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Genome-Wide Association Study (GWAS) is a hypothesis-free method for identifying associations between genetic regions and traits. GWAS analysis are usually used to identify genes involved in human diseaseBy applying GWAS analysis to variant data we will be able to identify a given variant (or a set of variants) involved in a given phenotype or disorder.Based on a statistical test, GWAS analysis will provide a level of significance (or p-value) for each variant. OpenCGA implements GWAS analysis based on the statistical tests: chi-square and Fisher.

Implementation

OpenCGA GWAS analysis extends Oskar GWAS implementation. GWAS is implemented using Hadoop MapReduce over HBase.

Input

Parameters

OpenCGA support different input parameters:

  • Variant data with sample genotypes
  • Two list of samples (case-control study)
  • Statistical test: chi square or fisher.

Instead of providing two lists of samples, users can provide:

  • A phenotype, and
  • A pedigree for those variant samples

Configuration

OpenCGA implementation supports different configuration variables. This can be setup in the OpenCGA installation folder or specified during execution.

Output

A text file

Files

GWAS analysis result includes a text file with score and plot image (WIP)

Text file that consists of a header line (starting with #), and then one line per variant with the following 12-13 columns:

chromosomeChromosome code
startStart base-pair coordinate
endEnd base-pair coordinate
strandStrand
referenceReference allele
alternateAlternate allele
dbSNPVariant identifier
geneGene name
biotypeBioytpe
conseq. typesList of consequence types
chi squareAllelic test chi-square statistic. Not present with 'fisher' test.
p-valueAllelic test p-value
odd ratioOdd ratio: odds(allele 1 | case) / odds(allele 1 | control)


Next, it shows the first lines of a result file after executing a GWAS analysis using the chi square test:

Code Block
themeRDark
#chromosome	start	end	strand	reference	alternate	dbSNP	gene	biotype	conseq. types	chi square	p-value	odd ratio
22	16054454	16054454	+	C	T	rs373998521			intergenic_variant	2.4727272727272727	0.11583677431831574	0.0
22	16065809	16065809	+	T	C		ENSG00000233866	lincRNA	downstream_gene_variant	0.053968253968253915	0.8162967146689325	0.8
22	16065809	16065809	+	T	C				regulatory_region_variant	0.053968253968253915	0.8162967146689325	0.8
22	16077310	16077310	+	T	A		ENSG00000229286	unprocessed_pseudogene	2KB_upstream_variant	0.9714285714285711	0.3243241555798487	3.0
22	16077310	16077310	+	T	A				regulatory_region_variant	0.9714285714285711	0.3243241555798487	3.0
22	16080499	16080499	+	A	G	rs200119791	ENSG00000229286	unprocessed_pseudogene	upstream_gene_variant	1.8888888888888886	0.16932729721206297	Infinity
22	16080499	16080499	+	A	G	rs200119791	ENSG00000235265	unprocessed_pseudogene	downstream_gene_variant	1.8888888888888886	0.16932729721206297	Infinity
22	16084621	16084621	+	T	C		ENSG00000235265	unprocessed_pseudogene	non_coding_transcript_exon_variant,non_coding_transcript_variant	2.4425287356321843	0.11808572685033702	Infinity



Index

Score can be indexed.

Useful Links

Table of Contents:

Table of Contents
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