Gene-Drop Iteration Convergence

How many gene-drop iterations does a pedigree need?

The Genetic Value Analysis ranks animals by mean kinship and genome uniqueness. Mean kinship is deterministic, but genome uniqueness (gu) is estimated by a gene-drop simulation: alleles are dropped down the pedigree a chosen number of times, and gu is the average result. Like any estimate from a finite number of draws, it carries sampling noise, so the precision of gu – and, more importantly, the selection order it produces – depends on how many iterations you run.

There is no single iteration count that is right for every colony. A pedigree with many animals whose true genome uniqueness sits near a ranking boundary needs more iterations before the chosen set of animals stops changing run to run; a pedigree whose ranked animals are well separated settles almost immediately. gvaConvergence() answers the question for the pedigree in hand: it reports how reproducible the selection order is at a range of iteration counts, and the smallest count at which it has settled.

What it measures

Because the gene-drop iteration columns are independent replicates, the whole convergence picture is recoverable from a single gene drop. gvaConvergence() runs one gene drop at a budget nMax, then for each candidate iteration count N it splits the columns into two disjoint halves of N columns each – two genuinely independent N-iteration runs – ranks each half through the same ordering pipeline the report uses, and compares the two orderings on two criteria:

top-k overlap – do the two runs choose the same top animals? (default k = 20, threshold oMin = 0.90)
Kendall rank agreement – do the chosen animals come out in the same order? (threshold rhoMin = 0.95)

The run is reproducible at N when both hold, and the recommended iteration count is the smallest N meeting both. The issue #76 “Undetermined” animals (both parents unknown, no recorded origin) are a policy constant with rank NA; they are excluded from the order and reported separately as nUndetermined.

This is distinct from seed reproducibility. A fixed seed already makes gu bit-identical run to run – that is reproducibility of the process. gvaConvergence() reports the sampling reproducibility of the estimate: would a fresh, independent run lead to the same animals being chosen, in the same order?

A pedigree where the iteration count matters

No bundled pedigree exercises the diagnostic well – after the issue #76 de-inflation the shipped pedigrees have no gu signal left to rank on, so their selection order is settled at the smallest iteration count. To show a pedigree where the count does matter, we build a small half-sib web in which the founders (the sources of the private alleles) are excluded from the analyzed population, so their alleles survive among the offspring only through descendants. Overlapping sire/dam mating windows give the offspring distinct true genome-uniqueness values that straddle the ranking boundary, so at low iteration counts the gene-drop estimate randomly crosses the boundary and the selection order churns.

## A deterministic dense-mid-range pedigree: 14 founder sires, each mated to a
## wrapping window of 5 of 15 founder dams (one offspring per pair). Windows
## overlap, so dam fan sizes vary and the 70 offspring get distinct genome
## uniqueness straddling the 10% ranking cutoff. Founders are excluded from the
## analyzed population (`pop`); the offspring are the probands.
makeConvergenceFixture <- function() {
  w <- rep(5L, 14L)          # sire window widths
  b <- 15L                   # number of founder dams
  a <- length(w)
  sids <- sprintf("S%03d", seq_len(a))
  dids <- sprintf("D%03d", seq_len(b))
  id <- c(sids, dids)
  sire <- rep(NA_character_, a + b)
  dam <- rep(NA_character_, a + b)
  sex <- c(rep("M", a), rep("F", b))
  off <- character(0L)
  ocount <- 0L
  start <- 1L
  for (i in seq_len(a)) {
    for (j in seq_len(w[i])) {
      dj <- ((start + j - 2L) %% b) + 1L
      ocount <- ocount + 1L
      o <- sprintf("O%04d", ocount)
      id <- c(id, o)
      sire <- c(sire, sids[i])
      dam <- c(dam, dids[dj])
      sex <- c(sex, if (ocount %% 2L == 0L) "F" else "M")
      off <- c(off, o)
    }
    start <- start + max(1L, w[i] - 1L)
  }
  ped <- data.frame(id = id, sire = sire, dam = dam, sex = sex,
                    stringsAsFactors = FALSE)
  ped$gen <- findGeneration(ped$id, ped$sire, ped$dam)
  list(ped = ped, pop = off)
}

fx <- makeConvergenceFixture()
nrow(fx$ped)      # total animals (founders + offspring)
#> [1] 99
length(fx$pop)    # offspring (the analyzed population)
#> [1] 70

We assess iteration counts from 25 up to 1500 (a fixed seed makes the curve reproducible):

conv <- gvaConvergence(
  fx$ped, pop = fx$pop, nMax = 3000L,
  grid = c(25L, 50L, 100L, 200L, 400L, 800L, 1500L), seed = 11L
)

kable(
  conv$convergence,
  digits = c(0L, 3L, 3L),
  col.names = c("Iterations (N)", "Top-20 overlap", "Kendall agreement"),
  caption = "Selection-order reproducibility vs. iteration count (half-sib web)."
) |>
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE)

Selection-order reproducibility vs. iteration count (half-sib web).
Iterations (N)	Top-20 overlap	Kendall agreement
25	0.75	0.701
50	0.80	0.781
100	0.85	0.853
200	1.00	0.916
400	1.00	0.945
800	1.00	0.953
1500	1.00	0.971


conv$recommendedIter   # smallest N meeting both criteria
#> [1] 800
conv$converged
#> [1] TRUE
conv$nRankable
#> [1] 70
conv$nUndetermined
#> [1] 0

At the smallest counts the two independent half-runs disagree on both which animals are chosen and their order; both measures climb as N grows, and the selection order has settled by the higher counts and stays settled there. recommendedIter (printed below the table) reports the smallest count meeting both criteria. For this pedigree the default of 1000 iterations is in the right range, and gvaConvergence() is how you would confirm that rather than guess it.

A pedigree that converges immediately

Contrast the bundled qcPed. After the issue #76 de-inflation none of its ranked animals carry a non-zero genome uniqueness, so the selection order is driven by deterministic mean kinship and does not move with the iteration count.

convQc <- gvaConvergence(
  nprcgenekeepr::qcPed, nMax = 400L,
  grid = c(25L, 50L, 100L, 200L), seed = 11L
)

kable(
  convQc$convergence,
  digits = c(0L, 3L, 3L),
  col.names = c("Iterations (N)", "Top-20 overlap", "Kendall agreement"),
  caption = "Selection-order reproducibility vs. iteration count (qcPed)."
) |>
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE)

Selection-order reproducibility vs. iteration count (qcPed).
Iterations (N)	Top-20 overlap	Kendall agreement
25	1	1
50	1	1
100	1	1
200	1	1


convQc$recommendedIter   # converges at the grid floor
#> [1] 25
convQc$nRankable
#> [1] 156
convQc$nUndetermined     # the excluded issue #76 set
#> [1] 124

Overlap and agreement are at their maximum from the smallest count, so the recommended count is the floor of the grid: this pedigree needs almost no iterations for the order to be reproducible. The nUndetermined count reports how many animals were set aside without a rank.

Choosing an iteration count

Run gvaConvergence() on your own pedigree and read recommendedIter. If converged is FALSE, no count in the grid settled the order – raise nMax and extend the grid (each candidate N needs 2 * N <= nMax), or accept that this pedigree is intrinsically near a ranking boundary and interpret the ranking with care. The thresholds k, oMin, and rhoMin are arguments, so you can make the definition of “reproducible” stricter or looser for your purpose.

Keep two ideas separate. The per-animal sampling standard error (guSE, reported beside gu in reportGV()) tells you how precise the genome uniqueness number is; it shrinks like one over the square root of the iteration count. The selection order – what gvaConvergence() measures – is what actually decides which animals are chosen for breeding. A small guSE does not by itself mean the order has settled, which is exactly why the convergence check exists.

See also reportGV() (the analysis whose iterations this advises on), calcGUSE() (the per-animal standard error), and the Colony Manager Tutorial vignette.

nprcgenekeepr: an R Package for the Genetic Management of Colonies

R. Mark Sharp, Ph.D.

2026-06-25

How many gene-drop iterations does a pedigree need?

What it measures

A pedigree where the iteration count matters

A pedigree that converges immediately

Choosing an iteration count