Homozygosity Haplotype and Whole-Exome Sequencing Analysis to Identify Potentially Functional Rare Variants Involved in Multiple Sclerosis among Sardinian Families

Multiple Sclerosis (MS) is a complex multifactorial autoimmune disease, whose sex- and age-adjusted prevalence in Sardinia (Italy) is among the highest worldwide. To date, 233 loci were associated with MS and almost 20% of risk heritability is attributable to common genetic variants, but many low-frequency and rare variants remain to be discovered. Here, we aimed to contribute to the understanding of the genetic basis of MS by investigating potentially functional rare variants. To this end, we analyzed thirteen multiplex Sardinian families with Immunochip genotyping data. For five families, Whole Exome Sequencing (WES) data were also available. Firstly, we performed a non-parametric Homozygosity Haplotype analysis for identifying the Region from Common Ancestor (RCA). Then, on these potential disease-linked RCA, we searched for the presence of rare variants shared by the affected individuals by analyzing WES data. We found: (i) a variant (43181034 T > G) in the splicing region on exon 27 of CUL9; (ii) a variant (50245517 A > C) in the splicing region on exon 16 of ATP9A; (iii) a non-synonymous variant (43223539 A > C), on exon 9 of TTBK1; (iv) a non-synonymous variant (42976917 A > C) on exon 9 of PPP2R5D; and v) a variant (109859349-109859354) in 3′UTR of MYO16.     

Whole-Exome Sequencing of 2,000 Danish Individuals and the Role of Rare Coding Variants in Type 2 Diabetes

It has been hypothesized that, in aggregate, rare variants in coding regions of genes explain a substantial fraction of the heritability of common diseases. We sequenced the exomes of 1,000 Danish cases with common forms of type 2 diabetes (including body mass index > 27.5 kg/m² and hypertension) and 1,000 healthy controls to an average depth of 56×. Our simulations suggest that our study had the statistical power to detect at least one causal gene (a gene containing causal mutations) if the heritability of these common diseases was explained by rare variants in the coding regions of a limited number of genes. We applied a series of gene-based tests to detect such susceptibility genes. However, no gene showed a significant association with disease risk after we corrected for the number of genes analyzed. Thus, we could reject a model for the genetic architecture of type 2 diabetes where rare nonsynonymous variants clustered in a modest number of genes (fewer than 20) are responsible for the majority of disease risk.     

Rare Risk Variants Identification by Identity-by-Descent Mapping and Whole-Exome Sequencing Implicates Neuronal Development Pathways in Schizophrenia and Bipolar Disorder

Schizophrenia (SCZ) and bipolar disorder (BPD) are highly heritable disorders with an estimated co-heritability of 68%. Hundreds of common alleles have been implicated, but recently a role for rare, high-penetrant variants has been also suggested in both disorders. This study investigated a familial cohort of SCZ and BPD patients from a closed population sample, where the high recurrence of the disorders and the homogenous genetic background indicate a possible enrichment in rare risk alleles. A total of 230 subjects (161 cases, 22 unaffected relatives, and 47 controls) were genetically investigated through an innovative strategy that integrates identity-by-descent (IBD) mapping and whole-exome sequencing (WES). IBD analysis allowed to track high-risk haplotypes (IBD_risk) shared exclusively by multiple patients from different families and possibly carrying the most penetrant alleles. A total of 444 non-synonymous sequence variants, of which 137 disruptive, were identified in IBD_risk haplotypes by WES. Interestingly, gene sets previously implicated in SCZ (i.e., post-synaptic density (PSD) proteins, voltage-gated calcium channels (VGCCs), and fragile X mental retardation protein (FMRP) targets) were found significantly enriched in genes carrying IBD_risk variants. Further, IBD_risk variants were preferentially affecting genes involved in the extracellular matrix (ECM) biology and axon guidance processes which appeared to be functionally connected in the pathway-derived meta-network analysis. Results thus confirm rare risk variants as key factors in SCZ and BPD pathogenesis and highlight a role for the development of neuronal connectivity in the etiology of both disorders.     

High Risk Population Isolate Reveals Low Frequency Variants Predisposing to Intracranial Aneurysms

3% of the population develops saccular intracranial aneurysms (sIAs), a complex trait, with a sporadic and a familial form. Subarachnoid hemorrhage from sIA (sIA-SAH) is a devastating form of stroke. Certain rare genetic variants are enriched in the Finns, a population isolate with a small founder population and bottleneck events. As the sIA-SAH incidence in Finland is >2× increased, such variants may associate with sIA in the Finnish population. We tested 9.4 million variants for association in 760 Finnish sIA patients (enriched for familial sIA), and in 2,513 matched controls with case-control status and with the number of sIAs. The most promising loci (p<5E-6) were replicated in 858 Finnish sIA patients and 4,048 controls. The frequencies and effect sizes of the replicated variants were compared to a continental European population using 717 Dutch cases and 3,004 controls. We discovered four new high-risk loci with low frequency lead variants. Three were associated with the case-control status: 2q23.3 (MAF 2.1%, OR 1.89, p 1.42×10-9); 5q31.3 (MAF 2.7%, OR 1.66, p 3.17×10-8); 6q24.2 (MAF 2.6%, OR 1.87, p 1.87×10-11) and one with the number of sIAs: 7p22.1 (MAF 3.3%, RR 1.59, p 6.08×-9). Two of the associations (5q31.3, 6q24.2) replicated in the Dutch sample. The 7p22.1 locus was strongly differentiated; the lead variant was more frequent in Finland (4.6%) than in the Netherlands (0.3%). Additionally, we replicated a previously inconclusive locus on 2q33.1 in all samples tested (OR 1.27, p 1.87×10-12). The five loci explain 2.1% of the sIA heritability in Finland, and may relate to, but not explain, the increased incidence of sIA-SAH in Finland. This study illustrates the utility of population isolates, familial enrichment, dense genotype imputation and alternate phenotyping in search for variants associated with complex diseases.     

Whole-Exome Sequencing Efficiently Detects Rare Mutations in Autosomal Recessive Nonsyndromic Hearing Loss

Identification of the pathogenic mutations underlying autosomal recessive nonsyndromic hearing loss (ARNSHL) is difficult, since causative mutations in 39 different genes have so far been reported. After excluding mutations in the most common ARNSHL gene, GJB2, via Sanger sequencing, we performed whole-exome sequencing (WES) in 30 individuals from 20 unrelated multiplex consanguineous families with ARNSHL. Agilent SureSelect Human All Exon 50 Mb kits and an Illumina Hiseq2000 instrument were used. An average of 93%, 84% and 73% of bases were covered to 1X, 10X and 20X within the ARNSHL-related coding RefSeq exons, respectively. Uncovered regions with WES included those that are not targeted by the exome capture kit and regions with high GC content. Twelve homozygous mutations in known deafness genes, of which eight are novel, were identified in 12 families: MYO15A-p.Q1425X, -p.S1481P, -p.A1551D; LOXHD1-p.R1494X, -p.E955X; GIPC3-p.H170N; ILDR1-p.Q274X; MYO7A-p.G2163S; TECTA-p.Y1737C; TMC1-p.S530X; TMPRSS3-p.F13Lfs*10; TRIOBP-p.R785Sfs*50. Each mutation was within a homozygous run documented via WES. Sanger sequencing confirmed co-segregation of the mutation with deafness in each family. Four rare heterozygous variants, predicted to be pathogenic, in known deafness genes were detected in 12 families where homozygous causative variants were already identified. Six heterozygous variants that had similar characteristics to those abovementioned variants were present in 15 ethnically-matched individuals with normal hearing. Our results show that rare causative mutations in known ARNSHL genes can be reliably identified via WES. The excess of heterozygous variants should be considered during search for causative mutations in ARNSHL genes, especially in small-sized families.     

Proxy Molecular Diagnosis from Whole-Exome Sequencing Reveals Papillon-Lefevre Syndrome Caused by a Missense Mutation in CTSC

Papillon-Lefevre syndrome (PLS) is an autosomal recessive disorder characterised by severe early onset periodontitis and palmoplantar hyperkeratosis. A previously reported missense mutation in the CTSC gene (NM_001814.4:c.899G>A:p.(G300D)) was identified in a homozygous state in two siblings diagnosed with PLS in a consanguineous family of Arabic ancestry. The variant was initially identified in a heterozygous state in a PLS unaffected sibling whose whole exome had been sequenced as part of a previous Primary ciliary dyskinesia study. Using this information, a proxy molecular diagnosis was made on the PLS affected siblings after consent was given to study this second disorder found to be segregating within the family. The prevalence of the mutation was then assayed in the local population using a representative sample of 256 unrelated individuals. The variant was absent in all subjects indicating that the variant is rare in Saudi Arabia. This family study illustrates how whole-exome sequencing can generate findings and inferences beyond its primary goal.     

General Framework for Meta-analysis of Rare Variants in Sequencing Association Studies

We propose a general statistical framework for meta-analysis of gene- or region-based multimarker rare variant association tests in sequencing association studies. In genome-wide association studies, single-marker meta-analysis has been widely used to increase statistical power by combining results via regression coefficients and standard errors from different studies. In analysis of rare variants in sequencing studies, region-based multimarker tests are often used to increase power. We propose meta-analysis methods for commonly used gene- or region-based rare variants tests, such as burden tests and variance component tests. Because estimation of regression coefficients of individual rare variants is often unstable or not feasible, the proposed method avoids this difficulty by calculating score statistics instead that only require fitting the null model for each study and then aggregating these score statistics across studies. Our proposed meta-analysis rare variant association tests are conducted based on study-specific summary statistics, specifically score statistics for each variant and between-variant covariance-type (linkage disequilibrium) relationship statistics for each gene or region. The proposed methods are able to incorporate different levels of heterogeneity of genetic effects across studies and are applicable to meta-analysis of multiple ancestry groups. We show that the proposed methods are essentially as powerful as joint analysis by directly pooling individual level genotype data. We conduct extensive simulations to evaluate the performance of our methods by varying levels of heterogeneity across studies, and we apply the proposed methods to meta-analysis of rare variant effects in a multicohort study of the genetics of blood lipid levels.     

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

As DNA sequencing technology has markedly advanced in recent years², it has become increasingly evident that the amount of genetic variation between any two individuals is greater than previously thought³. In contrast, array-based genotyping has failed to identify a significant contribution of common sequence variants to the phenotypic variability of common disease^4,5. Taken together, these observations have led to the evolution of the Common Disease / Rare Variant hypothesis suggesting that the majority of the "missing heritability" in common and complex phenotypes is instead due to an individual''s personal profile of rare or private DNA variants^6-8. However, characterizing how rare variation impacts complex phenotypes requires the analysis of many affected individuals at many genomic loci, and is ideally compared to a similar survey in an unaffected cohort. Despite the sequencing power offered by today''s platforms, a population-based survey of many genomic loci and the subsequent computational analysis required remains prohibitive for many investigators.To address this need, we have developed a pooled sequencing approach^1,9 and a novel software package¹ for highly accurate rare variant detection from the resulting data. The ability to pool genomes from entire populations of affected individuals and survey the degree of genetic variation at multiple targeted regions in a single sequencing library provides excellent cost and time savings to traditional single-sample sequencing methodology. With a mean sequencing coverage per allele of 25-fold, our custom algorithm, SPLINTER, uses an internal variant calling control strategy to call insertions, deletions and substitutions up to four base pairs in length with high sensitivity and specificity from pools of up to 1 mutant allele in 500 individuals. Here we describe the method for preparing the pooled sequencing library followed by step-by-step instructions on how to use the SPLINTER package for pooled sequencing analysis (http://www.ibridgenetwork.org/wustl/splinter). We show a comparison between pooled sequencing of 947 individuals, all of whom also underwent genome-wide array, at over 20kb of sequencing per person. Concordance between genotyping of tagged and novel variants called in the pooled sample were excellent. This method can be easily scaled up to any number of genomic loci and any number of individuals. By incorporating the internal positive and negative amplicon controls at ratios that mimic the population under study, the algorithm can be calibrated for optimal performance. This strategy can also be modified for use with hybridization capture or individual-specific barcodes and can be applied to the sequencing of naturally heterogeneous samples, such as tumor DNA.     

Novel Rare Missense Variations and Risk of Autism Spectrum Disorder: Whole-Exome Sequencing in Two Families with Affected Siblings and a Two-Stage Follow-Up Study in a Japanese Population

Rare inherited variations in multiplex families with autism spectrum disorder (ASD) are suggested to play a major role in the genetic etiology of ASD. To further investigate the role of rare inherited variations, we performed whole-exome sequencing (WES) in two families, each with three affected siblings. We also performed a two-stage follow-up case-control study in a Japanese population. WES of the six affected siblings identified six novel rare missense variations. Among these variations, CLN8 R24H was inherited in one family by three affected siblings from an affected father and thus co-segregated with ASD. In the first stage of the follow-up study, we genotyped the six novel rare missense variations identified by WES in 241 patients and 667 controls (the Niigata sample). Only CLN8 R24H had higher mutant allele frequencies in patients (1/482) compared with controls (1/1334). In the second stage, this variation was further genotyped, yet was not detected in a sample of 309 patients and 350 controls (the Nagoya sample). In the combined Niigata and Nagoya samples, there was no significant association (odds ratio = 1.8, 95% confidence interval = 0.1–29.6). These results suggest that CLN8 R24H plays a role in the genetic etiology of ASD, at least in a subset of ASD patients.     

Rare Variants Association Analysis in Large-Scale Sequencing Studies at the Single Locus Level

Genetic association analyses of rare variants in next-generation sequencing (NGS) studies are fundamentally challenging due to the presence of a very large number of candidate variants at extremely low minor allele frequencies. Recent developments often focus on pooling multiple variants to provide association analysis at the gene instead of the locus level. Nonetheless, pinpointing individual variants is a critical goal for genomic researches as such information can facilitate the precise delineation of molecular mechanisms and functions of genetic factors on diseases. Due to the extreme rarity of mutations and high-dimensionality, significances of causal variants cannot easily stand out from those of noncausal ones. Consequently, standard false-positive control procedures, such as the Bonferroni and false discovery rate (FDR), are often impractical to apply, as a majority of the causal variants can only be identified along with a few but unknown number of noncausal variants. To provide informative analysis of individual variants in large-scale sequencing studies, we propose the Adaptive False-Negative Control (AFNC) procedure that can include a large proportion of causal variants with high confidence by introducing a novel statistical inquiry to determine those variants that can be confidently dispatched as noncausal. The AFNC provides a general framework that can accommodate for a variety of models and significance tests. The procedure is computationally efficient and can adapt to the underlying proportion of causal variants and quality of significance rankings. Extensive simulation studies across a plethora of scenarios demonstrate that the AFNC is advantageous for identifying individual rare variants, whereas the Bonferroni and FDR are exceedingly over-conservative for rare variants association studies. In the analyses of the CoLaus dataset, AFNC has identified individual variants most responsible for gene-level significances. Moreover, single-variant results using the AFNC have been successfully applied to infer related genes with annotation information.