Insight into Neutral and Disease-Associated Human Genetic Variants through Interpretable Predictors

A variety of methods that predict human nonsynonymous single nucleotide polymorphisms (SNPs) to be neutral or disease-associated have been developed over the last decade. These methods are used for pinpointing disease-associated variants in the many variants obtained with next-generation sequencing technologies. The high performances of current sequence-based predictors indicate that sequence data contains valuable information about a variant being neutral or disease-associated. However, most predictors do not readily disclose this information, and so it remains unclear what sequence properties are most important. Here, we show how we can obtain insight into sequence characteristics of variants and their surroundings by interpreting predictors. We used an extensive range of features derived from the variant itself, its surrounding sequence, sequence conservation, and sequence annotation, and employed linear support vector machine classifiers to enable extracting feature importance from trained predictors. Our approach is useful for providing additional information about what features are most important for the predictions made. Furthermore, for large sets of known variants, it can provide insight into the mechanisms responsible for variants being disease-associated.     

Estimation of age and rate of increase of rare variants.

The problem considered is that of estimating the age or rate of increase of a variant on the basis of the present number of replicates observed in a population. In place of previous diffusion equation analyses of age probability distributions, the likelihood for the age is studied on the basis of a discrete branching process model. It is shown that variations inherent in the process of gene evolution in natural populations make it impossible to provide a reliable point estimate of the age of a specified variant, although the likelihood analysis provides a confidence interval which may place useful bounds on the period in which a variant originated. The observed distribution of numbers of several variants may also provide useful information. The problems of estimation are discussed with reference to rare variants arising in American Indian populations.     

SNP analysis to dissect human traits

The analysis of complex human diseases has been spurred by the number of published genomic sequence variants - many identified in the course of sequencing the human genome. But, to be useful for genetic analysis, variants have to be mapped accurately, their frequencies in various populations determined, and automated high-throughput assay techniques developed. Recently proposed methods address these issues: the use of 'reduced representation shotgun' methods for more efficient detection of single nucleotide polymorphisms (SNPs), the employment of high-throughput genotyping techniques, the development of SNP maps that incorporate information about linkage disequilibrium, and the use of SNPs in identifying susceptibility genes for common illnesses.     

Genetic analysis of the capsid region of norovirus GII.4 variants isolated in South Korea

Norovirus is one of the major causes of non-bacterial gastroenteritis in humans. The aim of this study was to analyze the amino acid variation of open reading frame 2 of GII.4 variants in South Korea during the period from November 2006 to December 2012. Sixty-nine complete nucleotide sequences of open reading frame 2 were obtained from 113 GII.4 strains. The GII.4 2006b variants were detected predominantly between 2006 and 2009; however, new GII.4 variants, which were termed the 2010 variant and the 2012 variant, emerged in 2010 and 2012, respectively. The number of GII.4 2006b variants steadily decreased until 2012, whereas the number of gastroenteritis cases caused by the new variants increased between 2010 and 2012. The amino acid sequence in the ORF2 region obtained in this study was compared with other GII.4 variants isolated in various countries. Amino acid variations were observed primarily at epitope sites and the surrounding regions. Amino acids 294, 359, 393, and 413 of the P2 subdomain were the most variable sites among the GII.4 variants. The information in this study can be useful in basic research to predict the emergence and determine the genetic functions of new GII.4 variants.     

MosaicBase:A Knowledgebase of Postzygotic Mosaic Variants in Noncancer Disease-related and Healthy Human Individuals

Mosaic variants resulting from postzygotic mutations are prevalent in the human genome and play important roles in human diseases. However, except for cancer-related variants, there is no collection of postzygotic mosaic variants in noncancer disease-related and healthy individuals. Here, we present MosaicBase, a comprehensive database that includes 6698 mosaic variants related to 266 noncancer diseases and 27,991 mosaic variants identified in 422 healthy individuals. Genomic and phenotypic information of each variant was manually extracted and curated from 383 publications. MosaicBase supports the query of variants with Online Mendelian Inheritance in Man (OMIM) entries, genomic coordinates, gene symbols, or Entrez IDs. We also provide an integrated genome browser for users to easily access mosaic variants and their related annotations for any genomic region. By analyzing the variants collected in MosaicBase, we find that mosaic variants that directly contribute to disease phenotype show features distinct from those of variants in individuals with mild or no phenotypes, in terms of their genomic distribution, mutation signatures, and fraction of mutant cells. MosaicBase will not only assist clinicians in genetic counseling and diagnosis but also provide a useful resource to understand the genomic baseline of postzygotic mutations in the general human population. MosaicBase is publicly available at http://mosaicbase.com/ or http://49.4.21.8:8000.     

VarSite: Disease variants and protein structure

VarSite is a web server mapping known disease‐associated variants from UniProt and ClinVar, together with natural variants from gnomAD, onto protein 3D structures in the Protein Data Bank. The analyses are primarily image‐based and provide both an overview for each human protein, as well as a report for any specific variant of interest. The information can be useful in assessing whether a given variant might be pathogenic or benign. The structural annotations for each position in the protein include protein secondary structure, interactions with ligand, metal, DNA/RNA, or other protein, and various measures of a given variant's possible impact on the protein's function. The 3D locations of the disease‐associated variants can be viewed interactively via the 3dmol.js JavaScript viewer, as well as in RasMol and PyMOL. Users can search for specific variants, or sets of variants, by providing the DNA coordinates of the base change(s) of interest. Additionally, various agglomerative analyses are given, such as the mapping of disease and natural variants onto specific Pfam or CATH domains. The server is freely accessible to all at: https://www.ebi.ac.uk/thornton-srv/databases/VarSite .     

Comprehensive candidate gene study highlights UGT1A and BNC2 as new genes determining continuous skin color variation in Europeans

Natural variation in human skin pigmentation is primarily due to genetic causes rooted in recent evolutionary history. Genetic variants associated with human skin pigmentation confer risk of skin cancer and may provide useful information in forensic investigations. Almost all previous gene-mapping studies of human skin pigmentation were based on categorical skin color information known to oversimplify the continuous nature of human skin coloration. We digitally quantified skin color into hue and saturation dimensions for 5,860 Dutch Europeans based on high-resolution skin photographs. We then tested an extensive list of 14,185 single nucleotide polymorphisms in 281 candidate genes potentially involved in human skin pigmentation for association with quantitative skin color phenotypes. Confirmatory association was revealed for several known skin color genes including HERC2, MC1R, IRF4, TYR, OCA2, and ASIP. We identified two new skin color genes: genetic variants in UGT1A were significantly associated with hue and variants in BNC2 were significantly associated with saturation. Overall, digital quantification of human skin color allowed detecting new skin color genes. The variants identified in this study may also contribute to the risk of skin cancer. Our findings are also important for predicting skin color in forensic investigations.     

Strategies and computational tools for improving randomized protein libraries

In the last decade, directed evolution has become a routine approach for engineering proteins with novel or altered properties. Concurrently, a trend away from purely 'blind' randomization strategies and towards more 'semi-rational' approaches has also become apparent. In this review, we discuss ways in which structural information and predictive computational tools are playing an increasingly important role in guiding the design of randomized libraries: web servers such as ConSurf-HSSP and SCHEMA allow the prediction of sites to target for producing functional variants, while algorithms such as GLUE, PEDEL and DRIVeR are useful for estimating library completeness and diversity. In addition, we review recent methodological developments that facilitate the construction of unbiased libraries, which are inherently more diverse than biased libraries and therefore more likely to yield improved variants.     

Novel putative drugs and key initiating genes for neurodegenerative disease determined using network-based genetic integrative analysis

Understanding the genetic causes of neurodegenerative disease (ND) can be useful for their prevention and treatment. Among the genetic variations responsible for ND, heritable germline variants have been discovered in genome-wide association studies (GWAS), and nonheritable somatic mutations have been discovered in sequencing projects. Distinguishing the important initiating genes in ND and comparing the importance of heritable and nonheritable genetic variants for treating ND are important challenges. In this study, we analysed GWAS results, somatic mutations and drug targets of ND from large databanks by performing directed network-based analysis considering a randomised network hypothesis testing procedure. A disease-associated biological network was created in the context of the functional interactome, and the nonrandom topological characteristics of directed-edge classes were interpreted. Hierarchical network analysis indicated that drug targets tend to lie upstream of somatic mutations and germline variants. Furthermore, using directed path length information and biological explanations, we provide information on the most important genes in these created node classes and their associated drugs. Finally, we identified nine germline variants overlapping with drug targets for ND, seven somatic mutations close to drug targets from the hierarchical network analysis and six crucial genes in controlling other genes from the network analysis. Based on these findings, some drugs have been proposed for treating ND via drug repurposing. Our results provide new insights into the therapeutic actionability of GWAS results and somatic mutations for ND. The interesting properties of each node class and the existing relationships between them can broaden our knowledge of ND.     

Runs of homozygosity: current knowledge and applications in livestock

This review presents a broader approach to the implementation and study of runs of homozygosity (ROH) in animal populations, focusing on identifying and characterizing ROH and their practical implications. ROH are continuous homozygous segments that are common in individuals and populations. The ability of these homozygous segments to give insight into a population's genetic events makes them a useful tool that can provide information about the demographic evolution of a population over time. Furthermore, ROH provide useful information about the genetic relatedness among individuals, helping to minimize the inbreeding rate and also helping to expose deleterious variants in the genome. The frequency, size and distribution of ROH in the genome are influenced by factors such as natural and artificial selection, recombination, linkage disequilibrium, population structure, mutation rate and inbreeding level. Calculating the inbreeding coefficient from molecular information from ROH (F_ROH) is more accurate for estimating autozygosity and for detecting both past and more recent inbreeding effects than are estimates from pedigree data (F_PED). The better results of F_ROH suggest that F_ROH can be used to infer information about the history and inbreeding levels of a population in the absence of genealogical information. The selection of superior animals has produced large phenotypic changes and has reshaped the ROH patterns in various regions of the genome. Additionally, selection increases homozygosity around the target locus, and deleterious variants are seen to occur more frequently in ROH regions. Studies involving ROH are increasingly common and provide valuable information about how the genome's architecture can disclose a population's genetic background. By revealing the molecular changes in populations over time, genome‐wide information is crucial to understanding antecedent genome architecture and, therefore, to maintaining diversity and fitness in endangered livestock breeds.     

Genome-wide analysis of human disease alleles reveals that their locations are correlated in paralogous proteins

The millions of mutations and polymorphisms that occur in human populations are potential predictors of disease, of our reactions to drugs, of predisposition to microbial infections, and of age-related conditions such as impaired brain and cardiovascular functions. However, predicting the phenotypic consequences and eventual clinical significance of a sequence variant is not an easy task. Computational approaches have found perturbation of conserved amino acids to be a useful criterion for identifying variants likely to have phenotypic consequences. To our knowledge, however, no study to date has explored the potential of variants that occur at homologous positions within paralogous human proteins as a means of identifying polymorphisms with likely phenotypic consequences. In order to investigate the potential of this approach, we have assembled a unique collection of known disease-causing variants from OMIM and the Human Genome Mutation Database (HGMD) and used them to identify and characterize pairs of sequence variants that occur at homologous positions within paralogous human proteins. Our analyses demonstrate that the locations of variants are correlated in paralogous proteins. Moreover, if one member of a variant-pair is disease-causing, its partner is likely to be disease-causing as well. Thus, information about variant-pairs can be used to identify potentially disease-causing variants, extend existing procedures for polymorphism prioritization, and provide a suite of candidates for further diagnostic and therapeutic purposes.     

Comprehensive approach to analyzing rare genetic variants

Recent findings suggest that rare variants play an important role in both monogenic and common diseases. Due to their rarity, however, it remains unclear how to appropriately analyze the association between such variants and disease. A common approach entails combining rare variants together based on a priori information and analyzing them as a single group. Here one must make some assumptions about what to aggregate. Instead, we propose two approaches to empirically determine the most efficient grouping of rare variants. The first considers multiple possible groupings using existing information. The second is an agnostic "step-up" approach that determines an optimal grouping of rare variants analytically and does not rely on prior information. To evaluate these approaches, we undertook a simulation study using sequence data from genes in the one-carbon folate metabolic pathway. Our results show that using prior information to group rare variants is advantageous only when information is quite accurate, but the step-up approach works well across a broad range of plausible scenarios. This agnostic approach allows one to efficiently analyze the association between rare variants and disease while avoiding assumptions required by other approaches for grouping such variants.     

A statistical framework for the interpretation of mtDNA mixtures: forensic and medical applications

Background

Mitochondrial DNA (mtDNA) variation is commonly analyzed in a wide range of different biomedical applications. Cases where more than one individual contribute to a stain genotyped from some biological material give rise to a mixture. Most forensic mixture cases are analyzed using autosomal markers. In rape cases, Y-chromosome markers typically add useful information. However, there are important cases where autosomal and Y-chromosome markers fail to provide useful profiles. In some instances, usually involving small amounts or degraded DNA, mtDNA may be the only useful genetic evidence available. Mitochondrial DNA mixtures also arise in studies dealing with the role of mtDNA variation in tumorigenesis. Such mixtures may be generated by the tumor, but they could also originate in vitro due to inadvertent contamination or a sample mix-up.

Methods/Principal Findings

We present the statistical methods needed for mixture interpretation and emphasize the modifications required for the more well-known methods based on conventional markers to generalize to mtDNA mixtures. Two scenarios are considered. Firstly, only categorical mtDNA data is assumed available, that is, the variants contributing to the mixture. Secondly, quantitative data (peak heights or areas) on the allelic variants are also accessible. In cases where quantitative information is available in addition to allele designation, it is possible to extract more precise information by using regression models. More precisely, using quantitative information may lead to a unique solution in cases where the qualitative approach points to several possibilities. Importantly, these methods also apply to clinical cases where contamination is a potential alternative explanation for the data.

Conclusions/Significance

We argue that clinical and forensic scientists should give greater consideration to mtDNA for mixture interpretation. The results and examples show that the analysis of mtDNA mixtures contributes substantially to forensic casework and may also clarify erroneous claims made in clinical genetics regarding tumorigenesis.     

The Curation of Genetic Variants: Difficulties and Possible Solutions

The curation of genetic variants from biomedical articles is required for various clinical and research purposes. Nowadays, establishment of variant databases that include overall information about variants is becoming quite popular. These databases have immense utility, serving as a user-friendly information storehouse of variants for information seekers. While manual curation is the gold standard method for curation of variants, it can turn out to be time-consuming on a large scale thus necessitating the need for automation. Curation of variants described in biomedical literature may not be straightforward mainly due to various nomenclature and expression issues. Though current trends in paper writing on variants is inclined to the standard nomenclature such that variants can easily be retrieved, we have a massive store of variants in the literature that are present as non-standard names and the online search engines that are predominantly used may not be capable of finding them. For effective curation of variants, knowledge about the overall process of curation, nature and types of difficulties in curation, and ways to tackle the difficulties during the task are crucial. Only by effective curation, can variants be correctly interpreted. This paper presents the process and difficulties of curation of genetic variants with possible solutions and suggestions from our work experience in the field including literature support. The paper also highlights aspects of interpretation of genetic variants and the importance of writing papers on variants following standard and retrievable methods.     

Resistance of cultured human fibroblasts and other cells to purine and pyrimidine analogues in relation to mutagenesis detection

In vitro enumeration of diploid human cell variants that are resistant to purine analogues is a possible method of detecting mutagenesis. Their incidences can be increased by the known mutagens, X-rays and N-methyl-N′-nitro-N-nitrosoguanidine (MNNG). Usefulness of this method depends on the kinds of hereditary changes that confer analogue-resistance on somatic cells. If resistance usually results from changes in genetic material, in vitro studies could be useful indicators of mutagenic effects on somatic cells and germ cells in vivo. If epigenetic changes are primarily responsible for analogue-resistant variants, their enumeration might not provide information relevant to germinal mutations but would still be a useful way to detect induction of general kinds of stable phenotypic changes that could cause cancer. This article outlines hypothetical epigenetic and genetic causes of somatic cell variation and a prospective genetic analysis of human cell variants that are resistant to 8-azaguanine (AG) or 2,6-diaminopurine ( (DAP).Recent evidences and arguments favoring epigenetic origins of resistance to base-analogues are inconclusive. The often cited high rate of changes causing impermeability to BUdR in hamster cells is based on one improperly executed determination. Comparisons of rates of variation conferring BUdR-resistance on cultured haploid and diploid frog cells included diploid variants that did not behave as mutants and ignored major sources of error in estimating mutation rates. AG-resistance could result from recessive mutations in X-chromosomal genes but comparisons of rates of mutation in hamster cells of different ploidies did not provide information about the numbers of X-chromosomes in the variants. Reports that normal rodent HGPRT reappeared in hybrids of enzyme-deficient rodent cells and HGPRT-containing cells of other species or in the rodent cells alone in response to the conditions of cell hybridization did not include adequate controls for reversions in mutant genes of the rodent cells. Questions about the epigenetic and genetic origins of analogue-resistance are mostly unanswered. It remains possible that some kinds of abnormal epigenetic changes cause somatic disease. Specific methods for detecting their occurrence and responsiveness to environmental factors should be devised by focusing efforts on traits that are normally subject to epigenetic regulation. Derepression of genes on the inactive X-chromosome and of liver phenylalanine hydroxylase production are presented as possible examples of abnormal epigenetic changes that could be quantitatively studied by direct selection in vitro.     

ATRIUM: Testing Untyped SNPs in Case-Control Association Studies with Related Individuals

In genome-wide association studies, only a subset of all genomic variants are typed by current, high-throughput, SNP-genotyping platforms. However, many of the untyped variants can be well predicted from typed variants, with linkage disequilibrium (LD) information among typed and untyped variants available from an external reference panel such as HapMap. Incorporation of such external information can allow one to perform tests of association between untyped variants and phenotype, thereby making more efficient use of the available genotype data. When related individuals are included in case-control samples, the dependence among their genotypes must be properly addressed for valid association testing. In the context of testing untyped variants, an additional analytical challenge is that the dependence, across related individuals, of the partial information on untyped-SNP genotypes must also be assessed and incorporated into the analysis for valid inference. We address this challenge with ATRIUM, a method for case-control association testing with untyped SNPs, based on genome screen data in samples in which some individuals are related. ATRIUM uses LD information from an external reference panel to specify a one-degree-of-freedom test of association with an untyped SNP. It properly accounts for dependence in the partial information on untyped-SNP genotypes across related individuals. We demonstrate that ATRIUM is robust in that it maintains the nominal type I error rate even when the external reference panel is not well matched to the case-control sample. We apply the method to detect association between type 2 diabetes and variants on chromosome 10 in the Framingham SHARe data.     

PAS染色在骨骼肌糖原贮积症诊断中的应用

目的探讨PAS染色在骨骼肌糖原贮积症诊断中的作用。方法用组织化学方法高碘酸-schiff(periodic acid schiff,PAS)染色方法显示糖原贮积症肌纤维胞浆内糖原的贮积。结果糖原贮积症患者肌纤维胞浆内聚集的红至红紫色颗粒为糖原。结论 PAS染色对于判断细胞内糖原贮积的糖原病和多糖体贮积性疾病的诊断是必要的。     

Novel alleles of the D16S752 polymorphic genetic marker linked to E-cadherin gene--a potential population marker

Seven DNA variants that polymorphic genetic marker D16S752 reveals in Croatian population are reported in this paper. The marker is a GATA tetranucleotide repeat linked to human E-cadherin gene (CDH1). Prior studies involving this marker revealed only four DNA allele variants. The reported DNA variants contribute to the collection of hypervariable DNA polymorphisms data useful in the field of anthropological and population genetic and forensic medicine.