Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole-Genome Sequencing Studies

Whole-genome sequencing in 333,100 individuals reveals rare non-coding single variant and aggregate associations with height

The role of rare non-coding variation in complex human phenotypes is still largely unknown. To elucidate the impact of rare variants in regulatory elements, we performed a whole-genome sequencing association analysis for height using 333,100 individuals from three datasets: UK Biobank (N = 200,003), TOPMed (N = 87,652) and All of Us (N = 45,445). We performed rare ( < 0.1% minor-allele-frequency) single-variant and aggregate testing of non-coding variants in regulatory regions based on proximal-regulatory, intergenic-regulatory and deep-intronic annotation. We observed 29 independent variants associated with height at P <  6 × 10 - 10 after conditioning on previously reported variants, with effect sizes ranging from -7cm to +4.7 cm. We also identified and replicated non-coding aggregate-based associations proximal to HMGA1 containing variants associated with a 5 cm taller height and of highly-conserved variants in MIR497HG on chromosome 17. We have developed an approach for identifying non-coding rare variants in regulatory regions with large effects from whole-genome sequencing data associated with complex traits.

Whole-genome sequencing reveals rare variants associated with gout in Taiwanese males

To identify rare variants (RVs) of gout, we sequenced the whole genomes of 321 male gout patients and combined these with those of 64 male gout patients and 682 normal controls at Taiwan Biobank. We performed ACAT-O to identify 682 significant RVs (p < 3.8 × 10-8) clustered on chromosomes 1, 7, 10, 16, and 18. To prioritize causal variants effectively, we sifted them by Combined Annotation-Dependent Depletion score >10 or |effect size| ≥ 1.5 for those without CADD scores. In particular, to the best of our knowledge, we identified the rare variants rs559954634, rs186763678, and 13-85340782-G-A for the first time to be associated with gout in Taiwanese males. Importantly, the RV rs559954634 positively affects gout, and its neighboring gene NPHS2 is involved in serum urate and expressed in kidney tissues. The kidneys play a major role in regulating uric acid levels. This suggests that rs559954634 may be involved in gout. Furthermore, rs186763678 is in the intron of NFIA that interacts with SLC2A9, which has the most significant effect on serum urate. Note that gene-gene interaction NFIA-SLC2A9 is significantly associated with serum urate in the Italian MICROS population and a Croatian population. Moreover, 13-85340782-G-A significantly affects gout susceptibility (odds ratio 6.0; P = 0.038). The >1% carrier frequencies of these potentially pathogenic (protective) RVs in cases (controls) suggest the revealed associations may be true; these RVs deserve further studies for the mechanism. Finally, multivariate logistic regression analysis shows that the rare variants rs559954634 and 13-85340782-G-A jointly are significantly associated with gout susceptibility.

The Genetic Determinants and Genomic Consequences of Non-Leukemogenic Somatic Point Mutations

Clonal hematopoiesis (CH) is defined by the expansion of a lineage of genetically identical cells in blood. Genetic lesions that confer a fitness advantage, such as point mutations or mosaic chromosomal alterations (mCAs) in genes associated with hematologic malignancy, are frequent mediators of CH. However, recent analyses of both single cell-derived colonies of hematopoietic cells and population sequencing cohorts have revealed CH frequently occurs in the absence of known driver genetic lesions. To characterize CH without known driver genetic lesions, we used 51,399 deeply sequenced whole genomes from the NHLBI TOPMed sequencing initiative to perform simultaneous germline and somatic mutation analyses among individuals without leukemogenic point mutations (LPM), which we term CH-LPMneg. We quantified CH by estimating the total mutation burden. Because estimating somatic mutation burden without a paired-tissue sample is challenging, we developed a novel statistical method, the Genomic and Epigenomic informed Mutation (GEM) rate, that uses external genomic and epigenomic data sources to distinguish artifactual signals from true somatic mutations. We performed a genome-wide association study of GEM to discover the germline determinants of CH-LPMneg. After fine-mapping and variant-to-gene analyses, we identified seven genes associated with CH-LPMneg (TCL1A, TERT, SMC4, NRIP1, PRDM16, MSRA, SCARB1), and one locus associated with a sex-associated mutation pathway (SRGAP2C). We performed a secondary analysis excluding individuals with mCAs, finding that the genetic architecture was largely unaffected by their inclusion. Functional analyses of SMC4 and NRIP1 implicated altered HSC self-renewal and proliferation as the primary mediator of mutation burden in blood. We then performed comprehensive multi-tissue transcriptomic analyses, finding that the expression levels of 404 genes are associated with GEM. Finally, we performed phenotypic association meta-analyses across four cohorts, finding that GEM is associated with increased white blood cell count and increased risk for incident peripheral artery disease, but is not significantly associated with incident stroke or coronary disease events. Overall, we develop GEM for quantifying mutation burden from WGS without a paired-tissue sample and use GEM to discover the genetic, genomic, and phenotypic correlates of CH-LPMneg.

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.

Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection

Machine learning (ML) methods are increasingly becoming crucial in genome-wide association studies for identifying key genetic variants or SNPs that statistical methods might overlook. Statistical methods predominantly identify SNPs with notable effect sizes by conducting association tests on individual genetic variants, one at a time, to determine their relationship with the target phenotype. These genetic variants are then used to create polygenic risk scores (PRSs), estimating an individual's genetic risk for complex diseases like cancer or cardiovascular disorders. Unlike traditional methods, ML algorithms can identify groups of low-risk genetic variants that improve prediction accuracy when combined in a mathematical model. However, the application of ML strategies requires addressing the feature selection challenge to prevent overfitting. Moreover, ensuring the ML model depends on a concise set of genomic variants enhances its clinical applicability, where testing is feasible for only a limited number of SNPs. In this study, we introduce a robust pipeline that applies ML algorithms in combination with feature selection (ML-FS algorithms), aimed at identifying the most significant genomic variants associated with the coronary artery disease (CAD) phenotype. The proposed computational approach was tested on individuals from the UK Biobank, differentiating between CAD and non-CAD individuals within this extensive cohort, and benchmarked against standard PRS-based methodologies like LDpred2 and Lassosum. Our strategy incorporates cross-validation to ensure a more robust evaluation of genomic variant-based prediction models. This method is commonly applied in machine learning strategies but has often been neglected in previous studies assessing the predictive performance of polygenic risk scores. Our results demonstrate that the ML-FS algorithm can identify panels with as few as 50 genetic markers that can achieve approximately 80% accuracy when used in combination with known risk factors. The modest increase in accuracy over PRS performances is noteworthy, especially considering that PRS models incorporate a substantially larger number of genetic variants. This extensive variant selection can pose practical challenges in clinical settings. Additionally, the proposed approach revealed novel CAD-genetic variant associations.

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.

FiMAP: A fast identity-by-descent mapping test for biobank-scale cohorts

Although genome-wide association studies (GWAS) have identified tens of thousands of genetic loci, the genetic architecture is still not fully understood for many complex traits. Most GWAS and sequencing association studies have focused on single nucleotide polymorphisms or copy number variations, including common and rare genetic variants. However, phased haplotype information is often ignored in GWAS or variant set tests for rare variants. Here we leverage the identity-by-descent (IBD) segments inferred from a random projection-based IBD detection algorithm in the mapping of genetic associations with complex traits, to develop a computationally efficient statistical test for IBD mapping in biobank-scale cohorts. We used sparse linear algebra and random matrix algorithms to speed up the computation, and a genome-wide IBD mapping scan of more than 400,000 samples finished within a few hours. Simulation studies showed that our new method had well-controlled type I error rates under the null hypothesis of no genetic association in large biobank-scale cohorts, and outperformed traditional GWAS single-variant tests when the causal variants were untyped and rare, or in the presence of haplotype effects. We also applied our method to IBD mapping of six anthropometric traits using the UK Biobank data and identified a total of 3,442 associations, 2,131 (62%) of which remained significant after conditioning on suggestive tag variants in the ± 3 centimorgan flanking regions from GWAS.

Finding Needles in the Haystack: Strategies for Uncovering Noncoding Regulatory Variants

Despite accumulating evidence implicating noncoding variants in human diseases, unraveling their functionality remains a significant challenge. Systematic annotations of the regulatory landscape and the growth of sequence variant data sets have fueled the development of tools and methods to identify causal noncoding variants and evaluate their regulatory effects. Here, we review the latest advances in the field and discuss potential future research avenues to gain a more in-depth understanding of noncoding regulatory variants.

A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.

Weighted Selection Probability to Prioritize Susceptible Rare Variants in Multi-Phenotype Association Studies with Application to a Soybean Genetic Data Set

Rare variant association studies with multiple traits or diseases have drawn a lot of attention since association signals of rare variants can be boosted if more than one phenotype outcome is associated with the same rare variants. Most of the existing statistical methods to identify rare variants associated with multiple phenotypes are based on a group test, where a pre-specified genetic region is tested one at a time. However, these methods are not designed to locate susceptible rare variants within the genetic region. In this article, we propose new statistical methods to prioritize rare variants within a genetic region when a group test for the genetic region identifies a statistical association with multiple phenotypes. It computes the weighted selection probability (WSP) of individual rare variants and ranks them from largest to smallest according to their WSP. In simulation studies, we demonstrated that the proposed method outperforms other statistical methods in terms of true positive selection, when multiple phenotypes are correlated with each other. We also applied it to our soybean single nucleotide polymorphism (SNP) data with 13 highly correlated amino acids, where we identified some potentially susceptible rare variants in chromosome 19.

Ravages: An R package for the simulation and analysis of rare variants in multicategory phenotypes

Current software packages for the analysis and the simulations of rare variants are only available for binary and continuous traits. Ravages provides solutions in a single R package to perform rare variant association tests for multicategory, binary and continuous phenotypes, to simulate datasets under different scenarios and to compute statistical power. Association tests can be run in the whole genome thanks to C++ implementation of most of the functions, using either RAVA-FIRST, a recently developed strategy to filter and analyse genome-wide rare variants, or user-defined candidate regions. Ravages also includes a simulation module that generates genetic data for cases who can be stratified into several subgroups and for controls. Through comparisons with existing programmes, we show that Ravages complements existing tools and will be useful to study the genetic architecture of complex diseases. Ravages is available on the CRAN at https://cran.r-project.org/web/packages/Ravages/ and maintained on Github at https://github.com/genostats/Ravages.

The sequence kernel association test for multicategorical outcomes

Disease heterogeneity is ubiquitous in biomedical and clinical studies. In genetic studies, researchers are increasingly interested in understanding the distinct genetic underpinning of subtypes of diseases. However, existing set-based analysis methods for genome-wide association studies are either inadequate or inefficient to handle such multicategorical outcomes. In this paper, we proposed a novel set-based association analysis method, sequence kernel association test (SKAT)-MC, the sequence kernel association test for multicategorical outcomes (nominal or ordinal), which jointly evaluates the relationship between a set of variants (common and rare) and disease subtypes. Through comprehensive simulation studies, we showed that SKAT-MC effectively preserves the nominal type I error rate while substantially increases the statistical power compared to existing methods under various scenarios. We applied SKAT-MC to the Polish breast cancer study (PBCS), and identified gene FGFR2 was significantly associated with estrogen receptor (ER)+ and ER- breast cancer subtypes. We also investigated educational attainment using UK Biobank data (N = 127 , 127 $N=127,127$ ) with SKAT-MC, and identified 21 significant genes in the genome. Consequently, SKAT-MC is a powerful and efficient analysis tool for genetic association studies with multicategorical outcomes. A freely distributed R package SKAT-MC can be accessed at https://github.com/Zhiwen-Owen-Jiang/SKATMC.

Weighted multiple testing procedures in genome-wide association studies

Multiple testing procedures controlling the false discovery rate (FDR) are increasingly used in the context of genome wide association studies (GWAS), and weighted multiple testing procedures that incorporate covariate information are efficient to improve the power to detect associations. In this work, we evaluate some recent weighted multiple testing procedures in the specific context of GWAS through a simulation study. We also present a new efficient procedure called wBHa that prioritizes the detection of genetic variants with low minor allele frequencies while maximizing the overall detection power. The results indicate good performance of our procedure compared to other weighted multiple testing procedures. In particular, in all simulated settings, wBHa tends to outperform other procedures in detecting rare variants while maintaining good overall power. The use of the different procedures is illustrated with a real dataset.

Aberrant activation of TCL1A promotes stem cell expansion in clonal haematopoiesis

Mutations in a diverse set of driver genes increase the fitness of haematopoietic stem cells (HSCs), leading to clonal haematopoiesis1. These lesions are precursors for blood cancers2-6, but the basis of their fitness advantage remains largely unknown, partly owing to a paucity of large cohorts in which the clonal expansion rate has been assessed by longitudinal sampling. Here, to circumvent this limitation, we developed a method to infer the expansion rate from data from a single time point. We applied this method to 5,071 people with clonal haematopoiesis. A genome-wide association study revealed that a common inherited polymorphism in the TCL1A promoter was associated with a slower expansion rate in clonal haematopoiesis overall, but the effect varied by driver gene. Those carrying this protective allele exhibited markedly reduced growth rates or prevalence of clones with driver mutations in TET2, ASXL1, SF3B1 and SRSF2, but this effect was not seen in clones with driver mutations in DNMT3A. TCL1A was not expressed in normal or DNMT3A-mutated HSCs, but the introduction of mutations in TET2 or ASXL1 led to the expression of TCL1A protein and the expansion of HSCs in vitro. The protective allele restricted TCL1A expression and expansion of mutant HSCs, as did experimental knockdown of TCL1A expression. Forced expression of TCL1A promoted the expansion of human HSCs in vitro and mouse HSCs in vivo. Our results indicate that the fitness advantage of several commonly mutated driver genes in clonal haematopoiesis may be mediated by TCL1A activation.

Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

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.

Simultaneous detection of novel genes and SNPs by adaptive p-value combination

Combining SNP p-values from GWAS summary data is a promising strategy for detecting novel genetic factors. Existing statistical methods for the p-value-based SNP-set testing confront two challenges. First, the statistical power of different methods depends on unknown patterns of genetic effects that could drastically vary over different SNP sets. Second, they do not identify which SNPs primarily contribute to the global association of the whole set. We propose a new signal-adaptive analysis pipeline to address these challenges using the omnibus thresholding Fisher’s method (oTFisher). The oTFisher remains robustly powerful over various patterns of genetic effects. Its adaptive thresholding can be applied to estimate important SNPs contributing to the overall significance of the given SNP set. We develop efficient calculation algorithms to control the type I error rate, which accounts for the linkage disequilibrium among SNPs. Extensive simulations show that the oTFisher has robustly high power and provides a higher balanced accuracy in screening SNPs than the traditional Bonferroni and FDR procedures. We applied the oTFisher to study the genetic association of genes and haplotype blocks of the bone density-related traits using the summary data of the Genetic Factors for Osteoporosis Consortium. The oTFisher identified more novel and literature-reported genetic factors than existing p-value combination methods. Relevant computation has been implemented into the R package TFisher to support similar data analysis.

Scalable approaches for functional analyses of whole-genome sequencing non-coding variants

Non-coding genetic variants outside of protein-coding genome regions play an important role in genetic and epigenetic regulation. It has become increasingly important to understand their roles, as non-coding variants often make up the majority of top findings of genome-wide association studies (GWAS). In addition, the growing popularity of disease-specific whole-genome sequencing (WGS) efforts expands the library of and offers unique opportunities for investigating both common and rare non-coding variants, which are typically not detected in more limited GWAS approaches. However, the sheer size and breadth of WGS data introduce additional challenges to predicting functional impacts in terms of data analysis and interpretation. This review focuses on the recent approaches developed for efficient, at-scale annotation and prioritization of non-coding variants uncovered in WGS analyses. In particular, we review the latest scalable annotation tools, databases and functional genomic resources for interpreting the variant findings from WGS based on both experimental data and in silico predictive annotations. We also review machine learning-based predictive models for variant scoring and prioritization. We conclude with a discussion of future research directions which will enhance the data and tools necessary for the effective functional analyses of variants identified by WGS to improve our understanding of disease etiology.

Whole genome sequence analysis of blood lipid levels in >66,000 individuals

Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

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.

eSCAN: scan regulatory regions for aggregate association testing using whole-genome sequencing data

Multiple statistical methods for aggregate association testing have been developed for whole-genome sequencing (WGS) data. Many aggregate variants in a given genomic window and ignore existing knowledge to define test regions, resulting in many identified regions not clearly linked to genes, and thus, limiting biological understanding. Functional information from new technologies (such as Hi-C and its derivatives), which can help link enhancers to their effector genes, can be leveraged to predefine variant sets for aggregate testing in WGS data. Here, we propose the eSCAN (scan the enhancers) method for genome-wide assessment of enhancer regions in sequencing studies, combining the advantages of dynamic window selection in SCANG (SCAN the Genome), a previously developed method, with the advantages of incorporating putative regulatory regions from annotation. eSCAN, by searching in putative enhancers, increases statistical power and aids mechanistic interpretation, as demonstrated by extensive simulation studies. We also apply eSCAN for blood cell traits using NHLBI Trans-Omics for Precision Medicine WGS data. Results from real data analysis show that eSCAN is able to capture more significant signals, and these signals are of shorter length (indicating higher resolution fine-mapping capability) and drive association of larger regions detected by other methods.

Leveraging Methylation Alterations to Discover Potential Causal Genes Associated With the Survival Risk of Cervical Cancer in TCGA Through a Two-Stage Inference Approach

BACKGROUND

Multiple genes were previously identified to be associated with cervical cancer; however, the genetic architecture of cervical cancer remains unknown and many potential causal genes are yet to be discovered.

METHODS

To explore potential causal genes related to cervical cancer, a two-stage causal inference approach was proposed within the framework of Mendelian randomization, where the gene expression was treated as exposure, with methylations located within the promoter regions of genes serving as instrumental variables. Five prediction models were first utilized to characterize the relationship between the expression and methylations for each gene; then, the methylation-regulated gene expression (MReX) was obtained and the association was evaluated via Cox mixed-effect model based on MReX. We further implemented the aggregated Cauchy association test (ACAT) combination to take advantage of respective strengths of these prediction models while accounting for dependency among the p-values.

RESULTS

A total of 14 potential causal genes were discovered to be associated with the survival risk of cervical cancer in TCGA when the five prediction models were separately employed. The total number of potential causal genes was brought to 23 when conducting ACAT. Some of the newly discovered genes may be novel (e.g., YJEFN3, SPATA5L1, IMMP1L, C5orf55, PPIP5K2, ZNF330, CRYZL1, PPM1A, ESCO2, ZNF605, ZNF225, ZNF266, FICD, and OSTC). Functional analyses showed that these genes were enriched in tumor-associated pathways. Additionally, four genes (i.e., COL6A1, SYDE1, ESCO2, and GIPC1) were differentially expressed between tumor and normal tissues.

CONCLUSION

Our study discovered promising candidate genes that were causally associated with the survival risk of cervical cancer and thus provided new insights into the genetic etiology of cervical cancer.

Advances and challenges in quantitative delineation of the genetic architecture of complex traits

Background

Genome-wide association studies (GWAS) have been widely adopted in studies of human complex traits and diseases.

Results

This review surveys areas of active research: quantifying and partitioning trait heritability, fine mapping functional variants and integrative analysis, genetic risk prediction of phenotypes, and the analysis of sequencing studies that have identified millions of rare variants. Current challenges and opportunities are highlighted.

Conclusion

GWAS have fundamentally transformed the field of human complex trait genetics. Novel statistical and computational methods have expanded the scope of GWAS and have provided valuable insights on the genetic architecture underlying complex phenotypes.

A unifying framework for rare variant association testing in family-based designs, including higher criticism approaches, SKATs, and burden tests

MOTIVATION

Analysis of rare variants in family-based studies remains a challenge. Transmission-based approaches provide robustness against population stratification, but the evaluation of the significance of test statistics based on asymptotic theory can be imprecise. Also, power will depend heavily on the choice of the test statistic and on the underlying genetic architecture of the locus, which will be generally unknown.

RESULTS

In our proposed framework, we utilize the FBAT haplotype algorithm to obtain the conditional offspring genotype distribution under the null hypothesis given the sufficient statistic. Based on this conditional offspring genotype distribution, the significance of virtually any association test statistic can be evaluated based on simulations or exact computations, without the need for asymptotic approximations. Besides standard linear burden-type statistics, this enables our approach to also evaluate other test statistics such as variance components statistics, higher criticism approaches, and maximum-single-variant-statistics, where asymptotic theory might be involved or does not provide accurate approximations for rare variant data. Based on these P-values, combined test statistics such as the aggregated Cauchy association test (ACAT) can also be utilized. In simulation studies, we show that our framework outperforms existing approaches for family-based studies in several scenarios. We also applied our methodology to a TOPMed whole-genome sequencing dataset with 897 asthmatic trios from Costa Rica.

AVAILABILITY AND IMPLEMENTATION

FBAT software is available at https://sites.google.com/view/fbatwebpage. Simulation code is available at https://github.com/julianhecker/FBAT_rare_variant_test_simulations. Whole-genome sequencing data for 'NHLBI TOPMed: The Genetic Epidemiology of Asthma in Costa Rica' is available at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000988.v4.p1.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Detecting local genetic correlations with scan statistics

Genetic correlation analysis has quickly gained popularity in the past few years and provided insights into the genetic etiology of numerous complex diseases. However, existing approaches oversimplify the shared genetic architecture between different phenotypes and cannot effectively identify precise genetic regions contributing to the genetic correlation. In this work, we introduce LOGODetect, a powerful and efficient statistical method to identify small genome segments harboring local genetic correlation signals. LOGODetect automatically identifies genetic regions showing consistent associations with multiple phenotypes through a scan statistic approach. It uses summary association statistics from genome-wide association studies (GWAS) as input and is robust to sample overlap between studies. Applied to seven phenotypically distinct but genetically correlated neuropsychiatric traits, we identify 227 non-overlapping genome regions associated with multiple traits, including multiple hub regions showing concordant effects on five or more traits. Our method addresses critical limitations in existing analytic strategies and may have wide applications in post-GWAS analysis.

xGAP: A python based efficient, modular, extensible and fault tolerant genomic analysis pipeline for variant discovery

MOTIVATION

Since the first human genome was sequenced in 2001, there has been a rapid growth in the number of bioinformatic methods to process and analyze next generation sequencing (NGS) data for research and clinical studies that aim to identify genetic variants influencing diseases and traits. To achieve this goal, one first needs to call genetic variants from NGS data which requires multiple computationally intensive analysis steps. Unfortunately, there is a lack of an open source pipeline that can perform all these steps on NGS data in a manner which is fully automated, efficient, rapid, scalable, modular, user-friendly and fault tolerant. To address this, we introduce xGAP, an extensible Genome Analysis Pipeline, which implements modified GATK best practice to analyze DNA-seq data with aforementioned functionalities.

RESULTS

xGAP implements massive parallelization of the modified GATK best practice pipeline by splitting a genome into many smaller regions with efficient load-balancing to achieve high scalability. It can process 30x coverage whole-genome sequencing (WGS) data in approximately 90 minutes. In terms of accuracy of discovered variants, xGAP achieves average F1 scores of 99.37% for SNVs and 99.20% for Indels across seven benchmark WGS datasets. We achieve highly consistent results across multiple on-premises (SGE & SLURM) high performance clusters. Compared to the Churchill pipeline, with similar parallelization, xGAP is 20% faster when analyzing 50X coverage WGS in AWS. Finally, xGAP is user-friendly and fault tolerant where it can automatically re-initiate failed processes to minimize required user intervention.

AVAILABILITY

xGAP is available at https://github.com/Adigorla/xgap.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identifying rare variants for quantitative traits in extreme samples of population via Kullback-Leibler distance

Background

The rapid development of sequencing technology and simultaneously the availability of large quantities of sequence data has facilitated the identification of rare variant associated with quantitative traits. However, existing statistical methods depend on certain assumptions and thus lacking uniform power. The present study focuses on mapping rare variant associated with quantitative traits.

Results

In the present study, we proposed a two-stage strategy to identify rare variant of quantitative traits using phenotype extreme selection design and Kullback-Leibler distance, where the first stage was association analysis and the second stage was fine mapping. We presented a statistic and a linkage disequilibrium measure for the first stage and the second stage, respectively. Theory analysis and simulation study showed that (1) the power of the proposed statistic for association analysis increased with the stringency of the sample selection and was affected slightly by non-causal variants and opposite effect variants, (2) the statistic here achieved higher power than three commonly used methods, and (3) the linkage disequilibrium measure for fine mapping was independent of the frequencies of non-causal variants and simply dependent on the frequencies of causal variants.

Conclusions

We conclude that the two-stage strategy here can be used effectively to mapping rare variant associated with quantitative traits.

Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale

Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce 'annotation principal components', multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.

PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection

Germline disease-causing variants are generally more spatially clustered in protein 3-dimensional structures than benign variants. Motivated by this tendency, we develop a fast and powerful protein-structure-based scan (PSCAN) approach for evaluating gene-level associations with complex disease and detecting signal variants. We validate PSCAN's performance on synthetic data and two real data sets for lipid traits and Alzheimer's disease. Our results demonstrate that PSCAN performs competitively with existing gene-level tests while increasing power and identifying more specific signal variant sets. Furthermore, PSCAN enables generation of hypotheses about the molecular basis for the associations in the context of protein structures and functional domains.

Rare variant association testing in the non-coding genome

The development of next-generation sequencing technologies has opened-up some new possibilities to explore the contribution of genetic variants to human diseases and in particular that of rare variants. Statistical methods have been developed to test for association with rare variants that require the definition of testing units and, in these testing units, the selection of qualifying variants to include in the test. In the coding regions of the genome, testing units are usually the different genes and qualifying variants are selected based on their functional effects on the encoded proteins. Extending these tests to the non-coding regions of the genome is challenging. Testing units are difficult to define as the non-coding genome organisation is still rather unknown. Qualifying variants are difficult to select as the functional impact of non-coding variants on gene expression is hard to predict. These difficulties could explain why very few investigators so far have analysed the non-coding parts of their whole genome sequencing data. These non-coding parts yet represent the vast majority of the genome and some studies suggest that they could play a major role in disease susceptibility. In this review, we discuss recent experimental and statistical developments to gain knowledge on the non-coding genome and how this knowledge could be used to include rare non-coding variants in association tests. We describe the few studies that have considered variants from the non-coding genome in association tests and how they managed to define testing units and select qualifying variants.

A fast and powerful eQTL weighted method to detect genes associated with complex trait using GWAS summary data

Although genomewide association studies (GWASs) have identified many genetic variants underlying complex traits, a large fraction of heritability still remains unexplained. Integrative analysis that incorporates additional information, such as expression quantitativetrait locus (eQTL) data into sequencing studies (denoted as transcriptomewide association study [TWAS]), can aid the discovery of trait-associated genetic variants. However, general TWAS methods only incorporate one eQTL-derived weight (e.g., cis-effect), and thus can suffer a substantial loss of power when the single estimated cis-effect is not predictive for the effect size of a genetic variant or when there are estimation errors in the estimated cis-effect, or if the data are not consistent with the model assumption. In this study, we propose an omnibus test (OT) which utilizes a Cauchy association test to integrate association evidence demonstrated by three different traditional tests (burden test, quadratic test, and adaptive test) using GWAS summary data with multiple eQTL-derived weights. The p value of the proposed test can be calculated analytically, and thus it is fast and efficient. We applied our proposed test to two schizophrenia (SCZ) GWAS summary data sets and two lipids trait (HDL) GWAS summary data sets. Compared with the three traditional tests, our proposed OT can identify more trait-associated genes.