Case study: QTL discovery

QTL Discovery using TORUS

Data Preparation

The QTL discovery requires same data format as in the enrichment analysis. It takes summary statistics in forms of point estimates and standard errors, pre-computed (log10) Bayes factors, or simply z-scores, all from single-SNP association analysis.

Functional annotations for SNPs and their position information can also be used in the QTL discovery. The data format can be found in above links.

Running QTL discovery

Running QTL discovery analysis is simply achieved by executing torus with command line option "-qtl". Here we demonstrate two examples

• QTL discovery in GEUVADIS eQTL data. This analysis uses the annotations of SNP distance

torus -d geuv.summary.bf.gz --load_bf -smap geuv.snp.map.gz -gmap geuv.gene.map.gz -qtl > geuv.egene.rst

• GWAS analysis of HDL.

 torus -d HDL.z_score.gz -annot 1000G.annot.gz --load_zval -qtl > HDL.qtl.rst

If no annotations provided, the QTL discovery procedure treats every SNP equally a priori.

Output from QTL discovery

The output from the QTL discovery has the following format

    1       ENSG00000164308    3.406e-106    1
    2       ENSG00000166750    4.212e-105    1
    3       ENSG00000198468    1.544e-104    1
    4       ENSG00000174652    6.366e-102    1
    5       ENSG00000197728    1.568e-101    1
    6       ENSG00000233927    1.364e-99    1
    7       ENSG00000237651    5.658e-95    1
    8       ENSG00000006282    8.019e-91    1
    9       ENSG00000112306    5.995e-89    1
   10       ENSG00000166839    9.625e-88    1

The output is a ranked list of all tested loci. The first and the second columns represent the rank and the name of a gene, respectively. Column 3 represents the false discovery probability of the corresponding gene (smaller value indicates that the locus likely to harbor QTNs). Finally, column 4 represents a indicator for the hypothesis testing outcome: 1 indicates rejection of the null hypothesis at the pre-defined FDR level (5% by default).