Selection Probability for Rare Variant Association Studies.

Genotype-phenotype (G-P) analyses for complex morphological traits typically utilize simple, predetermined anatomical measures or features derived via unsupervised dimension reduction techniques (e.g. principal component analysis (PCA) or eigen-shapes). Despite the popularity of these approaches, they do not necessarily reveal axes of phenotypic variation that are genetically relevant. Therefore, we introduce a framework to optimize phenotyping for G-P analyses, such as genome-wide association studies (GWAS) of common variants or rare variant association studies (RVAS) of rare variants. Our strategy is two-fold: (i) we construct a multidimensional feature space spanning a wide range of phenotypic variation, and (ii) within this feature space, we use an optimization algorithm to search for directions or feature combinations that are genetically enriched. To test our approach, we examine human facial shape in the context of GWAS and RVAS. In GWAS, we optimize for phenotypes exhibiting high heritability, estimated from either family data or genomic relatedness measured in unrelated individuals. In RVAS, we optimize for the skewness of phenotype distributions, aiming to detect commingled distributions that suggest single or few genomic loci with major effects. We compare our approach with eigen-shapes as baseline in GWAS involving 8246 individuals of European ancestry and in gene-based tests of rare variants with a subset of 1906 individuals. After applying linkage disequilibrium score regression to our GWAS results, heritability-enriched phenotypes yielded the highest SNP heritability, followed by eigen-shapes, while commingling-based traits displayed the lowest SNP heritability. Heritability-enriched phenotypes also exhibited higher discovery rates, identifying the same number of independent genomic loci as eigen-shapes with a smaller effective number of traits. For RVAS, commingling-based traits resulted in more genes passing the exome-wide significance threshold than eigen-shapes, while heritability-enriched phenotypes lead to only a few associations. Overall, our results demonstrate that optimized phenotyping allows for the extraction of genetically relevant traits that can specifically enhance discovery efforts of common and rare variants, as evidenced by their increased power in facial GWAS and RVAS.

Genetic counseling in direct-to-consumer exome sequencing: a case report.

Genome-wide associations studies (GWAS) have been very successful in identifying common genetic variation associated to numerous complex diseases [1]. However, most of the identified common genetic variants appear to confer modest risk and few causal alleles have been identified [2]. Furthermore, these associations account for a small portion of the total heritability of inherited disease variation [1]. This has led to the reexamination of the contribution of environment, gene-gene and gene-environment interactions, and rare genetic variants in complex diseases [1, 3, 4]. There is strong evidence that rare variants play an important role in complex disease etiology and may have larger genetic effects than common variants [2]. Currently, much of what we know regarding the contribution of rare genetic variants to disease risk is based on a limited number of phenotypes and candidate genes. However, rapid advancement of second generation sequencing technologies will invariably lead to widespread association studies comparing whole exome and eventually whole genome sequencing of cases and controls. A tremendous challenge for enabling these "next generation" medical genomic studies is developing statistical approaches for correlating rare genetic variants with disease outcome. The analysis of rare variants is challenging since methods used for common variants are woefully underpowered. Therefore, methods that can deal with genetic heterogeneity at the trait-associated locus have been developed to analyze rare variants. These methods instead analyzing individual variants analyze variants within a region/gene as a group and usually rely on collapsing. They can be applied to both in cases vs. controls and quantitative trait studies are needed. The paper of Bansal et al. in this volume describes the application of a number of statistical methods for testing associations between rare variants in two genes to obesity. The authors considered the relative merits of the different methods as well as important implementation details, such as the leveraging of genomic annotations and determining p-values. Knowledge of haplotypes can increase the power of GWAS studies and also highlight associations that are impossible to detect without haplotype phase (e.g. loss of heterozygosity). Even more complicated phase-dependent interactions of variants in linkage equilibrium have also been suggested as possible causes of missing heritability. In their work, Hallsorsson et al. formulate algorithmic strategies for haplotype phasing by multi-assembly of shared haplotypes from next-generation sequencing data. These methods would allow testing haplotypes harboring rare variants for association and potentially increase their explanatory power. Since single SNP tests are often underpowered in rare variant association analysis, Zeggini and Asimit propose a locus-based method that has high power in the presence of rare variants and that incorporate base quality scores available for sequencing data. Their results suggest that this multi-marker approach may be best suited for smaller regions, or after some filtering to reduce the number of SNPs that are jointly tested to reduce loss of power due to multiple-testing adjustments. Finally, the paper of Zhou et al., presents a penalized regression framework for association testing on sequence data, in the presence of both common and rare variants. This method also introduces the use of weights to incorporate available biological information on the variants. Although these tactics improve both false positive and false negative rates, they represent an incremental development and there is still significant room for improvement. With the development of sequencing technologies and methods to detect complex trait rare variant associations many new and exciting discovery are imminent. The analysis of rare variants is still in its infancy and the next few years promises to produce many new methods to meet the special demands of analyzing this type of data. Note from Publisher: This article contains the abstract and references.

In recent years, there has been an increasing interest in detecting disease-related rare variants in sequencing studies. Numerous studies have shown that common variants can only explain a small proportion of the phenotypic variance for complex diseases. More and more evidence suggests that some of this missing heritability can be explained by rare variants. Considering the importance of rare variants, researchers have proposed a considerable number of methods for identifying the rare variants associated with complex diseases. Extensive research has been carried out on testing the association between rare variants and dichotomous, continuous or ordinal traits. So far, however, there has been little discussion about the case in which both genotypes and phenotypes are ordinal variables. This paper introduces a method based on the γ-statistic, called OV-RV, for examining disease-related rare variants when both genotypes and phenotypes are ordinal. At present, little is known about the asymptotic distribution of the γ-statistic when conducting association analyses for rare variants. One advantage of OV-RV is that it provides a robust estimation of the distribution of the γ-statistic by employing the permutation approach proposed by Fisher. We also perform extensive simulations to investigate the numerical performance of OV-RV under various model settings. The simulation results reveal that OV-RV is valid and efficient; namely, it controls the type I error approximately at the pre-specified significance level and achieves greater power at the same significance level. We also apply OV-RV for rare variant association studies of diastolic blood pressure.

Molecular diagnosis for growth hormone deficiency in Chinese children and adolescents and evaluation of impact of rare genetic variants on treatment efficacy of growth hormone

Next-generation sequencing technology makes directly testing rare variants possible. However, existing statistical methods to detect common variants may not be optimal for testing rare variants because of allelic heterogeneity as well as the extreme rarity of individual variants. Recently, several statistical methods to detect associations of rare variants were developed, including population-based and family-based methods. Compared with population-based methods, family-based methods have more power and can prevent bias induced by population substructure. Both population-based and family-based methods for rare variant association studies are essentially testing the effect of a weighted combination of variants or its function. How to model the weights is critical for the testing power because the number of observations for any given rare variant is small and the multiple-test correction is more stringent for rare variants. We propose 4 weighting schemes for the family-based rare variants test (FBAT-v) to test for the effects of both rare and common variants across the genome. Applying FBAT-v with the proposed weighting schemes on the Genetic Analysis Workshop 19 family data indicates that the power of FBAT-v can be comparatively enhanced in most circumstances.

Some of the well-known functional alcohol dehydrogenase (ADH) gene variants (e.g. ADH1B*2, ADH1B*3 and ADH1C*2) that significantly affect the risk of alcohol dependence are rare variants in most populations. In the present study, we comprehensively examined the associations between rare ADH variants [minor allele frequency (MAF) <0.05] and alcohol dependence, with several other neuropsychiatric and neurological disorders as reference. A total of 49,358 subjects in 22 independent cohorts with 11 different neuropsychiatric and neurological disorders were analyzed, including 3 cohorts with alcohol dependence. The entire ADH gene cluster (ADH7-ADH1C-ADH1B-ADH1A-ADH6-ADH4-ADH5 at Chr4) was imputed in all samples using the same reference panels that included whole-genome sequencing data. We stringently cleaned the phenotype and genotype data to obtain a total of 870 single nucleotide polymorphisms with 0< MAF <0.05 for association analysis. We found that a rare variant constellation across the entire ADH gene cluster was significantly associated with alcohol dependence in European-Americans (Fp1: simulated global P=0.045), European-Australians (Fp5: global P=0.027; collapsing: P=0.038) and African-Americans (Fp5: global P=0.050; collapsing: P=0.038), but not with any other neuropsychiatric disease. Association signals in this region came principally from ADH6, ADH7, ADH1B and ADH1C. In particular, a rare ADH6 variant constellation showed a replicable association with alcohol dependence across these three independent cohorts. No individual rare variants were statistically significantly associated with any disease examined after group- and region-wide correction for multiple comparisons. We conclude that rare ADH variants are specific for alcohol dependence. The ADH gene cluster may harbor a causal variant(s) for alcohol dependence.

Possible role of rare variants in Trace amine associated receptor 1 in schizophrenia

In children and adolescents, osteosarcoma (OS) is the most common malignant bone tumor. OS occurs in certain cancer predisposition syndromes at a higher than expected frequency. However, outside of these rare syndromes, OS is significantly more common and its genetic etiology remains poorly understood. We conducted an evaluation of rare exonic variants in 545 unselected OS cases compared with 1061 cancer-free controls using whole-exome sequencing of blood or buccal cell DNA to estimate the prevalence and burden of rare deleterious germline variants. We assessed potentially pathogenic rare variants in 126 established cancer predisposing genes (CPG) and known somatically mutated genes. We also surveyed the exome for genes with a higher burden of rare variants in 342 EUR cases with 994 EUR controls sequenced at the same time. Rare genetic variants, defined by a MAF &amp;lt;0.01 in 1000G, ESP or ExAC, that passed quality control filters were classified based on well-annotated databases (IARC-TP53, Telomerase Database, ARUP, BIC, Insight and COSMIC-hotspots), variant databases (e.g. Clinvar, HGMD and LOVD), and in silico predictions (Sift, Polyphen, Mutation assessor, MutationTaster, FATHMM and LTR). Rare variants matching known pathogenic variants, or inexact matches at the same residue, in selected well-annotated databases were labeled as putative pathogenic variants. High impact variants (frameshift, stop gain/loss, or known splice sites) not matching a previously identified pathogenic variant in the selected databases were categorized as probably pathogenic. Predictions based solely on in silico predictions were categorized as possibly pathogenic, uncertain or likely benign. A total of 14.5% of cases had a predicted pathogenic or high impact variant in an autosomal dominant CPG. A significantly higher pathogenic rare variant burden was present in EUR cases compared with EUR controls (Pburden=3.7x10-05). TP53 had the highest prevalence of pathogenic mutations (5% of EUR cases; Pburden=1.05x10-09). CDKN2A, MEN1, MLH1, MUTYH, PALB2 and VHL had a significantly higher rare variant burden in the EUR cases. 7.5% of cases had a predicted pathogenic or probably pathogenic variant in an autosomal recessive CPG (Pburden=5.4x10-03). Additionally, two males had a pathogenic variant in the X-linked genes, DKC1 and GPC3. In total, 21% of cases had a predicted pathogenic or high impact variant in a CPG (Pburden=2.4x10-07). Exome-wide analysis identified two novel genes with significantly higher rare variant burdens in the cases compared to controls. In conclusion, several CPGs and two novel genes, not previously associated with OS, had an enrichment of rare variants in OS and warrant further follow-up. Our results indicate that a clinically significant fraction of OS cases may harbor one or more mutations worthy of consideration for further investigation and genetic counseling. Citation Format: Roelof Koster, Bin Zhu, Meredith Yeager, Michael Dean, Matthew Gianferante, Lei Song, Joshua Sampson, NCI DCEG Cancer Genomics Research Laboratory, Julie Gastier-Foster, Richard Gorlick, Silvia Regina Caminada de Toledo, Antonio Petrilli, Ana Patiño-Garcia, Fernando Lecanda, Massimo Serra, Claudia Hattinger, Piero Picci, Katia Scotlandi, Adrienne Flanagan, Roberto Tirabosco, Maria Amary, Nilgün Kurucu, Inci Ergurhan Ilhan, Neriman Sari, Mandy Ballinger, David Thomas, Donald Barkauskas, Belynda Hicks, Margaret Tucker, Neil Caporaso, Robert Hoover, Stephen Chanock, Sharon Savage, Lisa Mirabello. Whole-exome sequencing identifies a high frequency of germline deleterious variants in cancer predisposition genes in individuals with osteosarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4871. doi:10.1158/1538-7445.AM2017-4871

&lt;div&gt;Abstract&lt;p&gt;&lt;b&gt;Background:&lt;/b&gt; Common variants have been associated with prostate cancer risk. Unfortunately, few are reproducibly linked to aggressive disease, the phenotype of greatest clinical relevance. One possible explanation is that rare genetic variants underlie a significant proportion of the risk for aggressive disease.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Method:&lt;/b&gt; To identify such variants, we performed a two-stage approach using whole-exome sequencing followed by targeted sequencing of 800 genes in 652 aggressive prostate cancer patients and 752 disease-free controls in both African and European Americans. In each population, we tested rare variants for association using two gene-based aggregation tests. We established a study-wide significance threshold of 3.125 × 10&lt;sup&gt;−5&lt;/sup&gt; to correct for multiple testing.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Results:&lt;/b&gt; &lt;i&gt;TET2&lt;/i&gt; in African Americans was associated with aggressive disease, with 24.4% of cases harboring a rare deleterious variant compared with 9.6% of controls (FET &lt;i&gt;P&lt;/i&gt; = 1.84 × 10&lt;sup&gt;−5&lt;/sup&gt;, OR = 3.0; SKAT-O &lt;i&gt;P&lt;/i&gt; = 2.74 × 10&lt;sup&gt;−5&lt;/sup&gt;). We report 8 additional genes with suggestive evidence of association, including the DNA repair genes &lt;i&gt;PARP2&lt;/i&gt; and &lt;i&gt;MSH6&lt;/i&gt;. Finally, we observed an excess of rare truncation variants in 5 genes, including the DNA repair genes &lt;i&gt;MSH6, BRCA1&lt;/i&gt;, and &lt;i&gt;BRCA2&lt;/i&gt;. This adds to the growing body of evidence that DNA repair pathway defects may influence susceptibility to aggressive prostate cancer.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Conclusions:&lt;/b&gt; Our findings suggest that rare variants influence risk of clinically relevant prostate cancer and, if validated, could serve to identify men for screening, prophylaxis, and treatment.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Impact:&lt;/b&gt; This study provides evidence that rare variants in &lt;i&gt;TET2&lt;/i&gt; may help identify African American men at increased risk for clinically relevant prostate cancer. &lt;i&gt;Cancer Epidemiol Biomarkers Prev; 25(11); 1456–63. ©2016 AACR&lt;/i&gt;.&lt;/p&gt;&lt;/div&gt;

To compare the frequency of rare variants in genes of the pathophysiologically relevant endosomal Toll-like receptor (eTLR) pathway and any quantifiable differences in variant rarity, predicted deleteriousness, or molecular proximity in patients with systemic lupus erythematosus (SLE) and healthy controls. 65 genes associated with the eTLR pathway were identified by literature search and pathway analysis. Using next generation sequencing techniques, these were compared in two randomised cohorts of patients with SLE (n = 114 and n = 113) with 197 healthy controls. Genetically determined ethnicity was used to normalise minor allele frequencies (MAF) for the identified genetic variants and these were then compared by their frequency: rare (MAF < 0.005), uncommon (MAF 0.005-0.02), and common (MAF >0.02). This was compared to the results for 65 randomly selected genes. Patients with SLE are more likely to carry a rare nonsynonymous variant affecting proteins within the eTLR pathway than healthy controls. Furthermore, individuals with SLE are more likely to have multiple rare variants in this pathway. There were no differences in rarity, Combined Annotation Dependent Depletion (CADD) score, or molecular proximity for rare eTLR pathway variants. Rare non-synonymous variants are enriched in patients with SLE in the eTLR pathway. This supports the hypothesis that SLE arises from several rare variants of relatively large effect rather than many common variants of small effect.

Intellectual disability (ID) is a common disorder, yet there is a wide spectrum of impairment from mild to profoundly affected individuals. Mild ID is seen as the low extreme of the general distribution of intelligence, while severe ID is often seen as a monogenic disorder caused by rare, pathogenic, highly penetrant variants. To investigate the genetic factors influencing mild and severe ID, we evaluated rare and common variation in the Northern Finland Intellectual Disability cohort (n = 1096 ID patients), a cohort with a high percentage of mild ID (n = 550) and from a population bottleneck enriched in rare, damaging variation. Despite this enrichment, we found only a small percentage of ID was due to recessive Finnish-enriched variants (0.5%). A larger proportion was linked to dominant variation, with a significant burden of rare, damaging variation in both mild and severe ID. This rare variant burden was enriched in more severe ID (p = 2.4e-4), patients without a relative with ID (p = 4.76e-4), and in those with features associated with monogenic disorders. We also found a significant burden of common variants associated with decreased cognitive function, with no difference between mild and more severe ID. When we included common and rare variants in a joint model, the rare and common variants had additive effects in both mild and severe ID. A multimodel inference approach also found that common and rare variants together best explained ID status (ΔAIC = 16.8, ΔBIC = 10.2). Overall, we report evidence for the additivity of rare and common variant burden throughout the spectrum of intellectual disability.

Genome-wide association studies (GWAS) have identified hundreds of common variants associated with alcohol consumption. In contrast, genetic studies of alcohol consumption that use rare variants are still in their early stages. No prior studies of alcohol consumption have examined whether common and rare variants implicate the same genes and molecular networks, leaving open the possibility that the two approaches might identify distinct biology. To address this knowledge gap, we used publicly available alcohol consumption GWAS summary statistics (GSCAN, N = 666,978) and whole exome sequencing data (Genebass, N = 393,099) to identify a set of common and rare variants for alcohol consumption. We used gene-based analysis to implicate genes from common and rare variant analyses, which we then propagated onto a shared molecular network using a network colocalization procedure. Gene-based analysis of each dataset implicated 294 (common variants) and 35 (rare variants) genes, including ethanol metabolizing genes ADH1B and ADH1C, which were identified by both analyses, and ANKRD12, GIGYF1, KIF21B, and STK31, which were identified in only the rare variant analysis, but have been associated with other neuropsychiatric traits. Network colocalization revealed significant network overlap between the genes identified via common and rare variants. The shared network identified gene families that function in alcohol metabolism, including ADH, ALDH, CYP, and UGT. Seventy-one of the genes in the shared network were previously implicated in neuropsychiatric or substance use disorders but not alcohol-related behaviors (e.g. EXOC2, EPM2A, and CACNG4). Differential gene expression analysis showed enrichment in the liver and several brain regions. Genes implicated by network colocalization identify shared biology relevant to alcohol consumption, which also underlie neuropsychiatric traits and substance use disorders that are comorbid with alcohol use, providing a more holistic understanding of two disparate sources of genetic information.

Histology is one of the most important factors determining the prognosis of bladder cancers and rare variants are generally associated with decreased disease specific survival compared to pure transitional cell carcinomas. We believe that rare bladder cancer variants are likely underdiagnosed in the absence of a dedicated uro-pathologist in many centers. The objective of this study is to evaluate the contribution of a dedicated uro-pathologist on the identification of rare bladder cancer variants. We retrospectively analyzed the clinical and histological records of all patients which underwent a radical cystectomy and lymph node dissection between January 2000 and September 2015. The sample was divided in two groups: Group A, consists of patients who underwent radical cystectomy in the absence of a dedicated uro-pathologist at our institution, whereas the Group B consists of patients who underwent surgery when a dedicated uro-pathology service was available. We then evaluated the impact of a dedicated uro-pathologist on rare variants detection. One hundred thirty-seven out of 551 (24.9%) of patients who underwent RC had at least one rare variant. In Group A 38/238 (16%) of patients showed a rare variant, while 99/313 (31.6%; P<0.001) in group B. Furthermore, the diagnosis of sarcomatoid variant was statistically significantly less common in group A (P=0.0026). The concordance between final radical cystectomy histology and previous transurethral resection of bladder tumor (TURBT) histology was poor in both groups (overall 50.4%). The presence of a dedicated urological anatomical pathologist is of paramount importance and significantly increases the detection rate of non-transitional cell carcinoma bladder cancer types, but it does not increase the concordance rate between histological diagnoses in TURBT and radical cystectomy specimens.

Several gene-based tests, e.g., sequence kernel association test, have been developed for association testing of rare single nucleotide variants (SNVs) in genomic regions with disease traits. A common limitation of these aggregate methods is their inability to discriminate potentially causal variants from null variants within the tested regions. We propose a novel clustering method to classify rare variants into null and signal variant groups using summary statistics from the gene-based tests based on a Gaussian mixture model (GMM). We classify the signal variants into potentially risk and protective subgroups of different effect sizes. We evaluate the performance of the proposed method by a simulation study, considering several statistics such as the adjusted rand index (ARI), mean square error (MSE), and accuracy in specifying the number of clusters. We apply the proposed clustering method to identify possibly risk and protective rare variants in six genes that are significantly associated with blood pressure (BP) traits in the most recent large genomewide association study (GWAS) and meta-analysis. This proposed method may facilitate the identification of potentially causal rare variant clusters in genomic regions and ultimately help understand the genetic architecture underlying human complex traits for the discovery of drug target and the design of gene therapy.