Rare-variant association analysis: study designs and statistical tests

Rare copy number variation in posttraumatic stress disorder

Posttraumatic stress disorder (PTSD) is a heritable (h2 = 24-71%) psychiatric illness. Copy number variation (CNV) is a form of rare genetic variation that has been implicated in the etiology of psychiatric disorders, but no large-scale investigation of CNV in PTSD has been performed. We present an association study of CNV burden and PTSD symptoms in a sample of 114,383 participants (13,036 cases and 101,347 controls) of European ancestry. CNVs were called using two calling algorithms and intersected to a consensus set. Quality control was performed to remove strong outlier samples. CNVs were examined for association with PTSD within each cohort using linear or logistic regression analysis adjusted for population structure and CNV quality metrics, then inverse variance weighted meta-analyzed across cohorts. We examined the genome-wide total span of CNVs, enrichment of CNVs within specified gene-sets, and CNVs overlapping individual genes and implicated neurodevelopmental regions. The total distance covered by deletions crossing over known neurodevelopmental CNV regions was significant (beta = 0.029, SE = 0.005, P = 6.3 × 10-8). The genome-wide neurodevelopmental CNV burden identified explains 0.034% of the variation in PTSD symptoms. The 15q11.2 BP1-BP2 microdeletion region was significantly associated with PTSD (beta = 0.0206, SE = 0.0056, P = 0.0002). No individual significant genes interrupted by CNV were identified. 22 gene pathways related to the function of the nervous system and brain were significant in pathway analysis (FDR q < 0.05), but these associations were not significant once NDD regions were removed. A larger sample size, better detection methods, and annotated resources of CNV are needed to explore this relationship further.

Genetic heterogeneity: Challenges, impacts, and methods through an associative lens

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

KDR genetic predictor of toxicities induced by sorafenib and regorafenib

No biomarkers are available to predict toxicities induced by VEGFR TKIs. This study aimed to identify markers of toxicities induced by these drugs using a discovery-validation approach. The discovery set included 140 sorafenib-treated cancer patients (TARGET study) genotyped for SNPs in 56 genes. The most significant SNPs associated with grade ≥2 hypertension, diarrhea, dermatologic toxicities, and composite toxicity (any one of the toxicities) were tested for association with grade ≥2 toxicity in a validation set of 201 sorafenib-treated patients (Alliance/CALGB 80802). The validated SNP was tested for association with grade ≥2 toxicity in 107 (LCCC 1029) and 82 (Italian cohort) regorafenib-treated patients. SNP-toxicity associations were evaluated using logistic regression, and a meta-analysis between the studies was performed by inverse variance. Variant rs4864950 in KDR increased the risk of grade ≥2 composite toxicity in TARGET, Alliance/CALGB 80802, and the Italian cohort (meta-analysis p = 6.79 × 10^-4, OR = 2.01, 95% CI 1.34-3.01). We identified a predictor of toxicities induced by VEGFR TKIs. CLINICALTRIALS.GOV IDENTIFIER: NCT00073307 (TARGET), NCT01015833 (Alliance/CALGB 80802), and NCT01298570 (LCCC 1029).

Gene Sequencing Identifies Perturbation in Nitric Oxide Signaling as a Nonlipid Molecular Subtype of Coronary Artery Disease

BACKGROUND

A key goal of precision medicine is to disaggregate common, complex diseases into discrete molecular subtypes. Rare coding variants in the low-density lipoprotein receptor gene (LDLR) are identified in 1% to 2% of coronary artery disease (CAD) patients, defining a molecular subtype with risk driven by hypercholesterolemia.

METHODS

To search for additional subtypes, we compared the frequency of rare, predicted loss-of-function and damaging missense variants aggregated within a given gene in 41 081 CAD cases versus 217 115 controls.

RESULTS

Rare variants in LDLR were most strongly associated with CAD, present in 1% of cases and associated with 4.4-fold increased CAD risk. A second subtype was characterized by variants in endothelial nitric oxide synthase gene (NOS3), a key enzyme regulating vascular tone, endothelial function, and platelet aggregation. A rare predicted loss-of-function or damaging missense variants in NOS3 was present in 0.6% of cases and associated with 2.42-fold increased risk of CAD (95% CI, 1.80-3.26; P=5.50×10-9). These variants were associated with higher systolic blood pressure (+3.25 mm Hg; [95% CI, 1.86-4.65]; P=5.00×10-6) and increased risk of hypertension (adjusted odds ratio 1.31; [95% CI, 1.14-1.51]; P=2.00×10-4) but not circulating cholesterol concentrations, suggesting that, beyond lipid pathways, nitric oxide synthesis is a key nonlipid driver of CAD risk.

CONCLUSIONS

Beyond LDLR, we identified an additional nonlipid molecular subtype of CAD characterized by rare variants in the NOS3 gene.

Inherited genetic effects on arsenic metabolism: A comparison of effects on arsenic species measured in urine and in blood

Inorganic arsenic (iAs) is a carcinogen, and chronic exposure is associated with adverse health outcomes, including cancer and cardiovascular disease. Consumed iAs can undergo two methylation reactions catalyzed by arsenic methyltransferase (AS3MT), producing monomethylated and dimethylated forms of arsenic (MMA and DMA). Methylation of iAs helps facilitate excretion of arsenic in urine, with DMA composing the majority of arsenic species excreted. Past studies have identified genetic variation in the AS3MT (10q24.32) and FTCD (21q22.3) regions associated with arsenic metabolism efficiency (AME), measured as the proportion of each species present in urine (iAs%, MMA%, and DMA%), but their association with arsenic species present in blood has not been examined. We use data from three studies nested within the Health Effects and Longitudinal Study (HEALS)-the Nutritional Influences on Arsenic Toxicity Study, the Folate and Oxidative Stress study, and the Folic Acid and Creatine Trial-to examine the association of previously identified genetic variants with arsenic species in both urine and blood of 334 individuals. We confirm that the genetic variants in AS3MT and FTCD known to effect arsenic species composition in urine (an excreted byproduct of metabolism) have similar effects on arsenic species in blood (a tissue type that directly interacts with many organs, including those prone to arsenic toxicity). This consistency we observe provides further support for the hypothesis the AME SNPs identified to date impact the efficiency of arsenic metabolism and elimination, thereby influencing internal dose of arsenic and the dose delivered to toxicity-prone organs and tissues.

A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

Using coding and non-coding rare variants to target candidate genes in patients with severe tinnitus

Tinnitus is the phantom percept of an internal non-verbal set of noises and tones. It is reported by 15% of the population and it is usually associated with hearing and/or brain disorders. The role of structural variants (SVs) in coding and non-coding regions has not been investigated in patients with severe tinnitus. In this study, we performed whole-genome sequencing in 97 unrelated Swedish individuals with chronic tinnitus (TIGER cohort). Rare single nucleotide variants (SNV), large structural variants (LSV), and copy number variations (CNV) were retrieved to perform a gene enrichment analysis in TIGER and in a subgroup of patients with severe tinnitus (SEVTIN, n = 34), according to the tinnitus handicap inventory (THI) scores. An independent exome sequencing dataset of 147 Swedish tinnitus patients was used as a replication cohort (JAGUAR cohort) and population-specific datasets from Sweden (SweGen) and Non-Finish Europeans (NFE) from gnomAD were used as control groups. SEVTIN patients showed a higher prevalence of hyperacusis, hearing loss, and anxiety when they were compared to individuals in the TIGER cohort. We found an enrichment of rare missense variants in 6 and 8 high-constraint genes in SEVTIN and TIGER cohorts, respectively. Of note, an enrichment of missense variants was found in the CACNA1E gene in both SEVTIN and TIGER. We replicated the burden of missense variants in 9 high-constrained genes in the JAGUAR cohort, including the gene NAV2, when data were compared with NFE. Moreover, LSVs in constrained regions overlapping CACNA1E, NAV2, and TMEM132D genes were observed in TIGER and SEVTIN.

Gene-Level Associations in Patients With and Without Pathogenic Germline Variants in CDKN2A and Pancreatic Cancer

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is a component of familial melanoma due to germline pathogenic variants (GPVs) in CDKN2A. However, it is unclear what role this gene or other genes play in its etiology.

MATERIALS AND METHODS

We analyzed 189 cancer predisposition genes using parametric rare-variant association (RVA) tests and nonparametric permutation tests to identify gene-level associations in PDAC for patients with (CDKN2A+) and without (CDKN2A-) GPV. Exome sequencing was performed on 84 patients with PDAC, 47 CDKN2A+ and 37 CDKN2A-. After variant filtering, various RVA tests and permutation tests were run separately by CDKN2A status. Genes with the strongest nominal associations were evaluated in patients with PDAC from The Cancer Genome Atlas and the UK Biobank (UKB). A secondary analysis including only GPV from UKB was also performed.

RESULTS

In RVA tests, ERCC4 and RET showed the most compelling evidence as plausible PDAC candidate genes for CDKN2A+ patients. In contrast, the findings in CDKN2A- patients provided evidence for HMBS, EPCAM, and MRE11 as potential new candidate genes and confirmed ATM, BRCA2, and PALB2 as PDAC genes, consistent with findings in The Cancer Genome Atlas and the UKB. As expected, CDKN2A- patients were more likely to harbor GPVs from the 189 genes investigated. When including only GPVs from UKB, significant associations with PDAC were seen for ATM, BRCA2, and CDKN2A.

CONCLUSION

These results suggest that variants in other genes likely play a role in PDAC in all patients and that PDAC in CDKN2A+ patients has a distinct etiology from PDAC in CDKN2A- patients.

A combined polygenic score of 21,293 rare and 22 common variants improves diabetes diagnosis based on hemoglobin A1C levels

Polygenic scores (PGSs) combine the effects of common genetic variants1,2 to predict risk or treatment strategies for complex diseases3-7. Adding rare variation to PGSs has largely unknown benefits and is methodically challenging. Here, we developed a method for constructing rare variant PGSs and applied it to calculate genetically modified hemoglobin A1C thresholds for type 2 diabetes (T2D) diagnosis7-10. The resultant rare variant PGS is highly polygenic (21,293 variants across 154 genes), depends on ultra-rare variants (72.7% observed in fewer than three people) and identifies significantly more undiagnosed T2D cases than expected by chance (odds ratio = 2.71; P = 1.51 × 10-6). A PGS combining common and rare variants is expected to identify 4.9 million misdiagnosed T2D cases in the United States-nearly 1.5-fold more than the common variant PGS alone. These results provide a method for constructing complex trait PGSs from rare variants and suggest that rare variants will augment common variants in precision medicine approaches for common disease.

CLIN_SKAT: an R package to conduct association analysis using functionally relevant variants

Background

Availability of next generation sequencing data, allows low-frequency and rare variants to be studied through strategies other than the commonly used genome-wide association studies (GWAS). Rare variants are important keys towards explaining the heritability for complex diseases that remains to be explained by common variants due to their low effect sizes. However, analysis strategies struggle to keep up with the huge amount of data at disposal therefore creating a bottleneck. This study describes CLIN_SKAT, an R package, that provides users with an easily implemented analysis pipeline with the goal of (i) extracting clinically relevant variants (both rare and common), followed by (ii) gene-based association analysis by grouping the selected variants.

Results

CLIN_SKAT offers four simple functions that can be used to obtain clinically relevant variants, map them to genes or gene sets, calculate weights from global healthy populations and conduct weighted case–control analysis. CLIN_SKAT introduces improvements by adding certain pre-analysis steps and customizable features to make the SKAT results clinically more meaningful. Moreover, it offers several plot functions that can be availed towards obtaining visualizations for interpretation of the analyses results. CLIN_SKAT is available on Windows/Linux/MacOS and is operative for R version 4.0.4 or later. It can be freely downloaded from https://github.com/ShihChingYu/CLIN_SKAT, installed through devtools::install_github("ShihChingYu/CLIN_SKAT", force=T) and executed by loading the package into R using library(CLIN_SKAT). All outputs (tabular and graphical) can be downloaded in simple, publishable formats.

Conclusions

Statistical association analysis is often underpowered due to low sample sizes and high numbers of variants to be tested, limiting detection of causal ones. Therefore, retaining a subset of variants that are biologically meaningful seems to be a more effective strategy for identifying explainable associations while reducing the degrees of freedom. CLIN_SKAT offers users a one-stop R package that identifies disease risk variants with improved power via a series of tailor-made procedures that allows dimension reduction, by retaining functionally relevant variants, and incorporating ethnicity based priors. Furthermore, it also eliminates the requirement for high computational resources and bioinformatics expertise.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04987-2.

Genome-wide association studies and genomic selection assays made in a large sample of cacao (Theobroma cacao L.) germplasm reveal significant marker-trait associations and good predictive value for improving yield potential

A genome-wide association study (GWAS) was undertaken to unravel marker-trait associations (MTAs) between SNP markers and phenotypic traits. It involved a subset of 421 cacao accessions from the large and diverse collection conserved ex situ at the International Cocoa Genebank Trinidad. A Mixed Linear Model (MLM) in TASSEL was used for the GWAS and followed by confirmatory analyses using GAPIT FarmCPU. An average linkage disequilibrium (r2) of 0.10 at 5.2 Mb was found across several chromosomes. Seventeen significant (P ≤ 8.17 × 10-5 (-log10 (p) = 4.088)) MTAs of interest, including six that pertained to yield-related traits, were identified using TASSEL MLM. The latter accounted for 5 to 17% of the phenotypic variation expressed. The highly significant association (P ≤ 8.17 × 10-5) between seed length to width ratio and TcSNP 733 on chromosome 5 was verified with FarmCPU (P ≤ 1.12 × 10-8). Fourteen MTAs were common to both the TASSEL and FarmCPU models at P ≤ 0.003. The most significant yield-related MTAs involved seed number and seed length on chromosome 7 (P ≤ 1.15 × 10-14 and P ≤ 6.75 × 10-05, respectively) and seed number on chromosome 1 (P ≤ 2.38 × 10-05), based on the TASSEL MLM. It was noteworthy that seed length, seed length to width ratio and seed number were associated with markers at different loci, indicating their polygenic nature. Approximately 40 candidate genes that encode embryo and seed development, protein synthesis, carbohydrate transport and lipid biosynthesis and transport were identified in the flanking regions of the significantly associated SNPs and in linkage disequilibrium with them. A significant association of fruit surface anthocyanin intensity co-localised with MYB-related protein 308 on chromosome 4. Testing of a genomic selection approach revealed good predictive value (genomic estimated breeding values (GEBV)) for economic traits such as seed number (GEBV = 0.611), seed length (0.6199), seed width (0.5435), seed length to width ratio (0.5503), seed/cotyledon mass (0.6014) and ovule number (0.6325). The findings of this study could facilitate genomic selection and marker-assisted breeding of cacao thereby expediting improvement in the yield potential of cacao planting material.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score

Rare variant association tests (RVAT) have been developed to study the contribution of rare variants widely accessible through high-throughput sequencing technologies. RVAT require to aggregate rare variants in testing units and to filter variants to retain only the most likely causal ones. In the exome, genes are natural testing units and variants are usually filtered based on their functional consequences. However, when dealing with whole-genome sequence (WGS) data, both steps are challenging. No natural biological unit is available for aggregating rare variants. Sliding windows procedures have been proposed to circumvent this difficulty, however they are blind to biological information and result in a large number of tests. We propose a new strategy to perform RVAT on WGS data: “RAVA-FIRST” (RAre Variant Association using Functionally-InfoRmed STeps) comprising three steps. (1) New testing units are defined genome-wide based on functionally-adjusted Combined Annotation Dependent Depletion (CADD) scores of variants observed in the gnomAD populations, which are referred to as “CADD regions”. (2) A region-dependent filtering of rare variants is applied in each CADD region. (3) A functionally-informed burden test is performed with sub-scores computed for each genomic category within each CADD region. Both on simulations and real data, RAVA-FIRST was found to outperform other WGS-based RVAT. Applied to a WGS dataset of venous thromboembolism patients, we identified an intergenic region on chromosome 18 enriched for rare variants in early-onset patients. This region that was missed by standard sliding windows procedures is included in a TAD region that contains a strong candidate gene. RAVA-FIRST enables new investigations of rare non-coding variants in complex diseases, facilitated by its implementation in the R package Ravages.

Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes

Here we present an exome-wide rare genetic variant association study for 30 blood biomarkers in 191,971 individuals in the UK Biobank. We compare gene-based association tests for separate functional variant categories to increase interpretability and identify 193 significant gene-biomarker associations. Genes associated with biomarkers were ~ 4.5-fold enriched for conferring Mendelian disorders. In addition to performing weighted gene-based variant collapsing tests, we design and apply variant-category-specific kernel-based tests that integrate quantitative functional variant effect predictions for missense variants, splicing and the binding of RNA-binding proteins. For these tests, we present a computationally efficient combination of the likelihood-ratio and score tests that found 36% more associations than the score test alone while also controlling the type-1 error. Kernel-based tests identified 13% more associations than their gene-based collapsing counterparts and had advantages in the presence of gain of function missense variants. We introduce local collapsing by amino acid position for missense variants and use it to interpret associations and identify potential novel gain of function variants in PIEZO1. Our results show the benefits of investigating different functional mechanisms when performing rare-variant association tests, and demonstrate pervasive rare-variant contribution to biomarker variability.

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

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

Endophenotype effect sizes support variant pathogenicity in monogenic disease susceptibility genes

Accurate and efficient classification of variant pathogenicity is critical for research and clinical care. Using data from three large studies, we demonstrate that population-based associations between rare variants and quantitative endophenotypes for three monogenic diseases (low-density-lipoprotein cholesterol for familial hypercholesterolemia, electrocardiographic QTc interval for long QT syndrome, and glycosylated hemoglobin for maturity-onset diabetes of the young) provide evidence for variant pathogenicity. Effect sizes are associated with pathogenic ClinVar assertions (P < 0.001 for each trait) and discriminate pathogenic from non-pathogenic variants (area under the curve 0.82-0.84 across endophenotypes). An effect size threshold of ≥ 0.5 times the endophenotype standard deviation nominates up to 35% of rare variants of uncertain significance or not in ClinVar in disease susceptibility genes with pathogenic potential. We propose that variant associations with quantitative endophenotypes for monogenic diseases can provide evidence supporting pathogenicity.

A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies

BACKGROUND

Multilocus analysis on a set of single nucleotide polymorphisms (SNPs) pre-assigned within a gene constitutes a valuable complement to single-marker analysis by aggregating data on complex traits in a biologically meaningful way. However, despite the existence of a wide variety of SNP-set methods, few comprehensive comparison studies have been previously performed to evaluate the effectiveness of these methods.

RESULTS

We herein sought to fill this knowledge gap by conducting a comprehensive empirical comparison for 22 commonly-used summary-statistics based SNP-set methods. We showed that only seven methods could effectively control the type I error, and that these well-calibrated approaches had varying power performance under the simulation scenarios. Overall, we confirmed that the burden test was generally underpowered and score-based variance component tests (e.g., sequence kernel association test) were much powerful under the polygenic genetic architecture in both common and rare variant association analyses. We further revealed that two linkage-disequilibrium-free P value combination methods (e.g., harmonic mean P value method and aggregated Cauchy association test) behaved very well under the sparse genetic architecture in simulations and real-data applications to common and rare variant association analyses as well as in expression quantitative trait loci weighted integrative analysis. We also assessed the scalability of these approaches by recording computational time and found that all these methods can be scalable to biobank-scale data although some might be relatively slow.

CONCLUSION

In conclusion, we hope that our findings can offer an important guidance on how to choose appropriate multilocus association analysis methods in post-GWAS era. All the SNP-set methods are implemented in the R package called MCA, which is freely available at https://github.com/biostatpzeng/ .

Genetic association-based functional analysis detects HOGA1 as a potential gene involved in fat accumulation

Although there are a number of discoveries from genome-wide association studies (GWAS) for obesity, it has not been successful in linking GWAS results to biology. We sought to discover causal genes for obesity by conducting functional studies on genes detected from genetic association analysis. Gene-based association analysis of 917 individual exome sequences showed that HOGA1 attains exome-wide significance (p-value &lt; 2.7 × 10–6) for body mass index (BMI). The mRNA expression of HOGA1 is significantly increased in human adipose tissues from obese individuals in the Genotype-Tissue Expression (GTEx) dataset, which supports the genetic association of HOGA1 with BMI. Functional analyses employing cell- and animal model-based approaches were performed to gain insights into the functional relevance of Hoga1 in obesity. Adipogenesis was retarded when Hoga1 was knocked down by siRNA treatment in a mouse 3T3-L1 cell line and a similar inhibitory effect was confirmed in mice with down-regulated Hoga1. Hoga1 antisense oligonucleotide (ASO) treatment reduced body weight, blood lipid level, blood glucose, and adipocyte size in high-fat diet-induced mice. In addition, several lipogenic genes including Srebf1, Scd1, Lp1, and Acaca were down-regulated, while lipolytic genes Cpt1l, Ppara, and Ucp1 were up-regulated. Taken together, HOGA1 is a potential causal gene for obesity as it plays a role in excess body fat development.

Importance of Including Non-European Populations in Large Human Genetic Studies to Enhance Precision Medicine

One goal of genomic medicine is to uncover an individual's genetic risk for disease, which generally requires data connecting genotype to phenotype, as done in genome-wide association studies (GWAS). While there may be clinical promise to employing prediction tools such as polygenic risk scores (PRS), it currently stands that individuals of non-European ancestry may not reap the benefits of genomic medicine because of underrepresentation in large-scale genetics studies. Here, we discuss why this inequity poses a problem for genomic medicine and the reasons for the low transferability of PRS across populations. We also survey the ancestry representation of published GWAS and investigate how estimates of ancestry diversity in GWASparticipants might be biased. We highlight the importance of expanding genetic research in Africa, one of the most underrepresented regions in human genomics research, and discuss issues of ethics, resources, and technology for equitable advancement of genomic medicine.

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

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

Uncovering the Contribution of Moderate-Penetrance Susceptibility Genes to Breast Cancer by Whole-Exome Sequencing and Targeted Enrichment Sequencing of Candidate Genes in Women of European Ancestry

Rare variants in at least 10 genes, including BRCA1, BRCA2, PALB2, ATM, and CHEK2, are associated with increased risk of breast cancer; however, these variants, in combination with common variants identified through genome-wide association studies, explain only a fraction of the familial aggregation of the disease. To identify further susceptibility genes, we performed a two-stage whole-exome sequencing study. In the discovery stage, samples from 1528 breast cancer cases enriched for breast cancer susceptibility and 3733 geographically matched unaffected controls were sequenced. Using five different filtering and gene prioritization strategies, 198 genes were selected for further validation. These genes, and a panel of 32 known or suspected breast cancer susceptibility genes, were assessed in a validation set of 6211 cases and 6019 controls for their association with risk of breast cancer overall, and by estrogen receptor (ER) disease subtypes, using gene burden tests applied to loss-of-function and rare missense variants. Twenty genes showed nominal evidence of association (p-value < 0.05) with either overall or subtype-specific breast cancer. Our study had the statistical power to detect susceptibility genes with effect sizes similar to ATM, CHEK2, and PALB2, however, it was underpowered to identify genes in which susceptibility variants are rarer or confer smaller effect sizes. Larger sample sizes would be required in order to identify such genes.

Analysis of the docking property of host variants of hACE2 for SARS-CoV-2 in a large cohort

The recent novel coronavirus disease (COVID-19) outbreak, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is threatening global health. However, an understanding of the interaction of SARS-CoV-2 with human cells, including the physical docking property influenced by the host's genetic diversity, is still lacking. Here, based on germline variants in the UK Biobank covering 502,543 individuals, we revealed the molecular interactions between human angiotensin-converting enzyme 2 (hACE2), which is the representative receptor for SARS-CoV-2 entry, and COVID-19 infection. We identified six nonsense and missense variants of hACE2 from 2585 subjects in the UK Biobank covering 500000 individuals. Using our molecular dynamics simulations, three hACE2 variants from 2585 individuals we selected showed higher levels of binding free energy for docking in the range of 1.44-3.69 kcal/mol. Although there are diverse contributors to SARS-CoV-2 infections, including the mobility of individuals, we analyzed the diagnosis records of individuals with these three variants of hACE2. Our molecular dynamics simulations combined with population-based genomic data provided an atomistic understanding of the interaction between hACE2 and the spike protein of SARS-CoV-2.

Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots

For any given level of overall adiposity, individuals vary considerably in fat distribution. The inherited basis of fat distribution in the general population is not fully understood. Here, we study up to 38,965 UK Biobank participants with MRI-derived visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes. Because these fat depot volumes are highly correlated with BMI, we additionally study six local adiposity traits: VAT adjusted for BMI and height (VATadj), ASATadj, GFATadj, VAT/ASAT, VAT/GFAT, and ASAT/GFAT. We identify 250 independent common variants (39 newly-identified) associated with at least one trait, with many associations more pronounced in female participants. Rare variant association studies extend prior evidence for PDE3B as an important modulator of fat distribution. Local adiposity traits (1) highlight depot-specific genetic architecture and (2) enable construction of depot-specific polygenic scores that have divergent associations with type 2 diabetes and coronary artery disease. These results - using MRI-derived, BMI-independent measures of local adiposity - confirm fat distribution as a highly heritable trait with important implications for cardiometabolic health outcomes.

Rare Variant Analysis and Molecular Dynamics Simulation in Alzheimer’s Disease Identifies Exonic Variants in FLG

Background: Although an increasing number of common variants contributing to Alzheimer’s disease (AD) are uncovered by genome-wide association studies, they can only explain less than half of the heritability of AD. Rare variant association studies (RVAS) has become an increasingly important area to explain the risk or trait variability of AD. Method: To investigate the potential rare variants that cause AD, we screened 70,209 rare variants from two cohorts of a 175 AD cohort and a 214 cognitively normal cohort from the Alzheimer’s Disease Neuroimaging Initiative database. MIRARE, a novel RVAS method, was performed on 232 non-synonymous variants selected by ANNOVAR annotation. Molecular docking and molecular dynamics (MD) simulation were adopted to verify the interaction between the chosen functional variants and BACE1. Results: MIRAGE analysis revealed significant associations between AD and six potential pathogenic genes, including PREX2, FLG, DHX16, NID2, ZnF585B and ZnF875. Only interactions between FLG (including wild type and rs3120654(SER742TYR)) and BACE1 were verified by molecular docking and MD simulation. The interaction of FLG(SER742TYR) with BACE1 was greater than that of wildtype FLG with BACE1. Conclusions: According to the literature search, bio-informatics analysis, and molecular docking and MD simulation, we find non-synonymous rare variants in six genes, especially FLG(rs3120654), that may play key roles in AD.

Whole-genome sequencing identifies rare missense variants of WNT16 and ERVW-1 causing the systemic lupus erythematosus

Systemic lupus erythematosus (SLE, OMIM 152700) is a rare autoimmune disease with high heritability that affects ~0.1% of the population. Previous studies have revealed several common variants with small effects in European and East Asian SLE patients. However, there is still no rare variant study on Chinese SLE patients using the whole-genome sequencing technology (WGS). Here, we designed a family based WGS study to identify novel rare variants with large effects. Based on large-scale allele frequency data from the gnomAD database, we identified rare protein-coding gene variants with disruptive and sequence-altering impacts in SLE patients. We found that the burden of rare variants was significantly higher than that of common variants in patients, suggesting a larger effect of rare variants on the SLE pathogenesis. We identified the pathogenic risk of rare missense variants with significant odds ratios (p < 0.05) in two genes, including WNT16 (NC_000007.14:g.121329757G > C, NP_057171.2:p.(Ala86Pro) and 7 g.121329760G > C, NP_057171.2:p.(Ala87Pro)), which explains five out of seven patients covering all three families but are absent from all controls, and ERVW-1 (NC_000007.14:g.92469882A > G, NP_001124397.1:p.(Leu167Pro), rs74545114; NC_000007.14:g.92469907G > A, NP_001124397.1:p.(Arg159Cys), rs201142302; NC_000007.14:g.92469919G > A, NP_001124397.1:p.(His155Tyr), rs199552228), which explains the other two patients. None of these variants were identified in any of the controls. These associations are supported by known gene expression studies in SLE patients based on literature review. We further tested the wild and mutant types using the luciferase assays and qPCR in cells. We found that WNT16 can activate the canonical Wnt/β-catenin pathway while the mutant cannot. Additionally, the wild ERVW-1 expression can be significantly up-regulated by cAMP while the mutant cannot. Our study provides the first direct genetic and in vitro evidence for the pathogenic risk of mutant WNT16 and ERVW-1, which may facilitate the design of precision therapy for SLE.

RAVAQ: An integrative pipeline from quality control to region-based rare variant association analysis

Next-generation sequencing technologies have opened up the possibility to sequence large samples of cases and controls to test for association with rare variants. To limit cost and increase sample sizes, data from controls could be used in multiple studies and might thus be generated on different sequencing platforms. This could pose some problems of comparability between cases and controls due to batch effects that could be confounding factors, leading to false-positive association signals. To limit batch effects and ensure comparability of datasets, stringent quality controls are required. We propose an integrative five-steps pipeline, RAVAQ, that (a) performs a specific three-step quality control taking into account the case-control status to ensure data comparability, (b) selects qualifying variants as defined by the user, and (c) performs rare variant association tests per genomic region. The RAVAQ pipeline is wrapped in an R package. It is user-friendly and flexible in its arguments to adapt to the specificity of each research project. We provide examples showing how RAVAQ improves rare variant association tests. The default RAVAQ quality control outperformed the widely used Variant Quality Score Recalibration method, removing inflation due to spurious signals. RAVAQ is open source and freely available at https://gitlab.com/gmarenne/ravaq.

From pharmacogenetics to pharmaco-omics: Milestones and future directions

The origins of pharmacogenetics date back to the 1950s, when it was established that inter-individual differences in drug response are partially determined by genetic factors. Since then, pharmacogenetics has grown into its own field, motivated by the translation of identified gene-drug interactions into therapeutic applications. Despite numerous challenges ahead, our understanding of the human pharmacogenetic landscape has greatly improved thanks to the integration of tools originating from disciplines as diverse as biochemistry, molecular biology, statistics, and computer sciences. In this review, we discuss past, present, and future developments of pharmacogenetics methodology, focusing on three milestones: how early research established the genetic basis of drug responses, how technological progress made it possible to assess the full extent of pharmacological variants, and how multi-dimensional omics datasets can improve the identification, functional validation, and mechanistic understanding of the interplay between genes and drugs. We outline novel strategies to repurpose and integrate molecular and clinical data originating from biobanks to gain insights analogous to those obtained from randomized controlled trials. Emphasizing the importance of increased diversity, we envision future directions for the field that should pave the way to the clinical implementation of pharmacogenetics.

Whole Exome Sequencing Identifies Genes Associated With Non-Obstructive Azoospermia

Background: Non-obstructive azoospermia (NOA) affects nearly 1% of men; however, the landscape of the causative genes is largely unknown.

Objective: To explore the genetic etiology which is the fundamental cause of NOA, a prospective case-control study and parental–proband trio linkage analysis were performed.

Materials: A total of 133 patients with clinicopathological NOA and 343 fertile controls were recruited from a single large academic fertility center located in Northeast China; in addition, eleven trio families were available and enrolled.

Results: Whole exome sequencing-based rare variant association study between the cases and controls was performed using the gene burden association testing. Linkage analysis on the trio families was also interrogated. In total, 648 genes were identified to be associated with NOA (three of which were previously reported), out of which six novel genes were found further associated based on the linkage analysis in the trio families, and involved in the meiosis-related network.

Discussion and Conclusion: The six currently identified genes potentially account for a fraction (3.76%, 5 out of 133 patients) of the heritability of unidentified NOA, and combining the six novel genes and the three previously reported genes together would potentially account for an overall 6.77% (9 out of 133 patients) heritability of unidentified NOA in this study.

Evaluation of conformational transitions of h-BRCA2 functional domain and unclassified variant Arg2502Cys using multimodal approach

Breast cancer type 2 susceptibility (BRCA2) protein plays an essential role in the repair mechanism of DNA double-strand breaks and interstrand cross-links by Homologous recombination. Germline mutations identified in the BRCA2 gene confer an increased risk of hereditary breast and ovarian cancer. Missense mutations are identified all over the gene, including the DNA binding region of BRCA2 that interacts with FANCD2. However, the majority of these missense mutations are classified as 'Variants of Uncertain Significance' due to a lack of structural, functional and clinical correlations. Therefore, multi-disciplinary in-silico, in-vitro and biophysical approaches have been explored to characterize an unclassified missense mutation, BRCA2 Arg2502Cys, identified from a case-control study. Circular-dichroism and Fluorescence spectroscopy show that the Arg2502Cys mutation in hBRCA2 (residues 2350-2545) decreases the α-helical/β-sheet propensity of the wild-type protein and perturb the tertiary structure conformation. Molecular dynamics simulations revealed alteration in the intramolecular H-bonds, overall compactness and stability of the hydrophobic core were observed in the mutant protein. Principle component analysis indicated that Arg2502Cys mutant exhibited comparatively large conformational transitions and periodic fluctuation. Therefore, to our conclusion, BRCA2 Arg2502Cys mutant perturbed the structural integrity and conformational dynamics of BRCA2.

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.

A whole genome sequencing study of moderate to severe asthma identifies a lung function locus associated with asthma risk

Genome-wide association studies (GWAS) have identified many common variant loci associated with asthma susceptibility, but few studies investigate the genetics underlying moderate-to-severe asthma risk. Here, we present a whole-genome sequencing study comparing 3181 moderate-to-severe asthma patients to 3590 non-asthma controls. We demonstrate that asthma risk is genetically correlated with lung function measures and that this component of asthma risk is orthogonal to the eosinophil genetics that also contribute to disease susceptibility. We find that polygenic scores for reduced lung function are associated with younger asthma age of onset. Genome-wide, seven previously reported common asthma variant loci and one previously reported lung function locus, near THSD4, reach significance. We replicate association of the lung function locus in a recently published GWAS of moderate-to-severe asthma patients. We additionally replicate the association of a previously reported rare (minor allele frequency < 1%) coding variant in IL33 and show significant enrichment of rare variant burden in genes from common variant allergic disease loci. Our findings highlight the contribution of lung function genetics to moderate-to-severe asthma risk, and provide initial rare variant support for associations with moderate-to-severe asthma risk at several candidate genes from common variant loci.

Whole-genome sequencing reveals that variants in the Interleukin 18 Receptor Accessory Protein 3'UTR protect against ALS

The noncoding genome is substantially larger than the protein-coding genome but has been largely unexplored by genetic association studies. Here, we performed region-based rare variant association analysis of >25,000 variants in untranslated regions of 6,139 amyotrophic lateral sclerosis (ALS) whole genomes and the whole genomes of 70,403 non-ALS controls. We identified interleukin-18 receptor accessory protein (IL18RAP) 3' untranslated region (3'UTR) variants as significantly enriched in non-ALS genomes and associated with a fivefold reduced risk of developing ALS, and this was replicated in an independent cohort. These variants in the IL18RAP 3'UTR reduce mRNA stability and the binding of double-stranded RNA (dsRNA)-binding proteins. Finally, the variants of the IL18RAP 3'UTR confer a survival advantage for motor neurons because they dampen neurotoxicity of human induced pluripotent stem cell (iPSC)-derived microglia bearing an ALS-associated expansion in C9orf72, and this depends on NF-κB signaling. This study reveals genetic variants that protect against ALS by reducing neuroinflammation and emphasizes the importance of noncoding genetic association studies.

Genetic Determinants of Sudden Unexpected Death in Pediatrics

PURPOSE

This study aimed to evaluate genetic contributions to sudden unexpected death in pediatrics (SUDP).

METHODS

We phenotyped and performed exome sequencing for 352 SUDP cases. We analyzed variants in 294 "SUDP genes" with mechanisms plausibly related to sudden death. In a subset of 73 cases with parental data (trios), we performed exome-wide analyses and conducted cohort-wide burden analyses.

RESULTS

In total, we identified likely contributory variants in 37 of 352 probands (11%). Analysis of SUDP genes identified pathogenic/likely pathogenic variants in 12 of 352 cases (SCN1A, DEPDC5 [2], GABRG2, SCN5A [2], TTN [2], MYBPC3, PLN, TNNI3, and PDHA1) and variants of unknown significance-favor-pathogenic in 17 of 352 cases. Exome-wide analyses of the 73 cases with family data additionally identified 4 de novo pathogenic/likely pathogenic variants (SCN1A [2], ANKRD1, and BRPF1) and 4 de novo variants of unknown significance-favor-pathogenic. Comparing cases with controls, we demonstrated an excess burden of rare damaging SUDP gene variants (odds ratio, 2.94; 95% confidence interval, 2.37-4.21) and of exome-wide de novo variants in the subset of 73 with trio data (odds ratio, 3.13; 95% confidence interval, 1.91-5.16).

CONCLUSION

We provide strong evidence for a role of genetic factors in SUDP, involving both candidate genes and novel genes for SUDP and expanding phenotypes of disease genes not previously associated with sudden death.

Rare coding variants in ten genes confer substantial risk for schizophrenia

Rare coding variation has historically provided the most direct connections between gene function and disease pathogenesis. By meta-analysing the whole exomes of 24,248 schizophrenia cases and 97,322 controls, we implicate ultra-rare coding variants (URVs) in 10 genes as conferring substantial risk for schizophrenia (odds ratios of 3-50, P < 2.14 × 10-6) and 32 genes at a false discovery rate of <5%. These genes have the greatest expression in central nervous system neurons and have diverse molecular functions that include the formation, structure and function of the synapse. The associations of the NMDA (N-methyl-D-aspartate) receptor subunit GRIN2A and AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid) receptor subunit GRIA3 provide support for dysfunction of the glutamatergic system as a mechanistic hypothesis in the pathogenesis of schizophrenia. We observe an overlap of rare variant risk among schizophrenia, autism spectrum disorders1, epilepsy and severe neurodevelopmental disorders2, although different mutation types are implicated in some shared genes. Most genes described here, however, are not implicated in neurodevelopment. We demonstrate that genes prioritized from common variant analyses of schizophrenia are enriched in rare variant risk3, suggesting that common and rare genetic risk factors converge at least partially on the same underlying pathogenic biological processes. Even after excluding significantly associated genes, schizophrenia cases still carry a substantial excess of URVs, which indicates that more risk genes await discovery using this approach.

A multiethnic whole genome sequencing study to identify novel loci for bone mineral density

At present, there have only been a few DNA sequencing-based studies to explore the genetic determinants of bone mineral density (BMD). We carried out the largest whole genome sequencing analysis to date for femoral neck and spine BMD (n = 4981), with one of the highest average sequencing depths implemented thus far at 22×, in a multiethnic sample (58% Caucasian and 42% African American) from the Louisiana Osteoporosis Study (LOS). The LOS samples were combined with summary statistics from the GEFOS consortium and several independent samples of various ethnicities to perform GWAS meta-analysis (n = 44 506). We identified 31 and 30 genomic risk loci for femoral neck and spine BMD, respectively. The findings substantiate many previously reported susceptibility loci (e.g. WNT16 and ESR1) and reveal several others that are either novel or have not been widely replicated in GWAS for BMD, including two for femoral neck (IGF2 and ZNF423) and one for spine (SIPA1). Although we were not able to uncover ethnicity specific differences in the genetic determinants of BMD, we did identify several loci which demonstrated sex-specific associations, including two for women (PDE4D and PIGN) and three for men (TRAF3IP2, NFIB and LYSMD4). Gene-based rare variant association testing detected MAML2, a regulator of the Notch signaling pathway, which has not previously been suggested, for association with spine BMD. The findings provide novel insights into the pathophysiological mechanisms of osteoporosis.

Whole-exome sequencing in a Japanese multiplex family identifies new susceptibility genes for intracranial aneurysms

Background

Intracranial aneurysms (IAs) cause subarachnoid hemorrhage, which has high rates of mortality and morbidity when ruptured. Recently, the role of rare variants in the genetic background of complex diseases has been increasingly recognized. The aim of this study was to identify rare variants for susceptibility to IA.

Methods

Whole-exome sequencing was performed on seven members of a Japanese pedigree with highly aggregated IA. Candidate genes harboring co-segregating rare variants with IA were re-sequenced and tested for association with IA using additional 500 probands and 323 non-IA controls. Functional analysis of rare variants detected in the pedigree was also conducted.

Results

We identified two gene variants shared among all four affected participants in the pedigree. One was the splicing donor c.1515+1G>A variant in NPNT (Nephronectin), which was confirmed to cause aberrant splicing by a minigene assay. The other was the missense p.P83T variant in CBY2 (Chibby family member 2). Overexpression of p.P83T CBY2 fused with red fluorescent protein tended to aggregate in the cytoplasm. Although Nephronectin has been previously reported to be involved in endothelial angiogenic functions, CBY2 is a novel molecule in terms of vascular pathophysiology. We confirmed that CBY2 was expressed in cerebrovascular smooth muscle cells in an isoform2-specific manner. Targeted CBY2 re-sequencing in additional case-control samples identified three deleterious rare variants (p.R46H, p.P83T, and p.L183R) in seven probands, showing a significant enrichment in the overall probands (8/501) compared to the controls (0/323) (p = 0.026, Fisher’s extract test).

Conclusions

NPNT and CBY2 were identified as novel susceptibility genes for IA. The highly heterogeneous and polygenic architecture of IA susceptibility can be uncovered by accumulating extensive analyses that focus on each pedigree with a high incidence of IA.

GenRisk: A tool for comprehensive genetic risk modeling

Summary

The genetic architecture of complex traits can be influenced by both many common regulatory variants with small effect sizes and rare deleterious variants in coding regions with larger effect sizes. However, the two kinds of genetic contributions are typically analyzed independently. Here, we present GenRisk, a python package for the computation and the integration of gene scores based on the burden of rare deleterious variants and common-variants-based polygenic risk scores. The derived scores can be analyzed within GenRisk to perform association tests or to derive phenotype prediction models by testing multiple classification and regression approaches. GenRisk is compatible with VCF input file formats.

Availability and implementation

GenRisk is an open source publicly available python package that can be downloaded or installed from Github (https://github.com/AldisiRana/GenRisk).

Supplementary information

Supplementary data are available at Bioinformatics online.

Simulation Research on the Methods of Multi-Gene Region Association Analysis Based on a Functional Linear Model

Genome-wide association analysis is an important approach to identify genetic variants associated with complex traits. Complex traits are not only affected by single gene loci, but also by the interaction of multiple gene loci. Studies of association between gene regions and quantitative traits are of great significance in revealing the genetic mechanism of biological development. There have been a lot of studies on single-gene region association analysis, but the application of functional linear models in multi-gene region association analysis is still less. In this paper, a functional multi-gene region association analysis test method is proposed based on the functional linear model. From the three directions of common multi-gene region method, multi-gene region weighted method and multi-gene region loci weighted method, that test method is studied combined with computer simulation. The following conclusions are obtained through computer simulation: (a) The functional multi-gene region association analysis test method has higher power than the functional single gene region association analysis test method; (b) The functional multi-gene region weighted method performs better than the common functional multi-gene region method; (c) the functional multi-gene region loci weighted method is the best method for association analysis on three directions of the common multi-gene region method; (d) the performance of the Step method and Multi-gene region loci weighted Step for multi-gene regions is the best in general. Functional multi-gene region association analysis test method can theoretically provide a feasible method for the study of complex traits affected by multiple genes.

Adaptive and powerful microbiome multivariate association analysis via feature selection

The important role of human microbiome is being increasingly recognized in health and disease conditions. Since microbiome data is typically high dimensional, one popular mode of statistical association analysis for microbiome data is to pool individual microbial features into a group, and then conduct group-based multivariate association analysis. A corresponding challenge within this approach is to achieve adequate power to detect an association signal between a group of microbial features and the outcome of interest across a wide range of scenarios. Recognizing some existing methods' susceptibility to the adverse effects of noise accumulation, we introduce the Adaptive Microbiome Association Test (AMAT), a novel and powerful tool for multivariate microbiome association analysis, which unifies both blessings of feature selection in high-dimensional inference and robustness of adaptive statistical association testing. AMAT first alleviates the burden of noise accumulation via distance correlation learning, and then conducts a data-adaptive association test under the flexible generalized linear model framework. Extensive simulation studies and real data applications demonstrate that AMAT is highly robust and often more powerful than several existing methods, while preserving the correct type I error rate. A free implementation of AMAT in R computing environment is available at https://github.com/kzb193/AMAT.

Genome-Wide Association Study Adjusted for Occupational and Environmental Factors for Bladder Cancer Susceptibility

This study examined the effects of single-nucleotide polymorphisms (SNPs) on the development of bladder cancer, adding longest-held occupational and industrial history as regulators. The genome purified from blood was genotyped, followed by SNP imputation. In the genome-wide association study (GWAS), several patterns of industrial/occupational classifications were added to logistic regression models. The association test between bladder cancer development and the calculated genetic score for each gene region was evaluated (gene-wise analysis). In the GWAS and gene-wise analysis, the gliomedin gene satisfied both suggestive association levels of 10⁻⁵ in the GWAS and 10⁻⁴ in the gene-wise analysis for male bladder cancer. The expression of the gliomedin protein in the nucleus of bladder cancer cells decreased in cancers with a tendency to infiltrate and those with strong cell atypia. It is hypothesized that gliomedin is involved in the development of bladder cancer.

Genomic and Phenotypic Characterization of Experimentally Selected Resistant Leishmania donovani Reveals a Role for Dynamin-1-Like Protein in the Mechanism of Resistance to a Novel Antileishmanial Compound

The implementation of prospective drug resistance (DR) studies in the research-and-development (R&D) pipeline is a common practice for many infectious diseases but not for neglected tropical diseases (NTDs). Here, we explored and demonstrated the importance of this approach using as paradigms Leishmania donovani, the etiological agent of visceral leishmaniasis (VL), and TCMDC-143345, a promising compound of the GlaxoSmithKline (GSK) "Leishbox" to treat VL. We experimentally selected resistance to TCMDC-143345 in vitro and characterized resistant parasites at the genomic and phenotypic levels. We found that it took more time to develop resistance to TCMDC-143345 than to other drugs in clinical use and that there was no cross-resistance to these drugs, suggesting a new and unique mechanism. By whole-genome sequencing, we found two mutations in the gene encoding the L. donovani dynamin-1-like protein (LdoDLP1) that were fixed at the highest drug pressure. Through phylogenetic analysis, we identified LdoDLP1 as a family member of the dynamin-related proteins, a group of proteins that impacts the shapes of biological membranes by mediating fusion and fission events, with a putative role in mitochondrial fission. We found that L. donovani lines genetically engineered to harbor the two identified LdoDLP1 mutations were resistant to TCMDC-143345 and displayed altered mitochondrial properties. By homology modeling, we showed how the two LdoDLP1 mutations may influence protein structure and function. Taken together, our data reveal a clear involvement of LdoDLP1 in the adaptation/reduced susceptibility of L. donovani to TCMDC-143345. IMPORTANCE Humans and their pathogens are continuously locked in a molecular arms race during which the eventual emergence of pathogen drug resistance (DR) seems inevitable. For neglected tropical diseases (NTDs), DR is generally studied retrospectively once it has already been established in clinical settings. We previously recommended to keep one step ahead in the host-pathogen arms race and implement prospective DR studies in the R&D pipeline, a common practice for many infectious diseases but not for NTDs. Here, using Leishmania donovani, the etiological agent of visceral leishmaniasis (VL), and TCMDC-143345, a promising compound of the GSK Leishbox to treat VL, as paradigms, we experimentally selected resistance to the compound and proceeded to genomic and phenotypic characterization of DR parasites. The results gathered in the present study suggest a new DR mechanism involving the L. donovani dynamin-1-like protein (LdoDLP1) and demonstrate the practical relevance of prospective DR studies.

Whole genome sequence analysis of platelet traits in the NHLBI Trans-Omics for Precision Medicine (TOPMed) initiative

Platelets play a key role in thrombosis and hemostasis. Platelet count (PLT) and mean platelet volume (MPV) are highly heritable quantitative traits, with hundreds of genetic signals previously identified, mostly in European ancestry populations. We here utilize whole genome sequencing (WGS) from NHLBI's Trans-Omics for Precision Medicine initiative (TOPMed) in a large multi-ethnic sample to further explore common and rare variation contributing to PLT (n = 61 200) and MPV (n = 23 485). We identified and replicated secondary signals at MPL (rs532784633) and PECAM1 (rs73345162), both more common in African ancestry populations. We also observed rare variation in Mendelian platelet-related disorder genes influencing variation in platelet traits in TOPMed cohorts (not enriched for blood disorders). For example, association of GP9 with lower PLT and higher MPV was partly driven by a pathogenic Bernard-Soulier syndrome variant (rs5030764, p.Asn61Ser), and the signals at TUBB1 and CD36 were partly driven by loss of function variants not annotated as pathogenic in ClinVar (rs199948010 and rs571975065). However, residual signal remained for these gene-based signals after adjusting for lead variants, suggesting that additional variants in Mendelian genes with impacts in general population cohorts remain to be identified. Gene-based signals were also identified at several genome-wide association study identified loci for genes not annotated for Mendelian platelet disorders (PTPRH, TET2, CHEK2), with somatic variation driving the result at TET2. These results highlight the value of WGS in populations of diverse genetic ancestry to identify novel regulatory and coding signals, even for well-studied traits like platelet traits.

Contribution of Rare Genetic Variation to Disease Susceptibility in a Large Scandinavian Myositis Cohort

OBJECTIVE

Idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of complex autoimmune conditions characterized by inflammation in skeletal muscle and extramuscular compartments, and interferon (IFN) system activation. We undertook this study to examine the contribution of genetic variation to disease susceptibility and to identify novel avenues for research in IIMs.

METHODS

Targeted DNA sequencing was used to mine coding and potentially regulatory single nucleotide variants from ~1,900 immune-related genes in a Scandinavian case-control cohort of 454 IIM patients and 1,024 healthy controls. Gene-based aggregate testing, together with rare variant- and gene-level enrichment analyses, was implemented to explore genotype-phenotype relations.

RESULTS

Gene-based aggregate tests of all variants, including rare variants, identified IFI35 as a potential genetic risk locus for IIMs, suggesting a genetic signature of type I IFN pathway activation. Functional annotation of the IFI35 locus highlighted a regulatory network linked to the skeletal muscle-specific gene PTGES3L, as a potential candidate for IIM pathogenesis. Aggregate genetic associations with AGER and PSMB8 in the major histocompatibility complex locus were detected in the antisynthetase syndrome subgroup, which also showed a less marked genetic signature of the type I IFN pathway. Enrichment analyses indicated a burden of synonymous and noncoding rare variants in IIM patients, suggesting increased disease predisposition associated with these classes of rare variants.

CONCLUSION

Our study suggests the contribution of rare genetic variation to disease susceptibility in IIM and specific patient subgroups, and pinpoints genetic associations consistent with previous findings by gene expression profiling. These features highlight genetic profiles that are potentially relevant to disease pathogenesis.

Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy

Rapid methodological advances in statistical and computational genomics have enabled researchers to better identify and interpret both rare and common variants responsible for complex human diseases. As we continue to see an expansion of these advances in the field, it is now imperative for researchers to understand the resources and methodologies available for various data types and study designs. In this review, we provide an overview of recent methods for identifying rare and common variants and understanding their roles in disease etiology. Additionally, we discuss the strategy, challenge, and promise of gene therapy. As computational and statistical approaches continue to improve, we will have an opportunity to translate human genetic findings into personalized health care.

Deep Resequencing of 9 Candidate Genes Identifies a Role for ARAP1 and IGF2BP2 in Modulating Insulin Secretion Adjusted for Insulin Resistance in Obese Southern Europeans

Type 2 diabetes is characterized by impairment in insulin secretion, with an established genetic contribution. We aimed to evaluate common and low-frequency (1–5%) variants in nine genes strongly associated with insulin secretion by targeted sequencing in subjects selected from the extremes of insulin release measured by the disposition index. Collapsing data by gene and/or function, the association between disposition index and nonsense variants were significant, also after adjustment for confounding factors (OR = 0.25, 95% CI = 0.11–0.59, p = 0.001). Evaluating variants individually, three novel variants in ARAP1, IGF2BP2 and GCK, out of eight reaching significance singularly, remained associated after adjustment. Constructing a genetic risk model combining the effects of the three variants, only carriers of the ARAP1 and IGF2BP2 variants were significantly associated with a reduced probability to be in the lower, worst, extreme of insulin secretion (OR = 0.223, 95% CI = 0.105–0.473, p < 0.001). Observing a high number of normal glucose tolerance between carriers, a regression posthoc analysis was performed. Carriers of genetic risk model variants had higher probability to be normoglycemic, also after adjustment (OR = 2.411, 95% CI = 1.136–5.116, p = 0.022). Thus, in our southern European cohort, nonsense variants in all nine candidate genes showed association with better insulin secretion adjusted for insulin resistance, and we established the role of ARAP1 and IGF2BP2 in modulating insulin secretion.

Contribution of Rare and Low-Frequency Variants to Multiple Sclerosis Susceptibility in the Italian Continental Population

Genome-wide association studies identified over 200 risk loci for multiple sclerosis (MS) focusing on common variants, which account for about 50% of disease heritability. The goal of this study was to investigate whether low-frequency and rare functional variants, located in MS-established associated loci, may contribute to disease risk in a relatively homogeneous population, testing their cumulative effect (burden) with gene-wise tests. We sequenced 98 genes in 588 Italian patients with MS and 408 matched healthy controls (HCs). Variants were selected using different filtering criteria based on allelic frequency and in silico functional impacts. Genes showing a significant burden (n = 17) were sequenced in an independent cohort of 504 MS and 504 HC. The highest signal in both cohorts was observed for the disruptive variants (stop-gain, stop-loss, or splicing variants) located in EFCAB13, a gene coding for a protein of an unknown function (p < 10^-4). Among these variants, the minor allele of a stop-gain variant showed a significantly higher frequency in MS versus HC in both sequenced cohorts (p = 0.0093 and p = 0.025), confirmed by a meta-analysis on a third independent cohort of 1298 MS and 1430 HC (p = 0.001) assayed with an SNP array. Real-time PCR on 14 heterozygous individuals for this variant did not evidence the presence of the stop-gain allele, suggesting a transcript degradation by non-sense mediated decay, supported by the evidence that the carriers of the stop-gain variant had a lower expression of this gene (p = 0.0184). In conclusion, we identified a novel low-frequency functional variant associated with MS susceptibility, suggesting the possible role of rare/low-frequency variants in MS as reported for other complex diseases.

Genetics and functional genomics of multiple sclerosis

Multiple sclerosis (MS) is an inflammatory neurodegenerative disease with genetic predisposition. Over the last decade, genome-wide association studies with increasing sample size led to the discovery of robustly associated genetic variants at an exponential rate. More than 200 genetic loci have been associated with MS susceptibility and almost half of its heritability can be accounted for. However, many challenges and unknowns remain. Definitive studies of disease progression and endophenotypes are yet to be performed, whereas the majority of the identified MS variants are not yet functionally characterized. Despite these shortcomings, the unraveling of MS genetics has opened up a new chapter on our understanding MS causal mechanisms.