Rare Variant Studies Research Articles

Clinically Useful Genetic Markers of Antidepressant Response: How Do We Get There From Here?

Predicting who will respond to an antidepressant—and who will not—remains anurgent but unfulfilled need in psychiatry (1). Clinical indicators of response have a long history, but probably have low predictive value. This state of affairs has fueled an energetic search for biomarkers. Genetic markers are ideal biomarkers, since they do not suffer from reverse causation. Reverse causation arises when abiomarkervalue is actually the consequenceof the outcome, rather than its cause. Genetic markers that represent the inherited sequence of DNA cannot reflect the result of a health outcome. The best genetic markers would identify individuals most likely to benefit from a particular treatment and least likely to suffer adverse events. Years of work with candidate genes have yielded some reproduciblefindings, but their individual effects are small, with little impact on clinical decision making (2). Studies that use genome-wide genotyping technology have implicated additional genes (3), but well-replicated findings that can help guide clinical decision making remain elusive. In this issue, Schatzberg et al. (4) report that common genetic markers in ABCB1 provide some information about which patients are most likely to respond to certain selective serotonin reuptake inhibitors.ABCB1 encodes P-glycoprotein, which influences the efficiency with which certain antidepressants (and other drugs) cross the blood-brain barrier. Common variants in ABCB1 have been associated with antidepressant outcome in some previous studies (5), so replication in an independent sample was the next logical step. Ten common markers in and around ABCB1 were tested in 683 patients with major depressive disorder whowere treated for at least 2 weeks and an additional 576 patients who were treated for 8weeks as part of the International Study to Predict Optimized Treatment in Depression (iSPOT-D) trial. The samples were carefully ascertained and evaluated, with prospective measures of efficacy and side effect burden, using standard instruments. General and emotional cognition were further assessed with a battery of 13 tests. Antidepressants included escitalopram, sertraline, and extended-release venlafaxine, all of which are known to bind P-glycoprotein. One singlenucleotide polymorphism (SNP) upstream of ABCB1 was associated with remission and subjective side effects, and the direction of the effect differed among the medications tested. How far does this take us, and what’s next? Treatment assignmentwas randomized, but not blinded, and therewere no placebo controls and no direct assessments of adherence. Despite these limitations, this study represents one of the few replications in the field of antidepressant pharmacogenomics, and is thus a real step forward. Still, the effect sizes are too small to be useful in clinical decision making. It seems likely that antidepressant response, like depression itself, is a polygenic trait, influenced by numerous genes, each of small effect. Therefore, many more genetic markers will be needed before we can achieve clinically significant prediction. This will require much larger samples than have been studied so far. We now know that genome-wide association studies (GWAS) require very large sample sizes to identify genetic markers of polygenic traits. Samples in the tensof thousandsarenowroutine,yetnoGWASofantidepressant outcome to date has exceeded 2,000. As pharmacogenomics begins to bear fruit in psychiatry, we can hope for a renewed focus on the large sample sizes necessary for robust findings. Theheritability of antidepressant response puts anupper limit onwhatcan be predicted by genetic markers alone. Heritability measures how much of the individual differences in a trait can be explained by inherited genetic differences. This is typically estimated from twin studies, but these are generally impractical for treatment response studies, since twins would only rarely be treated for the same illness in the same way during the time frame of a study. An alternative approach directly compares genetic similarity (estimated from a genome-wide set of SNPs) with phenotypic similarityusingsophisticatedregressionmethods(6).Onestudy used this approach to estimate that antidepressant response was about 40% heritable, but with a large confidence interval (7). Other statistical models show that traits with this degree of heritability cannot be predicted verywell fromgeneticmarkers alone(8).Evenapanelofgeneticmarkersthatexplainsaquarter of the genetic variance—more than anyGWAS todate—gives an area under the curve of only about 64%.Thus, a combination of clinical data and genetic and other biomarkerswill be needed if we are to push predictability into the clinically useful range. Advances in cancer chemotherapy may light the way. We can already tailor treatment for breast cancer based on a Studies like that of Schatzberg et al. are encouraging because they suggest that the ultimate goal of clinically useful outcome prediction can be achieved.

Genetics and Epigenetics of Eating Disorders.

Eating disorders (EDs) are serious psychiatric conditions influenced by biological, psychological, and sociocultural factors. A better understanding of the genetics of these complex traits and the development of more sophisticated molecular biology tools have advanced our understanding of the etiology of EDs. The aim of this review is to critically evaluate the literature on the genetic research conducted on three major EDs: anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED). We will first review the diagnostic criteria, clinical features, prevalence, and prognosis of AN, BN, and BED, followed by a review of family, twin, and adoption studies. We then review the history of genetic studies of EDs covering linkage analysis, candidate gene association studies, genome-wide association studies, and the study of rare variants in EDs. Our review also incorporates a translational perspective by covering animal models of ED-related phenotypes. Finally, we review the nascent field of epigenetics of EDs and a look forward to future directions for ED genetic research.

Assessing the effects of multiple markers in genetic association studies.

Assessing the effects of multiple markers in genetic association studies.

Association of genetic variants of GRIN2B with autism.

Autism (MIM 209850) is a complex neurodevelopmental disorder characterized by social communication impairments and restricted repetitive behaviors. It has a high heritability, although much remains unclear. To evaluate genetic variants of GRIN2B in autism etiology, we performed a system association study of common and rare variants of GRIN2B and autism in cohorts from a Chinese population, involving a total sample of 1,945 subjects. Meta-analysis of a triad family cohort and a case-control cohort identified significant associations of multiple common variants and autism risk (Pmin = 1.73 × 10−4). Significantly, the haplotype involved with the top common variants also showed significant association (P = 1.78 × 10−6). Sanger sequencing of 275 probands from a triad cohort identified several variants in coding regions, including four common variants and seven rare variants. Two of the common coding variants were located in the autism-related linkage disequilibrium (LD) block, and both were significantly associated with autism (P < 9 × 10−3) using an independent control cohort. Burden analysis and case-only analysis of rare coding variants identified by Sanger sequencing did not find this association. Our study for the first time reveals that common variants and related haplotypes of GRIN2B are associated with autism risk.

Assessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regression.

Accounting for gene-environment (G×E) interactions in complex trait association studies can facilitate our understanding of genetic heterogeneity under different environmental exposures, improve the ability to discover susceptible genes that exhibit little marginal effect, provide insight into the biological mechanisms of complex diseases, help to identify high-risk subgroups in the population, and uncover hidden heritability. However, significant G×E interactions can be difficult to find. The sample sizes required for sufficient power to detect association are much larger than those needed for genetic main effects, and interactions are sensitive to misspecification of the main-effects model. These issues are exacerbated when working with binary phenotypes and rare variants, which bear less information on association. In this work, we present a similarity-based regression method for evaluating G×E interactions for rare variants with binary traits. The proposed model aggregates the genetic and G×E information across markers, using genetic similarity, thus increasing the ability to detect G×E signals. The model has a random effects interpretation, which leads to robustness against main-effect misspecifications when evaluating G×E interactions. We construct score tests to examine G×E interactions and a computationally efficient EM algorithm to estimate the nuisance variance components. Using simulations and data applications, we show that the proposed method is a flexible and powerful tool to study the G×E effect in common or rare variant studies with binary traits.

Abstract 15265: Exome-wide Association Study of the Human Metabolome in a Community-based Cohort

Introduction: Because metabolites are hypothesized to play key roles as markers and effectors of cardio-metabolic diseases, recent studies have sought to annotate the genetic determinants of circulating metabolite levels. We hypothesize that the study of low frequency and rare variants of potential functional significance will refine our understanding of the genetic determinants of human metabolism. Methods: We analyzed the association between 217 plasma metabolites and exome variants captured on the Illumina HumanExome Beadchip in 2,076 Framingham Heart Study (FHS) participants. Of the &gt;240,000 variants on the exome array, we restricted our analysis to the subset of 92,633 variants that were polymorphic and nonsynonymous, nonsense, or located in a splice site. We performed association analyses that relate each single variant to each metabolite, as well as gene-based burden tests that evaluate the aggregate effects of all variants within a gene. Statistical analyses accounted for number of SNPs or genes (for burden tests). Results: We identified a total of 15 gene-metabolite associations, nine from single variant analysis ( gene : metabolite): CTH (Cystathionine); BPIFA2 (Betaine); OR1F1 (Creatinine); TCF19 (Glycerol); GMPS (Xanthosine); SIGLEC6 (Creatine); ANKUB1 (Carnosine); KIAA1751 (Serotonin); MAP1A (TAG 58:6). (Bonferroni-adjusted significance threshold of P &lt; 5.4 x 10 -7 for all). All but the ANKUB1findings are attributable to rare variants with MAF 0.01 or less. We identified six additional associations from burden tests, including three at established human disease loci: HAL (histidine); PAH (phenylalananine); UPB1 (ureidopropionate); TG (indole propionate); CDH5 (SM 22:1); PHLDB1 (LPE 18:0) (Bonferroni-adjusted significance threshold of P &lt; 3.9 x10 -6 for all). Conclusion: Our findings highlight genes with a direct biochemical relationship with the given metabolite as well as unanticipated genetic effectors of select metabolites. We show how an examination of variants across the spectrum of allele frequency is able to identify independent association signals at select loci as well as generate a more integrated view of genetic determinants of metabolites.

Hierarchical Bayesian model for rare variant association analysis integrating genotype uncertainty in human sequence data.

Next-generation sequencing (NGS) has led to the study of rare genetic variants, which possibly explain the missing heritability for complex diseases. Most existing methods for rare variant (RV) association detection do not account for the common presence of sequencing errors in NGS data. The errors can largely affect the power and perturb the accuracy of association tests due to rare observations of minor alleles. We developed a hierarchical Bayesian approach to estimate the association between RVs and complex diseases. Our integrated framework combines the misclassification probability with shrinkage-based Bayesian variable selection. It allows for flexibility in handling neutral and protective RVs with measurement error, and is robust enough for detecting causal RVs with a wide spectrum of minor allele frequency (MAF). Imputation uncertainty and MAF are incorporated into the integrated framework to achieve the optimal statistical power. We demonstrate that sequencing error does significantly affect the findings, and our proposed model can take advantage of it to improve statistical power in both simulated and real data. We further show that our model outperforms existing methods, such as sequence kernel association test (SKAT). Finally, we illustrate the behavior of the proposed method using a Finnish low-density lipoprotein cholesterol study, and show that it identifies an RV known as FH North Karelia in LDLR gene with three carriers in 1,155 individuals, which is missed by both SKAT and Granvil.

Identification of Rare, Single-Nucleotide Mutations in NDE1 and Their Contributions to Schizophrenia Susceptibility.

Nuclear distribution E homolog 1 (NDE1), located within chromosome 16p13.11, plays an essential role in microtubule organization, mitosis, and neuronal migration and has been suggested by several studies of rare copy number variants to be a promising schizophrenia (SCZ) candidate gene. Recently, increasing attention has been paid to rare single-nucleotide variants (SNVs) discovered by deep sequencing of candidate genes, because such SNVs may have large effect sizes and their functional analysis may clarify etiopathology. We conducted mutation screening of NDE1 coding exons using 433 SCZ and 145 pervasive developmental disorders samples in order to identify rare single nucleotide variants with a minor allele frequency ≤5%. We then performed genetic association analysis using a large number of unrelated individuals (3554 SCZ, 1041 bipolar disorder [BD], and 4746 controls). Among the discovered novel rare variants, we detected significant associations between SCZ and S214F (P = .039), and between BD and R234C (P = .032). Furthermore, functional assays showed that S214F affected axonal outgrowth and the interaction between NDE1 and YWHAE (14-3-3 epsilon; a neurodevelopmental regulator). This study strengthens the evidence for association between rare variants within NDE1 and SCZ, and may shed light into the molecular mechanisms underlying this severe psychiatric disorder.

Challenges of linkage analysis in the era of whole-genome sequencing.

Whole-genome sequencing (WGS) is becoming an affordable technology for the study of the genetics of complex traits. With any new technology, experimental designs and statistical methods, both old and new, must be evaluated. One design seeing a resurgence of interest is the use of families. Genetic Analysis Workshop 18 provided the opportunity to evaluate statistical methods applied to WGS data for family-based studies. We summarize the results of five contributions that used linkage in the context of WGS. The investigators took differing approaches, including assessment of false-positive rates in classic two-point linkage, the effects of heterogeneity on linkage and association tests, and the use of linkage to focus association tests. We describe the primary findings of each contribution and note challenges that are not new to those working in family designs or specific to WGS data; for example, choice of phenotype definition, covariate adjustment, and use of longitudinal data may produce different results, making comparisons challenging. We detail new issues brought about by WGS, such as the elevated genome-wide false-positive rate for classic two-point parametric linkage analysis, computational demands in multipoint calculations, and lack of clarity in how to best use linkage to focus association testing. Finally, we comment on when linkage may be helpful for WGS, highlighting where additional research is needed; for example, although linkage analysis has been successful in the study of rare variants of large effect, how to best use family information in the context of rare variants of moderate effect remains an open research question.

Novel rare variants in F-box protein 45 (FBXO45) in schizophrenia

The ubiquitin ligase F-box protein 45 (FBXO45) is critical for synaptogenesis, neuronal migration, and synaptic transmission. FBXO45 is included in the 3q29 microdeletion region that confers a significant risk for schizophrenia, as shown by rare structural variant studies. Thus, FBXO45 is considered a prominent candidate for mediating schizophrenia pathogenesis. Here, we investigated rare, deleterious single nucleotide variants (SNVs) as well as small insertions and deletions (INDELs) in FBXO45 that may contribute to schizophrenia susceptibility.Using Sanger sequencing, we performed mutation screening in FBXO45 exon regions in 337 schizophrenia patients. Novel missense or nonsense variants were followed up with a genetic association study in an independent sample set of 601 schizophrenia patients and 916 controls, a case report for assessing the clinical consequence of the mutations, a pedigree study for measuring mutation inheritance in the proband's family, bioinformatics analyses for evaluating mutation effect on protein structure and function, and mRNA expression analysis for examining mutation transcriptional influence on FBXO45 expression.One heterozygous, novel, and rare missense mutation (R108C) was identified in a single schizophrenia patient and in his healthy mother. At age 20, this patient was diagnosed with paranoid schizophrenia and carried some clinical features of 3q29 deletion phenotypes, including premorbid IQ decline. With follow-up genotyping, this mutation was not found in either the schizophrenia group (0/601) or the healthy control group (0/916). Bioinformatics analyses predicted that R108C probably pathologically impacted the structure and function of the FBXO45 protein. The relative expression of FBXO45 in SCZ case with R108C mutation was relatively low when compared to 50 schizophrenia patients and 52 healthy controls.The R108C mutation in FBXO45 is a rare variant with a modest effect on schizophrenia risk that may disrupt the structure and function of the FBXO45 protein. Our findings also suggest that FBXO45 may be a new attractive candidate gene for schizophrenia.

Rare variants and autoimmune disease.

The study of rare variants in monogenic forms of autoimmune disease has offered insight into the aetiology of more complex pathologies. Research in complex autoimmune disease initially focused on sequencing candidate genes, with some early successes, notably in uncovering low-frequency variation associated with Type 1 diabetes mellitus. However, other early examples have proved difficult to replicate, and a recent study across six autoimmune diseases, re-sequencing 25 autoimmune disease-associated genes in large sample sizes, failed to find any associated rare variants. The study of rare and low-frequency variation in autoimmune diseases has been made accessible by the inclusion of such variants on custom genotyping arrays (e.g. Immunochip and Exome arrays). Whole-exome sequencing approaches are now also being utilised to uncover the contribution of rare coding variants to disease susceptibility, severity and treatment response. Other sequencing strategies are starting to uncover the role of regulatory rare variation.

Rare variants and cardiovascular disease.

Cardiovascular disease (CVD) is a leading cause of mortality and morbidity in the Western world. Large genome-wide association studies (GWASs) of coronary artery disease, myocardial infarction, stroke and dilated cardiomyopathy have identified a number of common genetic variants with modest effects on disease risk. Similarly, studies of important modifiable risk factors of CVD have identified a large number of predominantly common variant associations, for example, with blood pressure and blood lipid levels. In each case, despite the often large numbers of loci identified, only a small proportion of the phenotypic variance is explained. It has been hypothesised that rare variants with large effects may account for some of the missing variance but large-scale studies of rare variation are in their infancy for cardiovascular traits and have yet to produce fruitful results. Studies of monogenic CVDs, inherited disorders believed to be entirely driven by individual rare mutations, have highlighted genes that play a key role in disease aetiology. In this review, we discuss how findings from studies of rare variants in monogenic disease and GWAS of predominantly common variants are converging to provide further insight into biological disease mechanisms.

Genetics, genomics, and their relevance to pathology and therapy.

Genetic and genomic investigations are a starting point for the study of human disease, seeking to discover causative variants relevant to disease pathophysiology. Over the past 5 years, massively parallel, high-throughput, next-generation sequencing techniques have revolutionized genetics and genomics, identifying the causes of many Mendelian diseases. The application of whole-genome sequencing and whole-exome sequencing to large populations has produced several publicly available sequence datasets that have revealed the scope of human genetic variation and have contributed to important methodological advances in the study of both common and rare genetic variants in genetically complex diseases. The importance of noncoding genetic variation has been highlighted by the Encyclopedia of DNA Elements (ENCODE) project and National Institutes of Health (NIH) Roadmap Epigenomics Program and integrated analyses of these datasets, together with disease-specific datasets, will provide an important and powerful tool for determining the mechanisms through which disease-associated, noncoding variation influences disease risk.

Evaluating the impact of genotype errors on rare variant tests of association.

The new class of rare variant tests has usually been evaluated assuming perfect genotype information. In reality, rare variant genotypes may be incorrect, and so rare variant tests should be robust to imperfect data. Errors and uncertainty in SNP genotyping are already known to dramatically impact statistical power for single marker tests on common variants and, in some cases, inflate the type I error rate. Recent results show that uncertainty in genotype calls derived from sequencing reads are dependent on several factors, including read depth, calling algorithm, number of alleles present in the sample, and the frequency at which an allele segregates in the population. We have recently proposed a general framework for the evaluation and investigation of rare variant tests of association, classifying most rare variant tests into one of two broad categories (length or joint tests). We use this framework to relate factors affecting genotype uncertainty to the power and type I error rate of rare variant tests. We find that non-differential genotype errors (an error process that occurs independent of phenotype) decrease power, with larger decreases for extremely rare variants, and for the common homozygote to heterozygote error. Differential genotype errors (an error process that is associated with phenotype status), lead to inflated type I error rates which are more likely to occur at sites with more common homozygote to heterozygote errors than vice versa. Finally, our work suggests that certain rare variant tests and study designs may be more robust to the inclusion of genotype errors. Further work is needed to directly integrate genotype calling algorithm decisions, study costs and test statistic choices to provide comprehensive design and analysis advice which appropriately accounts for the impact of genotype errors.

Rare Variant Testing of Imputed Data: An Analysis Pipeline Typified

Important methodological advancements in rare variant association testing have been made recently, among them collapsing tests, kernel methods and the variable threshold (VT) technique. Typically, rare variants from a region of interest are tested for association as a group (‘bin'). Rare variant studies are already routinely performed as whole-exome sequencing studies. As an alternative approach, we propose a pipeline for rare variant analysis of imputed data and develop respective quality control criteria. We provide suggestions for the choice and construction of analysis bins in whole-genome application and support the analysis with implementations of standard burden tests (COLL, CMAT) in our INTERSNP-RARE software. In addition, three rare variant regression tests (REG, FRACREG and COLLREG) are implemented. All tests are accompanied with the VT approach which optimizes the definition of ‘rareness'. We integrate kernel tests as implemented in SKAT/SKAT-O into the suggested strategies. Then, we apply our analysis scheme to a genome-wide association study of Alzheimer's disease. Further, we show that our pipeline leads to valid significance testing procedures with controlled type I error rates. Strong association signals surrounding the known APOE locus demonstrate statistical power. In addition, we highlight several suggestive rare variant association findings for follow-up studies, including genomic regions overlapping MCPH1, MED18 and NOTCH3. In summary, we describe and support a straightforward and cost-efficient rare variant analysis pipeline for imputed data and demonstrate its feasibility and validity. The strategy can complement rare variant studies with next generation sequencing data.

A powerful association test of multiple genetic variants using a random-effects model

There is an emerging interest in sequencing-based association studies of multiple rare variants. Most association tests suggested in the literature involve collapsing rare variants with or without weighting. Recently, a variance-component score test [sequence kernel association test (SKAT)] was proposed to address the limitations of collapsing method. Although SKAT was shown to outperform most of the alternative tests, its applications and power might be restricted and influenced by missing genotypes. In this paper, we suggest a new method based on testing whether the fraction of causal variants in a region is zero. The new association test, T REM , is derived from a random-effects model and allows for missing genotypes, and the choice of weighting function is not required when common and rare variants are analyzed simultaneously. We performed simulations to study the type I error rates and power of four competing tests under various conditions on the sample size, genotype missing rate, variant frequency, effect directionality, and the number of non-causal rare variant and/or causal common variant. The simulation results showed that T REM was a valid test and less sensitive to the inclusion of non-causal rare variants and/or low effect common variants or to the presence of missing genotypes. When the effects were more consistent in the same direction, T REM also had better power performance. Finally, an application to the Shanghai Breast Cancer Study showed that rare causal variants at the FGFR2 gene were detected by T REM and SKAT, but T REM produced more consistent results for different sets of rare and common variants.

PhenoMan: phenotypic data exploration, selection, management and quality control for association studies of rare and common variants

Next-generation sequencing and other high-throughput technology advances have promoted great interest in detecting associations between complex traits and genetic variants. Phenotype selection, quality control (QC) and control of confounders are crucial and can have a great impact on the ability to detect associations. Although there are programs to perform association analyses, e.g. PLINK and GenABEL, they cannot be used for comprehensive management and QC of phenotype data. To address this need PhenoMan was developed: to select individuals based on multiple phenotype criteria or population membership; control for missing covariate data; remove related individuals, duplicate samples and individuals with incorrect sex specification; recode primary traits and covariates; transform data; remove or winsorize outliers; select covariates for analysis; and create residuals. To ensure consistency and harmonization between analyses, a report is generated for every dataset. Summary statistics are also provided in graphical or text format. PhenoMan can be used for selection and manipulation of quantitative, disease and control data. Phenoman is freeware that provides approaches for efficient exploration and management of phenotype data. Proper QC of phenotypes before proceeding to the association analysis is critical to ensure control of type I and II errors, reliable effect estimates and consistent results between studies. PhenoMan is highly beneficial for the preparation of qualitative and quantitative trait data for association studies using new datasets as well as those obtained from public repositories. code.google.com/p/phenoman

Monogenic causes of elevated HDL cholesterol and implications for development of new therapeutics.

Identification of the CETP, LIPG (encoding endothelial lipase) and APOC3 genes, and ana lysis of rare genetic variants in them, have allowed researchers to increase understanding of HDL metabolism significantly. However, development of cardiovascular risk-reducing therapeutics targeting the proteins encoded by these genes has been less straightforward. The failure of two CETP inhibitors is complex but illustrates a possible over-reliance on HDL cholesterol as a marker of therapeutic efficacy. The case of endothelial lipase exemplifies the importance of utilizing population-wide genetic studies of rare variants in potential therapeutic targets to gain information on cardiovascular disease end points. Similar population-wide studies of cardiovascular end points make apoC-III a potentially attractive target for lipid-related drug discovery. These three cases illustrate the positives and negatives of single-gene studies relating to HDL-related cardiovascular drug discovery; such studies should focus not only on HDL cholesterol and other components of the lipid profile, but also on the effect genetic variants have on cardiovascular end points.

Rare variants in LRRK1 and Parkinson's disease.

Approximately 20 % of individuals with Parkinson's disease (PD) report a positive family history. Yet, a large portion of causal and disease-modifying variants is still unknown. We used exome sequencing in two affected individuals from a family with late-onset PD to identify 15 potentially causal variants. Segregation analysis and frequency assessment in 862 PD cases and 1,014 ethnically matched controls highlighted variants in EEF1D and LRRK1 as the best candidates. Mutation screening of the coding regions of these genes in 862 cases and 1,014 controls revealed several novel non-synonymous variants in both genes in cases and controls. An in silico multi-model bioinformatics analysis was used to prioritize identified variants in LRRK1 for functional follow-up. However, protein expression, subcellular localization, and cell viability were not affected by the identified variants. Although it has yet to be proven conclusively that variants in LRRK1 are indeed causative of PD, our data strengthen a possible role for LRRK1 in addition to LRRK2 in the genetic underpinnings of PD but, at the same time, highlight the difficulties encountered in the study of rare variants identified by next-generation sequencing in diseases with autosomal dominant or complex patterns of inheritance.Electronic supplementary materialThe online version of this article (doi:10.1007/s10048-013-0383-8) contains supplementary material, which is available to authorized users.

Rare Variants in PLXNA4 and Parkinson’s Disease

Approximately 20% of individuals with Parkinson’s disease (PD) report a positive family history. Yet, a large portion of causal and disease-modifying variants is still unknown. We used exome sequencing in two affected individuals from a family with late-onset familial PD followed by frequency assessment in 975 PD cases and 1014 ethnically-matched controls and linkage analysis to identify potentially causal variants. Based on the predicted penetrance and the frequencies, a variant in PLXNA4 proved to be the best candidate and PLXNA4 was screened for additional variants in 862 PD cases and 940 controls, revealing an excess of rare non-synonymous coding variants in PLXNA4 in individuals with PD. Although we cannot conclude that the variant in PLXNA4 is indeed the causative variant, these findings are interesting in the light of a surfacing role of axonal guidance mechanisms in neurodegenerative disorders but, at the same time, highlight the difficulties encountered in the study of rare variants identified by next-generation sequencing in diseases with autosomal dominant or complex patterns of inheritance.