@@ -57,7 +57,9 @@ At present, GADGETS does not support the use of genotypes imputed with uncertain

 ## Pre-process Data

-The second step in the analysis pipeline is to pre-process the data. Below, we default to the assumption that SNPs located on the same biological chromosome are in linkage, but users need not make this assumption and are encouraged to more carefully tailor this argument based on individual circumstances. For the example data, the SNPs are drawn from chromosomes 10-13, with the columns sorted by chromosome and 25 SNPs per chromosome. We therefore construct the vector as follows:

+The second step in the analysis pipeline is to pre-process the data. Below, we default to the assumption that SNPs located on the same biological chromosome are in linkage, but users need not make this assumption and are encouraged to more carefully tailor this argument based on individual circumstances. We recommend using external data for determining LD blocks. A determination of LD based on mothers would be distorted by maternal epistasis, and a determination of LD based on fathers could be distorted by being the father of a case, hence more likely to be a co-donor of risk-related offspring SNPs.

+

+For the example data, the SNPs are drawn from chromosomes 10-13, with the columns sorted by chromosome and 25 SNPs per chromosome. We therefore construct the vector as follows:

 ```{r}

 ld.block.vec <- rep(25, 4)

@@ -373,7 +375,7 @@ As a final step in the analytic pipeline, we recommend users examine network plo

 Briefly, we use our epistasis test 'p-values' to assign graphical scores to pairs of SNPs identified in top-scoring chromosomes, with higher scores corresponding to lower epistasis p-values (more substantial evidence for epistasis). We then aggregate those pair-scores across chromosome sizes to generate a final collection of SNP-pairs, which we display in a single network plot.

-We start by computing the SNP-pair scores using `compute.graphical.scores`. This function takes as required arguments a list of data.tables containing the results from GADGETS run for different chromosome sizes and the list of pre-processed data from the function `preprocess.genetic.data`. The `compute.graphical.scores` function uses the `epistasis.test` function under the hood, so, as mentioned above, if all SNPs are in the same linkage block then a warning will print. Due to the way the permutation algorithm works, if a set of SNPs are on the same linkage block, they will not get a good h-value. This is because LD blocks are permuted together, so there will be no variation in the permutations. Thus, they cannot be detected as epistatic, regardless of the underlying truth. This phenomenon may also occur if the majority of an epistatic set lies within the same LD block. The user may see fit to enforce a different criterion, perhaps based on LD, or perhaps no criterion at all and let all the SNPs permute individually.

+We start by computing the SNP-pair scores using `compute.graphical.scores`. This function takes as required arguments a list of data.tables containing the results from GADGETS run for different chromosome sizes and the list of pre-processed data from the function `preprocess.genetic.data`. The `compute.graphical.scores` function uses the `epistasis.test` function under the hood, so, as mentioned above, if all SNPs are in the same designated linkage block then a warning will print. Due to the way the permutation algorithm works, if a set of SNPs are on the same linkage block, they will not get a good h-value. This is because LD blocks are permuted together, so there will be no variation in the permutations. Thus, they cannot be detected as epistatic, regardless of the underlying truth. This phenomenon may also occur if many SNPs of an epistatic set lie within the same designated LD block. The user may see fit to enforce a different criterion, perhaps based on LD, or perhaps no criterion at all and let all the SNPs permute individually.

 **For analysts who have run the permutation-based global test:** we recommend restricting attention to chromosomes with fitness scores higher than what we would expect for null data. Specifically, the `global.test` function output contains a vector, `max.perm.95th.pctl`, that reports the $95^{th}$ percentile of the maximum observed fitness score across the null permutes for each chromosome size. We restrict our network plots to the chromosomes with fitness scores exceeding the corresponding null threshold for each chromosome size: