Genetic approaches to studying common diseases and complex traits

Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: Scoping Review

BACKGROUND

An increasing body of literature highlights the integration of machine learning with genomic data in psychiatry, particularly for complex mental health disorders such as schizophrenia. These advanced techniques offer promising potential for uncovering various facets of these disorders. A comprehensive review of the current applications of machine learning in conjunction with genomic data within this context can significantly enhance our understanding of the current state of research and its future directions.

OBJECTIVE

This study aims to conduct a systematic scoping review of the use of machine learning algorithms with genomic data in the field of schizophrenia.

METHODS

To conduct a systematic scoping review, a search was performed in the electronic databases MEDLINE, Web of Science, PsycNet (PsycINFO), and Google Scholar from 2013 to 2024. Studies at the intersection of schizophrenia, genomic data, and machine learning were evaluated.

RESULTS

The literature search identified 2437 eligible articles after removing duplicates. Following abstract screening, 143 full-text articles were assessed, and 121 were subsequently excluded. Therefore, 21 studies were thoroughly assessed. Various machine learning algorithms were used in the identified studies, with support vector machines being the most common. The studies notably used genomic data to predict schizophrenia, identify schizophrenia features, discover drugs, classify schizophrenia amongst other mental health disorders, and predict the quality of life of patients.

CONCLUSIONS

Several high-quality studies were identified. Yet, the application of machine learning with genomic data in the context of schizophrenia remains limited. Future research is essential to further evaluate the portability of these models and to explore their potential clinical applications.

Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery

MOTIVATION

Genome-wide association studies (GWAS) have enabled large-scale analysis of the role of genetic variants in human disease. Despite impressive methodological advances, subsequent clinical interpretation and application remains challenging when GWAS suffer from a lack of statistical power. In recent years, however, the use of information diffusion algorithms with molecular networks has led to fruitful insights on disease genes.

RESULTS

We present an overview of the design choices and pitfalls that prove crucial in the application of network propagation methods to GWAS summary statistics. We highlight general trends from the literature, and present benchmark experiments to expand on these insights selecting as case study three diseases and five molecular networks. We verify that the use of gene-level scores based on GWAS P-values offers advantages over the selection of a set of 'seed' disease genes not weighted by the associated P-values if the GWAS summary statistics are of sufficient quality. Beyond that, the size and the density of the networks prove to be important factors for consideration. Finally, we explore several ensemble methods and show that combining multiple networks may improve the network propagation approach.

Essential Role of Multi-Omics Approaches in the Study of Retinal Vascular Diseases

Retinal vascular disease is a highly prevalent vision-threatening ocular disease in the global population; however, its exact mechanism remains unclear. The expansion of omics technologies has revolutionized a new medical research methodology that combines multiple omics data derived from the same patients to generate multi-dimensional and multi-evidence-supported holistic inferences, providing unprecedented opportunities to elucidate the information flow of complex multi-factorial diseases. In this review, we summarize the applications of multi-omics technology to further elucidate the pathogenesis and complex molecular mechanisms underlying retinal vascular diseases. Moreover, we proposed multi-omics-based biomarker and therapeutic strategy discovery methodologies to optimize clinical and basic medicinal research approaches to retinal vascular diseases. Finally, the opportunities, current challenges, and future prospects of multi-omics analyses in retinal vascular disease studies are discussed in detail.

Polygenic and Network-Based Studies in Risk Identification and Demystification of cancer

INTRODUCTION

Diseases were initially thought to be the consequence of a single gene mutation. Advances in DNA sequencing tools and our understanding of gene behavior have revealed that complex diseases, such as cancer, are the product of genes cooperating with each other and with their environment in orchestrated communication networks. Seeing that the function of individual genes is still used to analyze cancer, the shift to using functionally interacting groups of genes as a new unit of study holds promise for demystifying cancer.

AREAS COVERED

The literature search focused on three types of cancer, namely breast, lung, and prostate, but arguments from other cancers were also included. The aim was to prove that multigene analyses can accurately predict and prognosticate cancer risk, subtype cancer for more personalized and effective treatments, and discover anti-cancer therapies. Computational intelligence is being harnessed to analyze this type of data and is proving indispensable to scientific progress.

EXPERT OPINION

In the future, comprehensive profiling of all kinds of patient data (e.g., serum molecules, environmental exposures) can be used to build universal networks that should help us elucidate the molecular mechanisms underlying diseases and provide appropriate preventive measures, ensuring lifelong health and longevity.

Multi-Omics Profiling Approach to Asthma: An Evolving Paradigm

Asthma is a complex multifactorial and heterogeneous respiratory disease. Although genetics is a strong risk factor of asthma, external and internal exposures and their interactions with genetic factors also play important roles in the pathophysiology of asthma. Over the past decades, the application of high-throughput omics approaches has emerged and been applied to the field of asthma research for screening biomarkers such as genes, transcript, proteins, and metabolites in an unbiased fashion. Leveraging large-scale studies representative of diverse population-based omics data and integrating with clinical data has led to better profiling of asthma risk. Yet, to date, no omic-driven endotypes have been translated into clinical practice and management of asthma. In this article, we provide an overview of the current status of omics studies of asthma, namely, genomics, transcriptomics, epigenomics, proteomics, exposomics, and metabolomics. The current development of the multi-omics integrations of asthma is also briefly discussed. Biomarker discovery following multi-omics profiling could be challenging but useful for better disease phenotyping and endotyping that can translate into advances in asthma management and clinical care, ultimately leading to successful precision medicine approaches.

Genetic variations (eQTLs) in muscle transcriptome and mitochondrial genes, and trans-eQTL molecular pathways in feed efficiency from Danish breeding pigs

Feed efficiency (FE) is a key trait in pig production, as improvement in FE has positive economic and environmental impact. FE is a complex phenotype and testing animals for FE is costly. Therefore, there has been a desire to find functionally relevant single nucleotide polymorphisms (SNPs) as biomarkers, to improve our biological understanding of FE as well as accuracy of genomic prediction for FE. We have performed a cis- and trans- eQTL (expression quantitative trait loci) analysis, in a population of Danbred Durocs (N = 11) and Danbred Landrace (N = 27) using both a linear and ANOVA model based on muscle tissue RNA-seq. We analyzed a total of 1425x19179 or 2.7x10⁷ Gene-SNP combinations in eQTL detection models for FE. The 1425 genes were from RNA-Seq based differential gene expression analyses using 25880 genes related to FE and additionally combined with mitochondrial genes. The 19179 SNPs were from applying stringent quality control and linkage disequilibrium filtering on genotype data using a GGP Porcine HD 70k SNP array. We applied 1000 fold bootstrapping and enrichment analysis to further validate and analyze our detected eQTLs. We identified 13 eQTLs with FDR < 0.1, affecting several genes found in previous studies of commercial pig breeds. Examples include MYO19, CPT1B, ACSL1, IER5L, CPT1A, SUCLA2, CSRNP1, PARK7 and MFF. The bootstrapping results showed statistically significant enrichment (p-value<2.2x10^-16) of eQTLs with p-value < 0.01 in both cis and trans-eQTLs. Enrichment analysis of top trans-eQTLs revealed high enrichment for gene categories and gene ontologies associated with genomic context and expression regulation. This included transcription factors (p-value = 1.0x10^-13), DNA-binding (GO:0003677, p-value = 8.9x10^-14), DNA-binding transcription factor activity (GO:0003700,) nucleus gene (GO:0005634, p-value<2.2x10^-16), negative regulation of expression (GO:0010629, p-value<2.2x10^-16). These results would be useful for future genome assisted breeding of pigs to improve FE, and in the improved understanding of the functional mechanism of trans eQTLs.

Simple and accurate visual detection of single nucleotide polymorphism based on colloidal gold nucleic acid strip biosensor and primer-specific PCR

Single nucleotide polymorphism (SNP) was associated with many human diseases, therefore, SNP detection was important for early diagnosis and clinical prognosis. Herein, a simple and accurate method for visual detection SNP sites (A/A, G/G, A/G) in CYP1A1 gene related to cancers based on colloidal gold nucleic acid strip biosensor and primer-specific polymerase chain reaction (PCR) was established. This method could directly distinguish SNP sites on strip biosensor by introducing twice PCR amplifications. The second PCR (primer-specific PCR) was performed using specific product of the first PCR as template, thus this twice PCR could reduce non-specific amplification greatly and obtain target product. In addition, single-strand or double-strand DNA (ssDNA or dsDNA) was accurately produced by introducing mismatched base at the 3' end of forward primers in primer-specific PCR. The designed strip biosensor could only combine with the ssDNA, thus visual detection of SNP could be achieved within 10 min by color difference of a pair of strips. 61 human blood samples by this method were identical with those of pyrosequencing. This method had the advantages of rapid, visual and low-cost and was expected to be applied in medical diagnosis.

Contributions of Interactions Between Lifestyle and Genetics on Coronary Artery Disease Risk

PURPOSE OF THE REVIEW

To summarize current knowledge on interactions between genetic variants and lifestyle factors (G×L) associated with the development of coronary artery disease (CAD) and prioritize future research.

RECENT FINDINGS

Genetic risk and combined lifestyle factors and behaviors have a log-additive effect on the risk of developing CAD. First, we describe genetic and lifestyle factors associated with CAD and then focus on G×L interactions. The majority of G×L interaction studies are small-scale candidate gene studies that lack replication and therefore provide spurious results. Only a few studies, of which most use genetic risk scores or genome-wide approaches to test interactions, are robust in number and analysis strategy. These studies provide evidence for the existence of G×L interactions in the development of CAD. Further G×L interactions studies are important as they contribute to our understanding of disease pathophysiology and possibly provide insights for improving interventions or personalized recommendations.

Genetics and the heart rate response to exercise

The acute heart rate response to exercise, i.e., heart rate increase during and heart rate recovery after exercise, has often been associated with all-cause and cardiovascular mortality. The long-term response of heart rate to exercise results in favourable changes in chronotropic function, including decreased resting and submaximal heart rate as well as increased heart rate recovery. Both the acute and long-term heart rate response to exercise have been shown to be heritable. Advances in genetic analysis enable researchers to investigate this hereditary component to gain insights in possible molecular mechanisms underlying interindividual differences in the heart rate response to exercise. In this review, we comprehensively searched candidate gene, linkage, and genome-wide association studies that investigated the heart rate response to exercise. A total of ten genes were associated with the acute heart rate response to exercise in candidate gene studies. Only one gene (CHRM2), related to heart rate recovery, was replicated in recent genome-wide association studies (GWASs). Additional 17 candidate causal genes were identified for heart rate increase and 26 for heart rate recovery in these GWASs. Nine of these genes were associated with both acute increase and recovery of the heart rate during exercise. These genes can be broadly categorized into four categories: (1) development of the nervous system (CCDC141, PAX2, SOX5, and CAV2); (2) prolongation of neuronal life span (SYT10); (3) cardiac development (RNF220 and MCTP2); (4) cardiac rhythm (SCN10A and RGS6). Additional 10 genes were linked to long-term modification of the heart rate response to exercise, nine with heart rate increase and one with heart rate recovery. Follow-up will be essential to get functional insights in how candidate causal genes affect the heart rate response to exercise. Future work will be required to translate these findings to preventive and therapeutic applications.

Genetic Influences on Behavioral Outcomes After Childhood TBI: A Novel Systems Biology-Informed Approach

Objectives: To test whether genetic associations with behavioral outcomes after early childhood traumatic brain injury (TBI) are enriched for biologic pathways underpinning neurocognitive and behavioral networks.

Design: Cross-sectional evaluation of the association of genetic factors with early (~ 6 months) and long-term (~ 7 years) post-TBI behavioral outcomes. We combined systems biology and genetic association testing methodologies to identify biologic pathways associated with neurocognitive and behavior outcomes after TBI. We then evaluated whether genes/single nucleotide polymorphism (SNPs) associated with these biologic pathways were more likely to demonstrate a relationship (i.e., enrichment) with short and long-term behavioral outcomes after early childhood TBI compared to genes/SNPs not associated with these biologic pathways.

Setting: Outpatient research setting.

Participants:140 children, ages 3–6:11 years at time of injury, admitted for a TBI or orthopedic injury (OI).

Interventions: Not Applicable.

Main Outcome Measures: Child behavior checklist total problems T score.

Results: Systems biology methodology identified neuronal systems and neurotransmitter signaling (Glutamate receptor, dopamine, serotonin, and calcium signaling), inflammatory response, cell death, immune systems, and brain development as important biologic pathways to neurocognitive and behavioral outcomes after TBI. At 6 months post injury, the group (TBI versus OI) by polymorphism interaction was significant when the aggregate signal from the highest ranked 40% of case gene associations was compared to the control set of genes. At ~ 7 years post injury, the selected polymorphisms had a significant main effect after controlling for injury type when the aggregate signal from the highest ranked 10% of the case genes were compared to the control set of genes

Conclusions: Findings demonstrate the promise of applying a genomics approach, informed by systems biology, to understanding behavioral recovery after pediatric TBI. A mixture of biologic pathways and processes are associated with behavioral recovery, specifically genes associated with cell death, inflammatory response, neurotransmitter signaling, and brain development. These results provide insights into the complex biology of TBI recovery.

The Post-GWAS Era: From Association to Function

During the past 12 years, genome-wide association studies (GWASs) have uncovered thousands of genetic variants that influence risk for complex human traits and diseases. Yet functional studies aimed at delineating the causal genetic variants and biological mechanisms underlying the observed statistical associations with disease risk have lagged. In this review, we highlight key advances in the field of functional genomics that may facilitate the derivation of biological meaning post-GWAS. We highlight the evidence suggesting that causal variants underlying disease risk often function through regulatory effects on the expression of target genes and that these expression effects might be modest and cell-type specific. We moreover discuss specific studies as proof-of-principle examples for current statistical, bioinformatic, and empirical bench-based approaches to downstream elucidation of GWAS-identified disease risk loci.

Dopamine pathway gene variants may modulate cognitive performance in the DHS - Mind Study

BACKGROUND

There is an established association between type 2 diabetes and accelerated cognitive decline. The exact mechanism linking type 2 diabetes and reduced cognitive function is less clear. The monoamine system, which is extensively involved in cognition, can be altered by type 2 diabetes status. Thus, this study hypothesized that sequence variants in genes linked to dopamine metabolism and associated pathways are associated with cognitive function as assessed by the Digit Symbol Substitution Task, the Modified Mini-Mental State Examination, the Stroop Task, the Rey Auditory-Verbal Learning Task, and the Controlled Oral Word Association Task for Phonemic and Semantic Fluency in the Diabetes Heart Study, a type 2 diabetes-enriched familial cohort (n = 893).

METHODS

To determine the effects of candidate variants on cognitive performance, genetic association analyses were performed on the well-documented variable number tandem repeat located in the 3' untranslated region of the dopamine transporter, as well as on single-nucleotide polymorphisms covering genes in the dopaminergic pathway, the insulin signaling pathway, and the convergence of both. Next, polymorphisms in loci of interest with strong evidence for involvement in dopamine processing were extracted from genetic datasets available in a subset of the cohort (n = 572) derived from Affymetrix(®) Genome-Wide Human SNP Array 5.0 and 1000 Genomes imputation from this array.

RESULTS

The candidate gene analysis revealed one variant from the DOPA decarboxylase gene, rs10499695, to be associated with poorer performance on a subset of Rey Auditory-Verbal Learning Task measuring retroactive interference (P = 0.001, β = -0.45). Secondary analysis of genome-wide and imputed data uncovered another DOPA decarboxylase variant, rs62445903, also associated with retroactive interference (P = 7.21 × 10(-7), β = 0.3). These data suggest a role for dopaminergic genes, specifically a gene involved in regulation of dopamine synthesis, in cognitive performance in type 2 diabetes.

Integrative Biology of Diabetic Kidney Disease

BACKGROUND

The leading cause of ESRD in the U.S. is diabetic kidney disease (DKD). Despite significant efforts to improve outcomes in DKD, the impact on disease progression has been disappointing. This has prompted clinicians and researchers to search for alternative approaches to identify persons at risk, and to search for more effective therapies to halt progression of DKD. Identification of novel therapies is critically dependent on a more comprehensive understanding of the pathophysiology of DKD, specifically at the molecular level. A more expansive and exploratory view of DKD is needed to complement more traditional research approaches that have focused on single molecules.

SUMMARY

In recent years, sophisticated research methodologies have emerged within systems biology that should allow for a more comprehensive disease definition of DKD. Systems biology provides an inter-disciplinary approach to describe complex interactions within biological systems including how these interactions influence systems' functions and behaviors. Computational modeling of large, system-wide, quantitative data sets is used to generate molecular interaction pathways, such as metabolic and cell signaling networks.

KEY MESSAGES

Importantly, interpretation of data generated by systems biology tools requires integration with enhanced clinical research data and validation using model systems. Such an integrative biological approach has already generated novel insights into pathways and molecules involved in DKD. In this review, we highlight recent examples of how combining systems biology with traditional clinical and model research efforts results in an integrative biology approach that has significantly added to the understanding of the complex pathophysiology of DKD.

Clinical genetic research 2: Genetic epidemiology of complex phenotypes

Genetic factors play a substantive role in the susceptibility to common diseases. Due to recent and rapid advancements in characterization of genetic variants and large-scale genotyping platforms, multiple genes and genetic variants have now been identified for common, complex diseases. The most efficient method for gene identification at present appears to be large-scale association-based studies, which integrate genetic and epidemiological principles. As the strategy for gene identification studies has shifted towards genetic association-based methods rather than traditional linkage analysis, epidemiological methods are increasingly being integrated into genetic investigations. Consequently, the disciplines of genetics and epidemiology, which historically have functioned separately, have been integrated into a discipline referred to as genetic epidemiology. In this chapter, we review methods for establishing the genetic burden of complex genetic disease, followed by methods for gene and/or genetic variant identification and when appropriate we highlight the epidemiological issues that guide these methods.

Global secretome characterization of A549 human alveolar epithelial carcinoma cells during Mycoplasma pneumoniae infection

BACKGROUND

Mycoplasma pneumoniae (M. pneumoniae) is one of the major etiological agents for community-acquired pneumonia (CAP) in all age groups. The early host response to M. pneumoniae infection relies on the concerted release of proteins with various biological activities. However, no comprehensive analysis of the secretory proteins has been conducted to date regarding the host response upon M. pneumoniae infection.

RESULTS

We employed the liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based label-free quantitative proteomic technology to identify and characterize the members of the human alveolar epithelial carcinoma A549 cell secretome during M. pneumoniae infection. A total of 256 proteins were identified, with 113 being differentially expressed (>1.5-fold change), among which 9 were only expressed in control cells, 10 only in M. pneumoniae-treated cells, while 55 were up-regulated and 39 down-regulated by M. pneumoniae. The changed expression of some of the identified proteins was validated by RT-PCR and immunoblot analysis. Cellular localization analysis of the secretome data revealed 59.38% of the proteins were considered as "putative secretory proteins". Functional analysis revealed that the proteins affected upon M. pneumoniae infection were mainly related to metabolic process, stress response, and immune response. We further examined the level of one up-regulated protein, IL-33, in clinical samples. The result showed that IL-33 levels were significantly higher in the plasma and bronchoalveolar lavage fluid (BALF) of M. pneumoniae pneumonia (MPP) patients.

CONCLUSIONS

The present study provided systematic information about the changes in the expression of secretory proteins during M. pneumoniae infection, which is useful for the discovery of specific biomarkers and targets for pharmacological intervention.

Lumbar disc degeneration is linked to a carbohydrate sulfotransferase 3 variant

Lumbar disc degeneration (LDD) is associated with both genetic and environmental factors and affects many people worldwide. A hallmark of LDD is loss of proteoglycan and water content in the nucleus pulposus of intervertebral discs. While some genetic determinants have been reported, the etiology of LDD is largely unknown. Here we report the findings from linkage and association studies on a total of 32,642 subjects consisting of 4,043 LDD cases and 28,599 control subjects. We identified carbohydrate sulfotransferase 3 (CHST3), an enzyme that catalyzes proteoglycan sulfation, as a susceptibility gene for LDD. The strongest genome-wide linkage peak encompassed CHST3 from a Southern Chinese family–based data set, while a genome-wide association was observed at rs4148941 in the gene in a meta-analysis using multiethnic population cohorts. rs4148941 lies within a potential microRNA-513a-5p (miR-513a-5p) binding site. Interaction between miR-513a-5p and mRNA transcribed from the susceptibility allele (A allele) of rs4148941 was enhanced in vitro compared with transcripts from other alleles. Additionally, expression of CHST3 mRNA was significantly reduced in the intervertebral disc cells of human subjects carrying the A allele of rs4148941. Together, our data provide new insights into the etiology of LDD, implicating an interplay between genetic risk factors and miRNA.

Prevention of Osteoporosis and Bone Fragility

The importance of optimal bone growth in childhood and adolescence has been recognized as one of the key strategies in osteoporotic fracture prevention. Low birth size, poor childhood growth, and low peak bone mass at the cessation of growth have been linked to the later risk of osteoporosis and hip fracture. Formerly, the focus was merely on maximizing bone mineral accrual because a high peak bone mineral mass may prevent attainment of a critical “fracture threshold” associated with age-related bone loss and osteoporosis. More recently, the focus has shifted away from bone mineral accrual—as measured by dual-energy X-ray absorptiometry (DXA)—toward the optimization of bone strength. This is partly because of the advances in bone imaging that have enabled estimation of bone strength beyond bone mass. In this review, we briefly describe long-bone growth and structural development and our abilities to assess bone properties by medical imaging tools. In addition, we summarize the evidence of factors contributing to skeletal growth, bone fragility, and the development of strong, healthy bones.

Empirical hierarchical bayes approach to gene-environment interactions: development and application to genome-wide association studies of lung cancer in TRICL

The analysis of gene-environment (G × E) interactions remains one of the greatest challenges in the postgenome-wide association studies (GWASs) era. Recent methods constitute a compromise between the robust but underpowered case-control and powerful case-only methods. Inferences of the latter are biased when the assumption of gene-environment (G-E) independence in controls fails. We propose a novel empirical hierarchical Bayes approach to G × E interaction (EHB-GE), which benefits from greater rank power while accounting for population-based G-E correlation. Building on Lewinger et al.'s ([2007] Genet Epidemiol 31:871-882) hierarchical Bayes prioritization approach, the method first obtains posterior G-E correlation estimates in controls for each marker, borrowing strength from G-E information across the genome. These posterior estimates are then subtracted from the corresponding case-only G × E estimates. We compared EHB-GE with rival methods using simulation. EHB-GE has similar or greater rank power to detect G × E interactions in the presence of large numbers of G-E correlations with weak to strong effects or only a low number of such correlations with large effect. When there are no or only a few weak G-E correlations, Murcray et al.'s method ([2009] Am J Epidemiol 169:219-226) identifies markers with low G × E interaction effects better. We applied EHB-GE and competing methods to four lung cancer case-control GWAS from the Interdisciplinary Research in Cancer of the Lung/International Lung Cancer Consortium with smoking as environmental factor. A number of genes worth investigating were identified by the EHB-GE approach.

Bioinformatics and systems biology analysis of genes network involved in OLP (Oral Lichen Planus) pathogenesis

BACKGROUND

Genes involved in different biological processes form complex interaction networks. However, only few of them have a high number of interactions with the other genes in the network and therefore they may play a major role. In previous bioinformatics and experimental studies, these genes were identified and termed as "leader genes". In the current ab initio theoretical study, genes involved in human OLP (Oral Lichen Planus) pathogenesis are identified and ranked according to their number of interactions, in order to obtain a broader view of its molecular mechanisms and to plan targeted experimentations.

METHODS

Genes involved or potentially involved in OLP were identified by systematically querying several databases until the identification of a final set of genes. Interactions among these genes were mapped and given a significance score using STRING database. For each gene, significance scores were summed to obtain a weighted number of links (WNL) and subsequently genes were clustered according to this parameter. The genes in the highest cluster were termed as leader genes; the other ones were ranked as class B genes, class C genes, and so on. This study was complemented by a topological analysis of the network, carried out using Cytoscape, BinGO and FANMOD software.

RESULTS

The interactions in the obtained network showed power law behaviour, in agreement with the scale-free topology theory of the biological graphs. 132 genes were identified and five of them (namely, JUN, EGFR, FOS, IL2, ITGB4) were classified as leaders. Interestingly, all of them but EGFR were up-regulated and were widely distributed in the network (in term of topological parameters such as stress, eccentricity and radiality) and showed higher topological coefficients than the other genes.

CONCLUSIONS

Even with the limitations of any ab initio analysis, this study can suggest targeted experimentation, focused on the leader genes and therefore simpler to be analysed than mass scale molecular genomics. Moreover, it may suggest new potential risk factors and therapeutic targets.

Genetics of pelvic organ prolapse: crossing the bridge between bench and bedside in urogynecologic research

An increasing number of scientists have studied the molecular and biochemical basis of pelvic organ prolapse (POP). The extracellular matrix content of the pelvic floor is the major focus of those investigations and pointed for potential molecular markers of the dysfunction. The identification of women predisposed to develop POP would help in the patients' management and care. This article includes a critical analysis of the literature up to now; discusses implications for future research and the role of the genetics in POP.

Update in chronic obstructive pulmonary disease: role of antioxidant and metabolizing gene polymorphisms

Chronic obstructive pulmonary disease (COPD) is characterized by systemic and local chronic inflammation and oxidative stress. The sources of the increased oxidative stress in COPD patients derive from the increased burden of inhaled oxidants such as cigarette smoke and other forms of particulate or gaseous air pollution and from the increase in reactive oxygen species (ROS) generated by several inflammatory, immune, and structural airways cells. There is increasing evidence that genetic factors may also contribute to the pathogenesis if COPD, particularly antioxidant genes, which may confer a susceptibility to environmental insults such as cigarette smoke and thereafter development of COPD. Consequently, heme oxygenase-1 (HO-1), superoxide dismutase (SOD), catalase (CAT), glutathione S-transferase (GST), microsomal epoxide hydrolase (EPHX1), and cytochrome P450 (CYP) genetic polymorphisms may have an important role in COPD pathogenesis. In this review the authors summarized the most recent findings dealing with these antioxidant genes contributing to the free radical neutralization and xenobiotic enzymes playing a role in different phases of cell detoxification reactions related to the redox status imbalance in COPD, with an emphasis on their possible roles in disease progression.

Revisiting Mendelian disorders through exome sequencing

Over the past several years, more focus has been placed on dissecting the genetic basis of complex diseases and traits through genome-wide association studies. In contrast, Mendelian disorders have received little attention mainly due to the lack of newer and more powerful methods to study these disorders. Linkage studies have previously been the main tool to elucidate the genetics of Mendelian disorders; however, extremely rare disorders or sporadic cases caused by de novo variants are not amendable to this study design. Exome sequencing has now become technically feasible and more cost-effective due to the recent advances in high-throughput sequence capture methods and next-generation sequencing technologies which have offered new opportunities for Mendelian disorder research. Exome sequencing has been swiftly applied to the discovery of new causal variants and candidate genes for a number of Mendelian disorders such as Kabuki syndrome, Miller syndrome and Fowler syndrome. In addition, de novo variants were also identified for sporadic cases, which would have not been possible without exome sequencing. Although exome sequencing has been proven to be a promising approach to study Mendelian disorders, several shortcomings of this method must be noted, such as the inability to capture regulatory or evolutionary conserved sequences in non-coding regions and the incomplete capturing of all exons.

Equine clinical genomics: A clinician's primer

The objective of this review is to introduce equine clinicians to the rapidly evolving field of clinical genomics with a vision of improving the health and welfare of the domestic horse. For 15 years a consortium of veterinary geneticists and clinicians has worked together under the umbrella of The Horse Genome Project. This group, encompassing 22 laboratories in 12 countries, has made rapid progress, developing several iterations of linkage, physical and comparative gene maps of the horse with increasing levels of detail. In early 2006, the research was greatly facilitated when the US National Human Genome Research Institute of the National Institutes of Health added the horse to the list of mammalian species scheduled for whole genome sequencing. The genome of the domestic horse has now been sequenced and is available to researchers worldwide in publicly accessible databases. This achievement creates the potential for transformative change within the horse industry, particularly in the fields of internal medicine, sports medicine and reproduction. The genome sequence has enabled the development of new genome-wide tools and resources for studying inherited diseases of the horse. To date, researchers have identified 11 mutations causing 10 clinical syndromes in the horse. Testing is commercially available for all but one of these diseases. Future research will probably identify the genetic bases for other equine diseases, produce new diagnostic tests and generate novel therapeutics for some of these conditions. This will enable equine clinicians to play a critical role in ensuring the thoughtful and appropriate application of this knowledge as they assist clients with breeding and clinical decision-making.

Genetic determinants of pubertal timing in the general population

Puberty is an important developmental stage during which reproductive capacity is attained. The timing of puberty varies greatly among healthy individuals in the general population and is influenced by both genetic and environmental factors. Although genetic variation is known to influence the normal spectrum of pubertal timing, the specific genes involved remain largely unknown. Genetic analyses have identified a number of genes responsible for rare disorders of pubertal timing such as hypogonadotropic hypogonadism and Kallmann syndrome. Recently, the first loci with common variation reproducibly associated with population variation in the timing of puberty were identified at 6q21 in or near LIN28B and at 9q31.2. However, these two loci explain only a small fraction of the genetic contribution to population variation in pubertal timing, suggesting the need to continue to consider other loci and other types of variants. Here we provide an update of the genes implicated in disorders of puberty, discuss genes and pathways that may be involved in the timing of normal puberty, and suggest additional avenues of investigation to identify genetic regulators of puberty in the general population.

Transgenerational genetic effects of the paternal Y chromosome on daughters' phenotypes

AIMS

Recent evidence suggests that transgenerational genetic effects contribute to phenotypic variation in complex traits. To test for the general occurrence of these effects and to estimate their strength, we took advantage of chromosome substitution strains (CSSs) of mice where the Y chromosome of the host strain has been replaced with the Y chromosome of the donor strain. Daughters of these CSS-Y males and host strain females are genetically identical and should be phenotypically indistinguishable in the absence of transgenerational genetic effects of the fathers' Y chromosome on daughters' phenotypes.

MATERIALS & METHODS

Assay results for a broad panel of physiological traits and behaviors were compared for genetically identical daughters of CSS-Y males and host strain females from the B6-Chr(A/J) and B6-Chr(PWD) panels of CSSs. In addition, behavioral traits including specific tests for anxiety-related behaviors were tested in daughters of B6-Chr(129) and 129-Chr(B6) CSS-Y males.

RESULTS

Across a panel of 41 multigenic traits assayed in the B6-Chr(A/J) panel of CSSs females and 21 multigenic traits in the B6-Chr(PWD) panel females, the frequency and strength for transgenerational genetic effects were remarkably similar to those for conventional inheritance of substituted chromosomes. In addition, we found strong evidence that the Y chromosome from the 129 inbred strain significantly reduced anxiety levels among daughters of B6-Chr(129) CSS-Y males.

CONCLUSION

We found that transgenerational genetic effects rival conventional genetic effects in frequency and strength, we suggest that some phenotypic variation found in conventional studies of complex traits are attributable in part to the action of genetic variants in previous generations, and we propose that transgenerational genetic effects contribute to 'missing heritability'.

Epidemiology of asthma: risk factors for development

This comprehensive review of the recent literature was undertaken to determine the current state of knowledge of the risk factors involved in the development of asthma in order to focus investigations in a proposed new longitudinal birth cohort study. The origins of asthma appear to lie in the prenatal and early postnatal period, and renewed investigations in this period with long-term close follow-up and objective phenotypic characterization will help to unravel the role of the multiple putative environmental factors in the development of asthma. It is only after understanding these effects that one can hope to design rational prevention studies for asthma.

Systems analysis of bone

The genetic variants contributing to variability in skeletal traits has been well studied, and several hundred QTLs have been mapped and several genes contributing to trait variation have been identified. However, many questions remain unanswered. In particular, it is unclear whether variation in a single gene leads to alterations in function. Bone is a highly adaptive system and genetic variants affecting one trait are often accompanied by compensatory changes in other traits. The functional interactions among traits, which is known as phenotypic integration, has been observed in many biological systems, including bone. Phenotypic integration is a property of bone that is critically important for establishing a mechanically functional structure that is capable of supporting the forces imparted during daily activities. In this paper, bone is reviewed as a system and primarily in the context of functionality. A better understanding of the system properties of bone will lead to novel targets for future genetic analyses and the identification of genes that are directly responsible for regulating bone strength. This systems analysis has the added benefit of leaving a trail of valuable information about how the skeletal system works. This information will provide novel approaches to assessing skeletal health during growth and aging and for developing novel treatment strategies to reduce the morbidity and mortality associated with fragility fractures.

Vitamin D receptor polymorphisms are associated with increased susceptibility to primary biliary cirrhosis in Japanese and Italian populations

BACKGROUND/AIMS

Vitamin D receptor (VDR) agonists have recently been identified as potent immunomodulators capable of inhibiting Th1-mediated immune response, leading us to consider the hypothesis that functional VDR polymorphisms might contribute to enhanced risk for developing primary biliary cirrhosis (PBC), a Th1-mediated autoimmune disease. In the current study, we aimed at elucidating the genetic association of VDR polymorphisms with susceptibility to PBC in Japanese and Italian populations.

METHODS

We enrolled 334 PBC patients (195 Japanese and 139 Italians), as well as 334 age- and sex-matched controls (179 Japanese and 156 Italians). VDR genotyping was performed by PCR-RFLP, using BsmI, ApaI and TaqI endonucleases.

RESULTS

The genotype BB of BsmI polymorphism was significantly associated with PBC (OR = 1.80 [95% CI; 1.19-2.73], p = 0.005). The association of the genotype BB was observed in Japanese (OR = 13.77, p = 0.001), and Italians (OR = 1.83, p = 0.019), respectively, although not significant in Italians after Bonferroni correction. The frequency of the B allele at the BsmI polymorphism was significantly higher in PBC patients (OR = 1.27 [95% CI; 1.02-1.59], p = 0.040).

CONCLUSIONS

The genotype 'BB' as well as 'B' allele at BsmI polymorphism of the VDR gene contribute to the risk of PBC development.

Measures of autozygosity in decline: globalization, urbanization, and its implications for medical genetics

This research investigates the influence of demographic factors on human genetic sub-structure. In our discovery cohort, we show significant demographic trends for decreasing autozygosity associated with population variation in chronological age. Autozygosity, the genomic signature of consanguinity, is identifiable on a genome-wide level as extended tracts of homozygosity. We identified an average of 28.6 tracts of extended homozygosity greater than 1 Mb in length in a representative population of 809 unrelated North Americans of European descent ranging in chronological age from 19–99 years old. These homozygous tracts made up a population average of 42 Mb of the genome corresponding to 1.6% of the entire genome, with each homozygous tract an average of 1.5 Mb in length. Runs of homozygosity are steadily decreasing in size and frequency as time progresses (linear regression, p<0.05). We also calculated inbreeding coefficients and showed a significant trend for population-wide increasing heterozygosity outside of linkage disequilibrium. We successfully replicated these associations in a demographically similar cohort comprised of a subgroup of 477 Baltimore Longitudinal Study of Aging participants. We also constructed statistical models showing predicted declining rates of autozygosity spanning the 20th century. These predictive models suggest a 14.0% decrease in the frequency of these runs of homozygosity and a 24.3% decrease in the percent of the genome in runs of homozygosity, as well as a 30.5% decrease in excess homozygosity based on the linkage pruned inbreeding coefficients. The trend for decreasing autozygosity due to panmixia and larger effective population sizes will likely affect the frequency of rare recessive genetic diseases in the future. Autozygosity has declined, and it seems it will continue doing so.

Population geneticists use genetic markers to quantify and compare levels of inbreeding in populations and identify disease-associated loci; epidemiologists utilize demographic factors to quantify disease risk modifiers. Our research group sought to investigate the intersection of these two disciplines and examine the way in which demographic trends associated with decreasing levels of inbreeding may influence genomic structure and how this may affect medical genetics research. By examining two age-heterogeneous populations of outbred North Americans, we were able to ascertain genetic changes occurring over the past century that have been likely brought about by recent increases in mobility, urbanization, and population admixture. Using multiple measures of the genomic manifestations of distant consanguinity, we showed significant trends towards decreasing levels of autozygosity and more marginal inbreeding coefficients as study participant birth years neared the chronological present day. We believe this finding is particularly important, as decreasing autozygosity and less homozygosity genome-wide may help to slightly reduce the burden of rare recessive diseases in the future.

An evaluation of the genetic-matched pair study design using genome-wide SNP data from the European population

Genetic matching potentially provides a means to alleviate the effects of incomplete Mendelian randomization in population-based gene-disease association studies. We therefore evaluated the genetic-matched pair study design on the basis of genome-wide SNP data (309,790 markers; Affymetrix GeneChip Human Mapping 500K Array) from 2457 individuals, sampled at 23 different recruitment sites across Europe. Using pair-wise identity-by-state (IBS) as a matching criterion, we tried to derive a subset of markers that would allow identification of the best overall matching (BOM) partner for a given individual, based on the IBS status for the subset alone. However, our results suggest that, by following this approach, the prediction accuracy is only notably improved by the first 20 markers selected, and increases proportionally to the marker number thereafter. Furthermore, in a considerable proportion of cases (76.0%), the BOM of a given individual, based on the complete marker set, came from a different recruitment site than the individual itself. A second marker set, specifically selected for ancestry sensitivity using singular value decomposition, performed even more poorly and was no more capable of predicting the BOM than randomly chosen subsets. This leads us to conclude that, at least in Europe, the utility of the genetic-matched pair study design depends critically on the availability of comprehensive genotype information for both cases and controls.

Calculation and use of the Hardy-Weinberg model in association studies

Hardy-Weinberg equilibrium (HWE) is an important tool for understanding population structure. If certain assumptions are met, genotype and allele frequencies can be estimated from one generation to the next. In genetic association studies, HWE principles have been applied to detect genotyping error and disease susceptibility loci. The focus of this unit is to review the key principles and assumptions of HWE. There is a brief discussion on how the significance of HWE is tested, and a review of current applications of HWE in association studies. The applications discussed include estimating penetrance, evaluating genotyping errors, testing for population stratification, and testing for association.

The genetic basis of psoriasis

For a complex genetic disease, psoriasis has a high penetration within families and a concordance rate of up to 70% in identical twins. Despite this and the endeavors of many research groups for more than a decade, no susceptibility allele has so far been unequivocally identified, although about 20 genetic loci associated with psoriasis have been reported from linkage-based studies. Moreover, only 1 of these linkage-based loci, PSORS1, that includes the HLA-C gene on chromosome 6p21, has been universally confirmed. Very recent data strongly indicate that HLA-Cw*0602 is the susceptibility allele in this locus, a finding that is consistent with the notion that the pathogenesis of psoriasis involves autoantigen recognition by epidermal CD8+ T lymphocytes. Several candidate genes in some of the other 7 PSORS designated loci are currently being evaluated. The relative lack of success in elucidating the genetic basis of psoriasis highlights the formidable challenge of dissecting the genetic basis of diseases with a complex mode of inheritance.

Genetic epidemiology of complex phenotypes

Genetic factors play a substantive role in the susceptibility to common diseases. Due to recent advancements in the characterization of genetic variants and large-scale genotyping platforms, multiple genes have now been identified for common, complex diseases. As a result, there is an immense interest in elucidating genetic factors in complex traits. Therefore, it is important to understand the design as well as the technical and statistical issues of the two most common approaches for gene identification: linkage and association.

Maternal and fetal variation in genes of cholesterol metabolism is associated with preterm delivery

OBJECTIVE

To examine the contribution of variants in fetal and maternal cholesterol metabolism genes in preterm delivery (PTD).

STUDY DESIGN

A total of 40 single-nucleotide polymorphisms (SNPs) in 16 genes related to cholesterol metabolism were examined for 414 preterm infants (gestational ages 22 to 36 weeks; comprising 305 singletons and 109 twins) and at least 1 parent. Fetal effects were assessed using the transmission disequilibrium test (TDT) for each SNP, followed by a log linear model-based approach to utilize families with missing parental genotypes for those SNPs showing significance under TDT. Genetic variant effects were examined for a role in PTD, gestational age and birth weight. Maternal effects were estimated using a log linear model-based approach.

RESULT

Among singleton gestations, suggestive association (P<0.01 without adjusting for multiple comparisons) was found between birth weight and fetal DHCR7 gene/SNP combinations (rs1630498, P=0.002 and rs2002064, P=0.003). Among all gestations, suggestive associations were found between PTD and fetal HMGCR (rs2303152, P=0.002) and APOA1 (rs 5070, P=0.004). The result for HMGCR was further supported by the log linear model-based test in the single births (P=0.007) and in all births (P=0.006). New associations (APOE and ABCA1) were observed when birth weight was normalized for gestational age suggesting independent effects of variants on birth weight separate from effects on PTD. Testing for maternally mediated genetic effects has identified suggestive association between ABCA1 (rs4149313, P=0.004) and decreased gestational age.

CONCLUSION

Variants in maternal and fetal genes for cholesterol metabolism were associated with PTD and decreased birth weight or gestational age in this study. Genetic markers may serve as one mechanism to identify high-risk mothers and fetuses for targeted nutritional treatment and/or prevention of low birth weight or PTD.

Aspects génétiques de l'obésité

Genetic, specifically monogenic, obesity must be suspected: in cases of early severe obesity, developing in the first months of life; in cases of severely abnormal dietary behavior; in cases of associated endocrine abnormalities or pigmentation disorders; in cases of parental consanguinity. Mutations of the MC4R gene are the leading cause of oligogenic obesity. Discovery of a mutation of the MC4R gene furnishes a biologic source for some patients' susceptibility to obesity. Most human obesity nonetheless results from the interaction of multiple genetic factors and the environment. Molecules acting on the melanocortin pathway are currently under development by the pharmaceutical industry.

A ribonuclease protection assay-based approach for analysis of angiogenic gene expression in archival tissues

Archival, formalin-fixed and paraffin-embedded tissues routinely stored in pathology departments represent an invaluable resource for retrospective molecular biology studies for diagnostic and prognostic purposes. In such specimens extraction of transcriptionally competent RNA to be analyzed by conventional techniques, such as reverse transcription-polymerase chain reaction, is a challenging task. Therefore, we developed a novel methodological approach that allows successful detection and semiquantitative analysis of specific mRNAs obtained from archival formalin-fixed, paraffin-embedded specimens by ribonuclease protection assay. Specifically, we measured a panel of 7 angiogenic markers in selected archival tissues stored at room temperature and retrieved over a wide time span (10 y). The study series consisted in samples of benign and malignant melanocytic lesions. In our model, expression of FLT-1, the vascular-endothelial growth factor receptor-1, correlated with the expression of mRNAs encoding other tyrosine kinase receptors, such as TIE-1 and TIE-2, as well as with angiopoietin and with the protease-activated receptor-1 and vascular-endothelial growth factor itself. Relative to control (normal skin), in melanoma the expression of the selected angiogenic markers was significantly higher. In conclusion, our study provides evidence that ribonuclease protection assay on archival specimens would be highly valuable for retrospective studies, for diagnosis or prognosis.

Strategies for identifying modifier genes in cystic fibrosis

Even in patients with cystic fibrosis (CF) with identical CFTR genotypes, there is a wide range in the severity of lung disease, with some individuals facing death or lung transplantation early in life and others demonstrating mild lung disease well into adulthood. Although numerous environmental factors have been identified that influence CF pulmonary phenotype, there is now growing evidence that polymorphic variants in genes besides CFTR play an important role in determining severity of CF lung disease. This article reviews the most recent findings regarding genetic modifiers in CF and also discusses in detail the strategies currently being used to identify novel modifiers of CF pulmonary phenotype. These include single- and multicenter studies, twin and sib studies, microarray approaches, and whole genome association studies.

The genetics of the polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is a very common endocrine disorder that has a strong genetic component and is characterized by polycystic ovaries, hyperandrogenemia, and menstrual irregularity. During the past decade, the roles of more than 70 candidate genes have been evaluated for a causal role in PCOS; however, because of genetic and phenotypic heterogeneity and underpowered studies, the results of many of these studies remain inconclusive. Here, the results of the genetic analysis of several candidate genes and gene regions-CYP11A (encoding cytochrome P450, family 11, subfamily A polypeptides), CAPN10 (encoding calpain 10), the insulin gene VNTR (variable number of tandem repeats), and D19S884 (a dinucleotide repeat marker mapping to chromosome 19p13.2)-are discussed in detail. Although past genetic studies of PCOS have yielded only modest results, resources and techniques have been assembled to remedy the major deficits of these early studies, promising that the next few years will be a very exciting and rewarding era for the genetic analysis of PCOS.