Statistical Learning of Large-Scale Genetic Data: How to Run a Genome-Wide Association Study of Gene-Expression Data Using the 1000 Genomes Project Data.

Anton Sugolov, Eric Emmenegger, Andrew D Paterson, Lei Sun

Open access · hybridAbstract read

In one paragraph

Article in Statistics in biosciences, 2024. 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

0.9field-weighted citation impact, top 20% of its field

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, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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

4 authors at 1 institution in 1 country.

Anton Sugolov *Department of Mathematics,Faculty of Arts and Sciences, University of Toronto, Toronto, Canada.ORCID 0000-0003-2484-8236

Eric Emmenegger *Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada.

Andrew D PatersonProgram in Genetics & Genome Biology The Hospital for Sick Children, University of Toronto, Toronto, ON Canada.ORCID 0000-0003-2159-0487

Lei SunDalla Lana School of Public Health, University of Toronto, Toronto, Canada.ORCID 0000-0002-5640-937X

University of Toronto · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Teaching statistics through engaging applications to contemporary large-scale datasets is essential to attracting students to the field. To this end, we developed a hands-on, week-long workshop for senior high-school or junior undergraduate students, without prior knowledge in statistical genetics but with some basic knowledge in data science, to conduct their own genome-wide association study (GWAS). The GWAS was performed for open source gene expression data, using publicly available human genetics data. Assisted by a detailed instruction manual, students were able to obtain

Indexed as

1000 Genomes ProjectData VisualizationGene ExpressionGenome-wide Association StudyHands-on ExperienceLarge-scale Data AnalysisMultiple Hypothesis TestingOpen ResourceReproducible Research

Identifiers

PMID38495080

DOI10.1007/s12561-023-09375-9

PMCPMC10940486

OpenAlexW4382795050

What OpenQuestion holds

Textmetadata

LicenceCC BY

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 OpenQuestion graph.