Single nucleotide differences (SNDs) in the dbSNP database may lead to errors in genotyping and haplotyping studies

Exploiting public databases of genomic variation to quantify evolutionary constraint on the branch point sequence in 30 plant and animal species

The branch point sequence is a degenerate intronic heptamer required for the assembly of the spliceosome during pre-mRNA splicing. Disruption of this motif may promote alternative splicing and eventually cause phenotype variation. Despite its functional relevance, the branch point sequence is not included in most genome annotations. Here, we predict branch point sequences in 30 plant and animal species and attempt to quantify their evolutionary constraints using public variant databases. We find an implausible variant distribution in the databases from 16 of 30 examined species. Comparative analysis of variants from whole-genome sequencing shows that variants submitted from exome sequencing or false positive variants are widespread in public databases and cause these irregularities. We then investigate evolutionary constraint with largely unbiased public variant databases in 14 species and find that the fourth and sixth position of the branch point sequence are more constrained than coding nucleotides. Our findings show that public variant databases should be scrutinized for possible biases before they qualify to analyze evolutionary constraint.

Surfing the Big Data Wave: Omics Data Challenges in Transplantation

In both research and care, patients, caregivers, and researchers are facing a leap forward in the quantity of data that are available for analysis and interpretation, marking the daunting "big data era." In the biomedical field, this quantitative shift refers mostly to the -omics that permit measuring and analyzing biological features of the same type as a whole. Omics studies have greatly impacted transplantation research and highlighted their potential to better understand transplant outcomes. Some studies have emphasized the contribution of omics in developing personalized therapies to avoid graft loss. However, integrating omics data remains challenging in terms of analytical processes. These data come from multiple sources. Consequently, they may contain biases and systematic errors that can be mistaken for relevant biological information. Normalization methods and batch effects have been developed to tackle issues related to data quality and homogeneity. In addition, imputation methods handle data missingness. Importantly, the transplantation field represents a unique analytical context as the biological statistical unit is the donor-recipient pair, which brings additional complexity to the omics analyses. Strategies such as combined risk scores between 2 genomes taking into account genetic ancestry are emerging to better understand graft mechanisms and refine biological interpretations. The future omics will be based on integrative biology, considering the analysis of the system as a whole and no longer the study of a single characteristic. In this review, we summarize omics studies advances in transplantation and address the most challenging analytical issues regarding these approaches.

Extension of the Human Fibrinogen Database with Detailed Clinical Information—The αC-Connector Segment

Fibrinogen, an abundant plasma glycoprotein, is involved in the final stage of blood coagulation. Decreased fibrinogen levels, which may be caused by mutations, are manifested mainly in bleeding and thrombotic disorders. Clinically relevant mutations of fibrinogen are listed in the Human Fibrinogen Database. For the αC-connector (amino acids Aα240–410, nascent chain numbering), we have extended this database, with detailed descriptions of the clinical manifestations among members of reported families. This includes the specification of bleeding and thrombotic events and results of coagulation assays. Where available, the impact of a mutation on clotting and fibrinolysis is reported. The collected data show that the Human Fibrinogen Database reports considerably fewer missense and synonymous mutations than the general COSMIC and dbSNP databases. Homozygous nonsense or frameshift mutations in the αC-connector are responsible for most clinically relevant symptoms, while heterozygous mutations are often asymptomatic. Symptomatic subjects suffer from bleeding and, less frequently, from thrombotic events. Miscarriages within the first trimester and prolonged wound healing were reported in a few subjects. All mutations inducing thrombotic phenotypes are located at the identical positions within the consensus sequence of the tandem repeats.

Overview of human 20 alpha-hydroxysteroid dehydrogenase (AKR1C1): Functions, regulation, and structural insights of inhibitors

Human aldo-keto reductase family 1C1 (AKR1C1) is an important enzyme involved in human hormone metabolism, which is mainly responsible for the metabolism of progesterone in the human body. AKR1C1 is highly expressed and has an important relationship with the occurrence and development of various diseases, especially some cancers related to hormone metabolism. Nowadays, many inhibitors against AKR1C1 have been discovered, including some synthetic compounds and natural products, which have certain inhibitory activity against AKR1C1 at the target level. Here we briefly reviewed the physiological and pathological functions of AKR1C1 and the relationship with the disease, and then summarized the development of AKR1C1 inhibitors, elucidated the interaction between inhibitors and AKR1C1 through molecular docking results and existing co-crystal structures. Finally, we discussed the design ideals of selective AKR1C1 inhibitors from the perspective of AKR1C1 structure, discussed the prospects of AKR1C1 in the treatment of human diseases in terms of biomarkers, pre-receptor regulation and single nucleotide polymorphisms, aiming to provide new ideas for drug research targeting AKR1C1.

Identification and functional characterization of new missense SNPs in the coding region of the TP53 gene

Infrequent and rare genetic variants in the human population vastly outnumber common ones. Although they may contribute significantly to the genetic basis of a disease, these seldom-encountered variants may also be miss-identified as pathogenic if no correct references are available. Somatic and germline TP53 variants are associated with multiple neoplastic diseases, and thus have come to serve as a paradigm for genetic analyses in this setting. We searched 14 independent, globally distributed datasets and recovered TP53 SNPs from 202,767 cancer-free individuals. In our analyses, 19 new missense TP53 SNPs, including five novel variants specific to the Asian population, were recurrently identified in multiple datasets. Using a combination of in silico, functional, structural, and genetic approaches, we showed that none of these variants displayed loss of function compared to the normal TP53 gene. In addition, classification using ACMG criteria suggested that they are all benign. Considered together, our data reveal that the TP53 coding region shows far more polymorphism than previously thought and present high ethnic diversity. They furthermore underline the importance of correctly assessing novel variants in all variant-calling pipelines associated with genetic diagnoses for cancer.

Molecular genetic analysis of AKR1C2-4 and HSD17B6 genes in subjects 46,XY with hypospadias

BACKGROUND

The formation of the male urethra depends to enzyme-mediated testosterone (T) conversion into 5α-dihydrotestosterone (DHT). Two metabolic pathways could be operating in the fetal testis to synthesize androgens: 1) the "classic" route (T→DHT) mediated by SRD5A2 and 2) a "backdoor" pathway in which DHT is synthesized by aldo-keto reductase family 1, member C2 (AKR1C2), AKR1C3, and AKR1C4 enzymes without formation of a T intermediate.

OBJECTIVE

We studied four genes of the "backdoor" pathway in karyotypic males with hypospadias to ascertain whether gene defects in AKRs impair urethral DHT formation that result in hypospadias.

DESIGN AND PATIENTS

The coding regions of the AKR1C2-4 and HSD17B6 genes were analyzed by PCR-SSCP and sequencing in a cohort of 25 Mexican patients (0.3-9 year-old-children) with 46,XY-hypospadias. Chi-squared tests was performed to evaluate the distribution of genotypes, alleles, and the Hardy-Weinberg (H-W) equilibrium. The effect of the genetic variants was investigated by in silico studies.

RESULTS

Screening studies revealed distinct genotypic patterns at different exons of AKR1C2-4 whereas HSD17B6 presented a wild-type sequence. The DNA analyses detected two synonymous variants (c.327C>T, c.666T>C/unreported) in AKR1C2. The AKR1C3 had two variants (c.15C>G, c.230A>G), two unreported variants (c.538T>C, c.596G>A), and one silent variant (c.312G>A). Two variants (c.434C>G, c.931C>G) were identified in AKR1C4. All variants were in H-W equilibrium without structural changes.

DISCUSSION

Hypospadias have been associated with defects that alter androgen biosynthesis in the human fetal testis, specifically 5α-DHT. We selected four candidate genes involved in the "backdoor" pathway for the formation of 5α-DHT. Molecular assays of the AKR1C2, AKR1C3, and AKR1C4 genes revealed a total of nine genetic single nucleotide variants. Several variants in the AKR1C genes have been associated with a variety of human pathologies. However, our studies suggest that active steroid biosynthesis via AKR1C might not be involved in hypospadias. Additionally, genetic research suggests a low involvement in the "backdoor" 5α-DHT pathway during human sexual development, specifically, the differentiation of male external genitalia.

CONCLUSION

These results indicate that substitutions in AKR1C2-4 are polymorphisms and all genetic variants lacks deleterious significant association with hypospadias. The data suggest that inactivating mutations in the AKR1C2-4 and HSD17B6 genes are an infrequent cause of hypospadias, which might weaken the contribution of the "backdoor" pathway to embryonic urethral masculinization.

Comprehensive elaboration of database resources utilized in next-generation sequencing-based tumor somatic mutation detection

The rapid evolution of next-generation sequencing (NGS)-based tumor genomic profile detection and the emergence of molecularly targeted therapies have enabled precision oncology. In NGS-based analysis, various types of databases have been developed to perform different functions. However, many problems still exist when using these public databases. Therefore, it is important to better understand the characteristics and limitations of each database and have them complement each other to provide useful clinical evidence for NGS testing. In this review, we elaborate on the important role of databases and their concrete applications in NGS-based somatic mutation detection. We introduce the typically used databases for sequence alignment, variant filtration, and variant interpretation, and compare the differences between the databases with similar functions. Subsequently, we determine the limitations of each database and provide the corresponding solutions. Furthermore, we present an overview diagram to clearly illustrate the database used in the entire NGS-based somatic mutation detection pipeline.

High prevalence of cancer-associated TP53 variants in the gnomAD database: A word of caution concerning the use of variant filtering

The 1,000 genome project, the Exome Aggregation Consortium (ExAC) or the Genome Aggregation database (gnomAD) datasets, were developed to provide large-scale reference data of genetic variations for various populations to filter out common benign variants and identify rare variants of clinical importance based on their frequency in the human population. Using a TP53 repository of 80,000 cancer variants, as well as TP53 variants from multiple cancer genome projects, we have defined a set of certified oncogenic TP53 variants. This specific set has been independently validated by functional and in silico predictive analysis. Here we show that a significant number of these variants are included in gnomAD and ExAC. Most of them correspond to TP53 hotspot variants occurring as somatic and germline events in human cancer. Similarly, disease-associated variants for five other tumor suppressor genes, including BRCA1, BRCA2, APC, PTEN, and MLH1, have also been identified. This study demonstrates that germline TP53 variants in the human population are more frequent than previously thought. Furthermore, population databases such as gnomAD or ExAC must be used with caution and need to be annotated for the presence of oncogenic variants to improve their clinical utility.

Allele balance bias identifies systematic genotyping errors and false disease associations

In recent years, next‐generation sequencing (NGS) has become a cornerstone of clinical genetics and diagnostics. Many clinical applications require high precision, especially if rare events such as somatic mutations in cancer or genetic variants causing rare diseases need to be identified. Although random sequencing errors can be modeled statistically and deep sequencing minimizes their impact, systematic errors remain a problem even at high depth of coverage. Understanding their source is crucial to increase precision of clinical NGS applications. In this work, we studied the relation between recurrent biases in allele balance (AB), systematic errors, and false positive variant calls across a large cohort of human samples analyzed by whole exome sequencing (WES). We have modeled the AB distribution for biallelic genotypes in 987 WES samples in order to identify positions recurrently deviating significantly from the expectation, a phenomenon we termed allele balance bias (ABB). Furthermore, we have developed a genotype callability score based on ABB for all positions of the human exome, which detects false positive variant calls that passed state‐of‐the‐art filters. Finally, we demonstrate the use of ABB for detection of false associations proposed by rare variant association studies. Availability: https://github.com/Francesc-Muyas/ABB.

Architecture of polymorphisms in the human genome reveals functionally important and positively selected variants in immune response and drug transporter genes

Background

Genetic polymorphisms can contribute to phenotypic differences amongst individuals, including disease risk and drug response. Characterization of genetic polymorphisms that modulate gene expression and/or protein function may facilitate the identification of the causal variants. Here, we present the architecture of genetic polymorphisms in the human genome focusing on those predicted to be potentially functional/under natural selection and the pathways that they reside.

Results

In the human genome, polymorphisms that directly affect protein sequences and potentially affect function are the most constrained variants with the lowest single-nucleotide variant (SNV) density, least population differentiation and most significant enrichment of rare alleles. SNVs which potentially alter various regulatory sites, e.g. splicing regulatory elements, are also generally under negative selection.

Interestingly, genes that regulate the expression of transcription/splicing factors and histones are conserved as a higher proportion of these genes is non-polymorphic, contain ultra-conserved elements (UCEs) and/or has no non-synonymous SNVs (nsSNVs)/coding INDELs. On the other hand, major histocompatibility complex (MHC) genes are the most polymorphic with SNVs potentially affecting the binding of transcription/splicing factors and microRNAs (miRNA) exhibiting recent positive selection (RPS). The drug transporter genes carry the most number of potentially deleterious nsSNVs and exhibit signatures of RPS and/or population differentiation. These observations suggest that genes that interact with the environment are highly polymorphic and targeted by RPS.

Conclusions

In conclusion, selective constraints are observed in coding regions, master regulator genes, and potentially functional SNVs. In contrast, genes that modulate response to the environment are highly polymorphic and under positive selection.

Electronic supplementary material

The online version of this article (10.1186/s40246-018-0175-1) contains supplementary material, which is available to authorized users.

Bioinformatics-based tools in drug discovery: the cartography from single gene to integrative biological networks

Originally developed for the analysis of biological sequences, bioinformatics has advanced into one of the most widely recognized domains in the scientific community. Despite this technological evolution, there is still an urgent need for nontoxic and efficient drugs. The onus now falls on the 'omics domain to meet this need by implementing bioinformatics techniques that will allow for the introduction of pioneering approaches in the rational drug design process. Here, we categorize an updated list of informatics tools and explore the capabilities of integrative bioinformatics in disease control. We believe that our review will serve as a comprehensive guide toward bioinformatics-oriented disease and drug discovery research.

Structural and functional impact of SNPs in P-selectin gene: A comprehensive in silico analysis

P-selectin is an adhesion molecule which plays an important role in the development of inflammation. It is encoded by the SELP gene located on chromosome 1q21-q24. Various single nucleotide polymorphisms (SNPs) ofSELPhave been reported to be associated with various inflammatory disease conditions. The genetics behind these diseases could be better understood by knowing the structural and functional impact of various genetic determinants ofSELP. So far, this is the first comprehensive and systematicin silicoanalysis of SNPs inSELP. A total of 2780 SNPs ofSELPwere retrieved from NCBI dbSNP. Only conserved and validated SNPs with minor allele frequency (MAF) ≥ 0.05 were subjected to further analysis. Based on these criteria, we selected 4 non-synonymous SNPs (nsSNPs) and 119 non-coding SNPs (ncSNPs). The nsSNPs were analyzed for deleterious effects using SIFT, Polyphen-2, nsSNPAnalyzer, SNP & Go, SNPs3, Mutperd and I-mutant web tools. The template prediction for variant structure modeling was performed using MUSTER and SWISS-MODEL. The functional impact of ncSNPs was analyzed by SNPinfo and RegulomeDB. Thein silicoanalysis predicted 3 nsSNPs and 21 ncSNPs as potential candidates for future case-control association studies and functional analysis ofSELP.

Fine-Mapping of the 1p11.2 Breast Cancer Susceptibility Locus

The Cancer Genetic Markers of Susceptibility genome-wide association study (GWAS) originally identified a single nucleotide polymorphism (SNP) rs11249433 at 1p11.2 associated with breast cancer risk. To fine-map this locus, we genotyped 92 SNPs in a 900kb region (120,505,799-121,481,132) flanking rs11249433 in 45,276 breast cancer cases and 48,998 controls of European, Asian and African ancestry from 50 studies in the Breast Cancer Association Consortium. Genotyping was done using iCOGS, a custom-built array. Due to the complicated nature of the region on chr1p11.2: 120,300,000-120,505,798, that lies near the centromere and contains seven duplicated genomic segments, we restricted analyses to 429 SNPs excluding the duplicated regions (42 genotyped and 387 imputed). Per-allelic associations with breast cancer risk were estimated using logistic regression models adjusting for study and ancestry-specific principal components. The strongest association observed was with the original identified index SNP rs11249433 (minor allele frequency (MAF) 0.402; per-allele odds ratio (OR) = 1.10, 95% confidence interval (CI) 1.08-1.13, P = 1.49 x 10-21). The association for rs11249433 was limited to ER-positive breast cancers (test for heterogeneity P≤8.41 x 10-5). Additional analyses by other tumor characteristics showed stronger associations with moderately/well differentiated tumors and tumors of lobular histology. Although no significant eQTL associations were observed, in silico analyses showed that rs11249433 was located in a region that is likely a weak enhancer/promoter. Fine-mapping analysis of the 1p11.2 breast cancer susceptibility locus confirms this region to be limited to risk to cancers that are ER-positive.

Different evolutionary patterns of SNPs between domains and unassigned regions in human protein-coding sequences

Protein evolution plays an important role in the evolution of each genome. Because of their functional nature, in general, most of their parts or sites are differently constrained selectively, particularly by purifying selection. Most previous studies on protein evolution considered individual proteins in their entirety or compared protein-coding sequences with non-coding sequences. Less attention has been paid to the evolution of different parts within each protein of a given genome. To this end, based on PfamA annotation of all human proteins, each protein sequence can be split into two parts: domains or unassigned regions. Using this rationale, single nucleotide polymorphisms (SNPs) in protein-coding sequences from the 1000 Genomes Project were mapped according to two classifications: SNPs occurring within protein domains and those within unassigned regions. With these classifications, we found: the density of synonymous SNPs within domains is significantly greater than that of synonymous SNPs within unassigned regions; however, the density of non-synonymous SNPs shows the opposite pattern. We also found there are signatures of purifying selection on both the domain and unassigned regions. Furthermore, the selective strength on domains is significantly greater than that on unassigned regions. In addition, among all of the human protein sequences, there are 117 PfamA domains in which no SNPs are found. Our results highlight an important aspect of protein domains and may contribute to our understanding of protein evolution.

Impact of host genetic polymorphisms on vaccine induced antibody response

Many host- and vaccine-specific factors modulate an antibody response. Host genetic polymorphisms, in particular, modulate the immune response in multiple ways on different scales. This review article describes how information on host genetic polymorphisms and corresponding immune cascades may be used to generate personalized vaccine strategies to optimize the antibody response.

Identification of Low-Confidence Regions in the Pig Reference Genome (Sscrofa10.2)

Many applications of high throughput sequencing rely on the availability of an accurate reference genome. Variant calling often produces large data sets that cannot be realistically validated and which may contain large numbers of false-positives. Errors in the reference assembly increase the number of false-positives. While resources are available to aid in the filtering of variants from human data, for other species these do not yet exist and strict filtering techniques must be employed which are more likely to exclude true-positives. This work assesses the accuracy of the pig reference genome (Sscrofa10.2) using whole genome sequencing reads from the Duroc sow whose genome the assembly was based on. Indicators of structural variation including high regional coverage, unexpected insert sizes, improper pairing and homozygous variants were used to identify low quality (LQ) regions of the assembly. Low coverage (LC) regions were also identified and analyzed separately. The LQ regions covered 13.85% of the genome, the LC regions covered 26.6% of the genome and combined (LQLC) they covered 33.07% of the genome. Over half of dbSNP variants were located in the LQLC regions. Of copy number variable regions identified in a previous study, 86.3% were located in the LQLC regions. The regions were also enriched for gene predictions from RNA-seq data with 42.98% falling in the LQLC regions. Excluding variants in the LQ, LC, or LQLC from future analyses will help reduce the number of false-positive variant calls. Researchers using WGS data should be aware that the current pig reference genome does not give an accurate representation of the copy number of alleles in the original Duroc sow's genome.

Single nucleotide differences (SNDs) continue to contaminate the dbSNP database with consequences for human genomics and health

It has been established that up to 8.3% of the biallelic coding SNPs present in dbSNP are actually artefactual polymorphism-like errors, previously termed single nucleotide differences, or SNDs. In this study, a previous analysis of SNPs in dbSNP was extended and updated to examine how the incidence of SNDs has changed over an intervening five year period. The incidence of SNDs was found to be lower than in the previous analysis at 2.2% of all biallelic SNPs. There was only a modest reduction in the percentage of SNDs in the original set of biallelic coding SNPs tested. This suggests that the overall reduction in the incidence of SNDs over the intervening 5-year period is related to an improvement in SNP detection methods and more rigorous curation, rather than efforts to ameliorate the presence of SNDs. We note that SNDs contaminating the dbSNP may lead to erroneous conclusions on human conditions.

Computational approaches to study the effects of small genomic variations

Advances in DNA sequencing technologies have led to an avalanche-like increase in the number of gene sequences deposited in public databases over the last decade as well as the detection of an enormous number of previously unseen nucleotide variants therein. Given the size and complex nature of the genome-wide sequence variation data, as well as the rate of data generation, experimental characterization of the disease association of each of these variations or their effects on protein structure/function would be costly, laborious, time-consuming, and essentially impossible. Thus, in silico methods to predict the functional effects of sequence variations are constantly being developed. In this review, we summarize the major computational approaches and tools that are aimed at the prediction of the functional effect of mutations, and describe the state-of-the-art databases that can be used to obtain information about mutation significance. We also discuss future directions in this highly competitive field.

Analysis of the genetic basis of periodic fever with aphthous stomatitis, pharyngitis, and cervical adenitis (PFAPA) syndrome

PFAPA syndrome is the most common autoinflammatory syndrome in children from Western countries. In spite of its strong familial clustering, its genetic basis and inheritance pattern are still unknown. We performed a comprehensive genetic study on 68 individuals from 14 families. Linkage analysis suggested a susceptibility locus on chromosome 8, but direct molecular sequencing did not support this initial statistical finding. Exome sequencing revealed the absence of any gene that was mutated in all patients. Exhaustive screening of genes involved in other autoinflammatory syndromes or encoding components of the human inflammasome showed no DNA variants that could be linked to PFAPA molecular pathology. Among these, the previously-reported missense mutation V198M in the NLRP3 gene was clearly shown not to co-segregate with PFAPA. Our results on this relatively large cohort indicate that PFAPA syndrome is unlikely to be a monogenic condition. Moreover, none of the several genes known to be involved in inflammation or in autoinflammatory disorders seem to be relevant, alone, to its etiology, suggesting that PFAPA results from oligogenic or complex inheritance of variants in multiple disease genes and/or non-genetic factors.

Identifying Highly Penetrant Disease Causal Mutations Using Next Generation Sequencing: Guide to Whole Process

Recent technological advances have created challenges for geneticists and a need to adapt to a wide range of new bioinformatics tools and an expanding wealth of publicly available data (e.g., mutation databases, and software). This wide range of methods and a diversity of file formats used in sequence analysis is a significant issue, with a considerable amount of time spent before anyone can even attempt to analyse the genetic basis of human disorders. Another point to consider that is although many possess “just enough” knowledge to analyse their data, they do not make full use of the tools and databases that are available and also do not fully understand how their data was created. The primary aim of this review is to document some of the key approaches and provide an analysis schema to make the analysis process more efficient and reliable in the context of discovering highly penetrant causal mutations/genes. This review will also compare the methods used to identify highly penetrant variants when data is obtained from consanguineous individuals as opposed to nonconsanguineous; and when Mendelian disorders are analysed as opposed to common-complex disorders.

RADIA: RNA and DNA integrated analysis for somatic mutation detection

The detection of somatic single nucleotide variants is a crucial component to the characterization of the cancer genome. Mutation calling algorithms thus far have focused on comparing the normal and tumor genomes from the same individual. In recent years, it has become routine for projects like The Cancer Genome Atlas (TCGA) to also sequence the tumor RNA. Here we present RADIA (RNA and DNA Integrated Analysis), a novel computational method combining the patient-matched normal and tumor DNA with the tumor RNA to detect somatic mutations. The inclusion of the RNA increases the power to detect somatic mutations, especially at low DNA allelic frequencies. By integrating an individual's DNA and RNA, we are able to detect mutations that would otherwise be missed by traditional algorithms that examine only the DNA. We demonstrate high sensitivity (84%) and very high precision (98% and 99%) for RADIA in patient data from endometrial carcinoma and lung adenocarcinoma from TCGA. Mutations with both high DNA and RNA read support have the highest validation rate of over 99%. We also introduce a simulation package that spikes in artificial mutations to patient data, rather than simulating sequencing data from a reference genome. We evaluate sensitivity on the simulation data and demonstrate our ability to rescue back mutations at low DNA allelic frequencies by including the RNA. Finally, we highlight mutations in important cancer genes that were rescued due to the incorporation of the RNA.

Congenital cataracts: de novo gene conversion event in CRYBB2

PURPOSE

To identify the cause of congenital cataracts in a consanguineous family of Ashkenazi Jewish ancestry.

METHODS

We performed genome-wide linkage analysis and whole-exome sequencing for the initial discovery of variants, and we confirmed the variants using gene-specific primers and Sanger sequencing.

RESULTS

We found significant evidence of linkage to chromosome 22, under an autosomal dominant inheritance model, with a maximum logarithm of the odds (LOD) score of 3.91 (16.918 to 25.641 Mb). Exome sequencing identified three nonsynonymous changes in the CRYBB2 exon 5 coding sequence that are consistent with the sequence of the corresponding region of the pseudogene CRYBB2P1. The identification of these changes was complicated by possible mismapping of some mutated CRYBB2 sequences to CRYBB2P1. Sequencing with gene-specific primers confirmed that the changes--rs2330991, c.433 C>T (p.R145W); rs2330992, c.440A>G (p.Q147R); and rs4049504, c.449C>T (p.T150M)--present in all ten affected family members are located in CRYBB2 and are not artifacts of cross-reaction with CRYBB2P1. We did not find these changes in six unaffected family members, including the unaffected grandfather who contributed the affected haplotype, nor did we find them in the 100 Ashkenazi Jewish controls.

CONCLUSIONS

Our data are consistent with a de novo gene conversion event, transferring 270 base pairs at most from CRYBB2P1 to exon 5 of CRYBB2. This study highlights how linkage mapping can be complicated by de novo mutation events, as well as how sequence-analysis pipeline mapping of short reads from next-generation sequencing can be complicated by the existence of pseudogenes or other highly homologous sequences.

Role of aldo-keto reductase family 1 (AKR1) enzymes in human steroid metabolism

Human aldo-keto reductases AKR1C1-AKR1C4 and AKR1D1 play essential roles in the metabolism of all steroid hormones, the biosynthesis of neurosteroids and bile acids, the metabolism of conjugated steroids, and synthetic therapeutic steroids. These enzymes catalyze NADPH dependent reductions at the C3, C5, C17 and C20 positions on the steroid nucleus and side-chain. AKR1C1-AKR1C4 act as 3-keto, 17-keto and 20-ketosteroid reductases to varying extents, while AKR1D1 acts as the sole Δ(4)-3-ketosteroid-5β-reductase (steroid 5β-reductase) in humans. AKR1 enzymes control the concentrations of active ligands for nuclear receptors and control their ligand occupancy and trans-activation, they also regulate the amount of neurosteroids that can modulate the activity of GABAA and NMDA receptors. As such they are involved in the pre-receptor regulation of nuclear and membrane bound receptors. Altered expression of individual AKR1C genes is related to development of prostate, breast, and endometrial cancer. Mutations in AKR1C1 and AKR1C4 are responsible for sexual development dysgenesis and mutations in AKR1D1 are causative in bile-acid deficiency.

Caveat emptor: single nucleotide polymorphism reporting in pharmacogenomics

While it is arguably the most comprehensive source of genetic information, the NCBI's dbSNP database (National Center for Biotechnology Information database of single nucleotide polymorphisms; http://www.ncbi.nlm.nih.gov/projects/SNP/) is imperfect. In this commentary, we highlight the issues surrounding this database, while considering the great importance and utility of this resource for those in the pharmacology and pharmacogenomics communities. We describe our experience with the information in this database as a cautionary tale for those who will utilize such information in the future. We also discuss several measures that could render it more reliable.

Application of high-throughput sequencing for studying genomic variations in congenital heart disease

Congenital heart diseases (CHD) represent the most common birth defect in human. The majority of cases are caused by a combination of complex genetic alterations and environmental influences. In the past, many disease-causing mutations have been identified; however, there is still a large proportion of cardiac malformations with unknown precise origin. High-throughput sequencing technologies established during the last years offer novel opportunities to further study the genetic background underlying the disease. In this review, we provide a roadmap for designing and analyzing high-throughput sequencing studies focused on CHD, but also with general applicability to other complex diseases. The three main next-generation sequencing (NGS) platforms including their particular advantages and disadvantages are presented. To identify potentially disease-related genomic variations and genes, different filtering steps and gene prioritization strategies are discussed. In addition, available control datasets based on NGS are summarized. Finally, we provide an overview of current studies already using NGS technologies and showing that these techniques will help to further unravel the complex genetics underlying CHD.

Using false discovery rates to benchmark SNP-callers in next-generation sequencing projects

Sequence alignments form the basis for many comparative and population genomic studies. Alignment tools provide a range of accuracies dependent on the divergence between the sequences and the alignment methods. Despite widespread use, there is no standard method for assessing the accuracy of a dataset and alignment strategy after resequencing. We present a framework and tool for determining the overall accuracies of an input read dataset, alignment and SNP-calling method providing an isolate in that dataset has a corresponding, or closely related reference sequence available. In addition to this tool for comparing False Discovery Rates (FDR), we include a method for determining homozygous and heterozygous positions from an alignment using binomial probabilities for an expected error rate. We benchmark this method against other SNP callers using our FDR method with three fungal genomes, finding that it was able achieve a high level of accuracy. These tools are available at http://cfdr.sourceforge.net/.

BlackOPs: increasing confidence in variant detection through mappability filtering

Identifying variants using high-throughput sequencing data is currently a challenge because true biological variants can be indistinguishable from technical artifacts. One source of technical artifact results from incorrectly aligning experimentally observed sequences to their true genomic origin ('mismapping') and inferring differences in mismapped sequences to be true variants. We developed BlackOPs, an open-source tool that simulates experimental RNA-seq and DNA whole exome sequences derived from the reference genome, aligns these sequences by custom parameters, detects variants and outputs a blacklist of positions and alleles caused by mismapping. Blacklists contain thousands of artifact variants that are indistinguishable from true variants and, for a given sample, are expected to be almost completely false positives. We show that these blacklist positions are specific to the alignment algorithm and read length used, and BlackOPs allows users to generate a blacklist specific to their experimental setup. We queried the dbSNP and COSMIC variant databases and found numerous variants indistinguishable from mapping errors. We demonstrate how filtering against blacklist positions reduces the number of potential false variants using an RNA-seq glioblastoma cell line data set. In summary, accounting for mapping-caused variants tuned to experimental setups reduces false positives and, therefore, improves genome characterization by high-throughput sequencing.

Stochastic choice of allelic expression in human neural stem cells

Monoallelic gene expression, such as genomic imprinting, is well described. Less well-characterized are genes undergoing stochastic monoallelic expression (MA), where specific clones of cells express just one allele at a given locus. We performed genome-wide allelic expression assessment of human clonal neural stem cells derived from cerebral cortex, striatum, and spinal cord, each with differing genotypes. We assayed three separate clonal lines from each donor, distinguishing stochastic MA from genotypic effects. Roughly 2% of genes showed evidence for autosomal MA, and in about half of these, allelic expression was stochastic between different clones. Many of these loci were known neurodevelopmental genes, such as OTX2 and OLIG2. Monoallelic genes also showed increased levels of DNA methylation compared to hypomethylated biallelic loci. Identified monoallelic gene loci showed altered chromatin signatures in fetal brain, suggesting an in vivo correlate of this phenomenon. We conclude that stochastic allelic expression is prevalent in neural stem cells, providing clonal diversity to developing tissues such as the human brain.

Online tools for bioinformatics analyses in nutrition sciences

Recent advances in "omics" research have resulted in the creation of large datasets that were generated by consortiums and centers, small datasets that were generated by individual investigators, and bioinformatics tools for mining these datasets. It is important for nutrition laboratories to take full advantage of the analysis tools to interrogate datasets for information relevant to genomics, epigenomics, transcriptomics, proteomics, and metabolomics. This review provides guidance regarding bioinformatics resources that are currently available in the public domain, with the intent to provide a starting point for investigators who want to take advantage of the opportunities provided by the bioinformatics field.

Strategies for excluding false Y-chromosomal SNP entries from human genome databases

Current human genome databases for public single nucleotide polymorphisms (SNPs) still contain a substantial fraction of false entries. The main reasons for errors include sequencing or assembly errors, paralogous sequence-, and private variants. In the course of our studies on the Y chromosome, we established a set of internal laboratory guidelines for reliably identifying false SNP entries in databases.

FOXO3 gene variants and human aging: coding variants may not be key players

FOXO3 is generally recognized as a "master" gene in aging since its association with longevity has been replicated in multiple organisms and human populations. A group of single nucleotide polymorphisms in linkage disequilibrium with a coding region has been associated with human longevity, but the actual functional variant is unidentified. Therefore, we sequenced the coding region in our long-lived Japanese American population in order to enhance resources for fine mapping this region. We demonstrate that of 38 published variants, 6 are misalignments with homologous nonallelic sequences from FOXO3B (ZNF286B), a pseudogene on a different chromosome; 2 are attributable to ZNF286B only, and the remaining 30 were unconfirmed, indicating that they are very rare and not likely involved in longevity. Furthermore, we identified a novel, unique, nonsynonymous coding variant in exon 3 (Gly566Ala; rs138174682) that is prevalent in multiple ethnic groups but appeared too rare for major longevity effects in our study populations.

Unrecognized sequence homologies may confound genome-wide association studies

Genome-wide association studies (GWAS) have become a preferred method to identify new genetic susceptibility loci. This technique aims to understanding the molecular etiology of common diseases, but in many cases, it has led to the identification of loci with no obvious biological relevance. Herein, we show that previously unrecognized sequence homologies have caused single-nucleotide polymorphism (SNP) microarrays to incorrectly associate a phenotype to a given locus when in fact the linkage is to another distant locus. Using genetic differences between male and female subjects as a model to study the effect of one specific genomic region on the whole SNP microarray, we provide strong evidence that the use of standard methods for GWAS can be misleading. We suggest a new systematic quality control step in the biological interpretation of previous and future GWAS.

pfSNP: An integrated potentially functional SNP resource that facilitates hypotheses generation through knowledge syntheses

Currently, >14,000,000 single nucleotide polymorphisms (SNPs) are reported. Identifying phenotype-affecting SNPs among these many SNPs pose significant challenges. Although several Web resources are available that can inform about the functionality of SNPs, these resources are mainly annotation databases and are not very comprehensive. In this article, we present a comprehensive, well-annotated, integrated pfSNP (potentially functional SNPs) Web resource (http://pfs.nus.edu.sg/), which is aimed to facilitate better hypothesis generation through knowledge syntheses mediated by better data integration and a user-friendly Web interface. pfSNP integrates >40 different algorithms/resources to interrogate >14,000,000 SNPs from the dbSNP database for SNPs of potential functional significance based on previous published reports, inferred potential functionality from genetic approaches as well as predicted potential functionality from sequence motifs. Its query interface has the user-friendly "auto-complete, prompt-as-you-type" feature and is highly customizable, facilitating different combination of queries using Boolean-logic. Additionally, to facilitate better understanding of the results and aid in hypotheses generation, gene/pathway-level information with text clouds highlighting enriched tissues/pathways as well as detailed-related information are also provided on the results page. Hence, the pfSNP resource will be of great interest to scientists focusing on association studies as well as those interested to experimentally address the functionality of SNPs.

Accessing and selecting genetic markers from available resources

The history of genetic markers accurately partitions the progression of molecular genetics into three phases: the RFLP (restriction fragment length polymorphism), microsatellite and SNP (single nucleotide polymorphism) eras. This chapter focuses predominately on the current workhorse, the SNP, though briefly covers the former two and overviews current online databases and portals that act as central repositories as well as hubs to further detailed information. Central gene or disease-based searches are considered and then followed through systematically.

Variation in the mutation rate across mammalian genomes

It has been known for many years that the mutation rate varies across the genome. However, only with the advent of large genomic data sets is the full extent of this variation becoming apparent. The mutation rate varies over many different scales, from adjacent sites to whole chromosomes, with the strongest variation seen at the smallest scales. Some of these patterns have clear mechanistic bases, but much of the rate variation remains unexplained, and some of it is deeply perplexing. Variation in the mutation rate has important implications in evolutionary biology and underexplored implications for our understanding of hereditary disease and cancer.

High-quality DNA sequence capture of 524 disease candidate genes

The accurate and complete selection of candidate genomic regions from a DNA sample before sequencing is critical in molecular diagnostics. Several recently developed technologies await substantial improvements in performance, cost, and multiplex sample processing. Here we present the utility of long padlock probes (LPPs) for targeted exon capture followed by array-based sequencing. We found that on average 92% of 5,471 exons from 524 nuclear-encoded mitochondrial genes were successfully amplified from genomic DNA from 63 individuals. Only 144 exons did not amplify in any sample due to high GC content. One LPP was sufficient to capture sequences from <100-500 bp in length and only a single-tube capture reaction and one microarray was required per sample. Our approach was highly reproducible and quick (<8 h) and detected DNA variants at high accuracy (false discovery rate 1%, false negative rate 3%) on the basis of known sample SNPs and Sanger sequence verification. In a patient with clinical and biochemical presentation of ornithine transcarbamylase (OTC) deficiency, we identified copy-number differences in the OTC gene at exon-level resolution. This shows the ability of LPPs to accurately preserve a sample's genome information and provides a cost-effective strategy to identify both single nucleotide changes and structural variants in targeted resequencing.

In silico analysis of the exome for gene discovery

Here we describe a bioinformatic strategy for extracting and analyzing the list of variants revealed from an exome sequencing project to identify potential disease genes. This in silico method filters out the majority of common SNPs and extracts a list of potential candidate protein-coding and non-coding RNA (ncRNA) genes. The workflow employs Galaxy, a publically available Web-based software, to filter and sort sequence variants identified by capture-based target enrichment and sequencing from exomes including selected ncRNAs.

Modeling single nucleotide polymorphisms in the human AKR1C1 and AKR1C2 genes: implications for functional and genotyping analyses

Enzymes encoded by the AKR1C1 and AKR1C2 genes are responsible for the metabolism of progesterone and 5α-dihydrotestosterone (DHT), respectively. The effect of amino acid substitutions, resulting from single nucleotide polymorphisms (SNPs) in the AKR1C2 gene, on the enzyme kinetics of the AKR1C2 gene product were determined experimentally by Takashi et al. In this paper, we used homology modeling to predict and analyze the structure of AKR1C1 and AKR1C2 genetic variants. The experimental reduction in enzyme activity in the AKR1C2 variants F46Y and L172Q, as determined by Takahashi et al., is predicted to be due to increased instability in cofactor binding, caused by disruptions to the hydrogen bonds between NADP and AKR1C2, resulting from the insertion of polar residues into largely non-polar environments near the site of cofactor binding. Other AKR1C2 variants were shown to involve either conservative substitutions or changes taking place on the surface of the molecule and distant from the active site, confirming the experimental finding of Takahashi et al. that these variants do not result in any statistically significant reduction in enzyme activity. The AKR1C1 R258C variant is predicted to have no effect on enzyme activity for similar reasons. Thus, we provide further insight into the molecular mechanism of the enzyme kinetics of these proteins. Our data also highlight previously reported difficulties with online databases.

Impact of DNA physical properties on local sequence bias of human mutation

In selectively neutral regions of the human genome, nucleotide substitutions do not occur at random with respect to the local DNA sequence neighborhood. However, apart from the hypermutability of methylated CpG dinucleotides, which can explain the overrepresentation of nucleotide transitions in this context, the sequence-specific factors underlying point mutation bias remain largely to be determined, both in nature and in quantitative impact. One hypothesis suggests that the physical characteristics of a DNA context could have a modulating effect on its mutability, adjusting the impact of damage or the efficiency of repair. Here, we report a genome-wide computational test of this hypothesis, in which we utilize a constrained set of human non-CpG SNPs as the source of selectively neutral germline mutations. Interestingly, we observe that the quantitative context-dependencies of some substitution types display significant associations to measures of local structural topography and helix stability in DNA. Most prominently, we find that the local sequence bias of transition mutations is significantly associated with the sequence-dependent level of helix instability imposed by the potentially underlying DNA mismatches. The results of our work indicate the extent to which DNA physical properties could have shaped the recent point mutational spectrum in the human genome.

New tools and methods for direct programmatic access to the dbSNP relational database

Genome-wide association studies often incorporate information from public biological databases in order to provide a biological reference for interpreting the results. The dbSNP database is an extensive source of information on single nucleotide polymorphisms (SNPs) for many different organisms, including humans. We have developed free software that will download and install a local MySQL implementation of the dbSNP relational database for a specified organism. We have also designed a system for classifying dbSNP tables in terms of common tasks we wish to accomplish using the database. For each task we have designed a small set of custom tables that facilitate task-related queries and provide entity-relationship diagrams for each task composed from the relevant dbSNP tables. In order to expose these concepts and methods to a wider audience we have developed web tools for querying the database and browsing documentation on the tables and columns to clarify the relevant relational structure. All web tools and software are freely available to the public at http://cgsmd.isi.edu/dbsnpq. Resources such as these for programmatically querying biological databases are essential for viably integrating biological information into genetic association experiments on a genome-wide scale.