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Conditioning analysis

Multiple association signals could exist in one locus, especially when observing complex LD structures in the regional plot. Conditioning on one signal allows the separation of independent signals.

Several ways to perform the conditioning analysis:

Adding the lead variant to the covariates

First, extract the individual genotype (dosage) to the text file. Then add it to covariates.

plink2 \
  --pfile chr1.dose.Rsq0.3 vzs \
  --extract chr1.list \
  --threads 1 \
  --export A \
  --out genotype/chr1

The exported format could be found in Export non-PLINK 2 fileset.

Note

Major allele dosage would be outputted. If adding ref-first, REF allele would be outputted. It does not matter as a covariate.

Then just paste it to the covariates table and run the association test.

Note

Some association test software will also provide options for condition analysis. For example, in PLINK, you can use --condition <variant ID> for condition analysis. You can simply provide a list of variant IDs to run the condition analysis.

GCTA-COJO

If raw genotypes and phenotypes are not available, GCTA-COJO performs conditioning analysis using sumstats and external LD reference.

cojo-top-SNPs 10 will perform a step-wise model selection to select 10 independently associated SNPs (including non-significant ones).

gcta \
  --bfile chr1 \
  --chr 1 \
  --maf 0.001 \
  --cojo-file chr1_cojo.input \
  --cojo-top-SNPs 10 \
  --extract-region-bp 1 152383617 5000 \
  --out chr1_cojo.output

Note

bfile is used to generate LD. A size of > 4000 unrelated samples is suggested. Estimation of LD in GATC is based on the hard-call genotype.

Input file format less chr1_cojo.input: 

ID      ALLELE1 ALLELE0 A1FREQ  BETA    SE      P       N
chr1:11171:CCTTG:C      C       CCTTG   0.0831407       -0.0459889      0.0710074       0.5172  180590
chr1:13024:G:A  A       G       1.63957e-05     -3.2714 3.26302 0.3161  180590
Here A1 is the effect allele.

Then --cojo-cond could be used to generate new sumstats conditioned on the above-selected variant(s).

Reference:

  • https://www.cog-genomics.org/plink/1.9/assoc
  • Yang, J., Ferreira, T., Morris, A. P., Medland, S. E., Genetic Investigation of ANthropometric Traits (GIANT) Consortium, DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, ... & Visscher, P. M. (2012). Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nature genetics, 44(4), 369-375.