Global inference of disease-causing single nucleotide variants from exome sequencing data.
BACKGROUND
Whole exome sequencing (WES) has recently emerged as an effective approach for identifying genetic variants underlying human diseases. However, considerable time and labour is needed for careful investigation of candidate variants. Although filtration based on population frequencies and functional prediction scores could effectively remove common and neutral variants, hundreds or even thousands of rare deleterious variants still remain. In addition, current WES platforms also provide variant information in flanking noncoding regions, such as promoters, introns and splice sites. Despite of being recognized to harbour causal variants, these regions are usually ignored by current analysis pipelines.


RESULTS
We present a novel computational method, called Glints, to overcome the above limitations. Glints is capable of identifying disease-causing SNVs in both coding and flanking noncoding regions from exome sequencing data. The principle behind Glints is that disease-causing variants should manifest their effect at both variant and gene levels. Specifically, Glints integrates 14 types of functional scores, including predictions for both coding and noncoding variants, and 9 types of association scores, which help identifying disease relevant genes. We conducted a large-scale simulation studies based on 1000 Genomes Project data and demonstrated the effectiveness of our method in both coding and flanking noncoding regions. We also applied Glints in two real exome sequencing and demonstrated its effectiveness for uncovering disease-causing SNVs. Both standalone software and web server are available at our website http://bioinfo.au.tsinghua.edu.cn/jianglab/glints .


CONCLUSIONS
Glints is effective for uncovering disease-causing SNVs in coding and flanking noncoding regions, which is supported by both simulation and real case studies. Glints is expected to be a useful tool for human genetics research based on exome sequencing data.
MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, Tsinghua National Laboratory for Information Science and Technology, Beijing, China.|Department of Computer Science, Tsinghua University, Beijing, China.
Mengmeng
M
Wu
MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, Tsinghua National Laboratory for Information Science and Technology, Beijing, China.|Department of Computer Science, Tsinghua University, Beijing, China.
Ting
T
Chen
MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, Tsinghua National Laboratory for Information Science and Technology, Beijing, China. ruijiang@tsinghua.edu.cn.|Department of Automation, Tsinghua University, Beijing, China. ruijiang@tsinghua.edu.cn.
Rui
R
Jiang
D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D059472:Exome; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D007438:Introns; D020641:Polymorphism, Single Nucleotide; D011401:Promoter Regions, Genetic; D022821:RNA Splice Sites; D017422:Sequence Analysis, DNA / Q000379:methods; D012984:Software
D022821:RNA Splice Sites