This document provides an overview of exome analysis for identifying causal genes for Mendelian disorders. It discusses technological advances that have enabled exome sequencing, key publications in the field, strategies and tools used for data analysis, and exome sequencing service providers. The document is intended as a useful resource for those interested in how exome analysis is used to identify genes underlying Mendelian conditions.
Iowa State Bioinformatics BCB Symposium 2018 - There and Back Again
Fundamentals of Exome Analysis
1. Fundamentals of Analysis of Exomes
Diego Forero, MD, PhD
Assistant Professor
Director, Laboratory of NeuroPsychiatric Genetics
Director, Medical Research Office
School of Medicine
Antonio Nariño University
Bogotá, Colombia
Editor, hum-molgen.org
diego.forero@uan.edu.co
2. CAUSAL GENES FOR MENDELIAN
DISORDERS
Mendelian Autosomal Sex-linked Total
Disorders
Causal Gene 3.175 266 3.441
Known
Causal Gene 1.633 140 1.773
Unknown
Online Mendelian Inheritance in Man
8. A timeline illustrating technological breakthroughs and
hallmark publications for Mendelian disease gene
identification
Gilissen, Genom Biol 2011
9. A timeline illustrating technological breakthroughs and
hallmark publications for Mendelian disease gene
identification
Gilissen, Genom Biol 2011
10. First Published Exome
"We focus here on the variants in a person’s ‘exome,’ which is
the set of exons in a genome..."
Ng, PLoS Genet 2008
11. A representation of the relationship between the size of
the mutational target and the frequency of disease for
disorders caused by de novo mutations
Gilissen, Genom Biol 2011
13. Strategies for finding disease-causing rare variants
using exome sequencing
Bamshad, Nat Rev Genet 2011
14. Typical heuristic filtering applied to exome sequencing
projects aimed at novel gene discovery for Mendelian
disorders
Stitziel, Genom Biol 2011
15. Mean number of coding variants in two populations
Bamshad, Nat Rev Genet 2011
16. First identification of the causal gene for a monogenic disorder
by exome sequencing
Freeman–Sheldon syndrome (MYH3)
Ng, Nature 2009
17. Exome Analysis for One Patient
Perrault syndrome (HSD17B4)
Pierce, Am J Hum Genet 2010
18. Useful In Silico Tools
VarSifter
http://research.nhgri.nih.gov/software/VarSifter/index.shtml
Exome Variant Server (6503 exomes)
http://evs.gs.washington.edu/EVS/
wANNOVAR
http://wannovar.usc.edu/
19. Useful In Silico Tools
Atlas2 Suite
http://sourceforge.net/p/atlas2/wiki/Atlas2%20Suite/
IBD2
http://compbio.charite.de/contao/index.php/ibd2.html
EVA
http://plateforme-genomique-irib.univ-rouen.fr/EVA/index.php
TREAT
http://ndc.mayo.edu/mayo/research/biostat/stand-alone-packages.cfm
KGGSeq
http://statgenpro.psychiatry.hku.hk/limx/kggseq/
20. Exomes-Service Providers
Axeq (USA)
50x; Illumina TrueSeq/HiSeq200. $2,300 USD per sample
Macrogen (Korea)
50x; Illumina TrueSeq/HiSeq200. $2,499 USD per sample
BaseClear (Netherlands)
30x; Nimblegen/Illumina. € 2,499 per sample
PerkinElmer (USA)
30x; Agilent/Illumina. $3,500 USD per sample
BGI Americas (USA)
30x; $3,500 USD per sample
EdgeBio (USA)
50x; SOLiD 4. $5,500 USD per sample
DNAVision (Belgium)
30x; Agilent/Illumina. 5,990 € per sample
Knome (USA)
30x; Illumina. $8,750 USD per sample
Source BioScience (UK)
50x; Agilent/Illumina. 5,850GBP per sample
21. Example of Data Provided
http://www.ncbi.nlm.nih.gov/omim
Need for in-house exome data.
Possible Noise from dbSNP and OMIM
Data from Human Gene Mutation Database?
23. Tools for mutation pathogenicity prediction
Thusberg J, Olatubosun A, Vihinen M.
Performance of mutation pathogenicity prediction methods on missense variants.
Hum Mutat. 2011 Apr;32(4):358-68.
25. www.daforerog.co.cc
“This is an excellent resource for anyone who is generally interested in
how these technologies work”.
Stephen Turner, PhD
Center for Human Genetics Research, Vanderbilt University.