| Title: | SNP-based heritability and selection analyses: Improved models and new results |
| Journal: | BioEssays |
| Published: | 13 Mar 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35279859/ |
| DOI: | https://doi.org/10.1002/bies.202100170 |
| Citations: | 16 (13 in last 2 years) as of 8 Aug 2024 |
Publication 12476
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Abstract
Complex-trait genetics has advanced dramatically through methods to estimate the heritability tagged by SNPs, both genome-wide and in genomic regions of interest such as those defined by functional annotations. The models underlying many of these analyses are inadequate, and consequently many SNP-heritability results published to date are inaccurate. Here, we review the modelling issues, both for analyses based on individual genotype data and association test statistics, highlighting the role of a low-dimensional model for the heritability of each SNP. We use state-of-art models to present updated results about how heritability is distributed with respect to functional annotations in the human genome, and how it varies with allele frequency, which can reflect purifying selection. Our results give finer detail to the picture that has emerged in recent years of complex trait heritability widely dispersed across the genome. Confounding due to population structure remains a problem that summary statistic analyses cannot reliably overcome. Also see the video abstract here: https://youtu.be/WC2u03V65MQ.</p>
9 Keywords
- Gene Frequency
- Genome, Human
- Genome-Wide Association Study
- Genotype
- Humans
- Models, Genetic
- Phenotype
- Polymorphism, Single Nucleotide
- Quantitative Trait, Heritable
3 Authors
- Doug Speed
- Anubhav Kaphle
- David J. Balding
1 Application
| Application ID | Title |
|---|---|
| 45918 | Heritability enrichment analysis, prediction and genetic correlation analysis for multiple traits. |
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