ArticleGenetic epidemiology2016
Uncovering Local Trends in Genetic Effects of Multiple Phenotypes via Functional Linear Models.
Article in Genetic epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models.European journal of human genetics : EJHG · 2017Pooled it
- AIGen: an artificial intelligence software for complex genetic data analysis.Briefings in bioinformatics · 2024Article
- Constructing a polygenic risk score for childhood obesity using functional data analysis.Econometrics and statistics · 2023Article
- Gene-Based Association Testing of Dichotomous Traits With Generalized Functional Linear Mixed Models Using Extended Pedigrees: Applications to Age-Related Macular Degeneration.Journal of the American Statistical Association · 2021Article
- Gene-based association analysis of survival traits via functional regression-based mixed effect cox models for related samples.Genetic epidemiology · 2019Article
- Linear mixed models for association analysis of quantitative traits with next-generation sequencing data.Genetic epidemiology · 2019Article
- A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next-generation sequencing.Genetic epidemiology · 2017Article
- A Comparison Study of Fixed and Mixed Effect Models for Gene Level Association Studies of Complex Traits.Genetic epidemiology · 2016Article
- An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance.Human heredity · 2016Article
- Functional Neural Networks for High-Dimensional Genetic Data Analysis.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
Recent technological advances equipped researchers with capabilities that go beyond traditional genotyping of loci known to be polymorphic in a general population. Genetic sequences of study participants can now be assessed directly. This capability removed technology-driven bias toward scoring predominantly common polymorphisms and let researchers reveal a wealth of rare and sample-specific variants. Although the relative contributions of rare and common polymorphisms to trait variation are being debated, researchers are faced with the need for new statistical tools for simultaneous evaluation of all variants within a region. Several research groups demonstrated flexibility and good statistical power of the functional linear model approach. In this work we extend previous developments to allow inclusion of multiple traits and adjustment for additional covariates. Our functional approach is unique in that it provides a nuanced depiction of effects and interactions for the variables in the model by representing them as curves varying over a genetic region. We demonstrate flexibility and competitive power of our approach by contrasting its performance with commonly used statistical tools and illustrate its potential for discovery and characterization of genetic architecture of complex traits using sequencing data from the Dallas Heart Study.
Indexed as
Identifiers
What OpenQuestion holds
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.