Gene-level evolutionary conservation and human constraint decline at comparable rates across the age of onset of Mendelian disease

Abstract

Background Gene-level constraint and conservation scores are widely used to prioritise disease genes and to benchmark genomic language models. Purifying selection acts weakly on phenotypes manifesting after reproduction, so these scores are expected to carry less information about late-onset disease. An onset-dependent gradient has been demonstrated for human allele frequencies but not for the gene-level scores used in practice. Results We built an age-of-onset axis for 2,885 Mendelian diseases from Orphanet and fitted cross-species conservation (241-way phyloP) and human-population constraint (shet) on the same 5,322 gene-disease pairs against matched controls, to test whether one decays faster with onset. Their onset slopes differed by 0.23 logit units (97.5% confidence interval −0.10 to 0.56), equivalent to 0.024 units of area under the receiver operating characteristic curve (AUROC) in a design resolving 0.049; the interval excludes constraint losing discriminative power faster than conservation by more than 0.011 AUROC units, widening to 0.033 when somatic associations are dropped. The usual explanation, that early-onset disease genes are developmental, pleiotropic and haploinsufficient by construction, did not account for the contrast; conditioning barely moved it. The answer did, however, depend on the constraint metric: three alternatives yielded larger contrasts excluding zero, though paired comparison could not distinguish the estimates. The metrics also diverged on recessive disease genes, with shet ranked below the gene pool (AUROC 0.487) and loss-of-function depletion above it (0.533), though, against matched controls, both ranked them below (0.457 and 0.450). Evolutionary scores improved a non-evolutionary baseline by 17 to 23%, with no onset gradient. Conclusions Conservation and constraint do not lose their value at measurably different rates as disease onset rises, and with only 283 late-onset Mendelian diseases annotated genome-wide, the question cannot be settled more finely. The gene-class explanation does not account for the contrast. Metrics that rank genes near-identically can still yield opposite onset-stratified answers, so a result reported by one metric carries a sensitivity that its confidence interval does not show. No constraint metric identifies recessive disease genes against matched controls, so a pipeline with recessive disease in scope needs a correction that switching metric does not supply.