Evidence map›Paper›PMID 34174829›Full record

ArticleBMC bioinformatics2021

A novel nonlinear dimension reduction approach to infer population structure for low-coverage sequencing data.

Miao Zhang, Yiwen Liu, Hua Zhou, Joseph Watkins, Jin Zhou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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.

Miao Zhang *Interdisciplinary Program in Statistics and Data Science, University of Arizona, 617 N. Santa Rita Ave., 85721, Tucson, USA.
Yiwen Liu *Department of Mathematics, University of Arizona, 617 N. Santa Rita Ave., 85721, Tucson, USA.
Hua ZhouDepartment of Biostatistics, University of California, Los Angeles, 650 Charles E. Young Dr. South, 90095, Los Angeles, USA.
Joseph WatkinsDepartment of Mathematics, University of Arizona, 617 N. Santa Rita Ave., 85721, Tucson, USA.
Jin ZhouDepartment of Epidemiology and Biostatistics, University of Arizona, 1295 N. Martin Ave., 85724, Tucson, USA. jzhou@email.arizona.edu.ORCID http://orcid.org/0000-0001-7983-0274

Funding

SOUTHWEST ENVIRONMENTAL HEALTH SCIENCES CENTERP30ES006694 · NIEHS · UNIVERSITY OF ARIZONA · PI Nathan J Cherrington · 1994 to 2026
$36.7M
STATISTICAL METHODS FOR GENE MAPPINGR01GM053275 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LANGE, KENNETH L · 1995 to 2019
$9.3M
Genomics, GPUs, and Next Generation Computational StatisticsR01HG006139 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SOBEL, ERIC · 2011 to 2023
$5.1M
Develop T2D Patient-Centered Treatment Suggestion Rule using EMR dataK01DK106116 · NIDDK · UNIVERSITY OF ARIZONA · PI ZHOU, JIN · 2016 to 2019
$524k
A Role for Glycemic Variation in Optimizing Management of Diabetes and Vascular ComplicationsR21HL150374 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI REAVEN, PETER D, ZHOU, JIN · 2020 to 2021
$250k
NHGRI NIH HHS HG006139NHGRI NIH HHS R01 HG006139NHLBI NIH HHS R21 HL150374NHLBI NIH HHS R21HL150374NIDDK NIH HHS K01 DK106116NIDDK NIH HHS K01DK106116NIEHS NIH HHS P30 ES006694NIGMS NIH HHS R01 GM053275
6 · The paper itself

Abstract

backgroundLow-depth sequencing allows researchers to increase sample size at the expense of lower accuracy. To incorporate uncertainties while maintaining statistical power, we introduce MCPCA_PopGen to analyze population structure of low-depth sequencing data.

resultsThe method optimizes the choice of nonlinear transformations of dosages to maximize the Ky Fan norm of the covariance matrix. The transformation incorporates the uncertainty in calling between heterozygotes and the common homozygotes for loci having a rare allele and is more linear when both variants are common.

conclusionsWe apply MCPCA_PopGen to samples from two indigenous Siberian populations and reveal hidden population structure accurately using only a single chromosome. The MCPCA_PopGen package is available on https://github.com/yiwenstat/MCPCA_PopGen .

Indexed as

High-Throughput Nucleotide SequencingPolymorphism, Single NucleotideAllelesGenotypeSoftwareData-adaptiveDimension reductionLow-coverageNon-linear kernelPopulation structure

Identifiers

PMID34174829
PMCPMC8236193

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.