pg-phenotype

Phenotypes in the context of a pedigree, as an extension of pedigree-graph: a Rust core with Python and R bindings. Relationships and kinship come from pedigree-graph's core; each method is a module on top.

Python

import pg_phenotype as pgp
from pg_phenotype import pafgrs

trait = pgp.Trait(dx, kind="binary")  # one value per pedigree row
prep = pafgrs.prepare(pedigree, ndegree=2)  # once per pedigree
cip = pafgrs.Cip(ages, cip_values)  # K is the last value
scores = pafgrs.score_univariate(prep, trait, age=dx_age, cip=cip, h2=0.4)
scores.est, scores.var, scores.n_relatives, scores.metadata

both = pafgrs.score_bivariate(prep, (t1, t2), age=(a1, a2), cip=(c1, c2), h2=(0.4, 0.6), rg=0.5)

• pedigree: columns id, mother, father (-1 or NA when missing) and optional twin, sex (0 female, 1 male), as a polars or pandas frame or a mapping of arrays. It is validated by pedigree-graph's rules.
• Trait(values, kind=None): one phenotype column on the pedigree's rows, NA where unknown. The kind is continuous, binary, ordinal or categorical, inferred when unambiguous.
• PA-FGRS takes a binary trait. age is the onset age for a case and the last observed age for a control; a control without an age is unobserved and counted.
• probands= restricts the scored rows; relatives are drawn from the whole pedigree. Output is one record per proband in input-row order.
• configure_threads(n) or PG_PHENOTYPE_THREADS sets the thread budget (default 1). Results are identical for every budget.

Errors are pg_phenotype.ValidationError, ParameterError and ResourceError, each with a stable .code and .fields.

Assortative mating

from pg_phenotype.assortative import mate_correlation

res = mate_correlation(pedigree, [pgp.Trait(liab), pgp.Trait(dx)], stratum=birth_decade, bootstrap=1000, seed=1)
cell = res.cell(0, 1)  # mothers' trait 0 x fathers' trait 1
cell.primary.value, cell.primary.ci, cell.primary.permutation.p
cell.stratified.result.value  # within birth-decade strata
res.within_person["mothers"]

The Mate Correlation is the correlation between the mother's and the father's value of a trait over a pedigree's Mating Pairs. Each cell uses the estimators its trait kinds call for (Pearson, tetrachoric, polychoric, biserial or polyserial, with their closed-form companions), a Mate Network sandwich SE, an optional Mate Network bootstrap, and a sequential father-permutation p-value. docs/assortative-mating.md describes the inputs, rules and every result field, and docs/assortative-mating-design.md explains the design.

R

library(pgphenotype)

dx <- trait(ped$dx, kind = "binary")
prep <- pafgrs_prepare(ped, ndegree = 2L)
cip <- pafgrs_cip(ages, cip_values)
scores <- pafgrs_score_univariate(prep, dx, age = ped$dx_age, cip = cip, h2 = 0.4)
both <- pafgrs_score_bivariate(prep, list(t1, t2), ages = list(a1, a2),
                               cips = list(c1, c2), h2 = c(0.4, 0.6), rg = 0.5)
am <- assortative_mate_correlation(ped, list(trait(ped$liab), trait(ped$dx)),
                                   stratum = ped$birth_decade, bootstrap = 1000, seed = 1)

The R package runs the same core as Python and returns the same scores bit for bit. Errors are conditions of class pgphenotype_error (with pgphenotype_validation_error and friends) carrying $code and $fields, positions 1-based.

Correctness (PA-FGRS)

• Rust against NumPy reference cores (fitACE's, verbatim) given the same covariance and order: within 1e-10.
• Against BioPsyk/PAFGRS's own pa_fgrs on stored fixtures: within 1e-10 (1.1e-15 observed).
• Every kinship value bit-identical to pedigree-graph's pair_kinship.

Correctness (assortative mating)

• Against pedsum #13 at 142adf3 on 74 stored cases (28 fixtures, pedsum's own benchmark pedigrees at 10^4 pairs, and 40 random pedigrees), every count, reason, flag and p-value is identical. Closed-form estimates agree within 4.5e-15, and latent ones within 3.2e-10, inside Brent's 1e-7 tolerance. One case differs on float noise in pedsum. The gate report has the details.
• On pedsum's benchmark pedigrees at 10^4 to 10^6 pairs and up to 12 threads, pg-phenotype's median wall time and peak memory are at most 0.994 and 0.981 of pedsum's (benchmark report).
• The SciPy and C-library functions pedsum calls are ported bit for bit (ADR 0005).

Design decisions are in docs/adr/; vocabulary in CONTEXT.md.

Development

pixi run test        # rebuilds the extension with test hooks, then pytest (no slow tests)
pixi run test-all    # the same with the slow tests, as CI runs it
pixi run test-rust
pixi run -e r r-test
pixi run test-against-pg ../pedigree-graph   # every suite against a pg checkout

To release a version, follow How to release pg-phenotype.