Evidence map›Paper›PMID 38821058›Full record

ArticleAmerican journal of human genetics2024

KnockoffHybrid: A knockoff framework for hybrid analysis of trio and population designs in genome-wide association studies.

Yi Yang, Qi Wang, Chen Wang, Joseph Buxbaum, Iuliana Ionita-Laza

Abstract read
In one paragraph

Article in American journal of human genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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.

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yi YangDepartment of Biostatistics, City University of Hong Kong, Hong Kong SAR, China; School of Data Science, City University of Hong Kong, Hong Kong SAR, China. Electronic address: yi.yang@cityu.edu.hk.
Qi WangSchool of Data Science, City University of Hong Kong, Hong Kong SAR, China.
Chen WangDepartment of Biostatistics, Columbia University, New York, NY 10032, USA.
Joseph BuxbaumDepartments of Psychiatry, Neuroscience, and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Iuliana Ionita-LazaDepartment of Biostatistics, Columbia University, New York, NY 10032, USA; Department of Statistics, Lund University, Lund, Sweden.

Funding

Novel Statistical methods for DNA Sequencing Data, and applications to Autism.R01MH095797 · NIMH · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI IONITA, IULIANA · 2012 to 2022
$3.1M
Integrative methods for the identification of causal variants in mental disorderR01MH106910 · NIMH · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI IONITA, IULIANA, XU, BIN · 2016 to 2018
$1.2M
NIMH NIH HHS R01 MH095797NIMH NIH HHS R01 MH106910
6 · The paper itself

Abstract

Both trio and population designs are popular study designs for identifying risk genetic variants in genome-wide association studies (GWASs). The trio design, as a family-based design, is robust to confounding due to population structure, whereas the population design is often more powerful due to larger sample sizes. Here, we propose KnockoffHybrid, a knockoff-based statistical method for hybrid analysis of both the trio and population designs. KnockoffHybrid provides a unified framework that brings together the advantages of both designs and produces powerful hybrid analysis while controlling the false discovery rate (FDR) in the presence of linkage disequilibrium and population structure. Furthermore, KnockoffHybrid has the flexibility to leverage different types of summary statistics for hybrid analyses, including expression quantitative trait loci (eQTL) and GWAS summary statistics. We demonstrate in simulations that KnockoffHybrid offers power gains over non-hybrid methods for the trio and population designs with the same number of cases while controlling the FDR with complex correlation among variants and population structure among subjects. In hybrid analyses of three trio cohorts for autism spectrum disorders (ASDs) from the Autism Speaks MSSNG, Autism Sequencing Consortium, and Autism Genome Project with GWAS summary statistics from the iPSYCH project and eQTL summary statistics from the MetaBrain project, KnockoffHybrid outperforms conventional methods by replicating several known risk genes for ASDs and identifying additional associations with variants in other genes, including the PRAME family genes involved in axon guidance and which may act as common targets for human speech/language evolution and related disorders.

Indexed as

Autism Spectrum DisorderGenome-Wide Association StudyLinkage DisequilibriumQuantitative Trait LociComputer SimulationGenetic Predisposition to DiseaseHumansModels, GeneticPolymorphism, Single NucleotideautismeQTLfamily-based designGWAShybrid analysisknockoff statisticspopulation admixturepopulation designstatistical geneticsTWAS

Identifiers

PMID38821058
PMCPMC11267528

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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.