Exome sequencing in families with severe mental illness identifies novel and rare variants in genes implicated in Mendelian neuropsychiatric syndromes

AbstractIntroductionSevere Mental Illnesses (SMI), such as bipolar disorder and schizophrenia, are highly heritable, and have a complex pattern of inheritance. Genome wide association studies detect a part of the heritability, which can be attributed to common genetic variation. Examination of rare variants with Next Generation Sequencing (NGS) may add to the understanding of genetic architecture of SMIs.MethodsWe analyzed 32 ill subjects (with diagnosis of Bipolar Disorder, n=26; schizophrenia, n=4; schizoaffective disorder, n=1 schizophrenia like psychosis, n=1) from 8 multiplex families; and 33 healthy individuals by whole exome sequencing. Prioritized variants were selected by a 4-step filtering process, which included deleteriousness by 5 in silico algorithms; sharing within families, absence in the controls and rarity in South Asian sample of Exome Aggregation Consortium.ResultsWe identified a total of 42 unique rare, non-synonymous deleterious variants in this study with an average of 5 variants per family. None of the variants were shared across families, indicating a ‘private’ mutational profile. Twenty (47.6%) of the variant harboring genes identified in this sample have been previously reported to contribute to the risk of neuropsychiatric syndromes. These include genes which are related to neurodevelopmental processes, or have been implicated in different monogenic syndromes with a severe neurodevelopmental phenotype.ConclusionNGS approaches in family based studies are useful to identify novel and rare variants in genes for complex disorders like SMI. The study further validates the phenotypic burden of rare variants in Mendelian disease genes, indicating pleiotropic effects in the etiology of severe mental illnesses.

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Family-based exome-sequencing approach identifies rare susceptibility variants for lithium-responsive bipolar disorder

Genome ◽

10.1139/gen-2013-0081 ◽

2013 ◽

Vol 56 (10) ◽

pp. 634-640 ◽

Cited By ~ 32

Author(s):

Cristiana Cruceanu ◽

Amirthagowri Ambalavanan ◽

Dan Spiegelman ◽

Julie Gauthier ◽

Ronald G. Lafrenière ◽

...

Keyword(s):

Bipolar Disorder ◽

Rare Variants ◽

Association Studies ◽

Phenotypic Heterogeneity ◽

Exome Capture ◽

Genome Wide Association Studies ◽

Complex Disorder ◽

Genome Wide ◽

High Throughput Dna Sequencing ◽

Family Based

Bipolar disorder (BD) is a psychiatric condition characterized by the occurrence of at least two episodes of clinically disturbed mood including mania and depression. A vast literature describing BD studies suggests that a strong genetic contribution likely underlies this condition; heritability is estimated to be as high as 80%. Many studies have identified BD susceptibility loci, but because of the genetic and phenotypic heterogeneity observed across individuals, very few loci were subsequently replicated. Research in BD genetics to date has consisted of classical linkage or genome-wide association studies, which have identified candidate genes hypothesized to present common susceptibility variants. Although the observation of such common variants is informative, they can only explain a small fraction of the predicted BD heritability, suggesting a considerable contribution would come from rare and highly penetrant variants. We are seeking to identify such rare variants, and to increase the likelihood of being successful, we aimed to reduce the phenotypic heterogeneity factor by focusing on a well-defined subphenotype of BD: excellent response to lithium monotherapy. Our group has previously shown positive response to lithium therapy clusters in families and has a consistent clinical presentation with minimal comorbidity. To identify such rare variants, we are using a targeted exome capture and high-throughput DNA sequencing approach, and analyzing the entire coding sequences of BD affected individuals from multigenerational families. We are prioritizing rare variants with a frequency of less than 1% in the population that segregate with affected status within each family, as well as being potentially highly penetrant (e.g., protein truncating, missense, or frameshift) or functionally relevant (e.g., 3′UTR, 5′UTR, or splicing). By focusing on rare variants in a familial cohort, we hope to explain a significant portion of the missing heritability in BD, as well as to narrow our current insight on the key biochemical pathways implicated in this complex disorder.

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Demographic history mediates the effect of stratification on polygenic scores

eLife ◽

10.7554/elife.61548 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 1

Author(s):

Arslan A Zaidi ◽

Iain Mathieson

Keyword(s):

Principal Components ◽

Population Stratification ◽

Rare Variants ◽

Association Studies ◽

Demographic History ◽

Hybrid Approach ◽

Genome Wide Association Studies ◽

Environmental Structure ◽

Polygenic Scores ◽

Family Based

Population stratification continues to bias the results of genome-wide association studies (GWAS). When these results are used to construct polygenic scores, even subtle biases can cumulatively lead to large errors. To study the effect of residual stratification, we simulated GWAS under realistic models of demographic history. We show that when population structure is recent, it cannot be corrected using principal components of common variants because they are uninformative about recent history. Consequently, polygenic scores are biased in that they recapitulate environmental structure. Principal components calculated from rare variants or identity-by-descent segments can correct this stratification for some types of environmental effects. While family-based studies are immune to stratification, the hybrid approach of ascertaining variants in GWAS but reestimating effect sizes in siblings reduces but does not eliminate stratification. We show that the effect of population stratification depends not only on allele frequencies and environmental structure but also on demographic history.

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Deep genotype imputation captures virtually all heritability of autoimmune vitiligo

Human Molecular Genetics ◽

10.1093/hmg/ddaa005 ◽

2020 ◽

Vol 29 (5) ◽

pp. 859-863 ◽

Cited By ~ 3

Author(s):

Genevieve H L Roberts ◽

Stephanie A Santorico ◽

Richard A Spritz

Keyword(s):

Complex Disease ◽

Rare Variants ◽

Association Studies ◽

Genotype Imputation ◽

Genome Wide Association Studies ◽

Common Variants ◽

Genome Wide ◽

Autoimmune Vitiligo ◽

Family Based ◽

Project Data

Abstract Autoimmune vitiligo is a complex disease involving polygenic risk from at least 50 loci previously identified by genome-wide association studies. The objectives of this study were to estimate and compare vitiligo heritability in European-derived patients using both family-based and ‘deep imputation’ genotype-based approaches. We estimated family-based heritability (h2FAM) by vitiligo recurrence among a total 8034 first-degree relatives (3776 siblings, 4258 parents or offspring) of 2122 unrelated vitiligo probands. We estimated genotype-based heritability (h2SNP) by deep imputation to Haplotype Reference Consortium and the 1000 Genomes Project data in unrelated 2812 vitiligo cases and 37 079 controls genotyped genome wide, achieving high-quality imputation from markers with minor allele frequency (MAF) as low as 0.0001. Heritability estimated by both approaches was exceedingly high; h2FAM = 0.75–0.83 and h2SNP = 0.78. These estimates are statistically identical, indicating there is essentially no remaining ‘missing heritability’ for vitiligo. Overall, ~70% of h2SNP is represented by common variants (MAF > 0.01) and 30% by rare variants. These results demonstrate that essentially all vitiligo heritable risk is captured by array-based genotyping and deep imputation. These findings suggest that vitiligo may provide a particularly tractable model for investigation of complex disease genetic architecture and predictive aspects of personalized medicine.

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Linking common and rare disease genetics through gene regulatory networks

10.1101/2021.10.21.21265342 ◽

2021 ◽

Author(s):

Olivier B. Bakker ◽

Annique Claringbould ◽

Harm-Jan Westra ◽

Henry H. Wiersma ◽

Floranne Boulogne ◽

...

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Association Studies ◽

Genetic Diseases ◽

Common Disease ◽

Disease Genes ◽

Mendelian Disease ◽

Genome Wide Association Studies ◽

Gene Regulatory ◽

Key Genes

Genetic variants identified through genome-wide association studies (GWAS) are typically non-coding and exert small regulatory effects on downstream genes, but which downstream genes are ultimately impacted and how they confer risk remains mostly unclear. Conversely, variants that cause rare Mendelian diseases are often coding and have a more direct impact on disease development. We demonstrate that common and rare genetic diseases can be linked by studying the gene regulatory networks impacted by common disease-associated variants. We implemented this in the 'Downstreamer' method and applied it to 44 GWAS traits and find that predicted downstream "key genes" are enriched with Mendelian disease genes, e.g. key genes for height are enriched for genes that cause skeletal abnormalities and Ehlers-Danlos syndromes. We find that 82% of these key genes are located outside of GWAS loci, suggesting that they result from complex trans regulation rather than being impacted by disease-associated variants in cis. Finally, we discuss the challenges in reconstructing gene regulatory networks and provide a roadmap to improve identification of these highly connected genes for common traits and diseases.

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Detection of rare disease-related genetic variants using the birthday model

10.1101/464842 ◽

2018 ◽

Author(s):

Yael Berstein ◽

Shane E. McCarthy ◽

Melissa Kramer ◽

W. Richard McCombie

Keyword(s):

Bipolar Disorder ◽

Exome Sequencing ◽

Case Control Study ◽

Rare Variants ◽

Autism Spectrum ◽

Case Control ◽

Supplementary Information ◽

Complex Disorders ◽

Link Type ◽

Control Study

AbstractMotivationExome sequencing is a powerful technique for the identification of disease-causing genes. A number of Mendelian inherited disease genes have been identified through this method. However, it remains a challenge to leverage exome sequencing for the study of complex disorders, such as schizophrenia and bipolar disorder, due to the genetic and phenotypic heterogeneity of these disorders. Although not feasible for many studies, sequencing large sample sizes (>10,000) may improve statistical power to associate more variants, while the aggregation of distinct rare variants associated with a given disease can make the identification of causal genes statistically challenging. Therefore, new methods for rare variant association are imperative to identify causative genes of complex disorders.ResultsHere we propose a method to predict causative rare variants using a popular probabilistic problem: The Birthday Model, which estimates the probability that multiple individuals in a group share the same birthday. We consider the probability and coincidence of samples sharing a variant akin to the chance of individuals sharing the same birthday. We investigated the parameter effects of our model, providing guidelines for its use and interpretation of the results. Using published data on autism spectrum disorder, hypertriglyceridemia in addition to a current case-control study on bipolar disorder, we evaluated this probabilistic method to identify potential causative variants. Several genes in the top results of the case-control study were associated with autism spectrum and bipolar disorder. Given that the core probability based on the birthday model is very sensitive to low recurrence, the method successfully tests the association of rare variants, which generally do not provide enough signal in commonly used statistical tests. Importantly, the simplicity of the model allows quick interpretation of genomic data, enabling users to select gene candidates for further biological validation of specific mutations and downstream functional or other studies.Availabilityhttps://github.com/yberstein/Birthday-Alqorithmhttp://labshare.cshl.edu/shares/mccombielab/www-data/Birthday-Algorithm/[email protected] (or [email protected])Supplementary informationSupplementary data are available online.

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Rare variants in neuronal excitability genes influence risk for bipolar disorder

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1424958112 ◽

2015 ◽

Vol 112 (11) ◽

pp. 3576-3581 ◽

Cited By ~ 93

Author(s):

Seth A. Ament ◽

Szabolcs Szelinger ◽

Gustavo Glusman ◽

Justin Ashworth ◽

Liping Hou ◽

...

Keyword(s):

Bipolar Disorder ◽

Candidate Genes ◽

Neuronal Excitability ◽

Rare Variants ◽

Association Studies ◽

Missense Variant ◽

Genome Wide Association Studies ◽

Regulatory Variants ◽

Genes Encoding ◽

Coding Variants

We sequenced the genomes of 200 individuals from 41 families multiply affected with bipolar disorder (BD) to identify contributions of rare variants to genetic risk. We initially focused on 3,087 candidate genes with known synaptic functions or prior evidence from genome-wide association studies. BD pedigrees had an increased burden of rare variants in genes encoding neuronal ion channels, including subunits of GABAA receptors and voltage-gated calcium channels. Four uncommon coding and regulatory variants also showed significant association, including a missense variant in GABRA6. Targeted sequencing of 26 of these candidate genes in an additional 3,014 cases and 1,717 controls confirmed rare variant associations in ANK3, CACNA1B, CACNA1C, CACNA1D, CACNG2, CAMK2A, and NGF. Variants in promoters and 5′ and 3′ UTRs contributed more strongly than coding variants to risk for BD, both in pedigrees and in the case-control cohort. The genes and pathways identified in this study regulate diverse aspects of neuronal excitability. We conclude that rare variants in neuronal excitability genes contribute to risk for BD.

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Candidate Genes for Nonsyndromic Cleft Palate Detected by Exome Sequencing

Journal of Dental Research ◽

10.1177/0022034517722761 ◽

2017 ◽

Vol 96 (11) ◽

pp. 1314-1321 ◽

Cited By ~ 15

Author(s):

A.K. Hoebel ◽

D. Drichel ◽

M. van de Vorst ◽

A.C. Böhmer ◽

S. Sivalingam ◽

...

Keyword(s):

Cleft Palate ◽

Exome Sequencing ◽

Candidate Genes ◽

Rare Variants ◽

Association Studies ◽

Low Frequency ◽

Missense Variant ◽

Genome Wide Association Studies ◽

Loss Of Function ◽

Novel Variant

Nonsyndromic cleft palate only (nsCPO) is a facial malformation that has a livebirth prevalence of 1 in 2,500. Research suggests that the etiology of nsCPO is multifactorial, with a clear genetic component. To date, genome-wide association studies have identified only 1 conclusive common variant for nsCPO, that is, a missense variant in the gene grainyhead-like-3 ( GRHL3). Thus, the underlying genetic causes of nsCPO remain largely unknown. The present study aimed at identifying rare variants that might contribute to nsCPO risk, via whole-exome sequencing (WES), in multiply affected Central European nsCPO pedigrees. WES was performed in 2 affected first-degree relatives from each family. Variants shared between both individuals were analyzed for their potential deleterious nature and a low frequency in the general population. Genes carrying promising variants were annotated for 1) reported associations with facial development, 2) multiple occurrence of variants, and 3) expression in mouse embryonic palatal shelves. This strategy resulted in the identification of a set of 26 candidate genes that were resequenced in 132 independent nsCPO cases and 623 independent controls of 2 different ethnicities, using molecular inversion probes. No rare loss-of-function mutation was identified in either WES or resequencing step. However, we identified 2 or more missense variants predicted to be deleterious in each of 3 genes ( ACACB, PTPRS, MIB1) in individuals from independent families. In addition, the analyses identified a novel variant in GRHL3 in 1 patient and a variant in CREBBP in 2 siblings. Both genes underlie different syndromic forms of CPO. A plausible hypothesis is that the apparently nonsyndromic clefts in these 3 patients might represent hypomorphic forms of the respective syndromes. In summary, the present study identified rare variants that might contribute to nsCPO risk and suggests candidate genes for further investigation.

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Finding the Sources of Missing Heritability within Rare Variants Through Simulation

Bioinformatics and Biology Insights ◽

10.1177/1177932217735096 ◽

2017 ◽

Vol 11 ◽

pp. 117793221773509 ◽

Cited By ~ 7

Author(s):

Baishali Bandyopadhyay ◽

Veda Chanda ◽

Yupeng Wang

Keyword(s):

Genetic Association ◽

Rare Variant ◽

Genetic Variants ◽

Rare Variants ◽

Association Studies ◽

Genome Wide Association Studies ◽

Missing Heritability ◽

Promoter Regions ◽

Complex Disorders ◽

Term Births

Thousands of genome-wide association studies (GWAS) have been conducted to identify the genetic variants associated with complex disorders. However, only a small proportion of phenotypic variances can be explained by the reported variants. Moreover, many GWAS failed to identify genetic variants associated with disorders displaying hereditary features. The “missing heritability” problem can be partly explained by rare variants. We simulated a causality scenario that gestational ages, a quantitative trait that can distinguish preterm (<37 weeks) and term births, were significantly correlated with the rare variant aggregations at 1000 single-nucleotide polymorphism loci. These 1000 simulated causal rare variants were embedded into randomly selected subsets of 9642 promoter regions from the 1000 Genomes Project genotypic data according to different proportions of causal rare variants within the embedded promoters. Through analysis of the correlations between rare variant aggregations and gestational ages, we found that the embedded promoters as a whole showed weaker genetic association when the proportion of causal rare variants decreased, and no individual embedded promoters showed genetic association when the proportion of causal rare variants was smaller than 0.4. Our analyses indicate that association signals can be greatly diluted when causal rare variants are dispersedly and sparsely distributed in the genome, accounting for an important source of missing heritability.

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Genotype-phenotype association mining in bipolar disorder: market research meets complex genetics

10.1101/116624 ◽

2017 ◽

Author(s):

René Breuer ◽

Manuel Mattheisen ◽

Josef Frank ◽

Bertram Krumm ◽

Jens Treutlein ◽

...

Keyword(s):

Data Mining ◽

Bipolar Disorder ◽

Market Research ◽

Association Studies ◽

Genetic Research ◽

Gene Discovery ◽

Genome Wide Association Studies ◽

Complex Disorders ◽

Complex Genetics ◽

Genome Wide

AbstractDisentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: applying a data mining procedure to identify yet undetected genotype-phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2,835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted. Two of these - one associated with eating disorder and the other with anxiety - remained significant in an independent dataset after robust correction for multiple testing, showing considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively). Our approach may help detect novel specific genotype-phenotype relationships in BD typically not explored by analyses like GWAS. While we adapted the data mining tool within the context of BD gene discovery, it may facilitate identifying highly specific genotype-phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.

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Retracted: Identification of de novo mutations in prenatal neurodevelopment-associated genes in schizophrenia in two Han Chinese patient-sibling family-based cohorts

Translational Psychiatry ◽

10.1038/s41398-020-00987-z ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Shan Jiang ◽

Daizhan Zhou ◽

Yin-Ying Wang ◽

Peilin Jia ◽

Chunling Wan ◽

...

Keyword(s):

Drug Targets ◽

Developmental Disorders ◽

Rare Variants ◽

De Novo ◽

Association Studies ◽

Chinese Patient ◽

Genome Wide Association Studies ◽

De Novo Mutations ◽

Burden Test ◽

Family Based

AbstractSchizophrenia (SCZ) is a severe psychiatric disorder with a strong genetic component. High heritability of SCZ suggests a major role for transmitted genetic variants. Furthermore, SCZ is also associated with a marked reduction in fecundity, leading to the hypothesis that alleles with large effects on risk might often occur de novo. In this study, we conducted whole-genome sequencing for 23 families from two cohorts with unaffected siblings and parents. Two nonsense de novo mutations (DNMs) in GJC1 and HIST1H2AD were identified in SCZ patients. Ten genes (DPYSL2, NBPF1, SDK1, ZNF595, ZNF718, GCNT2, SNX9, AACS, KCNQ1, and MSI2) were found to carry more DNMs in SCZ patients than their unaffected siblings by burden test. Expression analyses indicated that these DNM implicated genes showed significantly higher expression in prefrontal cortex in prenatal stage. The DNM in the GJC1 gene is highly likely a loss function mutation (pLI = 0.94), leading to the dysregulation of ion channel in the glutamatergic excitatory neurons. Analysis of rare variants in independent exome sequencing dataset indicates that GJC1 has significantly more rare variants in SCZ patients than in unaffected controls. Data from genome-wide association studies suggested that common variants in the GJC1 gene may be associated with SCZ and SCZ-related traits. Genes co-expressed with GJC1 are involved in SCZ, SCZ-associated pathways, and drug targets. These evidences suggest that GJC1 may be a risk gene for SCZ and its function may be involved in prenatal and early neurodevelopment, a vulnerable period for developmental disorders such as SCZ.

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