Genotyping technologies for genetic research

Multi-omics and multi-stages integration identified a novel variant associated with silicosis risk

Assessing the association between candidate single-nucleotide polymorphisms (SNPs) identified by multi-omics approaches and susceptibility to silicosis. RNA-seq analysis was performed to screen the differentially expressed mRNAs in the fibrotic lung tissues of mice exposed to silica particles. Following this, we integrated the SNPs located in the above human homologenes with the silicosis-related genome-wide association study (GWAS) data to select the candidate SNPs. Then, expression quantitative trait locus (eQTL)-SNPs were identified by the GTEx database. Next, we validated the associations between the functional eQTL-SNPs and silicosis susceptibility by additional case-control study. And the contribution of the identified SNP and its host gene in the fibrosis process was further validated by functional experiments. A total of 12 eQTL-SNPs were identified in the screening stage. The results of the validation stage suggested that the variant T allele of rs419540 located in IL12RB1 significantly increased the risk of developing silicosis [additive model: odds ratio (OR) = 1.78, 95% confidence interval (CI) 1.11-2.85, P = 0.017]. Furthermore, the combination of GWAS and the results of validation stage also indicated that the variant T allele of rs419540 in IL12RB1 was associated with increased silicosis risk (additive model: OR = 2.07, 95% CI 1.38-3.12, P < 0.001). Additionally, after knockdown or overexpression of IL12RB1, the levels of pro-inflammatory factors, such as IL-12, IFN-γ, and other pro-inflammatory factors, were correspondingly decreased or increased. The novel eQTL-SNP, rs419540, might increase the risk of silicosis by modulating the expression levels of IL12RB1.

Understanding and applying gene-environment interactions: a guide for nutrition professionals with an emphasis on integration in African research settings

Noncommunicable diseases (NCDs) are influenced by the interplay between genetics and environmental exposures, particularly diet. However, many healthcare professionals, including nutritionists and dietitians, have limited genetic background and, therefore, they may lack understanding of gene-environment interactions (GxEs) studies. Even researchers deeply involved in nutrition studies, but with a focus elsewhere, can struggle to interpret, evaluate, and conduct GxE studies. There is an urgent need to study African populations that bear a heavy burden of NCDs, demonstrate unique genetic variability, and have cultural practices resulting in distinctive environmental exposures compared with Europeans or Americans, who are studied more. Although diverse and rapidly changing environments, as well as the high genetic variability of Africans and difference in linkage disequilibrium (ie, certain gene variants are inherited together more often than expected by chance), provide unparalleled potential to investigate the omics fields, only a small percentage of studies come from Africa. Furthermore, research evidence lags behind the practices of companies offering genetic testing for personalized medicine and nutrition. We need to generate more evidence on GxEs that also considers continental African populations to be able to prevent unethical practices and enable tailored treatments. This review aims to introduce nutrition professionals to genetics terms and valid methods to investigate GxEs and their challenges, and proposes ways to improve quality and reproducibility. The review also provides insight into the potential contributions of nutrigenetics and nutrigenomics to the healthcare sphere, addresses direct-to-consumer genetic testing, and concludes by offering insights into the field's future, including advanced technologies like artificial intelligence and machine learning.

Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

Applications of multi-omics analysis in human diseases

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

Circulating cell-free DNA sequencing for early detection of lung cancer

INTRODUCTION

Lung cancer is a leading cause of death in patients with cancer. Early diagnosis is crucial to improve the prognosis of patients with lung cancer. Plasma circulating cell-free DNA (cfDNA) contains comprehensive genetic and epigenetic information from tissues throughout the body, suggesting that early detection of lung cancer can be done non-invasively, conveniently, and cost-effectively using high-sensitivity techniques such as sequencing.

AREAS COVERED

In this review, we summarize the latest technological innovations, coupled with next-generation sequencing (NGS), regarding genomic alterations, methylation, and fragmentomic features of cfDNA for the early detection of lung cancer, as well as their clinical advances. Additionally, we discuss the suitability of study designs for diagnostic accuracy evaluation for different target populations and clinical questions.

EXPERT OPINION

Currently, cfDNA-based early screening and diagnosis of lung cancer faces many challenges, such as unsatisfactory performance, lack of quality control standards, and poor repeatability. However, the progress of several large prospective studies employing epigenetic features has shown promising predictive performance, which has inspired cfDNA sequencing for future clinical applications. Furthermore, the development of multi-omics markers for lung cancer, including genome-wide methylation and fragmentomics, is expected to play an increasingly important role in the future.

Optimal implementation of genomic selection in clone breeding programs-Exemplified in potato: I. Effect of selection strategy, implementation stage, and selection intensity on short-term genetic gain

Genomic selection (GS) is used in many animal and plant breeding programs to enhance genetic gain for complex traits. However, its optimal integration in clone breeding programs, for example potato, that up to now relied on phenotypic selection (PS) requires further research. In this study, we performed computer simulations based on an empirical genomic dataset of tetraploid potato to (i) investigate under a fixed budget how the weight of GS relative to PS, the stage of implementing GS, the correlation between an auxiliary trait and the target trait, the variance components, and the prediction accuracy affect the genetic gain of the target trait, (ii) determine the optimal allocation of resources maximizing the genetic gain of the target trait, and (iii) make recommendations to breeders how to implement GS in clone and especially potato breeding programs. In our simulation results, any selection strategy involving GS had a higher short-term genetic gain for the target trait than Standard-PS. In addition, we showed that implementing GS in consecutive selection stages can largely enhance short-term genetic gain and recommend the breeders to implement GS at single hills and A clone stages. Furthermore, we observed for selection strategies involving GS that the optimal allocation of resources maximizing the genetic gain of the target trait differed considerably from those typically used in potato breeding programs and, thus, require the adjustment of the selection and phenotyping intensities. The trends are described in our study. Therefore, our study provides new insight for breeders regarding how to optimally implement GS in a commercial potato breeding program to improve the short-term genetic gain for their target trait.

SNP Genotyping with Amplifluor-Like Method

For SNP genotyping, amplification of fluorescence (Amplifluor) is a popular and actively developing method in the plant sciences. The "Amplifluor-like" is a "home-made" modification of the original commercial Amplifluor method. Amplifluor-like genotyping requires two essential components: (1) two allele-specific forward primers targeting the SNP site with one common reverse primer; and (2) a universal part with two non-allele-specific molecular probes containing one of the two used fluorophores and a quencher. Allele discrimination is based on the fluorescence score, where the dominance of one dye over the other confirms the presence of each specific SNP allele. The Amplifluor-like method is similar to commercial KASP and original Amplifluor methods but is much cheaper because all components can be ordered as regular and modified oligos. The easily adaptable Amplifluor-like method can be modified by any researcher to make it suitable for available instruments, reagents and conditions in low-budget laboratories for SNP genotyping of any plant species with identified genetic polymorphism.

A Guide to Genome-Wide Association Study Design for Diabetic Retinopathy

Diabetic retinopathy (DR) is the most common microvascular complication related to diabetes. There is evidence that genetics play an important role in DR pathogenesis, but the complexity of the disease makes genetic studies a challenge. This chapter is a practical overview of the basic steps for genome-wide association studies with respect to DR and its associated traits. Also described are approaches that can be adopted in future DR studies. This is intended to serve as a guide for beginners and to provide a framework for further in-depth analysis.

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.

Limitations and opportunities of technologies for the analysis of cell-free DNA in cancer diagnostics

Cell-free DNA (cfDNA) in the circulating blood plasma of patients with cancer contains tumour-derived DNA sequences that can serve as biomarkers for guiding therapy, for the monitoring of drug resistance, and for the early detection of cancers. However, the analysis of cfDNA for clinical diagnostic applications remains challenging because of the low concentrations of cfDNA, and because cfDNA is fragmented into short lengths and is susceptible to chemical damage. Barcodes of unique molecular identifiers have been implemented to overcome the intrinsic errors of next-generation sequencing, which is the prevailing method for highly multiplexed cfDNA analysis. However, a number of methodological and pre-analytical factors limit the clinical sensitivity of the cfDNA-based detection of cancers from liquid biopsies. In this Review, we describe the state-of-the-art technologies for cfDNA analysis, with emphasis on multiplexing strategies, and discuss outstanding biological and technical challenges that, if addressed, would substantially improve cancer diagnostics and patient care.

Modified "Allele-Specific qPCR" Method for SNP Genotyping Based on FRET

The proposed method is a modified and improved version of the existing "Allele-specific q-PCR" (ASQ) method for genotyping of single nucleotide polymorphism (SNP) based on fluorescence resonance energy transfer (FRET). This method is similar to frequently used techniques like Amplifluor and Kompetitive allele specific PCR (KASP), as well as others employing common universal probes (UPs) for SNP analyses. In the proposed ASQ method, the fluorophores and quencher are located in separate complementary oligonucleotides. The ASQ method is based on the simultaneous presence in PCR of the following two components: an allele-specific mixture (allele-specific and common primers) and a template-independent detector mixture that contains two or more (up to four) universal probes (UP-1 to 4) and a single universal quencher oligonucleotide (Uni-Q). The SNP site is positioned preferably at a penultimate base in each allele-specific primer, which increases the reaction specificity and allele discrimination. The proposed ASQ method is advanced in providing a very clear and effective measurement of the fluorescence emitted, with very low signal background-noise, and simple procedures convenient for customized modifications and adjustments. Importantly, this ASQ method is estimated as two- to ten-fold cheaper than Amplifluor and KASP, and much cheaper than all those methods that rely on dual-labeled probes without universal components, like TaqMan and Molecular Beacons. Results for SNP genotyping in the barley genes HvSAP16 and HvSAP8, in which stress-associated proteins are controlled, are presented as proven and validated examples. This method is suitable for bi-allelic uniplex reactions but it can potentially be used for 3- or 4-allelic variants or different SNPs in a multiplex format in a range of applications including medical, forensic, or others involving SNP genotyping.

Responsible, practical genomic data sharing that accelerates research

Data sharing anchors reproducible science, but expectations and best practices are often nebulous. Communities of funders, researchers and publishers continue to grapple with what should be required or encouraged. To illuminate the rationales for sharing data, the technical challenges and the social and cultural challenges, we consider the stakeholders in the scientific enterprise. In biomedical research, participants are key among those stakeholders. Ethical sharing requires considering both the value of research efforts and the privacy costs for participants. We discuss current best practices for various types of genomic data, as well as opportunities to promote ethical data sharing that accelerates science by aligning incentives.

Copy Number Variations and Expression Levels of Guanylate-Binding Protein 6 Gene Associated with Growth Traits of Chinese Cattle

Association studies have indicated profound effects of copy number variations (CNVs) on various phenotypes in different species. In this study, we identified the CNV distributions and expression levels of guanylate-binding protein 6 (GBP6) associated with the growth traits of Chinese cattle. The results showed that the phenotypic values of body size and weight of Xianan (XN) cattle were higher than those of Nanyang (NY) cattle. The medium CNV types were mostly identified in the XN and NY breeds, but their CNV distributions were significantly different (adjusted p < 0.05). The association analysis revealed that the body weight, cannon circumference and chest circumference of XN cattle had significantly different values in different CNV types (p < 0.05), with CNV gain types (Log22-ΔΔCt > 0.5) displaying superior phenotypic values. We also found that transcription levels varied in different tissues (p < 0.001) and the CNV gain types showed the highest relative gene expression levels in the muscle tissue, consistent with the highest phenotypic values of body weight and cannon circumference among the three CNV types. Consequently, our results suggested that CNV gain types of GBP6 could be used as the candidate markers in the cattle-breeding program for growth traits.

Whole-genome resequencing from bulked-segregant analysis reveals gene set based association analyses for the Vibrio anguillarum resistance of turbot (Scophthalmus maximus)

Many achievements have been made to develop quantitative trait loci (QTLs) and gene-associated single nucleotide polymorphisms (SNPs) to facilitate practical marker-assisted selection (MAS) in aquatic animals. However, the systematic studies of SNPs associated with extreme threshold traits were poor in populations lacking of parental genomic information. Coupling next generation sequencing with bulked segregant analysis (BSA) should allow identification of numerous associated SNPs with extreme phenotypes. In the present study, using combination of SNP frequency difference and Euclidean distance, we conducted linkage analysis of SNPs located in genes involved in immune responses, and identified markers associated with Vibrio anguillarum resistance in turbot (Scophthalmus maximus). A total of 221 SNPs was found as candidate SNPs between resistant and susceptible individuals. Among these SNPs, 35 loci located in immune related genes were genotyped in verification population and 7 of them showed significant association with V. anguillarum resistance in both alleles and genotypes (P < 0.05). Among these 7 genes, PIK3CA-like, CYLD, VCAM1, RhoB and RhoGEF are involved in PI3K/Akt/mTOR pathway and NF-κB pathway, which influence the efficiency of bacteria entering the host and inflammation. SNP-SNP interaction analysis was performed by generalized multifactor dimensionality reduction (GMDR). The combination of SNP loci in RhoB, PIK3CA-like and ADCY3 showed a significant effect on V. anguillarum resistance with the verification rate in the sequencing population up to 70.8%. Taken all, our findings demonstrated the feasibility of BSA-seq approach in identifying genes responsible for the extreme phenotypes and will aid in performing MAS in turbot.

Nucleic acid testing and molecular characterization of HIV infections

Significant advances have been made in the molecular assays used for the detection of human immunodeficiency virus (HIV), which are crucial in preventing HIV transmission and monitoring disease progression. Molecular assays for HIV diagnosis have now reached a high degree of specificity, sensitivity and reproducibility, and have less operator involvement to minimize risk of contamination. Furthermore, analyses have been developed for the characterization of host gene polymorphisms and host responses to better identify and monitor HIV-1 infections in the clinic. Currently, molecular technologies including HIV quantitative and qualitative assays are mainly based on the polymerase chain reaction (PCR), transcription-mediated amplification (TMA), nucleic acid sequence-based amplification (NASBA), and branched chain (b) DNA methods and widely used for HIV detection and characterization, such as blood screening, point-of-care testing (POCT), pediatric diagnosis, acute HIV infection (AHI), HIV drug resistance testing, antiretroviral (AR) susceptibility testing, host genome polymorphism testing, and host response analysis. This review summarizes the development and the potential utility of molecular assays used to detect and characterize HIV infections.

Evaluating the Accuracy of Imputation Methods in a Five-Way Admixed Population

Genotype imputation is a powerful tool for increasing statistical power in an association analysis. Meta-analysis of multiple study datasets also requires a substantial overlap of SNPs for a successful association analysis, which can be achieved by imputation. Quality of imputed datasets is largely dependent on the software used, as well as the reference populations chosen. The accuracy of imputation of available reference populations has not been tested for the five-way admixed South African Colored (SAC) population. In this study, imputation results obtained using three freely-accessible methods were evaluated for accuracy and quality. We show that the African Genome Resource is the best reference panel for imputation of missing genotypes in samples from the SAC population, implemented via the freely accessible Sanger Imputation Server.

eDNA as a tool for identifying freshwater species in sustainable forestry: A critical review and potential future applications

Environmental DNA (eDNA) is an emerging biological monitoring tool that can aid in assessing the effects of forestry and forest manufacturing activities on biota. Monitoring taxa across broad spatial and temporal scales is necessary to ensure forest management and forest manufacturing activities meet their environmental goals of maintaining biodiversity. Our objectives are to describe potential applications of eDNA across the wood products supply chain extending from regenerating forests, harvesting, and wood transport, to manufacturing facilities, and to review the current state of the science in this context. To meet our second objective, we summarize the taxa examined with targeted (PCR, qPCR or ddPCR) or metagenomic eDNA methods (eDNA metabarcoding), evaluate how estimated species richness compares between traditional field sampling and eDNA metabarcoding approaches, and compare the geographical representation of prior eDNA studies in freshwater ecosystems to global wood baskets. Potential applications of eDNA include evaluating the effects of forestry and forest manufacturing activities on aquatic biota, delineating fish-bearing versus non fish-bearing reaches, evaluating effectiveness of constructed road crossings for freshwater organism passage, and determining the presence of at-risk species. Studies using targeted eDNA approaches focused on fish, amphibians, and invertebrates, while metagenomic studies focused on fish, invertebrates, and microorganisms. Rare, threatened, or endangered species received the least attention in targeted eDNA research, but are arguably of greatest interest to sustainable forestry and forest manufacturing that seek to preserve freshwater biodiversity. Ultimately, using eDNA methods will enable forestry and forest manufacturing managers to have data-driven prioritization for conservation actions for all freshwater species.

Digital versatile discs as platforms for multiplexed genotyping based on selective ligation and universal microarray detection

The development of a high-performance assay readout using integrated detectors is a current challenge in the implementation of DNA tests in diagnostic laboratories, particularly for supporting pharmacogenetic tests.

The sensitive detection of IVSII-1(G˃A) mutation in beta globin gene using a Nano-based ligation genotyping system

Beta-thalassemia (β-thalassemia) is a globally genetic diseases, and is most prevalent in the Middle East, particularly in Iran. Carrier detection and prenatal diagnosis are the best ways to managing it, and to prevent new community cases from emerging. We report on a simple method for rapid detection of the worst β-thalassemia point mutation in Iran (IVS-II-1 G>A), using a nano-based ligation assay, this was performed using probes with labeled magnetic nanoparticles and quantum dots. After optimizing the technique, 50 DNA samples were genotyped with this method. We found a frequency of 72% for IVSII-1 (G˃A) mutation (42% heterozygote, and 30% mutant homozygote) with a highly sensitive nano-based ligation genotyping system, offering excellent sensitivity and specificity for point mutation detection; it has been demonstrated to be inaccurate, sensitive, cost-effective, and rapid technique for single nucleotide polymorphism (SNP) genotyping.

Applications of the 1000 Genomes Project resources

The 1000 Genomes Project created a valuable, worldwide reference for human genetic variation. Common uses of the 1000 Genomes dataset include genotype imputation supporting Genome-wide Association Studies, mapping expression Quantitative Trait Loci, filtering non-pathogenic variants from exome, whole genome and cancer genome sequencing projects, and genetic analysis of population structure and molecular evolution. In this article, we will highlight some of the multiple ways that the 1000 Genomes data can be and has been utilized for genetic studies.

Multi-omics approaches to disease

High-throughput technologies have revolutionized medical research. The advent of genotyping arrays enabled large-scale genome-wide association studies and methods for examining global transcript levels, which gave rise to the field of “integrative genetics”. Other omics technologies, such as proteomics and metabolomics, are now often incorporated into the everyday methodology of biological researchers. In this review, we provide an overview of such omics technologies and focus on methods for their integration across multiple omics layers. As compared to studies of a single omics type, multi-omics offers the opportunity to understand the flow of information that underlies disease.

Genetic Disorders of Dental Development: Tales from the Bony Crypt

PURPOSE OF REVIEW

The ebb and flow of genetic influence relative to the understanding of craniofacial and dental disorders has evolved into a tacit acceptance of the current genetic paradigm. This review explores the science behind craniofacial and dental disorders through the lens of recent past and current findings and using tooth agenesis as a model of advances in craniofacial genetics.

RECENT FINDINGS

Contemporary studies of craniofacial biology takes advantage of the technological resources stemming from the genomic and post-genomic eras. Emerging data highlights the role of key genes and the epigenetic landscape controlling these genes, in causing dentofacial abnormalities. We also report here a novel Glu78FS MSX1 mutation in one family segregating an autosomal dominant form of severe tooth agenesis as an illustration of an evolving theme, i.e., different mutations in the same gene can result in a spectrum of dentofacial phenotypic severity. The future of clinical therapeutics will benefit from advances in genetics and molecular biology that refine the genotype-phenotype correlation. Indeed, the past century suggests a continued convergence of genetic science in the practice of clinical dentistry.

Gene set based association analyses for the WSSV resistance of Pacific white shrimp Litopenaeus vannamei

White Spot Syndrome Virus (WSSV) is regarded as a virus with the strongest pathogenicity to shrimp. For the threshold trait such as disease resistance, marker assisted selection (MAS) was considered to be a more effective approach. In the present study, association analyses of single nucleotide polymorphisms (SNPs) located in a set of immune related genes were conducted to identify markers associated with WSSV resistance. SNPs were detected by bioinformatics analysis on RNA sequencing data generated by Illimina sequencing platform and Roche 454 sequencing technology. A total of 681 SNPs located in the exons of immune related genes were selected as candidate SNPs. Among these SNPs, 77 loci were genotyped in WSSV susceptible group and resistant group. Association analysis was performed based on logistic regression method under an additive and dominance model in GenABEL package. As a result, five SNPs showed associations with WSSV resistance at a significant level of 0.05. Besides, SNP-SNP interaction analysis was conducted. The combination of SNP loci in TRAF6, Cu/Zn SOD and nLvALF2 exhibited a significant effect on the WSSV resistance of shrimp. Gene expression analysis revealed that these SNPs might influence the expression of these immune-related genes. This study provides a useful method for performing MAS in shrimp.

Fluorescent detection of point mutation via ligase reaction assisted by quantum dots and magnetic nanoparticle-based probes

A nanodiagnostic genotyping method was presented for point mutation detection directly in human genomic DNA based on ligase reaction coupled with quantum dots and magnetic nanoparticle-based probes.

Genomics era and complex disorders: Implications of GWAS with special reference to coronary artery disease, type 2 diabetes mellitus, and cancers

The Human Genome Project (HGP) has identified millions of single nucleotide polymorphisms (SNPs) and their association with several diseases, apart from successfully characterizing the Mendelian/monogenic diseases. However, the dissection of precise etiology of complex genetic disorders still poses a challenge for human geneticists. This review outlines the landmark results of genome-wide association studies (GWAS) with respect to major complex diseases - Coronary artery disease (CAD), type 2 diabetes mellitus (T2DM), and predominant cancers. A brief account on the current Indian scenario is also given. All the relevant publications till mid-2015 were accessed through web databases such as PubMed and Google. Several databases providing genetic information related to these diseases were tabulated and in particular, the list of the most significant SNPs identified through GWAS was made, which may be useful for designing studies in functional validation. Post-GWAS implications and emerging concepts such as epigenomics and pharmacogenomics were also discussed.

Development of Novel High-Resolution Melting-Based Assays for Genotyping Two Alu Insertion Polymorphisms (FXIIIB and PV92)

Insertion/Deletion polymorphisms (InDels) are a common type of genetic variation, with a growing role in population genetics and applied genomics. There is the need for the development of novel cost-effective assays for genotyping InDels of high importance. The main objective of this study was to develop high-resolution melting-based assays for genotyping two commonly studied Alu insertion polymorphisms: FXIIIB and PV92 (rs70942849 and rs3138523). Three primers (two forward and one reverse) were designed for each marker, and high-resolution melting (HRM) analyses in a qPCR platform were performed, using EvaGreen fluorescent dye. For each one of the two Alu insertion polymorphisms, HRM analyses identified distinguishable peaks for the three genotypes, allowing a robust genotyping. Results were validated using 96 DNA samples previously genotyped and the assays worked with different DNA concentrations. In this study, we developed novel cost-effective assays, using qPCR, for genotyping two Alu insertion polymorphisms (widely used as ancestry markers). Our results highlight the feasibility of using HRM analyses for genotyping InDel polymorphisms of medical and biotechnological importance.

The Importance of Patient-Specific Factors for Hepatic Drug Response and Toxicity

Responses to drugs and pharmacological treatments differ considerably between individuals. Importantly, only 50%-75% of patients have been shown to react adequately to pharmacological interventions, whereas the others experience either a lack of efficacy or suffer from adverse events. The liver is of central importance in the metabolism of most drugs. Because of this exposed status, hepatotoxicity is amongst the most common adverse drug reactions and hepatic liabilities are the most prevalent reason for the termination of development programs of novel drug candidates. In recent years, more and more factors were unveiled that shape hepatic drug responses and thus underlie the observed inter-individual variability. In this review, we provide a comprehensive overview of different principle mechanisms of drug hepatotoxicity and illustrate how patient-specific factors, such as genetic, physiological and environmental factors, can shape drug responses. Furthermore, we highlight other parameters, such as concomitantly prescribed medications or liver diseases and how they modulate drug toxicity, pharmacokinetics and dynamics. Finally, we discuss recent progress in the field of in vitro toxicity models and evaluate their utility in reflecting patient-specific factors to study inter-individual differences in drug response and toxicity, as this understanding is necessary to pave the way for a patient-adjusted medicine.

Developing a Risk-scoring Model for Ankylosing Spondylitis Based on a Combination of HLA-B27, Single-nucleotide Polymorphism, and Copy Number Variant Markers

OBJECTIVE

To develop a genotype-based ankylosing spondylitis (AS) risk prediction model that is more sensitive and specific than HLA-B27 typing.

METHODS

To develop the AS genetic risk scoring (AS-GRS) model, 648 individuals (285 cases and 363 controls) were examined for 5 copy number variants (CNV), 7 single-nucleotide polymorphisms (SNP), and an HLA-B27 marker by TaqMan assays. The AS-GRS model was developed using logistic regression and validated with a larger independent set (576 cases and 680 controls).

RESULTS

Through logistic regression, we built the AS-GRS model consisting of 5 genetic components: HLA-B27, 3 CNV (1q32.2, 13q13.1, and 16p13.3), and 1 SNP (rs10865331). All significant associations of genetic factors in the model were replicated in the independent validation set. The discriminative ability of the AS-GRS model measured by the area under the curve was excellent: 0.976 (95% CI 0.96-0.99) in the model construction set and 0.951 (95% CI 0.94-0.96) in the validation set. The AS-GRS model showed higher specificity and accuracy than the HLA-B27-only model when the sensitivity was set to over 94%. When we categorized the individuals into quartiles based on the AS-GRS scores, OR of the 4 groups (low, intermediate-1, intermediate-2, and high risk) showed an increasing trend with the AS-GRS scores (r² = 0.950) and the highest risk group showed a 494× higher risk of AS than the lowest risk group (95% CI 237.3-1029.1).

CONCLUSION

Our AS-GRS could be used to identify individuals at high risk for AS before major symptoms appear, which may improve the prognosis for them through early treatment.

From genetic associations to functional studies in multiple sclerosis

Genetic screens steadily reveal more loci that show robust associations to complex human diseases, including multiple sclerosis (MS). Although some of the identified genetic variants are easily interpreted into a biological function, most of the genetic associations are frequently challenging to interpret. Underlying these difficulties is the fact that chip-based assays typically detect single nucleotide polymorphisms (SNPs) representative of a stretch of DNA containing many genomic variants in linkage disequilibrium. Furthermore, a large proportion of the SNPs with strongest association to MS are located in regions of the DNA that do not directly code for proteins. Here we discuss challenges faced by MS researchers to follow up the large-scale genetic screens that have been published over the past years in search of functional consequences of the identified MS-associated SNPs. We discuss experimental design, tools and methods that may provide the much-needed biological insights in both disease etiology and disease manifestations.

Development of new SNP derived cleaved amplified polymorphic sequence marker set and its successful utilization in the genetic analysis of seed color variation in barley

The aim of the present study was to develop a new cost effective PCR based CAPS marker set using advantages of high-throughput SNP genotyping. Initially, SNP survey was made using 20 diverse barley genotypes via 9k iSelect array genotyping that resulted in 6334 polymorphic SNP markers. Principle component analysis using this marker data showed fine differentiation of barley diverse gene pool. Till this end, we developed 200 SNP derived CAPS markers distributed across the genome covering around 991cM with an average marker density of 5.09cM. Further, we genotyped 68 CAPS markers in an F2 population (Cheri×ICB181160) segregating for seed color variation in barley. Genetic mapping of seed color revealed putative linkage of single nuclear gene on chromosome 1H. These findings showed the proof of concept for the development and utility of a newer cost effective genomic tool kit to analyze broader genetic resources of barley worldwide.

Genetics and psychotic disorders: A fresh look at consanguinity

Consanguineous unions refer to marriages between related individuals who share a common ancestor. These unions are still commonplace in certain regions of the world such as the southern coast of the Mediterranean, throughout the Middle East and South-East Asia. According to available data, couples of second cousins or closer and their offspring currently represent 10.4% of the world's population, thus resulting in increased frequencies of autosomal recessive disorders. Furthermore, consanguinity may be implicated in the increased frequency of multifactorial pathologies such as mental disorders. The few existing epidemiological studies in consanguineous and/or geographically isolated populations confirm that there is a significant association between consanguinity and mental disorders and a higher risk of schizophrenia or bipolar disorders among offspring from consanguineous couples. There exists a strong and complex genetic component in the predisposition to psychotic disorders that has been confirmed in numerous studies. However, the genetic basis of these disorders remains poorly understood. GWAS studies (Genome Wide Association Studies) over the past 10 years have identified a few weak associations, thus refuting the "common diseases-common variants" hypothesis. A model implicating numerous rare variants has been supported by the recent discovery of CNVs (Copy Number Variants) and their statistically significant association with psychiatric disorders such as schizophrenia, bipolar disorders and autism. The study of consanguineous families may contribute to identifying rare variants in homogenous populations who have conserved certain alleles. Major developments in molecular biology techniques would facilitate these studies as well as contributing to identifying major genes. These results emphasize the need for genetic counseling in high-risk communities and the importance of implementing preventive actions and raising awareness concerning the risk of consanguineous unions.

PBAP: a pipeline for file processing and quality control of pedigree data with dense genetic markers

MOTIVATION

Huge genetic datasets with dense marker panels are now common. With the availability of sequence data and recognition of importance of rare variants, smaller studies based on pedigrees are again also common. Pedigree-based samples often start with a dense marker panel, a subset of which may be used for linkage analysis to reduce computational burden and to limit linkage disequilibrium between single-nucleotide polymorphisms (SNPs). Programs attempting to select markers for linkage panels exist but lack flexibility.

RESULTS

We developed a pedigree-based analysis pipeline (PBAP) suite of programs geared towards SNPs and sequence data. PBAP performs quality control, marker selection and file preparation. PBAP sets up files for MORGAN, which can handle analyses for small and large pedigrees, typically human, and results can be used with other programs and for downstream analyses. We evaluate and illustrate its features with two real datasets.

AVAILABILITY AND IMPLEMENTATION

PBAP scripts may be downloaded from http://faculty.washington.edu/wijsman/software.shtml.

CONTACT

wijsman@uw.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Efficient SNP Discovery by Combining Microarray and Lab-on-a-Chip Data for Animal Breeding and Selection

The genetic markers associated with economic traits have been widely explored for animal breeding. Among these markers, single-nucleotide polymorphism (SNPs) are gradually becoming a prevalent and effective evaluation tool. Since SNPs only focus on the genetic sequences of interest, it thereby reduces the evaluation time and cost. Compared to traditional approaches, SNP genotyping techniques incorporate informative genetic background, improve the breeding prediction accuracy and acquiesce breeding quality on the farm. This article therefore reviews the typical procedures of animal breeding using SNPs and the current status of related techniques. The associated SNP information and genotyping techniques, including microarray and Lab-on-a-Chip based platforms, along with their potential are highlighted. Examples in pig and poultry with different SNP loci linked to high economic trait values are given. The recommendations for utilizing SNP genotyping in nimal breeding are summarized.

Tourette Syndrome: Bridging the Gap between Genetics and Biology

Tourette syndrome is a childhood neuropsychiatric disorder, which presents with disruptive motor and vocal tics. The disease also has a high comorbidity with obsessive-compulsive disorder and attention deficit hyperactivity disorder, which may further increase the distress experienced by patients. Current treatments act with varying efficacies in alleviating symptoms, as the underlying biology of the disease is not fully understood to provide precise therapeutic targets. Moreover, the genetic complexity of the disorder presents a substantial challenge to the identification of genetic alterations that contribute to the Tourette's phenotype. Nevertheless, genetic studies have suggested involvement of dopaminergic, serotonergic, glutamatergic, and histaminergic pathways in the pathophysiology of at least some cases. In addition, genetic overlaps with other neuropsychiatric disorders may point toward a shared biology. The findings that are emerging from genetic studies will allow researchers to piece together the underlying components of the disease, in the hopes that a deeper understanding of Tourette's can lead to improved treatments for those affected by it.

Review: High-performance computing to detect epistasis in genome scale data sets

It is becoming clear that most human diseases have a complex etiology that cannot be explained by single nucleotide polymorphisms (SNPs) or simple additive combinations; the general consensus is that they are caused by combinations of multiple genetic variations. The limited success of some genome-wide association studies is partly a result of this focus on single genetic markers. A more promising approach is to take into account epistasis, by considering the association of multiple SNP interactions with disease. However, as genomic data continues to grow in resolution, and genome and exome sequencing become more established, the number of combinations of variants to consider increases rapidly. Two potential solutions should be considered: the use of high-performance computing, which allows us to consider a larger number of variables, and heuristics to make the solution more tractable, essential in the case of genome sequencing. In this review, we look at different computational methods to analyse epistatic interactions within disease-related genetic data sets created by microarray technology. We also review efforts to use epistatic analysis results to produce biomarkers for diagnostic tests and give our views on future directions in this field in light of advances in sequencing technology and variants in non-coding regions.

Molecular Epidemiology of Mycoplasma pneumoniae: Genotyping Using Single Nucleotide Polymorphisms and SNaPshot Technology

Molecular typing of Mycoplasma pneumoniae is an important tool for identifying grouped cases and investigating outbreaks. In the present study, we developed a new genotyping method based on single nucleotide polymorphisms (SNPs) selected from the whole-genome sequencing of eight M. pneumoniae strains, using the SNaPshot minisequencing assay. Eight SNPs, localized in housekeeping genes, predicted lipoproteins, and adhesin P1 genes were selected for genotyping. These SNPs were evaluated on 140 M. pneumoniae clinical isolates previously genotyped by multilocus variable-number tandem-repeat analysis (MLVA-5) and adhesin P1 typing. This method was also adapted for direct use with clinical samples and evaluated on 51 clinical specimens. The analysis of the clinical isolates using the SNP typing method showed nine distinct SNP types with a Hunter and Gaston diversity index (HGDI) of 0.836, which is higher than the HGDI of 0.583 retrieved for the MLVA-4 typing method, where the nonstable Mpn1 marker was removed. A strong correlation with the P1 adhesin gene typing results was observed. The congruence was poor between MLVA-5 and SNP typing, indicating distinct genotyping schemes. Combining the results increased the discriminatory power. This new typing method based on SNPs and the SNaPshot technology is a method for rapid M. pneumoniae typing directly from clinical specimens, which does not require any sequencing step. This method is based on stable markers and provides information distinct from but complementary to MLVA typing. The combined use of SNPs and MLVA typing provides powerful discrimination of strains.

Complex and multi-allelic copy number variation in human disease

Hundreds of copy number variants are complex and multi-allelic, in that they have many structural alleles and have rearranged multiple times in the ancestors who contributed chromosomes to current humans. Not only are the relationships of these multi-allelic CNVs (mCNVs) to phenotypes generally unknown, but many mCNVs have not yet been described at the basic levels—alleles, allele frequencies, structural features—that support genetic investigation. To date, most reported disease associations to these variants have been ascertained through candidate gene studies. However, only a few associations have reached the level of acceptance defined by durable replications in many cohorts. This likely stems from longstanding challenges in making precise molecular measurements of the alleles individuals have at these loci. However, approaches for mCNV analysis are improving quickly, and some of the unique characteristics of mCNVs may assist future association studies. Their various structural alleles are likely to have different magnitudes of effect, creating a natural allelic series of growing phenotypic impact and giving investigators a set of natural predictions and testable hypotheses about the extent to which each allele of an mCNV predisposes to a phenotype. Also, mCNVs’ low-to-modest correlation to individual single-nucleotide polymorphisms (SNPs) may make it easier to distinguish between mCNVs and nearby SNPs as the drivers of an association signal, and perhaps, make it possible to preliminarily screen candidate loci, or the entire genome, for the many mCNV–disease relationships that remain to be discovered.

Hybrid, multiplexed, functional DNA nanotechnology for bioanalysis

DNA nanotechnology allows for the realization of novel multiplexed assays in bioanalytical sciences.

Highly sensitive DNA methylation analysis at CpG resolution by surface-enhanced Raman scattering via ligase chain reaction

Highly sensitive DNA methylation analysis at CpG resolution is demonstrated by employing SERS nanotags via ligase chain reaction (LCR) and validated with sequencing.

Characterizing the genetic basis of bacterial phenotypes using genome-wide association studies: a new direction for bacteriology

Genome-wide association studies (GWASs) have become an increasingly important approach for eukaryotic geneticists, facilitating the identification of hundreds of genetic polymorphisms that are responsible for inherited diseases. Despite the relative simplicity of bacterial genomes, the application of GWASs to identify polymorphisms responsible for important bacterial phenotypes has only recently been made possible through advances in genome sequencing technologies. Bacterial GWASs are now about to come of age thanks to the availability of massive datasets, and because of the potential to bridge genomics and traditional genetic approaches that is provided by improving validation strategies. A small number of pioneering GWASs in bacteria have been published in the past 2 years, examining from 75 to more than 3,000 strains. The experimental designs have been diverse, taking advantage of different processes in bacteria for generating variation. Analysis of data from bacterial GWASs can, to some extent, be performed using software developed for eukaryotic systems, but there are important differences in genome evolution that must be considered. The greatest experimental advantage of bacterial GWASs is the potential to perform downstream validation of causality and dissection of mechanism. We review the recent advances and remaining challenges in this field and propose strategies to improve the validation of bacterial GWASs.

A powerful tool for genome analysis in maize: development and evaluation of the high density 600 k SNP genotyping array

Background

High density genotyping data are indispensable for genomic analyses of complex traits in animal and crop species. Maize is one of the most important crop plants worldwide, however a high density SNP genotyping array for analysis of its large and highly dynamic genome was not available so far.

Results

We developed a high density maize SNP array composed of 616,201 variants (SNPs and small indels). Initially, 57 M variants were discovered by sequencing 30 representative temperate maize lines and then stringently filtered for sequence quality scores and predicted conversion performance on the array resulting in the selection of 1.2 M polymorphic variants assayed on two screening arrays. To identify high-confidence variants, 285 DNA samples from a broad genetic diversity panel of worldwide maize lines including the samples used for sequencing, important founder lines for European maize breeding, hybrids, and proprietary samples with European, US, semi-tropical, and tropical origin were used for experimental validation. We selected 616 k variants according to their performance during validation, support of genotype calls through sequencing data, and physical distribution for further analysis and for the design of the commercially available Affymetrix® Axiom® Maize Genotyping Array. This array is composed of 609,442 SNPs and 6,759 indels. Among these are 116,224 variants in coding regions and 45,655 SNPs of the Illumina® MaizeSNP50 BeadChip for study comparison. In a subset of 45,974 variants, apart from the target SNP additional off-target variants are detected, which show only a minor bias towards intermediate allele frequencies. We performed principal coordinate and admixture analyses to determine the ability of the array to detect and resolve population structure and investigated the extent of LD within a worldwide validation panel.

Conclusions

The high density Affymetrix® Axiom® Maize Genotyping Array is optimized for European and American temperate maize and was developed based on a diverse sample panel by applying stringent quality filter criteria to ensure its suitability for a broad range of applications. With 600 k variants it is the largest currently publically available genotyping array in crop species.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-823) contains supplementary material, which is available to authorized users.

Assessing accuracy of genotype imputation in American Indians

Background

Genotype imputation is commonly used in genetic association studies to test untyped variants using information on linkage disequilibrium (LD) with typed markers. Imputing genotypes requires a suitable reference population in which the LD pattern is known, most often one selected from HapMap. However, some populations, such as American Indians, are not represented in HapMap. In the present study, we assessed accuracy of imputation using HapMap reference populations in a genome-wide association study in Pima Indians.

Results

Data from six randomly selected chromosomes were used. Genotypes in the study population were masked (either 1% or 20% of SNPs available for a given chromosome). The masked genotypes were then imputed using the software Markov Chain Haplotyping Algorithm. Using four HapMap reference populations, average genotype error rates ranged from 7.86% for Mexican Americans to 22.30% for Yoruba. In contrast, use of the original Pima Indian data as a reference resulted in an average error rate of 1.73%.

Conclusions

Our results suggest that the use of HapMap reference populations results in substantial inaccuracy in the imputation of genotypes in American Indians. A possible solution would be to densely genotype or sequence a reference American Indian population.