Genetic linkage studies

Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data provided new insights into the understanding of epigenetic heterogeneity and transcriptional regulation. With the increasing abundance of dataset resources, there is an urgent need to extract more useful information through high-quality data analysis methods specifically designed for scATAC-seq. However, analyzing scATAC-seq data poses challenges due to its near binarization, high sparsity and ultra-high dimensionality properties. Here, we proposed a novel network diffusion-based computational method to comprehensively analyze scATAC-seq data, named Single-Cell ATAC-seq Analysis via Network Refinement with Peaks Location Information (SCARP). SCARP formulates the Network Refinement diffusion method under the graph theory framework to aggregate information from different network orders, effectively compensating for missing signals in the scATAC-seq data. By incorporating distance information between adjacent peaks on the genome, SCARP also contributes to depicting the co-accessibility of peaks. These two innovations empower SCARP to obtain lower-dimensional representations for both cells and peaks more effectively. We have demonstrated through sufficient experiments that SCARP facilitated superior analyses of scATAC-seq data. Specifically, SCARP exhibited outstanding cell clustering performance, enabling better elucidation of cell heterogeneity and the discovery of new biologically significant cell subpopulations. Additionally, SCARP was also instrumental in portraying co-accessibility relationships of accessible regions and providing new insight into transcriptional regulation. Consequently, SCARP identified genes that were involved in key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diseases and predicted reliable cis-regulatory interactions. To sum up, our studies suggested that SCARP is a promising tool to comprehensively analyze the scATAC-seq data.

Discovering SNP-disease relationships in genome-wide SNP data using an improved harmony search based on SNP locus and genetic inheritance patterns

Advances in high-throughput sequencing technologies have made it possible to access millions of measurements from thousands of people. Single nucleotide polymorphisms (SNPs), the most common type of mutation in the human genome, have been shown to play a significant role in the development of complex and multifactorial diseases. However, studying the synergistic interactions between different SNPs in explaining multifactorial diseases is challenging due to the high dimensionality of the data and methodological complexities. Existing solutions often use a multi-objective approach based on metaheuristic optimization algorithms such as harmony search. However, previous studies have shown that using a multi-objective approach is not sufficient to address complex disease models with no or low marginal effect. In this research, we introduce a locus-driven harmony search (LDHS), an improved harmony search algorithm that focuses on using SNP locus information and genetic inheritance patterns to initialize harmony memories. The proposed method integrates biological knowledge to improve harmony memory initialization by adding SNP combinations that are likely candidates for interaction and disease causation. Using a SNP grouping process, LDHS generates harmonies that include SNPs with a higher potential for interaction, resulting in greater power in detecting disease-causing SNP combinations. The performance of the proposed algorithm was evaluated on 200 synthesized datasets for disease models with and without marginal effect. The results show significant improvement in the power of the algorithm to find disease-related SNP sets while decreasing computational cost compared to state-of-the-art algorithms. The proposed algorithm also demonstrated notable performance on real breast cancer data, showing that integrating prior knowledge can significantly improve the process of detecting disease-related SNPs in both real and synthesized data.

Structural neuroimaging phenotypes and associated molecular and genomic underpinnings in autism: a review

Autism has been associated with differences in the developmental trajectories of multiple neuroanatomical features, including cortical thickness, surface area, cortical volume, measures of gyrification, and the gray-white matter tissue contrast. These neuroimaging features have been proposed as intermediate phenotypes on the gradient from genomic variation to behavioral symptoms. Hence, examining what these proxy markers represent, i.e., disentangling their associated molecular and genomic underpinnings, could provide crucial insights into the etiology and pathophysiology of autism. In line with this, an increasing number of studies are exploring the association between neuroanatomical, cellular/molecular, and (epi)genetic variation in autism, both indirectly and directly in vivo and across age. In this review, we aim to summarize the existing literature in autism (and neurotypicals) to chart a putative pathway from (i) imaging-derived neuroanatomical cortical phenotypes to (ii) underlying (neuropathological) biological processes, and (iii) associated genomic variation.

The emergence of genotypic divergence and future precision medicine applications

Genotypic divergence is a term adapted from population genetics and intimately linked to evolution. We use divergence here to emphasize the differences that set individuals apart in any cohort. The history of genetics is filled with descriptions of genotypic differences, but causal inference of interindividual biological variation has been scarce. We suggest that the practice of precision medicine requires a divergent approach, an approach dependent on the causal interpretation of previous convergent (and preliminary) knowledge in the field. This knowledge has relied on convergent descriptive syndromology (lumping), which has overemphasized a reductionistic gene determinism on the quest of seeking associations without causal understanding. Regulatory variants with small effect and somatic mutations are some of the modifying factors that lead to incomplete penetrance and intrafamilial variable expressivity often observed in apparently monogenic clinical disorders. A truly divergent approach to precision medicine requires splitting, that is, the consideration of different layers of genetic phenomena that interact causally in a nonlinear fashion. This chapter reviews convergences and divergences in genetics and genomics, aiming to discuss what can be causally understood to approximate the as-yet utopian lands of Precision Medicine for patients with neurodegenerative disorders.

Systematic approach to identify therapeutic targets and functional pathways for the cervical cancer

BACKGROUND

In today's society, cancer has become a big concern. The most common cancers in women are breast cancer (BC), endometrial cancer (EC), ovarian cancer (OC), and cervical cancer (CC). CC is a type of cervix cancer that is the fourth most common cancer in women and the fourth major cause of death.

RESULTS

This research uses a network approach to discover genetic connections, functional enrichment, pathways analysis, microRNAs transcription factors (miRNA-TF) co-regulatory network, gene-disease associations, and therapeutic targets for CC. Three datasets from the NCBI's GEO collection were considered for this investigation. Then, using a comparison approach between the datasets, 315 common DEGs were discovered. The PPI network was built using a variety of combinatorial statistical approaches and bioinformatics tools, and the PPI network was then utilized to identify hub genes and critical modules.

CONCLUSION

Furthermore, we discovered that CC has specific similar links with the progression of different tumors using Gene Ontology terminology and pathway analysis. Transcription factors-gene linkages, gene-disease correlations, and the miRNA-TF co-regulatory network were revealed to have functional enrichments. We believe the candidate drugs identified in this study could be effective for advanced CC treatment.

Globally distributed mining-impacted environments are underexplored hotspots of multidrug resistance genes

Mining is among the human activities with widest environmental impacts, and mining-impacted environments are characterized by high levels of metals that can co-select for antibiotic resistance genes (ARGs) in microorganisms. However, ARGs in mining-impacted environments are still poorly understood. Here, we conducted a comprehensive study of ARGs in such environments worldwide, taking advantage of 272 metagenomes generated from a global-scale data collection and two national sampling efforts in China. The average total abundance of the ARGs in globally distributed studied mine sites was 1572 times per gigabase, being rivaling that of urban sewage but much higher than that of freshwater sediments. Multidrug resistance genes accounted for 40% of the total ARG abundance, tended to co-occur with multimetal resistance genes, and were highly mobile (e.g. on average 16% occurring on plasmids). Among the 1848 high-quality metagenome-assembled genomes (MAGs), 85% carried at least one multidrug resistance gene plus one multimetal resistance gene. These high-quality ARG-carrying MAGs considerably expanded the phylogenetic diversity of ARG hosts, providing the first representatives of ARG-carrying MAGs for the Archaea domain and three bacterial phyla. Moreover, 54 high-quality ARG-carrying MAGs were identified as potential pathogens. Our findings suggest that mining-impacted environments worldwide are underexplored hotspots of multidrug resistance genes.

Tendon and Ligament Genetics: How Do They Contribute to Disease and Injury? A Narrative Review

A significant proportion of patients requiring musculoskeletal management present with tendon and ligament pathology. Our understanding of the intrinsic and extrinsic mechanisms that lead to such disabilities is increasing. However, the complexity underpinning these interactive multifactorial elements is still not fully characterised. Evidence highlighting the genetic components, either reducing or increasing susceptibility to injury, is increasing. This review examines the present understanding of the role genetic variations contribute to tendon and ligament injury risk. It examines the different elements of tendon and ligament structure and considers our knowledge of genetic influence on form, function, ability to withstand load, and undertake repair or regeneration. The role of epigenetic factors in modifying gene expression in these structures is also explored. It considers the challenges to interpreting present knowledge, the requirements, and likely pathways for future research, and whether such information has reached the point of clinical utility.

From APC to the genetics of hereditary and familial colon cancer syndromes

Hereditary colorectal cancer (CRC) syndromes attributable to high penetrance mutations represent 9-26% of young-onset CRC cases. The clinical significance of many of these mutations is understood well enough to be used in diagnostics and as an aid in patient care. However, despite the advances made in the field, a significant proportion of familial and early-onset cases remains molecularly uncharacterized and extensive work is still needed to fully understand the genetic nature of CRC susceptibility. With the emergence of next-generation sequencing and associated methods, several predisposition loci have been unraveled, but validation is incomplete. Individuals with cancer-predisposing mutations are currently enrolled in life-long surveillance, but with the development of new treatments, such as cancer vaccinations, this might change in the not so distant future for at least some individuals. For individuals without a known cause for their disease susceptibility, prevention and therapy options are less precise. Herein, we review the progress achieved in the last three decades with a focus on how CRC predisposition genes were discovered. Furthermore, we discuss the clinical implications of these discoveries and anticipate what to expect in the next decade.

PATRIOT: A Pipeline for Tracing Identity-by-Descent for Chromosome Segments to Improve Genomic Prediction in Self-Pollinating Crop Species

The lowering genotyping cost is ushering in a wider interest and adoption of genomic prediction and selection in plant breeding programs worldwide. However, improper conflation of historical and recent linkage disequilibrium between markers and genes restricts high accuracy of genomic prediction (GP). Multiple ancestors may share a common haplotype surrounding a gene, without sharing the same allele of that gene. This prevents parsing out genetic effects associated with the underlying allele of that gene among the set of ancestral haplotypes. We present "Parental Allele Tracing, Recombination Identification, and Optimal predicTion" (i.e., PATRIOT) approach that utilizes marker data to allow for a rapid identification of lines carrying specific alleles, increases the accuracy of genomic relatedness and diversity estimates, and improves genomic prediction. Leveraging identity-by-descent relationships, PATRIOT showed an improvement in GP accuracy by 16.6% relative to the traditional rrBLUP method. This approach will help to increase the rate of genetic gain and allow available information to be more effectively utilized within breeding programs.

Genomics in medicine: A new era in medicine

The sequencing of complete human genome revolutionized the genomic medicine. However, the complex interplay of gene-environment-lifestyle and influence of non-coding genomic regions on human health remain largely unexplored. Genomic medicine has great potential for diagnoses or disease prediction, disease prevention and, targeted treatment. However, many of the promising tools of genomic medicine are still in their infancy and their application may be limited because of the limited knowledge we have that precludes its use in many clinical settings. In this review article, we have reviewed the evolution of genomic methodologies/tools, their limitations, and scope, for current and future clinical application.

Genome-Wide Linkage Analysis of the Risk of Contracting a Bloodstream Infection in 47 Pedigrees Followed for 23 Years Assembled From a Population-Based Cohort (the HUNT Study)

OBJECTIVES

Bloodstream infection is an important cause of death worldwide. The main objective of this study was to identify genetic loci linked to risk of contracting a bloodstream infection.

DESIGN

Genome-wide linkage analysis.

SETTING

Population-based, Norwegian cohort, followed between 1995 and 2017.

SUBJECTS

Among 69,423 genotyped subjects, there were 47 families with two or more second-degree relatives with bloodstream infection in the follow-up period. There were 365 subjects in these families, of which 110 were affected.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The cohort was genotyped using Illumina HumanCoreExome (Illumina, San Diego, CA) arrays. Before linkage analysis, single-nucleotide polymorphisms were pruned and clumped. In nonparametric linkage analysis using an exponential model, we found three loci with a suggestive linkage to bloodstream infection, all on chromosome 4, at 46.6 centimorgan (logarithm of odds, 2.3), 57.7 centimorgan (logarithm of odds, 3.2), and 70.0 centimorgan (logarithm of odds, 2.1). At the peak of the lead region are three genes: TLR10, TLR1, and TLR6.

CONCLUSIONS

Variations in the TLR10/1/6 locus appear to be linked with the risk of contracting a bloodstream infection.

The Impact of Modern Technologies on Molecular Diagnostic Success Rates, with a Focus on Inherited Retinal Dystrophy and Hearing Loss

The identification of pathogenic variants in monogenic diseases has been of interest to researchers and clinicians for several decades. However, for inherited diseases with extremely high genetic heterogeneity, such as hearing loss and retinal dystrophies, establishing a molecular diagnosis requires an enormous effort. In this review, we use these two genetic conditions as examples to describe the initial molecular genetic identification approaches, as performed since the early 90s, and subsequent improvements and refinements introduced over the years. Next, the history of DNA sequencing from conventional Sanger sequencing to high-throughput massive parallel sequencing, a.k.a. next-generation sequencing, is outlined, including their advantages and limitations and their impact on identifying the remaining genetic defects. Moreover, the development of recent technologies, also coined "third-generation" sequencing, is reviewed, which holds the promise to overcome these limitations. Furthermore, we outline the importance and complexity of variant interpretation in clinical diagnostic settings concerning the massive number of different variants identified by these methods. Finally, we briefly mention the development of novel approaches such as optical mapping and multiomics, which can help to further identify genetic defects in the near future.

Machine learning approaches for predicting biomolecule-disease associations

Biomolecules, such as microRNAs, circRNAs, lncRNAs and genes, are functionally interdependent in human cells, and all play critical roles in diverse fundamental and vital biological processes. The dysregulations of such biomolecules can cause diseases. Identifying the associations between biomolecules and diseases can uncover the mechanisms of complex diseases, which is conducive to their diagnosis, treatment, prognosis and prevention. Due to the time consumption and cost of biologically experimental methods, many computational association prediction methods have been proposed in the past few years. In this study, we provide a comprehensive review of machine learning-based approaches for predicting disease-biomolecule associations with multi-view data sources. Firstly, we introduce some databases and general strategies for integrating multi-view data sources in the prediction models. Then we discuss several feature representation methods for machine learning-based prediction models. Thirdly, we comprehensively review machine learning-based prediction approaches in three categories: basic machine learning methods, matrix completion-based methods and deep learning-based methods, while discussing their advantages and disadvantages. Finally, we provide some perspectives for further improving biomolecule-disease prediction methods.

Genetic Architecture of Depression: Where Do We Stand Now?

The research of depression genetics has been occupied by historical candidate genes which were tested by candidate gene association studies. However, these studies were mostly not replicable. Thus, genetics of depression have remained elusive for a long time. As research moves from candidate gene association studies to GWAS, the hypothesis-free non-candidate gene association studies in genome-wide level, this trend will likely change. Despite the fact that the earlier GWAS of depression were not successful, the recent GWAS suggest robust findings for depression genetics. These altogether will catalyze a new wave of multidisciplinary research to pin down the neurobiology of depression.

The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease in which genetic factors contribute approximately 70% of etiological effects. Studies have found many significant genetic and environmental factors, but the pathogenesis of AD is still unclear. With the application of microarray and next-generation sequencing technologies, research using genetic data has shown explosive growth. In addition to conventional statistical methods for the processing of these data, artificial intelligence (AI) technology shows obvious advantages in analyzing such complex projects. This article first briefly reviews the application of AI technology in medicine and the current status of genetic research in AD. Then, a comprehensive review is focused on the application of AI in the genetic research of AD, including the diagnosis and prognosis of AD based on genetic data, the analysis of genetic variation, gene expression profile, gene-gene interaction in AD, and genetic analysis of AD based on a knowledge base. Although many studies have yielded some meaningful results, they are still in a preliminary stage. The main shortcomings include the limitations of the databases, failing to take advantage of AI to conduct a systematic biology analysis of multilevel databases, and lack of a theoretical framework for the analysis results. Finally, we outlook the direction of future development. It is crucial to develop high quality, comprehensive, large sample size, data sharing resources; a multi-level system biology AI analysis strategy is one of the development directions, and computational creativity may play a role in theory model building, verification, and designing new intervention protocols for AD.

Next Generation Sequencing and Bioinformatics Analysis of Family Genetic Inheritance

Mendelian and complex genetic trait diseases continue to burden and affect society both socially and economically. The lack of effective tests has hampered diagnosis thus, the affected lack proper prognosis. Mendelian diseases are caused by genetic mutations in a singular gene while complex trait diseases are caused by the accumulation of mutations in either linked or unlinked genomic regions. Significant advances have been made in identifying novel diseases associated mutations especially with the introduction of next generation and third generation sequencing. Regardless, some diseases are still without diagnosis as most tests rely on SNP genotyping panels developed from population based genetic analyses. Analysis of family genetic inheritance using whole genomes, whole exomes or a panel of genes has been shown to be effective in identifying disease-causing mutations. In this review, we discuss next generation and third generation sequencing platforms, bioinformatic tools and genetic resources commonly used to analyze family based genomic data with a focus on identifying inherited or novel disease-causing mutations. Additionally, we also highlight the analytical, ethical and regulatory challenges associated with analyzing personal genomes which constitute the data used for family genetic inheritance.

Human leukocyte antigen association with familial steroid-sensitive nephrotic syndrome

Steroid-sensitive nephrotic syndrome (SSNS) is the most common form of nephrotic syndrome in childhood. Cases with the familial occurrence of SSNS suggest that genetics may play a role in the disease. Human leucocyte antigen (HLA) alleles have been associated with SSNS. We present genetic findings in nine families (44 participants), each with at least two affected siblings. A total of 19 patients were affected with familial SSNS. Six of nine families showed linkage to markers on chromosome 6p (27.29-33.97 Mbp) (Hg19), especially to markers D6S1629 and D6S1560 on HLA dense region in this location. Interestingly, we also found linkage of disease phenotype of familial SSNS on chromosome 15 (91.7-96.9 Mbp) (Hg19) with a logarithm of odds (LOD) score Z = 3.02.Conclusion: Our findings confirm the linkage of HLA markers on chromosome 6, which strengthens the association of HLA alleles in SSNS. What is Known: • Human leukocyte antigen (HLA) alleles have been associated with idiopathic steroid-sensitive nephrotic syndrome (SSNS). Only few studies have investigated the association between HLA alleles and familial SSNS. What is New: • We present evidence of linkage of familial SSNS to chromosome 6p (27.29-33.97 Mbp) (Hg19), especially to markers D6S1629 and D6S1560 on HLA dense region in this location. We also found linkage of the disease phenotype of familial SSNS on chromosome 15 (91.7-96.9 Mbp) (Hg19) with a logarithm of odds (LOD) score of Z = 3.02 following autosomal recessive inheritance pattern.

Genetics in bicuspid aortic valve disease: Where are we?

Bicuspid aortic valve (BAV) is the most common congenital heart defect, found in up to 2% of the population and associated with a 30% lifetime risk of complications. BAV is inherited as an autosomal dominant trait with incomplete penetrance and variable expressivity due to a complex genetic architecture that involves many interacting genes. In this review, we highlight the current state of knowledge about BAV genetics, principles and methods for BAV gene discovery, clinical applications of BAV genetics, and important future directions.

Unravelling the complex genetics of common kidney diseases: from variants to mechanisms

Genome-wide association studies (GWAS) have identified hundreds of loci associated with kidney-related traits such as glomerular filtration rate, albuminuria, hypertension, electrolyte and metabolite levels. However, these impressive, large-scale mapping approaches have not always translated into an improved understanding of disease or development of novel therapeutics. GWAS have several important limitations. Nearly all disease-associated risk loci are located in the non-coding region of the genome and therefore, their target genes, affected cell types and regulatory mechanisms remain unknown. Genome-scale approaches can be used to identify associations between DNA sequence variants and changes in gene expression (quantified through bulk and single-cell methods), gene regulation and other molecular quantitative trait studies, such as chromatin accessibility, DNA methylation, protein expression and metabolite levels. Data obtained through these approaches, used in combination with robust computational methods, can deliver robust mechanistic inferences for translational exploitation. Understanding the genetic basis of common kidney diseases means having a comprehensive picture of the genes that have a causal role in disease development and progression, of the cells, tissues and organs in which these genes act to affect the disease, of the cellular pathways and mechanisms that drive disease, and of potential targets for disease prevention, detection and therapy.

Review: Understanding Rare Genetic Diseases in Low Resource Regions Like Jammu and Kashmir - India

Rare diseases (RDs) are the clinical conditions affecting a few percentage of individuals in a general population compared to other diseases. Limited clinical information and a lack of reliable epidemiological data make their timely diagnosis and therapeutic management difficult. Emerging Next-Generation DNA Sequencing technologies have enhanced our horizons on patho-physiological understanding of many of the RDs and ushered us into an era of diagnostic and therapeutic research related to this ignored health challenge. Unfortunately, relevant research is meager in developing countries which lack a reliable estimate of the exact burden of most of the RDs. India is to be considered as the "Pandora's Box of genetic disorders." Owing to its huge population heterogeneity and high inbreeding or endogamy rates, a higher burden of rare recessive genetic diseases is expected and supported by the literature findings that endogamy is highly detrimental to health as it enhances the degree of homozygosity of recessive alleles in the general population. The population of a low resource region Jammu and Kashmir (J&K) - India, is highly inbred. Some of its population groups variably practice consanguinity. In context with the region's typical geographical topography, highly inbred population structure and unique but heterogeneous gene pool, a huge burden of known and uncharacterized genetic disorders is expected. Unfortunately, many suspected cases of genetic disorders remain undiagnosed or misdiagnosed due to lack of appropriate clinical as well as diagnostic resources in the region, causing patients to face a huge psycho-socio-economic crisis and many a time suffer life-long with their ailment. In this review, the major challenges associated with RDs are highlighted in general and an account on the methods that can be adopted for conducting fruitful molecular genetic studies in genetically vulnerable and low resource regions is also provided, with an example of a region like J&K - India.

The Play of Genes and Non-genetic Factors on Type 2 Diabetes

Diabetes has been a disease of public health concern for a number of decades. It was in the 1930s when scientists made an interesting discovery that the disease is actually divided into two types as some patients were insensitive to insulin treatment then. Type 2 Diabetes which happens to be the non-insulin dependent one is the most common form of the disease and is caused by the interaction between genetic and non-genetic factors. Despite conflicting results, numerous studies have identified genetic and non-genetic factors associated with this common type of diabetes. This review has summarized literature on some genes and non-genetic factors which have been identified to be associated with Type 2 diabetes. It has sourced literature from PubMed, Web of Science and Medline without any limitation to regions, publication types, or languages. The paper has started with the introduction, the play of non-genetic factors, the impact of genes in general, and ended with the interaction between some genes and environmental factors.

Opioid Addiction, Genetic Susceptibility, and Medical Treatments: A Review

Opioid addiction is a chronic and complex disease characterized by relapse and remission. In the past decade, the opioid epidemic or opioid crisis in the United States has raised public awareness. Methadone, buprenorphine, and naloxone have proven their effectiveness in treating addicted individuals, and each of them has different effects on different opioid receptors. Classic and molecular genetic research has provided valuable information and revealed the possible mechanism of individual differences in vulnerability for opioid addiction. The polygenic risk score based on the results of a genome-wide association study (GWAS) may be a promising tool to evaluate the association between phenotypes and genetic markers across the entire genome. A novel gene editing approach, clustered, regularly-interspaced short palindromic repeats (CRISPR), has been widely used in basic research and potentially applied to human therapeutics such as mental illness; many applications against addiction based on CRISPR are currently under research, and some are successful in animal studies. In this article, we summarized the biological mechanisms of opioid addiction and medical treatments, and we reviewed articles about the genetics of opioid addiction, the promising approach to predict the risk of opioid addiction, and a novel gene editing approach. Further research on medical treatments based on individual vulnerability is needed.

Statistical genomics in rare cancer

Rare cancers make of more than 20% of cancer cases. Due to the rare nature, less research has been conducted on rare cancers resulting in worse outcomes for patients with rare cancers compared to common cancers. The ability to study rare cancers is impaired by the ability to collect a large enough set of patients to complete an adequately powered genomic study. In this manuscript we outline analytical approaches and public genomic datasets that have been used in genomic studies of rare cancers. These statistical analysis approaches and study designs include: gene set / pathway analyses, pedigree and consortium studies, meta-analysis or horizontal integration, and integration of multiple types of genomic information or vertical integration. We also discuss some of the publicly available resources that can be leveraged in rare cancer genomic studies.

Genetics of Diabetic Retinopathy

PURPOSE OF REVIEW

The goal of this paper is to review the latest findings in understanding the genetics of diabetic retinopathy. We highlight recent literature using a variety of molecular genetic techniques to identify variants which contribute to genetic susceptibility for diabetic retinopathy.

RECENT FINDINGS

New genome-wide association study (GWAS) and whole-exome sequencing approaches have been utilized to identify both common and rare variants associated with diabetic retinopathy. While variants have been identified in isolated studies, no variants have been replicated across multiple studies. The identification of genetic factors associated with diabetic retinopathy remains elusive. This is due to the multifactorial nature of the disease, small sample sizes for GWAS, and difficulty in controlling covariates of the disease. Larger populations as well as utilization of new sequencing and data analysis techniques may lead to new insights into genetic factors associated with diabetic retinopathy in the future.

Applied Statistics for Human Genetics Using R

This chapter considers the fundamental concepts in the theory of probability and applied statistics in epidemiology, including the biostatistical concepts and measures in genetic association and familial aggregation studies, including: Additional Approaches in Familial Aggregation Studies Twin Studies Adoption Studies Inbreeding Studies Randomization Test Segregation studies, Linkage studies, Association studies Genome-wide Association Studies (GWAS) Big Data and Human Genomics.

Defining the genetic profile of endometriosis

Endometriosis is a pathological condition which has been extensively studied, since its pathophysiology stems from a broad spectrum of environmental influences and genetic factors. Familial studies aim at defining inheritance trends, while linkage analysis studies focus on the identification of genetic sites related to endometriosis susceptibility. Genetic association studies take into account candidate genes and single nucleotide polymorphisms, and hence target at unraveling the association between disease severity and genetic variation. The common goal of various types of studies is, through genetic mapping methods, the timely identification of therapeutic strategies for disease symptoms, including pelvic pain and infertility, as well as efficient counselling. While genome-wide association studies (GWAS) play a primary role in depicting genetic contributions to disease development, they entail a certain bias as regards the case-control nature of their design and the reproducibility of the results. Nevertheless, genetic-oriented studies and the implementation of the results through clinical tests, hold a considerable advantage in proper disease management. In this review article, we present information about gene-gene and gene-environment interactions involved in endometriosis and discuss the effectiveness of GWAS in identitying novel potential therapeutic targets in an attempt to develop novel therapeutic strategies for a better management and treatment of patients with endometriosis.

IMI - Myopia Genetics Report

The knowledge on the genetic background of refractive error and myopia has expanded dramatically in the past few years. This white paper aims to provide a concise summary of current genetic findings and defines the direction where development is needed. We performed an extensive literature search and conducted informal discussions with key stakeholders. Specific topics reviewed included common refractive error, any and high myopia, and myopia related to syndromes. To date, almost 200 genetic loci have been identified for refractive error and myopia, and risk variants mostly carry low risk but are highly prevalent in the general population. Several genes for secondary syndromic myopia overlap with those for common myopia. Polygenic risk scores show overrepresentation of high myopia in the higher deciles of risk. Annotated genes have a wide variety of functions, and all retinal layers appear to be sites of expression. The current genetic findings offer a world of new molecules involved in myopiagenesis. As the missing heritability is still large, further genetic advances are needed. This Committee recommends expanding large-scale, in-depth genetic studies using complementary big data analytics, consideration of gene-environment effects by thorough measurement of environmental exposures, and focus on subgroups with extreme phenotypes and high familial occurrence. Functional characterization of associated variants is simultaneously needed to bridge the knowledge gap between sequence variance and consequence for eye growth.

Established and emerging strategies to crack the genetic code of obesity

Tremendous progress has been made in the genetic elucidation of obesity over the past two decades, driven largely by technological, methodological and organizational innovations. Current strategies for identifying obesity-predisposing loci/genes, including cytogenetics, linkage analysis, homozygosity mapping, admixture mapping, candidate gene studies, genome-wide association studies, custom genotyping arrays, whole-exome sequencing and targeted exome sequencing, have achieved differing levels of success, and the identified loci in aggregate explain only a modest fraction of the estimated heritability of obesity. This review outlines the successes and limitations of these approaches and proposes novel strategies, including the use of exceptionally large sample sizes, the study of diverse ethnic groups and deep phenotypes and the application of innovative methods and study designs, to identify the remaining obesity-predisposing genes. The use of both established and emerging strategies has the potential to crack the genetic code of obesity in the not-too-distant future. The resulting knowledge is likely to yield improvements in obesity prediction, prevention and care.

Network-based association analysis to infer new disease-gene relationships using large-scale protein interactions

Protein-protein interactions integrated with disease-gene associations represent important information for revealing protein functions under disease conditions to improve the prevention, diagnosis, and treatment of complex diseases. Although several studies have attempted to identify disease-gene associations, the number of possible disease-gene associations is very small. High-throughput technologies have been established experimentally to identify the association between genes and diseases. However, these techniques are still quite expensive, time consuming, and even difficult to perform. Thus, based on currently available data and knowledge, computational methods have served as alternatives to provide more possible associations to increase our understanding of disease mechanisms. Here, a new network-based algorithm, namely, Disease-Gene Association (DGA), was developed to calculate the association score of a query gene to a new possible set of diseases. First, a large-scale protein interaction network was constructed, and the relationship between two interacting proteins was calculated with regard to the disease relationship. Novel plausible disease-gene pairs were identified and statistically scored by our algorithm using neighboring protein information. The results yielded high performance for disease-gene prediction, with an F-measure of 0.78 and an AUC of 0.86. To identify promising candidates of disease-gene associations, the association coverage of genes and diseases were calculated and used with the association score to perform gene and disease selection. Based on gene selection, we identified promising pairs that exhibited evidence related to several important diseases, e.g., inflammation, lipid metabolism, inborn errors, xanthomatosis, cerebellar ataxia, cognitive deterioration, malignant neoplasms of the skin and malignant tumors of the cervix. Focusing on disease selection, we identified target genes that were important to blistering skin diseases and muscular dystrophy. In summary, our developed algorithm is simple, efficiently identifies disease–gene associations in the protein-protein interaction network and provides additional knowledge regarding disease-gene associations. This method can be generalized to other association studies to further advance biomedical science.

Genetic predisposition to preeclampsia is conferred by fetal DNA variants near FLT1, a gene involved in the regulation of angiogenesis

Preeclampsia risk is influenced by both the mother's genetic background and the genetics of her fetus; however, the specific genes responsible for conferring preeclampsia risk have largely remained elusive. Evidence that preeclampsia has a genetic predisposition was first detailed in the early 1960s, and overall preeclampsia heritability is estimated at ∼55%. Many traditional gene discovery approaches have been used to investigate the specific genes that contribute to preeclampsia risk, but these have largely not been successful or reproducible. Over the past decade, genome-wide association studies have allowed for significant advances in the understanding of the genetic basis of many common diseases. Genome-wide association studies are predicated on the idea that the genetic basis of many common diseases are complex and polygenic with many variants, each with modest effects that contribute to disease risk. Using this approach in preeclampsia, a large genome-wide association study recently identified and replicated the first robust fetal genomic region associated with excess risk. A screen of >7 million genetic variants in 2658 offspring from preeclamptic women and 308,292 population controls identified a single association signal close to the Fms-like tyrosine kinase 1 gene, on chromosome 13. Fms-like tyrosine kinase 1 encodes soluble Fms-like tyrosine kinase 1, a splice variant of the vascular endothelial growth factor receptor that exerts antiangiogenic activity by inhibiting signaling of proangiogenic factors. The Fms-like tyrosine kinase 1 pathway is central in preeclampsia pathogenesis because excess circulating soluble Fms-like tyrosine kinase 1 in the maternal plasma leads to the hallmark clinical features of preeclampsia, including hypertension and proteinuria. The success of this landmark fetal preeclampsia genome-wide association study suggests that well-powered, larger maternal and fetal genome-wide association study will be fruitful in identifying additional common variants that implicate causal preeclampsia genes and pathways. Such efforts will rely on the continued development of large preeclampsia consortia focused on preeclampsia genetics to obtain adequate sample sizes, detailed clinical phenotyping, and matched maternal-fetal samples. In summary, the fetal preeclampsia genome-wide association study represents an exciting advance in preeclampsia biology, suggesting that dysregulation at the Fms-like tyrosine kinase 1 locus in the fetal genome (likely in the placenta) is a fundamental molecular defect in preeclampsia.

Role of Genetics and Epigenetics in the Pathogenesis of Alzheimer's Disease and Frontotemporal Dementia

Alzheimer's disease (AD) and frontotemporal dementia (FTD) represent the first cause of dementia in senile and pre-senile population, respectively. A percentage of cases have a genetic cause, inherited with an autosomal dominant pattern of transmission. The majority of cases, however, derive from complex interactions between a number of genetic and environmental factors. Gene variants may act as risk or protective factors. Their combination with a variety of environmental exposures may result in increased susceptibility to these diseases or may influence their course. The scenario is even more complicated considering the effect of epigenetics, which encompasses mechanisms able to alter the expression of genes without altering the DNA sequence. In this review, an overview of the current genetic and epigenetic progresses in AD and FTD will be provided, with particular focus on 1) causative genes, 2) genetic risk factors and disease modifiers, and 3) epigenetics, including methylation, non-coding RNAs and chromatin remodeling.

New insights into the genetics of fibromyalgia

Although debate on the concept of fibromyalgia (FM) has been vigorous ever since the classification criteria were first published, FM is now better understood and has become recognized as a disorder. Recently, FM has come to be considered a major health problem, affecting 1% to 5% of the general population. As familial aggregations have been observed among some FM patients, genetic research on FM is logical. In fact, genome-wide association studies and linkage analysis, and studies on candidate genes, have uncovered associations between certain genetic factors and FM. Genetic susceptibility is now considered to influence the etiology of FM. At the same time, novel genetic techniques, such as microRNA analysis, have been used in attempts to improve our understanding of the genetic predisposition to FM. In this article, we review recent advances in, and continuing challenges to, the identification of genes contributing to the development of, and symptom severity in, FM.

Multiple Testing in the Context of Gene Discovery in Sickle Cell Disease Using Genome-Wide Association Studies

The issue of multiple testing, also termed multiplicity, is ubiquitous in studies where multiple hypotheses are tested simultaneously. Genome-wide association study (GWAS), a type of genetic association study that has gained popularity in the past decade, is most susceptible to the issue of multiple testing. Different methodologies have been employed to address the issue of multiple testing in GWAS. The purpose of the review is to examine the methodologies employed in dealing with multiple testing in the context of gene discovery using GWAS in sickle cell disease complications.

The Genetics of Ischemic Heart Disease: From Current Knowledge to Clinical Implications

Ischemic heart disease continues to cause high morbidity and mortality. Its prevalence is expected to increase due to population aging, and its prevention is a major goal of health policies. The risk of developing ischemic heart disease is related to a complex interplay between genetic, environmental, and lifestyle factors. In the last decade, considerable progress has been made in knowledge of the genetic architecture of this disease. This narrative review provides an overview of current knowledge of the genetics of ischemic heart disease and of its translation to clinical practice: identification of new therapeutic targets, assessment of the causal relationship between biomarkers and disease, improved risk prediction, and identification of responders and nonresponders to specific drugs (pharmacogenomics).

Recent advances in predicting gene-disease associations

Deciphering gene–disease association is a crucial step in designing therapeutic strategies against diseases. There are experimental methods for identifying gene–disease associations, such as genome-wide association studies and linkage analysis, but these can be expensive and time consuming. As a result, various in silico methods for predicting associations from these and other data have been developed using different approaches. In this article, we review some of the recent approaches to the computational prediction of gene–disease association. We look at recent advancements in algorithms, categorising them into those based on genome variation, networks, text mining, and crowdsourcing. We also look at some of the challenges faced in the computational prediction of gene–disease associations.

Predicting the pathogenicity of aminoacyl-tRNA synthetase mutations

Aminoacyl-tRNA synthetases (ARSs) are ubiquitously expressed, essential enzymes responsible for charging tRNA with cognate amino acids-the first step in protein synthesis. ARSs are required for protein translation in the cytoplasm and mitochondria of all cells. Surprisingly, mutations in 28 of the 37 nuclear-encoded human ARS genes have been linked to a variety of recessive and dominant tissue-specific disorders. Current data indicate that impaired enzyme function is a robust predictor of the pathogenicity of ARS mutations. However, experimental model systems that distinguish between pathogenic and non-pathogenic ARS variants are required for implicating newly identified ARS mutations in disease. Here, we outline strategies to assist in predicting the pathogenicity of ARS variants and urge cautious evaluation of genetic and functional data prior to linking an ARS mutation to a human disease phenotype.

Posterior tibial tendinopathy: what are the risk factors?

BACKGROUND

Posterior tibial tendinopathy (PTT) is the most common cause of acquired (progressive) flatfoot deformity in adults. To date, PTT research has mainly focused on management rather than on causal mechanisms. The etiology of PTT is likely to be multifactorial because both intrinsic and extrinsic risk factors have been reported. We sought to critically evaluate reported etiologic factors for PTT and consider the concept of genetic risk factors.

METHODS

A detailed review of the literature published after 1936 was undertaken using English-language medical databases.

RESULTS

No clear consensus exists as to the relative importance of the risk factors reported, and neither has any consideration been given to a possible genetic basis for PTT.

CONCLUSIONS

To date, studies have examined various intrinsic and extrinsic risk factors implicated in the etiology of PTT. The interaction of these factors with an individual's genetic background may provide valuable data and help offer a more complete risk profile for PTT. A properly constructed genetic association study to determine the genetic basis of PTT would provide a novel and alternative approach to understanding this condition.

The genetic background of Parkinson's disease: current progress and future prospects

Almost two decades of genetic research in Parkinson's disease (PD) have remarkably increased our knowledge regarding the genetic basis of PD with numerous genes and genetic loci having been found to cause familial PD or affect the risk for PD. Approximately 5-10% of PD patients have monogenic forms of the disease, exhibiting a classical Mendelian type of inheritance, however, the majority PD cases are sporadic, probably caused by a combination of genetic and environmental risk factors. Nowadays, six genes, alpha synuclein, LRRK2, VPS35, Parkin, PINK1 and DJ-1, have definitely been associated with an autosomal dominant or recessive PD mode of inheritance. The advent of genome-wide association studies (GWAS) and the implementation of new technologies, like next generation sequencing (NGS) and exome sequencing has undoubtedly greatly aided the identification on novel risk variants for sporadic PD. In this review, we will summarize the current progress and future prospects in the field of PD genetics.

Identity-by-descent mapping for diastolic blood pressure in unrelated Mexican Americans

Population-based identity by descent (IBD) mapping is a statistical method for detection of genetic loci that share an ancestral segment among “unrelated” pairs of individuals for a disease. As a complementary method to genome-wide association studies, IBD mapping is robust to allelic heterogeneity and may identify rare inherited variants when combined with sequence data.

Our objective is to identify the causal genes for diastolic blood pressure (DBP). We applied a population-based IBD mapping method to 105 unrelated individuals selected from the family data provided for the Genetic Analysis Workshop 19. Using the genome-wide association study data (ie, the microarray data), chromosome 3 was scanned for IBD sharing segments among all pairs of these individuals. At the chromosomal region with the most significant relationship between IBD sharing and DBP, the whole genome sequence data were examined to identify the risk variants for DBP.

The most significant chromosomal region that was identified to have a relationship between the IBD sharing and DBP was at 3q12.3 (p = 0.0016), although it did not achieve the chromosome-wide significance level (p = 0.00012). This chromosomal region contains 1 gene, ZPLD1, which has been reported to be associated with cerebral cavernous malformations, a disease with enlarged small blood vessels (capillaries) in the brain. Although 24 deleterious variants were identified at this region, no significant association was found between these variants and DBP (p = 0.40).

We presented a mapping strategy which combined a population-based IBD mapping method with sequence data analyses. One gene was located at a chromosomal region identified by this method for DBP. However, further study with a large sample size is needed to assess this result.

Making sense of the cause of Crohn's - a new look at an old disease

The cause of Crohn’s disease (CD) has posed a conundrum for at least a century. A large body of work coupled with recent technological advances in genome research have at last started to provide some of the answers. Initially this review seeks to explain and to differentiate between bowel inflammation in the primary immunodeficiencies that generally lead to very early onset diffuse bowel inflammation in humans and in animal models, and the real syndrome of CD. In the latter, a trigger, almost certainly enteric infection by one of a multitude of organisms, allows the faeces access to the tissues, at which stage the response of individuals predisposed to CD is abnormal. Direct investigation of patients’ inflammatory response together with genome-wide association studies (GWAS) and DNA sequencing indicate that in CD the failure of acute inflammation and the clearance of bacteria from the tissues, and from within cells, is defective. The retained faecal products result in the characteristic chronic granulomatous inflammation and adaptive immune response. In this review I will examine the contemporary evidence that has led to this understanding, and look for explanations for the recent dramatic increase in the incidence of this disease.

Making sense of the cause of Crohn's - a new look at an old disease

The cause of Crohn's disease (CD) has posed a conundrum for at least a century. A large body of work coupled with recent technological advances in genome research have at last started to provide some of the answers. Initially this review seeks to explain and to differentiate between bowel inflammation in the primary immunodeficiencies that generally lead to very early onset diffuse bowel inflammation in humans and in animal models, and the real syndrome of CD. In the latter, a trigger, almost certainly enteric infection by one of a multitude of organisms, allows the faeces access to the tissues, at which stage the response of individuals predisposed to CD is abnormal. Direct investigation of patients' inflammatory response together with genome-wide association studies (GWAS) and DNA sequencing indicate that in CD the failure of acute inflammation and the clearance of bacteria from the tissues, and from within cells, is defective. The retained faecal products result in the characteristic chronic granulomatous inflammation and adaptive immune response. In this review I will examine the contemporary evidence that has led to this understanding, and look for explanations for the recent dramatic increase in the incidence of this disease.

Insights into genetic susceptibility in the etiology of spontaneous preterm birth

Preterm birth (PTB; <37 weeks of gestation) is a complex disorder, whose etiology is influenced by a variety of factors. A greater understanding of the biological mechanisms that contribute to PTB will facilitate identification of those at increased risk and may inform new treatments. To accomplish this, it is vital to elucidate the heritability patterns of this condition as well as the environment and lifestyle factors that increase risk for PTB. Identifying individual genes that contribute to the etiology of PTB presents particular challenges, and there has been little agreement among candidate gene and genome-wide studies performed to date. In this review we will evaluate recent genetic studies of spontaneous PTB, discuss common themes among their findings, and suggest approaches for future studies of PTB.

Linkage and whole genome sequencing identify a locus on 6q25-26 for formal thought disorder and implicate MEF2A regulation

Formal thought disorder is a major feature of schizophrenia and other psychotic disorders. It is heritable, found in healthy relatives of patients with schizophrenia and other mental disorders but knowledge of specific genetic factors is lacking. The aim of this study was to search for biologically relevant high-risk variants. Formal thought disorder was assessed in participants in the Copenhagen Schizophrenia Linkage Study (N=236), a unique high-risk family study comprised of six large pedigrees. Microsatellite linkage analysis of formal thought disorder was performed and subsequent haplotype analysis of the implicated region using phased microsatellite and SNP genotypes. Whole genome sequencing (N=3) was used in the attempt to identify causative variants in the linkage region. Linkage analysis of formal thought disorder resulted in a single peak at chromosome 6(q26-q27) centred on marker D6S1277, with a maximum LOD score of 4.0. Phasing and fine mapping of the linkage peak identified a 5.5Mb haplotype (chr6:162242322-167753547, hg18) in 31 individuals, all belonging to the same pedigree sharing the haplotype from a common ancestor. The haplotype segregated with increased total thought disorder index score (P=4.9 × 10(-5)) and qualitatively severe forms of thought disturbances. Whole genome sequencing identified a novel nucleotide deletion (chr6:164377205 AG>A, hg18) predicted to disrupt the potential binding of the transcription factor MEF2A. The MEF2A binding site is located between two genes previously reported to associate with schizophrenia, QKI (HGNC:21100) and PDE10A (HGNC:8772). The findings are consistent with MEF2A deregulation conferring risk of formal thought disorder.

Dissecting Complex and Multifactorial Nature of Alzheimer's Disease Pathogenesis: a Clinical, Genomic, and Systems Biology Perspective

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by loss of memory and other cognitive functions. AD can be classified into familial AD (FAD) and sporadic AD (SAD) based on heritability and into early onset AD (EOAD) and late onset AD (LOAD) based on age of onset. LOAD cases are more prevalent with genetically complex architecture. In spite of significant research focused on understanding the etiological mechanisms, search for diagnostic biomarker(s) and disease-modifying therapy is still on. In this article, we aim to comprehensively review AD literature on established etiological mechanisms including role of beta-amyloid and apolipoprotein E (APOE) along with promising newer etiological factors such as epigenetic modifications that have been associated with AD suggesting its multifactorial nature. As genomic studies have recently played a significant role in elucidating AD pathophysiology, a systematic review of findings from genome-wide linkage (GWL), genome-wide association (GWA), genome-wide expression (GWE), and epigenome-wide association studies (EWAS) was conducted. The availability of multi-dimensional genomic data has further coincided with the advent of computational and network biology approaches in recent years. Our review highlights the importance of integrative approaches involving genomics and systems biology perspective in elucidating AD pathophysiology. The promising newer approaches may provide reliable means of early and more specific diagnosis and help identify therapeutic interventions for LOAD.

Exploring Genetic Susceptibility to Fibromyalgia

Fibromyalgia (FM) affects 1% to 5% of the population, and approximately 90% of the affected individuals are women. FM patients experience impaired quality of life and the disorder places a considerable economic burden on the medical care system. With the recognition of FM as a major health problem, many recent studies have evaluated the pathophysiology of FM. Although the etiology of FM remains unknown, it is thought to involve some combination of genetic susceptibility and environmental exposure that triggers further alterations in gene expression. Because FM shows marked familial aggregation, most previous research has focused on genetic predisposition to FM and has revealed associations between genetic factors and the development of FM, including specific gene polymorphisms involved in the serotonergic, dopaminergic, and catecholaminergic pathways. The aim of this review was to discuss the current evidence regarding genetic factors that may play a role in the development and symptom severity of FM.

Aetiological coding sequence variants in non-syndromic premature ovarian failure: From genetic linkage analysis to next generation sequencing

Premature ovarian failure (POF) is a frequent pathology affecting 1-1.5% of women under 40 years old. Despite advances in diagnosing and treating human infertility, POF is still classified as being idiopathic in 50-80% of cases, strongly suggesting a genetic origin for the disease. Different types of autosomal and X-linked genetic anomalies can originate the phenotype in syndromic and non-syndromic POF cases. Particular interest has been focused on research into non-syndromic POF causative coding variants during the past two decades. This has been based on the assumption that amino acid substitutions might modify the intrinsic physicochemical properties of functional proteins, thereby inducing pathological phenotypes. In this case, a restricted number of mutations might originate the disease. However, like other complex pathologies, POF might result from synergistic/compensatory effects caused by several low-to-mildly drastic mutations which have frequently been classified as non-functional SNPs. Indeed, reproductive phenotypes can be considered as quantitative traits resulting from the subtle interaction of many genes. Although numerous sequencing projects have involved candidate genes, only a few coding mutations explaining a low percentage of cases have been described. Such apparent failure to identify aetiological coding sequence variations might have been due to the inherent molecular complexity of mammalian reproduction and to the difficulty of simultaneously analysing large genomic regions by Sanger sequencing. The purpose of this review is to present the molecular and cellular effects caused by non-synonymous mutations which have been formally associated, by functional tests, with the aetiology of hypergonadotropic non-syndromic POF. Considerations have also been included regarding the polygenic nature of reproduction and POF, as well as future approaches for identifying novel aetiological genes based on next generation sequencing (NGS).