Cohort-driven variant burden analysis and pathogenicity identification in monogenic autoinflammatory disorders.

BACKGROUND: Nearly 50 pathogenic genes and hundreds of pathogenic variants have been identified in monogenic autoinflammatory diseases (AIDs). Nonetheless, there are still many genes for which the pathogenic mechanisms are poorly understood, and the pathogenicity of many candidate variants needs to be determined. OBJECTIVE: Monogenic AIDs are a group of rare genetic diseases characterized by inflammation as the phenotype. With the development of next-generation sequencing, pathogenic genes have been widely reported and used for clinical screening and diagnosis. The International Society for Systemic Autoinflammatory Diseases has recognized approximately 50 pathogenic genes and hundreds of related pathogenic variants in monogenic AIDs. We plan to investigate these pathogenic variants by conducting a variant burden analysis to determine whether or not there are consistent characteristics. METHODS: We performed a variant burden analysis on the Genome Aggregation Database cohort using the currently reported genetic variants in monogenic AIDs, analyzing the enrichment of allelic signatures and deleterious predictions at the variants. Allelic signatures were extracted from Genome Aggregation Database, and the deleterious predictions were extracted from existing tools. The features obtained from the variant burden analysis were applied to the Random Forest model to classify the pathogenicity of novel mutations. RESULTS: Functional enrichment and network analysis of AID pathogenic genes have hinted at the possible involvement of unsuspected signals. The variant burden analysis demonstrated that the pathogenicity of a variant could not be reliably classified using only its allele frequency and deleterious predictions. However, variants of varying classifications of pathogenicity exhibited strikingly different patterns of the allelic signature in the upstream and downstream regions surrounding the variants. Furthermore, the distribution of deleterious variants surrounding the variants in the cohort varied significantly across pathogenicity categories. Finally, the cohort-based features extracted from the alleles were applied to the prediction of pathogenicity in monogenic AIDs, achieving superior prediction performance compared with other tools. The cohort-based features have potential applications across a more extensive variety of disease categories. CONCLUSIONS: The pathogenicity of a variant can be effectively classified on the basis of variant frequency and deleterious prediction of the allele in the cohort, and this information can be used to improve the accuracy of the current classification of the pathogenicity of the variant.

Structural Variants and Implicated Processes Associated with Familial Tourette Syndrome.

Gilles de la Tourette syndrome (GTS) is a neurodevelopmental psychiatric disorder with complex and elusive etiology with a significant role of genetic factors. The aim of this study was to identify structural variants that could be associated with familial GTS. The study group comprised 17 multiplex families with 80 patients. Structural variants were identified from whole-genome sequencing data and followed by co-segregation and bioinformatic analyses. The localization of these variants was used to select candidate genes and create gene sets, which were subsequently processed in gene ontology and pathway enrichment analysis. Seventy putative pathogenic variants shared among affected individuals within one family but not present in the control group were identified. Only four private or rare deletions were exonic in LDLRAD4, B2M, USH2A, and ZNF765 genes. Notably, the USH2A gene is involved in cochlear development and sensory perception of sound, a process that was associated previously with familial GTS. In addition, two rare variants and three not present in the control group were co-segregating with the disease in two families, and uncommon insertions in GOLM1 and DISC1 were co-segregating in three families each. Enrichment analysis showed that identified structural variants affected synaptic vesicle endocytosis, cell leading-edge organization, and signaling for neurite outgrowth. The results further support the involvement of the regulation of neurotransmission, neuronal migration, and sound-sensing in GTS.

Genetic influences on epilepsy outcomes: A whole-exome sequencing and health care records data linkage study.

OBJECTIVE: This study was undertaken to develop a novel pathway linking genetic data with routinely collected data for people with epilepsy, and to analyze the influence of rare, deleterious genetic variants on epilepsy outcomes. METHODS: We linked whole-exome sequencing (WES) data with routinely collected primary and secondary care data and natural language processing (NLP)-derived seizure frequency information for people with epilepsy within the Secure Anonymised Information Linkage Databank. The study participants were adults who had consented to participate in the Swansea Neurology Biobank, Wales, between 2016 and 2018. DNA sequencing was carried out as part of the Epi25 collaboration. For each individual, we calculated the total number and cumulative burden of rare and predicted deleterious genetic variants and the total of rare and deleterious variants in epilepsy and drug metabolism genes. We compared these measures with the following outcomes: (1) no unscheduled hospital admissions versus unscheduled admissions for epilepsy, (2) antiseizure medication (ASM) monotherapy versus polytherapy, and (3) at least 1 year of seizure freedom versus <1 year of seizure freedom. RESULTS: We linked genetic data for 107 individuals with epilepsy (52% female) to electronic health records. Twenty-six percent had unscheduled hospital admissions, and 70% were prescribed ASM polytherapy. Seizure frequency information was linked for 100 individuals, and 10 were seizure-free. There was no significant difference between the outcome groups in terms of the exome-wide and gene-based burden of rare and deleterious genetic variants. SIGNIFICANCE: We successfully uploaded, annotated, and linked genetic sequence data and NLP-derived seizure frequency data to anonymized health care records in this proof-of-concept study. We did not detect a genetic influence on real-world epilepsy outcomes, but our study was limited by a small sample size. Future studies will require larger (WES) data to establish genetic variant contribution to epilepsy outcomes.

Analysis of rare disruptive germline mutations in 2135 enriched BRCA-negative breast cancers excludes additional high-impact susceptibility genes.

BACKGROUND: Breast cancer has a significant heritable basis, of which ∼60% remains unexplained. Testing for BRCA1/BRCA2 offers useful discrimination of breast cancer risk within families, and identification of additional breast cancer susceptibility genes could offer clinical utility. PATIENTS AND METHODS: We included 2135 invasive breast cancer cases recruited via the Breast and Ovarian Cancer Susceptibility study, a retrospective UK study of familial breast cancer. ELIGIBILITY CRITERIA: female, BRCA-negative, white European ethnicity, and one of: (i) breast cancer family history, (ii) bilateral disease, (iii) young age of onset (<30 years), and (iv) concomitant ovarian cancer. We undertook exome sequencing of cases and carried out gene-level burden testing of rare damaging variants against those from 51 377 ethnicity-matched population controls from gnomAD. RESULTS: 159/2135 (7.4%) cases had a qualifying variant in an established breast cancer susceptibility gene, with minimal evidence of signal in other cancer susceptibility genes. Known breast cancer susceptibility genes PALB2, CHEK2, and ATM were the only genes to retain statistical significance after correcting for multiple testing. Due to the enrichment of hereditary cases in the series, we had good power (>80%) to detect a gene of BRCA1-like risk [odds ratio (OR) = 10.6] down to a population minor allele frequency of 4.6 × 10-5 (1 in 10 799, less than one-tenth that of BRCA1)and of PALB2-like risk (OR = 5.0) down to a population minor allele frequency of 2.8 × 10-4 (1 in 1779, less than half that of PALB2). Power was lower for identification of novel moderate penetrance genes (OR = 2-3) like CHEK2 and ATM. CONCLUSIONS: This is the largest case-control whole-exome analysis of enriched breast cancer published to date. Whilst additional breast cancer susceptibility genes likely exist, those of high penetrance are likely to be of very low mutational frequency. Contention exists regarding the clinical utility of such genes.

Gene-Wise Burden of Coding Variants Correlates to Noncoding Pharmacogenetic Risk Variants.

Genetic variability can modulate individual drug responses. A significant portion of pharmacogenetic variants reside in the noncoding genome yet it is unclear if the noncoding variants directly influence protein function and expression or are present on a haplotype including a functionally relevant genetic variation (synthetic association). Gene-wise variant burden (GVB) is a gene-level measure of deleteriousness, reflecting the cumulative effects of deleterious coding variants, predicted in silico. To test potential associations between noncoding and coding pharmacogenetic variants, we computed a drug-level GVB for 5099 drugs from DrugBank for 2504 genomes of the 1000 Genomes Project and evaluated the correlation between the long-known noncoding variant-drug associations in PharmGKB, with functionally relevant rare and common coding variants aggregated into GVBs. We obtained the area under the receiver operating characteristics curve (AUC) by comparing the drug-level GVB ranks against the corresponding pharmacogenetic variants-drug associations in PharmGKB. We obtained high overall AUCs (0.710 ± 0.022-0.734 ± 0.018) for six different methods (i.e., SIFT, MutationTaster, Polyphen-2 HVAR, Polyphen-2 HDIV, phyloP, and GERP++), and further improved the ethnicity-specific validations (0.759 ± 0.066-0.791 ± 0.078). These results suggest that a significant portion of the long-known noncoding variant-drug associations can be explained as synthetic associations with rare and common coding variants burden of the corresponding pharmacogenes.

Exome-Wide Association Study of Pancreatic Cancer Risk.

We conducted a case-control exome-wide association study to discover germline variants in coding regions that affect risk for pancreatic cancer, combining data from 5 studies. We analyzed exome and genome sequencing data from 437 patients with pancreatic cancer (cases) and 1922 individuals not known to have cancer (controls). In the primary analysis, BRCA2 had the strongest enrichment for rare inactivating variants (17/437 cases vs 3/1922 controls) (P = 3.27x10-6; exome-wide statistical significance threshold P < 2.5x10-6). Cases had more rare inactivating variants in DNA repair genes than controls, even after excluding 13 genes known to predispose to pancreatic cancer (adjusted odds ratio, 1.35; P = .045). At the suggestive threshold (P < .001), 6 genes were enriched for rare damaging variants (UHMK1, AP1G2, DNTA, CHST6, FGFR3, and EPHA1) and 7 genes had associations with pancreatic cancer risk, based on the sequence-kernel association test. We confirmed variants in BRCA2 as the most common high-penetrant genetic factor associated with pancreatic cancer and we also identified candidate pancreatic cancer genes. Large collaborations and novel approaches are needed to overcome the genetic heterogeneity of pancreatic cancer predisposition.

PERCH: A Unified Framework for Disease Gene Prioritization.

To interpret genetic variants discovered from next-generation sequencing, integration of heterogeneous information is vital for success. This article describes a framework named PERCH (Polymorphism Evaluation, Ranking, and Classification for a Heritable trait), available at http://BJFengLab.org/. It can prioritize disease genes by quantitatively unifying a new deleteriousness measure called BayesDel, an improved assessment of the biological relevance of genes to the disease, a modified linkage analysis, a novel rare-variant association test, and a converted variant call quality score. It supports data that contain various combinations of extended pedigrees, trios, and case-controls, and allows for a reduced penetrance, an elevated phenocopy rate, liability classes, and covariates. BayesDel is more accurate than PolyPhen2, SIFT, FATHMM, LRT, Mutation Taster, Mutation Assessor, PhyloP, GERP++, SiPhy, CADD, MetaLR, and MetaSVM. The overall approach is faster and more powerful than the existing quantitative method pVAAST, as shown by the simulations of challenging situations in finding the missing heritability of a complex disease. This framework can also classify variants of unknown significance (variants of uncertain significance) by quantitatively integrating allele frequencies, deleteriousness, association, and co-segregation. PERCH is a versatile tool for gene prioritization in gene discovery research and variant classification in clinical genetic testing.

Neutral and weakly nonneutral sequence variants may define individuality.

Large-scale computational analyses of the growing wealth of genome-variation data consistently tell two distinct stories. The first is expected: coding variants reported in disease-related databases significantly alter the function of affected proteins. The second is surprising: the genomes of healthy individuals appear to carry many variants that are predicted to have some effect on function. As long as the complete experimental analysis of all human genome variants remains impossible, computational methods, such as PolyPhen, SNAP, and SIFT, might provide important insights. These methods capture the effects of particular variants very well and can highlight trends in populations of variants. Diseases are, arguably, extreme phenotypic variations and are often attributable to one or a few severely functionally disruptive variants. Our findings suggest a genomic basis of the different nondisease phenotypes. Prediction methods indicate that variants in seemingly healthy individuals tend to be neutral or weakly disruptive for protein molecular function. These variant effects are predicted to be largely either experimentally undetectable or are not deemed significant enough to be published. This may suggest that nondisease phenotypes arise through combinations of many variants whose effects are weakly nonneutral (damaging or enhancing) to the molecular protein function but fall within the wild-type range of overall physiological function.

TREM2 Gene Compound Heterozygosity in Neurodegenerative Disorders.

BACKGROUND: Homozygous variants of the TREM2 and TYROBP genes have been shown to be causative for multiple bone cysts and neurodegeneration leading to progressive dementia (NHD, Nasu-Hakola disease). OBJECTIVE: To determine if biallelic variants of these genes and/or oligogenic inheritance could be responsible for a wider spectrum of neurodegenerative conditions. METHODS: We analyzed 52 genes associated with neurodegenerative disorders using targeted next generation sequencing in a selected group of 29 patients (n = 14 Alzheimer's disease, n = 8 frontotemporal dementia, n = 7 amyotrophic lateral sclerosis) carrying diverse already determined rare variants in exon 2 of TREM2. Molecular modeling was used to get an insight into the potential effects of the mutation. RESULTS: We identified a novel mutation c.401_406delinsTCTAT; p.(Asp134Valfs*55) in exon 3 of TREM2 in an Alzheimer's disease patient also carrying the p.Arg62His TREM2 variant. Molecular modeling revealed that the identified mutation prevents anchoring of the TREM2 protein in the membrane, leaving the core of the Ig-like domain intact. CONCLUSION: Our results expand the spectrum of neurodegenerative diseases, where the carriers of biallelic mutations in TREM2 have been described for Alzheimer's disease, and highlight the impact of variant burden in other genes on phenotypic heterogeneity.

Replication assessment of NUS1 variants in Parkinson's disease.

The NUS1 gene was recently associated with Parkinson's disease (PD) in the Chinese population. Here, as part of the International Parkinson's Disease Genomics Consortium, we have leveraged large-scale PD case-control cohorts to comprehensively assess damaging NUS1 variants in individuals of European descent. Burden analysis of rare nonsynonymous damaging variants across case-control individuals from whole-exome and -genome data sets did not find evidence of NUS1 association with PD. Overall, single-variant tests for rare (minor allele frequency<0.01) and common (minor allele frequency>0.01) variants, including 15 PD-GWAS cohorts and summary statistics from the largest PD GWAS meta-analysis to date, also did not uncover any associations. Our results indicate a lack of evidence for a role of rare damaging nonsynonymous NUS1 variants in PD in unrelated case-control cohorts of European descent, suggesting that the previously observed association could be driven by extremely rare population-specific variants.

Genomic Context Differs Between Human Dilated Cardiomyopathy and Hypertrophic Cardiomyopathy.

Background Inherited cardiomyopathies display variable penetrance and expression, and a component of phenotypic variation is genetically determined. To evaluate the genetic contribution to this variable expression, we compared protein coding variation in the genomes of those with hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM). Methods and Results Nonsynonymous single-nucleotide variants (nsSNVs) were ascertained using whole genome sequencing from familial cases of HCM (n=56) or DCM (n=70) and correlated with echocardiographic information. Focusing on nsSNVs in 102 genes linked to inherited cardiomyopathies, we correlated the number of nsSNVs per person with left ventricular measurements. Principal component analysis and generalized linear models were applied to identify the probability of cardiomyopathy type as it related to the number of nsSNVs in cardiomyopathy genes. The probability of having DCM significantly increased as the number of cardiomyopathy gene nsSNVs per person increased. The increase in nsSNVs in cardiomyopathy genes significantly associated with reduced left ventricular ejection fraction and increased left ventricular diameter for individuals carrying a DCM diagnosis, but not for those with HCM. Resampling was used to identify genes with aberrant cumulative allele frequencies, identifying potential modifier genes for cardiomyopathy. Conclusions Participants with DCM had more nsSNVs per person in cardiomyopathy genes than participants with HCM. The nsSNV burden in cardiomyopathy genes did not correlate with the probability or manifestation of left ventricular measures in HCM. These findings support the concept that increased variation in cardiomyopathy genes creates a genetic background that predisposes to DCM and increased disease severity.

The burden of rare damaging variants in hereditary atypical parkinsonism genes is increased in patients with Parkinson's disease.

Increased burdens of rare coding variants in genes related to lysosomal storage disease or mitochondrial pathways were reported to be associated with idiopathic Parkinson's disease. Under a hypothesis that the burden of damaging rare coding variants is increased in causative genes for hereditary parkinsonism, we analyzed the burdens of rare coding variants with a case-control design. Two cohorts of whole-exome sequencing data and a cohort of genome-wide genotyping data of clinically validated idiopathic Parkinson's disease cases and controls, which were open to the public, were used. The sequence kernel association test-optimal was used to analyze the burden of rare variants in the hereditary parkinsonism gene set, which was constructed from the Online Mendelian Inheritance in Man database through manual curation. The hereditary parkinsonism gene set consisted of 17 genes with a locus symbol prefix for familial Parkinson's disease and 75 hereditary atypical parkinsonism genes. We detected a significant association of enriched burdens of predicted damaging rare coding variants in hereditary parkinsonism genes in all three datasets. Meta-analyses of the rare variant burden test in a subgroup of gene sets revealed an association between burdens of rare damaging variants with PD in a hereditary atypical parkinsonism gene set, but not in a subgroup gene set with a locus symbol prefix for familial Parkinson's disease. Our results highlight the roles of rare damaging variants in causative genes for hereditary atypical parkinsonian disorders. We propose that Mendelian genes associated with hereditary disorders accompanying parkinsonism are involved in Parkinson's disease-related genetic networks.

Identification of sixteen novel candidate genes for late onset Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is a neurodegenerative movement disorder affecting 1-5% of the general population for which neither effective cure nor early diagnostic tools are available that could tackle the pathology in the early phase. Here we report a multi-stage procedure to identify candidate genes likely involved in the etiopathogenesis of PD. METHODS: The study includes a discovery stage based on the analysis of whole exome data from 26 dominant late onset PD families, a validation analysis performed on 1542 independent PD patients and 706 controls from different cohorts and the assessment of polygenic variants load in the Italian cohort (394 unrelated patients and 203 controls). RESULTS: Family-based approach identified 28 disrupting variants in 26 candidate genes for PD including PARK2, PINK1, DJ-1(PARK7), LRRK2, HTRA2, FBXO7, EIF4G1, DNAJC6, DNAJC13, SNCAIP, AIMP2, CHMP1A, GIPC1, HMOX2, HSPA8, IMMT, KIF21B, KIF24, MAN2C1, RHOT2, SLC25A39, SPTBN1, TMEM175, TOMM22, TVP23A and ZSCAN21. Sixteen of them have not been associated to PD before, were expressed in mesencephalon and were involved in pathways potentially deregulated in PD. Mutation analysis in independent cohorts disclosed a significant excess of highly deleterious variants in cases (p = 0.0001), supporting their role in PD. Moreover, we demonstrated that the co-inheritance of multiple rare variants (≥ 2) in the 26 genes may predict PD occurrence in about 20% of patients, both familial and sporadic cases, with high specificity (> 93%; p = 4.4 × 10- 5). Moreover, our data highlight the fact that the genetic landmarks of late onset PD does not systematically differ between sporadic and familial forms, especially in the case of small nuclear families and underline the importance of rare variants in the genetics of sporadic PD. Furthermore, patients carrying multiple rare variants showed higher risk of manifesting dyskinesia induced by levodopa treatment. CONCLUSIONS: Besides confirming the extreme genetic heterogeneity of PD, these data provide novel insights into the genetic of the disease and may be relevant for its prediction, diagnosis and treatment.

Novel and reported variants in Parkinson's disease genes confer high disease burden among Indians.

BACKGROUND: Genetic heterogeneity in Parkinson's disease (PD) has been unambiguously reported across different populations. Assuming a higher genetic load, we tested variant burden in PD genes to an early onset PD cohort from India. METHODS: Whole exome sequencing was performed in 250 PD patients recruited following MDS-UPDRS criteria. The number of rare variants in the 20 known PD genes per exome were used to calculate average rare variant burden with the 616 non-PD exomes available in-house as a comparison group. SKAT-O test was used for gene level analysis. RESULTS: 80 patients harboured rare variants in 20 PD genes, of which six had known pathogenic variants accounting for 2.4% of the cohort. Of 80 patients, 12 had homozygous and nine had likely compound heterozygous variants in recessive PD genes and 59 had heterozygous variants in only dominant PD genes. Of the 16 novel variants of as yet unknown significance identified, four homozygous across ATP13A2, PRKN, SYNJ1 and PARK7; and 12 heterozygous among LRRK2, VPS35, EIF4G1 and CHCHD2 were observed. SKAT-O test suggested a higher burden in GBA (punadjusted = 0.002). Aggregate rare variant analysis including 75 more individuals with only heterozygous variants in recessive PD genes (excluding GBA), with an average of 0.85 protein-altering rare variants per PD patient exome versus 0.51 in the non-PD group, revealed a significant enrichment (p < 0.0001). CONCLUSION: This first study in an early onset PD cohort among Indians identified 16 novel variants in known genes and also provides evidence for a high genetic burden in this ethnically distinct population.

Gene-wise variant burden and genomic characterization of nearly every gene.

Aim: Current gene-level prioritization methods aim to provide information for further prioritization of 'disease-causing' mutations. Since, they are inherently biased toward disease genes, methods specific to pharmacogenetic (PGx) genes are required. Methods: We proposed a gene-wise variant burden (GVB) method that integrates in silico deleteriousness scores of the multitude of variants of a given gene at a personal-genome level. Results: GVB in its simplest form outperformed the two state-of-the-art methods with regard to predicting pharmacogenes and complex disease genes but not for rare Mendelian disease genes. GVB* adjusted by paralog counts robustly performed well in most of the pharmacogenetic subcategories. Seven molecular genetic features well characterized the unique genomic properties of PGx, complex, and Mendelian disease genes. Conclusion: Altogether, GVB is an individual-specific genescore, especially advantageous for PGx studies.

Burden of tumor mutations, neoepitopes, and other variants are weak predictors of cancer immunotherapy response and overall survival.

BACKGROUND: Tumor mutational burden (TMB; the quantity of aberrant nucleotide sequences a given tumor may harbor) has been associated with response to immune checkpoint inhibitor therapy and is gaining broad acceptance as a result. However, TMB harbors intrinsic variability across cancer types, and its assessment and interpretation are poorly standardized. METHODS: Using a standardized approach, we quantify the robustness of TMB as a metric and its potential as a predictor of immunotherapy response and survival among a diverse cohort of cancer patients. We also explore the additive predictive potential of RNA-derived variants and neoepitope burden, incorporating several novel metrics of immunogenic potential. RESULTS: We find that TMB is a partial predictor of immunotherapy response in melanoma and non-small cell lung cancer, but not renal cell carcinoma. We find that TMB is predictive of overall survival in melanoma patients receiving immunotherapy, but not in an immunotherapy-naive population. We also find that it is an unstable metric with potentially problematic repercussions for clinical cohort classification. We finally note minimal additional predictive benefit to assessing neoepitope burden or its bulk derivatives, including RNA-derived sources of neoepitopes. CONCLUSIONS: We find sufficient cause to suggest that the predictive clinical value of TMB should not be overstated or oversimplified. While it is readily quantified, TMB is at best a limited surrogate biomarker of immunotherapy response. The data do not support isolated use of TMB in renal cell carcinoma.

Contribution of Rare Copy Number Variants to Bipolar Disorder Risk Is Limited to Schizoaffective Cases.

BACKGROUND: Genetic risk for bipolar disorder (BD) is conferred through many common alleles, while a role for rare copy number variants (CNVs) is less clear. Subtypes of BD including schizoaffective disorder bipolar type (SAB), bipolar I disorder (BD I), and bipolar II disorder (BD II) differ according to the prominence and timing of psychosis, mania, and depression. The genetic factors contributing to the combination of symptoms among these subtypes are poorly understood. METHODS: Rare large CNVs were analyzed in 6353 BD cases (3833 BD I [2676 with psychosis, 850 without psychosis, and 307 with unknown psychosis history], 1436 BD II, 579 SAB, and 505 BD not otherwise specified) and 8656 controls. CNV burden and a polygenic risk score (PRS) for schizophrenia were used to evaluate the relative contributions of rare and common variants to risk of BD, BD subtypes, and psychosis. RESULTS: CNV burden did not differ between BD and controls when treated as a single diagnostic entity. However, burden in SAB was increased relative to controls (p = .001), BD I (p = .0003), and BD II (p = .0007). Burden and schizophrenia PRSs were increased in SAB compared with BD I with psychosis (CNV p = .0007, PRS p = .004), and BD I without psychosis (CNV p = .0004, PRS p = 3.9 × 10-5). Within BD I, psychosis was associated with increased schizophrenia PRSs (p = .005) but not CNV burden. CONCLUSIONS: CNV burden in BD is limited to SAB. Rare and common genetic variants may contribute differently to risk for psychosis and perhaps other classes of psychiatric symptoms.

Star Allele-Based Haplotyping versus Gene-Wise Variant Burden Scoring for Predicting 6-Mercaptopurine Intolerance in Pediatric Acute Lymphoblastic Leukemia Patients.

Nudix Hydrolase 15 (NUDT15) and Thiopurine S-Methyltransferase (TPMT) are strong genetic determinants of thiopurine toxicity in pediatric acute lymphoblastic leukemia (ALL) patients. Since patients with NUDT15 or TPMT deficiency suffer severe adverse drug reactions, star (*) allele-based haplotypes have been used to predict an optimal 6-mercaptopurine (6-MP) dosing. However, star allele haplotyping suffers from insufficient, inconsistent, and even conflicting designations with uncertain and/or unknown functional alleles. Gene-wise variant burden (GVB) scoring enables us to utilize next-generation sequencing (NGS) data to predict 6-MP intolerance in children with ALL. Whole exome sequencing was performed for 244 pediatric ALL patients under 6-MP treatments. We assigned star alleles with PharmGKB haplotype set translational table. GVB for NUDT15 and TPMT was computed by aggregating in silico deleteriousness scores of multiple coding variants for each gene. Poor last-cycle dose intensity percent (DIP < 25%) was considered as 6-MP intolerance, resulting therapeutic failure of ALL. DIPs showed significant differences ( p < 0.05) among NUDT15 poor (PM, n = 1), intermediate (IM, n = 48), and normal (NM, n = 195) metabolizers. TPMT exhibited no PM and only seven IMs. GVB showed significant differences among the different haplotype groups of both NUDT15 and TPMT ( p < 0.05). Kruskal-Wallis test for DIP values showed statistical significances for the seven different GVB score bins of NUDT15. GVB NUDT15 outperformed the star allele-based haplotypes in predicting patients with reduced last-cycle DIPs at all DIP threshold levels (i.e., 5%, 10%, 15%, and 25%). In NUDT15-and-TPMT combined interaction analyses, GVB NUDT15 , TPMT outperformed star alleles [area under the receiver operating curve (AUROC) = 0.677 vs. 0.645] in specificity (0.813 vs. 0.796), sensitivity (0.526 vs. 0.474), and positive (0.192 vs. 0.164) and negative (0.953 vs. 0.947) predictive values. Overall, GVB correctly classified five more patients (i.e., one into below and four into above 25% DIP groups) than did star allele haplotypes. GVB analysis demonstrated that 6-MP intolerance in pediatric ALL can be reliably predicted by aggregating NGS-based common, rare, and novel variants together without hampering the predictive power of the conventional haplotype analysis.

Comprehensive Analysis of Rare Variants of 101 Autism-Linked Genes in a Hungarian Cohort of Autism Spectrum Disorder Patients.

BACKGROUND: Autism spectrum disorder (ASD) is genetically and phenotypically heterogeneous. Former genetic studies suggested that both common and rare genetic variants play a role in the etiology. In this study, we aimed to analyze rare variants detected by next generation sequencing (NGS) in an autism cohort from Hungary. METHODS: We investigated the yield of NGS panel sequencing of an unselected ASD cohort (N = 174 ) for the detection of ASD associated syndromes. Besides, we analyzed rare variants in a common disease-rare variant framework and performed rare variant burden analysis and gene enrichment analysis in phenotype based clusters. RESULTS: We have diagnosed 13 molecularly proven syndromic autism cases. Strongest indicators of syndromic autism were intellectual disability, epilepsy or other neurological plus symptoms. Rare variant analysis on a cohort level confirmed the association of five genes with autism (AUTS2, NHS, NSD1, SLC9A9, and VPS13). We found no correlation between rare variant burden and number of minor malformation or autism severity. We identified four phenotypic clusters, but no specific gene was enriched in a given cluster. CONCLUSION: Our study indicates that NGS panel gene sequencing can be useful, where the clinical picture suggests a clinically defined syndromic autism. In this group, targeted panel sequencing may provide reasonable diagnostic yield. Unselected NGS panel screening in the clinic remains controversial, because of uncertain utility, and difficulties of the variant interpretation. However, the detected rare variants may still significantly influence autism risk and subphenotypes in a polygenic model, but to detect the effects of these variants larger cohorts are needed.

Corrigendum: Candidate Gene Resequencing in a Large Bicuspid Aortic Valve-Associated Thoracic Aortic Aneurysm Cohort: SMAD6 as an Important Contributor.

[This corrects the article on p. 400 in vol. 8, PMID: 28659821.].