Exome sequencing followed by large-scale genotyping fails to identify single rare variants of large effect in idiopathic generalized epilepsy

Idiopathic generalized epilepsy (IGE) is a complex disease with high heritability, but little is known about its genetic architecture. Rare copy-number variants have been found to explain nearly 3% of individuals with IGE; however, it remains unclear whether variants with moderate effect size and frequencies below what are reliably detected with genome-wide association studies contribute significantly to disease risk. In this study, we compare the exome sequences of 118 individuals with IGE and 242 controls of European ancestry by using next-generation sequencing. The exome-sequenced epilepsy cases include study subjects with two forms of IGE, including juvenile myoclonic epilepsy (n = 93) and absence epilepsy (n = 25). However, our discovery strategy did not assume common genetic control between the subtypes of IGE considered. In the sequence data, as expected, no variants were significantly associated with the IGE phenotype or more specific IGE diagnoses. We then selected 3,897 candidate epilepsy-susceptibility variants from the sequence data and genotyped them in a larger set of 878 individuals with IGE and 1,830 controls. Again, no variant achieved statistical significance. However, 1,935 variants were observed exclusively in cases either as heterozygous or homozygous genotypes. It is likely that this set of variants includes real risk factors. The lack of significant association evidence of single variants with disease in this two-stage approach emphasizes the high genetic heterogeneity of epilepsy disorders, suggests that the impact of any individual single-nucleotide variant in this disease is small, and indicates that gene-based approaches might be more successful for future sequencing studies of epilepsy predisposition.     

A new testing strategy to identify rare variants with either risk or protective effect on disease

Rapid advances in sequencing technologies set the stage for the large-scale medical sequencing efforts to be performed in the near future, with the goal of assessing the importance of rare variants in complex diseases. The discovery of new disease susceptibility genes requires powerful statistical methods for rare variant analysis. The low frequency and the expected large number of such variants pose great difficulties for the analysis of these data. We propose here a robust and powerful testing strategy to study the role rare variants may play in affecting susceptibility to complex traits. The strategy is based on assessing whether rare variants in a genetic region collectively occur at significantly higher frequencies in cases compared with controls (or vice versa). A main feature of the proposed methodology is that, although it is an overall test assessing a possibly large number of rare variants simultaneously, the disease variants can be both protective and risk variants, with moderate decreases in statistical power when both types of variants are present. Using simulations, we show that this approach can be powerful under complex and general disease models, as well as in larger genetic regions where the proportion of disease susceptibility variants may be small. Comparisons with previously published tests on simulated data show that the proposed approach can have better power than the existing methods. An application to a recently published study on Type-1 Diabetes finds rare variants in gene IFIH1 to be protective against Type-1 Diabetes.     

Genetic mapping and exome sequencing identify variants associated with five novel diseases

The Clinic for Special Children (CSC) has integrated biochemical and molecular methods into a rural pediatric practice serving Old Order Amish and Mennonite (Plain) children. Among the Plain people, we have used single nucleotide polymorphism (SNP) microarrays to genetically map recessive disorders to large autozygous haplotype blocks (mean = 4.4 Mb) that contain many genes (mean = 79). For some, uninformative mapping or large gene lists preclude disease-gene identification by Sanger sequencing. Seven such conditions were selected for exome sequencing at the Broad Institute; all had been previously mapped at the CSC using low density SNP microarrays coupled with autozygosity and linkage analyses. Using between 1 and 5 patient samples per disorder, we identified sequence variants in the known disease-causing genes SLC6A3 and FLVCR1, and present evidence to strongly support the pathogenicity of variants identified in TUBGCP6, BRAT1, SNIP1, CRADD, and HARS. Our results reveal the power of coupling new genotyping technologies to population-specific genetic knowledge and robust clinical data.     

Identification of rare DNA variants in mitochondrial disorders with improved array-based sequencing

A common goal in the discovery of rare functional DNA variants via medical resequencing is to incur a relatively lower proportion of false positive base-calls. We developed a novel statistical method for resequencing arrays (SRMA, sequence robust multi-array analysis) to increase the accuracy of detecting rare variants and reduce the costs in subsequent sequence verifications required in medical applications. SRMA includes single and multi-array analysis and accounts for technical variables as well as the possibility of both low- and high-frequency genomic variation. The confidence of each base-call was ranked using two quality measures. In comparison to Sanger capillary sequencing, we achieved a false discovery rate of 2% (false positive rate 1.2 × 10⁻⁵, false negative rate 5%), which is similar to automated second-generation sequencing technologies. Applied to the analysis of 39 nuclear candidate genes in disorders of mitochondrial DNA (mtDNA) maintenance, we confirmed mutations in the DNA polymerase gamma POLG in positive control cases, and identified novel rare variants in previously undiagnosed cases in the mitochondrial topoisomerase TOP1MT, the mismatch repair enzyme MUTYH, and the apurinic-apyrimidinic endonuclease APEX2. Some patients carried rare heterozygous variants in several functionally interacting genes, which could indicate synergistic genetic effects in these clinically similar disorders.     

DNA bar coding and pyrosequencing to identify rare HIV drug resistance mutations

Treatment of HIV-infected individuals with antiretroviral agents selects for drug-resistant mutants, resulting in frequent treatment failures. Although the major antiretroviral resistance mutations are routinely characterized by DNA sequencing, treatment failures are still common, probably in part because undetected rare resistance mutations facilitate viral escape. Here we combined DNA bar coding and massively parallel pyrosequencing to quantify rare drug resistance mutations. Using DNA bar coding, we were able to analyze seven viral populations in parallel, overall characterizing 118093 sequence reads of average length 103bp. Analysis of a control HIV mixture showed that resistance mutations present as 5% of the population could be readily detected without false positive calls. In three samples of multidrug-resistant HIV populations from patients, all the drug-resistant mutations called by conventional analysis were identified, as well as four additional low abundance drug resistance mutations, some of which would be expected to influence the response to antiretroviral therapy. Methods for sensitive characterization of HIV resistance alleles have been reported, but only the pyrosequencing method allows all the positions at risk for drug resistance mutations to be interrogated deeply for many HIV populations in a single experiment.     

Two-stage design of sequencing studies for testing association with rare variants

Multiple rare variants have been suggested as accounting for some of the associations with common single nucleotide polymorphisms identified in genome-wide association studies or possibly some of the as yet undiscovered heritability. We consider the power of various approaches to designing substudies aimed at using next-generation sequencing technologies to discover novel variants and to select some subsets that are possibly causal for genotyping in the original case-control study and testing for association using various weighted sum indices. We find that the selection of variants based on the statistical significance of the case-control difference in the subsample yields good power for testing rare variant indices in the main study, and that multivariate models including both the summary index of rare variants and the associated common single nucleotide polymorphisms can distinguish which is the causal factor. By simulation, we explore the effects of varying the size of the discovery subsample, choice of index, and true causal model.     

Association of MHC and rheumatoid arthritis. HLA polymorphisms in phenotypic variants of rheumatoid arthritis

Genes in the human leukocyte antigen (HLA) region remain the most powerful disease risk genes in rheumatoid arthritis (RA). Several allelic variants of HLA-DRB1 genes have been associated with RA, supporting a role for T-cell receptor-HLA-antigen interactions in the pathologic process. Disease-associated HLA-DRB1 alleles are similar but not identical and certain allelic variants are preferentially enriched in patient populations with defined clinical characteristics. Also, a gene dosing effect of HLA-DRB1 alleles has been suggested by the accumulation of patients with two RA-associated alleles, especially in patient subsets with a severe disease course. Therefore, polymorphisms in HLA genes are being explored as tools to dissect the clinical heterogeneity of the rheumatoid syndrome. Besides HLA polymorphisms, other risk genes will be helpful in defining genotypic profiles correlating with disease phenotypes. One such phenotype is the type of synovial lesion generated by the patient. HLA genes in conjunction with other genetic determinants may predispose patients to a certain pathway of synovial inflammation. Also, patients may or may not develop extraarticular manifestations, which are critical in determining morbidity and mortality. HLA genes, complemented by other RA risk genes, are likely involved in shaping the T-cell repertoire, including the emergence of an unusual T-cell population characterized by the potential of vascular injury, such as seen in extraarticular RA.     

Association of MHC and rheumatoid arthritis: HLA polymorphisms in phenotypic variants of rheumatoid arthritis

Genes in the human leukocyte antigen (HLA) region remain the most powerful disease risk genes in rheumatoid arthritis (RA). Several allelic variants of HLA-DRB1 genes have been associated with RA, supporting a role for T-cell receptor-HLA-antigen interactions in the pathologic process. Disease-associated HLA-DRB1 alleles are similar but not identical and certain allelic variants are preferentially enriched in patient populations with defined clinical characteristics. Also, a gene dosing effect of HLA-DRB1 alleles has been suggested by the accumulation of patients with two RA-associated alleles, especially in patient subsets with a severe disease course. Therefore, polymorphisms in HLA genes are being explored as tools to dissect the clinical heterogeneity of the rheumatoid syndrome. Besides HLA polymorphisms, other risk genes will be helpful in defining genotypic profiles correlating with disease phenotypes. One such phenotype is the type of synovial lesion generated by the patient. HLA genes in conjunction with other genetic determinants may predispose patients to a certain pathway of synovial inflammation. Also, patients may or may not develop extraarticular manifestations, which are critical in determining morbidity and mortality. HLA genes, complemented by other RA risk genes, are likely involved in shaping the T-cell repertoire, including the emergence of an unusual T-cell population characterized by the potential of vascular injury, such as seen in extraarticular RA.     

A general framework for detecting disease associations with rare variants in sequencing studies

Biological and empirical evidence suggests that rare variants account for a large proportion of the genetic contributions to complex human diseases. Recent technological advances in high-throughput sequencing platforms have made it possible for researchers to generate comprehensive information on rare variants in large samples. We provide a general framework for association testing with rare variants by combining mutation information across multiple variant sites within a gene and relating the enriched genetic information to disease phenotypes through appropriate regression models. Our framework covers all major study designs (i.e., case-control, cross-sectional, cohort and family studies) and all common phenotypes (e.g., binary, quantitative, and age at onset), and it allows arbitrary covariates (e.g., environmental factors and ancestry variables). We derive theoretically optimal procedures for combining rare mutations and construct suitable test statistics for various biological scenarios. The allele-frequency threshold can be fixed or variable. The effects of the combined rare mutations on the phenotype can be in the same direction or different directions. The proposed methods are statistically more powerful and computationally more efficient than existing ones. An application to a deep-resequencing study of drug targets led to a discovery of rare variants associated with total cholesterol. The relevant software is freely available.     

Genome-wide studies of copy number variation and exome sequencing identify rare variants in BAG3 as a cause of dilated cardiomyopathy

Dilated cardiomyopathy commonly causes heart failure and is the most frequent precipitating cause of heart transplantation. Familial dilated cardiomyopathy has been shown to be caused by rare variant mutations in more than 30 genes but only ~35% of its genetic cause has been identified, principally by using linkage-based or candidate gene discovery approaches. In a multigenerational family with autosomal dominant transmission, we employed whole-exome sequencing in a proband and three of his affected family members, and genome-wide copy number variation in the proband and his affected father and unaffected mother. Exome sequencing identified 428 single point variants resulting in missense, nonsense, or splice site changes. Genome-wide copy number analysis identified 51 insertion deletions and 440 copy number variants > 1 kb. Of these, a 8733 bp deletion, encompassing exon 4 of the heat shock protein cochaperone BCL2-associated athanogene 3 (BAG3), was found in seven affected family members and was absent in 355 controls. To establish the relevance of variants in this protein class in genetic DCM, we sequenced the coding exons in BAG3 in 311 other unrelated DCM probands and identified one frameshift, two nonsense, and four missense rare variants absent in 355 control DNAs, four of which were familial and segregated with disease. Knockdown of bag3 in a zebrafish model recapitulated DCM and heart failure. We conclude that new comprehensive genomic approaches have identified rare variants in BAG3 as causative of DCM.     

HapFABIA: Identification of very short segments of identity by descent characterized by rare variants in large sequencing data

Identity by descent (IBD) can be reliably detected for long shared DNA segments, which are found in related individuals. However, many studies contain cohorts of unrelated individuals that share only short IBD segments. New sequencing technologies facilitate identification of short IBD segments through rare variants, which convey more information on IBD than common variants. Current IBD detection methods, however, are not designed to use rare variants for the detection of short IBD segments. Short IBD segments reveal genetic structures at high resolution. Therefore, they can help to improve imputation and phasing, to increase genotyping accuracy for low-coverage sequencing and to increase the power of association studies. Since short IBD segments are further assumed to be old, they can shed light on the evolutionary history of humans. We propose HapFABIA, a computational method that applies biclustering to identify very short IBD segments characterized by rare variants. HapFABIA is designed to detect short IBD segments in genotype data that were obtained from next-generation sequencing, but can also be applied to DNA microarray data. Especially in next-generation sequencing data, HapFABIA exploits rare variants for IBD detection. HapFABIA significantly outperformed competing algorithms at detecting short IBD segments on artificial and simulated data with rare variants. HapFABIA identified 160 588 different short IBD segments characterized by rare variants with a median length of 23 kb (mean 24 kb) in data for chromosome 1 of the 1000 Genomes Project. These short IBD segments contain 752 000 single nucleotide variants (SNVs), which account for 39% of the rare variants and 23.5% of all variants. The vast majority—152 000 IBD segments—are shared by Africans, while only 19 000 and 11 000 are shared by Europeans and Asians, respectively. IBD segments that match the Denisova or the Neandertal genome are found significantly more often in Asians and Europeans but also, in some cases exclusively, in Africans. The lengths of IBD segments and their sharing between continental populations indicate that many short IBD segments from chromosome 1 existed before humans migrated out of Africa. Thus, rare variants that tag these short IBD segments predate human migration from Africa. The software package HapFABIA is available from Bioconductor. All data sets, result files and programs for data simulation, preprocessing and evaluation are supplied at http://www.bioinf.jku.at/research/short-IBD.     

Hypogalactosylation of serum N-glycans fails to predict clinical response to methotrexate and TNF inhibition in rheumatoid arthritis

Introduction

Rheumatoid arthritis (RA) is associated with hypogalactosylation of immunoglobulin G (IgG). We examined whether a proxy measure for galactosylation of IgG N-glycans could predict response to therapy or was differentially affected by methotrexate (MTX) or TNF blockade.

Methods

Using a previously defined normal phase high-performance liquid chromatography approach, we ascertained the galactosylation status of whole serum N-glycans in two well-defined RA clinical cohorts: the Autoimmune Biomarkers Collaborative Network (n = 98) and Nested I (n = 64). The ratio of agalactosylated to monogalactosylated N-glycans in serum (sG0/G1) was determined before and during therapy with MTX or TNF inhibition and correlated with anticitrullinated peptide antibody (ACPA) status and clinical response as assessed by 28-joint Disease Activity Score utilizing C-reactive peptide and European League Against Rheumatism response criteria.

Results

RA patients from both cohorts exhibited elevation of sG0/G1 at baseline. Improvement in clinical scores correlated with a reduction in sG0/G1 (Spearman''s ρ = 0.31 to 0.37; P < 0.05 for each cohort). However, pretreatment sG0/G1 was not predictive of clinical response. Changes in sG0/G1 were similar in the MTX and TNF inhibitor groups. Corrected for disease activity, ACPA positivity correlated with higher sG0/G1.

Conclusions

Baseline serum N-glycan hypogalactosylation, an index previously correlated with hypogalactosylation of IgG N-glycans, did not distinguish patients with rheumatoid arthritis who were likely to experience a favorable clinical response to MTX or TNF blockade. Clinical improvement was associated with partial glycan normalization. ACPA-positive patients demonstrated enhanced N-glycan aberrancy compared with ACPA-negative patients.     

Targeted genomic capture and massively parallel sequencing to identify genes for hereditary hearing loss in middle eastern families

Background

Identification of genes responsible for medically important traits is a major challenge in human genetics. Due to the genetic heterogeneity of hearing loss, targeted DNA capture and massively parallel sequencing are ideal tools to address this challenge. Our subjects for genome analysis are Israeli Jewish and Palestinian Arab families with hearing loss that varies in mode of inheritance and severity.

Results

A custom 1.46 MB design of cRNA oligonucleotides was constructed containing 246 genes responsible for either human or mouse deafness. Paired-end libraries were prepared from 11 probands and bar-coded multiplexed samples were sequenced to high depth of coverage. Rare single base pair and indel variants were identified by filtering sequence reads against polymorphisms in dbSNP132 and the 1000 Genomes Project. We identified deleterious mutations in CDH23, MYO15A, TECTA, TMC1, and WFS1. Critical mutations of the probands co-segregated with hearing loss. Screening of additional families in a relevant population was performed. TMC1 p.S647P proved to be a founder allele, contributing to 34% of genetic hearing loss in the Moroccan Jewish population.

Conclusions

Critical mutations were identified in 6 of the 11 original probands and their families, leading to the identification of causative alleles in 20 additional probands and their families. The integration of genomic analysis into early clinical diagnosis of hearing loss will enable prediction of related phenotypes and enhance rehabilitation. Characterization of the proteins encoded by these genes will enable an understanding of the biological mechanisms involved in hearing loss.     

To identify associations with rare variants, just WHaIT: Weighted haplotype and imputation-based tests

Empirical evidences suggest that both common and rare variants contribute to complex disease etiology. Although the effects of common variants have been thoroughly assessed in recent genome-wide association studies (GWAS), our knowledge of the impact of rare variants on complex diseases remains limited. A number of methods have been proposed to test for rare variant association in sequencing-based studies, a study design that is becoming popular but is still not economically feasible. On the contrary, few (if any) methods exist to detect rare variants in GWAS data, the data we have collected on thousands of individuals. Here we propose two methods, a weighted haplotype-based approach and an imputation-based approach, to test for the effect of rare variants with GWAS data. Both methods can incorporate external sequencing data when available. We evaluated our methods and compared them with methods proposed in the sequencing setting through extensive simulations. Our methods clearly show enhanced statistical power over existing methods for a wide range of population-attributable risk, percentage of disease-contributing rare variants, and proportion of rare alleles working in different directions. We also applied our methods to the IFIH1 region for the type 1 diabetes GWAS data collected by the Wellcome Trust Case-Control Consortium. Our methods yield p values in the order of 10⁻³, whereas the most significant p value from the existing methods is greater than 0.17. We thus demonstrate that the evaluation of rare variants with GWAS data is possible, particularly when public sequencing data are incorporated.     

PTPN22 genetic variation: evidence for multiple variants associated with rheumatoid arthritis

The minor allele of the R620W missense single-nucleotide polymorphism (SNP) (rs2476601) in the hematopoietic-specific protein tyrosine phosphatase gene, PTPN22, has been associated with multiple autoimmune diseases, including rheumatoid arthritis (RA). These genetic data, combined with biochemical evidence that this SNP affects PTPN22 function, suggest that this phosphatase is a key regulator of autoimmunity. To determine whether other genetic variants in PTPN22 contribute to the development of RA, we sequenced the coding regions of this gene in 48 white North American patients with RA and identified 15 previously unreported SNPs, including 2 coding SNPs in the catalytic domain. We then genotyped 37 SNPs in or near PTPN22 in 475 patients with RA and 475 individually matched controls (sample set 1) and selected a subset of markers for replication in an additional 661 patients with RA and 1,322 individually matched controls (sample set 2). Analyses of these results predict 10 common (frequency >1%) PTPN22 haplotypes in white North Americans. The sole haplotype found to carry the previously identified W620 risk allele was strongly associated with disease in both sample sets, whereas another haplotype, identical at all other SNPs but carrying the R620 allele, showed no association. R620W, however, does not fully explain the association between PTPN22 and RA, since significant differences between cases and controls persisted in both sample sets after the haplotype data were stratified by R620W. Additional analyses identified two SNPs on a single common haplotype that are associated with RA independent of R620W, suggesting that R620W and at least one additional variant in the PTPN22 gene region influence RA susceptibility.     

Interferon regulatory factor 5 genetic variants are associated with cardiovascular disease in patients with rheumatoid arthritis

Introduction

Rheumatoid arthritis (RA) is a complex polygenic inflammatory disease associated with accelerated atherosclerosis and increased cardiovascular (CV) disease risk. Interferon regulatory factor 5 (IRF5) is a regulator of type I interferon induction. Recently, researchers have described an association between multiple single-nucleotide polymorphisms of the IRF5 gene and some rheumatic disorders. In this study, we aimed to evaluate whether three different haplotype blocks within the IRF5 locus which have been shown to alter the protein function are involved in the risk of CV events occurring in Spanish RA patients.

Methods

Three IRF5 polymorphisms (rs2004640, rs2070197 and rs10954213) representative of each haplotype group were genotyped by performing TaqMan assays using a 7900HT Fast Real-Time PCR System with tissue from a total of 2,137 Spanish patients diagnosed with RA. Among them, 390 (18.2%) had experienced CV events. The relationship of IRF5 genotypes and haplotypes to CV events was tested using Cox regression.

Results

Male sex, age at RA diagnosis and most traditional risk factors (hypertension, dyslipidemia and smoking habit) were associated with increased risk for CV events in the RA population. Interestingly, a protective effect of both IRF5 rs2004640 GG and IRF5 rs10954213 GG genotypes against the risk for CV events after adjusting the results for sex, age at RA diagnosis and traditional CV disease risk factors was observed (hazard ratio (HR) = 0.6, 95% confidence interval (CI) = 0.38 to 0.92, P = 0.02; and HR = 0.58, 95% CI = 0.36 to 0.95, P = 0.03, respectively). Moreover, we detected a protective effect of the GTG haplotype against the risk for CV events after adjusting the results for potential confounding factors (HR = 0.72, 95% CI = 0.56 to 0.93, P = 0.012).

Conclusions

Our results reveal that IRF5 gene variants are associated with risk of CV events in patients with RA.