scholarly journals The contribution of common regulatory and protein-coding TYR variants in the genetic architecture of albinism

2021 ◽  
Author(s):  
Vincent Michaud ◽  
Eulalie Lasseaux ◽  
David J Green ◽  
Dave T Gerrard ◽  
Claudio Plaisant ◽  
...  
Keyword(s):  
Genetic Architecture ◽  
Large Scale ◽  
Diagnostic Yield ◽  
Genetic Diseases ◽  
Gene Encoding ◽  
Protein Coding ◽  
Autosomal Recessive Disorders ◽  
Functional Variants ◽  
Prevalent Disease ◽  
Coding Variants

Genetic diseases have been historically segregated into rare Mendelian and common complex conditions. Large-scale studies using genome sequencing are eroding this distinction and are gradually unmasking the underlying complexity of human traits. We studied a cohort of 1,313 individuals with albinism aiming to gain insights into the genetic architecture of rare, autosomal recessive disorders. We investigated the contribution of regulatory and protein-coding variants at the common and rare ends of the allele-frequency spectrum. We focused on TYR, the gene encoding tyrosinase, and found that a promoter variant, TYR: c.-301C>T [rs4547091], modulates the penetrance of a prevalent, disease-associated missense change, TYR: c.1205G>A [rs1126809]. We also found that homozygosity for a haplotype formed by three common, functional variants, TYR: c.[-301C;575C>A;1205G>A], confers a high risk of albinism (OR>77) and is associated with reduced vision in UK Biobank participants. Finally, we report how the combined analysis of rare and common variants increases diagnostic yield and informs genetic counselling in families with albinism.

scholarly journals Rare schizophrenia risk variant burden is conserved in diverse human populations

2022 ◽  
Author(s):  
Dongjing Liu ◽  
Dara Meyer ◽  
Brian Fennessy ◽  
Claudia Feng ◽  
Esther Cheng ◽  
...  
Keyword(s):  
Genetic Architecture ◽  
Large Scale ◽  
Current Knowledge ◽  
European Ancestry ◽  
P Value ◽  
Human Populations ◽  
Protein Coding ◽  
Coding Regions ◽  
Meta Analyses ◽  
Shared Risk

Schizophrenia is a chronic mental illness that is amongst the most debilitating conditions encountered in medical practice. A recent landmark schizophrenia study of the protein-coding regions of the genome identified a causal role for ten genes and a concentration of rare variant signals in evolutionarily constrained genes1. This study -- and most other large-scale human genetic studies -- was mainly composed of individuals of European ancestry, and the generalizability of the findings in non-European populations is unclear. To address this gap in knowledge, we designed a custom sequencing panel based on current knowledge of the genetic architecture of schizophrenia and applied it to a new cohort of 22,135 individuals of diverse ancestries. Replicating earlier work, cases carried a significantly higher burden of rare protein-truncating variants among constrained genes (OR=1.48, p-value = 5.4 x 10-6). In meta-analyses with existing schizophrenia datasets totaling up to 35,828 cases and 107,877 controls, this excess burden was largely consistent across five continental populations. Two genes (SRRM2 and AKAP11) were newly implicated as schizophrenia risk genes, and one gene (PCLO) was identified as a shared risk gene for schizophrenia and autism. Overall, our results lend robust support to the rare allelic spectrum of the genetic architecture of schizophrenia being conserved across diverse human populations.

scholarly journals Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
James Zou ◽  
Gregory Valiant ◽  
Paul Valiant ◽  
Konrad Karczewski ◽  
Siu On Chan ◽  
...  
Keyword(s):  
Large Scale ◽  
Human Populations ◽  
Protein Coding ◽  
Coding Variants

scholarly journals Local genetic effects on gene expression across 44 human tissues

2016 ◽  
Author(s):  
François Aguet ◽  
Andrew A. Brown ◽  
Stephane E. Castel ◽  
Joe R. Davis ◽  
Pejman Mohammadi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Large Scale ◽  
Genetic Diseases ◽  
Tissue Expression ◽  
Specific Expression ◽  
Tissue Specific ◽  
Regulatory Variation ◽  
Functional Variants ◽  
Trait Locus

AbstractExpression quantitative trait locus (eQTL) mapping provides a powerful means to identify functional variants influencing gene expression and disease pathogenesis. We report the identification of cis-eQTLs from 7,051 post-mortem samples representing 44 tissues and 449 individuals as part of the Genotype-Tissue Expression (GTEx) project. We find a cis-eQTL for 88% of all annotated protein-coding genes, with one-third having multiple independent effects. We identify numerous tissue-specific cis-eQTLs, highlighting the unique functional impact of regulatory variation in diverse tissues. By integrating large-scale functional genomics data and state-of-the-art fine-mapping algorithms, we identify multiple features predictive of tissue-specific and shared regulatory effects. We improve estimates of cis-eQTL sharing and effect sizes using allele specific expression across tissues. Finally, we demonstrate the utility of this large compendium of cis-eQTLs for understanding the tissue-specific etiology of complex traits, including coronary artery disease. The GTEx project provides an exceptional resource that has improved our understanding of gene regulation across tissues and the role of regulatory variation in human genetic diseases.

scholarly journals Non-coding variants upstream of MEF2C cause severe developmental disorder through three distinct loss-of-function mechanisms

2020 ◽  
Author(s):  
Caroline F Wright ◽  
Nicholas M Quaife ◽  
Laura Ramos-Hernández ◽  
Petr Danecek ◽  
Matteo P Ferla ◽  
...  
Keyword(s):  
Developmental Disorders ◽  
Developmental Disorder ◽  
De Novo ◽  
Diagnostic Yield ◽  
Severe Disease ◽  
Loss Of Function ◽  
Single Nucleotide Variants ◽  
Clinical Genetic ◽  
Protein Coding ◽  
Coding Variants

AbstractClinical genetic testing of protein-coding regions identifies a likely causative variant in only ∼35% of severe developmental disorder (DD) cases. We screened 9,858 patients from the Deciphering Developmental Disorders (DDD) study for de novo mutations in the 5’untranslated regions (5’UTRs) of dominant haploinsufficient DD genes. We identify four single nucleotide variants and two copy number variants upstream of MEF2C that cause DD through three distinct loss-of-function mechanisms, disrupting transcription, translation, and/or protein function. These non-coding variants represent 23% of disease-causing variants identified in MEF2C in the DDD cohort. Our analyses show that non-coding variants upstream of known disease-causing genes are an important cause of severe disease and demonstrate that analysing 5’UTRs can increase diagnostic yield, even using existing exome sequencing datasets. We also show how non-coding variants can help inform both the disease-causing mechanism underlying protein-coding variants, and dosage tolerance of the gene.

scholarly journals Sequencing of 640,000 exomes identifies GPR75 variants associated with protection from obesity

Science ◽  
2021 ◽  
Vol 373 (6550) ◽  
pp. eabf8683
Author(s):  
Parsa Akbari ◽  
Ankit Gilani ◽  
Olukayode Sosina ◽  
Jack A. Kosmicki ◽  
Lori Khrimian ◽  
...  
Keyword(s):  
Complex Traits ◽  
Large Scale ◽  
The United States ◽  
Protein Coding ◽  
Knock Out ◽  
The United Kingdom ◽  
Body Adiposity ◽  
Coding Variants ◽  
G Protein Coupled ◽  
Diet Model

Large-scale human exome sequencing can identify rare protein-coding variants with a large impact on complex traits such as body adiposity. We sequenced the exomes of 645,626 individuals from the United Kingdom, the United States, and Mexico and estimated associations of rare coding variants with body mass index (BMI). We identified 16 genes with an exome-wide significant association with BMI, including those encoding five brain-expressed G protein–coupled receptors (CALCR, MC4R, GIPR, GPR151, and GPR75). Protein-truncating variants in GPR75 were observed in ~4/10,000 sequenced individuals and were associated with 1.8 kilograms per square meter lower BMI and 54% lower odds of obesity in the heterozygous state. Knock out of Gpr75 in mice resulted in resistance to weight gain and improved glycemic control in a high-fat diet model. Inhibition of GPR75 may provide a therapeutic strategy for obesity.

scholarly journals Quantifying the unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects

2015 ◽  
Author(s):  
James Zou ◽  
Gregory Valiant ◽  
Paul Valiant ◽  
Konrad Karczewski ◽  
Siu On Chan ◽  
...  
Keyword(s):  
Frequency Distribution ◽  
Statistical Power ◽  
Large Scale ◽  
Rare Variants ◽  
Human Populations ◽  
Loss Of Function ◽  
Protein Coding ◽  
Missense Variants ◽  
Healthy Humans ◽  
Coding Variants

As new proposals aim to sequence ever larger collection of humans, it is critical to have a quantitative framework to evaluate the statistical power of these projects. We developed a new algorithm, UnseenEst, and applied it to the exomes of 60,706 individuals to estimate the frequency distribution of all protein-coding variants, including rare variants that have not been observed yet in the current cohorts. Our results quantified the number of new variants that we expect to identify as sequencing cohorts reach hundreds of thousands of individuals. With 500K individuals, we find that we expect to capture 7.5% of all possible loss-of-function variants and 12% of all possible missense variants. We also estimate that 2,900 genes have loss-of-function frequency of less than 0.00001 in healthy humans, consistent with very strong intolerance to gene inactivation.

scholarly journals SMAP: A pipeline for sample matching in proteogenomics

2021 ◽  
Author(s):  
Ling Li ◽  
Mingming Niu ◽  
Alyssa Erickson ◽  
Jie Luo ◽  
Kincaid Rowbotham ◽  
...  
Keyword(s):  
Large Scale ◽  
Ribosome Profiling ◽  
Sequencing Data ◽  
Protein Coding ◽  
Web Based ◽  
Link Type ◽  
Genomics And Proteomics ◽  
Sample Data ◽  
Dependent Protein ◽  
Coding Variants

AbstractIntegration of genomics and proteomics (proteogenomics) offers unprecedented promise for in-depth understanding of human diseases. However, sample mix-up is a pervasive, recurring problem, due to complex sample processing in proteogenomics. Here we present a pipeline for Sample Matching in Proteogenomics (SMAP) for verifying sample identity to ensure data integrity. SMAP infers sample-dependent protein-coding variants from quantitative mass spectrometry (MS), and aligns the MS-based proteomic samples with genomic samples by two discriminant scores. Theoretical analysis with simulation data indicates that SMAP is capable of uniquely match proteomic and genomic samples, when ≥20% genotypes of individual samples are available. When SMAP was applied to a large-scale proteomics dataset from 288 biological samples generated by the PsychENCODE BrainGVEX project, we identified and corrected 18.8% (54/288) mismatched samples. The correction was further confirmed by ribosome profiling and assay for transposase-accessible chromatin sequencing data from the same set of samples. Thus our results demonstrate that SMAP is an effective tool for sample verification in a large-scale MS-based proteogenomics study. The source code, manual, and sample data of the SMAP are publicly available at https://github.com/UND-Wanglab/SMAP, and a web-based SMAP can be accessed at https://smap.shinyapps.io/smap/.

scholarly journals Computational Assessment of the Regulation-Modulating Potential for Noncoding Variants

2019 ◽  
Author(s):  
Fang-Yuan Shi ◽  
Yu Wang ◽  
Dong Huang ◽  
Yu Liang ◽  
Nan Liang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Genetic Diseases ◽  
Superior Performance ◽  
Massive Datasets ◽  
Functional Variants ◽  
False Discovery ◽  
Genome Wide ◽  
Causal Variants ◽  
Higher Sensitivity

AbstractLarge-scale genome-wide association and expression quantitative trait loci studies have identified multiple noncoding variants associated with genetic diseases via affecting gene expression. However, effectively and efficiently pinpointing causal variants remains a serious challenge. Here, we developed CARMEN, a novel algorithm to identify functional noncoding expression-modulating variants. Multiple evaluations demonstrated CARMEN’s superior performance over state-of-the-art tools. Its higher sensitivity and low false discovery rate enable CARMEN to identify multiple causal expression-modulating variants that other tools simply missed. Meanwhile, benefitting from extensive annotations generated, CARMEN provides mechanism hints on predicted expression-modulating variants, enabling effectively characterizing functional variants involved in gene expression and disease-related phenotypes. CARMEN scales well with the massive datasets and is available online as a Web server at http://carmen.gao-lab.org.

scholarly journals Mutational patterns and clonal evolution from diagnosis to relapse in pediatric acute lymphoblastic leukemia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shumaila Sayyab ◽  
Anders Lundmark ◽  
Malin Larsson ◽  
Markus Ringnér ◽  
Sara Nystedt ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Large Scale ◽  
Somatic Mutations ◽  
Lymphoblastic Leukemia ◽  
Clonal Evolution ◽  
Point Mutations ◽  
Driver Genes ◽  
Protein Coding ◽  
Pediatric Acute Lymphoblastic Leukemia ◽  
Evolutionary Trajectories

AbstractThe mechanisms driving clonal heterogeneity and evolution in relapsed pediatric acute lymphoblastic leukemia (ALL) are not fully understood. We performed whole genome sequencing of samples collected at diagnosis, relapse(s) and remission from 29 Nordic patients. Somatic point mutations and large-scale structural variants were called using individually matched remission samples as controls, and allelic expression of the mutations was assessed in ALL cells using RNA-sequencing. We observed an increased burden of somatic mutations at relapse, compared to diagnosis, and at second relapse compared to first relapse. In addition to 29 known ALL driver genes, of which nine genes carried recurrent protein-coding mutations in our sample set, we identified putative non-protein coding mutations in regulatory regions of seven additional genes that have not previously been described in ALL. Cluster analysis of hundreds of somatic mutations per sample revealed three distinct evolutionary trajectories during ALL progression from diagnosis to relapse. The evolutionary trajectories provide insight into the mutational mechanisms leading relapse in ALL and could offer biomarkers for improved risk prediction in individual patients.

scholarly journals Genetic architecture of schizophrenia: a review of major advancements

2021 ◽  
pp. 1-10
Author(s):  
Sophie E. Legge ◽  
Marcos L. Santoro ◽  
Sathish Periyasamy ◽  
Adeniran Okewole ◽  
Arsalan Arsalan ◽  
...  
Keyword(s):  
Genetic Architecture ◽  
Association Studies ◽  
Copy Number Variants ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Loss Of Function ◽  
Genetic Loci ◽  
Significant Enrichment ◽  
High Heritability ◽  
Coding Variants

Abstract Schizophrenia is a severe psychiatric disorder with high heritability. Consortia efforts and technological advancements have led to a substantial increase in knowledge of the genetic architecture of schizophrenia over the past decade. In this article, we provide an overview of the current understanding of the genetics of schizophrenia, outline remaining challenges, and summarise future directions of research. World-wide collaborations have resulted in genome-wide association studies (GWAS) in over 56 000 schizophrenia cases and 78 000 controls, which identified 176 distinct genetic loci. The latest GWAS from the Psychiatric Genetics Consortium, available as a pre-print, indicates that 270 distinct common genetic loci have now been associated with schizophrenia. Polygenic risk scores can currently explain around 7.7% of the variance in schizophrenia case-control status. Rare variant studies have implicated eight rare copy-number variants, and an increased burden of loss-of-function variants in SETD1A, as increasing the risk of schizophrenia. The latest exome sequencing study, available as a pre-print, implicates a burden of rare coding variants in a further nine genes. Gene-set analyses have demonstrated significant enrichment of both common and rare genetic variants associated with schizophrenia in synaptic pathways. To address current challenges, future genetic studies of schizophrenia need increased sample sizes from more diverse populations. Continued expansion of international collaboration will likely identify new genetic regions, improve fine-mapping to identify causal variants, and increase our understanding of the biology and mechanisms of schizophrenia.

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