Exome-wide analysis implicates rare protein-altering variants in human handedness.

Handedness is a manifestation of brain hemispheric specialization. Left-handedness occurs at increased rates in neurodevelopmental disorders. Genome-wide association studies have identified common genetic effects on handedness or brain asymmetry, which mostly involve variants outside protein-coding regions and may affect gene expression. Implicated genes include several that encode tubulins (microtubule components) or microtubule-associated proteins. Here we examine whether left-handedness is also influenced by rare coding variants (frequencies ≤ 1%), using exome data from 38,043 left-handed and 313,271 right-handed individuals from the UK Biobank. The beta-tubulin gene TUBB4B shows exome-wide significant association, with a rate of rare coding variants 2.7 times higher in left-handers than right-handers. The TUBB4B variants are mostly heterozygous missense changes, but include two frameshifts found only in left-handers. Other TUBB4B variants have been linked to sensorineural and/or ciliopathic disorders, but not the variants found here. Among genes previously implicated in autism or schizophrenia by exome screening, DSCAM and FOXP1 show evidence for rare coding variant association with left-handedness. The exome-wide heritability of left-handedness due to rare coding variants was 0.91%. This study reveals a role for rare, protein-altering variants in left-handedness, providing further evidence for the involvement of microtubules and disorder-relevant genes.

Exome-Based Amino Acid Optimization: A Dietary Strategy to Satisfy Human Nutritional Demands and Enhance Muscle Strength in Breast Tumor Mice Undergoing Chemotherapy.

Breast cancer patients undergoing chemotherapy often experience muscle wasting and weakness, which impact their quality of life. A potential solution lies in customizing amino acid compositions based on exome-derived formulations (ExAAs). The study hypothesized that tailoring dietary amino acids using ExAAs could enhance muscle health. Theoretical amino acid requirements were calculated from the genome's exome region, and a breast cancer mouse model undergoing paclitaxel treatment was established. The mice were supplemented with a cancer-specific nutritional formula (QJS), and the effects of QJS and amino acid-adjusted QJS (adjQJS) were compared. Both formulations improved the nutritional status without compromising tumor growth. Notably, adjQJS significantly enhanced muscle strength compared to QJS (1.51 ± 0.25 vs. 1.30 ± 0.08 fold change, p < 0.05). Transcriptome analysis revealed alterations in complement and coagulation cascades, with an observed upregulation of C3 gene expression in adjQJS. Immune regulation also changed, showing a decrease in B cells and an increase in monocytes in skeletal muscle with adjQJS. Importantly, adjQJS resulted in a notable increase in Alistipes abundance compared to QJS (10.19 ± 0.04% vs. 5.03 ± 1.75%). This study highlights the potential of ExAAs as valuable guide for optimizing amino acid composition in diets for breast cancer patients undergoing chemotherapy.

Implementation of whole-exome sequencing for pharmacogenomics profiling and exploring its potential clinical utilities.

Whole-exome sequencing (WES) is widely used in clinical settings; however, the exploration of its use in pharmacogenomic analysis remains limited. Our study compared the variant callings for 28 core absorption, distribution, metabolism and elimination genes by WES and array-based technology using clinical trials samples. The results revealed that WES had a positive predictive value of 0.71-0.92 and a sensitivity of single-nucleotide variants between 0.68 and 0.95, compared with array-based technology, for the variants in the commonly targeted regions of the WES and PhamacoScan™ assay. Besides the common variants detected by both assays, WES identified 200-300 exclusive variants per sample, totalling 55 annotated exclusive variants, including important modulators of metabolism such as rs2032582 (ABCB1) and rs72547527 (SULT1A1). This study highlights the potential clinical advantages of using WES to identify a wider range of genetic variations and enabling precision medicine.

[The foetal exome revealed]. / L'ADN fœtal révélé.

New developments in the analysis of maternal cell-free DNA now allow complete analysis of the foetal exome. This makes possible an important extension of the breadth of prenatal diagnostics but raises significant ethical questions.

A Rare Inherited Bone Marrow Failure Syndrome Disclosed by Reanalysis of the Exome Data of a Patient Evaluated for Cytopenia and Dysmorphic Features.

BACKGROUND: Multisystemic findings of inherited bone marrow failure syndromes may cause difficulty in diagnosis. Exome sequencing (ES) helps to define the etiology of rare diseases and reanalysis offers a valuable new diagnostic approach. Herein, we present the clinical and molecular characteristics of a girl who was referred for cytopenia and frequent infections. CASE REPORT: A 5-year-old girl with cytopenia, dysmorphism, short stature, developmental delay, and myopia was referred for genetic counseling. Reanalysis of the ES data revealed a homozygous splice-site variant in the DNAJC21 (NM_001012339.3:c.983+1G>A), causing Shwachman-Diamond Syndrome (SDS). It was shown by the RNA sequencing that exon 7 was skipped, causing an 88-nucleotide deletion. CONCLUSIONS: Precise genetic diagnosis enables genetic counseling and improves patient management by avoiding inappropriate treatment and unnecessary testing. This report would contribute to the clinical and molecular understanding of this rare type of SDS caused by DNAJC21 variants and expand the phenotypic features of this condition.

Rapid exome sequencing for children with severe acute encephalopathy - A case series.

Increasingly, next-generation sequencing (NGS) is becoming an invaluable tool in the diagnosis of unexplained acute neurological disorders, such as acute encephalopathy/encephalitis. Here, we describe a brief series of pediatric patients who presented at the pediatric intensive care unit with severe acute encephalopathy, initially suspected as infectious or inflammatory but subsequently diagnosed with a monogenic disorder. Rapid exome sequencing was performed during the initial hospitalization of three unrelated patients, and results were delivered within 7-21 days. All patients were previously healthy, 1.5-3 years old, of Muslim Arab descent, with consanguineous parents. One patient presenting with acute necrotizing encephalopathy (ANEC). Her sister presented with ANEC one year prior. Exome sequencing was diagnostic in all three patients. All were homozygous for pathogenic and likely-pathogenic variants associated with recessive disorders; MOCS2, NDUFS8 and DBR1. Surprisingly, the initial workup was not suggestive of the final diagnosis. This case series demonstrates that the use of rapid exome sequencing is shifting the paradigm of diagnostics even in critical care situations and should be considered early on in children with acute encephalopathy. A timely diagnosis can direct initial treatment as well as inform decisions regarding long-term care.

Investigation of Recessive Effects of Coding Variants on Common Clinical Phenotypes in Exome-Sequenced UK Biobank Participants.

INTRODUCTION: Previous studies have demonstrated effects of rare coding variants on common, clinically relevant phenotypes although the additive burden of these variants makes only a small contribution to overall trait variance. Although recessive effects of individual homozygous variants have been studied, little work has been done to elucidate the impact of rare coding variants occurring together as compound heterozygotes. METHODS: In this study, attempts were made to identify pairs of variants likely to be occurring as compound heterozygotes using 200,000 exome-sequenced subjects from the UK Biobank. Pairs of variants, which were seen together in the same subject more often than would be expected by chance, were excluded as it was assumed that these might be present in the same haplotype. Attention was restricted to variants with minor allele frequency ≤0.05 and to those predicted to alter amino acid sequence or prevent normal gene expression. For each gene, compound heterozygotes were assigned scores based on the rarity and predicted functional consequences of the constituent variants and the scores were used in a logistic regression analysis to test for association with hypertension, hyperlipidaemia, and type 2 diabetes. RESULTS: No statistically significant associations were observed and the results conformed to the distribution, which would be expected under the null hypothesis. The average number of apparently compound heterozygous subjects for each gene was only 282.2. CONCLUSION: It seems difficult to detect an effect of compound heterozygotes on the risk of these phenotypes. Even if recessive effects from compound heterozygotes do occur, they would only affect a small number of people and overall would not make a substantial contribution to phenotypic variance. This research has been conducted using the UK Biobank Resource.

Genetic and therapeutic landscapes in cohort of pancreatic adenocarcinomas: next-generation sequencing and machine learning for full tumor exome analysis.

About 7% of all cancer deaths are caused by pancreatic cancer (PCa). PCa is known for its lowest survival rates among all oncological diseases and heterogenic molecular profile. Enormous amount of genetic changes, including somatic mutations, exceeds the limits of routine clinical genetic laboratory tests and further stagnates the development of personalized treatments. We aimed to build a mutational landscape of PCa in the Russian population based on full exome next-generation sequencing (NGS) of the limited group of patients. Applying a machine learning model on full exome individual data we received personalized recommendations for targeted treatment options for each clinical case and summarized them in the unique therapeutic landscape.

Protocol for detecting rare and common genetic associations in whole-exome sequencing studies using MAGICpipeline.

Whole-exome sequencing (WES) is a major approach to uncovering gene-disease associations and pinpointing effector genes. Here, we present a protocol for estimating genetic associations of rare and common variants in large-scale case-control WES studies using MAGICpipeline, an open-access analysis pipeline. We describe steps for assessing gene-based rare-variant association analyses by incorporating multiple variant pathogenic annotations and statistical techniques. We then detail procedures for identifying disease-related modules and hub genes using weighted correlation network analysis, a systems biology approach. For complete details on the use and execution of this protocol, please refer to Su et al. (2023).1.

Exome-Wide Sequencing Study Identified Genetic Variants Associated With Sarcopenic Obesity.

Sarcopenic obesity (SO) is an age-related disease characterized by the coexistence of excessive adiposity and low muscle mass or function. Although obesity and sarcopenia are heritable conditions, the genetic determinants of SO have not been fully understood. We conducted a large-scale exome-wide association analysis of SO in a sequenced sample of 2 887 cases and 113 284 controls and an imputed sample of 4 003 cases and 161 990 controls in the UK Biobank cohort. Single-variant association analysis identified one locus 1q41 (lead SNP rs1417066, LYPLAL1-AS1, odds ratio [OR] = 1.15, 95% confidence interval [CI] = [1.11-1.19], p = 1.75 × 10-14) that was significantly associated with SO at the exome-wide significance level (p < 1 × 10-8). Colocalization analysis in the Genotype-Tissue Expression expression quantitative trait locus database showed that LYPLAL1-AS1 was colocalized with SO in multiple musculoskeletal-related tissues. Gene-based burden test of rare loss-of-function variants identified 5 genes at the gene-wise significance level (p < 4.3 × 10-6): PDE3B (OR = 2.48, p = 1.10 × 10-6), MYOZ3 (OR = 25.49, p = 1.41 × 10-7), SLC15A3 (OR = 4.75, p = 6.82 × 10-7), RNF130 (OR = 25.83, p = 4.07 × 10-6), and TNK2 (OR = 4.25, p = 8.75 × 10-8). Overall, our study uncovered the genetic effects of both common and rare variants on SO susceptibility, expanded existing knowledge of the genetic architecture of SO, and improved understanding of the genetic mechanisms underlying SO.

The cost of proband and trio exome and genome analysis in rare disease: A micro-costing study.

PURPOSE: Rare disease genomic testing is a complex process involving various resources. Accurate resource estimation is required for informed prioritization and reimbursement decisions. This study aims to analyze the costs and cost drivers of clinical genomic testing. METHODS: Based on genomic sequencing workflows we microcosted limited virtual panel analysis on exome sequencing backbone, proband and trio exome, and genome testing for proband and trio analysis in 2023 Australian Dollars ($). Deterministic and probabilistic sensitivity analyses were undertaken. RESULTS: Panel testing costs AUD $2373 ($733-$6166), and exome sequencing costs $2823 ($802-$7206) and $5670 ($2006-$11,539) for proband and trio analysis, respectively. Genome sequencing costs $4840 ($2153-$9890) and $11,589 ($5842-$16,562) for proband and trio analysis. The most expensive cost component of genomic testing was sequencing (36.9%-69.4% of total cost), with labor accounting for 27.1%-63.2% of total cost. CONCLUSION: We provide a comprehensive analysis of rare disease genomic testing costs, for a range of clinical testing types and contexts. This information will accurately inform economic evaluations of rare disease genomic testing and decision making on policy settings that assist with implementation, such as genomic testing reimbursement.

Public platform with 39,472 exome control samples enables association studies without genotype sharing.

Acquiring a sufficiently powered cohort of control samples matched to a case sample can be time-consuming or, in some cases, impossible. Accordingly, an ability to leverage genetic data from control samples that were already collected elsewhere could dramatically improve power in genetic association studies. Sharing of control samples can pose significant challenges, since most human genetic data are subject to strict sharing regulations. Here, using the properties of singular value decomposition and subsampling algorithm, we developed a method allowing selection of the best-matching controls in an external pool of samples compliant with personal data protection and eliminating the need for genotype sharing. We provide access to a library of 39,472 exome sequencing controls at http://dnascore.net enabling association studies for case cohorts lacking control subjects. Using this approach, control sets can be selected from this online library with a prespecified matching accuracy, ensuring well-calibrated association analysis for both rare and common variants.

Identification of Synonymous Pathogenic Variants in Monogenic Disorders by Integrating Exome with Transcriptome Sequencing.

Exome sequencing is becoming a first-tier clinical diagnostic test for Mendelian diseases, drastically reducing the time and cost of diagnostic odyssey and improving the diagnosis rate. Despite its success, exome sequencing faces practical challenges in assessing the pathogenicity of numerous intronic and synonymous variants, leaving a significant proportion of patients undiagnosed. In this study, a whole-blood transcriptome database was constructed that showed the expression profile of 2981 Online Mendelian Inheritance in Man disease genes in blood samples. Meanwhile, a workflow integrating exome sequencing, blood transcriptome sequencing, and in silico prediction tools to identify and validate splicing-altering intronic or synonymous variants was proposed. Following this pipeline, seven synonymous variants in eight patients were discovered. Of these, the functional evidence of c.981G>A (PIGN), c.1161A>G (ALPL), c.858G>A (ATP6AP2), and c.1011G>T (MTHFR) have not been reported previously. RNA sequencing validation confirmed that these variants induced aberrant splicing, expanding the disease-causing variant spectrum of these genes. Overall, this study shows the feasibility of combining multi-omics data to identify splicing-altering variants, especially the power of RNA sequencing. It also reveals that synonymous variants, which often are overlooked in standard diagnostic approaches, comprise an important portion of unresolved genetic diseases.

ECOLE: Learning to call copy number variants on whole exome sequencing data.

Copy number variants (CNV) are shown to contribute to the etiology of several genetic disorders. Accurate detection of CNVs on whole exome sequencing (WES) data has been a long sought-after goal for use in clinics. This was not possible despite recent improvements in performance because algorithms mostly suffer from low precision and even lower recall on expert-curated gold standard call sets. Here, we present a deep learning-based somatic and germline CNV caller for WES data, named ECOLE. Based on a variant of the transformer architecture, the model learns to call CNVs per exon, using high-confidence calls made on matched WGS samples. We further train and fine-tune the model with a small set of expert calls via transfer learning. We show that ECOLE achieves high performance on human expert labelled data for the first time with 68.7% precision and 49.6% recall. This corresponds to precision and recall improvements of 18.7% and 30.8% over the next best-performing methods, respectively. We also show that the same fine-tuning strategy using tumor samples enables ECOLE to detect RT-qPCR-validated variations in bladder cancer samples without the need for a control sample. ECOLE is available at https://github.com/ciceklab/ECOLE .

STIGMA: Single-cell tissue-specific gene prioritization using machine learning.

Clinical exome and genome sequencing have revolutionized the understanding of human disease genetics. Yet many genes remain functionally uncharacterized, complicating the establishment of causal disease links for genetic variants. While several scoring methods have been devised to prioritize these candidate genes, these methods fall short of capturing the expression heterogeneity across cell subpopulations within tissues. Here, we introduce single-cell tissue-specific gene prioritization using machine learning (STIGMA), an approach that leverages single-cell RNA-seq (scRNA-seq) data to prioritize candidate genes associated with rare congenital diseases. STIGMA prioritizes genes by learning the temporal dynamics of gene expression across cell types during healthy organogenesis. To assess the efficacy of our framework, we applied STIGMA to mouse limb and human fetal heart scRNA-seq datasets. In a cohort of individuals with congenital limb malformation, STIGMA prioritized 469 variants in 345 genes, with UBA2 as a notable example. For congenital heart defects, we detected 34 genes harboring nonsynonymous de novo variants (nsDNVs) in two or more individuals from a set of 7,958 individuals, including the ortholog of Prdm1, which is associated with hypoplastic left ventricle and hypoplastic aortic arch. Overall, our findings demonstrate that STIGMA effectively prioritizes tissue-specific candidate genes by utilizing single-cell transcriptome data. The ability to capture the heterogeneity of gene expression across cell populations makes STIGMA a powerful tool for the discovery of disease-associated genes and facilitates the identification of causal variants underlying human genetic disorders.

"Hole" Exome Sequences: The Importance of Phenotyping to Fill the Gaps in Whole Exome Sequencing.

BACKGROUND: Whole exome sequencing (WES) is commonly used for patients with nonspecific clinical features and conditions with genetic heterogeneity. However, a nondiagnostic exome does not exclude a genetic diagnosis, so history and physical examination is crucial to selecting appropriate genetic testing. CASES: We report three patients with three recognizable phenotypes: a seven-year-old female with classic Rett syndrome; a 28-year-old male with neuropathy, ataxia, and retinitis pigmentosa; and a 16-year-old male with mosaic, segmental, paternal uniparental disomy 14 who had nondiagnostic WES. CONCLUSIONS: Despite recognizable phenotypes they had diagnostic delays due to incorrect selection of genetic testing. This case series highlights the limitations of WES and reinforces the importance of utilizing patient history and physical examination to select initial testing. We will discuss appropriate testing for these patients and a consistent diagnostic algorithm that can be applied when approaching patients with unknown or uncertain clinical presentations.

Exome-wide comparative analyses revealed differentiating genomic regions for performance traits in Indian native buffaloes.

In India, 20 breeds of buffalo have been identified and registered, yet limited studies have been conducted to explore the performance potential of these breeds, especially in the Indian native breeds. This study is a maiden attempt to delineate the important variants and unique genes through exome sequencing for milk yield, milk composition, fertility, and adaptation traits in Indian local breeds of buffalo. In the present study, whole exome sequencing was performed on Chhattisgarhi (n = 3), Chilika (n = 4), Gojri (n = 3), and Murrah (n = 4) buffalo breeds and after stringent quality control, 4333, 6829, 4130, and 4854 InDels were revealed, respectively. Exome-wide FST along 100-kb sliding windows detected 27, 98, 38, and 35 outlier windows in Chhattisgarhi, Chilika, Gojri, and Murrah, respectively. The comparative exome analysis of InDels and subsequent gene ontology revealed unique breed specific genes for milk yield (CAMSAP3), milk composition (CLCN1, NUDT3), fertility (PTGER3) and adaptation (KCNA3, TH) traits. Study provides insight into mechanism of how these breeds have evolved under natural selection, the impact of these events on their respective genomes, and their importance in maintaining purity of these breeds for the traits under study. Additionally, this result will underwrite to the genetic acquaintance of these breeds for breeding application, and in understanding of evolution of these Indian local breeds.

Reanalysis of whole-exome sequencing (WES) data of children with neurodevelopmental disorders in a standard patient care context.

This study aims to inform future genetic reanalysis management by evaluating the yield of whole-exome sequencing (WES) reanalysis in standard patient care in the Netherlands. Single-center data of 159 patients with a neurodevelopmental disorder (NDD), in which WES analysis and reanalysis were performed between January 1, 2014, and December 31, 2021, was retrospectively collected. Patients were included if they were under the age of 18 years at initial analysis and if this initial analysis did not result in a diagnosis. Demographic, phenotypic, and genotypic characteristics of patients were collected and analyzed. The primary outcomes of our study were (i) diagnostic yield at reanalysis, (ii) reasons for detecting a new possibly causal variant at reanalysis, (iii) unsolicited findings, and (iv) factors associated with positive result of reanalysis. In addition, we conducted a questionnaire study amongst the 7 genetic department in the Netherlands creating an overview of used techniques, yield, and organization of WES reanalysis. The single-center data show that in most cases, WES reanalysis was initiated by the clinical geneticist (65%) or treating physician (30%). The mean time between initial WES analysis and reanalysis was 3.7 years. A new (likely) pathogenic variant or VUS with a clear link to the phenotype was found in 20 initially negative cases, resulting in a diagnostic yield of 12.6%. In 75% of these patients, the diagnosis had clinical consequences, as for example, a screening plan for associated signs and symptoms could be devised. Most (32%) of the (likely) causal variants identified at WES reanalysis were discovered due to a newly described gene-disease association. In addition to the 12.6% diagnostic yield based on new diagnoses, reclassification of a variant of uncertain significance found at initial analysis led to a definite diagnosis in three patients. Diagnostic yield was higher in patients with dysmorphic features compared to patients without clear dysmorphic features (yield 27% vs. 6%; p = 0.001). CONCLUSIONS: Our results show that WES reanalysis in patients with NDD in standard patient care leads to a substantial increase in genetic diagnoses. In the majority of newly diagnosed patients, the diagnosis had clinical consequences. Knowledge about the clinical impact of WES reanalysis, clinical characteristics associated with higher yield, and the yield per year after a negative WES in larger clinical cohorts is warranted to inform guidelines for genetic reanalysis. These guidelines will be of great value for pediatricians, pediatric rehabilitation specialists, and pediatric neurologists in daily care of patients with NDD. WHAT IS KNOWN: • Whole exome sequencing can cost-effectively identify a genetic cause of intellectual disability in about 30-40% of patients. • WES reanalysis in a research setting can lead to a definitive diagnosis in 10-20% of previously exome negative cases. WHAT IS NEW: • WES reanalysis in standard patient care resulted in a diagnostic yield of 13% in previously exome negative children with NDD. • The presence of dysmorphic features is associated with an increased diagnostic yield of WES reanalysis.

Inferring compound heterozygosity from large-scale exome sequencing data.

Recessive diseases arise when both copies of a gene are impacted by a damaging genetic variant. When a patient carries two potentially causal variants in a gene, accurate diagnosis requires determining that these variants occur on different copies of the chromosome (that is, are in trans) rather than on the same copy (that is, in cis). However, current approaches for determining phase, beyond parental testing, are limited in clinical settings. Here we developed a strategy for inferring phase for rare variant pairs within genes, leveraging genotypes observed in the Genome Aggregation Database (v2, n = 125,748 exomes). Our approach estimates phase with 96% accuracy, both in trio data and in patients with Mendelian conditions and presumed causal compound heterozygous variants. We provide a public resource of phasing estimates for coding variants and counts per gene of rare variants in trans that can aid interpretation of rare co-occurring variants in the context of recessive disease.