Evidence map›Paper›PMID 42330953›Full record

ArticleAmerican journal of human genetics2026

Individuals who deviate from polygenic expectation are enriched for damaging variants in genes linked to rare disease.

Nikolas A Baya, Frederik H Lassen, Barney Hill, Samvida S Venkatesh, Hannah Currant, Cecilia M Lindgren, Duncan S Palmer

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In one paragraph

Article in American journal of human genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Nikolas A BayaBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Department of Statistics, University of Oxford, Oxford, UK. Electronic address: nikolasbaya@gmail.com.
Frederik H LassenBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Barney HillBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK.
Samvida S VenkateshBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Hannah CurrantBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK.
Cecilia M LindgrenBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Duncan S PalmerBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Department of Statistics, University of Oxford, Oxford, UK; Broad Institute of MIT and Harvard, Cambridge, MA, USA; The Pioneer Centre for SMARTbiomed, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK. Electronic address: duncan.stuart.palmer@gmail.com.

Funding

The Massachusetts General Hospital Harvard Center for Reproductive MedicineP50HD104224 · NICHD · MASSACHUSETTS GENERAL HOSPITAL · PI TALKOWSKI, MICHAEL E · 2021 to 2025
$7.8M
Bill & Melinda Gates Foundation INV-024200NICHD NIH HHS P50 HD104224
6 · The paper itself

Abstract

Polygenic scores (PGSs) stratify disease risk but often fail to capture individual variation. "Misaligned" individuals, whose observed phenotypes deviate from their genetically expected values based on PGS, provide a powerful model for identifying factors beyond common-variant effects, including additional genetic factors. Here, we apply misalignment classification and enrichment testing frameworks to seven continuous traits and three diseases, assessing whether misaligned individuals in the UK Biobank are enriched for rare (minor-allele frequency [MAF] <0.1%) damaging genetic variation. We identified significant enrichment of predicted loss-of-function (pLoF) variants in COPB2 and GORAB among individuals with lower-than-expected bone mineral density. Regarding stature, shorter-than-expected individuals were enriched for pLoF variants in ACAN and IGF1, while taller-than-expected individuals showed enrichment for damaging missense variants in FBN1. Transitioning from validation to discovery, we performed an exome-wide scan for genes associated with misalignment and identified 74 significant genes, including KANK1, a gene which may have a protective role against primary ovarian insufficiency, and ACSL6, a lipid metabolism gene where damaging missense variation was associated with lower-than-expected BMI. For diseases, results supported a liability threshold model involving counteracting common and rare variant effects. Diagnosed type 2 diabetes mellitus patients with rare pathogenic variants in HNF1A and HNF4A possessed significantly lower polygenic risk than those without. Conversely, coronary artery disease controls harboring protective ANGPTL3 variants had nominally higher polygenic risk. This study highlights the power of misalignment-based analyses in complex continuous phenotypes and disease with the potential to validate known genetic contributors to traits and identify previously unassociated genes.

Indexed as

Genetic Predisposition to DiseaseGenetic VariationMultifactorial InheritanceRare DiseasesBone DensityDiabetes Mellitus, Type 2FemaleGene FrequencyGenetic Risk ScoreHumansMalePhenotypePolymorphism, Single Nucleotidecholesterolcomplex traitscoronary artery diseasediabetesheightliability thresholdmisalignedpolygenic scoresrare diseaserare variants

Identifiers

PMID42330953
PMCPMC13504349

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.