Xueqiu Jian

dbNSFP v2.0: A database of human non-synonymous SNVs and their functional predictions and annotations

dbNSFP is a database developed for functional prediction and annotation of all potential non‐synonymous single‐nucleotide variants (nsSNVs) in the human genome. This database significantly facilitates the process of querying predictions and annotations from different databases/web‐servers for large amounts of nsSNVs discovered in exome‐sequencing studies. Here we report a recent major update of the database to version 2.0. We have rebuilt the SNV collection based on GENCODE 9 and currently the database includes 87,347,043 nsSNVs and 2,270,742 essential splice site SNVs (an 18% increase compared to dbNSFP v1.0). For each nsSNV dbNSFP v2.0 has added two prediction scores (MutationAssessor and FATHMM) and two conservation scores (GERP++ and SiPhy). The original five prediction and conservation scores in v1.0 (SIFT, Polyphen2, LRT, MutationTaster and PhyloP) have been updated. Rich functional annotations for SNVs and genes have also been added into the new version, including allele frequencies observed in the 1000 Genomes Project phase 1 data and the NHLBI Exome Sequencing Project, various gene IDs from different databases, functional descriptions of genes, gene expression and gene interaction information, among others. dbNSFP v2.0 is freely available for download at http://sites.google.com/site/jpopgen/dbNSFP. ©2013 Wiley‐Liss, Inc.

Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies

Accurate deleteriousness prediction for nonsynonymous variants is crucial for distinguishing pathogenic mutations from background polymorphisms in whole exome sequencing (WES) studies. Although many deleteriousness prediction methods have been developed, their prediction results are sometimes inconsistent with each other and their relative merits are still unclear in practical applications. To address these issues, we comprehensively evaluated the predictive performance of 18 current deleteriousness-scoring methods, including 11 function prediction scores (PolyPhen-2, SIFT, MutationTaster, Mutation Assessor, FATHMM, LRT, PANTHER, PhD-SNP, SNAP, SNPs&GO and MutPred), 3 conservation scores (GERP++, SiPhy and PhyloP) and 4 ensemble scores (CADD, PON-P, KGGSeq and CONDEL). We found that FATHMM and KGGSeq had the highest discriminative power among independent scores and ensemble scores, respectively. Moreover, to ensure unbiased performance evaluation of these prediction scores, we manually collected three distinct testing datasets, on which no current prediction scores were tuned. In addition, we developed two new ensemble scores that integrate nine independent scores and allele frequency. Our scores achieved the highest discriminative power compared with all the deleteriousness prediction scores tested and showed low false-positive prediction rate for benign yet rare nonsynonymous variants, which demonstrated the value of combining information from multiple orthologous approaches. Finally, to facilitate variant prioritization in WES studies, we have pre-computed our ensemble scores for 87 347 044 possible variants in the whole-exome and made them publicly available through the ANNOVAR software and the dbNSFP database.

In silico prediction of splice-altering single nucleotide variants in the human genome

In silico tools have been developed to predict variants that may have an impact on pre-mRNA splicing. The major limitation of the application of these tools to basic research and clinical practice is the difficulty in interpreting the output. Most tools only predict potential splice sites given a DNA sequence without measuring splicing signal changes caused by a variant. Another limitation is the lack of large-scale evaluation studies of these tools. We compared eight in silico tools on 2959 single nucleotide variants within splicing consensus regions (scSNVs) using receiver operating characteristic analysis. The Position Weight Matrix model and MaxEntScan outperformed other methods. Two ensemble learning methods, adaptive boosting and random forests, were used to construct models that take advantage of individual methods. Both models further improved prediction, with outputs of directly interpretable prediction scores. We applied our ensemble scores to scSNVs from the Catalogue of Somatic Mutations in Cancer database. Analysis showed that predicted splice-altering scSNVs are enriched in recurrent scSNVs and known cancer genes. We pre-computed our ensemble scores for all potential scSNVs across the human genome, providing a whole genome level resource for identifying splice-altering scSNVs discovered from large-scale sequencing studies.

In silico tools for splicing defect prediction: a survey from the viewpoint of end users

RNA splicing is the process during which introns are excised and exons are spliced. The precise recognition of splicing signals is critical to this process, and mutations affecting splicing comprise a considerable proportion of genetic disease etiology. Analysis of RNA samples from the patient is the most straightforward and reliable method to detect splicing defects. However, currently, the technical limitation prohibits its use in routine clinical practice. In silico tools that predict potential consequences of splicing mutations may be useful in daily diagnostic activities. In this review, we provide medical geneticists with some basic insights into some of the most popular in silico tools for splicing defect prediction, from the viewpoint of end users. Bioinformaticians in relevant areas who are working on huge data sets may also benefit from this review. Specifically, we focus on those tools whose primary goal is to predict the impact of mutations within the 5′ and 3′ splicing consensus regions: the algorithms used by different tools as well as their major advantages and disadvantages are briefly introduced; the formats of their input and output are summarized; and the interpretation, evaluation, and prospection are also discussed.Genet Med 16 7, 497–503.

In silico prediction of deleteriousness for nonsynonymous and splice-altering single nucleotide variants in the human genome

In silico prediction methods have increasingly been valuable and popular in molecular biology, especially in human genetics, for deleteriousness prediction to filter and prioritize huge amounts of DNA variation identified by sequencing human genomes. There is a rich collection of available methods developed upon different levels/aspects of knowledge about how DNA variations affect gene expression. Given the fact that their predictions are not always consistent or even opposite of what was expected, using consensus prediction or majority vote among these methods is preferred to trusting any single one. Because querying different databases for different methods is both tedious and time-consuming for such big data sets, one database integrating predictions from multiple databases can facilitate the process. In this chapter, we describe the general steps of obtaining comprehensive predictions and annotations for large numbers of variants from dbNSFP, the first and probably the most widely used database of its kind.

Whole exome sequencing study identifies novel rare and common Alzheimer’s-Associated variants involved in immune response and transcriptional regulation

The Alzheimer’s Disease Sequencing Project (ADSP) undertook whole exome sequencing in 5,740 late-onset Alzheimer disease (AD) cases and 5,096 cognitively normal controls primarily of European ancestry (EA), among whom 218 cases and 177 controls were Caribbean Hispanic (CH). An age-, sex- and APOE based risk score and family history were used to select cases most likely to harbor novel AD risk variants and controls least likely to develop AD by age 85 years. We tested ~1.5 million single nucleotide variants (SNVs) and 50,000 insertion-deletion polymorphisms (indels) for association to AD, using multiple models considering individual variants as well as gene-based tests aggregating rare, predicted functional, and loss of function variants. Sixteen single variants and 19 genes that met criteria for significant or suggestive associations after multiple-testing correction were evaluated for replication in four independent samples; three with whole exome sequencing (2,778 cases, 7,262 controls) and one with genome-wide genotyping imputed to the Haplotype Reference Consortium panel (9,343 cases, 11,527 controls). The top findings in the discovery sample were also followed-up in the ADSP whole-genome sequenced family-based dataset (197 members of 42 EA families and 501 members of 157 CH families). We identified novel and predicted functional genetic variants in genes previously associated with AD. We also detected associations in three novel genes: IGHG3 (p = 9.8 × 10−7), an immunoglobulin gene whose antibodies interact with β-amyloid, a long non-coding RNA AC099552.4 (p = 1.2 × 10−7), and a zinc-finger protein ZNF655 (gene-based p = 5.0 × 10−6). The latter two suggest an important role for transcriptional regulation in AD pathogenesis.

Functional annotation of genomic variants in studies of late-onset Alzheimer’s disease

Abstract Motivation Annotation of genomic variants is an increasingly important and complex part of the analysis of sequence-based genomic analyses. Computational predictions of variant function are routinely incorporated into gene-based analyses of rare-variants, though to date most studies use limited information for assessing variant function that is often agnostic of the disease being studied. Results In this work, we outline an annotation process motivated by the Alzheimer’s Disease Sequencing Project, illustrate the impact of including tissue-specific transcript sets and sources of gene regulatory information and assess the potential impact of changing genomic builds on the annotation process. While these factors only impact a small proportion of total variant annotations (∼5%), they influence the potential analysis of a large fraction of genes (∼25%). Availability and implementation Individual variant annotations are available via the NIAGADS GenomicsDB, at https://www.niagads.org/genomics/ tools-and-software/databases/genomics-database. Annotations are also available for bulk download at https://www.niagads.org/datasets. Annotation processing software is available at http://www.icompbio.net/resources/software-and-downloads/. Supplementary information Supplementary data are available at Bioinformatics online.

Whole exome sequence analysis of white matter hyperintensities on cranial MRI

Michael W. Weiner, Andrew J. Saykin, the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Indiana University School of Medicine, Indianapolis, IN, USA; University of California, San Diego, La Jolla, CA, USA; Mayo Clinic, Rochester, MN, USA; University of Southern California, Los Angeles, CA, USA; Harvard Medical School, Boston, MA, USA; Brigham and Women’s Hospital, Boston, MA, USA; University of California Berkeley, Berkeley, CA, USA; University of California San Francisco, San Francisco, CA, USA. Contact e-mail: asaykin@iupui.edu

Imaging Endophenotypes of Stroke as a Target for Genetic Studies

Stroke is a heterogeneous disease leading to death of neural tissue and often resulting in the loss of motor and cognitive function. It is the fifth leading cause of death and a leading cause of severe long-term disability in the United States.1 Most strokes (≈80%–90%) are caused by an acute interruption of the brain arterial blood supply because of vascular occlusion, leading to brain tissue ischemia. Approximately 10% to 20% of strokes are caused by blood vessel rupture, leading to hemorrhage. Although a growing number of genetic loci have been identified for the major stroke risk factors, the genetic architecture of stroke and its subtypes remains largely uncharacterized. The use of imaging measures as endophenotypes in genetic studies of stroke is leading to new discoveries and may provide a better understanding of the biological mechanisms underlying stroke pathogenesis. The concept of endophenotype was first developed in the early 1970s by Gottesman and Shields2 for schizophrenia research. An endophenotype was originally defined as a quantitative characteristic of the disease, which cannot be observed by the naked eye (Endo- means internal or inside; Pheno- means showing or appearing).3 It is not a risk factor but rather an expression of the underlying disease liability. Gottesman and Gould3 identified 6 criteria that define an endophenotype (Table). Because endophenotypes are typically quantitative and lie in the causal pathway to the disease but are closer to the gene action than the clinical phenotype,3 they provide greater power than their corresponding clinical phenotypes in gene discovery, as has been shown in the genetic study of other complex diseases.4,5 This review will discuss selected imaging endophenotypes of stroke. Genetic studies referenced in this article mostly focus on genome-wide association studies (GWASs) in large population-based samples. View this table: Table. Criteria Defining an Endophenotype3 …

Genome-wide association study of 23,500 individuals identifies 7 loci associated with brain ventricular volume

The volume of the lateral ventricles (LV) increases with age and their abnormal enlargement is a key feature of several neurological and psychiatric diseases. Although lateral ventricular volume is heritable, a comprehensive investigation of its genetic determinants is lacking. In this meta-analysis of genome-wide association studies of 23,533 healthy middle-aged to elderly individuals from 26 population-based cohorts, we identify 7 genetic loci associated with LV volume. These loci map to chromosomes 3q28, 7p22.3, 10p12.31, 11q23.1, 12q23.3, 16q24.2, and 22q13.1 and implicate pathways related to tau pathology, S1P signaling, and cytoskeleton organization. We also report a significant genetic overlap between the thalamus and LV volumes (ρgenetic = −0.59, p-value = 3.14 × 10−6), suggesting that these brain structures may share a common biology. These genetic associations of LV volume provide insights into brain morphology.An increase in the volume of the brain lateral ventricles is a sign of normal aging, but can also be associated with neurological and psychiatric disorders. Here, Vojinovic et al. identify seven genetic loci in a GWA study for ventricular volume in 23,500 individuals and find correlation with thalamus volume.