Evidence map›Paper›PMID 37745394›Full record

ArticlebioRxiv : the preprint server for biology2023

A biobank-scale test of marginal epistasis reveals genome-wide signals of polygenic epistasis.

Boyang Fu, Ali Pazokitoroudi, Albert Xue, Aakarsh Anand, Prateek Anand, Noah Zaitlen, Sriram Sankararaman

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 9 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Boyang FuDepartment of Computer Science, UCLA, Los Angeles, CA, USA.
Ali PazokitoroudiDepartment of Computer Science, UCLA, Los Angeles, CA, USA.
Albert XueBioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA.
Aakarsh AnandDepartment of Computer Science, UCLA, Los Angeles, CA, USA.
Prateek AnandDepartment of Computer Science, UCLA, Los Angeles, CA, USA.
Noah ZaitlenDepartment of Neurology, UCLA, Los Angeles, CA, USA.
Sriram SankararamanDepartment of Computer Science, UCLA, Los Angeles, CA, USA.
University of California, Los Angeles · US

Funding

Identifying the genetic causes of depression in a deeply phenotyped population from South KoreaU01MH126798 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI AHN, YONG MIN, FLINT, JONATHAN · 2021 to 2025
$8.9M
Training Grant in Genomic Analysis and InterpretationT32HG002536 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Valerie A Arboleda, Harold Pimentel · 2002 to 2026
$8.6M
Methods for Genome-wide Association Studies in Admixed PopulationsR01HG006399 · NHGRI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI PRICE, ALKES L · 2011 to 2024
$6.3M
Improving the interpretability of genetic studies of major depressive disorder to identify risk genesR01MH130581 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI JONATHAN FLINT, KENNETH SEEDMAN KENDLER · 2022 to 2026
$2.8M
Rarely Common: Uncovering the dominant role of rare variants in the genetic architecture of complex human traits.R01GM142112 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HERNANDEZ, RYAN D. · 2021 to 2024
$2.2M
Identifying and quantifying genetic effects on neurodevelopmental trajectories in adolescentsR01MH122688 · NIMH · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FAN, CHUN CHIEH · 2020 to 2024
$1.8M
Statistical Models for Dissecting Human Population Admixture and its Role in Evolution and DiseaseR35GM125055 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SANKARARAMAN, SRIRAM · 2017 to 2021
$1.6M
NHGRI NIH HHS R01 HG006399NHGRI NIH HHS T32 HG002536NIGMS NIH HHS R01 GM142112NIGMS NIH HHS R35 GM125055NIMH NIH HHS R01 MH122688NIMH NIH HHS R01 MH130581NIMH NIH HHS U01 MH126798
6 · The paper itself

Abstract

The contribution of epistasis (interactions among genes or genetic variants) to human complex trait variation remains poorly understood. Methods that aim to explicitly identify pairs of genetic variants, usually single nucleotide polymorphisms (SNPs), associated with a trait suffer from low power due to the large number of hypotheses tested while also having to deal with the computational problem of searching over a potentially large number of candidate pairs. An alternate approach involves testing whether a single SNP modulates variation in a trait against a polygenic background. While overcoming the limitation of low power, such tests of polygenic or marginal epistasis (ME) are infeasible on Biobank-scale data where hundreds of thousands of individuals are genotyped over millions of SNPs. We present a method to test for ME of a SNP on a trait that is applicable to biobank-scale data. We performed extensive simulations to show that our method provides calibrated tests of ME. We applied our method to test for ME at SNPs that are associated with 53 quantitative traits across ≈ 300 K unrelated white British individuals in the UK Biobank (UKBB). Testing 15, 601 trait-loci associations that were significant in GWAS, we identified 16 trait-loci pairs across 12 traits that demonstrate strong evidence of ME signals (p-value

Identifiers

PMID37745394
PMCPMC10515811
OpenAlexW4386638905

What Socratic holds

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LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 Socratic graph.