. Selenium deficiency is considered to be the main cause of Keshan disease. However, the molecular mechanism underlying Keshan disease remains unclear. Our whole-exome sequencing from 68 patients with Keshan disease and 100 controls found 199 candidate genes by gene-level burden tests. Interestingly, using multiomics data, the selenium-related gene ALAD (δ-aminolevulinic acid dehydratase) was the only candidate causative gene identified by three different analysis approaches. Based on single-cell transcriptome data, ALAD was highly expressed in cardiomyocytes and double mutations of human ALAD dramatically reduced its enzyme activity in vitro compared to negative control. Functional analysis of ALAD inhibition in mice resulted in a Keshan phenotype with left ventricular enlargement and cardiac dysfunction, whereas administration of sodium selenite markedly reversed the changes caused by ALAD inhibition. In addition, sodium selenite reversed Keshan phenotypes by affecting energy metabolism and mitochondrial function in mice as shown by the transcriptomic and proteomic data and the ultrastructure of cardiac myocytes. Our findings are the first to demonstrate that the selenium-related gene ALAD is essential for cardiac function by maintaining normal mitochondrial activity, providing strong molecular evidence supporting the hypothesis of selenium deficiency in Keshan disease. These results identified ALAD as a novel target for therapeutic intervention in Keshan disease and Keshan disease-related dilated cardiomyopathy.

Analysis of rare disruptive germline mutations in 2135 enriched BRCA-negative breast cancers excludes additional high-impact susceptibility genes

Breast cancer has a significant heritable basis, of which ∼60% remains unexplained. Testing for BRCA1/BRCA2 offers useful discrimination of breast cancer risk within families, and identification of additional breast cancer susceptibility genes could offer clinical utility. We included 2135 invasive breast cancer cases recruited via the Breast and Ovarian Cancer Susceptibility study, a retrospective UK study of familial breast cancer. female, BRCA-negative, white European ethnicity, and one of: (i) breast cancer family history, (ii) bilateral disease, (iii) young age of onset (<30 years), and (iv) concomitant ovarian cancer. We undertook exome sequencing of cases and carried out gene-level burden testing of rare damaging variants against those from 51 377 ethnicity-matched population controls from gnomAD. 159/2135 (7.4%) cases had a qualifying variant in an established breast cancer susceptibility gene, with minimal evidence of signal in other cancer susceptibility genes. Known breast cancer susceptibility genes PALB2, CHEK2, and ATM were the only genes to retain statistical significance after correcting for multiple testing. Due to the enrichment of hereditary cases in the series, we had good power (>80%) to detect a gene of BRCA1-like risk [odds ratio (OR)= 10.6] down to a population minor allele frequency of 4.6× 10-5 (1 in 10 799, less than one-tenth that of BRCA1)and of PALB2-like risk (OR= 5.0) down to a population minor allele frequency of 2.8× 10-4 (1 in 1779, less than half that of PALB2). Power was lower for identification of novel moderate penetrance genes (OR= 2-3) like CHEK2 and ATM. This is the largest case-control whole-exome analysis of enriched breast cancer published to date. Whilst additional breast cancer susceptibility genes likely exist, those of high penetrance are likely to be of very low mutational frequency. Contention exists regarding the clinical utility of such genes.

Integrated Quantile RAnk Test (iQRAT) for gene-level associations

Gene-based testing is a commonly employed strategy in many genetic association studies. Gene-trait associations can be complex due to underlying population heterogeneity, gene-environment interactions, and various other reasons. Existing gene-based tests, such as burden and sequence kernel association tests (SKAT), are mean-based tests and may miss or underestimate higher-order associations that could be scientifically interesting. In this paper we propose a new family of gene-level association tests that integrate quantile rank score process to better accommodate complex associations. The resulting test statistics have multiple advantages: (1) they are almost as efficient as the best existing tests when the associations are homogeneous across quantile levels and have improved efficiency for complex and heterogeneous associations; (2) they provide useful insights into risk stratification; (3) the test statistics are distribution free and could hence accommodate a wide range of underlying distributions, and (4) they are computationally efficient. We established the asymptotic properties of the proposed tests under the null and alternative hypotheses and conducted large-scale simulation studies to investigate their finite sample performance. The performance of the proposed approach is compared with that of conventional mean-based tests, that is, the burden and SKAT tests, through simulation studies and applications to a metabochip dataset on lipid traits and to the genotype-tissue expression data in GTEx to identify eGenes, that is, genes whose expression levels are associated with cis-eQTLs.

Excise Taxes as a Policy Lever for Reaching Healthy North Carolina 2030 Targets.

Excise taxes can raise the price of unhealthy products, reducing consumption and associated health risks and costs. Raising state excise taxes on tobacco, alcohol, and sugary drinks and allowing local governments to do the same are win-win strategies for achieving three Healthy North Carolina 2030 health behavior targets while increasing state revenues.

EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing.

More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific gene expression measurements from single-cell RNA sequencing (scRNA-seq). We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We apply our framework to multiple scRNA-seq datasets from different platforms and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and scRNA-seq datasets and further validated using PubMed search and existing bulk case-control testing results.

ExomeChip-based rare variant association study in restless legs syndrome

Restless legs syndrome (RLS) is a common sleep-related movement disorder in populations of European descent and disease risk is strongly influenced by genetic factors. Common variants have been assessed extensively in several genome-wide association studies, but the contribution of rarer genetic variation has not been investigated at this scale. We therefore genotyped a case–control set of 9246 individuals for mainly rare and low frequency exonic variants using the Illumina ExomeChip. However, standard single variant and gene-level association tests were negative. This does not preclude a role of rare variants in RLS, but is likely due to the small sample size and the limited selection of rare genetic variation captured on the array. Therefore, exome or whole genome sequencing should be performed rather than increasing the sample size of ExomeChip studies in order to identify rare risk variants for RLS.

Omnibus testing approach for gene-based gene-gene interaction.

Genetic interaction is considered as one of the main heritable component of complex traits. With the emergence of genome-wide association studies (GWAS), a collection of statistical methods dedicated to the identification of interaction at the SNP level have been proposed. More recently, gene-based gene-gene interaction testing has emerged as an attractive alternative as they confer advantage in both statistical power and biological interpretation. Most of the gene-based interaction methods rely on a multidimensional modeling of the interaction, thus facing a lack of robustness against the huge space of interaction patterns. In this paper, we study a global testing approaches to address the issue of gene-based gene-gene interaction. Based on a logistic regression modeling framework, all SNP-SNP interaction tests are combined to produce a gene-level test for interaction. We propose an omnibus test that takes advantage of (1) the heterogeneity between existing global tests and (2) the complementarity between allele-based and genotype-based coding of SNPs. Through an extensive simulation study, it is demonstrated that the proposed omnibus test has the ability to detect with high power the most common interaction genetic models with one causal pair as well as more complex genetic models where more than one causal pair is involved. On the other hand, the flexibility of the proposed approach is shown to be robust and improves power compared to single global tests in replication studies. Furthermore, the application of our procedure to real datasets confirms the adaptability of our approach to replicate various gene-gene interactions.

Partitioning gene-level contributions to complex-trait heritability by allele frequency identifies disease-relevant genes

Recent works have shown that SNP heritability-which is dominated by low-effect common variants-may not be the most relevant quantity for localizing high-effect/critical disease genes. Here, we introduce methods to estimate the proportion of phenotypic variance explained by a given assignment of SNPs to a single gene ("gene-level heritability"). We partition gene-level heritability by minor allele frequency (MAF) to find genes whose gene-level heritability is explained exclusively by "low-frequency/rare" variants (0.5% ≤ MAF < 1%). Applying our method to ∼16K protein-coding genes and 25 quantitative traits in the UK Biobank (N = 290K "White British"), we find that, on average across traits, ∼2.5% of nonzero-heritability genes have a rare-variant component and only ∼0.8% (327 gene-trait pairs) have heritability exclusively from rare variants. Of these 327 gene-trait pairs, 114 (35%) were not detected by existing gene-level association testing methods. The additional genes we identify are significantly enriched for known disease genes, and we find several examples of genes that have been previously implicated in phenotypically related Mendelian disorders. Notably, the rare-variant component of gene-level heritability exhibits trends different from those of common-variant gene-level heritability. For example, while total gene-level heritability increases with gene length, the rare-variant component is significantly larger among shorter genes; the cumulative distributions of gene-level heritability also vary across traits and reveal differences in the relative contributions of rare/common variants to overall gene-level polygenicity. While nonzero gene-level heritability does not imply causality, if interpreted in the correct context, gene-level heritability can reveal useful insights into complex-trait genetic architecture.

Focused goodness of fit tests for gene set analyses

Gene set-based signal detection analyses are used to detect an association between a trait and a set of genes by accumulating signals across the genes in the gene set. Since signal detection is concerned with identifying whether any of the genes in the gene set are non-null, a goodness-of-fit (GOF) test can be used to compare whether the observed distribution of gene-level tests within the gene set agrees with the theoretical null distribution. Here, we present a flexible gene set-based signal detection framework based on tail-focused GOF statistics. We show that the power of the various statistics in this framework depends critically on two parameters: the proportion of genes within the gene set that are non-null and the degree of separation between the null and alternative distributions of the gene-level tests. We give guidance on which statistic to choose for a given situation and implement the methods in a fast and user-friendly R package, wHC (https://github.com/mqzhanglab/wHC). Finally, we apply these methods to a whole exome sequencing study of amyotrophic lateral sclerosis.

Genome-wide copy number variation analysis of hepatitis B infection in a Japanese population

Genome-wide association studies have been performed to identify common genetic variants associated with hepatitis B (HB). However, little is known about copy number variations (CNVs) in HB. In this study, we performed a genome-wide CNV analysis between 1830 healthy controls and 1031 patients with HB infection after quality control. Using signal calling by the Axiom Analysis Suite and CNV detection by PennCNV software, we obtained a total of 4494 CNVs across all individuals. The genes with CNVs that were found only in the HB patients were associated with the immune system, such as antigen processing. A gene-level CNV association test revealed statistically significant CNVs in the contactin 6 (CNTN6) gene. Moreover, we also performed gene-level CNV association tests in disease subgroups, including hepatocellular carcinoma patients, liver cirrhosis patients, and HBV carriers, including asymptomatic carriers and patients with HBV-derived chronic hepatitis. Our findings from germline cells suggested that patient-specific CNVs may be inherent genetic risk factors for HB.

Integrative modeling of transmitted and de novo variants identifies novel risk genes for congenital heart disease.

Whole-exome sequencing (WES) studies have identified multiple genes enriched for de novo mutations (DNMs) in congenital heart disease (CHD) probands. However, risk gene identification based on DNMs alone remains statistically challenging due to heterogenous etiology of CHD and low mutation rate in each gene. In this manuscript, we introduce a hierarchical Bayesian framework for gene-level association test which jointly analyzes de novo and rare transmitted variants. Through integrative modeling of multiple types of genetic variants, gene-level annotations, and reference data from large population cohorts, our method accurately characterizes the expected frequencies of both de novo and transmitted variants and shows improved statistical power compared to analyses based on DNMs only. Applied to WES data of 2,645 CHD proband-parent trios, our method identified 15 significant genes, half of which are novel, leading to new insights into the genetic bases of CHD. These results showcase the power of integrative analysis of transmitted and de novo variants for disease gene discovery.