The power of gene-based rare variant methods to detect disease-associated variation and test hypotheses about complex disease

EBV-specific T-cell immunity: relevance for multiple sclerosis

Genetic and environmental factors jointly determine the susceptibility to develop multiple sclerosis (MS). Improvements in the design of epidemiological studies have helped to identify consistent environmental risk associations such as the increased susceptibility for MS following Epstein-Barr virus (EBV) infection, while biological mechanisms that drive the association between EBV and MS remain incompletely understood. An increased and broadened repertoire of antibody and T-cell immune responses to EBV-encoded antigens, especially to the dominant CD4+ T-cell EBV nuclear antigen 1 (EBNA1), is consistently observed in patients with MS, indicating that protective EBV-specific immune responses are deregulated in MS and potentially contribute to disease development. Exploitation of B-cell trajectories by EBV infection might promote survival of autoreactive B-cell species and proinflammatory B:T-cell interactions. In this review article, we illustrate evidence for a causal role of EBV infection in MS, discuss how EBV-targeting adaptive immune responses potentially modulate disease susceptibility and progression, and provide future perspectives on how novel model systems could be utilized to better define the role of EBV and viral pathogens in MS. Insights gained from these studies might facilitate the development of prevention strategies and more effective treatments for MS.

Strategies in Aggregation Tests for Rare Variants

Genome-wide association studies (GWAS) successfully identified numerous common variants involved in complex diseases, but only limited heritability was explained by these findings. Advances in high-throughput sequencing technology made it possible to assess the contribution of rare variants in common diseases. However, study of rare variants introduces challenges due to low frequency of rare variants. Well-established common variant methods were underpowered to identify the rare variants in GWAS. To address this challenge, several new methods have been developed to examine the role of rare variants in complex diseases. These approaches are based on testing the aggregate effect of multiple rare variants in a predefined genetic region. Provided here is an overview of statistical approaches and the protocols explaining step-by-step analysis of aggregations tests with the hands-on experience using R scripts in four categories: burden tests, adaptive burden tests, variance-component tests, and combined tests. Also explained are the concepts of rare variants, permutation tests, kernel methods, and genetic variant annotation. At the end we discuss relevant topics of bioinformatics tools for annotation, family-based design of rare-variant analysis, population stratification adjustment, and meta-analysis. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.

Rare variant association on unrelated individuals in case-control studies using aggregation tests: existing methods and current limitations

Over the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations.

Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data

The development of high-throughput next-generation sequencing technologies and large-scale genetic association studies produced numerous advances in the biostatistics field. Various aggregation tests, i.e. statistical methods that analyze associations of a trait with multiple markers within a genomic region, have produced a variety of novel discoveries. Notwithstanding their usefulness, there is no single test that fits all needs, each suffering from specific drawbacks. Selecting the right aggregation test, while considering an unknown underlying genetic model of the disease, remains an important challenge. Here we propose a new ensemble method, called Excalibur, based on an optimal combination of 36 aggregation tests created after an in-depth study of the limitations of each test and their impact on the quality of result. Our findings demonstrate the ability of our method to control type I error and illustrate that it offers the best average power across all scenarios. The proposed method allows for novel advances in Whole Exome/Genome sequencing association studies, able to handle a wide range of association models, providing researchers with an optimal aggregation analysis for the genetic regions of interest.

Biallelic Mutations in P4HTM Cause Syndromic Obesity

We previously demonstrated that 50% of children with obesity from consanguineous families from Pakistan carry pathogenic variants in known monogenic obesity genes. Here, we have discovered a novel monogenetic recessive form of severe childhood obesity using an in-house computational staged approach. The analysis included whole-exome sequencing data of 366 children with severe obesity, 1,000 individuals of the Pakistan Risk of Myocardial Infarction Study (PROMIS) study, and 200,000 participants of the UK Biobank to prioritize genes harboring rare homozygous variants with putative effect on human obesity. We identified five rare or novel homozygous missense mutations predicted deleterious in five consanguineous families in P4HTM encoding prolyl 4-hydroxylase transmembrane (P4H-TM). We further found two additional homozygous missense mutations in children with severe obesity of Indian and Moroccan origin. Molecular dynamics simulation suggested that these mutations destabilized the active conformation of the substrate binding domain. Most carriers also presented with hypotonia, cognitive impairment, and/or developmental delay. Three of the five probands died of pneumonia during the first 2 years of the follow-up. P4HTM deficiency is a novel form of syndromic obesity, affecting 1.5% of our children with obesity associated with high mortality. P4H-TM is a hypoxia-inducible factor that is necessary for survival and adaptation under oxygen deprivation, but the role of this pathway in energy homeostasis and obesity pathophysiology remains to be elucidated.

Genetic Screening of Targeted Region on the Chromosome 22q11.2 in Patients with Microtia and Congenital Heart Defect

Microtia is a congenital malformation characterized by a small, abnormally shaped auricle (pinna) ranging in severity. Congenital heart defect (CHD) is one of the comorbid anomalies with microtia. However, the genetic basis of the co-existence of microtia and CHD remains unclear. Copy number variations (CNVs) of 22q11.2 contribute significantly to microtia and CHD, respectively, thus suggesting a possible shared genetic cause embedded in this genomic region. In this study, 19 sporadic patients with microtia and CHD, as well as a nuclear family, were enrolled for genetic screening of single nucleotide variations (SNVs) and CNVs in 22q11.2 by target capture sequencing. We detected a total of 105 potential deleterious variations, which were enriched in ear- or heart-development-related genes, including TBX1 and DGCR8. The gene burden analysis also suggested that these genes carry more deleterious mutations in the patients, as well as several other genes associated with cardiac development, such as CLTCL1. Additionally, a microduplication harboring SUSD2 was validated in an independent cohort. This study provides new insights into the underlying mechanisms for the comorbidity of microtia and CHD focusing on chromosome 22q11.2, and suggests that a combination of genetic variations, including SNVs and CNVs, may play a crucial role instead of single gene mutation.

Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants

Anthropometric traits, measuring body size and shape, are highly heritable and significant clinical risk factors for cardiometabolic disorders. These traits have been extensively studied in genome-wide association studies (GWASs), with hundreds of genome-wide significant loci identified. We performed a whole-exome sequence analysis of the genetics of height, body mass index (BMI) and waist/hip ratio (WHR). We meta-analyzed single-variant and gene-based associations of whole-exome sequence variation with height, BMI, and WHR in up to 22,004 individuals, and we assessed replication of our findings in up to 16,418 individuals from 10 independent cohorts from Trans-Omics for Precision Medicine (TOPMed). We identified four trait associations with single-nucleotide variants (SNVs; two for height and two for BMI) and replicated the LECT2 gene association with height. Our expression quantitative trait locus (eQTL) analysis within previously reported GWAS loci implicated CEP63 and RFT1 as potential functional genes for known height loci. We further assessed enrichment of SNVs, which were monogenic or syndromic variants within loci associated with our three traits. This led to the significant enrichment results for height, whereas we observed no Bonferroni-corrected significance for all SNVs. With a sample size of ∼20,000 whole-exome sequences in our discovery dataset, our findings demonstrate the importance of genomic sequencing in genetic association studies, yet they also illustrate the challenges in identifying effects of rare genetic variants.

Rare variant analyses across multiethnic cohorts identify novel genes for refractive error

Refractive error, measured here as mean spherical equivalent (SER), is a complex eye condition caused by both genetic and environmental factors. Individuals with strong positive or negative values of SER require spectacles or other approaches for vision correction. Common genetic risk factors have been identified by genome-wide association studies (GWAS), but a great part of the refractive error heritability is still missing. Some of this heritability may be explained by rare variants (minor allele frequency [MAF] ≤ 0.01.). We performed multiple gene-based association tests of mean Spherical Equivalent with rare variants in exome array data from the Consortium for Refractive Error and Myopia (CREAM). The dataset consisted of over 27,000 total subjects from five cohorts of Indo-European and Eastern Asian ethnicity. We identified 129 unique genes associated with refractive error, many of which were replicated in multiple cohorts. Our best novel candidates included the retina expressed PDCD6IP, the circadian rhythm gene PER3, and P4HTM, which affects eye morphology. Future work will include functional studies and validation. Identification of genes contributing to refractive error and future understanding of their function may lead to better treatment and prevention of refractive errors, which themselves are important risk factors for various blinding conditions.

A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

CLIN_SKAT: an R package to conduct association analysis using functionally relevant variants

Background

Availability of next generation sequencing data, allows low-frequency and rare variants to be studied through strategies other than the commonly used genome-wide association studies (GWAS). Rare variants are important keys towards explaining the heritability for complex diseases that remains to be explained by common variants due to their low effect sizes. However, analysis strategies struggle to keep up with the huge amount of data at disposal therefore creating a bottleneck. This study describes CLIN_SKAT, an R package, that provides users with an easily implemented analysis pipeline with the goal of (i) extracting clinically relevant variants (both rare and common), followed by (ii) gene-based association analysis by grouping the selected variants.

Results

CLIN_SKAT offers four simple functions that can be used to obtain clinically relevant variants, map them to genes or gene sets, calculate weights from global healthy populations and conduct weighted case–control analysis. CLIN_SKAT introduces improvements by adding certain pre-analysis steps and customizable features to make the SKAT results clinically more meaningful. Moreover, it offers several plot functions that can be availed towards obtaining visualizations for interpretation of the analyses results. CLIN_SKAT is available on Windows/Linux/MacOS and is operative for R version 4.0.4 or later. It can be freely downloaded from https://github.com/ShihChingYu/CLIN_SKAT, installed through devtools::install_github("ShihChingYu/CLIN_SKAT", force=T) and executed by loading the package into R using library(CLIN_SKAT). All outputs (tabular and graphical) can be downloaded in simple, publishable formats.

Conclusions

Statistical association analysis is often underpowered due to low sample sizes and high numbers of variants to be tested, limiting detection of causal ones. Therefore, retaining a subset of variants that are biologically meaningful seems to be a more effective strategy for identifying explainable associations while reducing the degrees of freedom. CLIN_SKAT offers users a one-stop R package that identifies disease risk variants with improved power via a series of tailor-made procedures that allows dimension reduction, by retaining functionally relevant variants, and incorporating ethnicity based priors. Furthermore, it also eliminates the requirement for high computational resources and bioinformatics expertise.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04987-2.

Antibody-mediated cell depletion therapies in multiple sclerosis

Development of disease-modifying therapies including monoclonal antibody (mAb)-based therapeutics for the treatment of multiple sclerosis (MS) has been extremely successful over the past decades. Most of the mAb-based therapies approved for MS deplete immune cell subsets and act through activation of cellular Fc-gamma receptors expressed by cytotoxic lymphocytes and phagocytes, resulting in antibody-dependent cellular cytotoxicity or by initiation of complement-mediated cytotoxicity. The therapeutic goal is to eliminate pathogenic immune cell components and to potentially foster the reconstitution of a new and healthy immune system. Ab-mediated immune cell depletion therapies include the CD52-targeting mAb alemtuzumab, CD20-specific therapeutics, and new Ab-based treatments which are currently being developed and tested in clinical trials. Here, we review recent developments in effector mechanisms and clinical applications of Ab-based cell depletion therapies, compare their immunological and clinical effects with the prototypic immune reconstitution treatment strategy, autologous hematopoietic stem cell transplantation, and discuss their potential to restore immunological tolerance and to achieve durable remission in people with MS.

RAREsim: A simulation method for very rare genetic variants

Identification of rare-variant associations is crucial to full characterization of the genetic architecture of complex traits and diseases. Essential in this process is the evaluation of novel methods in simulated data that mirror the distribution of rare variants and haplotype structure in real data. Additionally, importing real-variant annotation enables in silico comparison of methods, such as rare-variant association tests and polygenic scoring methods, that focus on putative causal variants. Existing simulation methods are either unable to employ real-variant annotation or severely under- or overestimate the number of singletons and doubletons, thereby reducing the ability to generalize simulation results to real studies. We present RAREsim, a flexible and accurate rare-variant simulation algorithm. Using parameters and haplotypes derived from real sequencing data, RAREsim efficiently simulates the expected variant distribution and enables real-variant annotations. We highlight RAREsim's utility across various genetic regions, sample sizes, ancestries, and variant classes.

Partitioning gene-level contributions to complex-trait heritability by allele frequency identifies disease-relevant genes

Recent works have shown that SNP heritability-which is dominated by low-effect common variants-may not be the most relevant quantity for localizing high-effect/critical disease genes. Here, we introduce methods to estimate the proportion of phenotypic variance explained by a given assignment of SNPs to a single gene ("gene-level heritability"). We partition gene-level heritability by minor allele frequency (MAF) to find genes whose gene-level heritability is explained exclusively by "low-frequency/rare" variants (0.5% ≤ MAF < 1%). Applying our method to ∼16K protein-coding genes and 25 quantitative traits in the UK Biobank (N = 290K "White British"), we find that, on average across traits, ∼2.5% of nonzero-heritability genes have a rare-variant component and only ∼0.8% (327 gene-trait pairs) have heritability exclusively from rare variants. Of these 327 gene-trait pairs, 114 (35%) were not detected by existing gene-level association testing methods. The additional genes we identify are significantly enriched for known disease genes, and we find several examples of genes that have been previously implicated in phenotypically related Mendelian disorders. Notably, the rare-variant component of gene-level heritability exhibits trends different from those of common-variant gene-level heritability. For example, while total gene-level heritability increases with gene length, the rare-variant component is significantly larger among shorter genes; the cumulative distributions of gene-level heritability also vary across traits and reveal differences in the relative contributions of rare/common variants to overall gene-level polygenicity. While nonzero gene-level heritability does not imply causality, if interpreted in the correct context, gene-level heritability can reveal useful insights into complex-trait genetic architecture.

Comparison of mixed model based approaches for correcting for population substructure with application to extreme phenotype sampling

Background

Mixed models are used to correct for confounding due to population stratification and hidden relatedness in genome-wide association studies. This class of models includes linear mixed models and generalized linear mixed models. Existing mixed model approaches to correct for population substructure have been previously investigated with both continuous and case-control response variables. However, they have not been investigated in the context of extreme phenotype sampling (EPS), where genetic covariates are only collected on samples having extreme response variable values. In this work, we compare the performance of existing binary trait mixed model approaches (GMMAT, LEAP and CARAT) on EPS data. Since linear mixed models are commonly used even with binary traits, we also evaluate the performance of a popular linear mixed model implementation (GEMMA).

Results

We used simulation studies to estimate the type I error rate and power of all approaches assuming a population with substructure. Our simulation results show that for a common candidate variant, both LEAP and GMMAT control the type I error rate while CARAT’s rate remains inflated. We applied all methods to a real dataset from a Québec, Canada, case-control study that is known to have population substructure. We observe similar type I error control with the analysis on the Québec dataset. For rare variants, the false positive rate remains inflated even after correction with mixed model approaches. For methods that control the type I error rate, the estimated power is comparable.

Conclusions

The methods compared in this study differ in their type I error control. Therefore, when data are from an EPS study, care should be taken to ensure that the models underlying the methodology are suitable to the sampling strategy and to the minor allele frequency of the candidate SNPs.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08297-y).

Contribution of Rare and Low-Frequency Variants to Multiple Sclerosis Susceptibility in the Italian Continental Population

Genome-wide association studies identified over 200 risk loci for multiple sclerosis (MS) focusing on common variants, which account for about 50% of disease heritability. The goal of this study was to investigate whether low-frequency and rare functional variants, located in MS-established associated loci, may contribute to disease risk in a relatively homogeneous population, testing their cumulative effect (burden) with gene-wise tests. We sequenced 98 genes in 588 Italian patients with MS and 408 matched healthy controls (HCs). Variants were selected using different filtering criteria based on allelic frequency and in silico functional impacts. Genes showing a significant burden (n = 17) were sequenced in an independent cohort of 504 MS and 504 HC. The highest signal in both cohorts was observed for the disruptive variants (stop-gain, stop-loss, or splicing variants) located in EFCAB13, a gene coding for a protein of an unknown function (p < 10^-4). Among these variants, the minor allele of a stop-gain variant showed a significantly higher frequency in MS versus HC in both sequenced cohorts (p = 0.0093 and p = 0.025), confirmed by a meta-analysis on a third independent cohort of 1298 MS and 1430 HC (p = 0.001) assayed with an SNP array. Real-time PCR on 14 heterozygous individuals for this variant did not evidence the presence of the stop-gain allele, suggesting a transcript degradation by non-sense mediated decay, supported by the evidence that the carriers of the stop-gain variant had a lower expression of this gene (p = 0.0184). In conclusion, we identified a novel low-frequency functional variant associated with MS susceptibility, suggesting the possible role of rare/low-frequency variants in MS as reported for other complex diseases.

Deviation from baseline mutation burden provides powerful and robust rare-variants association test for complex diseases

Identifying rare variants that contribute to complex diseases is challenging because of the low statistical power in current tests comparing cases with controls. Here, we propose a novel and powerful rare variants association test based on the deviation of the observed mutation burden of a gene in cases from a baseline predicted by a weighted recursive truncated negative-binomial regression (RUNNER) on genomic features available from public data. Simulation studies show that RUNNER is substantially more powerful than state-of-the-art rare variant association tests and has reasonable type 1 error rates even for stratified populations or in small samples. Applied to real case-control data, RUNNER recapitulates known genes of Hirschsprung disease and Alzheimer's disease missed by current methods and detects promising new candidate genes for both disorders. In a case-only study, RUNNER successfully detected a known causal gene of amyotrophic lateral sclerosis. The present study provides a powerful and robust method to identify susceptibility genes with rare risk variants for complex diseases.

Gene-level analysis of rare variants in 379,066 whole exome sequences identifies an association of GIGYF1 loss of function with type 2 diabetes

Sequencing of large cohorts offers an unprecedented opportunity to identify rare genetic variants and to find novel contributors to human disease. We used gene-based collapsing tests to identify genes associated with glucose, HbA1c and type 2 diabetes (T2D) diagnosis in 379,066 exome-sequenced participants in the UK Biobank. We identified associations for variants in GCK, HNF1A and PDX1, which are known to be involved in Mendelian forms of diabetes. Notably, we uncovered novel associations for GIGYF1, a gene not previously implicated by human genetics in diabetes. GIGYF1 predicted loss of function (pLOF) variants associated with increased levels of glucose (0.77 mmol/L increase, p = 4.42 × 10^–12) and HbA1c (4.33 mmol/mol, p = 1.28 × 10^–14) as well as T2D diagnosis (OR = 4.15, p = 6.14 × 10^–11). Multiple rare variants contributed to these associations, including singleton variants. GIGYF1 pLOF also associated with decreased cholesterol levels as well as an increased risk of hypothyroidism. The association of GIGYF1 pLOF with T2D diagnosis replicated in an independent cohort from the Geisinger Health System. In addition, a common variant association for glucose and T2D was identified at the GIGYF1 locus. Our results highlight the role of GIGYF1 in regulating insulin signaling and protecting from diabetes.

Role of ADAMTS13, VWF and F8 genes in deep vein thrombosis

Background

We previously described the association between rare ADAMTS13 single nucleotide variants (SNVs) and deep vein thrombosis (DVT). Moreover, DVT patients with at least one rare ADAMTS13 SNV had a lower ADAMTS13 activity than non-carriers.

Aims

To confirm ADAMTS13 variants association with DVT and reduced plasma ADAMTS13 activity levels in a larger population. To investigate the role of VWF and F8 variants.

Methods

ADAMTS13, VWF and F8 were sequenced using next-generation sequencing in 594 Italian DVT patients and 571 controls. Genetic association testing was performed using logistic regression and gene-based tests. The association between rare ADAMTS13 variants and the respective plasmatic activity, available for 365 cases and 292 controls, was determined using linear regression. All analyses were age-, sex- adjusted.

Results

We identified 48 low-frequency/common and 272 rare variants. Nine low-frequency/common variants had a P<0.05, but a false discovery rate between 0.06 and 0.24. Of them, 7 were found in ADAMTS13 (rs28641026, rs28503257, rs685523, rs3124768, rs3118667, rs739469, rs3124767; all protective) and 2 in VWF (rs1800382 [risk], rs7962217 [protective]). Rare ADAMTS13 variants were significantly associated with DVT using the burden, variable threshold (VT) and UNIQ (P<0.05), but not with C-ALPHA, SKAT and SKAT-O tests. Rare VWF and F8 variants were not associated with DVT. Carriers of rare ADAMTS13 variants had lower ADAMTS13 activity than non-carriers (ß -6.2, 95%CI -11,-1.5). This association was stronger for DVT patients than controls (ß -7.5, 95%CI -13.5,-1.5 vs. ß -2.9, 95%CI -10.4,4.5).

Conclusions

ADAMTS13 and VWF low-frequency/common variants mainly showed a protective effect, although their association with DVT was not confirmed. DVT patients carrying a rare ADAMTS13 variants had slightly reduced ADAMTS13 activity levels, but a higher DVT risk. Rare VWF and FVIII variants were not associated with DVT suggesting that other mechanisms are responsible for the high VWF and FVIII levels measured in DVT patients.

Controlling for human population stratification in rare variant association studies

Population stratification is a confounder of genetic association studies. In analyses of rare variants, corrections based on principal components (PCs) and linear mixed models (LMMs) yield conflicting conclusions. Studies evaluating these approaches generally focused on limited types of structure and large sample sizes. We investigated the properties of several correction methods through a large simulation study using real exome data, and several within- and between-continent stratification scenarios. We considered different sample sizes, with situations including as few as 50 cases, to account for the analysis of rare disorders. Large samples showed that accounting for stratification was more difficult with a continental than with a worldwide structure. When considering a sample of 50 cases, an inflation of type-I-errors was observed with PCs for small numbers of controls (≤ 100), and with LMMs for large numbers of controls (≥ 1000). We also tested a novel local permutation method (LocPerm), which maintained a correct type-I-error in all situations. Powers were equivalent for all approaches pointing out that the key issue is to properly control type-I-errors. Finally, we found that power of analyses including small numbers of cases can be increased, by adding a large panel of external controls, provided an appropriate stratification correction was used.

Integrative analysis of multi-omics data for discovering low-frequency variants associated with low-density lipoprotein cholesterol levels

MOTIVATION

The abundance of omics data has facilitated integrative analyses of single and multiple molecular layers with genome-wide association studies focusing on common variants. Built on its successes, we propose a general analysis framework to leverage multi-omics data with sequencing data to improve the statistical power of discovering new associations and understanding of the disease susceptibility due to low-frequency variants. The proposed test features its robustness to model misspecification, high power across a wide range of scenarios and the potential of offering insights into the underlying genetic architecture and disease mechanisms.

RESULTS

Using the Framingham Heart Study data, we show that low-frequency variants are predictive of DNA methylation, even after conditioning on the nearby common variants. In addition, DNA methylation and gene expression provide complementary information to functional genomics. In the Avon Longitudinal Study of Parents and Children with a sample size of 1497, one gene CLPTM1 is identified to be associated with low-density lipoprotein cholesterol levels by the proposed powerful adaptive gene-based test integrating information from gene expression, methylation and enhancer-promoter interactions. It is further replicated in the TwinsUK study with 1706 samples. The signal is driven by both low-frequency and common variants.

AVAILABILITY AND IMPLEMENTATION

Models are available at https://github.com/ytzhong/DNAm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

RBM20 is a candidate gene for hypertrophic cardiomyopathy

BACKGROUND

The genetic basis of a considerable fraction of hypertrophic cardiomyopathy (HCM) cases remains unknown. Whether the gene encoding RNA Binding Motif Protein 20 (RBM20) is implicated in HCM and the correlation of clinical characteristics of RBM20 heterozygotes with HCM remain unresolved. We aimed to investigate the association between RBM20 variants and HCM.

METHODS

We compared rare variants in the RBM20 gene by exome sequencing in 793 HCM patients and 414 healthy controls. Based on a case-control approach, we used SKAT-O to explore whether RBM20 is associated with HCM. The genetic distribution of RBM20 rare variants was then compared between HCM heterozygotes and dilated cardiomyopathy (DCM) heterozygotes. Clinical features and prognosis of RBM20 heterozygotes were compared with non-heterozygotes.

RESULTS

Gene-based association analysis implicated RBM20 as a susceptibility gene for developing HCM. Patients with RBM20 variants displayed a higher prevalence of sudden cardiac arrest (SCA) (6.7% vs. 0.9%, p = 0.001), increased sudden cardiac death (SCD) risk factor counts and impaired left ventricle systolic function. Further survival analysis revealed that RBM20 heterozygotes had higher incidences of resuscitated cardiac arrest, recurrent non-sustained ventricular tachycardia and malignant arrhythmias. Mendelian randomization suggested that RBM20 expression in left ventricle was causally associated with HCM and DCM with opposite effects.

CONCLUSIONS

This study identified RBM20 as a potential causal gene of HCM. RBM20 variants are associated with increased risk for SCA in HCM.

The Complex and Diverse Genetic Architecture of Dilated Cardiomyopathy

Our insight into the diverse and complex nature of dilated cardiomyopathy (DCM) genetic architecture continues to evolve rapidly. The foundations of DCM genetics rest on marked locus and allelic heterogeneity. While DCM exhibits a Mendelian, monogenic architecture in some families, preliminary data from our studies and others suggests that at least 20% to 30% of DCM may have an oligogenic basis, meaning that multiple rare variants from different, unlinked loci, determine the DCM phenotype. It is also likely that low-frequency and common genetic variation contribute to DCM complexity, but neither has been examined within a rare variant context. Other types of genetic variation are also likely relevant for DCM, along with gene-by-environment interaction, now established for alcohol- and chemotherapy-related DCM. Collectively, this suggests that the genetic architecture of DCM is broader in scope and more complex than previously understood. All of this elevates the impact of DCM genetics research, as greater insight into the causes of DCM can lead to interventions to mitigate or even prevent it and thus avoid the morbid and mortal scourge of human heart failure.

Study of the collagen type VI alpha 3 (COL6A3) gene in Parkinson's disease

Background

To date, the genetic contribution to Parkinson’s disease (PD) remains unclear. Mutations in the collagen type VI alpha 3 (COL6A3) gene were recently identified as a cause of isolated dystonia. Since PD and dystonia are closely related disorders with shared clinical and genetic characteristics, we explored the association between COL6A3 and PD in a Chinese cohort.

Methods

We performed genetic screening of COL6A3 in a Chinese cohort of 173 patients with sporadic PD and 200 healthy controls. We identified variants that are likely to have pathogenic effects based on: 1) a minor allele frequency of < 0.01; and 2) the variant being recognized as deleterious by at least 15 different in silico predicting tools. Finally, we tested the aggregate burden of COL6A3 on PD via SKAT-O analysis.

Results

First, we found compound heterozygous COL6A3 gene mutations in one early-onset PD patients. Then, we explored whether COL6A3 variants contributed to increased risk of developing PD in a Chinese population. We detected 21 rare non-synonymous variants. Pathogenicity predictions identified 7 novel non-synonymous variants as likely to be pathogenic. SKAT-O analysis further revealed that an aggregate burden of variants in COL6A3 contributes to PD (p = 0.038).

Conclusion

An increased aggregate burden of the COL6A3 gene was detected in patients with PD.

Detecting Clustered Independent Rare Variant Associations Using Genetic Algorithms

The availability of an increasing collection of sequencing data provides the opportunity to study genetic variation with an unprecedented level of detail. There is much interest in uncovering the role of rare variants and their contribution to disease. However, detecting associations of rare variants with small minor allele frequencies (MAF) and modest effects remains a challenge for rare variant association methods. Due to this low signal-to-noise ratio, most methods are underpowered to detect associations even when conducting rare variant association tests at the gene level. We present a new method for detecting rare variant associations. The algorithm consists of two steps. In the first step, a genetic algorithm searches for a promising genomic region containing a collection of genes with causal rare variants. In the second step, a genetic algorithm aims at removing false positives from the located genomic region. We tested the proposed method with a collection of datasets obtained from real exome data. The proposed method possesses sufficient power for detecting associations of rare variants with complex phenotypes. This method can be used for studying the contribution of rare variants with complex disease, particularly in cases where single-variant or gene-based tests are underpowered.

Increased Burden of Rare Sequence Variants in GnRH-Associated Genes in Women With Hypothalamic Amenorrhea

CONTEXT

Functional hypothalamic amenorrhea (HA) is a common, acquired form of hypogonadotropic hypogonadism that occurs in the setting of energy deficits and/or stress. Variability in individual susceptibility to these stressors, HA heritability, and previous identification of several rare sequence variants (RSVs) in genes associated with the rare disorder, isolated hypogonadotropic hypogonadism (IHH), in individuals with HA suggest a possible genetic contribution to HA susceptibility.

OBJECTIVE

We sought to determine whether the burden of RSVs in IHH-related genes is greater in women with HA than controls.

DESIGN

We compared patients with HA to control women.

SETTING

The study was conducted at secondary referral centers.

PATIENTS AND OTHER PARTICIPANTS

Women with HA (n = 106) and control women (ClinSeq study; n = 468).

INTERVENTIONS

We performed exome sequencing in all patients and controls.

MAIN OUTCOME MEASURE(S)

The frequency of RSVs in 53 IHH-associated genes was determined using rare variant burden and association tests.

RESULTS

RSVs were overrepresented in women with HA compared with controls (P = .007). Seventy-eight heterozygous RSVs in 33 genes were identified in 58 women with HA (36.8% of alleles) compared to 255 RSVs in 41 genes among 200 control women (27.2%).

CONCLUSIONS

Women with HA are enriched for RSVs in genes that cause IHH, suggesting that variation in genes associated with gonadotropin-releasing hormone neuronal ontogeny and function may be a major determinant of individual susceptibility to developing HA in the face of diet, exercise, and/or stress.

A unique missense variant in the E1A-binding protein P400 gene is implicated in schizophrenia by whole-exome sequencing and mutant mouse models

Genetic and epidemiological evidence has suggested that genetic factors are important in schizophrenia, although its pathophysiology is poorly understood. This study used whole-exome sequencing to investigate potential novel schizophrenia-causing genes in a Japanese family containing several members affected by severe or treatment-resistant schizophrenia. A missense variant, chr12:132064747C>T (rs200626129, P2805L), in the E1A-binding protein P400 (EP400) gene completely segregated with schizophrenia in this family. Furthermore, numerous other EP400 mutations were identified in the targeted sequencing of a schizophrenia patient cohort. We also created two lines of Ep400 gene-edited mice, which had anxiety-like behaviours and reduced axon diameters. Our findings suggest that rs200626129 in EP400 is likely to cause schizophrenia in this Japanese family, and may lead to a better understanding and treatment of schizophrenia.

Exome Chip Analyses and Genetic Risk for IgA Nephropathy among Han Chinese

BACKGROUND AND OBJECTIVES

IgA nephropathy is the most common form of primary GN worldwide. The evidence of geographic and ethnic differences, as well as familial aggregation of the disease, supports a strong genetic contribution to IgA nephropathy. Evidence for genetic factors in IgA nephropathy comes also from genome-wide association patient-control studies. However, few studies have systematically evaluated the contribution of coding variation in IgA nephropathy.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We performed a two-stage exome chip-based association study in 13,242 samples, including 3363 patients with IgA nephropathy and 9879 healthy controls of Han Chinese ancestry. Common variant functional annotation, gene-based low-frequency variants analysis, differential mRNA expression, and gene network integration were also explored.

RESULTS

We identified three non-HLA gene regions (FBXL21, CCR6, and STAT3) and one HLA gene region (GABBR1) with suggestive significance (Pmeta <5×10-5) in single-variant associations. These novel non-HLA variants were annotated as expression-associated single-nucleotide polymorphisms and were located in enhancer regions enriched in histone marks H3K4me1 in primary B cells. Gene-based low-frequency variants analysis suggests CFB as another potential susceptibility gene. Further combined expression and network integration suggested that the five novel susceptibility genes, TGFBI, CCR6, STAT3, GABBR1, and CFB, were involved in IgA nephropathy.

CONCLUSIONS

Five novel gene regions with suggestive significance for IgA nephropathy were identified and shed new light for further mechanism investigation.

Efficient and flexible Integration of variant characteristics in rare variant association studies using integrated nested Laplace approximation

Rare variants are thought to play an important role in the etiology of complex diseases and may explain a significant fraction of the missing heritability in genetic disease studies. Next-generation sequencing facilitates the association of rare variants in coding or regulatory regions with complex diseases in large cohorts at genome-wide scale. However, rare variant association studies (RVAS) still lack power when cohorts are small to medium-sized and if genetic variation explains a small fraction of phenotypic variance. Here we present a novel Bayesian rare variant Association Test using Integrated Nested Laplace Approximation (BATI). Unlike existing RVAS tests, BATI allows integration of individual or variant-specific features as covariates, while efficiently performing inference based on full model estimation. We demonstrate that BATI outperforms established RVAS methods on realistic, semi-synthetic whole-exome sequencing cohorts, especially when using meaningful biological context, such as functional annotation. We show that BATI achieves power above 70% in scenarios in which competing tests fail to identify risk genes, e.g. when risk variants in sum explain less than 0.5% of phenotypic variance. We have integrated BATI, together with five existing RVAS tests in the 'Rare Variant Genome Wide Association Study' (rvGWAS) framework for data analyzed by whole-exome or whole genome sequencing. rvGWAS supports rare variant association for genes or any other biological unit such as promoters, while allowing the analysis of essential functionalities like quality control or filtering. Applying rvGWAS to a Chronic Lymphocytic Leukemia study we identified eight candidate predisposition genes, including EHMT2 and COPS7A.

VINYL: Variant prIoritizatioN by survivaL analysis

MOTIVATION

Clinical applications of genome re-sequencing technologies typically generate large amounts of data that need to be carefully annotated and interpreted to identify genetic variants potentially associated with pathological conditions. In this context, accurate and reproducible methods for the functional annotation and prioritization of genetic variants are of fundamental importance.

RESULTS

In this paper, we present VINYL, a flexible and fully automated system for the functional annotation and prioritization of genetic variants. Extensive analyses of both real and simulated datasets suggest that VINYL can identify clinically relevant genetic variants in a more accurate manner compared to equivalent state of the art methods, allowing a more rapid and effective prioritization of genetic variants in different experimental settings. As such we believe that VINYL can establish itself as a valuable tool to assist healthcare operators and researchers in clinical genomics investigations.

AVAILABILITY

VINYL is available at http://beaconlab.it/VINYL and https://github.com/matteo14c/VINYL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Novel Mutations and Genes That Impact on Growth in Short Stature of Undefined Aetiology: The EPIGROW Study

BACKGROUND

Children with short stature of undefined aetiology (SS-UA) may have undiagnosed genetic conditions.

PURPOSE

To identify mutations causing short stature (SS) and genes related to SS, using candidate gene sequence data from the European EPIGROW study.

METHODS

First, we selected exonic single nucleotide polymorphisms (SNPs), in cases and not controls, with minor allele frequency (MAF) < 2%, whose carriage fitted the mode of inheritance. Known mutations were identified using Ensembl and gene-specific databases. Variants were classified as pathogenic, likely pathogenic, or variant of uncertain significance using criteria from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. If predicted by ≥ 5/10 algorithms (eg, Polyphen2) to be deleterious, this was considered supporting evidence of pathogenicity. Second, gene-based burden testing determined the difference in SNP frequencies between cases and controls across all and then rare SNPs. For genotype/phenotype relationships, we used PLINK, based on haplotype, MAF > 2%, genotype present in > 75%, and Hardy Weinberg equilibrium P > 10-4.

RESULTS

First, a diagnostic yield of 10% (27/263) was generated by 2 pathogenic (nonsense in ACAN) and a further 25 likely pathogenic mutations, including previously known missense mutations in FANCB, IGFIR, MMP13, NPR2, OBSL1, and PTPN11. Second, genes related to SS: all methods identified PEX2. Another 7 genes (BUB1B, FANCM, CUL7, FANCA, PTCH1, TEAD3, BCAS3) were identified by both gene-based approaches and 6 (A2M, EFEMP1, PRKCH, SOS2, RNF135, ZBTB38) were identified by gene-based testing for all SNPs and PLINK.

CONCLUSIONS

Such panels improve diagnosis in SS-UA, extending known disease phenotypes. Fourteen genes related to SS included some known to cause growth disorders as well as novel targets.

Pathogenic variants in actionable MODY genes are associated with type 2 diabetes

Genome-wide association studies have identified 240 independent loci associated with type 2 diabetes (T2D) risk, but this knowledge has not advanced precision medicine. In contrast, the genetic diagnosis of monogenic forms of diabetes (including maturity-onset diabetes of the young (MODY)) are textbook cases of genomic medicine. Recent studies trying to bridge the gap between monogenic diabetes and T2D have been inconclusive. Here, we show a significant burden of pathogenic variants in genes linked with monogenic diabetes among people with common T2D, particularly in actionable MODY genes, thus implying that there should be a substantial change in care for carriers with T2D. We show that, among 74,629 individuals, this burden is probably driven by the pathogenic variants found in GCK, and to a lesser extent in HNF4A, KCNJ11, HNF1B and ABCC8. The carriers with T2D are leaner, which evidences a functional metabolic effect of these mutations. Pathogenic variants in actionable MODY genes are more frequent than was previously expected in common T2D. These results open avenues for future interventions assessing the clinical interest of these pathogenic mutations in precision medicine.

Identifying disease-causing mutations in genomes of single patients by computational approaches

Over the last decade next generation sequencing (NGS) has been extensively used to identify new pathogenic mutations and genes causing rare genetic diseases. The efficient analyses of NGS data is not trivial and requires a technically and biologically rigorous pipeline that addresses data quality control, accurate variant filtration to minimize false positives and false negatives, and prioritization of the remaining genes based on disease genomics and physiological knowledge. This review provides a pipeline including all these steps, describes popular software for each step of the analysis, and proposes a general framework for the identification of causal mutations and genes in individual patients of rare genetic diseases.

Genetic and epigenetic analyses of panic disorder in the post-GWAS era

Panic disorder (PD) is a common and debilitating neuropsychiatric disorder characterized by panic attacks coupled with excessive anxiety. Both genetic factors and environmental factors play an important role in PD pathogenesis and response to treatment. However, PD is clinically heterogeneous and genetically complex, and the exact genetic or environmental causes of this disorder remain unclear. Various approaches for detecting disease-causing genes have recently been made available. In particular, genome-wide association studies (GWAS) have attracted attention for the identification of disease-associated loci of multifactorial disorders. This review introduces GWAS of PD, followed by a discussion about the limitations of GWAS and the major challenges facing geneticists in the post-GWAS era. Alternative strategies to address these challenges are then proposed, such as epigenome-wide association studies (EWAS) and rare variant association studies (RVAS) using next-generation sequencing. To date, however, few reports have described these analyses, and the evidence remains insufficient to confidently identify or exclude rare variants or epigenetic changes in PD. Further analyses are therefore required, using sample sizes in the tens of thousands, extensive functional annotations, and highly targeted hypothesis testing.

Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma

Protein-altering variants that are protective against human disease provide in vivo validation of therapeutic targets. Here we use genotyping data from UK Biobank (n = 337,151 unrelated White British individuals) and FinnGen (n = 176,899) to conduct a search for protein-altering variants conferring lower intraocular pressure (IOP) and protection against glaucoma. Through rare protein-altering variant association analysis, we find a missense variant in ANGPTL7 in UK Biobank (rs28991009, p.Gln175His, MAF = 0.8%, genotyped in 82,253 individuals with measured IOP and an independent set of 4,238 glaucoma patients and 250,660 controls) that significantly lowers IOP (β = -0.53 and -0.67 mmHg for heterozygotes, -3.40 and -2.37 mmHg for homozygotes, P = 5.96 x 10-9 and 1.07 x 10-13 for corneal compensated and Goldman-correlated IOP, respectively) and is associated with 34% reduced risk of glaucoma (P = 0.0062). In FinnGen, we identify an ANGPTL7 missense variant at a greater than 50-fold increased frequency in Finland compared with other populations (rs147660927, p.Arg220Cys, MAF Finland = 4.3%), which was genotyped in 6,537 glaucoma patients and 170,362 controls and is associated with a 29% lower glaucoma risk (P = 1.9 x 10-12 for all glaucoma types and also protection against its subtypes including exfoliation, primary open-angle, and primary angle-closure). We further find three rarer variants in UK Biobank, including a protein-truncating variant, which confer a strong composite lowering of IOP (P = 0.0012 and 0.24 for Goldman-correlated and corneal compensated IOP, respectively), suggesting the protective mechanism likely resides in the loss of interaction or function. Our results support inhibition or down-regulation of ANGPTL7 as a therapeutic strategy for glaucoma.

Identification of potential candidate genes and pathways in atrioventricular nodal reentry tachycardia by whole-exome sequencing

Background

Atrioventricular nodal reentry tachycardia (AVNRT) is the most common manifestation of paroxysmal supraventricular tachycardia (PSVT). Increasing data have indicated familial clustering and participation of genetic factors in AVNRT, and no pathogenic genes related to AVNRT have been reported.

Methods

Whole‐exome sequencing (WES) was performed in 82 patients with AVNRT and 100 controls. Reference genes, genome‐wide association analysis, gene‐based collapsing, and pathway enrichment analysis were performed. A protein‐protein interaction (PPI) network was then established; WES database in the UK Biobank and one only genetic study of AVNRT in Denmark were used for external data validation.

Results

Among 95 reference genes, 126 rare variants in 48 genes were identified in the cases (minor allele frequency < 0.001). Gene‐based collapsing analysis and pathway enrichment analysis revealed six functional pathways related to AVNRT as with neuronal system/neurotransmitter release cycles and ion channel/cardiac conduction among the top 30 enriched pathways, and then 36 candidate pathogenic genes were selected. By combining with PPI analysis, 10 candidate genes were identified, including RYR2, NOS1, SCN1A, CFTR, EPHB4, ROBO1, PRKAG2, MMP2, ASPH, and ABCC8. From the UK Biobank database, 18 genes from candidate genes including SCN1A, PRKAG2, NOS1, and CFTR had rare variants in arrhythmias, and the rare variants in PIK3CB, GAD2, and HIP1R were in patients with PSVT. Moreover, one rare variant of RYR2 (c.4652A > G, p.Asn1551Ser) in our study was also detected in the Danish study. Considering the gene functional roles and external data validation, the most likely candidate genes were SCN1A, PRKAG2, RYR2, CFTR, NOS1, PIK3CB, GAD2, and HIP1R.

Conclusion

The preliminary results first revealed potential candidate genes such as SCN1A, PRKAG2, RYR2, CFTR, NOS1, PIK3CB, GAD2, and HIP1R, and the pathways mediated by these genes, including neuronal system/neurotransmitter release cycles or ion channels/cardiac conduction, might be involved in AVNRT.

Excess of singleton loss-of-function variants in Parkinson's disease contributes to genetic risk

BACKGROUND

Parkinson's disease (PD) is a neurodegenerative disorder with complex genetic architecture. Besides rare mutations in high-risk genes related to monogenic familial forms of PD, multiple variants associated with sporadic PD were discovered via association studies.

METHODS

We studied the whole-exome sequencing data of 340 PD cases and 146 ethnically matched controls from the Parkinson's Progression Markers Initiative (PPMI) and performed burden analysis for different rare variant classes. Disease prediction models were built based on clinical, non-clinical and genetic features, including both common and rare variants, and two machine learning methods.

RESULTS

We observed a significant exome-wide burden of singleton loss-of-function variants (corrected p=0.037). Overall, no exome-wide burden of rare amino acid changing variants was detected. Finally, we built a disease prediction model combining singleton loss-of-function variants, a polygenic risk score based on common variants, and family history of PD as features and reached an area under the curve of 0.703 (95% CI 0.698 to 0.708). By incorporating a rare variant feature, our model increased the performance of the state-of-the-art classification model for the PPMI dataset, which reached an area under the curve of 0.639 based on common variants alone.

CONCLUSION

The main finding of this study is to highlight the contribution of singleton loss-of-function variants to the complex genetics of PD and that disease risk prediction models combining singleton and common variants can improve models built solely on common variants.

Evolutionary perspectives on polygenic selection, missing heritability, and GWAS

Genome-wide association studies (GWAS) have successfully identified many trait-associated variants, but there is still much we do not know about the genetic basis of complex traits. Here, we review recent theoretical and empirical literature regarding selection on complex traits to argue that "missing heritability" is as much an evolutionary problem as it is a statistical problem. We discuss empirical findings that suggest a role for selection in shaping the effect sizes and allele frequencies of causal variation underlying complex traits, and the limitations of these studies. We then use simulations of selection, realistic genome structure, and complex human demography to illustrate the results of recent theoretical work on polygenic selection, and show that statistical inference of causal loci is sharply affected by evolutionary processes. In particular, when selection acts on causal alleles, it hampers the ability to detect causal loci and constrains the transferability of GWAS results across populations. Last, we discuss the implications of these findings for future association studies, and suggest that future statistical methods to infer causal loci for genetic traits will benefit from explicit modeling of the joint distribution of effect sizes and allele frequencies under plausible evolutionary models.

Population genetic simulation study of power in association testing across genetic architectures and study designs

While it is well established that genetics can be a major contributor to population variation of complex traits, the relative contributions of rare and common variants to phenotypic variation remains a matter of considerable debate. Here, we simulate genetic and phenotypic data across different case/control panel sampling strategies, sequencing methods, and genetic architecture models based on evolutionary forces to determine the statistical performance of rare variant association tests (RVATs) widely in use. We find that the highest statistical power of RVATs is achieved by sampling case/control individuals from the extremes of an underlying quantitative trait distribution. We also demonstrate that the use of genotyping arrays, in conjunction with imputation from a whole-genome sequenced (WGS) reference panel, recovers the vast majority (90%) of the power that could be achieved by sequencing the case/control panel using current tools. Finally, we show that for dichotomous traits, the statistical performance of RVATs decreases as rare variants become more important in the trait architecture. Our results extend previous work to show that RVATs are insufficiently powered to make generalizable conclusions about the role of rare variants in dichotomous complex traits.

A region-based gene association study combined with a leave-one-out sensitivity analysis identifies SMG1 as a pancreatic cancer susceptibility gene

Pancreatic adenocarcinoma (PC) is a lethal malignancy that is familial or associated with genetic syndromes in 10% of cases. Gene-based surveillance strategies for at-risk individuals may improve clinical outcomes. However, familial PC (FPC) is plagued by genetic heterogeneity and the genetic basis for the majority of FPC remains elusive, hampering the development of gene-based surveillance programs. The study was powered to identify genes with a cumulative pathogenic variant prevalence of at least 3%, which includes the most prevalent PC susceptibility gene, BRCA2. Since the majority of known PC susceptibility genes are involved in DNA repair, we focused on genes implicated in these pathways. We performed a region-based association study using the Mixed-Effects Score Test, followed by leave-one-out characterization of PC-associated gene regions and variants to identify the genes and variants driving risk associations. We evaluated 398 cases from two case series and 987 controls without a personal history of cancer. The first case series consisted of 109 patients with either FPC (n = 101) or PC at ≤50 years of age (n = 8). The second case series was composed of 289 unselected PC cases. We validated this discovery strategy by identifying known pathogenic BRCA2 variants, and also identified SMG1, encoding a serine/threonine protein kinase, to be significantly associated with PC following correction for multiple testing (p = 3.22x10-7). The SMG1 association was validated in a second independent series of 532 FPC cases and 753 controls (p<0.0062, OR = 1.88, 95%CI 1.17-3.03). We showed segregation of the c.4249A>G SMG1 variant in 3 affected relatives in a FPC kindred, and we found c.103G>A to be a recurrent SMG1 variant associating with PC in both the discovery and validation series. These results suggest that SMG1 is a novel PC susceptibility gene, and we identified specific SMG1 gene variants associated with PC risk.

Die Rolle seltener Varianten bei häufigen Krankheiten

Häufige Krankheiten, die sog. Volkskrankheiten, sind in der Regel multifaktoriell verursacht, d. h. zu ihrer Entwicklung tragen sowohl genetische Faktoren als auch nicht-genetische Umgebungseinflüsse bei. Die geschätzte Gesamterblichkeit (‑heritabilität) reicht von moderat bis vergleichsweise hoch. Die genetische Architektur ist komplex und kann das gesamte allelische Spektrum, von häufigen Varianten mit niedriger Penetranz bis hin zu seltenen Varianten mit höherer Penetranz, sowie alle möglichen Kombinationen umfassen. Während häufige Varianten seit mehreren Jahren mit großem Erfolg durch genomweite Assoziationsstudien (GWAS) identifiziert werden, war bisher die Identifizierung seltener Varianten, insbesondere aufgrund der großen Zahl beitragender Gene, nur begrenzt erfolgreich. Dies ändert sich derzeit dank der Anwendung von Hochdurchsatz-Sequenziertechnologien („next-generation sequencing“, NGS) und der daraus resultierenden zunehmenden Verfügbarkeit von exom- und genomweiten Sequenzdaten großer Kollektive. In diesem Artikel geben wir einen Überblick über die Bedeutung seltener Varianten bei häufigen Erkrankungen sowie den aktuellen Stand in Bezug auf deren Identifizierung mittels NGS. Wir betrachten insbesondere die folgenden Fragen: Bei welchen häufigen Krankheiten ist ein Beitrag seltener Varianten zu erwarten, wie können diese Varianten identifiziert werden, und welches Potenzial bieten seltene Varianten für das Verständnis biologischer Prozesse bzw. für die Translation in die klinische Praxis?

Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls

Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 × 10-3) and candidate genes from knockout mice (P = 5.2 × 10-3). Within our study, the strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000-185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts.

Identification and characterization of rare toll-like receptor 3 variants in patients with autoimmune Addison's disease

Autoimmune Addison's disease (AAD) is a classic organ-specific autoimmune disease characterized by an immune-mediated attack on the adrenal cortex. As most autoimmune diseases, AAD is believed to be caused by a combination of genetic and environmental factors, and probably interactions between the two. Persistent viral infections have been suggested to play a triggering role, by invoking inflammation and autoimmune destruction. The inability of clearing infections can be due to aberrations in innate immunity, including mutations in genes involved in the recognition of conserved microbial patterns. In a whole exome sequencing study of anonymized AAD patients, we discovered several rare variants predicted to be damaging in the gene encoding Toll-like receptor 3 (TLR3). TLR3 recognizes double stranded RNAs, and is therefore a major factor in antiviral defense. We here report the occurrence and functional characterization of five rare missense variants in TLR3 of patients with AAD. Most of these variants occurred together with a common TLR3 variant that has been associated with a wide range of immunopathologies. The biological implications of these variants on TLR3 function were evaluated in a cell-based assay, revealing a partial loss-of-function effect of three of the rare variants. In addition, rare mutations in other members of the TLR3-interferon (IFN) signaling pathway were detected in the AAD patients. Together, these findings indicate a potential role for TLR3 and downstream signaling proteins in the pathogenesis in a subset of AAD patients.

Reconstructing recent population history while mapping rare variants using haplotypes

Haplotype-based methods are a cost-effective alternative to characterize unobserved rare variants and map disease-associated alleles. Moreover, they can be used to reconstruct recent population history, which shaped distribution of rare variants and thus can be used to guide gene mapping studies. In this study, we analysed Illumina 650 k genotyped dataset on three underrepresented populations from Eastern Europe, where ancestors of Russians came into contact with two indigenous ethnic groups, Bashkirs and Tatars. Using the IBD mapping approach, we identified two rare IBD haplotypes strongly enriched in asthma patients of distinct ethnic background. We reconstructed recent population history using haplotype-based methods to reconcile this contradictory finding. Our ChromoPainter analysis showed that these haplotypes each descend from a single ancestor coming from one of the ethnic groups studied. Next, we used DoRIS approach and showed that source populations for patients exchanged recent (<60 generations) asymmetric gene flow, which supported the ChromoPainter-based scenario that patients share haplotypes through inter-ethnic admixture. Finally, we show that these IBD haplotypes overlap with asthma-associated genomic regions ascertained in European population. This finding is consistent with the fact that the two donor populations for the rare IBD haplotypes: Russians and Tatars have European ancestry.

The Contribution of Low-Frequency and Rare Coding Variation to Susceptibility to Type 2 Diabetes

PURPOSE OF REVIEW

Soon after the first genome-wide association study (GWAS) for type 2 diabetes (T2D) was published, it was hypothesized that rare and low-frequency variants might explain a substantial proportion of disease risk. Rare coding variants in particular were emphasized given their large expected role in disease. This review summarizes the extent to which recent T2D genetic studies provide evidence for or against this hypothesis.

RECENT FINDINGS

Following a comprehensive study of T2D genetic architecture using three sequencing and genotyping technologies, four even larger studies have provided a yet higher resolution view of the role of rare and low-frequency coding variation in T2D susceptibility. Empirical evidence strongly suggests that common regulatory variants are the dominant contributor to T2D heritability. However, rare coding variants may nonetheless be pervasive across T2D-relevant genes. A strategy using common variants to map disease genes, and rare coding variants to link molecular gene perturbations to cellular and phenotypic effects, may be an effective means to investigate T2D pathogenesis and potential new therapies.

A novel nonsense variant in SUPT20H gene associated with Rheumatoid Arthritis identified by Whole Exome Sequencing of multiplex families

The triggering and development of Rheumatoid Arthritis (RA) is conditioned by environmental and genetic factors. Despite the identification of more than one hundred genetic variants associated with the disease, not all the cases can be explained. Here, we performed Whole Exome Sequencing in 9 multiplex families (N = 30) to identify rare variants susceptible to play a role in the disease pathogenesis. We pre-selected 77 genes which carried rare variants with a complete segregation with RA in the studied families. Follow-up linkage and association analyses with pVAAST highlighted significant RA association of 43 genes (p-value < 0.05 after 106 permutations) and pinpointed their most likely causal variant. We re-sequenced the 10 most significant likely causal variants (p-value ≤ 3.78*10-3 after 106 permutations) in the extended pedigrees and 9 additional multiplex families (N = 110). Only one SNV in SUPT20H: c.73A>T (p.Lys25*), presented a complete segregation with RA in an extended pedigree with early-onset cases. In summary, we identified in this study a new variant associated with RA in SUPT20H gene. This gene belongs to several biological pathways like macro-autophagy and monocyte/macrophage differentiation, which contribute to RA pathogenesis. In addition, these results showed that analyzing rare variants using a family-based approach is a strategy that allows to identify RA risk loci, even with a small dataset.

Psychiatric genetics and the structure of psychopathology

For over a century, psychiatric disorders have been defined by expert opinion and clinical observation. The modern DSM has relied on a consensus of experts to define categorical syndromes based on clusters of symptoms and signs, and, to some extent, external validators, such as longitudinal course and response to treatment. In the absence of an established etiology, psychiatry has struggled to validate these descriptive syndromes, and to define the boundaries between disorders and between normal and pathologic variation. Recent advances in genomic research, coupled with large-scale collaborative efforts like the Psychiatric Genomics Consortium, have identified hundreds of common and rare genetic variations that contribute to a range of neuropsychiatric disorders. At the same time, they have begun to address deeper questions about the structure and classification of mental disorders: To what extent do genetic findings support or challenge our clinical nosology? Are there genetic boundaries between psychiatric and neurologic illness? Do the data support a boundary between disorder and normal variation? Is it possible to envision a nosology based on genetically informed disease mechanisms? This review provides an overview of conceptual issues and genetic findings that bear on the relationships among and boundaries between psychiatric disorders and other conditions. We highlight implications for the evolving classification of psychopathology and the challenges for clinical translation.