The genetic contribution of the X chromosome in age-related hearing loss.

Age-related (AR) hearing loss (HL) is the most common sensory impairment with heritability of 55%. The aim of this study was to identify genetic variants on chromosome X associated with ARHL through the analysis of data obtained from the UK Biobank. We performed association analysis between self-reported measures of HL and genotyped and imputed variants on chromosome X from ∼460,000 white Europeans. We identified three loci associated with ARHL with a genome-wide significance level (p < 5 × 10-8), ZNF185 (rs186256023, p = 4.9 × 10-10) and MAP7D2 (rs4370706, p = 2.3 × 10-8) in combined analysis of males and females, and LOC101928437 (rs138497700, p = 8.9 × 10-9) in the sex-stratified analysis of males. In-silico mRNA expression analysis showed MAP7D2 and ZNF185 are expressed in mice and adult human inner ear tissues, particularly in the inner hair cells. We estimated that only a small amount of variation of ARHL, 0.4%, is explained by variants on the X chromosome. This study suggests that although there are likely a few genes contributing to ARHL on the X chromosome, the role that the X chromosome plays in the etiology of ARHL may be limited.

Rare-variant association analysis reveals known and new age-related hearing loss genes.

Age-related (AR) hearing loss (HL) is a prevalent sensory deficit in the elderly population. Several studies showed that common variants increase ARHL susceptibility. Here, we demonstrate that rare-variants play a crucial role in ARHL etiology. We analyzed exome and imputed data from white-European UK Biobank volunteers, performing both single-variant and rare-variant aggregate association analyses using self-reported ARHL phenotypes. We identified and replicated associations between ARHL and rare-variants in KLHDC7B, PDCD6, MYO6, SYNJ2, and TECTA. PUS7L and EYA4 also revealed rare-variant associations with ARHL. EYA4, MYO6, and TECTA are all known to underline Mendelian nonsyndromic HL. PDCD6, a new HL gene, plays an important role in apoptosis and has widespread inner ear expression, particularly in the inner hair cells. An unreplicated common variant association was previously observed for KHLDC7B, here we demonstrate that rare-variants in this gene also play a role in ARHL etiology. Additionally, the first replicated association between SYNJ2 and ARHL was detected. Analysis of common variants revealed several previously reported, i.e., ARHGEF28, and new, i.e., PIK3R3, ARHL associations, as well as ones we replicate here for the first time, i.e., BAIAP2L2, CRIP3, KLHDC7B, MAST2, and SLC22A7. It was also observed that the odds ratios for rare-variant ARHL associations, were higher than those for common variants. In conclusion, we demonstrate the vital role rare-variants, including those in Mendelian nonsyndromic HL genes, play in the etiology of ARHL.

Whole genome sequencing identifies candidate genes for familial essential tremor and reveals biological pathways implicated in essential tremor aetiology.

BACKGROUND: Essential tremor (ET), one of the most common neurological disorders, has a phenotypically heterogeneous presentation characterized by bilateral kinetic tremor of the arms and, in some patients, tremor involving other body regions (e.g., head, voice). Genetic studies suggest that ET is genetically heterogeneous. METHODS: We analyzed whole genome sequence data (WGS) generated on 104 multi-generational white families with European ancestry affected by ET. Genome-wide parametric linkage and association scans were analyzed using adjusted logistic regression models through the application of the Pseudomarker software. To investigate the additional contribution of rare variants in familial ET, we also performed an aggregate variant non-parametric linkage (NPL) analysis using the collapsed haplotype method implemented in CHP-NPL software. FINDINGS: Parametric linkage analysis of common variants identified several loci with significant evidence of linkage (HLOD ≥3.6). Among the gene regions within the strongest ET linkage peaks were BTC (4q13.3, HLOD=4.53), N6AMT1 (21q21.3, HLOD=4.31), PCDH9 (13q21.32, HLOD=4.21), EYA1 (8q13.3, HLOD=4.04), RBFOX1 (16p13.3, HLOD=4.02), MAPT (17q21.31, HLOD=3.99) and SCARB2 (4q21.1, HLOD=3.65). CHP-NPL analysis identified fifteen additional genes with evidence of significant linkage (LOD ≥3.8). These genes include TUBB2A, VPS33B, STEAP1B, SPINK5, ZRANB1, TBC1D3C, PDPR, NPY4R, ETS2, ZNF736, SPATA21, ARL17A, PZP, BLK and CCDC94. In one ET family contributing to the linkage peak on chromosome 16p13.3, we identified a likely pathogenic heterozygous canonical splice acceptor variant in exon 2 of RBFOX1 (ENST00000547372; c.4-2A>G), that co-segregated with the ET phenotype in the family. INTERPRETATION: Linkage and association analyses of WGS identified several novel ET candidate genes, which are implicated in four major pathways that include 1) the epidermal growth factor receptor-phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha-AKT serine/threonine kinase 1 (EGFR-PI3K-AKT) and Mitogen-activated protein Kinase 1 (ERK) pathways, 2) Reactive oxygen species (ROS) and DNA repair, 3) gamma-aminobutyric acid-ergic (GABAergic) system and 4) RNA binding and regulation of RNA processes. Our study provides evidence for a possible overlap in the genetic architecture of ET, neurological disease, cancer and aging. The genes and pathways identified can be prioritized in future genetic and functional studies. FUNDING: National Institutes of Health, NINDS, NS073872 (USA) and NIA AG058131(USA).

Regulatory variants in TCF7L2 are associated with thoracic aortic aneurysm.

Thoracic aortic aneurysm (TAA) is characterized by dilation of the aortic root or ascending/descending aorta. TAA is a heritable disease that can be potentially life threatening. While 10%-20% of TAA cases are caused by rare, pathogenic variants in single genes, the origin of the majority of TAA cases remains unknown. A previous study implicated common variants in FBN1 with TAA disease risk. Here, we report a genome-wide scan of 1,351 TAA-affected individuals and 18,295 control individuals from the Cardiovascular Health Improvement Project and Michigan Genomics Initiative at the University of Michigan. We identified a genome-wide significant association with TAA for variants within the third intron of TCF7L2 following replication with meta-analysis of four additional independent cohorts. Common variants in this locus are the strongest known genetic risk factor for type 2 diabetes. Although evidence indicates the presence of different causal variants for TAA and type 2 diabetes at this locus, we observed an opposite direction of effect. The genetic association for TAA colocalizes with an aortic eQTL of TCF7L2, suggesting a functional relationship. These analyses predict an association of higher expression of TCF7L2 with TAA disease risk. In vitro, we show that upregulation of TCF7L2 is associated with BCL2 repression promoting vascular smooth muscle cell apoptosis, a key driver of TAA disease.

A quantitative trait rare variant nonparametric linkage method with application to age-at-onset of Alzheimer's disease.

To analyze pedigrees with quantitative trait (QT) and sequence data, we developed a rare variant (RV) quantitative nonparametric linkage (QNPL) method, which evaluates sharing of minor alleles. RV-QNPL has greater power than the traditional QNPL that tests for excess sharing of minor and major alleles. RV-QNPL is robust to population substructure and admixture, locus heterogeneity, and inclusion of nonpathogenic variants and can be readily applied outside of coding regions. When QNPL was used to analyze common variants, it often led to loci mapping to large intervals, e.g., >40 Mb. In contrast, when RVs are analyzed, regions are well defined, e.g., a gene. Using simulation studies, we demonstrate that RV-QNPL is substantially more powerful than applying traditional QNPL methods to analyze RVs. RV-QNPL was also applied to analyze age-at-onset (AAO) data for 107 late-onset Alzheimer's disease (LOAD) pedigrees of Caribbean Hispanic and European ancestry with whole-genome sequence data. When AAO of AD was analyzed regardless of APOE ε4 status, suggestive linkage (LOD = 2.4) was observed with RVs in KNDC1 and nominally significant linkage (p < 0.05) was observed with RVs in LOAD genes ABCA7 and IQCK. When AAO of AD was analyzed for APOE ε4 positive family members, nominally significant linkage was observed with RVs in APOE, while when AAO of AD was analyzed for APOE ε4 negative family members, nominal significance was observed for IQCK and ADAMTS1. RV-QNPL provides a powerful resource to analyze QTs in families to elucidate their genetic etiology.

A Rare Variant Nonparametric Linkage Method for Nuclear and Extended Pedigrees with Application to Late-Onset Alzheimer Disease via WGS Data.

To analyze family-based whole-genome sequence (WGS) data for complex traits, we developed a rare variant (RV) non-parametric linkage (NPL) analysis method, which has advantages over association methods. The RV-NPL differs from the NPL in that RVs are analyzed, and allele sharing among affected relative-pairs is estimated only for minor alleles. Analyzing families can increase power because causal variants with familial aggregation usually have larger effect sizes than those underlying sporadic diseases. Differing from association analysis, for NPL only affected individuals are analyzed, which can increase power, since unaffected family members can be susceptibility variant carriers. RV-NPL is robust to population substructure and admixture, inclusion of nonpathogenic variants, as well as allelic and locus heterogeneity and can readily be applied outside of coding regions. In contrast to analyzing common variants using NPL, where loci localize to large genomic regions (e.g., >50 Mb), mapped regions are well defined for RV-NPL. Using simulation studies, we demonstrate that RV-NPL is substantially more powerful than applying traditional NPL methods to analyze RVs. The RV-NPL was applied to analyze 107 late-onset Alzheimer disease (LOAD) pedigrees of Caribbean Hispanic and European ancestry with WGS data, and statistically significant linkage (LOD ≥ 3.8) was found with RVs in PSMF1 and PTPN21 which have been shown to be involved in LOAD etiology. Additionally, nominally significant linkage was observed with RVs in ABCA7, ACE, EPHA1, and SORL1, genes that were previously reported to be associated with LOAD. RV-NPL is an ideal method to elucidate the genetic etiology of complex familial diseases.

SEQSpark: A Complete Analysis Tool for Large-Scale Rare Variant Association Studies Using Whole-Genome and Exome Sequence Data.

Massively parallel sequencing technologies provide great opportunities for discovering rare susceptibility variants involved in complex disease etiology via large-scale imputation and exome and whole-genome sequence-based association studies. Due to modest effect sizes, large sample sizes of tens to hundreds of thousands of individuals are required for adequately powered studies. Current analytical tools are obsolete when it comes to handling these large datasets. To facilitate the analysis of large-scale sequence-based studies, we developed SEQSpark which implements parallel processing based on Spark to increase the speed and efficiency of performing data quality control, annotation, and association analysis. To demonstrate the versatility and speed of SEQSpark, we analyzed whole-genome sequence data from the UK10K, testing for associations with waist-to-hip ratios. The analysis, which was completed in 1.5 hr, included loading data, annotation, principal component analysis, and single variant and rare variant aggregate association analysis of >9 million variants. For rare variant aggregate analysis, an exome-wide significant association (p < 2.5 × 10-6) was observed with CCDC62 (SKAT-O [p = 6.89 × 10-7], combined multivariate collapsing [p = 1.48 × 10-6], and burden of rare variants [p = 1.48 × 10-6]). SEQSpark was also used to analyze 50,000 simulated exomes and it required 1.75 hr for the analysis of a quantitative trait using several rare variant aggregate association methods. Additionally, the performance of SEQSpark was compared to Variant Association Tools and PLINK/SEQ. SEQSpark was always faster and in some situations computation was reduced to a hundredth of the time. SEQSpark will empower large sequence-based epidemiological studies to quickly elucidate genetic variation involved in the etiology of complex traits.

The Rare-Variant Generalized Disequilibrium Test for Association Analysis of Nuclear and Extended Pedigrees with Application to Alzheimer Disease WGS Data.

Whole-genome and exome sequence data can be cost-effectively generated for the detection of rare-variant (RV) associations in families. Causal variants that aggregate in families usually have larger effect sizes than those found in sporadic cases, so family-based designs can be a more powerful approach than population-based designs. Moreover, some family-based designs are robust to confounding due to population admixture or substructure. We developed a RV extension of the generalized disequilibrium test (GDT) to analyze sequence data obtained from nuclear and extended families. The GDT utilizes genotype differences of all discordant relative pairs to assess associations within a family, and the RV extension combines the single-variant GDT statistic over a genomic region of interest. The RV-GDT has increased power by efficiently incorporating information beyond first-degree relatives and allows for the inclusion of covariates. Using simulated genetic data, we demonstrated that the RV-GDT method has well-controlled type I error rates, even when applied to admixed populations and populations with substructure. It is more powerful than existing family-based RV association methods, particularly for the analysis of extended pedigrees and pedigrees with missing data. We analyzed whole-genome sequence data from families affected by Alzheimer disease to illustrate the application of the RV-GDT. Given the capability of the RV-GDT to adequately control for population admixture or substructure and analyze pedigrees with missing genotype data and its superior power over other family-based methods, it is an effective tool for elucidating the involvement of RVs in the etiology of complex traits.

Rare variant associations with waist-to-hip ratio in European-American and African-American women from the NHLBI-Exome Sequencing Project.

Waist-to-hip ratio (WHR), a relative comparison of waist and hip circumferences, is an easily accessible measurement of body fat distribution, in particular central abdominal fat. A high WHR indicates more intra-abdominal fat deposition and is an established risk factor for cardiovascular disease and type 2 diabetes. Recent genome-wide association studies have identified numerous common genetic loci influencing WHR, but the contributions of rare variants have not been previously reported. We investigated rare variant associations with WHR in 1510 European-American and 1186 African-American women from the National Heart, Lung, and Blood Institute-Exome Sequencing Project. Association analysis was performed on the gene level using several rare variant association methods. The strongest association was observed for rare variants in IKBKB (P=4.0 × 10(-8)) in European-Americans, where rare variants in this gene are predicted to decrease WHRs. The activation of the IKBKB gene is involved in inflammatory processes and insulin resistance, which may affect normal food intake and body weight and shape. Meanwhile, aggregation of rare variants in COBLL1, previously found to harbor common variants associated with WHR and fasting insulin, were nominally associated (P=2.23 × 10(-4)) with higher WHR in European-Americans. However, these significant results are not shared between African-Americans and European-Americans that may be due to differences in the allelic architecture of the two populations and the small sample sizes. Our study indicates that the combined effect of rare variants contribute to the inter-individual variation in fat distribution through the regulation of insulin response.

Generation of sequence-based data for pedigree-segregating Mendelian or Complex traits.

MOTIVATION: There is great interest in analyzing next generation sequence data that has been generated for pedigrees. However, unlike for population-based data there are only a limited number of rare variant methods to analyze pedigree data. One limitation is the ability to evaluate type I and II errors for family-based methods, due to lack of software that can simulate realistic sequence data for pedigrees. SUMMARY: We developed RarePedSim (Rare-variant Pedigree-based Simulator), a program to simulate region/gene-level genotype and phenotype data for complex and Mendelian traits for any given pedigree structure. Using a genetic model, sequence variant data can be generated either conditionally or unconditionally on pedigree members' qualitative or quantitative phenotypes. Additionally, qualitative or quantitative traits can be generated conditional on variant data. Sequence data can either be simulated using realistic population demographic models or obtained from sequence-based studies. Variant sites can be annotated with positions, allele frequencies and functionality. For rare variants, RarePedSim is the only program that can efficiently generate both genotypes and phenotypes, regardless of pedigree structure. Data generated by RarePedSim are in standard Linkage file (.ped) and Variant Call (.vcf) formats, ready to be used for a variety of purposes, including evaluation of type I error and power, for association methods including mixed models and linkage analysis methods. AVAILABILITY AND IMPLEMENTATION: bioinformatics.org/simped/rare CONTACT: sleal@bcm.edu.

Rare A2ML1 variants confer susceptibility to otitis media.

A duplication variant within the middle ear-specific gene A2ML1 cosegregates with otitis media in an indigenous Filipino pedigree (LOD score = 7.5 at reduced penetrance) and lies within a founder haplotype that is also shared by 3 otitis-prone European-American and Hispanic-American children but is absent in non-otitis-prone children and >62,000 next-generation sequences. We identified seven additional A2ML1 variants in six otitis-prone children. Collectively, our studies support a role for A2ML1 in the pathophysiology of otitis media.

Collapsed haplotype pattern method for linkage analysis of next-generation sequence data.

Recent advances in next-generation sequencing (NGS) make it possible to directly sequence genomes and exomes of individuals with Mendelian diseases and screen sequence data for causal variants. With the reduction in cost of NGS, DNA samples from entire families can be sequenced and linkage analysis can be performed directly using NGS data. Inspired by 'burden' tests, which are used for complex trait rare variant association studies, we developed the collapsed haplotype pattern (CHP) method for linkage analysis. Using data from several deafness genes we demonstrate that the CHP method is substantially more powerful than analyzing individual variants. Unlike applying NGS data filtering approaches, the CHP method provides statistical evidence of a gene's involvement in disease etiology and is also less likely to exclude causal variants in the presence of phenocopies and/or reduced penetrance. The CHP method was implemented in the SEQLinkage software package, which can perform linkage analysis on NGS data or can generate data compatible with many linkage analysis programs, reviving them for use in NGS era.

Challenges and solutions for gene identification in the presence of familial locus heterogeneity.

Next-generation sequencing (NGS) of exomes and genomes has accelerated the identification of genes involved in Mendelian phenotypes. However, many NGS studies fall short of identifying causal variants, with estimates for success rates as low as 25% for uncovering the pathological variant underlying disease etiology. An important reason for such failures is familial locus heterogeneity, where within a single pedigree causal variants in two or more genes underlie Mendelian trait etiology. As examples of intra- and inter-sibship familial locus heterogeneity, we present 10 consanguineous Pakistani families segregating hearing impairment due to homozygous variants in two different hearing impairment genes and a European-American pedigree in which hearing impairment is caused by four variants in three different genes. We have identified 41 additional pedigrees with syndromic and nonsyndromic hearing impairment for which a single previously reported hearing impairment gene has been identified but only segregates with the phenotype in a subset of affected pedigree members. We estimate that locus heterogeneity occurs in 15.3% (95% confidence interval: 11.9%, 19.9%) of the families in our collection. We demonstrate novel approaches to apply linkage analysis and homozygosity mapping (for autosomal recessive consanguineous pedigrees), which can be used to detect locus heterogeneity using either NGS or SNP array data. Results from linkage analysis and homozygosity mapping can also be used to group sibships or individuals most likely to be segregating the same causal variants and thereby increase the success rate of gene identification.

RNF213 rare variants in an ethnically diverse population with Moyamoya disease.

BACKGROUND AND PURPOSE: Moyamoya disease (MMD) is a rare, genetically heterogeneous cerebrovascular disease resulting from occlusion of the distal internal carotid arteries. A variant in the Ring Finger 213 gene (RNF213), altering arginine at position 4810 (p.R4810K), is associated with MMD in Asian populations. However, there are a lack of data on the role of RNF213 in patients with MMD of additional ethnicities and diasporic Asian populations. We investigate the contribution of RNF213 alterations to MMD in an ethnically diverse population based in the United States. METHODS: We initially sequenced RNF213 exons 43, 44, and 45 (encoding the eponymous RING finger domain) and exon 60 (encoding p.R4810K) in 86 ethnically diverse patients with MMD. Comprehensive exome sequencing data from 24 additional patients with MMD was then analyzed to identify RNF213 variants globally. Segregation of variants with MMD and other vascular diseases was assessed in families. RESULTS: RNF213 p.R4810K was identified in 56% (9/16) of patients with MMD of Asian descent and not in 94 patients of non-Asian descent. 3.6% (4/110) of patients had variants in the exons encoding the RING finger domain. Seven additional variants were identified in 29% (7/24) of patients with MMD who underwent exome sequencing. Segregation analysis supported an association with MMD for 2 variants and a lack of association with disease for 1 variant. CONCLUSIONS: These results confirm that alterations in RNF213 predispose patients of diverse ethnicities to MMD, and that the p.R4810K variant predisposes individuals of Asian descent in the United States to MMD.

Variant association tools for quality control and analysis of large-scale sequence and genotyping array data.

Currently there is great interest in detecting associations between complex traits and rare variants. In this report, we describe Variant Association Tools (VAT) and the VAT pipeline, which implements best practices for rare-variant association studies. Highlights of VAT include variant-site and call-level quality control (QC), summary statistics, phenotype- and genotype-based sample selection, variant annotation, selection of variants for association analysis, and a collection of rare-variant association methods for analyzing qualitative and quantitative traits. The association testing framework for VAT is regression based, which readily allows for flexible construction of association models with multiple covariates and weighting themes based on allele frequencies or predicted functionality. Additionally, pathway analyses, conditional analyses, and analyses of gene-gene and gene-environment interactions can be performed. VAT is capable of rapidly scanning through data by using multi-process computation, adaptive permutation, and simultaneously conducting association analysis via multiple methods. Results are available in text or graphic file formats and additionally can be output to relational databases for further annotation and filtering. An interface to R language also facilitates user implementation of novel association methods. The VAT's data QC and association-analysis pipeline can be applied to sequence, imputed, and genotyping array, e.g., "exome chip," data, providing a reliable and reproducible computational environment in which to analyze small- to large-scale studies with data from the latest genotyping and sequencing technologies. Application of the VAT pipeline is demonstrated through analysis of data from the 1000 Genomes project.

Power analysis and sample size estimation for sequence-based association studies.

MOTIVATION: Statistical methods have been developed to test for complex trait rare variant (RV) associations, in which variants are aggregated across a region, which is typically a gene. Power analysis and sample size estimation for sequence-based RV association studies are challenging because of the necessity to realistically model the underlying allelic architecture of complex diseases within a suitable analytical framework to assess the performance of a variety of RV association methods in an unbiased manner. SUMMARY: We developed SEQPower, a software package to perform statistical power analysis for sequence-based association data under a variety of genetic variant and disease phenotype models. It aids epidemiologists in determining the best study design, sample size and statistical tests for sequence-based association studies. It also provides biostatisticians with a platform to fairly compare RV association methods and to validate and assess novel association tests. AVAILABILITY AND IMPLEMENTATION: The SEQPower program, source code, multi-platform executables, documentation, list of association tests, examples and tutorials are available at http://bioinformatics.org/spower.