This is the location for the HTRX tool that was firstly proposed by Barrie, W., Yang, Y., Irving-Pease, E.K. et al. Elevated genetic risk for multiple sclerosis emerged in steppe pastoralist populations. Nature 625, 321–328 (2024).

and then illustrated in detail by

Yang Y, Lawson DJ. HTRX: an R package for learning non-contiguous haplotypes associated with a phenotype. Bioinformatics Advances 3.1 (2023): vbad038.

Authors:

Yaoling Yang (yaoling.yang@bristol.ac.uk)

Daniel Lawson (dan.lawson@bristol.ac.uk)License: GPL-3

Haplotype Trend Regression with eXtra flexibility (HTRX) searches a pre-defined set of SNPs for haplotype patterns that include single nucleotide polymorphisms (SNPs) and non-contiguous haplotypes.

We search over all possible templates which give a value for each SNP being ‘0’ or ‘1’, reflecting whether the reference allele of each SNP is present or absent, or an ‘X’ meaning either value is allowed.

We used a two-stage procedure to select the best HTRX model (function “do_cv”).

Stage 1: select candidate models;

Stage 2: select the best model using 10-fold cross-validation.

Longer haplotypes are important for discovering interactions. However, there are \(3^k-1\) haplotypes in HTRX if the region contains \(k\) SNPs, making it unrealistic for regions with large numbers of SNPs. To address this issue, we proposed “cumulative HTRX” (function “do_cumulative_htrx”) that enables HTRX to run on longer haplotypes, i.e. haplotypes which include at least 7 SNPs (we recommend). Besides, we provide a parameter “max_int” which controls the maximum number of SNPs that can interact.

This package is also available from CRAN. You can install it by

A tutorial of package HTRX can be found in vignettes/HTRX_vignette.pdf

```
library(HTRX)
## use dataset "example_hap1", "example_hap2" and "example_data_nosnp"
## "example_hap1" and "example_hap2" are both genomes of 8 SNPs for 5,000 individuals (diploid data)
## "example_data_nosnp" is an example dataset which contains the outcome (binary), sex, age and 18 PCs
## visualise the covariates data
head(HTRX::example_data_nosnp)
## visualise the genotype data for the first genome
head(HTRX::example_hap1)
## we perform HTRX on the first 4 SNPs
## we first generate all the haplotype data, as defined by HTRX
HTRX_matrix=make_htrx(HTRX::example_hap1[,1:4],HTRX::example_hap2[,1:4])
## If the data is haploid, please set
## HTRX_matrix=make_htrx(HTRX::example_hap1[,1:4],HTRX::example_hap1[,1:4])
## next compute the maximum number of independent features
featurecap=htrx_max(nsnp=4)
## then perform HTRX using 2-step cross-validation
## to compute additional variance explained by haplotypes
## If you want to compute total variance explained, please set gain=FALSE
htrx_results <- do_cv(HTRX::example_data_nosnp,
HTRX_matrix,train_proportion=0.5,
sim_times=3,featurecap=featurecap,usebinary=1,
method="stratified",criteria="BIC",
gain=TRUE,runparallel=FALSE)
## If we want to compute the total variance explained
## we can set gain=FALSE in the above example
## we perform cumulative HTRX on all the 8 SNPs using 2-step cross-validation
## to compute additional variance explained by haplotypes
## If the data is haploid, please set hap2=HTRX::example_hap1
## If you want to compute total variance explained, please set gain=FALSE
## For Linux/MAC users, we strongly encourage you to set runparallel=TRUE
cumu_htrx_results <- do_cumulative_htrx(data_nosnp=HTRX::example_data_nosnp,
hap1=HTRX::example_hap1,
hap2=HTRX::example_hap2,
train_proportion=0.5,sim_times=1,
featurecap=6,usebinary=1,
randomorder=TRUE,method="stratified",
criteria="BIC",runparallel=FALSE)
```