Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease

A novel frameshift variant in the TMPRSS3 gene causes nonsyndromic hearing loss in a consanguineous family

BACKGROUND

Hearing Loss (HL) is the most common sensorineural condition in humans. Mutations in the TMPRSS3 gene (DNFB8/10 locus) have been linked to autosomal recessive non-syndromic hearing loss (ARNSHL).

METHODS

Whole-exome sequencing (WES) was utilized to identify disease-causing variants in a proband from Iran with ARNSHL who presented clinically with sensorineural, bilateral, and prelingual HL. The pathogenicity and novelty of the identified variant were assessed using various databases. A co-segregation study was also performed to confirm the presence of the variant in the proband's parents. Additionally, the secondary and tertiary structures of the mutant TMPRSS3 protein were predicted using bioinformatics tools. Furthermore, a global mutational spectrum of TMPRSS3 was created and statistically analyzed. The Iranome database was also used to identify other putative mutations in the TMPRSS3 gene in the Iranian population.

RESULTS

We identified a novel homozygous single nucleotide deletion in TMPRSS3 (c.297delA, p.Asp100ThrfsTer52) in the proband. This is the first report of this mutation in a patient with ARNSHL. Sanger sequencing confirmed that this variant co-segregated from the proband's parents. Bioinformatic tools classified this novel variant as likely pathogenic. Additionally, 49.55% of families with TMPRSS3-related HL patients were shown to have consanguinity, consistent with our study. The Iranome database also revealed the c.268G > A variant as a putative novel mutation in TMPRSS3.

CONCLUSION

This research expanded the pool of evidence regarding the association between mutations in the TMPRSS3 gene and ARNSHL. The finding confirmed that a single nucleotide deletion caused HL in the proband, suggesting that genetic testing, such as WES, is a robust technique for diagnosing patients with this condition.

Genetic architecture reconciles linkage and association studies of complex traits

Linkage studies have successfully mapped loci underlying monogenic disorders, but mostly failed when applied to common diseases. Conversely, genome-wide association studies (GWASs) have identified replicable associations between thousands of SNPs and complex traits, yet capture less than half of the total heritability. In the present study we reconcile these two approaches by showing that linkage signals of height and body mass index (BMI) from 119,000 sibling pairs colocalize with GWAS-identified loci. Concordant with polygenicity, we observed the following: a genome-wide inflation of linkage test statistics; that GWAS results predict linkage signals; and that adjusting phenotypes for polygenic scores reduces linkage signals. Finally, we developed a method using recombination rate-stratified, identity-by-descent sharing between siblings to unbiasedly estimate heritability of height (0.76 ± 0.05) and BMI (0.55 ± 0.07). Our results imply that substantial heritability remains unaccounted for by GWAS-identified loci and this residual genetic variation is polygenic and enriched near these loci.

Exploring the influence of the DRD2 gene on mathematical ability: perspectives of gene association and gene-environment interaction

Mathematical ability is influenced by genes and environment. This study focused on the effect of DRD2, a candidate gene for working memory, on mathematical ability. The results in child participants revealed associations between the DRD2 gene and mathematical ability. It was found that individual's mathematical ability was influenced by Single Nucleotide Polymorphisms (SNPs) in DRD2, both in the form of haplotypes and in the way of interaction with parental education. These findings suggest that dopaminergic genes are linked to mathematical ability. This study provides evidence for the genetic basis of mathematical ability and offers guidance for personalized intervention in mathematical education.

Robotic microinjection enables large-scale transgenic studies of Caenorhabditis elegans

The nematode Caenorhabditis elegans is widely employed as a model organism to study basic biological mechanisms. However, transgenic C. elegans are generated by manual injection, which remains low-throughput and labor-intensive, limiting the scope of approaches benefitting from large-scale transgenesis. Here, we report a robotic microinjection system, integrating a microfluidic device capable of reliable worm immobilization, transfer, and rotation, for high-speed injection of C. elegans. The robotic system provides an injection speed 2-3 times faster than that of experts with 7-22 years of experience while maintaining comparable injection quality and only limited trials needed by users to become proficient. We further employ our system in a large-scale reverse genetic screen using multiplexed alternative splicing reporters, and find that the TDP-1 RNA-binding protein regulates alternative splicing of zoo-1 mRNA, which encodes variants of the zonula occludens tight junction proteins. With its high speed, high accuracy, and high efficiency in worm injection, this robotic system shows great potential for high-throughput transgenic studies of C. elegans.

Preliminary identification of somatic mutations profile in ACL injury

Anterior cruciate ligament (ACL) injury is a common orthopedic disease with a high incidence, long recovery time, and often requiring surgical treatment. However, the susceptibility factors for ACL injury are currently unclear, and there is a lack of analysis on the differences in the ligament itself. Previous studies have focused on germline mutations, with less research on somatic mutations. To determine the role of somatic mutations in ACL injuries, we recruited seven patients between the ages of 20 and 39 years diagnosed with ACL injuries, collected their peripheral blood, injured ligament ends, and healthy ligament ends tissues, and performed exome sequencing with gene function enrichment analysis. We detected multiple gene mutations and gene deletions, which were only present in some of the samples. Unfortunately, it was not possible to determine whether these somatic mutations are related to ligament structure or function, or are involved in ACL injury. However, this study provides valuable clues for future in-depth research.

Most Pleiotropic Effects of Gene Knockouts Are Evolutionarily Transient in Yeasts

Pleiotropy, the phenomenon in which a single gene influences multiple traits, is a fundamental concept in genetics. However, the evolutionary mechanisms underlying pleiotropy require further investigation. In this study, we conducted parallel gene knockouts targeting 100 transcription factors in 2 strains of Saccharomyces cerevisiae. We systematically examined and quantified the pleiotropic effects of these knockouts on gene expression levels for each transcription factor. Our results showed that the knockout of a single gene generally affected the expression levels of multiple genes in both strains, indicating various degrees of pleiotropic effects. Strikingly, the pleiotropic effects of the knockouts change rapidly between strains in different genetic backgrounds, and ∼85% of them were nonconserved. Further analysis revealed that the conserved effects tended to be functionally associated with the deleted transcription factors, while the nonconserved effects appeared to be more ad hoc responses. In addition, we measured 184 yeast cell morphological traits in these knockouts and found consistent patterns. In order to investigate the evolutionary processes underlying pleiotropy, we examined the pleiotropic effects of standing genetic variations in a population consisting of ∼1,000 hybrid progenies of the 2 strains. We observed that newly evolved expression quantitative trait loci impacted the expression of a greater number of genes than did old expression quantitative trait loci, suggesting that natural selection is gradually eliminating maladaptive or slightly deleterious pleiotropic responses. Overall, our results show that, although being prevalent for new mutations, the majority of pleiotropic effects observed are evolutionarily transient, which explains how evolution proceeds despite complicated pleiotropic effects.

Genomic landscape of hepatocellular carcinoma in Egyptian patients by whole exome sequencing

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common primary liver cancer. Chronic hepatitis and liver cirrhosis lead to accumulation of genetic alterations driving HCC pathogenesis. This study is designed to explore genomic landscape of HCC in Egyptian patients by whole exome sequencing.

METHODS

Whole exome sequencing using Ion Torrent was done on 13 HCC patients, who underwent surgical intervention (7 patients underwent living donor liver transplantation (LDLT) and 6 patients had surgical resection}.

RESULTS

Mutational signature was mostly S1, S5, S6, and S12 in HCC. Analysis of highly mutated genes in both HCC and Non-HCC revealed the presence of highly mutated genes in HCC (AHNAK2, MUC6, MUC16, TTN, ZNF17, FLG, MUC12, OBSCN, PDE4DIP, MUC5b, and HYDIN). Among the 26 significantly mutated HCC genes-identified across 10 genome sequencing studies-in addition to TCGA, APOB and RP1L1 showed the highest number of mutations in both HCC and Non-HCC tissues. Tier 1, Tier 2 variants in TCGA SMGs in HCC and Non-HCC (TP53, PIK3CA, CDKN2A, and BAP1). Cancer Genome Landscape analysis revealed Tier 1 and Tier 2 variants in HCC (MSH2) and in Non-HCC (KMT2D and ATM). For KEGG analysis, the significantly annotated clusters in HCC were Notch signaling, Wnt signaling, PI3K-AKT pathway, Hippo signaling, Apelin signaling, Hedgehog (Hh) signaling, and MAPK signaling, in addition to ECM-receptor interaction, focal adhesion, and calcium signaling. Tier 1 and Tier 2 variants KIT, KMT2D, NOTCH1, KMT2C, PIK3CA, KIT, SMARCA4, ATM, PTEN, MSH2, and PTCH1 were low frequency variants in both HCC and Non-HCC.

CONCLUSION

Our results are in accordance with previous studies in HCC regarding highly mutated genes, TCGA and specifically enriched pathways in HCC. Analysis for clinical interpretation of variants revealed the presence of Tier 1 and Tier 2 variants that represent potential clinically actionable targets. The use of sequencing techniques to detect structural variants and novel techniques as single cell sequencing together with multiomics transcriptomics, metagenomics will integrate the molecular pathogenesis of HCC in Egyptian patients.

DeepRisk: A deep learning approach for genome-wide assessment of common disease risk

The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant interest. Although widely applied, traditional polygenic risk scoring methods fall short, as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms (SNPs). This presents a limitation, as genetic diseases often arise from complex interactions between multiple SNPs. To address this challenge, we developed DeepRisk, a biological knowledge-driven deep learning method for modeling these complex, nonlinear associations among SNPs, to provide a more effective method for scoring the risk of common diseases with genome-wide genotype data. Evaluations demonstrated that DeepRisk outperforms existing PRS-based methods in identifying individuals at high risk for four common diseases: Alzheimer's disease, inflammatory bowel disease, type 2 diabetes, and breast cancer.

Cross-species modeling of muscular dystrophy in Caenorhabditis elegans using patient-derived extracellular vesicles

Reliable disease models are critical for medicine advancement. Here, we established a versatile human disease model system using patient-derived extracellular vesicles (EVs), which transfer a pathology-inducing cargo from a patient to a recipient naïve model organism. As a proof of principle, we applied EVs from the serum of patients with muscular dystrophy to Caenorhabditis elegans and demonstrated their capability to induce a spectrum of muscle pathologies, including lifespan shortening and robust impairment of muscle organization and function. This demonstrates that patient-derived EVs can deliver disease-relevant pathologies between species and can be exploited for establishing novel and personalized models of human disease. Such models can potentially be used for disease diagnosis, prognosis, analyzing treatment responses, drug screening and identification of the disease-transmitting cargo of patient-derived EVs and their cellular targets. This system complements traditional genetic disease models and enables modeling of multifactorial diseases and of those not yet associated with specific genetic mutations.

Joint Linkage and Association Analysis Using GENEHUNTER-MODSCORE with an Application to Familial Pancreatic Cancer

INTRODUCTION

Joint linkage and association (JLA) analysis combines two disease gene mapping strategies: linkage information contained in families and association information contained in populations. Such a JLA analysis can increase mapping power, especially when the evidence for both linkage and association is low to moderate. Similarly, an association analysis based on haplotypes instead of single markers can increase mapping power when the association pattern is complex.

METHODS

In this paper, we present an extension to the GENEHUNTER-MODSCORE software package that enables a JLA analysis based on haplotypes and uses information from arbitrary pedigree types and unrelated individuals. Our new JLA method is an extension of the MOD score approach for linkage analysis, which allows the estimation of trait-model and linkage disequilibrium (LD) parameters, i.e., penetrance, disease-allele frequency, and haplotype frequencies. LD is modeled between alleles at a single diallelic disease locus and up to three diallelic test markers. Linkage information is contributed by additional multi-allelic flanking markers. We investigated the statistical properties of our JLA implementation using extensive simulations, and we compared our approach to another commonly used single-marker JLA test. To demonstrate the applicability of our new method in practice, we analyzed pedigree data from the German National Case Collection for Familial Pancreatic Cancer (FaPaCa).

RESULTS

Based on the simulated data, we demonstrated the validity of our JLA-MOD score analysis implementation and identified scenarios in which haplotype-based tests outperformed the single-marker test. The estimated trait-model and LD parameters were in good accordance with the simulated values. Our method outperformed another commonly used JLA single-marker test when the LD pattern was complex. The exploratory analysis of the FaPaCa families led to the identification of a promising genetic region on chromosome 22q13.33, which can serve as a starting point for future mutation analysis and molecular research in pancreatic cancer.

CONCLUSION

Our newly proposed JLA-MOD score method proves to be a valuable gene mapping and characterization tool, especially when either linkage or association information alone provide insufficient power to identify the disease-causing genetic variants.

Computational analysis and molecular dynamics simulation of high-risk single nucleotide polymorphisms of the ADAM10 gene

Polymorphisms of the disintegrin and metalloproteinase domain-containing protein 10 (ADAM10) are linked to pathophysiological changes in lung inflammation, cancer, Alzheimer's disease (AD), encephalopathy, liver fibrosis, and cardiovascular diseases. In this study, we predicted the pathogenicity of ADAM10 non-synonymous single nucleotide polymorphisms (nsSNPs) in a wide array of mutation analyzing bioinformatics tools. We retrieved 423 nsSNPs from dbSNP-NCBI for the analysis, and 13 were predicted deleterious by each of the ten tools: SIFT, PROVEAN, CONDEL, PANTHER-PSEP, SNAP2, SuSPect, PolyPhen-2, Meta-SNP, Mutation Assessor and Predict-SNP. Further assessment of amino acid sequences, homology models, conservation profiles, and inter-atomic interactions identified C222G, G361E and C639Y as the most pathogenic mutations. We validated this prediction through structural stability analysis using DUET, I-Mutant Suite, SNPeffect and Dynamut. Molecular dynamics simulations and principal component analysis also indicated considerable instability of the C222G, G361E and C639Y variants. Therefore, these ADAM10 nsSNPs could be candidates for diagnostic genetic screening and therapeutic molecular targeting.Communicated by Ramaswamy H. Sarma.

Integrating non-mammalian model organisms in the diagnosis of rare genetic diseases in humans

Next-generation sequencing technology has rapidly accelerated the discovery of genetic variants of interest in individuals with rare diseases. However, showing that these variants are causative of the disease in question is complex and may require functional studies. Use of non-mammalian model organisms - mainly fruitflies (Drosophila melanogaster), nematode worms (Caenorhabditis elegans) and zebrafish (Danio rerio) - enables the rapid and cost-effective assessment of the effects of gene variants, which can then be validated in mammalian model organisms such as mice and in human cells. By probing mechanisms of gene action and identifying interacting genes and proteins in vivo, recent studies in these non-mammalian model organisms have facilitated the diagnosis of numerous genetic diseases and have enabled the screening and identification of therapeutic options for patients. Studies in non-mammalian model organisms have also shown that the biological processes underlying rare diseases can provide insight into more common mechanisms of disease and the biological functions of genes. Here, we discuss the opportunities afforded by non-mammalian model organisms, focusing on flies, worms and fish, and provide examples of their use in the diagnosis of rare genetic diseases.

Genetic overlap for ten cardiovascular diseases: A comprehensive gene-centric pleiotropic association analysis and Mendelian randomization study

Recent studies suggest that pleiotropic effects may explain the genetic architecture of cardiovascular diseases (CVDs). We conducted a comprehensive gene-centric pleiotropic association analysis for ten CVDs using genome-wide association study (GWAS) summary statistics to identify pleiotropic genes and pathways that may underlie multiple CVDs. We found shared genetic mechanisms underlying the pathophysiology of CVDs, with over two-thirds of the diseases exhibiting common genes and single-nucleotide polymorphisms (SNPs). Significant positive genetic correlations were observed in more than half of paired CVDs. Additionally, we investigated the pleiotropic genes shared between different CVDs, as well as their functional pathways and distribution in different tissues. Moreover, six hub genes, including ALDH2, XPO1, HSPA1L, ESR2, WDR12, and RAB1A, as well as 26 targeted potential drugs, were identified. Our study provides further evidence for the pleiotropic effects of genetic variants on CVDs and highlights the importance of considering pleiotropy in genetic association studies.

Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases

Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed "omnigenic" model postulates that effects of genetic variation on traits are mediated by core-genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. Here, we develop a positive-unlabeled graph representation-learning ensemble-approach based on a nested cross-validation to predict core-like genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validations, we demonstrate that core-like genes display several key properties of core genes: Mouse knockouts of genes corresponding to our most confident predictions give rise to relevant mouse phenotypes at rates on par with the Mendelian disorder genes, and all candidates exhibit core gene properties like transcriptional deregulation in disease and loss-of-function intolerance. Moreover, as predicted for core genes, our candidates are enriched for drug targets and druggable proteins. In contrast to Mendelian disorder genes the new core-like genes are enriched for druggable yet untargeted gene products, which are therefore attractive targets for drug development. Interpretation of the underlying deep learning model suggests plausible explanations for our core gene predictions in form of molecular mechanisms and physical interactions. Our results demonstrate the potential of graph representation learning for the interpretation of biological complexity and pave the way for studying core gene properties and future drug development.

Gene Variants Implicated in Steatotic Liver Disease: Opportunities for Diagnostics and Therapeutics

Non-alcoholic fatty liver disease (NAFLD) describes a steatotic (or fatty) liver occurring as a consequence of a combination of metabolic, environmental, and genetic factors, in the absence of significant alcohol consumption and other liver diseases. NAFLD is a spectrum of conditions. Steatosis in the absence of inflammation is relatively benign, but the disease can progress into more severe forms like non-alcoholic steatohepatitis (NASH), liver cirrhosis, and hepatocellular carcinoma. NAFLD onset and progression are complex, as it is affected by many risk factors. The interaction between genetic predisposition and other factors partially explains the large variability of NAFLD phenotype and natural history. Numerous genes and variants have been identified through large-scale genome-wide association studies (GWAS) that are associated with NAFLD and one or more subtypes of the disease. Among them, the largest effect size and most consistent association have been patatin-like phospholipase domain-containing protein 3 (PNPLA3), transmembrane 6 superfamily member 2 (TM6SF2), and membrane-bound O-acyltransferase domain containing 7 (MBOAT7) genes. Extensive in vitro and in vivo studies have been conducted on these variants to validate these associations. The focus of this review is to highlight the genetics underpinning the molecular mechanisms driving the onset and progression of NAFLD and how they could potentially be used to improve genetic-based diagnostic testing of the disease and develop personalized, targeted therapeutics.

Comparative yield of molecular diagnostic algorithms for autism spectrum disorder diagnosis in India: evidence supporting whole exome sequencing as first tier test

BACKGROUND

Autism spectrum disorder (ASD) affects 1 in 100 children globally with a rapidly increasing prevalence. To the best of our knowledge, no data exists on the genetic architecture of ASD in India. This study aimed to identify the genetic architecture of ASD in India and to assess the use of whole exome sequencing (WES) as a first-tier test instead of chromosomal microarray (CMA) for genetic diagnosis.

METHODS

Between 2020 and 2022, 101 patient-parent trios of Indian origin diagnosed with ASD according to the Diagnostic and Statistical Manual, 5th edition, were recruited. All probands underwent a sequential genetic testing pathway consisting of karyotyping, Fragile-X testing (in male probands only), CMA and WES. Candidate variant validation and parental segregation analysis was performed using orthogonal methods.

RESULTS

Of 101 trios, no probands were identified with a gross chromosomal anomaly or Fragile-X. Three (2.9%) and 30 (29.7%) trios received a confirmed genetic diagnosis from CMA and WES, respectively. Amongst diagnosis from WES, SNVs were detected in 27 cases (90%) and CNVs in 3 cases (10%), including the 3 CNVs detected from CMA. Segregation analysis showed 66.6% (n = 3 for CNVs and n = 17 for SNVs) and 16.6% (n = 5) of the cases had de novo and recessive variants respectively, which is in concordance with the distribution of variant types and mode of inheritance observed in ASD patients of non-Hispanic white/ European ethnicity. MECP2 gene was the most recurrently mutated gene (n = 6; 20%) in the present cohort. Majority of the affected genes identified in the study cohort are involved in synaptic formation, transcription and its regulation, ubiquitination and chromatin remodeling.

CONCLUSIONS

Our study suggests de novo variants as a major cause of ASD in the Indian population, with Rett syndrome as the most commonly detected disorder. Furthermore, we provide evidence of a significant difference in the diagnostic yield between CMA (3%) and WES (30%) which supports the implementation of WES as a first-tier test for genetic diagnosis of ASD in India.

PheSom: a term frequency-based method for measuring human phenotype similarity on the basis of MeSH vocabulary

Background: Phenotype similarity calculation should be used to help improve drug repurposing. In this study, based on the MeSH terms describing the phenotypes deposited in OMIM, we proposed a method, namely, PheSom (Phenotype Similarity On MeSH), to measure the similarity between phenotypes. PheSom counted the number of overlapping MeSH terms between two phenotypes and then took the weight of every MeSH term within each phenotype into account according to the term frequency-inverse document frequency (FIDC). Phenotype-related genes were used for the evaluation of our method. Results: A 7,739 × 7,739 similarity score matrix was finally obtained and the number of phenotype pairs was dramatically decreased with the increase of similarity score. Besides, the overlapping rates of phenotype-related genes were remarkably increased with the increase of similarity score between phenotypes, which supports the reliability of our method. Conclusion: We anticipate our method can be applied to identifying novel therapeutic methods for complex diseases.

Unified views on variant impact across many diseases

Genomic studies of human disorders are often performed by distinct research communities (i.e., focused on rare diseases, common diseases, or cancer). Despite underlying differences in the mechanistic origin of different disease categories, these studies share the goal of identifying causal genomic events that are critical for the clinical manifestation of the disease phenotype. Moreover, these studies face common challenges, including understanding the complex genetic architecture of the disease, deciphering the impact of variants on multiple scales, and interpreting noncoding mutations. Here, we highlight these challenges in depth and argue that properly addressing them will require a more unified vocabulary and approach across disease communities. Toward this goal, we present a unified perspective on relating variant impact to various genomic disorders.

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.

A crowdsourcing database for the copy-number variation of the Spanish population

BACKGROUND

Despite being a very common type of genetic variation, the distribution of copy-number variations (CNVs) in the population is still poorly understood. The knowledge of the genetic variability, especially at the level of the local population, is a critical factor for distinguishing pathogenic from non-pathogenic variation in the discovery of new disease variants.

RESULTS

Here, we present the SPAnish Copy Number Alterations Collaborative Server (SPACNACS), which currently contains copy number variation profiles obtained from more than 400 genomes and exomes of unrelated Spanish individuals. By means of a collaborative crowdsourcing effort whole genome and whole exome sequencing data, produced by local genomic projects and for other purposes, is continuously collected. Once checked both, the Spanish ancestry and the lack of kinship with other individuals in the SPACNACS, the CNVs are inferred for these sequences and they are used to populate the database. A web interface allows querying the database with different filters that include ICD10 upper categories. This allows discarding samples from the disease under study and obtaining pseudo-control CNV profiles from the local population. We also show here additional studies on the local impact of CNVs in some phenotypes and on pharmacogenomic variants. SPACNACS can be accessed at: http://csvs.clinbioinfosspa.es/spacnacs/ .

CONCLUSION

SPACNACS facilitates disease gene discovery by providing detailed information of the local variability of the population and exemplifies how to reuse genomic data produced for other purposes to build a local reference database.

Genetics of mitochondrial diseases: Current approaches for the molecular diagnosis

Mitochondrial diseases are a genetically and phenotypically variable set of monogenic disorders. The main characteristic of mitochondrial diseases is a defective oxidative phosphorylation. Both nuclear and mitochondrial DNA encode the approximately 1500 mitochondrial proteins. Since identification of the first mitochondrial disease gene in 1988 a total of 425 genes have been associated with mitochondrial diseases. Mitochondrial dysfunctions can be caused both by pathogenic variants in the mitochondrial DNA or the nuclear DNA. Hence, besides maternal inheritance, mitochondrial diseases can follow all modes of Mendelian inheritance. The maternal inheritance and tissue specificity distinguish molecular diagnostics of mitochondrial disorders from other rare disorders. With the advances made in the next-generation sequencing technology, whole exome sequencing and even whole-genome sequencing are now the established methods of choice for molecular diagnostics of mitochondrial diseases. They reach a diagnostic rate of more than 50% in clinically suspected mitochondrial disease patients. Moreover, next-generation sequencing is delivering a constantly growing number of novel mitochondrial disease genes. This chapter reviews mitochondrial and nuclear causes of mitochondrial diseases, molecular diagnostic methodologies, and their current challenges and perspectives.

The complementary roles of genome-wide approaches in identifying genes linked to an inherited risk of colorectal cancer

The current understanding of the inherited risk of colorectal cancer (CRC) started with an observational clinical era in the late 19^th century, which was followed by a genetic era starting in the late 20^th century. Genome-wide linkage analysis allowed mapping several high-risk genes, which marked the beginning of the genetic era. The current high-throughput genomic phase includes genome-wide association study (GWAS) and genome-wide sequencing approaches which have revolutionized the conception of the inherited risk of CRC. On the one hand, GWAS has allowed the identification of multiple low risk loci correlated with CRC. On the other, genome-wide sequencing has led to the discovery of a second batch of high-to-moderate-risk genes that correlate to atypical familial CRC and polyposis syndromes. In contrast to other common cancers, which are usually dominated by a polygenic background, CRC risk is believed to be equally explained by monogenic and polygenic architectures, which jointly contribute to a quarter of familial clustering. Despite the fact that genome-wide approaches have allowed the identification of a continuum of responsible high-to-moderate-to-low-risk variants, much of the predisposition and familial clustering of CRC has not yet been explained. Other genetic, epigenetic and environmental factors might be playing important roles as well. In this review we aim to provide insights on the complementary roles played by different genomic approaches in allowing the current understanding of the genetic architecture of inherited CRC.

Effect of SMAD4 gene polymorphism on breast cancer risk in Bangladeshi women

Background

Breast cancer, one of the most prevalent cancer types among women worldwide as well as in Bangladesh, is the leading cause of cancer death in women throughout the globe. The risk of breast cancer development was found to be associated with genetic polymorphism according to several studies. As a convenient prognostic marker, a biomarker helps to identify disease progression, can lead to an effective therapeutic strategy, development of prognostic marker is very important for any cancer to initiate treatment strategy early to increase the possibility of the success rate of the treatment along with reduction of the treatment cost. This study aims to establish the correlation between polymorphism of SMAD4 rs10502913 and risk of breast cancer development in Bangladeshi women. This study was conducted on 70 breast cancer patients and 60 healthy volunteers through blood sample collection followed by DNA separation between the intervals of August 2019–October 2019. The collected DNA sample was arranged for the RFLP analysis of a PCR amplified fragments followed by gel electrophoresis. The obtained data was analyzed by structured multinomial logistic regression model.

Results

Obtained different fragment size after gel electrophoresis indicated different genotypes in this experiment. Our findings demonstrated that mutant homozygous A/A genotype, plays a significant role in breast cancer development among Bangladeshi women (P = 0.006, OR = 4.9626, 95% CI = 1.9980–12.3261) compared to the reference homozygous G/G genotype. Moreover, heterozygous G/A genotype was also found to be significantly associated with the risk of breast cancer development (P = 0.0252, OR = 2.6574, CI = 1.1295–6.2525). Considering the A/A genotype and G/A genotype combined, it also indicates a strong association of breast cancer development in Bangladeshi women (P = 0.008, OR = 3.5630, CI = 1.6907–7.5068).

Conclusion

Our study indicated a novel association between SMAD4 (rs10502913) polymorphism and increased risk of breast cancer development in Bangladeshi women.

An In silico Approach towards Finding the Cancer-Causing Mutations in Human MET Gene

Mesenchymal-epithelial transition (MET) factor is a proto-oncogene encoding tyrosine kinase receptor with hepatocyte growth factor (HGF) or scatter factor (SF). It is found on the human chromosome number 7 and regulates the diverse cellular mechanisms of the human body. The impact of mutations occurring in the MET gene is demonstrated by their detrimental effects on normal cellular functions. These mutations can change the structure and function of MET leading to different diseases such as lung cancer, neck cancer, colorectal cancer, and many other complex syndromes. Hence, the current study focused on finding deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) and their subsequent impact on the protein's structure and functions, which may contribute to the emergence of cancers. These nsSNPs were first identified utilizing computational tools like SIFT, PROVEAN, PANTHER-PSEP, PolyPhen-2, I-Mutant 2.0, and MUpro. A total of 45359 SNPs of MET gene were accumulated from the database of dbSNP, and among them, 1306 SNPs were identified as non-synonymous or missense variants. Out of all 1306 nsSNPs, 18 were found to be the most deleterious. Moreover, these nsSNPs exhibited substantial effects on structure, binding affinity with ligand, phylogenetic conservation, secondary structure, and post-translational modification sites of MET, which were evaluated using MutPred2, RaptorX, ConSurf, PSIPRED, and MusiteDeep, respectively. Also, these deleterious nsSNPs were accompanied by changes in properties of MET like residue charge, size, and hydrophobicity. These findings along with the docking results are indicating the potency of the identified SNPs to alter the structure and function of the protein, which may lead to the development of cancers. Nonetheless, Genome-wide association study (GWAS) studies and experimental research are required to confirm the analysis of these nsSNPs.

Unraveling Pathophysiology of Takotsubo Syndrome: The Emerging Role of the Oxidative Stress's Systemic Status

Takotsubo Syndrome (TTS) is usually triggered by emotional or physical stressors, thus suggesting that an increased sympathetic activity, leading to myocardial perfusion abnormalities and ventricular dysfunction, plays a major pathogenetic role. However, it remains to be elucidated why severe emotional and physical stress might trigger TTS in certain individuals but not others. Clinical research has been focused mainly on mechanisms underlying the activation of the sympathetic nervous system and the occurrence of myocardial ischemia in TTS. However, scientific evidence shows that additional factors might play a pathophysiologic role in the condition's occurrence. In this regard, a significant contribution arrived from metabolomics studies that followed the systemic response to TTS. Specifically, preliminary data clearly show that there is an interplay between inflammation, genetics, and oxidative status which might explain susceptibility to the condition. This review aims to sum up the established pathogenetic factors underlying TTS and to appraise emerging mechanisms, with particular emphasis on oxidative status, which might better explain susceptibility to the condition.

Clinical and genetic analysis of familial neuromyelitis optica spectrum disorder in Chinese: associated with ubiquitin-specific peptidase USP18 gene variants

BACKGROUND

Familial clustering of neuromyelitis optica spectrum disorder (NMOSD) was present in Chinese. This study was to investigate the clinical characteristics and genetic background of familial NMOSD.

METHODS

Through questionnaires in four medical centres in 2016-2020, we identified 10 families with NMOSD aggregation. The statistical differences of clinical characteristics between familial and sporadic NMOSD (22 cases and 459 cases) were summarised. The whole-exome sequencing (WES) for seven families (13 cases and 13 controls) was analysed, compared with our previous WES data for sporadic NMOSD (228 cases and 1 400 controls). The family-based and population-based association and linkage analysis were conducted to identify the pathogenetic genes, the variant impacts were predicted.

RESULTS

The familial occurrence was 0.87% in Chinese. Familial patients had higher expanded disability status scale score than sporadic patients (p=0.03). The single-nucleotide polymorphism (SNP) rs2252257 in the promoter and enhancer of ubiquitin-specific peptidase USP18 was linked to familial NMOSD (p=7.8E-05, logarithm of the odds (LOD)=3.1), SNPs rs361553, rs2252257 and rs5746523 were related to sporadic NMOSD (p=1.29E-10, 3.45E-07 and 2.01E-09, respectively). Patients with the SNP rs361553 T/T genotype had higher recurrence rate than C/T or C/C genotype (1.22±0.85 vs 0.69±0.57 and 0.81±0.65, p=0.003 and 0.001, respectively). SNPs rs361553 and rs2252257 altered USP18 expression in brain and nerve tissues.

CONCLUSION

Most clinical characteristics of familial NMOSD were indistinguishable from sporadic NMOSD except for the worst episodes severity. USP18 with impaired intronic regulatory function contributed to the pathogenesis of NMOSD.

Research on the effects of rs1800566 C/T polymorphism of NAD(P)H quinone oxidoreductase 1 gene on cancer risk involves analysis of 43,736 cancer cases and 56,173 controls

Objective

A two-electron reductase known as NQO1 [NAD(P)H quinone oxidoreductase 1] is regarded as an excellent anticancer target. Studies have found that rs1800566 polymorphism of NQO1 is linked to different cancers, but their associations remain controversial.

Methods

In the present work, we selected to do a comprehensive meta-analysis to analyze their correlation. We performed searches on PubMed, Embase, Google Scholar, Chinese database, and Web of Science. The results we obtained covered all publications before April 3, 2022.

Results

There were 176 case-control studies among them, with 56,173 corresponding controls and 43,736 cancer cases. We determined that the NQO1 rs1800566 polymorphism was not related to the cancer risk by calculating 95% confidence intervals and odds ratios. However, stratified genotyping showed that this polymorphism was protective against hepatocellular carcinoma, renal cell carcinoma, and gastric cancer. In addition, on dividing cancer into six systems, the association with gastrointestinal cancer decreased. In the race-based subgroup, a decreasing trend was observed in Asians, while an increasing trend was found among Caucasians, Africans, and mixed populations. The decreased correlation in the hospital-based subgroup was also detected.

Conclusion

Current study shows that rs1800566 polymorphism of NQO1 was linked to cancer susceptibility and maybe as a tumor marker in their development.

The False Dawn of Polygenic Risk Scores for Human Disease Prediction

Polygenic risk scores (PRSs) are being constructed for many diseases and are presented today as a promising avenue in the field of human genetics. These scores aim at predicting the risk of developing a disease by leveraging the many genome-wide association studies (GWAS) conducted during the two last decades. Important investments are being made to improve score estimates by increasing GWAS sample sizes, by developing more sophisticated methods, and by proposing different corrections for potential biases. PRSs have entered the market with direct-to-consumer companies proposing to compute them from saliva samples and even recently to help parents select the healthiest embryos. In this paper, we recall how PRSs arose and question the credit they are given by revisiting underlying assumptions in light of the history of human genetics and by comparing them with estimated breeding values (EBVs) used for selection in livestock.

HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure

MOTIVATION

Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction.

RESULTS

We propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM's predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation.

CONCLUSIONS

The results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.

Whole exome analysis of patients in Japan with hearing loss reveals high heterogeneity among responsible and novel candidate genes

Background

Heterogeneous genetic loci contribute to hereditary hearing loss; more than 100 deafness genes have been identified, and the number is increasing. To detect pathogenic variants in multiple deafness genes, in addition to novel candidate genes associated with hearing loss, whole exome sequencing (WES), followed by analysis prioritizing genes categorized in four tiers, were applied.

Results

Trios from families with non-syndromic or syndromic hearing loss (n = 72) were subjected to WES. After segregation analysis and interpretation according to American College of Medical Genetics and Genomics guidelines, candidate pathogenic variants in 11 previously reported deafness genes (STRC, MYO15A, CDH23, PDZD7, PTPN11, SOX10, EYA1, MYO6, OTOF, OTOG, and ZNF335) were identified in 21 families. Discrepancy between pedigree inheritance and genetic inheritance was present in one family. In addition, eight genes (SLC12A2, BAIAP2L2, HKDC1, SVEP1, CACNG1, GTPBP4, PCNX2, and TBC1D8) were screened as single candidate genes in 10 families.

Conclusions

Our findings demonstrate that four-tier assessment of WES data is efficient and can detect novel candidate genes associated with hearing loss, in addition to pathogenic variants of known deafness genes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-022-02262-4.

Predicting Physical Appearance from DNA Data-Towards Genomic Solutions

The idea of forensic DNA intelligence is to extract from genomic data any information that can help guide the investigation. The clues to the externally visible phenotype are of particular practical importance. The high heritability of the physical phenotype suggests that genetic data can be easily predicted, but this has only become possible with less polygenic traits. The forensic community has developed DNA-based predictive tools by employing a limited number of the most important markers analysed with targeted massive parallel sequencing. The complexity of the genetics of many other appearance phenotypes requires big data coupled with sophisticated machine learning methods to develop accurate genomic predictors. A significant challenge in developing universal genomic predictive methods will be the collection of sufficiently large data sets. These should be created using whole-genome sequencing technology to enable the identification of rare DNA variants implicated in phenotype determination. It is worth noting that the correctness of the forensic sketch generated from the DNA data depends on the inclusion of an age factor. This, however, can be predicted by analysing epigenetic data. An important limitation preventing whole-genome approaches from being commonly used in forensics is the slow progress in the development and implementation of high-throughput, low DNA input sequencing technologies. The example of palaeoanthropology suggests that such methods may possibly be developed in forensics.

PDGNet: Predicting Disease Genes Using a Deep Neural Network With Multi-View Features

The knowledge of phenotype-genotype associations is crucial for the understanding of disease mechanisms. Numerous studies have focused on developing efficient and accurate computing approaches to predict disease genes. However, owing to the sparseness and complexity of medical data, developing an efficient deep neural network model to identify disease genes remains a huge challenge. Therefore, we develop a novel deep neural network model that fuses the multi-view features of phenotypes and genotypes to identify disease genes (termed PDGNet). Our model integrated the multi-view features of diseases and genes and leveraged the feedback information of training samples to optimize the parameters of deep neural network and obtain the deep vector features of diseases and genes. The evaluation experiments on a large data set indicated that PDGNet obtained higher performance than the state-of-the-art method (precision and recall improved by 9.55 and 9.63 percent). The analysis results for the candidate genes indicated that the predicted genes have strong functional homogeneity and dense interactions with known genes. We validated the top predicted genes of Parkinson's disease based on external curated data and published medical literatures, which indicated that the candidate genes have a huge potential to guide the selection of causal genes in the 'wet experiment'. The source codes and the data of PDGNet are available at https://github.com/yangkuoone/PDGNet.

Case Report: A Case of β-Ureidopropionase Deficiency Complicated With MELAS Syndrome Caused by UPB1 Variant and Mitochondrial Gene Variant

BACKGROUND

β-Ureidopropionase deficiency is a rare autosomal recessive disease affecting the last step of pyrimidine degradation. Mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) syndrome is a rare inherited disorder caused by genetic defects in mitochondrial DNA.

CASE PRESENTATION

One 8-year-old boy presented with dizziness, vomiting, and convulsions. The gas chromatography-mass spectrometry results suggested β-ureidopropionase deficiency. The whole-exome sequencing results revealed homozygous missense variant c.977G>A (p.R326Q) in UPB1. However, the patient presented with persistent hyperlactacidemia and metabolic acidosis, which did not correspond to the classic features of β-ureidopropionase deficiency. Combined with the manifestations of developmental delay, poor academic performance, and poor sports stamina, whole-mitochondrial-genome sequencing was performed. The results exhibited the variant m.3243A>G of MT-TL1 gene. The level of heterogeneity was 65% in the patient and 17.8% in his mother. Eventually, the final diagnosis of β-ureidopropionase deficiency combined with MELAS syndrome was made.

CONCLUSION

The report about β-ureidopropionase deficiency caused by a nuclear gene variant and MELAS syndrome caused by a mitochondrial gene variant coexisting in the same patient enriches the clinical study of these two rare diseases.

Genetic Differences between Physical Injury Patients With and Without Post-traumatic Syndrome: Focus on Secondary Findings and Potential Variants Revealed by Whole Exome Sequencing

Objective

Sudden traumatic physical injuries often cause psychological distress, which may be associated with chronic disability. Although considerable effort has been expended to identify genetic predictors of post-traumatic stress disorder (PTSD) after traumatic events, genetic predictors of psychological distress in response to severe physical injuries have been yet to be elucidated using whole exome sequencing (WES). Here, the genetic architecture of post-traumatic syndrome (PTS), which encompasses a broad range of psychiatric disorders after traumatic events including depression, anxiety disorder, acute stress disorder, and PTSD, was explored using WES in severely physically injured patients, focusing on secondary findings and potential PTS-related variants.

Methods

In total, 141 severely physically injured patients were consecutively recruited, and PTS was evaluated within 1 month of the injury. Secondary findings were analyzed according to PTS status. To identify PTS-related variants, genome-wide association analyses and the optimal sequencing kernel association test were performed.

Results

Of the 141 patients, 88 (62%) experienced PTS. There were 108 disease-causing variants in severely physically injured patients. As secondary findings, the stress- and inflammation-related signaling pathways were enriched in the PTS patients, while the glucose metabolism pathway was enriched in those without PTS. However, no significant PTS-related variants were identified.

Conclusion

Our findings suggest that genetic alterations in stress and inflammatory pathways might increase the likelihood of PTS immediately after severe physical injury. Future studies with larger samples and longitudinal designs are needed.

Problems and promises: How to tell the story of a Genome Wide Association Study?

The promise of treatments for common complex diseases (CCDs) is understood as an important force driving large scale genetics research over the last few decades. This paper considers the phenomenon of the Genome Wide Association Study (GWAS) via one high profile example, the Wellcome Trust Case Control Consortium (WTCCC). The WTCCC despite not fulfilling promises of new health interventions is still understood as an important step towards tackling CCDs clinically. The 'sociology of expectations' has considered many examples of failure to fulfil promises and the subsequent negative consequences including disillusionment, disappointment and disinvestment. In order to explore why some domains remain resilient in the face of apparent failure, I employ the concept of the 'problematic' found in the work of Giles Deleuze. This alternative theoretical framework challenges the idea that the failure to reach promised goals results in largely negative outcomes for a given field. I will argue that collective scientific action is motivated not only by hopes for the future but also by the drive to create solutions to the actual setbacks and successes which scientists encounter in their day-to-day work. I draw on eighteen interviews.

Exploring the Association Between Genetic Polymorphisms in Genes Involved in Craniofacial Development and Isolated Tooth Agenesis

Tooth agenesis is a common congenital anomaly in humans and is more common in oral cleft patients than in the general population. Many previous studies suggested that oral cleft and tooth agenesis share a similar genetic background. Therefore, this study explored the association between isolated tooth agenesis and genetic polymorphisms in genes that are crucial for craniofacial and tooth development. Panoramic radiographs, anamnesis, and genomic DNA from 273 patients were included. Patients were classified as tooth agenesis present, when at least one permanent tooth was congenitally missing. Patients with syndromes and oral cleft were excluded. Only unrelated patients were included. The genetic polymorphisms in BMP2 (rs235768 and rs1005464), BMP4 (rs17563), RUNX2 (rs59983488 and rs1200425), and SMAD6 (rs3934908 and rs2119261) were genotyped by real-time polymerase chain reaction. Genotype and allele distributions were compared between the tooth agenesis phenotypes and controls by Chi-square test. Haplotype and diplotype analysis were also performed, in addition to multivariate analysis (alpha of 0.05). A total of 86 tooth agenesis cases and 187 controls were evaluated. For the rs235768 in BMP2, patients carrying TT genotype have higher chance to present tooth agenesis [p < 0.001; prevalence ratio (PR) = 8.29; 95% confidence interval (CI) = 4.26–16.10]. The TT genotype in rs3934908 (SMAD6) was associated with higher chance to present third molar agenesis (p = 0.023; PR = 3.25; 95% CI = 1.17–8.99). BMP2 was also associated in haplotype and diplotype analysis with tooth agenesis. In conclusion, genetic polymorphisms in BMP2 and SMAD6 were associated with isolated tooth agenesis.

Appropriate Clinical Genetic Testing of Hemochromatosis Type 2-4, Including Ferroportin Disease

Hereditary hemochromatosis (HH) is an inherited iron overload disorder due to a deficiency of hepcidin, or a failure of hepcidin to degrade ferroportin. The most common form of HH, Type 1 HH, is most commonly due to a homozygous C282Y mutation in HFE and is relatively well understood in significance and action; however, other rare forms of HH (Types 2–4) exist and are more difficult to identify and diagnose in clinical practice. In this review, we describe the clinical characteristics of HH Type 2–4 and the mutation patterns that have been described in these conditions. We also review the different methods for genetic testing available in clinical practice and a pragmatic approach to the patient with suspected non-HFE HH.

Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach

Currently, the research of multi-omics, such as genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, and radiomics, are hot spots. The relationship between multi-omics data, drugs, and diseases has received extensive attention from researchers. At the same time, multi-omics can effectively predict the diagnosis, prognosis, and treatment of diseases. In essence, these research entities, such as genes, RNAs, proteins, microbes, metabolites, pathways as well as pathological and medical imaging data, can all be represented by the network at different levels. And some computer and biology scholars have tried to use computational methods to explore the potential relationships between biological entities. We summary a comprehensive research strategy, that is to build a multi-omics heterogeneous network, covering multimodal data, and use the current popular computational methods to make predictions. In this study, we first introduce the calculation method of the similarity of biological entities at the data level, second discuss multimodal data fusion and methods of feature extraction. Finally, the challenges and opportunities at this stage are summarized. Some scholars have used such a framework to calculate and predict. We also summarize them and discuss the challenges. We hope that our review could help scholars who are interested in the field of bioinformatics, biomedical image, and computer research.

Advances in colorectal cancer genomics and transcriptomics drive early detection and prevention

Colorectal carcinoma (CRC) is a high incidence cancer and leading cause of cancer mortality worldwide. The advances in genomics and transcriptomics in the past decades have improved the detection and prevention of CRC in familial CRC syndromes. Nevertheless, the ultimate goal of personalized medicine for sporadic CRC is still not within reach due no less to the difficulty in integrating population disparity and clinical data to combat what essentially is a very heterogenous disease. This minireview highlights the achievement of the past decades and present possible direction in the hope of early detection and metastasis prevention for reducing CRC-associated morbidity and mortality.

History of the methodology of disease gene identification

The past 45 years have witnessed a triumph in the discovery of genes and genetic variation that cause Mendelian disorders due to high impact variants. Important discoveries and organized projects have provided the necessary tools and infrastructure for the identification of gene defects leading to thousands of monogenic phenotypes. This endeavor can be divided in three phases in which different laboratory strategies were employed for the discovery of disease-related genes: (i) the biochemical phase, (ii) the genetic linkage followed by positional cloning phase, and (iii) the sequence identification phase. However, much more work is needed to identify all the high impact genomic variation that substantially contributes to the phenotypic variation.

Identifying Critical States of Complex Diseases by Single-Sample Jensen-Shannon Divergence

MOTIVATION

The evolution of complex diseases can be modeled as a time-dependent nonlinear dynamic system, and its progression can be divided into three states, i.e., the normal state, the pre-disease state and the disease state. The sudden deterioration of the disease can be regarded as the state transition of the dynamic system at the critical state or pre-disease state. How to detect the critical state of an individual before the disease state based on single-sample data has attracted many researchers' attention.

METHODS

In this study, we proposed a novel approach, i.e., single-sample-based Jensen-Shannon Divergence (sJSD) method to detect the early-warning signals of complex diseases before critical transitions based on individual single-sample data. The method aims to construct score index based on sJSD, namely, inconsistency index (ICI).

RESULTS

This method is applied to five real datasets, including prostate cancer, bladder urothelial carcinoma, influenza virus infection, cervical squamous cell carcinoma and endocervical adenocarcinoma and pancreatic adenocarcinoma. The critical states of 5 datasets with their corresponding sJSD signal biomarkers are successfully identified to diagnose and predict each individual sample, and some "dark genes" that without differential expressions but are sensitive to ICI score were revealed. This method is a data-driven and model-free method, which can be applied to not only disease prediction on individuals but also targeted drug design of each disease. At the same time, the identification of sJSD signal biomarkers is also of great significance for studying the molecular mechanism of disease progression from a dynamic perspective.

Advances and challenges in quantitative delineation of the genetic architecture of complex traits

Background

Genome-wide association studies (GWAS) have been widely adopted in studies of human complex traits and diseases.

Results

This review surveys areas of active research: quantifying and partitioning trait heritability, fine mapping functional variants and integrative analysis, genetic risk prediction of phenotypes, and the analysis of sequencing studies that have identified millions of rare variants. Current challenges and opportunities are highlighted.

Conclusion

GWAS have fundamentally transformed the field of human complex trait genetics. Novel statistical and computational methods have expanded the scope of GWAS and have provided valuable insights on the genetic architecture underlying complex phenotypes.

TP53 rs28934571 polymorphism increases the prognostic risk in hepatocellular carcinoma

Aim: Hepatocellular carcinoma (HCC) is considered to be the third leading cause of cancer death. The homologous gene of TP53 is significant in the occurrence and development of cancer. This study explored the relationship between TP53 rs28934571 polymorphism and HCC risk in Guangxi, China. Materials & methods: We first screened the association through bioinformatics. Additionally, a case-control study was performed to further verify the relationship between gene polymorphism and HCC risk after collecting clinical characteristics. Results: Results showed that allele A on TP53 rs28934571 was a risk factor for HCC and mutation from C to A on TP53 rs28934571 would increase the risk of poor prognosis of HCC. Conclusion: Therefore, the study concluded that TP53 rs28934571 may become a diagnostic indicator in judging the prognosis of HCC.

Heterogeneity Analysis of Esophageal Squamous Cell Carcinoma in Cell Lines, Tumor Tissues and Patient-Derived Xenografts

Esophageal Squamous Cell Carcinoma (ESCC) is the predominant type of Esophageal Cancer (EC), accounting for nearly 88% of EC incidents worldwide. Importantly, it is also a life-threatening cancer for patients diagnosed in advanced stages, with only a 20% 5-year survival rate due to a limited number of actionable targets and therapeutic options. Increasing evidence has shown that inter-tumor and intra-tumor heterogeneity are widely distributed across ESCC tumor tissues. In our work, multi-omics data from ESCC cell lines, tumor tissue, normal tissue and Patient-Derived Xenograft (PDX) tissues were analyzed to investigate the heterogeneity among ESCC samples at the DNA, RNA, and protein level. We identified enrichment of ECM-receptor interaction and Focal adhesion pathways from the subset of protein-coding genes with non-silent mutations in ESCC patients. We also found that TP53, TTN, KMT2D, CSMD3, DNAH5, MUC16 and DST are the most frequently mutated genes in ESCC patient samples. Out of the identified genes, TP53 is the most frequently mutated, with 84 distinct non-silent mutation variants. We observed that p.R248Q, p.R175G/H, and p.R273C/H are the most common TP53 mutation variants. The diversity of TP53 mutations reveal its importance in ESCC progression and may also provide promising targets for precision therapeutics. Additionally, we identified the Olfactory transduction as the top signaling pathway, enriched from genes uniquely expressed in The Cancer Genome Atlas (TCGA)-ESCC patient tumor tissues, which may provide implications for the exact roles of the corresponding genes in ESCC. Cyclic nucleotide-gated channel subunit beta 1(CNGB1), a gene belonging to the Olfactory transduction pathway, was found exclusively overexpressed in ESCC. Expression of CNGB1 could serve as a marker, indicating potential diagnostic or therapeutic value. Finally, we investigated heterogeneity in the context of the ESCC PDX model, which is an emerging tool used to predict drug response and recapitulate tumor behavior in vivo. We observed trans-species heterogeneity in as high as 75% of the identified proteins, indicating that the ambiguity of proteins should be addressed by specific strategies to avoid drawing false conclusions. The identification and characterization of gene mutation and expression heterogeneity across different ESCC datasets, including various novel TP53 mutations, ECM-receptor interaction, Focal adhesion, and Olfactory transduction pathways (CNGB1), provide researchers with evidence and implications for accurate research and precision therapeutic development.

Joo JW. MARS: leveraging allelic heterogeneity to increase power of association testing

In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.

NIDM: network impulsive dynamics on multiplex biological network for disease-gene prediction

The prediction of genes related to diseases is important to the study of the diseases due to high cost and time consumption of biological experiments. Network propagation is a popular strategy for disease-gene prediction. However, existing methods focus on the stable solution of dynamics while ignoring the useful information hidden in the dynamical process, and it is still a challenge to make use of multiple types of physical/functional relationships between proteins/genes to effectively predict disease-related genes. Therefore, we proposed a framework of network impulsive dynamics on multiplex biological network (NIDM) to predict disease-related genes, along with four variants of NIDM models and four kinds of impulsive dynamical signatures (IDSs). NIDM is to identify disease-related genes by mining the dynamical responses of nodes to impulsive signals being exerted at specific nodes. By a series of experimental evaluations in various types of biological networks, we confirmed the advantage of multiplex network and the important roles of functional associations in disease-gene prediction, demonstrated superior performance of NIDM compared with four types of network-based algorithms and then gave the effective recommendations of NIDM models and IDS signatures. To facilitate the prioritization and analysis of (candidate) genes associated to specific diseases, we developed a user-friendly web server, which provides three kinds of filtering patterns for genes, network visualization, enrichment analysis and a wealth of external links (http://bioinformatics.csu.edu.cn/DGP/NID.jsp). NIDM is a protocol for disease-gene prediction integrating different types of biological networks, which may become a very useful computational tool for the study of disease-related genes.