Hereditary retinoblastoma iPSC model reveals aberrant spliceosome function driving bone malignancies.

The RB1 gene is frequently mutated in human cancers but its role in tumorigenesis remains incompletely defined. Using an induced pluripotent stem cell (iPSC) model of hereditary retinoblastoma (RB), we report that the spliceosome is an up-regulated target responding to oncogenic stress in RB1-mutant cells. By investigating transcriptomes and genome occupancies in RB iPSC­derived osteoblasts (OBs), we discover that both E2F3a, which mediates spliceosomal gene expression, and pRB, which antagonizes E2F3a, coregulate more than one-third of spliceosomal genes by cobinding to their promoters or enhancers. Pharmacological inhibition of the spliceosome in RB1-mutant cells leads to global intron retention, decreased cell proliferation, and impaired tumorigenesis. Tumor specimen studies and genome-wide TCGA (The Cancer Genome Atlas) expression profile analyses support the clinical relevance of pRB and E2F3a in modulating spliceosomal gene expression in multiple cancer types including osteosarcoma (OS). High levels of pRB/E2F3a­regulated spliceosomal genes are associated with poor OS patient survival. Collectively, these findings reveal an undiscovered connection between pRB, E2F3a, the spliceosome, and tumorigenesis, pointing to the spliceosomal machinery as a potentially widespread therapeutic vulnerability of pRB-deficient cancers.

Polygenic risk impacts PDGFRA mutation penetrance in non-syndromic cleft lip and palate.

Non-syndromic cleft lip with or without cleft palate (NSCL/P) is a common, severe craniofacial malformation that imposes significant medical, psychosocial and financial burdens. NSCL/P is a multifactorial disorder with genetic and environmental factors playing etiologic roles. Currently, only 25% of the genetic variation underlying NSCL/P has been identified by linkage, candidate gene and genome-wide association studies. In this study, whole-genome sequencing and genome-wide genotyping followed by polygenic risk score (PRS) and linkage analyses were used to identify the genetic etiology of NSCL/P in a large three-generation family. We identified a rare missense variant in PDGFRA (c.C2740T; p.R914W) as potentially etiologic in a gene-based association test using pVAAST (P = 1.78 × 10-4) and showed decreased penetrance. PRS analysis suggested that variant penetrance was likely modified by common NSCL/P risk variants, with lower scores found among unaffected carriers. Linkage analysis provided additional support for PRS-modified penetrance, with a 7.4-fold increase in likelihood after conditioning on PRS. Functional characterization experiments showed that the putatively causal variant was null for signaling activity in vitro; further, perturbation of pdgfra in zebrafish embryos resulted in unilateral orofacial clefting. Our findings show that a rare PDGFRA variant, modified by additional common NSCL/P risk variants, have a profound effect on NSCL/P risk. These data provide compelling evidence for multifactorial inheritance long postulated to underlie NSCL/P and may explain some unusual familial patterns.

Host genetic effects in pneumonia.

Given the coronavirus disease 2019 (COVID-19) pandemic, investigations into host susceptibility to infectious diseases and downstream sequelae have never been more relevant. Pneumonia is a lung disease that can cause respiratory failure and hypoxia and is a common complication of infectious diseases, including COVID-19. Few genome-wide association studies (GWASs) of host susceptibility and severity of pneumonia have been conducted. We performed GWASs of pneumonia susceptibility and severity in the Vanderbilt University biobank (BioVU) with linked electronic health records (EHRs), including Illumina Expanded Multi-Ethnic Global Array (MEGAEX)-genotyped European ancestry (EA, n= 69,819) and African ancestry (AA, n = 15,603) individuals. Two regions of large effect were identified: the CFTR locus in EA (rs113827944; OR = 1.84, p value = 1.2 × 10-36) and HBB in AA (rs334 [p.Glu7Val]; OR = 1.63, p value = 3.5 × 10-13). Mutations in these genes cause cystic fibrosis (CF) and sickle cell disease (SCD), respectively. After removing individuals diagnosed with CF and SCD, we assessed heterozygosity effects at our lead variants. Further GWASs after removing individuals with CF uncovered an additional association in R3HCC1L (rs10786398; OR = 1.22, p value = 3.5 × 10-8), which was replicated in two independent datasets: UK Biobank (n = 459,741) and 7,985 non-overlapping BioVU subjects, who are genotyped on arrays other than MEGAEX. This variant was also validated in GWASs of COVID-19 hospitalization and lung function. Our results highlight the importance of the host genome in infectious disease susceptibility and severity and offer crucial insight into genetic effects that could potentially influence severity of COVID-19 sequelae.

A phase II trial of apalutamide for intermediate-risk prostate cancer and molecular correlates.

OBJECTIVES: To determine whether 6 months of preoperative apalutamide for intermediate-risk prostate cancer (IRPCa) reduces the aggregate postoperative radiotherapy risk and to evaluate associations of molecular perturbations with clinical outcomes in this study cohort. PATIENTS AND METHODS: Between May 2018 and February 2020, eligible patients with IRPCa (Gleason 3 + 4 or 4 + 3 and clinical T2b-c or prostate-specific antigen level of 10-20 ng/mL) were treated with apalutamide 240 mg/day for 6 months followed by radical prostatectomy (RP) in this single-arm, phase II trial. The primary endpoint was presence of any adverse pathological feature at risk of pelvic radiation (pathological T stage after neoadjuvant therapy [yp]T3 or ypN1 or positive surgical margins). Translational studies, including germline and somatic DNA alterations and RNA and protein expression, were performed on post-apalutamide RP specimens, and assessed for associations with clinical outcomes. RESULTS: A total of 40 patients underwent a RP, and only one patient discontinued apalutamide prior to 6 months. In all, 40% had adverse pathological features at time of RP, and the 3-year biochemical recurrence (BCR) rate was 15%, with 27.5% being not evaluable. Genomic alterations frequently seen in metastatic PCas, such as androgen receptor (AR), tumour protein p53 (TP53), phosphatase and tensin homologue (PTEN), or BReast CAncer associated gene (BRCA1/2) were underrepresented in this localised cohort. Adverse pathological features and BCR at 3-years were associated with increased expression of select cell cycle (e.g., E2F targets: adjusted P value [Padj] < 0.001, normalised enrichment score [NES] 2.47) and oxidative phosphorylation (Padj < 0.001, NES 1.62) pathways. CONCLUSIONS: Preoperative apalutamide did not reduce the aggregate postoperative radiation risk to the pre-specified threshold in unselected men with IRPCa. However, transcriptomic analysis identified key dysregulated pathways in tumours associated with adverse pathological outcomes and BCR, which warrant future study. Further investigation of preoperative therapy is underway for men with high-risk PCa.

Integrated case-control and somatic-germline interaction analyses of soft-tissue sarcoma.

PURPOSE: The contribution of rare genetic variation in the development of soft-tissue sarcoma (STS) remains underexplored. To address this gap, we conducted a whole-exome case-control and somatic-germline interaction study to identify and characterise STS susceptible genes. METHODS: The study involved 219 STS cases from The Cancer Genome Atlas and 3507 controls. All cases and controls were matched genetically onEuropean ancestry based on the 1000 Genomes project. Cross-platform technological stratification was performed with XPAT and gene-based association tests with VAAST 2. RESULTS: NF1 exhibited the strongest genome-wide signal across the six subtypes, with p=1×10-5. We also observed nominally significant association signals for three additional genes of interest, TP53 (p=0.0025), RB1 (p=0.0281), and MSH2 (p=0.0085). BAG1, which has not previously been implicated in STS, exhibited the strongest genome-wide signal after NF1, with p=6×10-5. The association signals for NF1 and MSH2 were driven primarily by truncating variants, with ORs of 39 (95% CI: 7.1 to 220) for NF1 and 33 (95% CI: 2.4 to 460) for MSH2. In contrast, the association signals for RB1 and BAG1 were driven primarily by predicted damaging missense variants, with estimated ORs of 12 (95% CI: 2.4 to 59) for RB1 and 20 (95% CI: 1.4 to 300) for BAG1. CONCLUSIONS: Our results confirm that pathogenic variants in NF1, RB1 and TP53 confer large increases in the risk of developing multiple STS subtypes, provide support for the role of MSH2 in STS susceptibility and identify BAG1 as a novel candidate STS risk gene.

Functionally oriented analysis of cardiometabolic traits in a trans-ethnic sample.

Interpretation of genetic association results is difficult because signals often lack biological context. To generate hypotheses of the functional genetic etiology of complex cardiometabolic traits, we estimated the genetically determined component of gene expression from common variants using PrediXcan (1) and determined genes with differential predicted expression by trait. PrediXcan imputes tissue-specific expression levels from genetic variation using variant-level effect on gene expression in transcriptome data. To explore the value of imputed genetically regulated gene expression (GReX) models across different ancestral populations, we evaluated imputed expression levels for predictive accuracy genome-wide in RNA sequence data in samples drawn from European-ancestry and African-ancestry populations and identified substantial predictive power using European-derived models in a non-European target population. We then tested the association of GReX on 15 cardiometabolic traits including blood lipid levels, body mass index, height, blood pressure, fasting glucose and insulin, RR interval, fibrinogen level, factor VII level and white blood cell and platelet counts in 15 755 individuals across three ancestry groups, resulting in 20 novel gene-phenotype associations reaching experiment-wide significance across ancestries. In addition, we identified 18 significant novel gene-phenotype associations in our ancestry-specific analyses. Top associations were assessed for additional support via query of S-PrediXcan (2) results derived from publicly available genome-wide association studies summary data. Collectively, these findings illustrate the utility of transcriptome-based imputation models for discovery of cardiometabolic effect genes in a diverse dataset.

XPAT: a toolkit to conduct cross-platform association studies with heterogeneous sequencing datasets.

High-throughput sequencing data are increasingly being made available to the research community for secondary analyses, providing new opportunities for large-scale association studies. However, heterogeneity in target capture and sequencing technologies often introduce strong technological stratification biases that overwhelm subtle signals of association in studies of complex traits. Here, we introduce the Cross-Platform Association Toolkit, XPAT, which provides a suite of tools designed to support and conduct large-scale association studies with heterogeneous sequencing datasets. XPAT includes tools to support cross-platform aware variant calling, quality control filtering, gene-based association testing and rare variant effect size estimation. To evaluate the performance of XPAT, we conducted case-control association studies for three diseases, including 783 breast cancer cases, 272 ovarian cancer cases, 205 Crohn disease cases and 3507 shared controls (including 1722 females) using sequencing data from multiple sources. XPAT greatly reduced Type I error inflation in the case-control analyses, while replicating many previously identified disease-gene associations. We also show that association tests conducted with XPAT using cross-platform data have comparable performance to tests using matched platform data. XPAT enables new association studies that combine existing sequencing datasets to identify genetic loci associated with common diseases and other complex traits.

Early history of Neanderthals and Denisovans.

Extensive DNA sequence data have made it possible to reconstruct human evolutionary history in unprecedented detail. We introduce a method to study the past several hundred thousand years. Our results show that (i) the Neanderthal-Denisovan lineage declined to a small size just after separating from the modern lineage, (ii) Neanderthals and Denisovans separated soon thereafter, and (iii) the subsequent Neanderthal population was large and deeply subdivided. They also (iv) support previous estimates of gene flow from Neanderthals into modern Eurasians. These results suggest an archaic human diaspora early in the Middle Pleistocene.

Evolutionary history of Tibetans inferred from whole-genome sequencing.

The indigenous people of the Tibetan Plateau have been the subject of much recent interest because of their unique genetic adaptations to high altitude. Recent studies have demonstrated that the Tibetan EPAS1 haplotype is involved in high altitude-adaptation and originated in an archaic Denisovan-related population. We sequenced the whole-genomes of 27 Tibetans and conducted analyses to infer a detailed history of demography and natural selection of this population. We detected evidence of population structure between the ancestral Han and Tibetan subpopulations as early as 44 to 58 thousand years ago, but with high rates of gene flow until approximately 9 thousand years ago. The CMS test ranked EPAS1 and EGLN1 as the top two positive selection candidates, and in addition identified PTGIS, VDR, and KCTD12 as new candidate genes. The advantageous Tibetan EPAS1 haplotype shared many variants with the Denisovan genome, with an ancient gene tree divergence between the Tibetan and Denisovan haplotypes of about 1 million years ago. With the exception of EPAS1, we observed no evidence of positive selection on Denisovan-like haplotypes.

Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium.

Genome-wide association studies (GWAS) have identified >300 loci associated with measures of adiposity including body mass index (BMI) and waist-to-hip ratio (adjusted for BMI, WHRadjBMI), but few have been identified through screening of the African ancestry genomes. We performed large scale meta-analyses and replications in up to 52,895 individuals for BMI and up to 23,095 individuals for WHRadjBMI from the African Ancestry Anthropometry Genetics Consortium (AAAGC) using 1000 Genomes phase 1 imputed GWAS to improve coverage of both common and low frequency variants in the low linkage disequilibrium African ancestry genomes. In the sex-combined analyses, we identified one novel locus (TCF7L2/HABP2) for WHRadjBMI and eight previously established loci at P < 5×10-8: seven for BMI, and one for WHRadjBMI in African ancestry individuals. An additional novel locus (SPRYD7/DLEU2) was identified for WHRadjBMI when combined with European GWAS. In the sex-stratified analyses, we identified three novel loci for BMI (INTS10/LPL and MLC1 in men, IRX4/IRX2 in women) and four for WHRadjBMI (SSX2IP, CASC8, PDE3B and ZDHHC1/HSD11B2 in women) in individuals of African ancestry or both African and European ancestry. For four of the novel variants, the minor allele frequency was low (<5%). In the trans-ethnic fine mapping of 47 BMI loci and 27 WHRadjBMI loci that were locus-wide significant (P < 0.05 adjusted for effective number of variants per locus) from the African ancestry sex-combined and sex-stratified analyses, 26 BMI loci and 17 WHRadjBMI loci contained ≤ 20 variants in the credible sets that jointly account for 99% posterior probability of driving the associations. The lead variants in 13 of these loci had a high probability of being causal. As compared to our previous HapMap imputed GWAS for BMI and WHRadjBMI including up to 71,412 and 27,350 African ancestry individuals, respectively, our results suggest that 1000 Genomes imputation showed modest improvement in identifying GWAS loci including low frequency variants. Trans-ethnic meta-analyses further improved fine mapping of putative causal variants in loci shared between the African and European ancestry populations.

Assessing the performance of in silico methods for predicting the pathogenicity of variants in the gene CHEK2, among Hispanic females with breast cancer.

The availability of disease-specific genomic data is critical for developing new computational methods that predict the pathogenicity of human variants and advance the field of precision medicine. However, the lack of gold standards to properly train and benchmark such methods is one of the greatest challenges in the field. In response to this challenge, the scientific community is invited to participate in the Critical Assessment for Genome Interpretation (CAGI), where unpublished disease variants are available for classification by in silico methods. As part of the CAGI-5 challenge, we evaluated the performance of 18 submissions and three additional methods in predicting the pathogenicity of single nucleotide variants (SNVs) in checkpoint kinase 2 (CHEK2) for cases of breast cancer in Hispanic females. As part of the assessment, the efficacy of the analysis method and the setup of the challenge were also considered. The results indicated that though the challenge could benefit from additional participant data, the combined generalized linear model analysis and odds of pathogenicity analysis provided a framework to evaluate the methods submitted for SNV pathogenicity identification and for comparison to other available methods. The outcome of this challenge and the approaches used can help guide further advancements in identifying SNV-disease relationships.

PADRE: Pedigree-Aware Distant-Relationship Estimation.

Accurate estimation of shared ancestry is an important component of many genetic studies; current prediction tools accurately estimate pairwise genetic relationships up to the ninth degree. Pedigree-aware distant-relationship estimation (PADRE) combines relationship likelihoods generated by estimation of recent shared ancestry (ERSA) with likelihoods from family networks reconstructed by pedigree reconstruction and identification of a maximum unrelated set (PRIMUS), improving the power to detect distant relationships between pedigrees. Using PADRE, we estimated relationships from simulated pedigrees and three extended pedigrees, correctly predicting 20% more fourth- through ninth-degree simulated relationships than when using ERSA alone. By leveraging pedigree information, PADRE can even identify genealogical relationships between individuals who are genetically unrelated. For example, although 95% of 13(th)-degree relatives are genetically unrelated, in simulations, PADRE correctly predicted 50% of 13(th)-degree relationships to within one degree of relatedness. The improvement in prediction accuracy was consistent between simulated and actual pedigrees. We also applied PADRE to the HapMap3 CEU samples and report new cryptic relationships and validation of previously described relationships between families. PADRE greatly expands the range of relationships that can be estimated by using genetic data in pedigrees.

ALPK1 missense pathogenic variant in five families leads to ROSAH syndrome, an ocular multisystem autosomal dominant disorder.

PURPOSE: To identify the molecular cause in five unrelated families with a distinct autosomal dominant ocular systemic disorder we called ROSAH syndrome due to clinical features of retinal dystrophy, optic nerve edema, splenomegaly, anhidrosis, and migraine headache. METHODS: Independent discovery exome and genome sequencing in families 1, 2, and 3, and confirmation in families 4 and 5. Expression of wild-type messenger RNA and protein in human and mouse tissues and cell lines. Ciliary assays in fibroblasts from affected and unaffected family members. RESULTS: We found the heterozygous missense variant in the ɑ-kinase gene, ALPK1, (c.710C>T, [p.Thr237Met]), segregated with disease in all five families. All patients shared the ROSAH phenotype with additional low-grade ocular inflammation, pancytopenia, recurrent infections, and mild renal impairment in some. ALPK1 was notably expressed in retina, retinal pigment epithelium, and optic nerve, with immunofluorescence indicating localization to the basal body of the connecting cilium of the photoreceptors, and presence in the sweat glands. Immunocytofluorescence revealed expression at the centrioles and spindle poles during metaphase, and at the base of the primary cilium. Affected family member fibroblasts demonstrated defective ciliogenesis. CONCLUSION: Heterozygosity for ALPK1, p.Thr237Met leads to ROSAH syndrome, an autosomal dominant ocular systemic disorder.

Integrated case-control and somatic-germline interaction analyses of melanoma susceptibility genes.

While a number of genes have been implicated in melanoma susceptibility, the role of protein-coding variation in melanoma development and progression remains underexplored. To better characterize the role of germline coding variation in melanoma, we conducted a whole-exome case-control and somatic-germline interaction study involving 322 skin cutaneous melanoma cases from The Cancer Genome Atlas and 3607 controls of European ancestry. We controlled for cross-platform technological stratification using XPAT and conducted gene-based association tests using VAAST 2. Four established melanoma susceptibility genes achieved nominal statistical significance, MC1R (p = .0014), MITF (p = .0165) BRCA2 (p = .0206), and MTAP (p = .0393). We also observed a suggestive association for FANCA (p = .002), a gene previously implicated in melanoma survival. The association signal for BRCA2 was driven primarily by likely gene disrupting (LGD) variants, with an Odds Ratio (OR) of 5.62 (95% Confidence Interval (CI) 1.03-30.1). In contrast, the association signals for MC1R and MITF were driven primarily by predicted pathogenic missense variants, with estimated ORs of 1.4 to 3.0 for MC1R and 4.1 for MITF. MTAP exhibited an excess of both LGD and predicted damaging missense variants among cases, with ORs of 5.62 and 3.72, respectively, although neither category was significant. For individuals with known or predicted damaging variants, age of disease onset was significantly lower for two of the four genes, MC1R (p = .005) and MTAP (p = .035). In an analysis of germline carrier status and overlapping copy number alterations, we observed no evidence to support a two-hit model of carcinogenesis in any of the four genes. Although MC1R carriers were represented proportionally among the four molecular tumor subtypes, these individuals accounted for 69% of ultraviolet (UV) radiation mutational signatures among triple-wild type tumors (p = .040), highlighting the increased sensitivity to UV exposure among individuals with loss-of-function variants in MC1R.

Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.

Phevor integrates phenotype, gene function, and disease information with personal genomic data for improved power to identify disease-causing alleles. Phevor works by combining knowledge resident in multiple biomedical ontologies with the outputs of variant-prioritization tools. It does so by using an algorithm that propagates information across and between ontologies. This process enables Phevor to accurately reprioritize potentially damaging alleles identified by variant-prioritization tools in light of gene function, disease, and phenotype knowledge. Phevor is especially useful for single-exome and family-trio-based diagnostic analyses, the most commonly occurring clinical scenarios and ones for which existing personal genome diagnostic tools are most inaccurate and underpowered. Here, we present a series of benchmark analyses illustrating Phevor's performance characteristics. Also presented are three recent Utah Genome Project case studies in which Phevor was used to identify disease-causing alleles. Collectively, these results show that Phevor improves diagnostic accuracy not only for individuals presenting with established disease phenotypes but also for those with previously undescribed and atypical disease presentations. Importantly, Phevor is not limited to known diseases or known disease-causing alleles. As we demonstrate, Phevor can also use latent information in ontologies to discover genes and disease-causing alleles not previously associated with disease.

Relationship estimation from whole-genome sequence data.

The determination of the relationship between a pair of individuals is a fundamental application of genetics. Previously, we and others have demonstrated that identity-by-descent (IBD) information generated from high-density single-nucleotide polymorphism (SNP) data can greatly improve the power and accuracy of genetic relationship detection. Whole-genome sequencing (WGS) marks the final step in increasing genetic marker density by assaying all single-nucleotide variants (SNVs), and thus has the potential to further improve relationship detection by enabling more accurate detection of IBD segments and more precise resolution of IBD segment boundaries. However, WGS introduces new complexities that must be addressed in order to achieve these improvements in relationship detection. To evaluate these complexities, we estimated genetic relationships from WGS data for 1490 known pairwise relationships among 258 individuals in 30 families along with 46 population samples as controls. We identified several genomic regions with excess pairwise IBD in both the pedigree and control datasets using three established IBD methods: GERMLINE, fastIBD, and ISCA. These spurious IBD segments produced a 10-fold increase in the rate of detected false-positive relationships among controls compared to high-density microarray datasets. To address this issue, we developed a new method to identify and mask genomic regions with excess IBD. This method, implemented in ERSA 2.0, fully resolved the inflated cryptic relationship detection rates while improving relationship estimation accuracy. ERSA 2.0 detected all 1(st) through 6(th) degree relationships, and 55% of 9(th) through 11(th) degree relationships in the 30 families. We estimate that WGS data provides a 5% to 15% increase in relationship detection power relative to high-density microarray data for distant relationships. Our results identify regions of the genome that are highly problematic for IBD mapping and introduce new software to accurately detect 1(st) through 9(th) degree relationships from whole-genome sequence data.

Genomic analysis of natural selection and phenotypic variation in high-altitude mongolians.

Deedu (DU) Mongolians, who migrated from the Mongolian steppes to the Qinghai-Tibetan Plateau approximately 500 years ago, are challenged by environmental conditions similar to native Tibetan highlanders. Identification of adaptive genetic factors in this population could provide insight into coordinated physiological responses to this environment. Here we examine genomic and phenotypic variation in this unique population and present the first complete analysis of a Mongolian whole-genome sequence. High-density SNP array data demonstrate that DU Mongolians share genetic ancestry with other Mongolian as well as Tibetan populations, specifically in genomic regions related with adaptation to high altitude. Several selection candidate genes identified in DU Mongolians are shared with other Asian groups (e.g., EDAR), neighboring Tibetan populations (including high-altitude candidates EPAS1, PKLR, and CYP2E1), as well as genes previously hypothesized to be associated with metabolic adaptation (e.g., PPARG). Hemoglobin concentration, a trait associated with high-altitude adaptation in Tibetans, is at an intermediate level in DU Mongolians compared to Tibetans and Han Chinese at comparable altitude. Whole-genome sequence from a DU Mongolian (Tianjiao1) shows that about 2% of the genomic variants, including more than 300 protein-coding changes, are specific to this individual. Our analyses of DU Mongolians and the first Mongolian genome provide valuable insight into genetic adaptation to extreme environments.

VAAST 2.0: improved variant classification and disease-gene identification using a conservation-controlled amino acid substitution matrix.

The need for improved algorithmic support for variant prioritization and disease-gene identification in personal genomes data is widely acknowledged. We previously presented the Variant Annotation, Analysis, and Search Tool (VAAST), which employs an aggregative variant association test that combines both amino acid substitution (AAS) and allele frequencies. Here we describe and benchmark VAAST 2.0, which uses a novel conservation-controlled AAS matrix (CASM), to incorporate information about phylogenetic conservation. We show that the CASM approach improves VAAST's variant prioritization accuracy compared to its previous implementation, and compared to SIFT, PolyPhen-2, and MutationTaster. We also show that VAAST 2.0 outperforms KBAC, WSS, SKAT, and variable threshold (VT) using published case-control datasets for Crohn disease (NOD2), hypertriglyceridemia (LPL), and breast cancer (CHEK2). VAAST 2.0 also improves search accuracy on simulated datasets across a wide range of allele frequencies, population-attributable disease risks, and allelic heterogeneity, factors that compromise the accuracies of other aggregative variant association tests. We also demonstrate that, although most aggregative variant association tests are designed for common genetic diseases, these tests can be easily adopted as rare Mendelian disease-gene finders with a simple ranking-by-statistical-significance protocol, and the performance compares very favorably to state-of-art filtering approaches. The latter, despite their popularity, have suboptimal performance especially with the increasing case sample size.