Genome-wide association studies: progress and potential for drug discovery and development

Seven-chain adaptive immune receptor repertoire analysis in rheumatoid arthritis reveals novel features associated with disease and clinically relevant phenotypes

BACKGROUND

In rheumatoid arthritis (RA), the activation of T and B cell clones specific for self-antigens leads to the chronic inflammation of the synovium. Here, we perform an in-depth quantitative analysis of the seven chains that comprise the adaptive immune receptor repertoire (AIRR) in RA.

RESULTS

In comparison to controls, we show that RA patients have multiple and strong differences in the B cell receptor repertoire including reduced diversity as well as altered isotype, chain, and segment frequencies. We demonstrate that therapeutic tumor necrosis factor inhibition partially restores this alteration but find a profound difference in the underlying biochemical reactivities between responders and non-responders. Combining the AIRR with HLA typing, we identify the specific T cell receptor repertoire associated with disease risk variants. Integrating these features, we further develop a molecular classifier that shows the utility of the AIRR as a diagnostic tool.

CONCLUSIONS

Simultaneous sequencing of the seven chains of the human AIRR reveals novel features associated with the disease and clinically relevant phenotypes, including response to therapy. These findings show the unique potential of AIRR to address precision medicine in immune-related diseases.

Unveiling the Mysteries of Non-Mendelian Heredity in Plant Breeding

Mendelian heredity is the cornerstone of plant breeding and has been used to develop new varieties of plants since the 19th century. However, there are several breeding cases, such as cytoplasmic inheritance, methylation, epigenetics, hybrid vigor, and loss of heterozygosity (LOH), where Mendelian heredity is not applicable, known as non-Mendelian heredity. This type of inheritance can be influenced by several factors besides the genetic architecture of the plant and its breeding potential. Therefore, exploring various non-Mendelian heredity mechanisms, their prevalence in plants, and the implications for plant breeding is of paramount importance to accelerate the pace of crop improvement. In this review, we examine the current understanding of non-Mendelian heredity in plants, including the mechanisms, inheritance patterns, and applications in plant breeding, provide an overview of the various forms of non-Mendelian inheritance (including epigenetic inheritance, cytoplasmic inheritance, hybrid vigor, and LOH), explore insight into the implications of non-Mendelian heredity in plant breeding, and the potential it holds for future research.

Starring Role of Biomarkers and Anticancer Agents as a Major Driver in Precision Medicine of Cancer Therapy

Precision medicine is the most modern contemporary medicine approach today, based on great amount of data on people's health, individual characteristics, and life circumstances, and employs the most effective ways to prevent and cure diseases. Precision medicine in cancer is the most precise and viable treatment for every cancer patient based on the disease's genetic profile. Precision medicine changes the standard one size fits all medication model, which focuses on average responses to care. Consolidating modern methodologies for streamlining and checking anticancer drugs can have long-term effects on understanding the results. Precision medicine can help explicit anticancer treatments using various drugs and even in discovery, thus becoming the paradigm of future cancer medicine. Cancer biomarkers are significant in precision medicine, and findings of different biomarkers make this field more promising and challenging. Naturally, genetic instability and the collection of extra changes in malignant growth cells are ways cancer cells adapt and survive in a hostile environment, for example, one made by these treatment modalities. Precision medicine centers on recognizing the best treatment for individual patients, dependent on their malignant growth and genetic characterization. This new era of genomics progressively referred to as precision medicine, has ignited a new episode in the relationship between genomics and anticancer drug development.

Genotype imputation for soybean nested association mapping population to improve precision of QTL detection

KEY MESSAGE

Software for high imputation accuracy in soybean was identified. Imputed dataset could significantly reduce the interval of genomic regions controlling traits, thus greatly improve the efficiency of candidate gene identification. Genotype imputation is a strategy to increase marker density of existing datasets without additional genotyping. We compared imputation performance of software BEAGLE 5.0, IMPUTE 5 and AlphaPlantImpute and tested software parameters that may help to improve imputation accuracy in soybean populations. Several factors including marker density, extent of linkage disequilibrium (LD), minor allele frequency (MAF), etc., were examined for their effects on imputation accuracy across different software. Our results showed that AlphaPlantImpute had a higher imputation accuracy than BEAGLE 5.0 or IMPUTE 5 tested in each soybean family, especially if the study progeny were genotyped with an extremely low number of markers. LD extent, MAF and reference panel size were positively correlated with imputation accuracy, a minimum number of 50 markers per chromosome and MAF of SNPs > 0.2 in soybean line were required to avoid a significant loss of imputation accuracy. Using the software, we imputed 5176 soybean lines in the soybean nested mapping population (NAM) with high-density markers of the 40 parents. The dataset containing 423,419 markers for 5176 lines and 40 parents was deposited at the Soybase. The imputed NAM dataset was further examined for the improvement of mapping quantitative trait loci (QTL) controlling soybean seed protein content. Most of the QTL identified were at identical or at similar position based on initial and imputed datasets; however, QTL intervals were greatly narrowed. The resulting genotypic dataset of NAM population will facilitate QTL mapping of traits and downstream applications. The information will also help to improve genotyping imputation accuracy in self-pollinated crops.

'Teratoid' Hepatoblastoma: An Intriguing Variant of Mixed Epithelial-Mesenchymal Hepatoblastoma

Liver neoplasms are quite rare in childhood. They often involve 6.7 cases per 10 million children aged 18 years or younger. Hepatoblastoma (HB) is the most frequent tumor, but this neoplasm's rarity points essentially to the difficulty of performing biologic studies and large-scale therapeutic trials. On the pathological ground, HB is separated into an entirely epithelial neoplasm or a mixed neoplasm with epithelial and mesenchymal components. This last category has been further subdivided into harboring teratoid features or not. The 'teratoid' HB includes a mixture of components with heterologous origin. The heterologous components include neuroectoderm, endoderm, or melanin-holding cells with or without mesenchymal components. The most important criterium for the teratoid component is neuroepithelium, melanin, and, more recently, a yolk-sac-like component and neuroendocrine components. The mesenchymal components include muscle, osteoid, and cartilage, which are most often observed mainly in 'teratoid' neoplasms. The teratoid component or mesenchymal components are diagnosed with biopsies. They appear more prominent after chemotherapy due to the response and shrinkage of epithelial elements and non- or low-responsive components of mixed HB. This review focuses on the clinical, radiological, and pathological findings of HB with teratoid features.

Identification and functional validation of HLA-C as a potential gene involved in colorectal cancer in the Korean population

BACKGROUND

Colorectal cancer (CRC) is the third most common cancer worldwide and is influenced by environmental and genetic factors. Although numerous genetic loci for CRC have been identified, the overall understanding of the genetic factors is yet to be elucidated. We sought to discover new genes involved in CRC applying genetic association analysis and functional study.

RESULTS

We conducted exome array analysis on 194 CRC and 600 control subjects for discovering new candidate CRC genes. Fisher's exact test detected one exome-wide significant functional locus for CRC on SMCO1 (P < 10^-6) and two suggestive functional loci on HLA-C and NUTM1 (10^-6 ≤ P < 10^-4). To evaluate the biological role of three candidate CRC genes, the differential expression of these genes between CRC and non-cancer colorectal cells was analyzed using qRT-PCR and publicly available gene expression data. Of three genes, HLA-C consistently revealed the significant down-regulation in CRC cells. In addition, we detected a reduction in cell viability in the HLA-C overexpression CRC cell line, implying the functional relevance of HLA-C in CRC. To understand the underlying mechanism exerted by HLA-C in CRC development, we conducted RNA sequencing analyses of HLA-C overexpression CRC cells and non-cancer colorectal cells. Pathway analysis detected that significantly down-regulated genes in HLA-C overexpression CRC cells were highly enriched in cancer-related signaling pathways such as JAK/STAT, ErbB, and Hedgehog signaling pathways.

CONCLUSIONS

Exome array CRC case-control analysis followed by functional validation demonstrated that HLA-C likely exerts its influence on CRC development via cancer-related signaling pathways.

Thalidomide Exerts Anti-Inflammatory Effects in Cutaneous Lupus by Inhibiting the IRF4/NF-ҡB and AMPK1/mTOR Pathways

Thalidomide is effective in patients with refractory cutaneous lupus erythematosus (CLE). However, the mechanism of action is not completely understood, and its use is limited by its potential, severe side-effects. Immune cell subset analysis in thalidomide’s CLE responder patients showed a reduction of circulating and tissue cytotoxic T-cells with an increase of iNKT cells and a shift towards a Th2 response. We conducted an RNA-sequencing study using CLE skin biopsies performing a Therapeutic Performance Mapping System (TMPS) analysis in order to generate a predictive model of its mechanism of action and to identify new potential therapeutic targets. Integrating RNA-seq data, public databases, and literature, TMPS analysis generated mathematical models which predicted that thalidomide acts via two CRBN-CRL4A- (CRL4CRBN) dependent pathways: IRF4/NF-ҡB and AMPK1/mTOR. Skin biopsies showed a significant reduction of IRF4 and mTOR in post-treatment samples by immunofluorescence. In vitro experiments confirmed the effect of thalidomide downregulating IRF4 in PBMCs and mTOR in keratinocytes, which converged in an NF-ҡB reduction that led to a resolution of the inflammatory lesion. These results emphasize the anti-inflammatory role of thalidomide in CLE treatment, providing novel molecular targets for the development of new therapies that could avoid thalidomide’s side effects while maintaining its efficacy.

Glucagon-like peptide-1 receptor and sarcoglycan delta genetic variants can affect cardiovascular risk in chronic kidney disease patients under hemodialysis

BACKGROUND

Chronic kidney disease (CKD) patients under hemodialysis show a higher risk of cardiovascular (CV) mortality and morbidity than the general population. This study aims to identify genetic markers that could explain the increased CV risk in hemodialysis.

METHODS

A total of 245 CKD patients under hemodialysis were recruited and followed up for 5 years to record CV events. Genetic analysis was performed using single-nucleotide polymorphisms (SNPs) genotyping by Infinium Expanded Multi-Ethnic Genotyping Array (Illumina, San Diego, CA, USA) comparing patients with and without a history of CV events [161 cardiovascular diseases (CVDs) and 84 no CVDs]. The fixation index (Fst) measure was used to identify the most differentiated SNPs, and gene ontology analysis [Protein Analysis THrough Evolutionary Relationships (PANTHER) and Ingenuity Pathway Analysis (IPA)] was applied to define the biological/pathological roles of the associated SNPs. Partitioning tree analysis interrogated the genotype-phenotype relationship between discovered genetic variants and CV phenotypes. Cox regression analysis measured the effect of these SNPs on new CV events during the follow-up (FU).

RESULTS

Fst analysis identified 3218 SNPs that were significantly different between CVD and no CVD. Gene ontology analysis identified two of these SNPs as involved in cardiovascular disease pathways (Ingenuity Pathway) and heart development (Panther) and belonging to 2 different genes: Glucagon-like peptide-1 receptor (GLP1R) and Sarcoglycan delta (SGCD). The phenotype-genotype analysis found a higher percentage of CVD patients carrying the GLP1R rs10305445 allele A (P = 0.03) and lower percentages of CVD patients carrying the SGCD rs145292439 allele A (P = 0.038). Moreover, SGCD rs145292439 was associated with higher levels of high-density lipoprotein (P = 0.015). Cox analysis confirmed the increased frequency of CV events during the 5-year FU in patients carrying GLP1R rs1035445 allele A but it did not show any significant association with SGCD rs145292439.

CONCLUSIONS

This study identified GLP1R rs10305445 and SCGD rs145292439 as potential genetic markers that may explain the higher risk of CVD in hemodialysis patients.

Interaction between Coffee Drinking and TRIB1 rs17321515 Single Nucleotide Polymorphism on Coronary Heart Disease in a Taiwanese Population

A complex interplay of several genetic and lifestyle factors influence coronary heart disease (CHD). We determined the interaction between coffee consumption and the tribbles pseudokinase 1 (TRIB1) rs17321515 variant on coronary heart disease (CHD). Data on CHD were obtained from the National Health Insurance Research Database (NHIRD) while genotype data were collected from the Taiwan Biobank (TWB) Database. From the linked electronic health record data, 1116 individuals were identified with CHD while 7853 were control individuals. Coffee consumption was associated with a lower risk of CHD. The multivariate-adjusted odds ratio (OR) and 95% confidence interval (CI) was 0.84 (0.72-0.99). Association of CHD with the TRIB1 rs17321515 variant was not significant. The OR (95% CI) was 1.01 (0.72-0.99). There was an interaction between TRIB1 rs17321515 and coffee consumption on CHD risk (p for interaction = 0.0330). After stratification by rs17321515 genotypes, coffee drinking remained significantly associated with a lower risk of CHD only among participants with GG genotype (OR, 0.62; 95% CI, 0.45-0.85). In conclusion, consumption of coffee was significantly associated with a decreased risk of CHD among Taiwanese adults with the TRIB1 GG genotype.

Review: Climate change impacts on food security- focus on perennial cropping systems and nutritional value

Anthropogenic increases in fossil fuel emissions have been a primary driver of increased concentrations of atmospheric carbon dioxide ([CO₂]) and other greenhouse gases resulting in warmer temperatures, alterations in precipitation patterns, and increased occurrence of extreme weather events in terrestrial areas across the globe. In agricultural growing regions, alterations in climate can challenge plant productivity in ways that impact the ability of the world to sustain adequate food production for a growing and increasingly affluent population with shifting access to affordable and nutritious food. While the knowledge gap that exists regarding potential climate change impacts is large across agriculture, it is especially large in specialty cropping systems. This includes fruit and vegetable crops, and perennial cropping systems which also contribute (along with row crops) to our global diet. In order to obtain a comprehensive view of the true impact of climate change on our global food supply, we must expand our narrow focus from improving yield and plant productivity to include the impact of climate change on the nutritional value of these crops. In order to address these questions, we need a multi-faceted approach that integrates physiology and genomics tools and conducts comprehensive experiments under realistic depictions of future projected climate. This review describes gaps in our knowledge in relation to these responses, and future questions and actions that are needed to develop a sustainable future food supply in light of global climate change.

GenEpi: gene-based epistasis discovery using machine learning

BACKGROUND

Genome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimer's disease (AD).

RESULTS

In this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting the ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power.

CONCLUSIONS

The results on simulation data and AD demonstrated that GenEpi has the ability to detect the epistasis associated with phenotypes effectively and efficiently. The released package can be generalized to largely facilitate the studies of many complex diseases in the near future.

Effect of Environmental Exposure and Pharmacogenomics on Drug Metabolism

BACKGROUND

Pesticides are major xenobiotic compounds and environmental pollutants, which are able to alter drug-metabolizing enzyme as well as pharmacokinetics of drugs. Subsequent to the release of the human genome project, genetic variations (polymorphism) become an integral part of drug development due to their influence on disease susceptibility/ progression of the disease and their impact on drug absorption, distribution, metabolism of active metabolites and finally excretion of the drug. Genetic polymorphisms crucially regulate pharmacokinetics and pharmacodynamics of drugs under the influence of physiological condition, lifestyle, as well as pathological conditions collectively.

OBJECTIVE

To review all the evidence concerning the effect of environmental exposure on drug metabolism with reference to pharmacogenomics.

METHODS

Scientific data search and review of basic, epidemiological, pharmacogenomics and pharmacokinetics studies were undertaken to evaluate the influence of environmental contaminants on drug metabolism.

RESULTS

Various environmental contaminants like pesticides effectively alter drug metabolism at various levels under the influence of pharmacogenomics, which interferes with pharmacokinetics of drug metabolism. Genetic polymorphism of phase I and phase II xenobiotic-metabolizing enzymes remarkably alters disease susceptibility as well as the progression of disease under the influence of various environmental contaminants at various levels.

CONCLUSION

Individual specific drug response may be attributed to a large variety of factors alone or in combination ranging from genetic variations (SNP, insertion, deletion, duplication etc.) to physiological setting (gender, age, body size, and ethnicity), environmental or lifestyle factors (radiation exposure, smoking, alcohol, nutrition, exposure to toxins, etc.); and pathological conditions (obesity, diabetes, liver and renal function).

Precision medicine in pediatric sepsis

PURPOSE OF REVIEW

Pediatric sepsis is a heterogeneous state associated with significant morbidity and mortality, but treatment strategies are limited. Clinical trials of immunomodulators in sepsis have shown no benefit, despite having a strong biological rationale. There is considerable interest in application of a precision medicine approach to pediatric sepsis to identify patients who are more likely to benefit from targeted therapeutic interventions.

RECENT FINDINGS

Precision medicine requires a clear understanding of the molecular basis of disease. 'Omics data' and bioinformatics tools have enabled identification of endotypes of pediatric septic shock, with corresponding biological pathways. Further, using a multibiomarker-based approach, patients at highest risk of poor outcomes can be identified at disease onset. Enrichment strategies, both predictive and prognostic, may be used to optimize patient selection in clinical trials and identify a subpopulation in whom therapy of interest may be trialed. A bedside-to-bench-to-bedside model may offer clinicians pragmatic tools to aid in decision-making.

SUMMARY

Precision medicine approaches may be used to subclassify, risk-stratify, and select pediatric patients with sepsis who may benefit from new therapies. Application of precision medicine will require robust basic and translational research, rigorous clinical trials, and infrastructure to collect and analyze big data.

Integration of target discovery, drug discovery and drug delivery: A review on computational strategies

Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure-activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing

Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome-all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n-of-1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.

Genetic Architecture of Soybean Yield and Agronomic Traits

Soybean is the world's leading source of vegetable protein and demand for its seed continues to grow. Breeders have successfully increased soybean yield, but the genetic architecture of yield and key agronomic traits is poorly understood. We developed a 40-mating soybean nested association mapping (NAM) population of 5,600 inbred lines that were characterized by single nucleotide polymorphism (SNP) markers and six agronomic traits in field trials in 22 environments. Analysis of the yield, agronomic, and SNP data revealed 23 significant marker-trait associations for yield, 19 for maturity, 15 for plant height, 17 for plant lodging, and 29 for seed mass. A higher frequency of estimated positive yield alleles was evident from elite founder parents than from exotic founders, although unique desirable alleles from the exotic group were identified, demonstrating the value of expanding the genetic base of US soybean breeding.

Early Diagnosis of Sepsis: Is an Integrated Omics Approach the Way Forward?

Sepsis remains one of the leading causes of death in the USA and it is expected to get worse as the population ages. Moreover, the standard of care, which recommends aggressive treatment with appropriate antibiotics, has led to an increase in multiple drug-resistant organisms. There is a dire need for the development of new antibiotics, improved antibiotic stewardship, and therapies that treat the host response. Development of new sepsis therapeutics has been a disappointment as no drugs are currently approved to treat the various complications from sepsis. Much of the failure has been blamed on animal models that do not accurately reflect the course of the disease. However, recent improvements in metabolomic, transcriptomic, genomic, and proteomic platforms have allowed for a broad-spectrum look at molecular changes in the host response using clinical samples. Integration of these multi-omic datasets allows researchers to perform systems biology approaches to identify novel pathophysiology of the disease. In this review, we highlight what is currently known about sepsis and how integrative omics has identified new diagnostic and predictive models of sepsis as well as novel mechanisms. These changes may improve patient care as well as guide future preclinical analysis of sepsis.

Next-generation sequencing in drug development: target identification and genetically stratified clinical trials

Next-generation sequencing (NGS) enabled high-throughput analysis of genotype-phenotype relationships on human populations, ushering in a new era of genetics-informed drug development. The year 2017 was remarkable, with the first FDA-approved gene therapy for cancer (Kymriah™) and for inherited diseases (LUXTURNA™), the first multiplex NGS panel for companion diagnostics (MSK-IMPACT™) and the first drug targeting a genetic signature rather than a disease (Keytruda^®). We envision that population-scale NGS with paired electronic health records (EHRs) will become a routine measure in the drug development process for the identification of novel drug targets, and that genetically stratified clinical trials could be widely adopted to improve power in precision-medicine-guided drug development.

Drug development in the era of precision medicine

For the past three decades, the use of genomics to inform drug discovery and development pipelines has generated both excitement and scepticism. Although earlier efforts successfully identified some new drug targets, the overall clinical efficacy of developed drugs has remained unimpressive, owing in large part to the heterogeneous causes of disease. Recent technological and analytical advances in genomics, however, have now made it possible to rapidly identify and interpret the genetic variation underlying a single patient's disease, thereby providing a window into patient-specific mechanisms that cause or contribute to disease, which could ultimately enable the 'precise' targeting of these mechanisms. Here, we first examine and highlight the successes and limitations of the earlier phases of genomics in drug discovery and development. We then review the current major efforts in precision medicine and discuss the potential broader utility of mechanistically guided treatments going forward.

Efficient estimation of disease odds ratios for follow-up genetic association studies

In the past decade, genome-wide association studies have identified thousands of susceptible variants associated with complex human diseases and traits. Conducting follow-up genetic association studies has become a standard approach to validate the findings of genome-wide association studies. One problem of high interest in genetic association studies is to accurately estimate the strength of the association, which is often quantified by odds ratios in case-control studies. However, estimating the association directly by follow-up studies is inefficient since this approach ignores information from the genome-wide association studies. In this article, an estimator called GFcom, which integrates information from genome-wide association studies and follow-up studies, is proposed. The estimator includes both the point estimate and corresponding confidence interval. GFcom is more efficient than competing estimators regarding MSE and the length of confidence intervals. The superiority of GFcom is particularly evident when the genome-wide association study suffers from severe selection bias. Comprehensive simulation studies and applications to three real follow-up studies demonstrate the performance of the proposed estimator. An R package, "GFcom", implementing our method is publicly available at https://github.com/JiyuanHu/GFcom .

Genetic studies of alcohol dependence in the context of the addiction cycle

Family, twin and adoption studies demonstrate clearly that alcohol dependence and alcohol use disorders are phenotypically complex and heritable. The heritability of alcohol use disorders is estimated at approximately 50-60% of the total phenotypic variability. Vulnerability to alcohol use disorders can be due to multiple genetic or environmental factors or their interaction which gives rise to extensive and daunting heterogeneity. This heterogeneity makes it a significant challenge in mapping and identifying the specific genes that influence alcohol use disorders. Genetic linkage and (candidate gene) association studies have been used now for decades to map and characterize genomic loci and genes that underlie the genetic vulnerability to alcohol use disorders. These approaches have been moderately successful in identifying several genes that contribute to the complexity of alcohol use disorders. Recently, genome-wide association studies have become one of the major tools for identifying genes for alcohol use disorders by examining correlations between millions of common single-nucleotide polymorphisms with diagnosis status. Genome-wide association studies are just beginning to uncover novel biology; however, the functional significance of results remains a matter of extensive debate and uncertainty. In this review, we present a select group of genome-wide association studies of alcohol dependence, as one example of a way to generate functional hypotheses, within the addiction cycle framework. This analysis may provide novel directions for validating the functional significance of alcohol dependence candidate genes. This article is part of the Special Issue entitled "Alcoholism".

Finding melanoma drugs through a probabilistic knowledge graph

Metastatic cutaneous melanoma is an aggressive skin cancer with some progression-slowing treatments but no known cure. The omics data explosion has created many possible drug candidates; however, filtering criteria remain challenging, and systems biology approaches have become fragmented with many disconnected databases. Using drug, protein and disease interactions, we built an evidence-weighted knowledge graph of integrated interactions. Our knowledge graph-based system, ReDrugS, can be used via an application programming interface or web interface, and has generated 25 high-quality melanoma drug candidates. We show that probabilistic analysis of systems biology graphs increases drug candidate quality compared to non-probabilistic methods. Four of the 25 candidates are novel therapies, three of which have been tested with other cancers. All other candidates have current or completed clinical trials, or have been studied in in vivo or in vitro. This approach can be used to identify candidate therapies for use in research or personalized medicine.

Fine-mapping of antipsychotic response genome-wide association studies reveals novel regulatory mechanisms

Aim: Noncoding variation has demonstrated regulatory effects on disease treatment outcomes. This study investigated the potential functionality of previously implicated noncoding variants on schizophrenia treatment response. Materials & methods: Predicted regulatory potential of variation identified from antipsychotic response genome-wide association studies was determined. Prioritized variants were assessed for association(s) with treatment outcomes in a South African first episode schizophrenia cohort (n = 103). Results: Bioinformatic and association results implicated a relationship between regulatory variants, expression of MANBA, COL9A2 and NFKB1, and treatment response. Three SNPs were associated with poor outcomes (rs230493: p = 1.88 × 10-6; rs3774959: p = 1.75 × 10-5; and rs230504: p = 1.48 × 10-4). Conclusion: This study has thoroughly investigated previous GWAS to pinpoint variants that may play a causal role in poor schizophrenia treatment outcomes, and provides potential candidate genes for further study in the field of antipsychotic response.

A BAYESIAN GRAPHICAL MODEL FOR GENOME-WIDE ASSOCIATION STUDIES (GWAS)

The analysis of GWAS data has long been restricted to simple models that cannot fully capture the genetic architecture of complex human diseases. As a shift from standard approaches, we propose here a general statistical framework for multi-SNP analysis of GWAS data based on a Bayesian graphical model. Our goal is to develop a general approach applicable to a wide range of genetic association problems, including GWAS and fine-mapping studies, and, more specifically, be able to: (1) Assess the joint effect of multiple SNPs that can be linked or unlinked and interact or not; (2) Explore the multi-SNP model space efficiently using the Mode Oriented Stochastic Search (MOSS) algorithm and determine the best models. We illustrate our new methodology with an application to the CGEM breast cancer GWAS data. Our algorithm selected several SNPs embedded in multi-locus models with high posterior probabilities. Most of the SNPs selected have a biological relevance. Interestingly, several of them have never been detected in standard single-SNP analyses. Finally, our approach has been implemented in the open source R package genMOSS.

Compound annotation with real time cellular activity profiles to improve drug discovery

INTRODUCTION

In the past decade, a range of innovative strategies have been developed to improve the productivity of pharmaceutical research and development. In particular, compound annotation, combined with informatics, has provided unprecedented opportunities for drug discovery.

AREAS COVERED

In this review, a literature search from 2000 to 2015 was conducted to provide an overview of the compound annotation approaches currently used in drug discovery. Based on this, a framework related to a compound annotation approach using real-time cellular activity profiles for probe, drug, and biology discovery is proposed.

EXPERT OPINION

Compound annotation with chemical structure, drug-like properties, bioactivities, genome-wide effects, clinical phenotypes, and textural abstracts has received significant attention in early drug discovery. However, these annotations are mostly associated with endpoint results. Advances in assay techniques have made it possible to obtain real-time cellular activity profiles of drug molecules under different phenotypes, so it is possible to generate compound annotation with real-time cellular activity profiles. Combining compound annotation with informatics, such as similarity analysis, presents a good opportunity to improve the rate of discovery of novel drugs and probes, and enhance our understanding of the underlying biology.

Genetic polymorphisms in the androgen metabolism pathway and risk of prostate cancer in low incidence Malaysian ethnic groups

Androgens are involved in prostate cancer (PCa) cell growth. Genes involved in androgen metabolism mediate key steps in sex steroid metabolism. This study attempted to investigate whether single nucleotide polymorphisms (SNPs) in the androgen metabolism pathway are associated with PCa risk in low incidence Asian ethnic groups. We genotyped 172 Malaysian subjects for cytochrome P450 family 17 (CYP17A1), steroid-5-alpha-reductase, polypeptide 1 and 2 (SRD5A1 and SRD5A2), and insulin-like growth factor 1 (IGF-1) genes of the androgen metabolism pathway and assessed the testosterone, dihydrotestosterone and IGF-1 levels. SNPs in the CYP17A1, SRD5A1, SRD5A2, and IGF-1 genes were genotyped using real-time polymerase chain reaction. Although we did not find significant association between SNPs analysed in this study with PCa risk, we observed however, significant association between androgen levels and the IGF-1 and several SNPs. Men carrying the GG genotype for SNP rs1004467 (CYP17A1) had significantly elevated testosterone (P = 0.012) and dihydrotestosterone (DHT) levels (P = 0.024) as compared to carriers of the A allele. The rs518673 of the SRD5A1 was associated with prostate specific antigen (PSA) levels. Our findings suggest CYP17A1 rs1004467 SNP is associated with testosterone and DHT levels indicating the importance of this gene in influencing androgen levels in the circulatory system of PCa patients, hence could be used as a potential marker in PCa assessment.

The current and potential impact of genetics and genomics on neuropsychopharmacology

One justification for the major scientific and financial investments in genetic and genomic studies in medicine is their therapeutic potential, both for revealing novel targets for drugs which treat the disease process, as well as allowing for more effective and safe use of existing medications. This review considers the extent to which this promise has yet been realised within psychopharmacology, how things are likely to develop in the foreseeable future, and the key issues involved. It draws primarily on examples from schizophrenia and its treatments. One observation is that there is evidence for a range of genetic influences on different aspects of psychopharmacology in terms of discovery science, but far less evidence that meets the standards required before such discoveries impact upon clinical practice. One reason is that results reveal complex genetic influences that are hard to replicate and usually of very small effect. Similarly, the slow progress being made in revealing the genes that underlie the major psychiatric syndromes hampers attempts to apply the findings to identify novel drug targets. Nevertheless, there are some intriguing positive findings of various kinds, and clear potential for genetics and genomics to play an increasing and major role in psychiatric drug discovery.

New approaches to establish genetic causality

Cardiovascular medicine has evolved rapidly in the era of genomics with many diseases having primary genetic origins becoming the subject of intense investigation. The resulting avalanche of information on the molecular causes of these disorders has prompted a revolution in our understanding of disease mechanisms and provided new avenues for diagnoses. At the heart of this revolution is the need to correctly classify genetic variants discovered during the course of research or reported from clinical genetic testing. This review will address current concepts related to establishing the cause and effect relationship between genomic variants and heart diseases. A survey of general approaches used for functional annotation of variants will also be presented.

Modified tetra-primer ARMS PCR as a single-nucleotide polymorphism genotyping tool

OBJECTIVES

Genotyping of single-nucleotide polymorphisms (SNPs) has been applied in various genetic contexts. Tetra-primer amplification refractory mutation system (ARMS) polymerase chain reaction (PCR) is reported as a prominent assay for SNP genotyping. However, there were published data that may question the reliability of this method on some occasions, in addition to a laborious and time-consuming procedure of the optimization step. In the current study, a new SNP genotyping method named modified tetra-primer ARMS (MTPA) PCR was developed based on tetra-primer ARMS PCR.

DESIGN AND METHODS

The modified method has two improvements in its instruction, including equalization of outer primer and inner primer strength by additional mismatch in outer primers, and consideration of equal annealing temperature of specific fragments more than melting temperature of primers. Advantageously, a new computer software was provided for designing primers based on novel concepts.

RESULTS

The usual tetra-primer ARMS PCR has a laborious process for optimization. In nonoptimal PCR programs, identification of the accurate genotype was found to be very difficult. However, in MTPA PCR, equalization of the amplicons and primer strength leads to increasing specificity and convenience of genotyping, which was validated by sequencing.

CONCLUSIONS

In the MTPA PCR technique, a new mismatch at -2 positions of outer primers and equal annealing temperature improve the genotyping procedure. Together, the introduced method could be suggested as a powerful tool for genotyping single-nucleotide mutations and polymorphisms.

Prospects for multitarget lipid-raft-coated silica beads: a remarkable online biomaterial for discovering multitarget antitumor lead compounds

Application of lipid raft biomaterial with multiple cancer-related receptors for screening novel multitarget antitumour lead compounds.

Progress toward personalized medicine for glaucoma

How will you respond when a patient asks, "Doctor, what can I do to prevent myself from going blind from glaucoma like mom?". There is optimism that genetic profiling will help target patients to individualized treatments based on validated disease risk alleles, validated pharmacogenetic markers and behavioral modification. Personalized medicine will become a reality through identification of disease and pharmacogenetic markers, followed by careful study of how to employ this information in order to improve treatment outcomes. With advances in genomic technologies, research has shifted from the simple monogenic disease model to a complex multigenic and environmental disease model to answer these questions. Our challenges lie in developing risk models that incorporate gene-gene interactions, gene copy-number variations, environmental interactions, treatment effects and clinical covariates.

Systems biology-embedded target validation: improving efficacy in drug discovery

The pharmaceutical industry is faced with a range of challenges with the ever-escalating costs of drug development and a drying out of drug pipelines. By harnessing advances in -omics technologies and moving away from the standard, reductionist model of drug discovery, there is significant potential to reduce costs and improve efficacy. Embedding systems biology approaches in drug discovery, which seek to investigate underlying molecular mechanisms of potential drug targets in a network context, will reduce attrition rates by earlier target validation and the introduction of novel targets into the currently stagnant market. Systems biology approaches also have the potential to assist in the design of multidrug treatments and repositioning of existing drugs, while stratifying patients to give a greater personalization of medical treatment.

Mutation in CYP27A1 identified in family with coronary artery disease

Coronary artery disease (CAD) is a leading cause of death worldwide. Myocardial infarction is the most severe outcome of CAD. Despite extensive efforts, the genetics of CAD is poorly understood. We aimed to identify the genetic cause of CAD in a pedigree with several affected individuals. Exome sequencing led to identification of a mutation in CYP27A1 that causes p.Arg225His in the encoded protein sterol 27-hydroxylase as the likely cause of CAD in the pedigree. The enzyme is multifunctional, and several of its functions including its functions in vitamin D metabolism and reverse cholesterol transport (RCT) are relevant to the CAD phenotype. Measurements of vitamin D levels suggested that the mutation does not affect CAD by affecting this parameter. We suggest that the mutation may cause CAD by affecting RCT. Screening of all coding regions of the CYP27A1 in 100 additional patients led to finding four variations (p.Arg14Gly, p.Arg26Lys, p.Ala27Arg, and p.Val86Met) in seven patients that may contribute to their CAD status. CYP27A1 is the known causative gene of cerebrotendinous xanthomatosis, a disorder which is sometimes accompanied by early onset atherosclerosis. This and the observation of potentially harmful variations in unrelated CAD patients provide additional evidence for the suggested causative role of the p.Arg225His mutation in CAD.

Characterization of schizophrenia adverse drug interactions through a network approach and drug classification

Antipsychotic drugs are medications commonly for schizophrenia (SCZ) treatment, which include two groups: typical and atypical. SCZ patients have multiple comorbidities, and the coadministration of drugs is quite common. This may result in adverse drug-drug interactions, which are events that occur when the effect of a drug is altered by the coadministration of another drug. Therefore, it is important to provide a comprehensive view of these interactions for further coadministration improvement. Here, we extracted SCZ drugs and their adverse drug interactions from the DrugBank and compiled a SCZ-specific adverse drug interaction network. This network included 28 SCZ drugs, 241 non-SCZs, and 991 interactions. By integrating the Anatomical Therapeutic Chemical (ATC) classification with the network analysis, we characterized those interactions. Our results indicated that SCZ drugs tended to have more adverse drug interactions than other drugs. Furthermore, SCZ typical drugs had significant interactions with drugs of the "alimentary tract and metabolism" category while SCZ atypical drugs had significant interactions with drugs of the categories "nervous system" and "antiinfectives for systemic uses." This study is the first to characterize the adverse drug interactions in the course of SCZ treatment and might provide useful information for the future SCZ treatment.

Diabetes and its comorbidities--where East meets West

Fetal programming associated with in utero exposure to maternal stress is thought to alter gene expression, resulting in phenotypes that promote survival in a pathogen-rich and nutrient-poor environment but substantially increase the risk of cardiovascular, metabolic and renal disorders (such as diabetes mellitus) in adults with obesity. These (epi)genetic phenomena are modified by environmental and socioeconomic factors, resulting in multiple subphenotypes and clinical consequences. In individuals from areas undergoing rapid economic development, which is associated with a transition from communicable to noncommunicable diseases, an efficient innate immune response can exaggerate obesity-associated inflammation. By contrast, in individuals with a genetic predisposition to autoimmune or monogenic diabetes mellitus, obesity can lead to atypical presentation of diabetes mellitus, termed 'double diabetes mellitus'. The increasingly young age at diagnosis of diabetes mellitus in developing countries results in prolonged exposure to glucolipotoxicity, low-grade inflammation and increased oxidative stress, which put enormous strain on pancreatic β cells and renal function. These conditions create a metabolic milieu conducive to cancer growth. This Review discusses how rapid changes in technology and human behaviour have brought on the global epidemic of metabolic diseases, and suggests that solutions will be based on using system change, technology and behavioural strategies to combat this societal-turned-medical problem.

Family-based association mapping in crop species

Identification of allelic variants associated with complex traits provides molecular genetic information associated with variability upon which both artificial and natural selections are based. Family-based association mapping (FBAM) takes advantage of linkage disequilibrium among segregating progeny within crosses and among parents to provide greater power than association mapping and greater resolution than linkage mapping. Herein, we discuss the potential adaption of human family-based association tests and quantitative transmission disequilibrium tests for use in crop species. The rapid technological advancement of next generation sequencing will enable sequencing of all parents in a planned crossing design, with subsequent imputation of genotypes for all segregating progeny. These technical advancements are easily adapted to mating designs routinely used by plant breeders. Thus, FBAM has the potential to be widely adopted for discovering alleles, common and rare, underlying complex traits in crop species.

Colorful DNA polymorphisms in humans

In this review article we summarize current knowledge on how variation on the DNA level influences human pigmentation including color variation of iris, hair, and skin. We review recent progress in the field of human pigmentation genetics by focusing on the genes and DNA polymorphisms discovered to be involved in determining human pigmentation traits, their association with diseases particularly skin cancers, and their power to predict human eye, hair, and skin colors with potential utilization in forensic investigations.

A novel generalized ridge regression method for quantitative genetics

As the molecular marker density grows, there is a strong need in both genome-wide association studies and genomic selection to fit models with a large number of parameters. Here we present a computationally efficient generalized ridge regression (RR) algorithm for situations in which the number of parameters largely exceeds the number of observations. The computationally demanding parts of the method depend mainly on the number of observations and not the number of parameters. The algorithm was implemented in the R package bigRR based on the previously developed package hglm. Using such an approach, a heteroscedastic effects model (HEM) was also developed, implemented, and tested. The efficiency for different data sizes were evaluated via simulation. The method was tested for a bacteria-hypersensitive trait in a publicly available Arabidopsis data set including 84 inbred lines and 216,130 SNPs. The computation of all the SNP effects required <10 sec using a single 2.7-GHz core. The advantage in run time makes permutation test feasible for such a whole-genome model, so that a genome-wide significance threshold can be obtained. HEM was found to be more robust than ordinary RR (a.k.a. SNP-best linear unbiased prediction) in terms of QTL mapping, because SNP-specific shrinkage was applied instead of a common shrinkage. The proposed algorithm was also assessed for genomic evaluation and was shown to give better predictions than ordinary RR.

Large-scale association analysis identifies new risk loci for coronary artery disease

Coronary artery disease (CAD) is the commonest cause of death. Here, we report an association analysis in 63,746 CAD cases and 130,681 controls identifying 15 loci reaching genome-wide significance, taking the number of susceptibility loci for CAD to 46, and a further 104 independent variants (r(2) < 0.2) strongly associated with CAD at a 5% false discovery rate (FDR). Together, these variants explain approximately 10.6% of CAD heritability. Of the 46 genome-wide significant lead SNPs, 12 show a significant association with a lipid trait, and 5 show a significant association with blood pressure, but none is significantly associated with diabetes. Network analysis with 233 candidate genes (loci at 10% FDR) generated 5 interaction networks comprising 85% of these putative genes involved in CAD. The four most significant pathways mapping to these networks are linked to lipid metabolism and inflammation, underscoring the causal role of these activities in the genetic etiology of CAD. Our study provides insights into the genetic basis of CAD and identifies key biological pathways.

Genetics of schizophrenia from a clinicial perspective

Schizophrenia (SZ) is a common disorder that runs in families. It has a relatively high heritability, i.e., inherited factors account for the major proportion of its etiology. The high heritability has motivated gene mapping studies that have improved in sophistication through the past two decades. Belying earlier expectations, it is now becoming increasingly clear that the cause of SZ does not reside in a single mutation, or even in a single gene. Rather, there are multiple DNA variants, not all of which have been identified. Additional risk may be conferred by interactions between individual DNA variants, as well as 'gene-environment' interactions. We review studies that have accounted for a fraction of the heritability. Their relevance to the practising clinician is discussed. We propose that continuing research in DNA variation, in conjunction with rapid ongoing advances in allied fields, will yield dividends from the perspective of diagnosis, treatment prediction through pharmacogenetics, and rational treatment through discoveries in pathogenesis.

Cancer proteomics

Cancer presents high mortality and morbidity globally, largely due to its complex and heterogenous nature, and lack of biomarkers for early diagnosis. A proteomics study of cancer aims to identify and characterize functional proteins that drive the transformation of malignancy, and to discover biomarkers to detect early-stage cancer, predict prognosis, determine therapy efficacy, identify novel drug targets, and ultimately develop personalized medicine. The various sources of human samples such as cell lines, tissues, and plasma/serum are probed by a plethora of proteomics tools to discover novel biomarkers and elucidate mechanisms of tumorigenesis. Innovative proteomics technologies and strategies have been designed for protein identification, quantitation, fractionation, and enrichment to delve deeper into the oncoproteome. In addition, there is the need for high-throughput methods for biomarker validation, and integration of the various platforms of oncoproteome data to fully comprehend cancer biology.

Next-generation community genetics for low- and middle-income countries

A recent report by the World Health Organization calls for implementation of community genetics programs in low- and middle-income countries (LMICs). Their focus is prevention of congenital disorders and genetic diseases at the population level, in addition to providing genetics services, including diagnosis and counseling. The proposed strategies include both newborn screening and population screening for carrier detection, in addition to lowering the incidence of congenital disorders and genetic diseases through the removal of environmental factors. In this article, we consider the potential impact of such testing on global health and highlight the near-term relevance of next-generation sequencing (NGS) and bioinformatic approaches to their implementation. Key attributes of NGS for community genetics programs are homogeneous approach, high multiplexing of diseases and samples, as well as rapidly falling costs of new technologies. In the near future, we estimate that appropriate use of population-specific test panels could cost as little as $10 for 10 Mendelian disorders and could have a major impact on diseases that currently affect 2% of children worldwide. However, the successful deployment of this technological innovation in LMICs will require high value for human life, thoughtful implementation, and autonomy of individual decisions, supported by appropriate genetic counseling and community education.

Genome-wide association study of skin complex diseases

Complex diseases are caused by both genetic and environmental factors. Over decades, scientists endeavored to uncover the genetic myth of complex diseases by linkage and association studies. Since 2005, the genome-wide association study (GWAS) has been proved to be the most powerful and efficient study design thus far in identifying genetic variants that are associated with complex diseases. More than 230 complex diseases and traits have been investigated by this approach. In dermatology, 10 skin complex diseases have been investigated, a wealth of common susceptibility variants conferring risk for skin complex diseases have been discovered. These findings point to genes and/or loci involved in biological systems worth further investigating by using other methodologies. Certainly, as our understanding of the genetic etiology of skin complex diseases continues to mature, important opportunities will emerge for developing more effective diagnostic and clinical management tools for these diseases.

Relevant Networks involving the p53 Signalling Pathway in Renal Cell Carcinoma

INTRODUCTION

Renal cell carcinoma is the most common type of kidney cancer. A better understanding of the critical pathways and interactions associated with alterations in renal function and renal tumour properties is required. Our final goal is to combine the knowledge provided by a regulatory network with experimental observations provided by the dataset.

METHODS

In this study, a systems biology approach was used, integrating immunohistochemistry protein expression profiles and protein interaction information with the STRING and MeV bioinformatics tools. A group consisting of 80 patients with renal cell carcinoma was studied. The expression of selected markers was assessed using tissue microarray technology on immunohistochemically stained slides. The immunohistochemical data of the molecular factors studied were analysed using a parametric statistical test, Pearson's correlation coefficient test.

RESULTS

Bioinformatics analysis of tumour samples resulted in 2 protein networks. The first network consists of proteins involved in the angiogenesis pathway and the apoptosis suppressor, BCL2, and includes both positive and negative correlations. The second network shows a negative interaction between the p53 tumour suppressor protein and the glucose transporter type 4.

CONCLUSION

The comprehensive pathway network will help us to realise the cooperative behaviours among pathways. Regulation of metabolic pathways is an important role of p53. The pathway involving the tumour suppressor gene p53 could regulate tumour angiogenesis. Further investigation of the proteins that interact with this pathway in this type of tumour may provide new strategies for cancer therapies to specifically inhibit the molecules that play crucial roles in tumour progression.