Interaction Study

In TarGene you can investigate the interacting effects between genetic variants and between genetic variants and the environment up to any order (GxG, GxE, GxGxG, ...). However, the computational complexity associated with interaction testing grows exponentially. Interaction studies are thus very often targeted, for instance towards a specific transcription factor TF. For this transcription factor, multiple variants may have been identified because they are believed to play different roles. In TarGene such a scenario can be encoded via the group mode of the ESTIMANDS_CONFIG file. An example is more informative than a long description. Let's save the following content to a "newinteractionconfig.yaml" file.

type: groups

estimands:
  - type: AIE
    orders: [2, 3]

variants:
  TF:
    variants_1:
      - "1:238411180:T:C"
      - "3:3502414:T:C"
    variants_2:
      - "2:14983:G:A"

extra_treatments:
  - "Cheese intake"

outcome_extra_covariates:
  - "Number of vehicles in household"

The estimands section describes the types of estimands we are interested in, here Average Interaction Effects (AIE). We would like to scan both orders 2 and 3.

The variants section contains groups of variants for which the interactions will be generated independently. Here there is only one group called TF, and it contains two subgroups: variants_1 and variants_2. This means that combinations of variants_1 and variants_2 will define the interactions under investigation. Here they are simply (1:238411180:T:C, 2:14983:G:A) and (3:3502414:T:C, 2:14983:G:A). Why did we set the interaction orders to be both 2 and 3 since only pairwise interactions can be generated here?

This is because the extra_treatments section can list additional environmental variables, thus increasing the potential interaction order. In this case, there is one: Cheese intake. This means that additional pairwise interactions will be generated: (1:238411180:T:C, Cheese intake), (2:14983:G:A, Cheese intake) and (3:3502414:T:C, Cheese intake). But also the following order 3 interactions: (1:238411180:T:C, 2:14983:G:A, Cheese intake) and (3:3502414:T:C, 2:14983:G:A, Cheese intake).

Finally, the outcome_extra_covariates describes additional variables predictive of the outcome, but that do not confound the effect of treatment variables (variants and extra_treatments).

Importantly, all traits variables in the TRAITS_DATASET that are not listed as either outcome_extra_covariates or extra_treatments will be considered as outcome variables.

We will now point to this newly created ESTIMANDS_CONFIG in our nextflow.config file:

params {
    ESTIMANDS_CONFIG = "new_interaction_config.yaml"

    // UK-Biobank specific parameters
    BED_FILES = "unphased_bed/ukb_chr{1,2,3}.{bed,bim,fam}"
    BGEN_FILES = "unphased_bgen/ukb_chr{1,2,3}.{bgen,bgen.bgi,sample}"
    UKB_CONFIG = "ukbconfig_small.yaml"
    TRAITS_DATASET = "dataset.csv"
    UKB_WITHDRAWAL_LIST = "withdrawal_list.txt"
}

As usual, the pipeline can then be run as follows:

nextflow run https://github.com/TARGENE/targene-pipeline -r v0.11.1 -profile local