ArticlePLoS genetics2011
Beyond missing heritability: prediction of complex traits.
Article in PLoS genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 164 papers.
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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.
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Who cites it
164 citing papers in PubMed.
- A genome-wide association study of host genetic determinants of the antibody response to Anthrax Vaccine Adsorbed.Vaccine · 2012Trial
- Trial
- Hiding in plain sight: uncovering the genetic basis of complex phenotypes through low-risk groups.Genetics · 2026Article
- PGS-GS: a framework integrating polygenic scores and genomic selection in animal breeding.Briefings in bioinformatics · 2026Article
- Genome-Wide Association Study and Genomic Selection for Average Daily Gain in Ashidan Yak.Animals : an open access journal from MDPI · 2026Article
- Comprehensive benchmarking single and multi ancestry polygenic score methods with the PGS-hub platform.Nature communications · 2026Article
- Harnessing big data for enhanced genome-wide prediction in winter wheat breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Population structure limits the use of genomic data for predicting phenotypes and managing genetic resources in forest trees.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Genomic prediction in Persian walnut: Optimization levers according to genetic architecture of complex traits.The plant genome · 2025Article
- Effect of breed composition in genomic prediction using crossbred pig reference population.Journal of animal science and technology · 2025Article
- Advancements and limitations in polygenic risk score methods for genomic prediction: a scoping review.Human genetics · 2024Article
- Bayesian hierarchical hypothesis testing in large-scale genome-wide association analysis.Genetics · 2024Article
- GWAS advancements to investigate disease associations and biological mechanisms.Clinical and translational discovery · 2024Article
- Can polygenic risk scores help explain disease prevalence differences around the world? A worldwide investigation.BMC genomic data · 2023Article
- Biobank-scale methods and projections for sparse polygenic prediction from machine learning.Scientific reports · 2023Article
- Statistical Methods for Disease Risk Prediction with Genotype Data.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data.The plant genome · 2022Article
- Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results.Heredity · 2022Article
- Integrating genome-wide association mapping of additive and dominance genetic effects to improve genomic prediction accuracy in Eucalyptus.The plant genome · 2022Article
- Average semivariance directly yields accurate estimates of the genomic variance in complex trait analyses.G3 (Bethesda, Md.) · 2022Article
104 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
Despite rapid advances in genomic technology, our ability to account for phenotypic variation using genetic information remains limited for many traits. This has unfortunately resulted in limited application of genetic data towards preventive and personalized medicine, one of the primary impetuses of genome-wide association studies. Recently, a large proportion of the "missing heritability" for human height was statistically explained by modeling thousands of single nucleotide polymorphisms concurrently. However, it is currently unclear how gains in explained genetic variance will translate to the prediction of yet-to-be observed phenotypes. Using data from the Framingham Heart Study, we explore the genomic prediction of human height in training and validation samples while varying the statistical approach used, the number of SNPs included in the model, the validation scheme, and the number of subjects used to train the model. In our training datasets, we are able to explain a large proportion of the variation in height (h(2) up to 0.83, R(2) up to 0.96). However, the proportion of variance accounted for in validation samples is much smaller (ranging from 0.15 to 0.36 depending on the degree of familial information used in the training dataset). While such R(2) values vastly exceed what has been previously reported using a reduced number of pre-selected markers (<0.10), given the heritability of the trait (∼ 0.80), substantial room for improvement remains.
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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.