Evidence map›Paper›PMID 28154505›Full record

ArticleGenomics & informatics2016

Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index.

Sunghwan Bae, Sungkyoung Choi, Sung Min Kim, Taesung Park

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genomics & informatics, 2016. 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.

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

3 citing papers in PubMed.

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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

4 authors.

Sunghwan BaeInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.; Bioinformatics and Biostatistics Lab, Seoul National University, Seoul 08826, Korea.
Sungkyoung ChoiInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.; Bioinformatics and Biostatistics Lab, Seoul National University, Seoul 08826, Korea.
Sung Min KimBioinformatics and Biostatistics Lab, Seoul National University, Seoul 08826, Korea.
Taesung ParkInterdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.; Bioinformatics and Biostatistics Lab, Seoul National University, Seoul 08826, Korea.; Department of Statistics, Seoul National University, Seoul 08826, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.

Indexed as

body mass indexclinical prediction rulegenome-wide association studypenalized regression modelsvariable selection

Identifiers

PMID28154505
PMCPMC5287118

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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.