Uncovering the roles of rare variants in common disease through whole-genome sequencing

A power-based sliding window approach to evaluate the clinical impact of rare genetic variants in the nucleotide sequence or the spatial position of the folded protein

Systematic determination of novel variant pathogenicity remains a major challenge, even when there is an established association between a gene and phenotype. Here we present Power Window (PW), a sliding window technique that identifies the impactful regions of a gene using population-scale clinico-genomic datasets. By sizing analysis windows on the number of variant carriers, rather than the number of variants or nucleotides, statistical power is held constant, enabling the localization of clinical phenotypes and removal of unassociated gene regions. The windows can be built by sliding across either the nucleotide sequence of the gene (through 1D space) or the positions of the amino acids in the folded protein (through 3D space). Using a training set of 350k exomes from the UK Biobank (UKB), we developed PW models for well-established gene-disease associations and tested their accuracy in two independent cohorts (117k UKB exomes and 65k exomes sequenced at Helix in the Healthy Nevada Project, myGenetics, or In Our DNA SC studies). The significant models retained a median of 49% of the qualifying variant carriers in each gene (range 2%-98%), with quantitative traits showing a median effect size improvement of 66% compared with aggregating variants across the entire gene, and binary traits' odds ratios improving by a median of 2.2-fold. PW showcases that electronic health record-based statistical analyses can accurately distinguish between novel coding variants in established genes that will have high phenotypic penetrance and those that will not, unlocking new potential for human genomics research, drug development, variant interpretation, and precision medicine.

Benchmarking computational variant effect predictors by their ability to infer human traits

BACKGROUND

Computational variant effect predictors offer a scalable and increasingly reliable means of interpreting human genetic variation, but concerns of circularity and bias have limited previous methods for evaluating and comparing predictors. Population-level cohorts of genotyped and phenotyped participants that have not been used in predictor training can facilitate an unbiased benchmarking of available methods. Using a curated set of human gene-trait associations with a reported rare-variant burden association, we evaluate the correlations of 24 computational variant effect predictors with associated human traits in the UK Biobank and All of Us cohorts.

RESULTS

AlphaMissense outperformed all other predictors in inferring human traits based on rare missense variants in UK Biobank and All of Us participants. The overall rankings of computational variant effect predictors in these two cohorts showed a significant positive correlation.

CONCLUSION

We describe a method to assess computational variant effect predictors that sidesteps the limitations of previous evaluations. This approach is generalizable to future predictors and could continue to inform predictor choice for personal and clinical genetics.

Human genetics of face recognition: discovery of MCTP2 mutations in humans with face blindness (congenital prosopagnosia)

Face recognition is important for both visual and social cognition. While prosopagnosia or face blindness has been known for seven decades and face-specific neurons for half a century, the molecular genetic mechanism is not clear. Here we report results after 17 years of research with classic genetics and modern genomics. From a large family with 18 congenital prosopagnosia (CP) members with obvious difficulties in face recognition in daily life, we uncovered a fully cosegregating private mutation in the MCTP2 gene which encodes a calcium binding transmembrane protein expressed in the brain. After screening through cohorts of 6589, we found more CPs and their families, allowing detection of more CP associated mutations in MCTP2. Face recognition differences were detected between 14 carriers with the frameshift mutation S80fs in MCTP2 and 19 noncarrying volunteers. Six families including one with 10 members showed the S80fs-CP correlation. Functional magnetic resonance imaging found association of impaired recognition of individual faces by MCTP2 mutant CPs with reduced repetition suppression to repeated facial identities in the right fusiform face area. Our results have revealed genetic predisposition of MCTP2 mutations in CP, 76 years after the initial report of prosopagnosia and 47 years after the report of the first CP. This is the first time a gene required for a higher form of visual social cognition was found in humans.

Monogenic lupus: insights into disease pathogenesis and therapeutic opportunities

PURPOSE OF REVIEW

This review aims to provide an overview of the genes and molecular pathways involved in monogenic lupus, the implications for genome diagnosis, and the potential therapies targeting these molecular mechanisms.

RECENT FINDINGS

To date, more than 30 genes have been identified as contributors to monogenic lupus. These genes are primarily related to complement deficiency, activation of the type I interferon (IFN) pathway, disruption of B-cell and T-cell tolerance and metabolic pathways, which reveal the multifaceted nature of systemic lupus erythematosus (SLE) pathogenesis.

SUMMARY

In-depth study of the causes of monogenic lupus can provide valuable insights into of pathogenic mechanisms of SLE, facilitate the identification of effective biomarkers, and aid in developing therapeutic strategies.

Investigating the Relationship Between Rare Genetic Variants and Fibrosis in Pediatric Nonalcoholic Fatty Liver Disease

Background and Aims

Nonalcoholic Fatty Liver Disease (NAFLD) is a complex human disease. Common genetic variation in the patatin-like phospholipase domain containing 3 (PNPLA3) and transmembrane 6 superfamily member 2 (TM6SF2) genes have been associated with an increased risk of developing NAFLD, nonalcoholic steatohepatitis (NASH), and fibrosis in adults. The role of rare genetic variants in the development and progression of NAFLD in children is not well known. We aimed to explore the role of rare genetic variants in pediatric patients with advanced fibrosis.

Methods

Whole exome sequencing data was generated for 229 pediatric patients diagnosed with NAFLD recruited from the NASH Clinical Research Network (NASH CRN). Case-control single variant and gene-based collapsing analyses were used to test for rare variants that were enriched or depleted within the pediatric NAFLD cohort specifically for advanced fibrosis (cases) versus those without fibrosis (controls) or six other histologic characteristics. Exome data from non-NAFLD population controls were also used for additional analyses. All results were adjusted for multiple testing using a Bonferroni correction.

Results

No genome-wide significant associations were found between rare variation and presence of advanced fibrosis or NASH, nor the severity of steatosis, inflammation, or hepatocellular ballooning. Significantly, no enrichment of rare variants in PNPLA3 or TM6SF2 was observed across phenotypes.

Conclusion

In a cohort of children with histologically proven NAFLD, no genome-wide significant associations were found between rare genetic variation and advanced fibrosis or six other histologic features. Of particular interest was the lack of association with genes of interest in adults: PNPLA3 and TM6SF2, though limitations in sample size may reduce the ability to detect associations, particularly with rare variation.

Leveraging Single-Cell Populations to Uncover the Genetic Basis of Complex Traits

The ease and throughput of single-cell genomics have steadily improved, and its current trajectory suggests that surveying single-cell populations will become routine. We discuss the merger of quantitative genetics with single-cell genomics and emphasize how this synergizes with advantages intrinsic to plants. Single-cell population genomics provides increased detection resolution when mapping variants that control molecular traits, including gene expression or chromatin accessibility. Additionally, single-cell population genomics reveals the cell types in which variants act and, when combined with organism-level phenotype measurements, unveils which cellular contexts impact higher-order traits. Emerging technologies, notably multiomics, can facilitate the measurement of both genetic changes and genomic traits in single cells, enabling single-cell genetic experiments. The implementation of single-cell genetics will advance the investigation of the genetic architecture of complex molecular traits and provide new experimental paradigms to study eukaryotic genetics.

A novel missense compound heterozygous variant in TLR1 gene is associated with susceptibility to rheumatoid arthritis - structural perspective and functional annotations

INTRODUCTION

Besides human leukocyte antigen (HLA-DRB1) locus, more than 100 loci across the genome have been identified and linked with the onset, expression and/or progression of rheumatoid arthritis (RA). However, there are still grey areas in our understanding of the key genetic contributors of the disease, particularly in familial cases.

METHODS

In the present study, we have performed the whole exome sequencing (WES) of RA patients from two consanguineous families of Pakistan in a quest to identify novel, high-impact, RA-susceptibility genetic variants.

RESULTS

Through stepwise filtering, around 17,000 variants (common in the affected members) were recognized, out of which 2651 were predicted to be deleterious. Of these, 196 had direct relevance to RA. When selected for homozygous recessive mode of inheritance, two novel pathogenic variants (c.1324T>C, p.Cys442→Arg442; c.2036T>C, p.Ile679→Thr679) in the TLR1 gene displayed the role of compound heterozygosity in modulating the phenotypic expression and penetrance of RA. The structural and functional consequences of the TLR1 missense single nucleotide mutations (Cys442→Arg442; Ile679→Thr679) were evaluated through molecular dynamic simulation (MDS) studies. Analysis showed domain's rigidification, conferring stability to mutant TLR1-TIR/TIRAP-TIR complex with concomitant increase in molecular interactions with pro-inflammatory cytokines, compared to the wild-type conformation. Gene co-expression network analysis highlighted interlinked partnering genes along with interleukin-6 production of TLR1 (corrected p-value 2.98e-4) and acetylcholine receptor activity of CHRNG (corrected p-value 6.12e-2) as highly enriched associated functions.

CONCLUSION

The results, validated through case-control study subjects, suggested that the variants identified through WES and confirmed through Sanger sequencing and MDS are the novel disease variants and are likely to confer RA-susceptibility, independently and/or in a family-specific context. Key Points • Exploration of population based/ethno-specific big data is imperative to identify novel causal variants of RA. • Two new deleterious missense mutations in mutational hotspot exon 4 of TLR1 gene have been identified in Pakistani RA patients. • MD simulation data provides evidence for domain's rigidification, conferring stability to mutant TLR1-TIR/TIRAP-TIR complex, with concomitant increase in production of pro-inflammatory cytokines, thus adding to the onset/erosive outcome of RA.

Strategies in Aggregation Tests for Rare Variants

Genome-wide association studies (GWAS) successfully identified numerous common variants involved in complex diseases, but only limited heritability was explained by these findings. Advances in high-throughput sequencing technology made it possible to assess the contribution of rare variants in common diseases. However, study of rare variants introduces challenges due to low frequency of rare variants. Well-established common variant methods were underpowered to identify the rare variants in GWAS. To address this challenge, several new methods have been developed to examine the role of rare variants in complex diseases. These approaches are based on testing the aggregate effect of multiple rare variants in a predefined genetic region. Provided here is an overview of statistical approaches and the protocols explaining step-by-step analysis of aggregations tests with the hands-on experience using R scripts in four categories: burden tests, adaptive burden tests, variance-component tests, and combined tests. Also explained are the concepts of rare variants, permutation tests, kernel methods, and genetic variant annotation. At the end we discuss relevant topics of bioinformatics tools for annotation, family-based design of rare-variant analysis, population stratification adjustment, and meta-analysis. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.

Impact of general incidence function on three-strain SEIAR model

We investigate the behavior of a complex three-strain model with a generalized incidence rate. The incidence rate is an essential aspect of the model as it determines the number of new infections emerging. The mathematical model comprises thirteen nonlinear ordinary differential equations with susceptible, exposed, symptomatic, asymptomatic and recovered compartments. The model is well-posed and verified through existence, positivity and boundedness. Eight equilibria comprise a disease-free equilibria and seven endemic equilibrium points following the existence of three strains. The basic reproduction numbers $ \mathfrak{R}_{01} $, $ \mathfrak{R}_{02} $ and $ \mathfrak{R}_{03} $ represent the dominance of strain 1, strain 2 and strain 3 in the environment for new strain emergence. The model establishes local stability at a disease-free equilibrium point. Numerical simulations endorse the impact of general incidence rates, including bi-linear, saturated, Beddington DeAngelis, non-monotone and Crowley Martin incidence rates.

Cauchy combination methods for the detection of gene-environment interactions for rare variants related to quantitative phenotypes

The characterization of gene-environment interactions (GEIs) can provide detailed insights into the biological mechanisms underlying complex diseases. Despite recent interest in GEIs for rare variants, published GEI tests are underpowered for an extremely small proportion of causal rare variants in a gene or a region. By extending the aggregated Cauchy association test (ACAT), we propose three GEI tests to address this issue: a Cauchy combination GEI test with fixed main effects (CCGEI-F), a Cauchy combination GEI test with random main effects (CCGEI-R), and an omnibus Cauchy combination GEI test (CCGEI-O). ACAT was applied to combine p values of single-variant GEI analyses to obtain CCGEI-F and CCGEI-R and p values of multiple GEI tests were combined in CCGEI-O. Through numerical simulations, for small numbers of causal variants, CCGEI-F, CCGEI-R and CCGEI-O provided approximately 5% higher power than the existing GEI tests INT-FIX and INT-RAN; however, they had slightly higher power than the existing GEI test TOW-GE. For large numbers of causal variants, although CCGEI-F and CCGEI-R exhibited comparable or slightly lower power values than the competing tests, the results were still satisfactory. Among all simulation conditions evaluated, CCGEI-O provided significantly higher power than that of competing GEI tests. We further applied our GEI tests in genome-wide analyses of systolic blood pressure or diastolic blood pressure to detect gene-body mass index (BMI) interactions, using whole-exome sequencing data from UK Biobank. At a suggestive significance level of 1.0 × 10-4, KCNC4, GAR1, FAM120AOS and NT5C3B showed interactions with BMI by our GEI tests.

Polygenic modelling and machine learning approaches in pharmacogenomics: Importance in downstream analysis of genome-wide association study data

Genome-wide association studies (GWAS) have identified genetic variations associated with adverse drug effects in pharmacogenomics (PGx) research. However, interpreting the biological implications of these associations remains a challenge. This review highlights 2 promising post-GWAS methods for PGx. First, we discuss the polygenic architecture of the PGx traits, especially for drug-induced liver injury. Experimental modelling using multiple donors' human primary hepatocytes and human liver organoids demonstrated the polygenic architecture of drug-induced liver injury susceptibility and found biological vulnerability in genetically high-risk tissue donors. Second, we discuss the challenges of interpreting the roles of variants in noncoding regions. Beyond methods involving expression quantitative trait locus analysis and massively parallel reporter assays, we suggest the use of in silico mutagenesis through machine learning methods to understand the roles of variants in transcriptional regulation. This review underscores the importance of these post-GWAS methods in providing critical insights into PGx, potentially facilitating drug development and personalized treatment.

Whole genome sequencing identifies genetic variants associated with neurogenic inflammation in rosacea

Rosacea is a chronic inflammatory skin disorder with high incidence rate. Although genetic predisposition to rosacea is suggested by existing evidence, the genetic basis remains largely unknown. Here we present the integrated results of whole genome sequencing (WGS) in 3 large rosacea families and whole exome sequencing (WES) in 49 additional validation families. We identify single rare deleterious variants of LRRC4, SH3PXD2A and SLC26A8 in large families, respectively. The relevance of SH3PXD2A, SLC26A8 and LRR family genes in rosacea predisposition is underscored by presence of additional variants in independent families. Gene ontology analysis suggests that these genes encode proteins taking part in neural synaptic processes and cell adhesion. In vitro functional analysis shows that mutations in LRRC4, SH3PXD2A and SLC26A8 induce the production of vasoactive neuropeptides in human neural cells. In a mouse model recapitulating a recurrent Lrrc4 mutation from human patients, we find rosacea-like skin inflammation, underpinned by excessive vasoactive intestinal peptide (VIP) release by peripheral neurons. These findings strongly support familial inheritance and neurogenic inflammation in rosacea development and provide mechanistic insight into the etiopathogenesis of the condition.

An exome-wide study of renal operational tolerance

Background

Renal operational tolerance is a rare and beneficial state of prolonged renal allograft function in the absence of immunosuppression. The underlying mechanisms are unknown. We hypothesized that tolerance might be driven by inherited protein coding genetic variants with large effect, at least in some patients.

Methods

We set up a European survey of over 218,000 renal transplant recipients and collected DNAs from 40 transplant recipients who maintained good allograft function without immunosuppression for at least 1 year. We performed an exome-wide association study comparing the distribution of moderate to high impact variants in 36 tolerant patients, selected for genetic homogeneity using principal component analysis, and 192 controls, using an optimal sequence-kernel association test adjusted for small samples.

Results

We identified rare variants of HOMER2 (3/36, FDR 0.0387), IQCH (5/36, FDR 0.0362), and LCN2 (3/36, FDR 0.102) in 10 tolerant patients vs. 0 controls. One patient carried a variant in both HOMER2 and LCN2. Furthermore, the three genes showed an identical variant in two patients each. The three genes are expressed at the primary cilium, a key structure in immune responses.

Conclusion

Rare protein coding variants are associated with operational tolerance in a sizable portion of patients. Our findings have important implications for a better understanding of immune tolerance in transplantation and other fields of medicine.ClinicalTrials.gov, identifier: NCT05124444.

From glucose sensing to exocytosis: takes from maturity onset diabetes of the young

Monogenic diabetes gave us simplified models of complex molecular processes occurring within β-cells, which allowed to explore the roles of numerous proteins from single protein perspective. Constellation of characteristic phenotypic features and wide application of genetic sequencing techniques to clinical practice, made the major form of monogenic diabetes - the Maturity Onset Diabetes of the Young to be distinguishable from type 1, type 2 as well as neonatal diabetes mellitus and understanding underlying molecular events for each type of MODY contributed to the advancements of antidiabetic therapy and stem cell research tremendously. The functional analysis of MODY-causing proteins in diabetes development, not only provided better care for patients suffering from diabetes, but also enriched our comprehension regarding the universal cellular processes including transcriptional and translational regulation, behavior of ion channels and transporters, cargo trafficking, exocytosis. In this review, we will overview structure and function of MODY-causing proteins, alterations in a particular protein arising from the deleterious mutations to the corresponding gene and their consequences, and translation of this knowledge into new treatment strategies.

Whole Genome Analysis of Venous Thromboembolism: the Trans-Omics for Precision Medicine Program

BACKGROUND

Risk for venous thromboembolism has a strong genetic component. Whole genome sequencing from the TOPMed program (Trans-Omics for Precision Medicine) allowed us to look for new associations, particularly rare variants missed by standard genome-wide association studies.

METHODS

The 3793 cases and 7834 controls (11.6% of cases were individuals of African, Hispanic/Latino, or Asian ancestry) were analyzed using a single variant approach and an aggregate gene-based approach using our primary filter (included only loss-of-function and missense variants predicted to be deleterious) and our secondary filter (included all missense variants).

RESULTS

Single variant analyses identified associations at 5 known loci. Aggregate gene-based analyses identified only PROC (odds ratio, 6.2 for carriers of rare variants; P=7.4×10-14) when using our primary filter. Employing our secondary variant filter led to a smaller effect size at PROC (odds ratio, 3.8; P=1.6×10-14), while excluding variants found only in rare isoforms led to a larger one (odds ratio, 7.5). Different filtering strategies improved the signal for 2 other known genes: PROS1 became significant (minimum P=1.8×10-6 with the secondary filter), while SERPINC1 did not (minimum P=4.4×10-5 with minor allele frequency <0.0005). Results were largely the same when restricting the analyses to include only unprovoked cases; however, one novel gene, MS4A1, became significant (P=4.4×10-7 using all missense variants with minor allele frequency <0.0005).

CONCLUSIONS

Here, we have demonstrated the importance of using multiple variant filtering strategies, as we detected additional genes when filtering variants based on their predicted deleteriousness, frequency, and presence on the most expressed isoforms. Our primary analyses did not identify new candidate loci; thus larger follow-up studies are needed to replicate the novel MS4A1 locus and to identify additional rare variation associated with venous thromboembolism.

The contribution of copy number variants to psychiatric symptoms and cognitive ability

Copy number variants (CNVs) are deletions and duplications of DNA sequence. The most frequently studied CNVs, which are described in this review, are recurrent CNVs that occur in the same locations on the genome. These CNVs have been strongly implicated in neurodevelopmental disorders, namely autism spectrum disorder (ASD), intellectual disability (ID), and developmental delay (DD), but also in schizophrenia. More recent work has also shown that CNVs increase risk for other psychiatric disorders, namely, depression, bipolar disorder, and post-traumatic stress disorder. Many of the same CNVs are implicated across all of these disorders, and these neuropsychiatric CNVs are also associated with cognitive ability in the general population, as well as with structural and functional brain alterations. Neuropsychiatric CNVs also show incomplete penetrance, such that carriers do not always develop any psychiatric disorder, and may show only mild symptoms, if any. Variable expressivity, whereby the same CNVs are associated with many different phenotypes of varied severity, also points to highly complex mechanisms underlying disease risk in CNV carriers. Comprehensive and longitudinal phenotyping studies of individual CNVs have provided initial insights into these mechanisms. However, more work is needed to estimate and predict the effect of non-recurrent, ultra-rare CNVs, which also contribute to psychiatric and cognitive outcomes. Moreover, delineating the broader phenotypic landscape of neuropsychiatric CNVs in both clinical and general population cohorts may also offer important mechanistic insights.

Risk scoring based on DNA methylation-driven related DEGs for colorectal cancer prognosis with systematic insights

Colorectal cancer is a common malignant tumor of the digestive tract. Despite advances in diagnostic techniques and medications. Its prognosis remains challenging. DNA methylation-driven related circulating tumor cells have attracted enormous interest in diagnosing owing to their non-invasive nature and early recognition properties. However, the mechanism through which risk biomarkers act remains elusive. Here, we designed a risk model based on differentially expressed genes, DNA methylation, robust, and survival-related factors in the framework of Cox regression. The model has satisfactory performance and is independently verified by an external and isolated dataset in terms of C-index value, ROC, and tROC. The model was applied to Colorectal cancer patients who were subsequently divided into high- and low-risk groups. Functional annotations, genomic alterations, tumor immune environment, and drug sensitivity were analyzed. We observed that up-regulated genes are associated with epithelial cell differentiation and MAPK signaling pathways. The down-regulated genes are related to IL-7 signaling and apoptosis-induced DNA fragmentation. Interestingly, the immune system was inhibited in high-risk groups. High-frequency mutation genes tend to co-occur. High-risk score patients are related to copy number amplification events. To address the challenges, we suggested eleven and twenty-one drugs that are sensitive to low- and high-risk patients. Finally, an artificial neural network was provided to evaluate the immunotherapeutic efficiency. Taken together, the findings demonstrated that our risk score model is robust and reliable for evaluating the prognosis with novel diagnostic and treatment targets. It also yields benefits for the treatment and provides unique insights into developing therapeutic strategies.

The role of next-generation sequencing in the examination of signaling genes in Brca1/2-negative breast cancer cases

INTRODUCTION

Breast cancer is the most prevalent malignancy in women worldwide. Although pathogenic variants in the BRCA1/2 genes are responsible for the majority of hereditary breast cancer cases, a substantial proportion of patients are negative for pathogenic variations in these genes. In cancers, the signal transduction pathways of the cell are usually affected first. Therefore, this study aimed to detect and classified genetic variations in non-BRCA signaling genes and investigate the underlying genetic causes of susceptibility to breast cancer.

METHODS

Ninety-six patients without pathogenic variants in the BRCA1/2 genes who met the inclusion criteria were enrolled in the study, and 34 genes were analyzed using next-generation sequencing (NGS) for genetic analysis.

RESULTS

Based on the ClinVar database or American College of Medical Genetics criteria, a total of 55 variants of 16 genes were detected in 43 (44.8%) of the 96 patients included in the study. The pathogenic variants were found in the TP53, CHEK2, and RET genes, whereas the likely pathogenic variants were found in the FGFR1, FGFR3, EGFR, and NOTCH1 genes.

CONCLUSION

The examination of signaling genes in patients who met the established criteria for hereditary breast cancer but were negative for BRCA1/2 pathogenic variants provided additional information for approximately 8% of the families. The results of the present study suggest that NGS is a powerful tool for investigating the underlying genetic causes of occurrence and progression of breast cancer.

Association of Single-Nucleotide Polymorphisms Rs2779249 (chr17:26128581 C>A) and Rs rs2297518 (chr17: chr17:27769571 G>A) of the NOS2 Gene with Tension-Type Headache and Arterial Hypertension Overlap Syndrome in Eastern Siberia

Inducible nitric oxide (NO) synthase (iNOS), encoded by the NOS2 gene, promotes the generation of high levels of NO to combat harmful environmental influences in a wide range of cells. iNOS can cause adverse effects, such as falling blood pressure, if overexpressed. Thus, according to some data, this enzyme is an important precursor of arterial hypertension (AH) and tension-type headache (TTH), which are the most common multifactorial diseases in adults. The purpose of this study was to investigate the association of rs2779249 (chr17:26128581 C>A) and rs2297518 (chr17: chr17:27769571 G>A) of the NOS2 gene with TTH and AH overlap syndrome (OS) in Caucasians in Eastern Siberia. The sample size was 91 participants: the first group-30 patients with OS; the second group-30 patients AH; and the third group-31 healthy volunteers. RT-PCR was used for the determination of alleles and genotypes of the SNPs rs2779249 and rs2297518 of the NOS2 gene in all groups of participants. We showed that the frequency of allele A was significantly higher among patients with AH compared with healthy volunteers (p-value < 0.05). The frequency of the heterozygous genotype CA of rs2779249 was higher in the first group vs. the control (p-value = 0.03), and in the second group vs. the control (p-value = 0.045). The frequency of the heterozygous genotype GA of rs2297518 was higher in the first group vs. the control (p-value = 0.035), and in the second group vs. the control (p-value = 0.001). The allele A of rs2779249 was associated with OS (OR = 3.17 [95% CI: 1.31-7.67], p-value = 0.009) and AH (OR = 2.94 [95% CI: 1.21-7.15], p-value = 0.015) risks compared with the control. The minor allele A of rs2297518 was associated with OS (OR = 4.0 [95% CI: 0.96-16.61], p-value = 0.035) and AH (OR = 8.17 [95% CI: 2.03-32.79], p-value = 0.001) risks compared with the control. Therefore, our pilot study demonstrated that the SNPs rs2779249 and rs229718 of the NOS2 gene could be promising genetic biomarkers for this OS risk in Caucasians from Eastern Siberia.

Common and rare variant associations with latent traits underlying depression, bipolar disorder, and schizophrenia

Genetic studies in psychiatry have primarily focused on the effects of common genetic variants, but few have investigated the role of rare genetic variants, particularly for major depression. In order to explore the role of rare variants in the gap between estimates of single nucleotide polymorphism (SNP) heritability and twin study heritability, we examined the contribution of common and rare genetic variants to latent traits underlying psychiatric disorders using high-quality imputed genotype data from the UK Biobank. Using a pre-registered analysis, we used items from the UK Biobank Mental Health Questionnaire relevant to three psychiatric disorders: major depression (N = 134,463), bipolar disorder (N = 117,376) and schizophrenia (N = 130,013) and identified a general hierarchical factor for each that described participants' responses. We calculated participants' scores on these latent traits and conducted single-variant genetic association testing (MAF > 0.05%), gene-based burden testing and pathway association testing associations with these latent traits. We tested for enrichment of rare variants (MAF 0.05-1%) in genes that had been previously identified by common variant genome-wide association studies, and genes previously associated with Mendelian disorders having relevant symptoms. We found moderate genetic correlations between the latent traits in our study and case-control phenotypes in previous genome-wide association studies, and identified one common genetic variant (rs72657988, minor allele frequency = 8.23%, p = 1.01 × 10^-9) associated with the general factor of schizophrenia, but no other single variants, genes or pathways passed significance thresholds in this analysis, and we did not find enrichment in previously identified genes.

Understanding Insulin in the Age of Precision Medicine and Big Data: Under-Explored Nature of Genomics

Insulin is amongst the human genome's most well-studied genes/proteins due to its connection to metabolic health. Within this article, we review literature and data to build a knowledge base of Insulin (INS) genetics that influence transcription, transcript processing, translation, hormone maturation, secretion, receptor binding, and metabolism while highlighting the future needs of insulin research. The INS gene region has 2076 unique variants from population genetics. Several variants are found near the transcriptional start site, enhancers, and following the INS transcripts that might influence the readthrough fusion transcript INS-IGF2. This INS-IGF2 transcript splice site was confirmed within hundreds of pancreatic RNAseq samples, lacks drift based on human genome sequencing, and has possible elevated expression due to viral regulation within the liver. Moreover, a rare, poorly characterized African population-enriched variant of INS-IGF2 results in a loss of the stop codon. INS transcript UTR variants rs689 and rs3842753, associated with type 1 diabetes, are found in many pancreatic RNAseq datasets with an elevation of the 3'UTR alternatively spliced INS transcript. Finally, by combining literature, evolutionary profiling, and structural biology, we map rare missense variants that influence preproinsulin translation, proinsulin processing, dimer/hexamer secretory storage, receptor activation, and C-peptide detection for quasi-insulin blood measurements.

Mapping the common gene networks that underlie related diseases

A longstanding goal of biomedicine is to understand how alterations in molecular and cellular networks give rise to the spectrum of human diseases. For diseases with shared etiology, understanding the common causes allows for improved diagnosis of each disease, development of new therapies and more comprehensive identification of disease genes. Accordingly, this protocol describes how to evaluate the extent to which two diseases, each characterized by a set of mapped genes, are colocalized in a reference gene interaction network. This procedure uses network propagation to measure the network 'distance' between gene sets. For colocalized diseases, the network can be further analyzed to extract common gene communities at progressive granularities. In particular, we show how to: (1) obtain input gene sets and a reference gene interaction network; (2) identify common subnetworks of genes that encompass or are in close proximity to all gene sets; (3) use multiscale community detection to identify systems and pathways represented by each common subnetwork to generate a network colocalized systems map; (4) validate identified genes and systems using a mouse variant database; and (5) visualize and further investigate select genes, interactions and systems for relevance to phenotype(s) of interest. We demonstrate the utility of this approach by identifying shared biological mechanisms underlying autism and congenital heart disease. However, this protocol is general and can be applied to any gene sets attributed to diseases or other phenotypes with suspected joint association. A typical NetColoc run takes less than an hour. Software and documentation are available at https://github.com/ucsd-ccbb/NetColoc .

Identification of potentially pathogenic variants for autism spectrum disorders using gene-burden analysis

Gene- burden analyses have lately become a very successful way for the identification of genes carrying risk variants underlying the analysed disease. This approach is also suitable for complex disorders like autism spectrum disorder (ASD). The gene-burden analysis using Testing Rare Variants with Public Data (TRAPD) software was conducted on whole exome sequencing data of Slovenian patients with ASD to determine potentially novel disease risk variants in known ASD-associated genes as well as in others. To choose the right control group for testing, principal component analysis based on the 1000 Genomes and ASD cohort samples was conducted. The subsequent protein structure and ligand binding analysis usingI-TASSER package were performed to detect changes in protein structure and ligand binding to determine a potential pathogenic consequence of observed mutation. The obtained results demonstrate an association of two variants-p.Glu198Lys (PPP2R5D:c.592G>A) and p.Arg253Gln (PPP2R5D:c.758G>A) with the ASD. Substitution p.Glu198Lys (PPP2R5D:c.592G>A) is a variant, previously described as pathogenic in association with ASD combined with intellectual disability, whereas p.Arg253Gln (PPP2R5D:c.758G>A) has not been described as an ASD-associated pathogenic variant yet. The results indicate that the filtering process was suitable and could be used in the future for detection of novel pathogenic variants when analysing groups of ASD patients.

Methodological quality assessment of genetic studies on brain arteriovenous malformation related hemorrhage: A cross-sectional study

Objectives: Rupture of a brain arteriovenous malformation (bAVM) can cause intracranial hemorrhage and severe clinical outcomes. At present, the mechanisms of bAVM-related hemorrhage are poorly understood. This study aimed to summarize the potential genetic risk factors for bAVM-related hemorrhage and appraise the methodological quality of existing genetic studies on bAVM-related hemorrhage using a cross-sectional design. Methods: A systematic literature search was conducted on genetic studies associated with bAVM-related hemorrhage published in PubMed, Embase, Web of Science, China National Knowledge Internet, and Wangfang databases, up to November 2022. Subsequently, a cross-sectional study was performed to describe the potential candidate genetic variants of bAVM associated with risk of hemorrhage and to evaluate the methodological quality of the identified studies using the Newcastle-Ottawa quality assessment scale and Q-genie tool. Results: Of the 1811 records identified in the initial search, nine studies met the filtering criteria and were included. Twelve single nucleotide polymorphisms (SNPs), including IL6 rs1800795, IL17A rs2275913, MMP9 rs9509, VEGFA rs1547651, and EPHB4 rs314353, rs314308, and rs314313, were associated with bAVM-related hemorrhage. However, only 12.5% of the evaluated SNPs showed statistical power> 0.80 (α = 0.05). Methodological quality assessment revealed significant flaws in the designs of the included studies, such as less reliable representativeness of recruited individuals, short follow-up periods in cohort studies, and less comparability between groups of hemorrhagic and non-hemorrhagic patients. Conclusion: IL1B, IL6, IL17A, APOE, MMP9, VEGFA and EPHB4 were potentially associated with bAVM-related hemorrhage. The methodological designs of the analyzed studies required improvement in order to obtain more reliable results. Regional alliances and rare disease banks need to be established to recruit large numbers of bAVM patients (especially familial and extreme-trait cases) in a multicenter, prospective cohort study with an adequate follow-up period. Furthermore, it is important to use advanced sequencing techniques and efficient measures to filter candidate genetic variants.

Noninvasive prenatal diagnosis targeting fetal nucleated red blood cells

Noninvasive prenatal diagnosis (NIPD) aims to detect fetal-related genetic disorders before birth by detecting markers in the peripheral blood of pregnant women, holding the potential in reducing the risk of fetal birth defects. Fetal-nucleated red blood cells (fNRBCs) can be used as biomarkers for NIPD, given their remarkable nature of carrying the entire genetic information of the fetus. Here, we review recent advances in NIPD technologies based on the isolation and analysis of fNRBCs. Conventional cell separation methods rely primarily on physical properties and surface antigens of fNRBCs, such as density gradient centrifugation, fluorescence-activated cell sorting, and magnetic-activated cell sorting. Due to the limitations of sensitivity and purity in Conventional methods, separation techniques based on micro-/nanomaterials have been developed as novel methods for isolating and enriching fNRBCs. We also discuss emerging methods based on microfluidic chips and nanostructured substrates for static and dynamic isolation of fNRBCs. Additionally, we introduce the identification techniques of fNRBCs and address the potential clinical diagnostic values of fNRBCs. Finally, we highlight the challenges and the future directions of fNRBCs as treatment guidelines in NIPD.

Genetic heterogeneity: Challenges, impacts, and methods through an associative lens

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

Phenotypic continuum of NFU1-related disorders

Bi-allelic variants in Iron-Sulfur Cluster Scaffold (NFU1) have previously been associated with multiple mitochondrial dysfunctions syndrome 1 (MMDS1) characterized by early-onset rapidly fatal leukoencephalopathy. We report 19 affected individuals from 10 independent families with ultra-rare bi-allelic NFU1 missense variants associated with a spectrum of early-onset pure to complex hereditary spastic paraplegia (HSP) phenotype with a longer survival (16/19) on one end and neurodevelopmental delay with severe hypotonia (3/19) on the other. Reversible or irreversible neurological decompensation after a febrile illness was common in the cohort, and there were invariable white matter abnormalities on neuroimaging. The study suggests that MMDS1 and HSP could be the two ends of the NFU1-related phenotypic continuum.

Scalable Functional Assays for the Interpretation of Human Genetic Variation

Scalable sequence-function studies have enabled the systematic analysis and cataloging of hundreds of thousands of coding and noncoding genetic variants in the human genome. This has improved clinical variant interpretation and provided insights into the molecular, biophysical, and cellular effects of genetic variants at an astonishing scale and resolution across the spectrum of allele frequencies. In this review, we explore current applications and prospects for the field and outline the principles underlying scalable functional assay design, with a focus on the study of single-nucleotide coding and noncoding variants.

Whole-Exome Sequencing identified Olfactory Receptor genes as a key contributor to extreme obesity with progression to nonalcoholic steatohepatitis in Mexican patients: Olfactory receptor genes in obese NASH patients

INTRODUCTION AND OBJECTIVES

Obesity is a global health problem that triggers fat liver accumulation. The prevalence of obesity and the risk of non-alcoholic steatohepatitis (NASH) among young obese Mexican is high. Furthermore, genetic predisposition is a key factor in weight gain and disrupts metabolism. Herein, we used Whole-Exome Sequencing to identify potential causal variants and the biological processes that lead to obesity with progression to NASH among Mexican patients.

MATERIALS AND METHODS

Whole-Exome Sequencing was performed in nine obese patients with NASH diagnosis with a BMI ≥30 kg/m² and one control (BMI=24.2 kg/m²) by using the Ion S5^TM platform. Genetic variants were determined by Ion Reporter software. Enriched GO biological set genes were identified by the WebGestalt tool. Genetic variants within ≥2 obese NASH patients and having scores of SIFT 0.0-0.05 and Polyphen 0.85-1.0 were categorized as pathogenic.

RESULTS

A total of 1359 variants with a probable pathogenic effect were determined in obese patients with NASH diagnosis. After several filtering steps, the most frequent pathogenic variants found were rs25640-HSD17B4, rs8105737-OR1I1, rs998544-OR5R1, and rs4916685, rs10037067, and rs2366926 in ADGRV1. Notably, the primary biological processes affected by these pathogenic variants were the sensory perception and detection of chemical stimulus pathways in which the olfactory receptor gene family was the most enriched.

CONCLUSIONS

Variants in the olfactory receptor genes were highly enriched in Mexican obese patients that progress to NASH and could be potential targets of association studies.

A combined polygenic score of 21,293 rare and 22 common variants improves diabetes diagnosis based on hemoglobin A1C levels

Polygenic scores (PGSs) combine the effects of common genetic variants1,2 to predict risk or treatment strategies for complex diseases3-7. Adding rare variation to PGSs has largely unknown benefits and is methodically challenging. Here, we developed a method for constructing rare variant PGSs and applied it to calculate genetically modified hemoglobin A1C thresholds for type 2 diabetes (T2D) diagnosis7-10. The resultant rare variant PGS is highly polygenic (21,293 variants across 154 genes), depends on ultra-rare variants (72.7% observed in fewer than three people) and identifies significantly more undiagnosed T2D cases than expected by chance (odds ratio = 2.71; P = 1.51 × 10-6). A PGS combining common and rare variants is expected to identify 4.9 million misdiagnosed T2D cases in the United States-nearly 1.5-fold more than the common variant PGS alone. These results provide a method for constructing complex trait PGSs from rare variants and suggest that rare variants will augment common variants in precision medicine approaches for common disease.

High Throughput Sequencing to Identify Monogenic Etiologies in a Preselected Polycystic Ovary Syndrome Cohort

Context

Polycystic ovary syndrome (PCOS) etiology remains to be elucidated, but familial clustering and twin studies have shown a strong heritable component.

Objective

The purpose of this study was to identify rare genetic variants that are associated with the etiology of PCOS in a preselected cohort.

Methods

This prospective study was conducted among a selected group of women with PCOS. The study’s inclusion criteria were patients with PCOS diagnosed by the Rotterdam criteria with the following phenotypes: severe insulin resistance (IR), normoandrogenic–normometabolic phenotype, adrenal hyperandrogenism, primary amenorrhea, and familial PCOS. Forty-five patients were studied by target sequencing, while 8 familial cases were studied by whole exome sequencing.

Results

Patients were grouped according to the inclusion criteria with the following distribution: 22 (41.5%) with severe IR, 13 (24.5%) with adrenal hyperandrogenism, 7 (13.2%) with normoandrogenic phenotype, 3 (5.7%) with primary amenorrhea, and 8 (15.1%) familial cases. DNA sequencing analysis identified 1 pathogenic variant in LMNA, 3 likely pathogenic variants in INSR, PIK3R1, and DLK1, and 6 variants of uncertain significance level with interesting biologic rationale in 5 genes (LMNA, GATA4, NR5A1, BMP15, and FSHR). LMNA was the most prevalent affected gene in this cohort (3 variants).

Conclusion

Several rare variants in genes related to IR were identified in women with PCOS. Although IR is a common feature of PCOS, patients with extreme or atypical phenotype should be carefully evaluated to rule out monogenic conditions.

Functional Allele Validation by Gene Editing to Leverage the Wealth of Genetic Resources for Crop Improvement

Advances in molecular technologies over the past few decades, such as high-throughput DNA marker genotyping, have provided more powerful plant breeding approaches, including marker-assisted selection and genomic selection. At the same time, massive investments in plant genetics and genomics, led by whole genome sequencing, have led to greater knowledge of genes and genetic pathways across plant genomes. However, there remains a gap between approaches focused on forward genetics, which start with a phenotype to map a mutant locus or QTL with the goal of cloning the causal gene, and approaches using reverse genetics, which start with large-scale sequence data and work back to the gene function. The recent establishment of efficient CRISPR-Cas-based gene editing promises to bridge this gap and provide a rapid method to functionally validate genes and alleles identified through studies of natural variation. CRISPR-Cas techniques can be used to knock out single or multiple genes, precisely modify genes through base and prime editing, and replace alleles. Moreover, technologies such as protoplast isolation, in planta transformation, and the use of developmental regulatory genes promise to enable high-throughput gene editing to accelerate crop improvement.

Genomic medicine on the frontier of precision medicine

Genomic medicine has created a great deal of hope since the completion of the Human Genome Project (HGP). Genomic medicine promises disease prevention and early diagnosis in the context of precision medicine. Precision medicine as a scientific discipline has introduced as an evolution in medicine. The rapid growth of high-development technologies permits the assessment of biological systems. Study of the integrated profiles of omics, such as genome, transcriptome, proteome and other omics information lead to significant advances in personalized and precision medicine. In the context of precision medicine, pharmacogenomics can play an important role in order to discriminate responders and non-responders to medications and avoiding toxicity and achieving the optimum dose. So precision medicine in accordance with genomic medicine will transform medicine from conventional evidence-based medicine in the diagnosis and treatment towards precision based-medicine. In this review, we have summarized the related issues for genomic medicine and precision medicine.

Evaluation of the correctable decoding sequencing as a new powerful strategy for DNA sequencing

This article proposed the correctable decoding sequencing technology with conservative theoretical error rate of 0.0009%, and evaluated its robustness by simulation. This technology can provide a powerful new protocol for NGS platforms, enabling accurate identification of rare mutations in medicine.

Next-generation sequencing (NGS) promises to revolutionize precision medicine, but the existing sequencing technologies are limited in accuracy. To overcome this limitation, we propose the correctable decoding sequencing strategy, which is a duplex sequencing protocol with conservative theoretical error rates of 0.0009%. This rate is lower than that for Sanger sequencing. Here, we simulate the sequencing reactions by the self-developed software, and find that this approach has great potential in NGS in terms of sequence decoding, reassembly, error correction, and sequencing accuracy. Besides, this approach can be compatible with most SBS-based sequencing platforms, and also has the ability to compensate for some of the shortcomings of NGS platforms, thereby broadening its application for researchers. Hopefully, it can provide a powerful new protocol that can be used as an alternative to the existing NGS platforms, enabling accurate identification of rare mutations in a variety of applications in biology and medicine.

High-Selectivity Single-Nucleotide Variant Capture Technology Based on the DNA Reaction Network

The extremely low abundance of circulating tumor DNA in blood samples has limited the development of liquid biopsy techniques for the early diagnosis of major diseases. In this study, we demonstrate a DRN-based screening technique, SCREEN, which achieves the specific capture and enrichment of low abundance SNV nucleic acid samples without selective amplification. The SCREEN technique achieved a 108-fold increase in the abundance of single-nucleotide variant (SNV) nucleic acids from highly homologous mixtures (from 0.01% to 1.08%) and has been shown to significantly increase the abundance of SNV nucleic acids from 0.1% to 51% further through two rounds of capture. As a highly effective pre-enrichment technique, SCREEN has demonstrated the ability to enhance NGS in detecting an ultralow abundance SNV nucleic acid powerfully and has high compatibility with existing molecular diagnostic methods.

Computational and Experimental Analysis of Genetic Variants

Genomics has grown exponentially over the last decade. Common variants are associated with physiological changes through statistical strategies such as Genome-Wide Association Studies (GWAS) and quantitative trail loci (QTL). Rare variants are associated with diseases through extensive filtering tools, including population genomics and trio-based sequencing (parents and probands). However, the genomic associations require follow-up analyses to narrow causal variants, identify genes that are influenced, and to determine the physiological changes. Large quantities of data exist that can be used to connect variants to gene changes, cell types, protein pathways, clinical phenotypes, and animal models that establish physiological genomics. This data combined with bioinformatics including evolutionary analysis, structural insights, and gene regulation can yield testable hypotheses for mechanisms of genomic variants. Molecular biology, biochemistry, cell culture, CRISPR editing, and animal models can test the hypotheses to give molecular variant mechanisms. Variant characterizations can be a significant component of educating future professionals at the undergraduate, graduate, or medical training programs through teaching the basic concepts and terminology of genetics while learning independent research hypothesis design. This article goes through the computational and experimental analysis strategies of variant characterization and provides examples of these tools applied in publications. © 2022 American Physiological Society. Compr Physiol 12:3303-3336, 2022.

Transcriptome characterization of Larrea tridentata and identification of genes associated with phenylpropanoid metabolic pathways

Larrea tridentata (Sesse and Moc. ex DC.) Coville (family: Zygophyllaceae) is an aromatic evergreen shrub with resin-covered leaves, known to use in traditional medicine for diverse ailments. It also has immense pharmacological significance due to presence of powerful phenylpropanoids antioxidant, nordihydroguaiaretic acid (NDGA). The RNA sequence/transcriptome analyses connect the genomic information into the discovery of gene function. Hence, the acquaint analysis of L. tridentata is in lieu to characterize the transcriptome, and to identify the candidate genes involved in the phenylpropanoid biosynthetic pathway. To gain molecular insight, the bioinformatics analysis of transcriptome was performed. The total bases covered 48,630 contigs of length greater than 200 bp and above came out to 21,590,549 with an average GC content of 45% and an abundance of mononucleotide, SSR, including C3H, FAR1, and MADS transcription gene families. The best enzyme commission (EC) classification obtained from the assembled sequences represented major abundant enzyme classes e.g., RING-type E3 ubiquitin transferase and non-specific serine/threonine protein kinase. The KEGG pathway analysis mapped into 377 KEGG different metabolic pathways. The enrichment of phenylpropanoid biosynthesis pathways (22 genes i.e., phenylalanine ammonia-lyase, trans-cinnamate 4-monooxygenase, 4-coumarate-CoA ligase, cinnamoyl-CoA reductase, beta-glucosidase, shikimate O-hydroxycinnamoyl transferase, 5-O-(4-coumaroyl)-D-quinate 3'-monooxygenase, cinnamyl-alcohol dehydrogenase, peroxidase, coniferyl-alcohol glucosyltransferase, caffeoyl shikimate esterase, caffeoyl-CoA O-methyltransferase, caffeate O-methyltransferase, coniferyl-aldehyde dehydrogenase, feruloyl-CoA 6-hydroxylase, and ferulate-5-hydroxylase), and expression profile indicated antioxidant, anti-arthritic, and anticancer properties of L. tridentata. The present results could provide an important resource for squeezing biotechnological applications of L. tridentata.

Assessing accuracy of genotype imputation in the Afrikaner and Brahman cattle breeds of South Africa

Imputation may be used to rescue genomic data from animals that would otherwise be eliminated due to a lower than desired call rate. The aim of this study was to compare the accuracy of genotype imputation for Afrikaner, Brahman, and Brangus cattle of South Africa using within- and multiple-breed reference populations. A total of 373, 309, and 101 Afrikaner, Brahman, and Brangus cattle, respectively, were genotyped using the GeneSeek Genomic Profiler 150 K panel that contained 141,746 markers. Markers with MAF ≤ 0.02 and call rates ≤ 0.95 or that deviated from Hardy Weinberg Equilibrium frequency with a probability of ≤ 0.0001 were excluded from the data as were animals with a call rate ≤ 0.90. The remaining data included 99,086 SNPs and 360 Afrikaner, 75,291 SNPs and 288 animals Brahman, and 97,897 SNPs and 99 Brangus animals. A total of 7986, 7002, and 7000 SNP from 50 Afrikaner and Brahman and 30 Brangus cattle, respectively, were masked and then imputed using BEAGLE v3 and FImpute v2. The within-breed imputation yielded accuracies ranging from 89.9 to 96.6% for the three breeds. The multiple-breed imputation yielded corresponding accuracies from 69.21 to 88.35%. The results showed that population homogeneity and numerical representation for within and across breed strategies, respectively, are crucial components for improving imputation accuracies.

A pharmacogenomic investigation of the cardiac safety profile of ondansetron in children and pregnant women

BACKGROUND

Ondansetron is a highly effective antiemetic for the treatment of nausea and vomiting. However, this medication has also been associated with QT prolongation. Pharmacogenomic information on therapeutic response to ondansetron exists, but no investigation has been performed on genetic factors that influence the cardiac safety of this medication.

METHODS

Three patient groups receiving ondansetron were recruited and followed prospectively (pediatric post-surgical patients n = 101; pediatric oncology patients n = 98; pregnant women n = 62). Electrocardiograms were conducted at baseline, and 5- and 30-min post-ondansetron administration, to determine the effect of ondansetron treatment on QT interval. Pharmacogenomic associations were assessed via analyses of comprehensive CYP2D6 genotyping and genome-wide association study data.

RESULTS

In the entire cohort, 62 patients (24.1%) met the criteria for prolonged QT, with 1.2% of the cohort exhibiting unsafe QT prolongation. The most significant shift from baseline occurred at five minutes post-ondansetron administration (P = 9.8 × 10^-4). CYP2D6 activity score was not associated with prolonged QT. Genome-wide analyses identified novel associations with a missense variant in TLR3 (rs3775291; P = 2.00 × 10^-7) and a variant linked to the expression of SLC36A1 (rs34124313; P = 1.97 × 10^-7).

CONCLUSIONS

This study has provided insight into the genomic basis of ondansetron-induced cardiac changes and has emphasized the importance of genes that have been implicated in serotonin-related traits. These biologically-relevant findings represent the first step towards understanding this adverse event with the overall goal to improve the safety of this commonly used antiemetic medication.

Improving one-step scarless genome editing in Drosophila melanogaster by combining ovoD co-CRISPR selection with sgRNA target site masking

The precise and rapid construction of alleles through CRISPR/Cas9-mediated genome engineering renders Drosophila melanogaster a powerful animal system for molecular structure–function analyses and human disease models. Application of the ovo^D co-selection method offers expedited generation and enrichment of scarlessly edited alleles without the need for linked transformation markers, which specifically in the case of exon editing can impact allele usability. However, we found that knockin procedures by homology-directed repair (HDR) under ovo^D co-selection resulted in low transformation efficiency. This is likely due to repeated rounds of Cas9 cleavage of HDR donor and/or engineered genomic locus DNA, as noted for other CRISPR/Cas9 editing strategies before, impeding the recovery of correctly edited alleles. Here we provide a one-step protocol to improve the generation of scarless alleles by ovo^D-co-selection with single-guide RNA (sgRNA) binding site masking. Using this workflow, we constructed human disease alleles for two Drosophila genes, unc-13/CG2999 and armadillo/CG11579. We show and quantify how a known countermeasure, the insertion of silent point mutations into protospacer adjacent motif (PAM) or sgRNA homology regions, can potently suppress unintended sequence modifications during CRISPR/Cas9 genome editing of D. melanogaster under ovo^D co-selection. This strongly increased the recovery frequency of disease alleles.

SNP and Haplotype Regional Heritability Mapping (SNHap-RHM): Joint Mapping of Common and Rare Variation Affecting Complex Traits

We describe a genome-wide analytical approach, SNP and Haplotype Regional Heritability Mapping (SNHap-RHM), that provides regional estimates of the heritability across locally defined regions in the genome. This approach utilises relationship matrices that are based on sharing of SNP and haplotype alleles at local haplotype blocks delimited by recombination boundaries in the genome. We implemented the approach on simulated data and show that the haplotype-based regional GRMs capture variation that is complementary to that captured by SNP-based regional GRMs, and thus justifying the fitting of the two GRMs jointly in a single analysis (SNHap-RHM). SNHap-RHM captures regions in the genome contributing to the phenotypic variation that existing genome-wide analysis methods may fail to capture. We further demonstrate that there are real benefits to be gained from this approach by applying it to real data from about 20,000 individuals from the Generation Scotland: Scottish Family Health Study. We analysed height and major depressive disorder (MDD). We identified seven genomic regions that are genome-wide significant for height, and three regions significant at a suggestive threshold (p-value < 1 × 10-5) for MDD. These significant regions have genes mapped to within 400 kb of them. The genes mapped for height have been reported to be associated with height in humans. Similarly, those mapped for MDD have been reported to be associated with major depressive disorder and other psychiatry phenotypes. The results show that SNHap-RHM presents an exciting new opportunity to analyse complex traits by allowing the joint mapping of novel genomic regions tagged by either SNPs or haplotypes, potentially leading to the recovery of some of the "missing" heritability.

Computational study of the impact of nucleotide variations on highly conserved proteins: In the case of actin

Sequencing of individual human genomes enables studying relationship among nucleotide variations, amino acid substitutions, effect on protein structures and diseases. Many studies have found general tendencies, for instance, that pathogenic variations tend to be found in the buried regions of the protein structures, that benign variations tend to be found on the surface of the proteins, and that variations on evolutionary conserved residues tend to be pathogenic. These tendencies were deduced from globular proteins with standard evolutionary changes in amino acid sequences. In this study, we investigated the variation distribution on actin, one of the highly conserved proteins. Many nucleotide variations and three-dimensional structures of actin have been registered in databases. By combining those data, we found that variations buried inside the protein were rather benign and variations on the surface of the protein were pathogenic. This idiosyncratic distribution of the variation impact is likely ascribed to the extensive use of the surface of the protein for protein-protein interactions in actin.

An assessment of prevalence of Type 1 CFI rare variants in European AMD, and why lack of broader genetic data hinders development of new treatments and healthcare access

PURPOSE

Advanced age-related macular degeneration (AAMD) risk is associated with rare complement Factor I (FI) genetic variants associated with low FI protein levels (termed 'Type 1'), but it is unclear how variant prevalences differ between AMD patients from different ethnicities.

METHODS

Collective prevalence of Type 1 CFI rare variant genotypes were examined in four European AAMD datasets. Collective minor allele frequencies (MAFs) were sourced from the natural history study SCOPE, the UK Biobank, the International AMD Genomics Consortium (IAMDGC), and the Finnish Biobank Cooperative (FINBB), and compared to paired control MAFs or background population prevalence rates from the Genome Aggregation Database (gnomAD). Due to a lack of available genetic data in non-European AAMD, power calculations were undertaken to estimate the AAMD population sizes required to identify statistically significant association between Type 1 CFI rare variants and disease risk in different ethnicities, using gnomAD populations as controls.

RESULTS

Type 1 CFI rare variants were enriched in all European AAMD cohorts, with odds ratios (ORs) ranging between 3.1 and 7.8, and a greater enrichment was observed in dry AMD from FINBB (OR 8.9, 95% CI 1.49-53.31). The lack of available non-European AAMD datasets prevented us exploring this relationship more globally, however a statistical association may be detectable by future sequencing studies that sample approximately 2,000 AAMD individuals from Ashkenazi Jewish and Latino/Admixed American ethnicities.

CONCLUSIONS

The relationship between Type 1 CFI rare variants increasing odds of AAMD are well established in Europeans, however the lack of broader genetic data in AAMD has adverse implications for clinical development and future commercialisation strategies of targeted FI therapies in AAMD. These findings emphasise the importance of generating more diverse genetic data in AAMD to improve equity of access to new treatments and address the bias in health care.

ezGeno: an automatic model selection package for genomic data analysis

MOTIVATION

To facilitate the process of tailor-making a deep neural network for exploring the dynamics of genomic DNA, we have developed a hands-on package called ezGeno. ezGeno automates the search process of various parameters and network structures and can be applied to any kind of 1D genomic data. Combinations of multiple abovementioned 1D features are also applicable.

RESULTS

For the task of predicting TF binding using genomic sequences as the input, ezGeno can consistently return the best performing set of parameters and network structure, as well as highlight the important segments within the original sequences. For the task of predicting tissue-specific enhancer activity using both sequence and DNase feature data as the input, ezGeno also regularly outperforms the hand-designed models. Furthermore, we demonstrate that ezGeno is superior in efficiency and accuracy compared to the one-layer DeepBind model and AutoKeras, an open-source AutoML package.

AVAILABILITY AND IMPLEMENTATION

The ezGeno package can be freely accessed at https://github.com/ailabstw/ezGeno.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genome sequencing reveals the role of rare genomic variants in Chinese patients with symptomatic intracranial atherosclerotic disease

Objectives

The predisposition of intracranial atherosclerotic disease (ICAD) to East Asians over Caucasians infers a genetic basis which, however, remains largely unknown. Higher prevalence of vascular risk factors (VRFs) in Chinese over Caucasian patients who had a stroke, and shared risk factors of ICAD with other stroke subtypes indicate genes related to VRFs and/or other stroke subtypes may also contribute to ICAD.

Methods

Unrelated symptomatic patients with ICAD were recruited for genome sequencing (GS, 60-fold). Rare and potentially deleterious single-nucleotide variants (SNVs) and small insertions/deletions (InDels) were detected in genome-wide and correlated to genes related to VRFs and/or other stroke subtypes. Rare aneuploidies, copy number variants (CNVs) and chromosomal structural rearrangements were also investigated. Lastly, candidate genes were used for pathway and gene ontology enrichment analysis.

Results

Among 92 patients (mean age at stroke onset 61.0±9.3 years), GS identified likely ICAD-associated rare genomic variants in 54.3% (50/92) of patients. Forty-eight patients (52.2%, 48/92) had 59 rare SNVs/InDels reported or predicted to be deleterious in genes related to VRFs and/or other stroke subtypes. None of the 59 rare variants were identified in local subjects without ICAD (n=126). 31 SNVs/InDels were related to conventional VRFs, and 28 were discovered in genes related to other stroke subtypes. Our study also showed that rare CNVs (n=7) and structural rearrangement (a balanced translocation) were potentially related to ICAD in 8.7% (8/92) of patients. Lastly, candidate genes were significantly enriched in pathways related to lipoprotein metabolism and cellular lipid catabolic process.

Conclusions

Our GS study suggests a role of rare genomic variants with various variant types contributing to the development of ICAD in Chinese patients.

Identification of a Rare Novel KMT2C Mutation That Presents with Schizophrenia in a Multiplex Family

Schizophrenia is a complex genetic disorder involving many common variants with modest effects and rare mutations with high penetrance. Rare mutations associated with schizophrenia are highly heterogeneous and private for affected individuals and families. Identifying such mutations can help establish the molecular diagnosis, elucidate the pathogenesis, and provide helpful genetic counseling for affected patients and families. We performed a whole-exome sequencing analysis to search for rare pathogenic mutations co-segregating with schizophrenia transmitted in a dominant inheritance in a two-generation multiplex family. We identified a rare missense mutation H1574R (Histidine1574Arginine, rs199796552) of KMT2C (lysine methyltransferase 2C) co-segregating with affected members in this family. The mutation is a novel deleterious mutation of KMT2C, not reported before in the literature. The KMT2C encodes a histone 3 lysine 4 (H3K4)-specific methyltransferase and involves epigenetic regulation of brain gene expression. Mutations of KMT2C have been found in neurodevelopmental disorders, such as Kleefstra syndrome, intellectual disability, and autism spectrum disorders. Our finding suggests that schizophrenia might be one of the clinical phenotype spectra of KMT2C mutations, and KMT2C might be a novel risk gene for schizophrenia. Nevertheless, the co-segregation of this mutation with schizophrenia in this family might also be due to chance; functional assays of this mutation are needed to address this issue.

High-impact FN1 mutation decreases chondrogenic potential and affects cartilage deposition via decreased binding to collagen type II

Osteoarthritis is the most prevalent joint disease worldwide, yet progress in development of effective disease-modifying treatments is slow because of lack of insight into the underlying disease pathways. Therefore, we aimed to identify the causal pathogenic mutation in an early-onset osteoarthritis family, followed by functional studies in human induced pluripotent stem cells (hiPSCs) in an in vitro organoid cartilage model. We demonstrated that the identified causal missense mutation in the gelatin-binding domain of the extracellular matrix protein fibronectin resulted in significant decreased binding capacity to collagen type II. Further analyses of formed hiPSC-derived neo-cartilage tissue highlighted that mutated fibronectin affected chondrogenic capacity and propensity to a procatabolic osteoarthritic state. Together, we demonstrate that binding of fibronectin to collagen type II is crucial for fibronectin downstream gene expression of chondrocytes. We advocate that effective treatment development should focus on restoring or maintaining proper binding between fibronectin and collagen type II.

Detecting methylation quantitative trait loci using a methylation random field method

DNA methylation may be regulated by genetic variants within a genomic region, referred to as methylation quantitative trait loci (mQTLs). The changes of methylation levels can further lead to alterations of gene expression, and influence the risk of various complex human diseases. Detecting mQTLs may provide insights into the underlying mechanism of how genotypic variations may influence the disease risk. In this article, we propose a methylation random field (MRF) method to detect mQTLs by testing the association between the methylation level of a CpG site and a set of genetic variants within a genomic region. The proposed MRF has two major advantages over existing approaches. First, it uses a beta distribution to characterize the bimodal and interval properties of the methylation trait at a CpG site. Second, it considers multiple common and rare genetic variants within a genomic region to identify mQTLs. Through simulations, we demonstrated that the MRF had improved power over other existing methods in detecting rare variants of relatively large effect, especially when the sample size is small. We further applied our method to a study of congenital heart defects with 83 cardiac tissue samples and identified two mQTL regions, MRPS10 and PSORS1C1, which were colocalized with expression QTL in cardiac tissue. In conclusion, the proposed MRF is a useful tool to identify novel mQTLs, especially for studies with limited sample sizes.