Human-genome single nucleotide polymorphisms affecting transcription factor binding and their role in pathogenesis

Single nucleotide polymorphisms (SNPs) are the most common type of variation in the human genome. The vast majority of SNPs identified in the human genome do not have any effect on the phenotype; however, some can lead to changes in the function of a gene or the level of its expression. Most SNPs associated with certain traits or pathologies are mapped to regulatory regions of the genome and affect gene expression by changing transcription factor binding sites. In recent decades, substantial effort has been invested in searching for such regulatory SNPs (rSNPs) and understanding the mechanisms by which they lead to phenotypic differences, primarily to individual differences in susceptibility to diseases and in sensitivity to drugs. The development of the NGS (next-generation sequencing) technology has contributed not only to the identification of a huge number of SNPs and to the search for their association (genome-wide association studies, GWASs) with certain diseases or phenotypic manifestations, but also to the development of more productive approaches to their functional annotation. It should be noted that the presence of an association does not allow one to identify a functional, truly disease-associated DNA sequence variant among multiple marker SNPs that are detected due to linkage disequilibrium. Moreover, determination of associations of genetic variants with a disease does not provide information about the functionality of these variants, which is necessary to elucidate the molecular mechanisms of the development of pathology and to design effective methods for its treatment and prevention. In this regard, the functional analysis of SNPs annotated in the GWAS catalog, both at the genome-wide level and at the level of individual SNPs, became especially relevant in recent years. A genome-wide search for potential rSNPs is possible without any prior knowledge of their association with a trait. Thus, mapping expression quantitative trait loci (eQTLs) makes it possible to identify an SNP for which - among transcriptomes of homozygotes and heterozygotes for its various alleles - there are differences in the expression level of certain genes, which can be located at various distances from the SNP. To predict rSNPs, approaches based on searches for allele-specific events in RNA-seq, ChIP-seq, DNase-seq, ATAC-seq, MPRA, and other data are also used. Nonetheless, for a more complete functional annotation of such rSNPs, it is necessary to establish their association with a trait, in particular, with a predisposition to a certain pathology or sensitivity to drugs. Thus, approaches to finding SNPs important for the development of a trait can be categorized into two groups: (1) starting from data on an association of SNPs with a certain trait, (2) starting from the determination of allele-specific changes at the molecular level (in a transcriptome or regulome). Only comprehensive use of strategically different approaches can considerably enrich our knowledge about the role of genetic determinants in the molecular mechanisms of trait formation, including predisposition to multifactorial diseases.

Towards Understanding the Genetic Nature of Vasovagal Syncope

Syncope, defined as a transient loss of consciousness caused by transient global cerebral hypoperfusion, affects 30-40% of humans during their lifetime. Vasovagal syncope (VVS) is the most common cause of syncope, the etiology of which is still unclear. This review summarizes data on the genetics of VVS, describing the inheritance pattern of the disorder, candidate gene association studies and genome-wide studies. According to this evidence, VVS is a complex disorder, which can be caused by the interplay between genetic factors, whose contribution varies from monogenic Mendelian inheritance to polygenic inherited predisposition, and external factors affecting the monogenic (resulting in incomplete penetrance) and polygenic syncope types.

SIGNAL: A web-based iterative analysis platform integrating pathway and network approaches optimizes hit selection from genome-scale assays

Hit selection from high-throughput assays remains a critical bottleneck in realizing the potential of omic-scale studies in biology. Widely used methods such as setting of cutoffs, prioritizing pathway enrichments, or incorporating predicted network interactions offer divergent solutions yet are associated with critical analytical trade-offs. The specific limitations of these individual approaches and the lack of a systematic way by which to integrate their rankings have contributed to limited overlap in the reported results from comparable genome-wide studies and costly inefficiencies in secondary validation efforts. Using comparative analysis of parallel independent studies as a benchmark, we characterize the specific complementary contributions of each approach and demonstrate an optimal framework to integrate these methods. We describe selection by iterative pathway group and network analysis looping (SIGNAL), an integrated, iterative approach that uses both pathway and network methods to optimize gene prioritization. SIGNAL is accessible as a rapid user-friendly web-based application (https://signal.niaid.nih.gov). A record of this paper's transparent peer review is included in the Supplemental information.

A Deep Learning Approach to Population Structure Inference in Inbred Lines of Maize

Analysis of population genetic variation and structure is a common practice for genome-wide studies, including association mapping, ecology, and evolution studies in several crop species. In this study, machine learning (ML) clustering methods, K-means (KM), and hierarchical clustering (HC), in combination with non-linear and linear dimensionality reduction techniques, deep autoencoder (DeepAE) and principal component analysis (PCA), were used to infer population structure and individual assignment of maize inbred lines, i.e., dent field corn (n = 97) and popcorn (n = 86). The results revealed that the HC method in combination with DeepAE-based data preprocessing (DeepAE-HC) was the most effective method to assign individuals to clusters (with 96% of correct individual assignments), whereas DeepAE-KM, PCA-HC, and PCA-KM were assigned correctly 92, 89, and 81% of the lines, respectively. These findings were consistent with both Silhouette Coefficient (SC) and Davies-Bouldin validation indexes. Notably, DeepAE-HC also had better accuracy than the Bayesian clustering method implemented in InStruct. The results of this study showed that deep learning (DL)-based dimensional reduction combined with ML clustering methods is a useful tool to determine genetically differentiated groups and to assign individuals into subpopulations in genome-wide studies without having to consider previous genetic assumptions.

[Genome-wide studies of primary open-angle glaucoma]

The article reviews literature on developmental stages of genome-wide association studies (GWAS) of primary open-angle glaucoma (POAG). This problem is currently developing and one of the most complex in ophthalmology. The article considers main GWAS of POAG and established GWAS-significant polymorphisms associated with the disease. The topic of genome-wide studies of primary open-angle glaucoma will be of certain interest to ophthalmologists, materials on GWAS-significant loci can be used both in the selection of polymorphisms in replicative studies of POAG in various populations of Russia, and to expand ideas about the molecular genetic mechanisms of the development of the disease.

Interaction of the Host and Viral Genome and Their Influence on HIV Disease

The course of Human Immunodeficiency Virus type 1 (HIV) infection is a dynamic interplay in which both host and viral genetic variation, among other factors, influence disease susceptibility and rate of progression. HIV set-point viral load (spVL), a key indicator of HIV disease progression, has an estimated 30% of variance attributable to common heritable effects and roughly 70% attributable to environmental factors and/or additional non-genetic factors. Genome-wide genotyping and sequencing studies have allowed for large-scale association testing studying host and viral genetic variants associated with infection and disease progression. Host genomics of HIV infection has been studied predominantly in Caucasian populations consistently identifying human leukocyte antigen (HLA) genes and C-C motif chemokine receptor 5 as key factors of HIV susceptibility and progression. However, these studies don’t fully assess all classes of genetic variation (e.g., very rare polymorphisms, copy number variants etc.) and do not inform on non-European ancestry groups. Additionally, viral sequence variability has been demonstrated to influence disease progression independently of host genetic variation. Viral sequence variation can be attributed to the rapid evolution of the virus within the host due to the selective pressure of the host immune response. As the host immune system responds to the virus, e.g., through recognition of HIV antigens, the virus is able to mitigate this response by evolving HLA-specific escape mutations. Diversity of viral genotypes has also been correlated with moderate to strong effects on CD4+ T cell decline and some studies showing weak to no correlation with spVL. There is evidence to support these viral genetic factors being heritable between individuals and the evolution of these factors having important consequences in the genetic epidemiology of HIV infection on a population level. This review will discuss the host-pathogen interaction of HIV infection, explore the importance of host and viral genetics for a better understanding of pathogenesis and identify opportunities for additional genetic studies.

Insights into genetic and epigenetic determinants with impact on vitamin d signaling and cancer association studies: the case of thyroid cancer

Vitamin D is a key regulator of calcium metabolism and has been implicated as a cancer preventive agent. However, clinical studies have revealed conflicting results on its cancer preventive properties, attributed in part to multiple metabolic and regulatory factors susceptible to affect individual responses to exogenous vitamin D. Vitamin D is obtained from dietary sources and sun exposure, which depends on numerous parameters such as skin type, latitude, and lifestyle factors. Focusing on thyroid cancer (TC), we document that genetic and epigenetic determinants can greatly impact individual response to vitamin D and may outweigh the classical clinical correlative studies that focus on sun exposure/dietary intake factors. In particular, genetic determinants innate to host intrinsic metabolic pathways such as highly polymorphic cytochromes P450s responsible for the metabolic activation of vitamin D are expressed in many organs, including the thyroid gland and can impact vitamin D interaction with its nuclear receptor (VDR) in thyroid tissue. Moreover, downstream regulatory pathways in vitamin D signaling as well as VDR are also subject to wide genetic variability among human populations as shown by genome-wide studies. These genetic variations in multiple components of vitamin D pathways are critical determinants for the revaluation of the potential preventive and anticancer properties of vitamin D in TC.

SNP prioritization using a Bayesian probability of association

Prioritization is the process whereby a set of possible candidate genes or SNPs is ranked so that the most promising can be taken forward into further studies. In a genome-wide association study, prioritization is usually based on the P-values alone, but researchers sometimes take account of external annotation information about the SNPs such as whether the SNP lies close to a good candidate gene. Using external information in this way is inherently subjective and is often not formalized, making the analysis difficult to reproduce. Building on previous work that has identified 14 important types of external information, we present an approximate Bayesian analysis that produces an estimate of the probability of association. The calculation combines four sources of information: the genome-wide data, SNP information derived from bioinformatics databases, empirical SNP weights, and the researchers' subjective prior opinions. The calculation is fast enough that it can be applied to millions of SNPS and although it does rely on subjective judgments, those judgments are made explicit so that the final SNP selection can be reproduced. We show that the resulting probability of association is intuitively more appealing than the P-value because it is easier to interpret and it makes allowance for the power of the study. We illustrate the use of the probability of association for SNP prioritization by applying it to a meta-analysis of kidney function genome-wide association studies and demonstrate that SNP selection performs better using the probability of association compared with P-values alone.

Nucleotide excision repair in cellular chromatin: studies with yeast from nucleotide to gene to genome

Here we review our development of, and results with, high resolution studies on global genome nucleotide excision repair (GGNER) in Saccharomyces cerevisiae. We have focused on how GGNER relates to histone acetylation for its functioning and we have identified the histone acetyl tranferase Gcn5 and acetylation at lysines 9/14 of histone H3 as a major factor in enabling efficient repair. We consider results employing primarily MFA2 as a model gene, but also those with URA3 located at subtelomeric sequences. In the latter case we also see a role for acetylation at histone H4. We then go on to outline the development of a high resolution genome-wide approach that enables one to examine correlations between histone modifications and the nucleotide excision repair (NER) of UV-induced cyclobutane pyrimidine dimers throughout entire genomes. This is an approach that will enable rapid advances in understanding the complexities of how compacted chromatin in chromosomes is processed to access DNA damage and then returned to its pre-damaged status to maintain epigenetic codes.

The genetic pleiotropy of musculoskeletal aging

Musculoskeletal aging is detrimental to multiple bodily functions and starts early, probably in the fourth decade of an individual's life. Sarcopenia is a health problem that is expected to only increase as a greater portion of the population lives longer; prevalence of the related musculoskeletal diseases is similarly expected to increase. Unraveling the biological and biomechanical associations and molecular mechanisms underlying these diseases represents a formidable challenge. There are two major problems making disentangling the biological complexity of musculoskeletal aging difficult: (a) it is a systemic, rather than "compartmental," problem, which should be approached accordingly, and (b) the aging per se is neither well defined nor reliably measurable. A unique challenge of studying any age-related condition is a need of distinguishing between the "norm" and "pathology," which are interwoven throughout the aging organism. We argue that detecting genes with pleiotropic functions in musculoskeletal aging is needed to provide insights into the potential biological mechanisms underlying inter-individual differences insusceptibility to the musculoskeletal diseases. However, exploring pleiotropic relationships among the system's components is challenging both methodologically and conceptually. We aimed to focus on genetic aspects of the cross-talk between muscle and its "neighboring" tissues and organs (tendon, bone, and cartilage), and to explore the role of genetics to find the new molecular links between skeletal muscle and other parts of the "musculoskeleton." Identification of significant genetic variants underlying the musculoskeletal system's aging is now possible more than ever due to the currently available advanced genomic technologies. In summary, a "holistic" genetic approach is needed to study the systems's normal functioning and the disease predisposition in order to improve musculoskeletal health.

Systems genetics of alcoholism

Alcoholism is a common disease resulting from the complex interaction of genetic, social, and environmental factors. Interest in the high heritability of alcoholism has resulted in many studies of how single genes, as well as an individual's entire genetic content (i.e., genome) and the proteins expressed by the genome, influence alcoholism risk. The use of large-scale methods to identify and characterize genetic material (i.e., high-throughput technologies) for data gathering and analysis recently has made it possible to investigate the complexity of the genetic architecture of susceptibility to common diseases such as alcoholism on a systems level. Systems genetics is the study of all genetic variations, their interactions with each other (i.e., epistasis), their interactions with the environment (i.e., plastic reaction norms), their relationship with interindividual variation in traits that are influenced by many genes and contribute to disease susceptibility (i.e., intermediate quantitative traits or endophenotypes) defined at different levels of hierarchical biochemical and physiological systems, and their relationship with health and disease. (An endophenotype is a genetically determined trait [i.e., phenotype] that is not immediately visible but may contribute to the susceptibility to develop a particular behavior or syndrome. See the glossary, p. 84, for descriptions of other technical terms used in this article.) The goal of systems genetics is to provide an understanding of the complex relationship between the genome and disease by investigating intermediate biological processes. After investigating main effects, the first step in a systems genetics approach, as described here, is to search for gene-gene (i.e., epistatic) reactions.