Rare-variant association testing for sequencing data with the sequence kernel association test

A dominant negative mutation of GhMYB25-like alters cotton fiber initiation, reducing lint and fuzz

Cotton (Gossypium hirsutum) fibers, vital natural textile materials, are single-cell trichomes that differentiate from the ovule epidermis. These fibers are categorized as lint (longer fibers useful for spinning) or fuzz (shorter, less useful fibers). Currently, developing cotton varieties with high lint yield but without fuzz remains challenging due to our limited knowledge of the molecular mechanisms underlying fiber initiation. This study presents the identification and characterization of a naturally occurring dominant negative mutation GhMYB25-like_AthapT, which results in a reduced lint and fuzzless phenotype. The GhMYB25-like_AthapT protein exerts its dominant negative effect by suppressing the activity of GhMYB25-like during lint and fuzz initiation. Intriguingly, the negative effect of GhMYB25-like_AthapT could be alleviated by high expression levels of GhMYB25-like. We also uncovered the role of GhMYB25-like in regulating the expression of key genes such as GhPDF2 (PROTODERMAL FACTOR 2), CYCD3; 1 (CYCLIN D3; 1), and PLD (Phospholipase D), establishing its significance as a pivotal transcription factor in fiber initiation. We identified other genes within this regulatory network, expanding our understanding of the determinants of fiber cell fate. These findings offer valuable insights for cotton breeding and contribute to our fundamental understanding of fiber development.

A high-dimensional omnibus test for set-based association analysis

Set-based association analysis is a valuable tool in studying the etiology of complex diseases in genome-wide association studies, as it allows for the joint testing of variants in a region or group. Two common types of single nucleotide polymorphism (SNP)-disease functional models are recognized when evaluating the joint function of a set of SNP: the cumulative weak signal model, in which multiple functional variants with small effects contribute to disease risk, and the dominating strong signal model, in which a few functional variants with large effects contribute to disease risk. However, existing methods have two main limitations that reduce their power. Firstly, they typically only consider one disease-SNP association model, which can result in significant power loss if the model is misspecified. Secondly, they do not account for the high-dimensional nature of SNPs, leading to low power or high false positives. In this study, we propose a solution to these challenges by using a high-dimensional inference procedure that involves simultaneously fitting many SNPs in a regression model. We also propose an omnibus testing procedure that employs a robust and powerful P-value combination method to enhance the power of SNP-set association. Our results from extensive simulation studies and a real data analysis demonstrate that our set-based high-dimensional inference strategy is both flexible and computationally efficient and can substantially improve the power of SNP-set association analysis. Application to a real dataset further demonstrates the utility of the testing strategy.

Genome-Wide Association Analyses and Population Verification Highlight the Potential Genetic Basis of Horned Morphology during Polled Selection in Tibetan Sheep

The types and morphology of sheep horns have been extensively researched, yet the genetic foundation underlying the emergence of diverse horn characteristics during the breeding of polled Tibetan sheep has remained elusive. Genome-wide association analysis (GWAS) was performed on 103 subtypes (normal large horn, scurs, and polled) differentiated from G2 (offspring (G2) of parent (G1) of polled) of the polled core herd. Six single nucleotide polymorphisms (SNPs) located on chromosome 10 of the relaxin family peptide receptor 2 (RXFP2) gene exhibited positive correlations with horn length, horn base circumference, and horn base interval. Furthermore, in genotyping 382 G2 individuals, significant variations were observed for each specific horn type. Three additional mutations were identified near the target SNP upstream of the amplification product. Finally, the RXFP2-specific haplotype associated with the horned trait effectively maintained horn length, horn base circumference, and horn base interval in Tibetan sheep, as confirmed by population validation of nine loci in a sample size of 1125 individuals. The present study offers novel insights into the genetic differentiation of the horned type during improvement breeding and evolution, thereby establishing a robust theoretical foundation for polled Tibetan sheep breeding and providing valuable guidance for practical production.

A power-based sliding window approach to evaluate the clinical impact of rare genetic variants in the nucleotide sequence or the spatial position of the folded protein

Systematic determination of novel variant pathogenicity remains a major challenge, even when there is an established association between a gene and phenotype. Here we present Power Window (PW), a sliding window technique that identifies the impactful regions of a gene using population-scale clinico-genomic datasets. By sizing analysis windows on the number of variant carriers, rather than the number of variants or nucleotides, statistical power is held constant, enabling the localization of clinical phenotypes and removal of unassociated gene regions. The windows can be built by sliding across either the nucleotide sequence of the gene (through 1D space) or the positions of the amino acids in the folded protein (through 3D space). Using a training set of 350k exomes from the UK Biobank (UKB), we developed PW models for well-established gene-disease associations and tested their accuracy in two independent cohorts (117k UKB exomes and 65k exomes sequenced at Helix in the Healthy Nevada Project, myGenetics, or In Our DNA SC studies). The significant models retained a median of 49% of the qualifying variant carriers in each gene (range 2%-98%), with quantitative traits showing a median effect size improvement of 66% compared with aggregating variants across the entire gene, and binary traits' odds ratios improving by a median of 2.2-fold. PW showcases that electronic health record-based statistical analyses can accurately distinguish between novel coding variants in established genes that will have high phenotypic penetrance and those that will not, unlocking new potential for human genomics research, drug development, variant interpretation, and precision medicine.

ADELLE: A global testing method for Trans-eQTL mapping

Understanding the genetic regulatory mechanisms of gene expression is a challenging and ongoing problem. Genetic variants that are associated with expression levels are readily identified when they are proximal to the gene (i.e., cis-eQTLs), but SNPs distant from the gene whose expression levels they are associated with (i.e., trans-eQTLs) have been much more difficult to discover, even though they account for a majority of the heritability in gene expression levels. A major impediment to the identification of more trans-eQTLs is the lack of statistical methods that are powerful enough to overcome the obstacles of small effect sizes and large multiple testing burden of trans-eQTL mapping. Here, we propose ADELLE, a powerful statistical testing framework that requires only summary statistics and is designed to be most sensitive to SNPs that are associated with multiple gene expression levels, a characteristic of many trans-eQTLs. In simulations, we show that for detecting SNPs that are associated with 0.1%-2% of 10,000 traits, among the 7 methods we consider ADELLE is clearly the most powerful overall, with either the highest power or power not significantly different from the highest for all settings in that range. We apply ADELLE to a mouse advanced intercross line data set and show its ability to find trans-eQTLs that were not significant under a standard analysis. This demonstrates that ADELLE is a powerful tool at uncovering trans regulators of genetic expression.

Large-scale whole-exome sequencing analyses identified protein-coding variants associated with immune-mediated diseases in 350,770 adults

The genetic contribution of protein-coding variants to immune-mediated diseases (IMDs) remains underexplored. Through whole exome sequencing of 40 IMDs in 350,770 UK Biobank participants, we identified 162 unique genes in 35 IMDs, among which 124 were novel genes. Several genes, including FLG which is associated with atopic dermatitis and asthma, showed converging evidence from both rare and common variants. 91 genes exerted significant effects on longitudinal outcomes (interquartile range of Hazard Ratio: 1.12-5.89). Mendelian randomization identified five causal genes, of which four were approved drug targets (CDSN, DDR1, LTA, and IL18BP). Proteomic analysis indicated that mutations associated with specific IMDs might also affect protein expression in other IMDs. For example, DXO (celiac disease-related gene) and PSMB9 (alopecia areata-related gene) could modulate CDSN (autoimmune hypothyroidism-, psoriasis-, asthma-, and Graves' disease-related gene) expression. Identified genes predominantly impact immune and biochemical processes, and can be clustered into pathways of immune-related, urate metabolism, and antigen processing. Our findings identified protein-coding variants which are the key to IMDs pathogenesis and provided new insights into tailored innovative therapies.

Systematic rare variant analyses identify RAB32 as a susceptibility gene for familial Parkinson's disease

Despite substantial progress, causal variants are identified only for a minority of familial Parkinson's disease (PD) cases, leaving high-risk pathogenic variants unidentified1,2. To identify such variants, we uniformly processed exome sequencing data of 2,184 index familial PD cases and 69,775 controls. Exome-wide analyses converged on RAB32 as a novel PD gene identifying c.213C > G/p.S71R as a high-risk variant presenting in ~0.7% of familial PD cases while observed in only 0.004% of controls (odds ratio of 65.5). This variant was confirmed in all cases via Sanger sequencing and segregated with PD in three families. RAB32 encodes a small GTPase known to interact with LRRK2 (refs. 3,4). Functional analyses showed that RAB32 S71R increases LRRK2 kinase activity, as indicated by increased autophosphorylation of LRRK2 S1292. Here our results implicate mutant RAB32 in a key pathological mechanism in PD-LRRK2 kinase activity5-7-and thus provide novel insights into the mechanistic connections between RAB family biology, LRRK2 and PD risk.

Identification of the Molecular Components of Enhancer-Mediated Gene Expression Variation in Multiple Tissues Regulating Blood Pressure

BACKGROUND

Inter-individual variation in blood pressure (BP) arises in part from sequence variants within enhancers modulating the expression of causal genes. We propose that these genes, active in tissues relevant to BP physiology, can be identified from tissue-level epigenomic data and genotypes of BP-phenotyped individuals.

METHODS

We used chromatin accessibility data from the heart, adrenal, kidney, and artery to identify cis-regulatory elements (CREs) in these tissues and estimate the impact of common human single-nucleotide variants within these CREs on gene expression, using machine learning methods. To identify causal genes, we performed a gene-wise association test. We conducted analyses in 2 separate large-scale cohorts: 77 822 individuals from the Genetic Epidemiology Research on Adult Health and Aging and 315 270 individuals from the UK Biobank.

RESULTS

We identified 309, 259, 331, and 367 genes (false discovery rate <0.05) for diastolic BP and 191, 184, 204, and 204 genes for systolic BP in the artery, kidney, heart, and adrenal, respectively, in Genetic Epidemiology Research on Adult Health and Aging; 50% to 70% of these genes were replicated in the UK Biobank, significantly higher than the 12% to 15% expected by chance (P<0.0001). These results enabled tissue expression prediction of these 988 to 2875 putative BP genes in individuals of both cohorts to construct an expression polygenic score. This score explained ≈27% of the reported single-nucleotide variant heritability, substantially higher than expected from prior studies.

CONCLUSIONS

Our work demonstrates the power of tissue-restricted comprehensive CRE analysis, followed by CRE-based expression prediction, for understanding BP regulation in relevant tissues and provides dual-modality supporting evidence, CRE and expression, for the causality genes.

Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease

Coronary artery disease (CAD) exists on a spectrum of disease represented by a combination of risk factors and pathogenic processes. An in silico score for CAD built using machine learning and clinical data in electronic health records captures disease progression, severity and underdiagnosis on this spectrum and could enhance genetic discovery efforts for CAD. Here we tested associations of rare and ultrarare coding variants with the in silico score for CAD in the UK Biobank, All of Us Research Program and BioMe Biobank. We identified associations in 17 genes; of these, 14 show at least moderate levels of prior genetic, biological and/or clinical support for CAD. We also observed an excess of ultrarare coding variants in 321 aggregated CAD genes, suggesting more ultrarare variant associations await discovery. These results expand our understanding of the genetic etiology of CAD and illustrate how digital markers can enhance genetic association investigations for complex diseases.

Discovering non-additive heritability using additive GWAS summary statistics

LD score regression (LDSC) is a method to estimate narrow-sense heritability from genome-wide association study (GWAS) summary statistics alone, making it a fast and popular approach. In this work, we present interaction-LD score (i-LDSC) regression: an extension of the original LDSC framework that accounts for interactions between genetic variants. By studying a wide range of generative models in simulations, and by re-analyzing 25 well-studied quantitative phenotypes from 349,468 individuals in the UK Biobank and up to 159,095 individuals in BioBank Japan, we show that the inclusion of a cis-interaction score (i.e. interactions between a focal variant and proximal variants) recovers genetic variance that is not captured by LDSC. For each of the 25 traits analyzed in the UK Biobank and BioBank Japan, i-LDSC detects additional variation contributed by genetic interactions. The i-LDSC software and its application to these biobanks represent a step towards resolving further genetic contributions of sources of non-additive genetic effects to complex trait variation.

Variance-components tests for genetic association with multiple interval-censored outcomes

Massive genetic compendiums such as the UK Biobank have become an invaluable resource for identifying genetic variants that are associated with complex diseases. Due to the difficulties of massive data collection, a common practice of these compendiums is to collect interval-censored data. One challenge in analyzing such data is the lack of methodology available for genetic association studies with interval-censored data. Genetic effects are difficult to detect because of their rare and weak nature, and often the time-to-event outcomes are transformed to binary phenotypes for access to more powerful signal detection approaches. However transforming the data to binary outcomes can result in loss of valuable information. To alleviate such challenges, this work develops methodology to associate genetic variant sets with multiple interval-censored outcomes. Testing sets of variants such as genes or pathways is a common approach in genetic association settings to lower the multiple testing burden, aggregate small effects, and improve interpretations of results. Instead of performing inference with only a single outcome, utilizing multiple outcomes can increase statistical power by aggregating information across multiple correlated phenotypes. Simulations show that the proposed strategy can offer significant power gains over a single outcome approach. We apply the proposed test to the investigation that motivated this study, a search for the genes that perturb risks of bone fractures and falls in the UK Biobank.

Whole-exome sequencing in a Chinese sample provides preliminary evidence for the link between rare/low-frequency immune-related variants and early-onset schizophrenia

Rare and low-frequency variants contribute to schizophrenia (SCZ), and may influence its age-at-onset (AAO). We examined the association of rare or low-frequency deleterious coding variants in Chinese patients with SCZ. We collected DNA samples in 197 patients with SCZ spectrum disorder and 82 healthy controls (HC), and performed exome sequencing. The AAO variable was ascertained in the majority of SCZ participants for identify the early-onset (EOS, AAO<=18) and adult-onset (AOS, AAO>18) subgroups. We examined the overall association of rare/low-frequency, damaging variants in SCZ versus HC, EOS versus HC, and AOS versus HC at the gene and gene-set levels using Sequence Kernel Association Test. The quantitative rare-variant association test of AAO was conducted. Resampling was used to obtain empirical p-values and to control for family-wise error rate (FWER). In binary-trait association tests, we identified 5 potential candidate risk genes and 10 gene ontology biological processes (GOBP) terms, among which PADI2 reached FWER-adjusted significance. In quantitative rare-variant association tests, we found marginally significant correlations of AAO with alterations in 4 candidate risk genes, and 5 GOBP pathways. Together, the biological and functional profiles of these genes and gene sets supported the involvement of perturbations of neural systems in SCZ, and altered immune functions in EOS.

Genetically proxied HTRA1 protease activity and circulating levels independently predict risk of ischemic stroke and coronary artery disease

Genetic variants in HTRA1 are associated with stroke risk. However, the mechanisms mediating this remain largely unknown, as does the full spectrum of phenotypes associated with genetic variation in HTRA1. Here we show that rare HTRA1 variants are linked to ischemic stroke in the UK Biobank and BioBank Japan. Integrating data from biochemical experiments, we next show that variants causing loss of protease function associated with ischemic stroke, coronary artery disease and skeletal traits in the UK Biobank and MyCode cohorts. Moreover, a common variant modulating circulating HTRA1 mRNA and protein levels enhances the risk of ischemic stroke and coronary artery disease while lowering the risk of migraine and macular dystrophy in genome-wide association study, UK Biobank, MyCode and BioBank Japan data. We found no interaction between proxied HTRA1 activity and levels. Our findings demonstrate the role of HTRA1 for cardiovascular diseases and identify two mechanisms as potential targets for therapeutic interventions.

Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features

BACKGROUND

Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated.

METHODS

A total of 184 probable AD patients and 3773 healthy individuals aged 65 and over were enrolled. AD-related genes (51 SNPs) and 8 environmental factors were selected as features for multilayer ANN modeling. Random Forest (RF) and Support Vector Machine with RBF kernel (SVM) were also employed for comparison. Model results were verified using traditional statistics.

RESULTS

The ANN achieved high accuracy (0.98), sensitivity (0.95), and specificity (0.96) in the intrinsic test for AD classification. Excluding age and genetic data still yielded favorable results (accuracy: 0.97, sensitivity: 0.94, specificity: 0.96). The assigned weights to ANN features highlighted the importance of mental evaluation, years of education, and specific genetic variations (CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650) for AD classification. Receiver operating characteristic analysis revealed AUC values of 0.99 (intrinsic test), 0.60 (TWB-GWA), and 0.72 (CG-WGS), with slightly lower AUC values (0.96, 0.80, 0.52) when excluding age in ANN. The performance of the ANN model in AD classification was comparable to RF, SVM (linear kernel), and SVM (RBF kernel).

CONCLUSION

The ANN model demonstrated good sensitivity, specificity, and accuracy in AD classification. The top-weighted SNPs for AD prediction were CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650. The ANN model performed similarly to RF and SVM, indicating its capability to handle the complexity of AD as a disease entity.

radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenotype in pancreatic cancer

Addressing the significant level of variability exhibited by pancreatic cancer necessitates the adoption of a systems biology approach that integrates molecular data, biological properties of the tumors, medical images, and clinical features of the patients. In this study, a comprehensive multi-omics methodology was employed to examine a distinctive collection of patient dataset containing rapid autopsy tumor and normal tissue samples as well as longitudinal imaging with a focus on pancreatic cancer. By performing a whole exome sequencing analysis on tumor and normal tissues to identify somatic gene variants and a radiomic feature analysis to tumor CT images, the genome-wide association approach established a connection between pancreatic cancer driver genes and relevant radiomic features, enabling a thorough and quantitative assessment of the heterogeneity of pancreatic tumors. The significant association between sets of genes and radiomic features revealed the involvement of genes in shaping tumor morphological heterogeneity. Some results of the association established a connection between the molecular level mechanism and their outcomes at the level of tumor structural heterogeneity. Because tumor structure and tumor structural heterogeneity are related to the patients' overall survival, patients who had pancreatic cancer driver gene mutations with an association to a certain radiomic feature have been observed to experience worse survival rates than cases without these somatic mutations. Furthermore, the association analysis has revealed potential gene mutations and radiomic feature candidates that warrant further investigation in future research endeavors.

The full spectrum of SLC22 OCT1 mutations illuminates the bridge between drug transporter biophysics and pharmacogenomics

Mutations in transporters can impact an individual's response to drugs and cause many diseases. Few variants in transporters have been evaluated for their functional impact. Here, we combine saturation mutagenesis and multi-phenotypic screening to dissect the impact of 11,213 missense single-amino-acid deletions, and synonymous variants across the 554 residues of OCT1, a key liver xenobiotic transporter. By quantifying in parallel expression and substrate uptake, we find that most variants exert their primary effect on protein abundance, a phenotype not commonly measured alongside function. Using our mutagenesis results combined with structure prediction and molecular dynamic simulations, we develop accurate structure-function models of the entire transport cycle, providing biophysical characterization of all known and possible human OCT1 polymorphisms. This work provides a complete functional map of OCT1 variants along with a framework for integrating functional genomics, biophysical modeling, and human genetics to predict variant effects on disease and drug efficacy.

Efficient and accurate mixed model association tool for single-cell eQTL analysis

Understanding the genetic basis of gene expression can help us understand the molecular underpinnings of human traits and disease. Expression quantitative trait locus (eQTL) mapping can help in studying this relationship but have been shown to be very cell-type specific, motivating the use of single-cell RNA sequencing and single-cell eQTLs to obtain a more granular view of genetic regulation. Current methods for single-cell eQTL mapping either rely on the "pseudobulk" approach and traditional pipelines for bulk transcriptomics or do not scale well to large datasets. Here, we propose SAIGE-QTL, a robust and scalable tool that can directly map eQTLs using single-cell profiles without needing aggregation at the pseudobulk level. Additionally, SAIGE-QTL allows for testing the effects of less frequent/rare genetic variation through set-based tests, which is traditionally excluded from eQTL mapping studies. We evaluate the performance of SAIGE-QTL on both real and simulated data and demonstrate the improved power for eQTL mapping over existing pipelines.

Mitochondrial citrate carrier SLC25A1 is a dosage-dependent regulator of metabolic reprogramming and morphogenesis in the developing heart

The developing mammalian heart undergoes an important metabolic shift from glycolysis toward mitochondrial oxidation, such that oxidative phosphorylation defects may present with cardiac abnormalities. Here, we describe a new mechanistic link between mitochondria and cardiac morphogenesis, uncovered by studying mice with systemic loss of the mitochondrial citrate carrier SLC25A1. Slc25a1 null embryos displayed impaired growth, cardiac malformations, and aberrant mitochondrial function. Importantly, Slc25a1 heterozygous embryos, which are overtly indistinguishable from wild type, exhibited an increased frequency of these defects, suggesting Slc25a1 haploinsuffiency and dose-dependent effects. Supporting clinical relevance, we found a near-significant association between ultrarare human pathogenic SLC25A1 variants and pediatric congenital heart disease. Mechanistically, SLC25A1 may link mitochondria to transcriptional regulation of metabolism through epigenetic control of gene expression to promote metabolic remodeling in the developing heart. Collectively, this work positions SLC25A1 as a novel mitochondrial regulator of ventricular morphogenesis and cardiac metabolic maturation and suggests a role in congenital heart disease.

Rare MED12L Variants Are Associated with Susceptibility to Guttate Psoriasis in the Han Chinese Population

INTRODUCTION

According to the common disease/rare variant hypothesis, it is important to study the role of rare variants in complex diseases. The association of rare variants with psoriasis has been demonstrated, but the association between rare variants and specific clinical subtypes of psoriasis has not been investigated.

METHODS

Gene-based and gene-level meta-analyses were performed on data extracted from our previous study data sets (2,483 patients with guttate psoriasis and 8,292 patients with non-guttate psoriasis) for genotyping. Then, haplotype analysis was performed for rare loss-of-function variants located in MED12L, and protein function prediction was performed for MED12L. Gene-based analysis at each stage had a moderate significance threshold (p < 0.05). A χ2 test was then conducted on the three potential genes, and the merged gene-based analysis was used to confirm the results. We also conducted association analysis and meta-analysis for functional variants located on the identified gene.

RESULTS

Through these gene-level analyses, we determined that MED12L is a guttate psoriasis susceptibility gene (p = 9.99 × 10-5), and the single-nucleotide polymorphism with the strongest association was rs199780529 (p_combine = 1 × 10-3, p_meta = 2 × 10-3).

CONCLUSIONS

In our study, a guttate psoriasis-specific subtype-associated susceptibility gene was confirmed in a Chinese Han population. These findings contribute to a better genetic understanding of different subtypes of psoriasis.

Prioritizing disease-related rare variants by integrating gene expression data

Rare variants, comprising a vast majority of human genetic variations, are likely to have more deleterious impact on human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the diseased patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. We also found strong excess of rare variants among the top prioritized genes in diseased patients compared to that in healthy individuals. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.

Variant-to-function analysis of the childhood obesity chr12q13 locus implicates rs7132908 as a causal variant within the 3' UTR of FAIM2

The ch12q13 locus is among the most significant childhood obesity loci identified in genome-wide association studies. This locus resides in a non-coding region within FAIM2; thus, the underlying causal variant(s) presumably influence disease susceptibility via cis-regulation. We implicated rs7132908 as a putative causal variant by leveraging our in-house 3D genomic data and public domain datasets. Using a luciferase reporter assay, we observed allele-specific cis-regulatory activity of the immediate region harboring rs7132908. We generated isogenic human embryonic stem cell lines homozygous for either rs7132908 allele to assess changes in gene expression and chromatin accessibility throughout a differentiation to hypothalamic neurons, a key cell type known to regulate feeding behavior. The rs7132908 obesity risk allele influenced expression of FAIM2 and other genes and decreased the proportion of neurons produced by differentiation. We have functionally validated rs7132908 as a causal obesity variant that temporally regulates nearby effector genes and influences neurodevelopment and survival.

The association between single-nucleotide polymorphisms within type 1 interferon pathway genes and human immunodeficiency virus type 1 viral load in antiretroviral-naïve participants

BACKGROUND

Human genetic contribution to HIV progression remains inadequately explained. The type 1 interferon (IFN) pathway is important for host control of HIV and variation in type 1 IFN genes may contribute to disease progression. This study assessed the impact of variations at the gene and pathway level of type 1 IFN on HIV-1 viral load (VL).

METHODS

Two cohorts of antiretroviral (ART) naïve participants living with HIV (PLWH) with either early (START) or advanced infection (FIRST) were analysed separately. Type 1 IFN genes (n = 17) and receptor subunits (IFNAR1, IFNAR2) were examined for both cumulated type 1 IFN pathway analysis and individual gene analysis. SKAT-O was applied to detect associations between the genotype and HIV-1 study entry viral load (log10 transformed) as a proxy for set point VL; P-values were corrected using Bonferroni (P < 0.0025).

RESULTS

The analyses among those with early infection included 2429 individuals from five continents. The median study entry HIV VL was 14,623 (IQR 3460-45100) copies/mL. Across 673 SNPs within 19 type 1 IFN genes, no significant association with study entry VL was detected. Conversely, examining individual genes in START showed a borderline significant association between IFNW1, and study entry VL (P = 0.0025). This significance remained after separate adjustments for age, CD4+ T-cell count, CD4+/CD8+ T-cell ratio and recent infection. When controlling for population structure using linear mixed effects models (LME), in addition to principal components used in the main model, this was no longer significant (p = 0.0244). In subgroup analyses stratified by geographical region, the association between IFNW1 and study entry VL was only observed among African participants, although, the association was not significant when controlling for population structure using LME. Of the 17 SNPs within the IFNW1 region, only rs79876898 (A > G) was associated with study entry VL (p = 0.0020, beta = 0.32; G associated with higher study entry VL than A) in single SNP association analyses. The findings were not reproduced in FIRST participants.

CONCLUSION

Across 19 type 1 IFN genes, only IFNW1 was associated with HIV-1 study entry VL in a cohort of ART-naïve individuals in early stages of their infection, however, this was no longer significant in sensitivity analyses that controlled for population structures using LME.

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

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

A genome-wide spectrum of tandem repeat expansions in 338,963 humans

The Genome Aggregation Database (gnomAD), widely recognized as the gold-standard reference map of human genetic variation, has largely overlooked tandem repeat (TR) expansions, despite the fact that TRs constitute ∼6% of our genome and are linked to over 50 human diseases. Here, we introduce the TR-gnomAD (https://wlcb.oit.uci.edu/TRgnomAD), a biobank-scale reference of 0.86 million TRs derived from 338,963 whole-genome sequencing (WGS) samples of diverse ancestries (39.5% non-European samples). TR-gnomAD offers critical insights into ancestry-specific disease prevalence using disparities in TR unit number frequencies among ancestries. Moreover, TR-gnomAD is able to differentiate between common, presumably benign TR expansions, which are prevalent in TR-gnomAD, from those potentially pathogenic TR expansions, which are found more frequently in disease groups than within TR-gnomAD. Together, TR-gnomAD is an invaluable resource for researchers and physicians to interpret TR expansions in individuals with genetic diseases.

Structured testing of genetic association with mixed clinical outcomes

Genetic factors play a fundamental role in disease development. Studying the genetic association with clinical outcomes is critical for understanding disease biology and devising novel treatment targets. However, the frequencies of genetic variations are often low, making it difficult to examine the variants one-by-one. Moreover, the clinical outcomes are complex, including patients' survival time and other binary or continuous outcomes such as recurrences and lymph node count, and how to effectively analyze genetic association with these outcomes remains unclear. In this article, we proposed a structured test statistic for testing genetic association with mixed types of survival, binary, and continuous outcomes. The structured testing incorporates known biological information of variants while allowing for their heterogeneous effects and is a powerful strategy for analyzing infrequent genetic factors. Simulation studies show that the proposed test statistic has correct type I error and is highly effective in detecting significant genetic variants. We applied our approach to a uterine corpus endometrial carcinoma study and identified several genetic pathways associated with the clinical outcomes.

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

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

Genetic associations of protein-coding variants in venous thromboembolism

Previous genetic studies of venous thromboembolism (VTE) have been largely limited to common variants, leaving the genetic determinants relatively incomplete. We performed an exome-wide association study of VTE among 14,723 cases and 334,315 controls. Fourteen known and four novel genes (SRSF6, PHPT1, CGN, and MAP3K2) were identified through protein-coding variants, with broad replication in the FinnGen cohort. Most genes we discovered exhibited the potential to predict future VTE events in longitudinal analysis. Notably, we provide evidence for the additive contribution of rare coding variants to known genome-wide polygenic risk in shaping VTE risk. The identified genes were enriched in pathways affecting coagulation and platelet activation, along with liver-specific expression. The pleiotropic effects of these genes indicated the potential involvement of coagulation factors, blood cell traits, liver function, and immunometabolic processes in VTE pathogenesis. In conclusion, our study unveils the valuable contribution of protein-coding variants in VTE etiology and sheds new light on its risk stratification.

Genetic factors associated with suicidal behaviors and alcohol use disorders in an American Indian population

American Indians (AI) demonstrate the highest rates of both suicidal behaviors (SB) and alcohol use disorders (AUD) among all ethnic groups in the US. Rates of suicide and AUD vary substantially between tribal groups and across different geographical regions, underscoring a need to delineate more specific risk and resilience factors. Using data from over 740 AI living within eight contiguous reservations, we assessed genetic risk factors for SB by investigating: (1) possible genetic overlap with AUD, and (2) impacts of rare and low-frequency genomic variants. Suicidal behaviors included lifetime history of suicidal thoughts and acts, including verified suicide deaths, scored using a ranking variable for the SB phenotype (range 0-4). We identified five loci significantly associated with SB and AUD, two of which are intergenic and three intronic on genes AACSP1, ANK1, and FBXO11. Nonsynonymous rare and low-frequency mutations in four genes including SERPINF1 (PEDF), ZNF30, CD34, and SLC5A9, and non-intronic rare and low-frequency mutations in genes OPRD1, HSD17B3 and one lincRNA were significantly associated with SB. One identified pathway related to hypoxia-inducible factor (HIF) regulation, whose 83 nonsynonymous rare and low-frequency variants on 10 genes were significantly linked to SB as well. Four additional genes, and two pathways related to vasopressin-regulated water metabolism and cellular hexose transport, also were strongly associated with SB. This study represents the first investigation of genetic factors for SB in an American Indian population that has high risk for suicide. Our study suggests that bivariate association analysis between comorbid disorders can increase statistical power; and rare and low-frequency variant analysis in a high-risk population enabled by whole-genome sequencing has the potential to identify novel genetic factors. Although such findings may be population specific, rare functional mutations relating to PEDF and HIF regulation align with past reports and suggest a biological mechanism for suicide risk and a potential therapeutic target for intervention.

Unravelling the Genetic Landscape of Hemiplegic Migraine: Exploring Innovative Strategies and Emerging Approaches

Migraine is a severe, debilitating neurovascular disorder. Hemiplegic migraine (HM) is a rare and debilitating neurological condition with a strong genetic basis. Sequencing technologies have improved the diagnosis and our understanding of the molecular pathophysiology of HM. Linkage analysis and sequencing studies in HM families have identified pathogenic variants in ion channels and related genes, including CACNA1A, ATP1A2, and SCN1A, that cause HM. However, approximately 75% of HM patients are negative for these mutations, indicating there are other genes involved in disease causation. In this review, we explored our current understanding of the genetics of HM. The evidence presented herein summarises the current knowledge of the genetics of HM, which can be expanded further to explain the remaining heritability of this debilitating condition. Innovative bioinformatics and computational strategies to cover the entire genetic spectrum of HM are also discussed in this review.

3Dmapper: a command line tool for BioBank-scale mapping of variants to protein structures

MOTIVATION

The interpretation of genomic data is crucial to understand the molecular mechanisms of biological processes. Protein structures play a vital role in facilitating this interpretation by providing functional context to genetic coding variants. However, mapping genes to proteins is a tedious and error-prone task due to inconsistencies in data formats. Over the past two decades, numerous tools and databases have been developed to automatically map annotated positions and variants to protein structures. However, most of these tools are web-based and not well-suited for large-scale genomic data analysis.

RESULTS

To address this issue, we introduce 3Dmapper, a stand-alone command-line tool developed in Python and R. It systematically maps annotated protein positions and variants to protein structures, providing a solution that is both efficient and reliable.

AVAILABILITY AND IMPLEMENTATION

https://github.com/vicruiser/3Dmapper.

BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases

Genome-wide Association Studies (GWAS) methods have identified individual single-nucleotide polymorphisms (SNPs) significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT (https://github.com/wangjr03/BayesKAT), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases.

Prioritizing disease-related rare variants by integrating gene expression data

Rare variants, comprising a vast majority of human genetic variations, are likely to have more deleterious impact on human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the diseased patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.

Deep sequencing of candidate genes identified 14 variants associated with smoking abstinence in an ethnically diverse sample

Despite the large public health toll of smoking, genetic studies of smoking cessation have been limited with few discoveries of risk or protective loci. We investigated common and rare variant associations with success in quitting smoking using a cohort from 8 randomized controlled trials involving 2231 participants and a total of 10,020 common and 24,147 rare variants. We identified 14 novel markers including 6 mapping to genes previously related to psychiatric and substance use disorders, 4 of which were protective (CYP2B6 (rs1175607105), HTR3B (rs1413172952; rs1204720503), rs80210037 on chr15), and 2 of which were associated with reduced cessation (PARP15 (rs2173763), SCL18A2 (rs363222)). The others mapped to areas associated with cancer including FOXP1 (rs1288980) and ZEB1 (rs7349). Network analysis identified significant canonical pathways for the serotonin receptor signaling pathway, nicotine and bupropion metabolism, and several related to tumor suppression. Two novel markers (rs6749438; rs6718083) on chr2 are flanked by genes associated with regulation of bodyweight. The identification of novel loci in this study can provide new targets of pharmacotherapy and inform efforts to develop personalized treatments based on genetic profiles.

Enrichment of Rare Variants of Hemophagocytic Lymphohistiocytosis Genes in Systemic Juvenile Idiopathic Arthritis

Objective

To evaluate whether there is an enrichment of rare variants in familial hemophagocytic lymphohistiocytosis (HLH) genes and systemic juvenile idiopathic arthritis (sJIA) with or without macrophage activation syndrome (MAS).

Methods

Targeted sequencing of HLH genes (LYST, PRF1, RAB27A, STX11, STXBP2, UNC13D) was performed in sJIA subjects from an established cohort. Sequence data from control subjects were obtained in silico (dbGaP:phs000280.v8.p2). Rare variant association testing (RVT) was performed with sequence kernel association test (SKAT) package. Significance was defined as p<0.05 after 100,000 permutations.

Results

Sequencing data from 524 sJIA cases were jointly called and harmonized with exome-derived target data from 3000 controls. Quality control operations produced a set of 481 cases and 2924 ancestrally-matched control subjects. RVT of sJIA cases and controls revealed a significant association with rare protein-altering variants (minor allele frequency [MAF]<0.01) of STXBP2 (p=0.020), and ultra-rare variants (MAF<0.001) of STXBP2 (p=0.007) and UNC13D (p=0.045). A subanalysis of 32 cases with known MAS and 90 without revealed significant association of rare UNC13D variants (p=0.0047). Additionally, sJIA patients more often carried ≥2 HLH variants than did controls (p=0.007), driven largely by digenic combinations involving LYST.

Conclusion

We identified an enrichment of rare HLH variants in sJIA patients compared with healthy controls, driven by STXBP2 and UNC13D. Biallelic variation in HLH genes was associated with sJIA, driven by LYST. Only UNC13D displayed enrichment in patients with MAS. This suggests that HLH variants may contribute to the pathophysiology of sJIA, even without MAS.

Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework

BACKGROUND

The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies.

METHODS

We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies.

RESULTS

We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue.

CONCLUSION

Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies.

Integration of whole-exome sequencing and structural neuroimaging analysis in major depressive disorder: a joint study

Major depressive disorder (MDD) is a common mental illness worldwide and is triggered by an intricate interplay between environmental and genetic factors. Although there are several studies on common variants in MDD, studies on rare variants are relatively limited. In addition, few studies have examined the genetic contributions to neurostructural alterations in MDD using whole-exome sequencing (WES). We performed WES in 367 patients with MDD and 161 healthy controls (HCs) to detect germline and copy number variations in the Korean population. Gene-based rare variants were analyzed to investigate the association between the genes and individuals, followed by neuroimaging-genetic analysis to explore the neural mechanisms underlying the genetic impact in 234 patients with MDD and 135 HCs using diffusion tensor imaging data. We identified 40 MDD-related genes and observed 95 recurrent regions of copy number variations. We also discovered a novel gene, FRMPD3, carrying rare variants that influence MDD. In addition, the single nucleotide polymorphism rs771995197 in the MUC6 gene was significantly associated with the integrity of widespread white matter tracts. Moreover, we identified 918 rare exonic missense variants in genes associated with MDD susceptibility. We postulate that rare variants of FRMPD3 may contribute significantly to MDD, with a mild penetration effect.

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these two fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we derive a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., Genome-Wide Association Studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur using analytical theory and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate this by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study of this, we re-examine an analysis testing for co-evolution of expression levels between genes across a fungal phylogeny, and show that including covariance matrix eigenvectors as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

Multi-omics Integration Identifies Genes Influencing Traits Associated with Cardiovascular Risks: The Long Life Family Study

The Long Life Family Study (LLFS) enrolled 4,953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that affect cardiovascular risks in the LLFS population, we developed a multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from rare-variant analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA). Across all traits, CMA identified 64 significant genes after Bonferroni correction (p ≤ 2.8×10-7), 29 of which replicated in the Framingham Heart Study (FHS) cohort. Notably, 20 of the 29 replicated genes do not have a previously known trait-associated variant in the GWAS Catalog within 50 kb. Thirteen modules in Protein-Protein Interaction (PPI) networks are significantly enriched in genes with low meta-analysis p-values for at least one trait, three of which are replicated in the FHS cohort. The functional annotation of genes in these modules showed a significant over-representation of trait-related biological processes including sterol transport, protein-lipid complex remodeling, and immune response regulation. Among major findings, our results suggest a role of triglyceride-associated and mast-cell functional genes FCER1A, MS4A2, GATA2, HDC, and HRH4 in atherosclerosis risks. Our findings also suggest that lower expression of ATG2A, a gene we found to be associated with BMI, may be both a cause and consequence of obesity. Finally, our results suggest that ENPP3 may play an intermediary role in triglyceride-induced inflammation. Our pipeline is freely available and implemented in the Nextflow workflow language, making it easily runnable on any compute platform (https://nf-co.re/omicsgenetraitassociation).

The prognostic value of systematic genetic screening in amyotrophic lateral sclerosis patients

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with complex genetic architecture. Emerging evidence has indicated comorbidity between ALS and autoimmune conditions, suggesting a potential shared genetic basis. The objective of this study is to assess the prognostic value of systematic screening for rare deleterious mutations in genes associated with ALS and aberrant inflammatory responses.

METHODS

A discovery cohort of 494 patients and a validation cohort of 69 patients were analyzed in this study, with population-matched healthy subjects (n = 4961) served as controls. Whole exome sequencing (WES) was performed to identify rare deleterious variants in 50 ALS genes and 1177 genes associated with abnormal inflammatory responses. Genotype-phenotype correlation was assessed, and an integrative prognostic model incorporating genetic and clinical factors was constructed.

RESULTS

In the discovery cohort, 8.1% of patients carried confirmed ALS variants, and an additional 15.2% of patients carried novel ALS variants. Gene burden analysis revealed 303 immune-implicated genes with enriched rare variants, and 13.4% of patients harbored rare deleterious variants in these genes. Patients with ALS variants exhibited a more rapid disease progression (HR 2.87 [95% CI 2.03-4.07], p < 0.0001), while no significant effect was observed for immune-implicated variants. The nomogram model incorporating genetic and clinical information demonstrated improved accuracy in predicting disease outcomes (C-index, 0.749).

CONCLUSION

Our findings enhance the comprehension of the genetic basis of ALS within the Chinese population. It also appears that rare deleterious mutations occurring in immune-implicated genes exert minimal influence on the clinical trajectories of ALS patients.

Gene selection by incorporating genetic networks into case-control association studies

Large-scale genome-wide association studies (GWAS) have been successfully applied to a wide range of genetic variants underlying complex diseases. The network-based regression approach has been developed to incorporate a biological genetic network and to overcome the challenges caused by the computational efficiency for analyzing high-dimensional genomic data. In this paper, we propose a gene selection approach by incorporating genetic networks into case-control association studies for DNA sequence data or DNA methylation data. Instead of using traditional dimension reduction techniques such as principal component analyses and supervised principal component analyses, we use a linear combination of genotypes at SNPs or methylation values at CpG sites in a gene to capture gene-level signals. We employ three linear combination approaches: optimally weighted sum (OWS), beta-based weighted sum (BWS), and LD-adjusted polygenic risk score (LD-PRS). OWS and LD-PRS are supervised approaches that depend on the effect of each SNP or CpG site on the case-control status, while BWS can be extracted without using the case-control status. After using one of the linear combinations of genotypes or methylation values in each gene to capture gene-level signals, we regularize them to perform gene selection based on the biological network. Simulation studies show that the proposed approaches have higher true positive rates than using traditional dimension reduction techniques. We also apply our approaches to DNA methylation data and UK Biobank DNA sequence data for analyzing rheumatoid arthritis. The results show that the proposed methods can select potentially rheumatoid arthritis related genes that are missed by existing methods.

Gene-based association study of rare variants in children of diverse ancestries implicates TNFRSF21 in the development of allergic asthma

BACKGROUND

Most genetic studies of asthma and allergy have focused on common variation in individuals primarily of European ancestry. Studying the role of rare variation in quantitative phenotypes and in asthma phenotypes in populations of diverse ancestries can provide additional, important insights into the development of these traits.

OBJECTIVE

We sought to examine the contribution of rare variants to different asthma- or allergy-associated quantitative traits in children with diverse ancestries and explore their role in asthma phenotypes.

METHODS

We examined whole-genome sequencing data from children participants in longitudinal studies of asthma (n = 1035; parent-identified as 67% Black and 25% Hispanic) to identify rare variants (minor allele frequency < 0.01). We assigned variants to genes and tested for associations using an omnibus variant-set test between each of 24,902 genes and 8 asthma-associated quantitative traits. On combining our results with external data on predicted gene expression in humans and mouse knockout studies, we identified 3 candidate genes. A burden of rare variants in each gene and in a combined 3-gene score was tested for its associations with clinical phenotypes of asthma. Finally, published single-cell gene expression data in lower airway mucosal cells after allergen challenge were used to assess transcriptional responses to allergen.

RESULTS

Rare variants in USF1 were significantly associated with blood neutrophil count (P = 2.18 × 10-7); rare variants in TNFRSF21 with total IgE (P = 6.47 × 10-6) and PIK3R6 with eosinophil count (P = 4.10 × 10-5) reached suggestive significance. These 3 findings were supported by independent data from human and mouse studies. A burden of rare variants in TNFRSF21 and in a 3-gene score was associated with allergy-related phenotypes in cohorts of children with mild and severe asthma. Furthermore, TNFRSF21 was significantly upregulated in bronchial basal epithelial cells from adults with allergic asthma but not in adults with allergies (but not asthma) after allergen challenge.

CONCLUSIONS

We report novel associations between rare variants in genes and allergic and inflammatory phenotypes in children with diverse ancestries, highlighting TNFRSF21 as contributing to the development of allergic asthma.

Synergistic toxicity with copper contributes to NAT2-associated isoniazid toxicity

Anti-tuberculosis (AT) medications, including isoniazid (INH), can cause drug-induced liver injury (DILI), but the underlying mechanism remains unclear. In this study, we aimed to identify genetic factors that may increase the susceptibility of individuals to AT-DILI and to examine genetic interactions that may lead to isoniazid (INH)-induced hepatotoxicity. We performed a targeted sequencing analysis of 380 pharmacogenes in a discovery cohort of 112 patients (35 AT-DILI patients and 77 controls) receiving AT treatment for active tuberculosis. Pharmacogenome-wide association analysis was also conducted using 1048 population controls (Korea1K). NAT2 and ATP7B genotypes were analyzed in a replication cohort of 165 patients (37 AT-DILI patients and 128 controls) to validate the effects of both risk genotypes. NAT2 ultraslow acetylators (UAs) were found to have a greater risk of AT-DILI than other genotypes (odds ratio [OR] 5.6 [95% confidence interval; 2.5-13.2], P = 7.2 × 10-6). The presence of ATP7B gene 832R/R homozygosity (rs1061472) was found to co-occur with NAT2 UA in AT-DILI patients (P = 0.017) and to amplify the risk in NAT2 UA (OR 32.5 [4.5-1423], P = 7.5 × 10-6). In vitro experiments using human liver-derived cell lines (HepG2 and SNU387 cells) revealed toxic synergism between INH and Cu, which were strongly augmented in cells with defective NAT2 and ATP7B activity, leading to increased mitochondrial reactive oxygen species generation, mitochondrial dysfunction, DNA damage, and apoptosis. These findings link the co-occurrence of ATP7B and NAT2 genotypes to the risk of INH-induced hepatotoxicity, providing novel mechanistic insight into individual AT-DILI susceptibility. Yoon et al. showed that individuals who carry NAT2 UAs and ATP7B 832R/R genotypes are at increased risk of developing isoniazid hepatotoxicity, primarily due to the increased synergistic toxicity between isoniazid and copper, which exacerbates mitochondrial dysfunction-related apoptosis.

A method to estimate the contribution of rare coding variants to complex trait heritability

It has been postulated that rare coding variants (RVs; MAF < 0.01) contribute to the "missing" heritability of complex traits. We developed a framework, the Rare variant heritability (RARity) estimator, to assess RV heritability (h2RV) without assuming a particular genetic architecture. We applied RARity to 31 complex traits in the UK Biobank (n = 167,348) and showed that gene-level RV aggregation suffers from 79% (95% CI: 68-93%) loss of h2RV. Using unaggregated variants, 27 traits had h2RV > 5%, with height having the highest h2RV at 21.9% (95% CI: 19.0-24.8%). The total heritability, including common and rare variants, recovered pedigree-based estimates for 11 traits. RARity can estimate gene-level h2RV, enabling the assessment of gene-level characteristics and revealing 11, previously unreported, gene-phenotype relationships. Finally, we demonstrated that in silico pathogenicity prediction (variant-level) and gene-level annotations do not generally enrich for RVs that over-contribute to complex trait variance, and thus, innovative methods are needed to predict RV functionality.

Rare and common coding variants in lipid metabolism-related genes and their association with coronary artery disease

BACKGROUND

Coronary artery disease (CAD) is a complex disease that is influenced by environmental and genetic factors. In this study, we aimed to investigate the relationship between coding variants in lipid metabolism-related genes and CAD in a Chinese Han population.

METHODS

A total of 252 individuals were recruited for this study, including 120 CAD patients and 132 healthy control individuals. Rare and common coding variants in 12 lipid metabolism-related genes (ANGPTL3, ANGPTL4, APOA1, APOA5, APOC1, APOC3, CETP, LDLR, LIPC, LPL, PCSK9 and SCARB1) were detected via next-generation sequencing (NGS)-based targeted sequencing. Associations between common variants and CAD were evaluated by Fisher's exact test. A gene-based association test of rare variants was performed by the sequence kernel association test-optimal (SKAT-O test).

RESULTS

We found 51 rare variants and 17 common variants in this study. One common missense variant, LIPC rs6083, was significantly associated with CAD after Bonferroni correction (OR = 0.47, 95% CI = 0.29-0.76, p = 1.9 × 10- 3). Thirty-three nonsynonymous rare variants were identified, including two novel variants located in the ANGPTL4 (p.Gly47Glu) and SCARB1 (p.Leu233Phe) genes. We did not find a significant association between rare variants and CAD via gene-based analysis via the SKAT-O test.

CONCLUSIONS

Targeted sequencing is a powerful tool for identifying rare and common variants in CAD. The common missense variant LIPC rs6083 confers protection against CAD. The clinical relevance of rare variants in CAD aetiology needs to be investigated in larger sample sizes in the future.

rvTWAS: identifying gene-trait association using sequences by utilizing transcriptome-directed feature selection

Toward the identification of genetic basis of complex traits, transcriptome-wide association study (TWAS) is successful in integrating transcriptome data. However, TWAS is only applicable for common variants, excluding rare variants in exome or whole-genome sequences. This is partly because of the inherent limitation of TWAS protocols that rely on predicting gene expressions. Our previous research has revealed the insight into TWAS: the 2 steps in TWAS, building and applying the expression prediction models, are essentially genetic feature selection and aggregations that do not have to involve predictions. Based on this insight disentangling TWAS, rare variants' inability of predicting expression traits is no longer an obstacle. Herein, we developed "rare variant TWAS," or rvTWAS, that first uses a Bayesian model to conduct expression-directed feature selection and then uses a kernel machine to carry out feature aggregation, forming a model leveraging expressions for association mapping including rare variants. We demonstrated the performance of rvTWAS by thorough simulations and real data analysis in 3 psychiatric disorders, namely schizophrenia, bipolar disorder, and autism spectrum disorder. We confirmed that rvTWAS outperforms existing TWAS protocols and revealed additional genes underlying psychiatric disorders. Particularly, we formed a hypothetical mechanism in which zinc finger genes impact all 3 disorders through transcriptional regulations. rvTWAS will open a door for sequence-based association mappings integrating gene expressions.

DYNATE: Localizing rare-variant association regions via multiple testing embedded in an aggregation tree

Rare-variants (RVs) genetic association studies enable researchers to uncover the variation in phenotypic traits left unexplained by common variation. Traditional single-variant analysis lacks power; thus, researchers have developed various methods to aggregate the effects of RVs across genomic regions to study their collective impact. Some existing methods utilize a static delineation of genomic regions, often resulting in suboptimal effect aggregation, as neutral subregions within the test region will result in an attenuation of signal. Other methods use varying windows to search for signals but often result in long regions containing many neutral RVs. To pinpoint short genomic regions enriched for disease-associated RVs, we developed a novel method, DYNamic Aggregation TEsting (DYNATE). DYNATE dynamically and hierarchically aggregates smaller genomic regions into larger ones and performs multiple testing for disease associations with a controlled weighted false discovery rate. DYNATE's main advantage lies in its strong ability to identify short genomic regions highly enriched for disease-associated RVs. Extensive numerical simulations demonstrate the superior performance of DYNATE under various scenarios compared with existing methods. We applied DYNATE to an amyotrophic lateral sclerosis study and identified a new gene, EPG5, harboring possibly pathogenic mutations.

NCAD v1.0: a database for non-coding variant annotation and interpretation

The application of whole genome sequencing is expanding in clinical diagnostics across various genetic disorders, and the significance of non-coding variants in penetrant diseases is increasingly being demonstrated. Therefore, it is urgent to improve the diagnostic yield by exploring the pathogenic mechanisms of variants in non-coding regions. However, the interpretation of non-coding variants remains a significant challenge, due to the complex functional regulatory mechanisms of non-coding regions and the current limitations of available databases and tools. Hence, we develop the non-coding variant annotation database (NCAD, http://www.ncawdb.net/), encompassing comprehensive insights into 665,679,194 variants, regulatory elements, and element interaction details. Integrating data from 96 sources, spanning both GRCh37 and GRCh38 versions, NCAD v1.0 provides vital information to support the genetic diagnosis of non-coding variants, including allele frequencies of 12 diverse populations, with a particular focus on the population frequency information for 230,235,698 variants in 20,964 Chinese individuals. Moreover, it offers prediction scores for variant functionality, five categories of regulatory elements, and four types of non-coding RNAs. With its rich data and comprehensive coverage, NCAD serves as a valuable platform, empowering researchers and clinicians with profound insights into non-coding regulatory mechanisms while facilitating the interpretation of non-coding variants.

Risk and protection of different rare protein-coding variants of complement component C4A in age-related macular degeneration

Introduction: Age-related macular degeneration (AMD) is the leading cause of central vision loss in the elderly. One-third of the genetic contribution to this disease remains unexplained. Methods: We analyzed targeted sequencing data from two independent cohorts (4,245 cases, 1,668 controls) which included genomic regions of known AMD loci in 49 genes. Results: At a false discovery rate of <0.01, we identified 11 low-frequency AMD variants (minor allele frequency <0.05). Two of those variants were present in the complement C4A gene, including the replacement of the residues that contribute to the Rodgers-1/Chido-1 blood group antigens: [VDLL1207-1210ADLR (V1207A)] with discovery odds ratio (OR) = 1.7 (p = 3.2 × 10-5) which was replicated in the UK Biobank dataset (3,294 cases, 200,086 controls, OR = 1.52, p = 0.037). A novel variant associated with reduced risk for AMD in our discovery cohort was P1120T, one of the four C4A-isotypic residues. Gene-based tests yielded aggregate effects of nonsynonymous variants in 10 genes including C4A, which were associated with increased risk of AMD. In human eye tissues, immunostaining demonstrated C4A protein accumulation in and around endothelial cells of retinal and choroidal vasculature, and total C4 in soft drusen. Conclusion: Our results indicate that C4A protein in the complement activation pathways may play a role in the pathogenesis of AMD.

Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery

MOTIVATION

Genome-wide association studies (GWAS) have enabled large-scale analysis of the role of genetic variants in human disease. Despite impressive methodological advances, subsequent clinical interpretation and application remains challenging when GWAS suffer from a lack of statistical power. In recent years, however, the use of information diffusion algorithms with molecular networks has led to fruitful insights on disease genes.

RESULTS

We present an overview of the design choices and pitfalls that prove crucial in the application of network propagation methods to GWAS summary statistics. We highlight general trends from the literature, and present benchmark experiments to expand on these insights selecting as case study three diseases and five molecular networks. We verify that the use of gene-level scores based on GWAS P-values offers advantages over the selection of a set of 'seed' disease genes not weighted by the associated P-values if the GWAS summary statistics are of sufficient quality. Beyond that, the size and the density of the networks prove to be important factors for consideration. Finally, we explore several ensemble methods and show that combining multiple networks may improve the network propagation approach.

Kernel Cox partially linear regression: Building predictive models for cancer patients' survival

Wide heterogeneity exists in cancer patients' survival, ranging from a few months to several decades. To accurately predict clinical outcomes, it is vital to build an accurate predictive model that relates the patients' molecular profiles with the patients' survival. With complex relationships between survival and high-dimensional molecular predictors, it is challenging to conduct nonparametric modeling and irrelevant predictors removing simultaneously. In this article, we build a kernel Cox proportional hazards semi-parametric model and propose a novel regularized garrotized kernel machine (RegGKM) method to fit the model. We use the kernel machine method to describe the complex relationship between survival and predictors, while automatically removing irrelevant parametric and nonparametric predictors through a LASSO penalty. An efficient high-dimensional algorithm is developed for the proposed method. Comparison with other competing methods in simulation shows that the proposed method always has better predictive accuracy. We apply this method to analyze a multiple myeloma dataset and predict the patients' death burden based on their gene expressions. Our results can help classify patients into groups with different death risks, facilitating treatment for better clinical outcomes.