ArticlePloS one2012
A latent variable partial least squares path modeling approach to regional association and polygenic effect with applications to a human obesity study.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Implications of publicly available genomic data resources in searching for therapeutic targets of obesity and type 2 diabetes.Experimental & molecular medicine · 2018Review
- A novel quantitative body shape score for detecting association between obesity and hypertension in China.BMC public health · 2015Article
- Influence of personal social network and coping skills on risk for suicidal ideation in Chinese university students.PloS one · 2015Article
- A powerful latent variable method for detecting and characterizing gene-based gene-gene interaction on multiple quantitative traits.BMC genetics · 2013Article
- A PLSPM-based test statistic for detecting gene-gene co-association in genome-wide association study with case-control design.PloS one · 2013Article
- Renal Protective Role of Xiexin Decoction with Multiple Active Ingredients Involves Inhibition of Inflammation through Downregulation of the Nuclear Factor-κB Pathway in Diabetic Rats.Evidence-based complementary and alternative medicine : eCAM · 2013Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
Abstract
Genetic association studies are now routinely used to identify single nucleotide polymorphisms (SNPs) linked with human diseases or traits through single SNP-single trait tests. Here we introduced partial least squares path modeling (PLSPM) for association between single or multiple SNPs and a latent trait that can involve single or multiple correlated measurement(s). Furthermore, the framework naturally provides estimators of polygenic effect by appropriately weighting trait-attributing alleles. We conducted computer simulations to assess the performance via multiple SNPs and human obesity-related traits as measured by body mass index (BMI), waist and hip circumferences. Our results showed that the associate statistics had type I error rates close to nominal level and were powerful for a range of effect and sample sizes. When applied to 12 candidate regions in data (N = 2,417) from the European Prospective Investigation of Cancer (EPIC)-Norfolk study, a region in FTO was found to have stronger association (rs7204609∼rs9939881 at the first intron P = 4.29×10(-7)) than single SNP analysis (all with P>10(-4)) and a latent quantitative phenotype was obtained using a subset sample of EPIC-Norfolk (N = 12,559). We believe our method is appropriate for assessment of regional association and polygenic effect on a single or multiple traits.
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Registered trials
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.