Application of computational methods in genetic study of inflammatory bowel disease

Table 1 Commonly used publically available software for each step in genomic studies

Analysis	Software	URL	Ref.
GWAS	PLINK	http://pngu.mgh.harvard.edu/~purcell/plink/	[7]
	EMMAX	http://genetics.cs.ucla.edu/emmax/	[10]
	FaST	http://research.microsoft.com/en-us/um/redmond/projects/MSCompBio/Fastlmm/	[13]
	GEMMA	http://www.xzlab.org/software.html	[14]
Imputation	SHAPEIT (pre-phasing)	https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html	[20]
	IMPUTE2 (pre-phasing and imputation)	https://mathgen.stats.ox.ac.uk/impute/impute_v2.html	[17]
	MACH (pre-phasing and imputation)	http://csg.sph.umich.edu//abecasis/MACH/tour/imputation.html	[18]
	fastPHASE (pre-phasing and imputation)	https://els.comotion.uw.edu/express_license_technologies/fastphase	[16]
	BEAGLE (pre-phasing and imputation)	http://faculty.washington.edu/browning/beagle/beagle.html	[19]
	SNPTEST (association testing)	https://mathgen.stats.ox.ac.uk/genetics_software/snptest/old/snptest.html	[22]
Meta-analysis	METAL	http://csg.sph.umich.edu//abecasis/Metal/	[27]
	META	https://mathgen.stats.ox.ac.uk/genetics_software/meta/meta.html	[22]
	MetABEL	http://www.genabel.org/packages/MetABEL	[30]
	GWAMA	http://www.well.ox.ac.uk/gwama/download.shtml	[31]
	PLINK	http://pngu.mgh.harvard.edu/~purcell/plink/metaanal.shtml	[7]
Pathway analysis	GenGen	http://gengen.openbioinformatics.org/en/latest/tutorial/pathway/	[34]
	ALIGATOR	http://x004.psycm.uwcm.ac.uk/~peter/	[39]
	i-GSEA4GWAS	http://gsea4gwas.psych.ac.cn/	[40]
	GSEA-SNP	https://http://www.nr.no/en/projects/software-genomics	[41]
	DAVID	http://david.ncifcrf.gov/summary.jsp	[42]
	GeneTrail	http://genetrail.bioinf.uni-sb.de/	[43]
	WebGestalt	http://bioinfo.vanderbilt.edu/webgestalt/	[44]
Network analysis	Cytoscape	http://www.cytoscape.org/	[45]
	DAPPLE	http://www.broadinstitute.org/mpg/dapple/dappleTMP.php	[46]
	STRINGS	http://string-db.org/	[47]
	FUNCOUP	http://funcoup.sbc.su.se/search/	[48]
Gene-gene interaction	PLINK	http://pngu.mgh.harvard.edu/~purcell/plink/epi.shtml	[7]
	BOOST	http://bioinformatics.ust.hk/BOOST.html	[52]
	MDR	https://ritchielab.psu.edu/mdr-downloads	[55]
Microbiome	QIIME	http://qiime.org/	[69]
	mothur	http://www.mothur.org/wiki/Main_Page	[71]
	VAMPS	https://vamps.mbl.edu/portals/hmp/hmp.php	[72]
	PhymmBL	http://ccb.jhu.edu/software/phymmbl/index.shtml	[73]
	MEGAN5	http://ab.inf.uni-tuebingen.de/software/megan5/	[74]
	PhyIOTU	https://github.com/sharpton/PhylOTU	[75]
	MLTreeMap	http://mltreemap.org/	[76]
Pleiotropy	CPMA	http://coruscant.itmat.upenn.edu/software.html	[92]
	ASSET	http://www.bioconductor.org/packages/release/bioc/html/ASSET.html	[93]
	GPA	https://github.com/dongjunchung/GPA	[98]
	SMAT	http://www.hsph.harvard.edu/xlin/software.html	[101]

Table 2 Take home messages from our discussion on the application of computational methods in genetic study of inflammatory bowel disease

Take home messages
GWAS is an unbiased method to identify common genetic variants that are significantly associated with complex human diseases. Sample structure needs to be carefully handled to avoid false positive results
Imputation is often employed to infer un-genotyped SNPs based on those genotyped ones, followed by meta-analysis to combine results from multiple studies, in order to increase the power in GWAS
Pathway analyses help to identify genetic variants that have modest individual effect but jointly make significant contribution to disease susceptibility
Both gene-gene interactions and gene-environment interactions are important underlying factors for IBD
Pleiotropy studies aim to identify genetic loci shared by IBD and other immune diseases
Risk prediction is one of the ways to translate GWAS discoveries to clinics - to identify patients at high risk

GWAS: Genome-wide association study; IBD: Inflammatory bowel disease.