If your starting point for DAEQTL mapping is not the triad of the three data objects introduced in vignette('daeqtlr') and you need to assemble the set of SNP pairs for DAEQTL testing first, then this vignette is for you.
Here we show how to assemble a table of SNP pairs starting from:
- a list of DAE SNPs
- a list of candidate SNPs
- genomic positional annotation for those SNPs
Let us read in the three data sets above from the example files bundled with this daeqtlr:
dae_snps <- read_dae_snps(file = daeqtlr_example("dae_snps.txt"))
dae_snps[1:10, ]
#> # A tibble: 10 × 1
#> dae_snp
#> <chr>
#> 1 rsX019
#> 2 rsX020
#> 3 rsX031
#> 4 rsX038
#> 5 rsX041
#> 6 rsX048
#> 7 rsX049
#> 8 rsX051
#> 9 rsX058
#> 10 rsX059
candidate_snps <-
read_candidate_snps(file = daeqtlr_example("candidate_snps.txt"))
candidate_snps[1:10, ]
#> # A tibble: 10 × 1
#> candidate_snp
#> <chr>
#> 1 rsX001
#> 2 rsX002
#> 3 rsX003
#> 4 rsX004
#> 5 rsX005
#> 6 rsX006
#> 7 rsX007
#> 8 rsX008
#> 9 rsX009
#> 10 rsX010
snp_gen_positions <-
read_snp_gen_positions(file = daeqtlr_example("snp_gen_positions.csv"))
snp_gen_positions[1:10, ]
#> snp chromosome position
#> 1: rsX001 4 177558514
#> 2: rsX002 19 442675
#> 3: rsX003 19 238404
#> 4: rsX004 4 135561143
#> 5: rsX005 19 48440
#> 6: rsX006 4 22474241
#> 7: rsX007 4 178157767
#> 8: rsX008 1 180682638
#> 9: rsX009 4 22571167
#> 10: rsX010 19 36346We start by creating an intermediate data table (snp_table) with all the SNPs involved and their genomic positions, as well as the indication of who is a DAE SNP and who is a candidate SNP (they can be both). For that we use the function create_snp_table():
snp_table <-
create_snp_table(snp_gen_pos = snp_gen_positions,
dae_snps = dae_snps,
candidate_snps = candidate_snps)
snp_table
#> snp chromosome position is_dae_snp is_candidate_snp
#> 1: rsX001 4 177558514 FALSE TRUE
#> 2: rsX002 19 442675 FALSE TRUE
#> 3: rsX003 19 238404 FALSE TRUE
#> 4: rsX004 4 135561143 FALSE TRUE
#> 5: rsX005 19 48440 FALSE TRUE
#> 6: rsX006 4 22474241 FALSE TRUE
#> 7: rsX007 4 178157767 FALSE TRUE
#> 8: rsX008 1 180682638 FALSE TRUE
#> 9: rsX009 4 22571167 FALSE TRUE
#> 10: rsX010 19 36346 FALSE TRUE
#> 11: rsX011 1 181006806 FALSE TRUE
#> 12: rsX012 1 181005024 FALSE TRUE
#> 13: rsX013 1 180709233 FALSE TRUE
#> 14: rsX014 19 106704 FALSE TRUE
#> 15: rsX015 1 181114464 FALSE TRUE
#> 16: rsX016 19 160870 FALSE TRUE
#> 17: rsX017 19 107075 FALSE TRUE
#> 18: rsX018 19 422637 FALSE TRUE
#> 19: rsX019 19 113462 TRUE TRUE
#> 20: rsX020 1 181020238 TRUE TRUE
#> 21: rsX021 1 181060085 FALSE TRUE
#> 22: rsX022 19 599943 FALSE TRUE
#> 23: rsX023 19 169787 FALSE TRUE
#> 24: rsX024 19 376879 FALSE TRUE
#> 25: rsX025 19 141544 FALSE TRUE
#> 26: rsX026 19 402954 FALSE TRUE
#> 27: rsX027 4 177956612 FALSE TRUE
#> 28: rsX028 4 178073499 FALSE TRUE
#> 29: rsX029 4 22275597 FALSE TRUE
#> 30: rsX030 1 180744030 FALSE TRUE
#> 31: rsX031 1 181058720 TRUE TRUE
#> 32: rsX032 4 22861685 FALSE TRUE
#> 33: rsX033 4 22682377 FALSE TRUE
#> 34: rsX034 19 622636 FALSE TRUE
#> 35: rsX035 4 135861949 FALSE TRUE
#> 36: rsX036 4 22924223 FALSE TRUE
#> 37: rsX037 1 181106008 FALSE TRUE
#> 38: rsX038 4 136047668 TRUE TRUE
#> 39: rsX039 1 180934838 FALSE TRUE
#> 40: rsX040 1 181058949 FALSE TRUE
#> 41: rsX041 4 22465713 TRUE TRUE
#> 42: rsX042 4 22470182 FALSE TRUE
#> 43: rsX043 19 190524 FALSE TRUE
#> 44: rsX044 1 181005912 FALSE TRUE
#> 45: rsX045 19 125316 FALSE TRUE
#> 46: rsX046 19 99669 FALSE TRUE
#> 47: rsX047 19 156172 FALSE TRUE
#> 48: rsX048 19 139587 TRUE TRUE
#> 49: rsX049 1 181040482 TRUE TRUE
#> 50: rsX050 19 138345 FALSE TRUE
#> 51: rsX051 19 139425 TRUE TRUE
#> 52: rsX052 1 180575433 FALSE TRUE
#> 53: rsX053 19 136349 FALSE TRUE
#> 54: rsX054 19 155716 FALSE TRUE
#> 55: rsX055 1 181116544 FALSE TRUE
#> 56: rsX056 4 136032256 FALSE TRUE
#> 57: rsX057 4 177971967 FALSE TRUE
#> 58: rsX058 4 177973123 TRUE TRUE
#> 59: rsX059 4 177973549 TRUE TRUE
#> 60: rsX060 19 128944 TRUE TRUE
#> 61: rsX061 1 180871514 FALSE TRUE
#> 62: rsX062 1 181065405 FALSE TRUE
#> 63: rsX063 4 177602406 FALSE TRUE
#> 64: rsX064 19 598122 FALSE TRUE
#> 65: rsX065 1 181180883 FALSE TRUE
#> 66: rsX066 1 180708005 FALSE TRUE
#> 67: rsX099 1 180709100 TRUE FALSE
#> snp chromosome position is_dae_snp is_candidate_snpTo create the set of SNP pairs, we use the function create_snp_pairs() that takes in the snp_table. Essentially, the create_snp_pairs() searches within a genomic neighborhood of each DAE SNP for other SNPs marked as candidate SNPs. This genomic neighborhood is specified via a genomic window size centered on the DAE SNP. The window size defaults to 500 kb (window_size = 500000L), meaning a total of 1 Mb around the DAE SNP’s position (500 kb to each side).
# Assembles a data table of SNP pairs
snp_pairs <- create_snp_pairs(snp_table = snp_table)
snp_pairs
#> dae_snp candidate_snp chromosome dae_snp_position candidate_snp_position
#> 1: rsX099 rsX052 1 180709100 180575433
#> 2: rsX099 rsX008 1 180709100 180682638
#> 3: rsX099 rsX066 1 180709100 180708005
#> 4: rsX099 rsX013 1 180709100 180709233
#> 5: rsX099 rsX030 1 180709100 180744030
#> ---
#> 207: rsX059 rsX057 4 177973549 177971967
#> 208: rsX059 rsX058 4 177973549 177973123
#> 209: rsX059 rsX059 4 177973549 177973549
#> 210: rsX059 rsX028 4 177973549 178073499
#> 211: rsX059 rsX007 4 177973549 178157767And there you have it, the SNP pairs needed for DAEQTL mapping as explained in vignette('daeqtlr').