For any pair of parents in a [hbr_advanced_decision()] result — not only the top-ranked crosses — reports which haplotype blocks are fixed, segregating, split between the parents (a recombinant is needed) or missing, the exact probability that a doubled-haploid progeny carries every segregating target haplotype, the F2 probabilities, and the number of plants needed to recover at least one carrier with the requested confidence.
Usage
hbr_cross_plan(
x,
parent_a,
parent_b = NULL,
mating = c("DH", "RIL"),
conf = 0.95
)Arguments
- x
A result of [hbr_advanced_decision()] that was run with a genetic map (so `x$target` is populated).
- parent_a
Line IDs. When `parent_b` is `NULL`, all pairs among `parent_a` are planned.
- parent_b
Optional line IDs, same length as `parent_a`.
- mating
`"DH"` or `"RIL"` (RIL by selfing) for the linked probability.
- conf
Confidence for the number of plants. Default `0.95`.
Value
A data.frame with one row per cross, columns `parent_a`, `parent_b`, `mid_index`, `n_blocks_fixed`, `n_blocks_segregating`, `n_blocks_split`, `n_blocks_incomplete`, `n_blocks_covered`, `n_blocks_target`, `p_pyramid_DH` or `p_pyramid_RIL`, `p_pyramid_F2_hom`, `plants_needed`, and, when available, `sd_index` and `uc`.
Details
The DH probability is computed at the gene level with Haldane's map function, so linkage in coupling or repulsion enters exactly. F2 probabilities ignore linkage and count one locus per segregating block or per differing gene of a split block. Plant numbers solve `1 - (1 - p)^N >= conf` (Sedcole 1977). The mid-parent index uses the result's multi-trait index (larger is better in the pipeline's direction). Usefulness and progeny SD are reported only for pairs that were computed in the result.
Examples
# A minimal hgsi_advanced stub, sufficient for planning only
res <- structure(list(
ids = c("A", "B", "C"),
index = c(1.0, 0.8, 0.6),
crosses = data.frame(parent_a = "A", parent_b = "B",
uc = 1.5, sd_index = 0.4),
target = list(
carry = matrix(c(TRUE, FALSE, TRUE, FALSE, TRUE, TRUE),
nrow = 3, ncol = 2,
dimnames = list(c("A", "B", "C"),
c("GENE01", "GENE02"))),
block = c(1L, 1L),
units = data.frame(gene = c("GENE01", "GENE02"),
chr = c("1", "1"),
cm = c(10, 10.5),
stringsAsFactors = FALSE))),
class = "hgsi_advanced")
hbr_cross_plan(res, "A", "B")
#> parent_a parent_b mid_index n_blocks_fixed n_blocks_segregating
#> n_blocks_fixed A B 0.9 0 0
#> n_blocks_split n_blocks_incomplete n_blocks_covered
#> n_blocks_fixed 1 0 1
#> n_blocks_target p_pyramid_DH p_pyramid_F2_hom plants_needed
#> n_blocks_fixed 1 0.002487542 0.0625 1203
#> sd_index uc
#> n_blocks_fixed 0.4 1.5
hbr_cross_plan(res, c("A", "B"), c("B", "C"))
#> parent_a parent_b mid_index n_blocks_fixed n_blocks_segregating
#> 1 A B 0.9 0 0
#> 2 B C 0.7 0 1
#> n_blocks_split n_blocks_incomplete n_blocks_covered n_blocks_target
#> 1 1 0 1 1
#> 2 0 0 1 1
#> p_pyramid_DH p_pyramid_F2_hom plants_needed sd_index uc
#> 1 0.002487542 0.0625 1203 0.4 1.5
#> 2 0.500000000 0.2500 5 NA NA