Quantifying the underestimation of relative risks from genome-wide association studies.
Genome-wide association studies (GWAS) have identified hundreds of associated loci across many common diseases. Most risk variants identified by GWAS will merely be tags for as-yet-unknown causal variants. It is therefore possible that identification of the causal variant, by fine mapping, will iden...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2011-03-01
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| Series: | PLoS Genetics |
| Online Access: | http://europepmc.org/articles/PMC3060077?pdf=render |
| _version_ | 1817969317337104384 |
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| author | Chris Spencer Eliana Hechter Damjan Vukcevic Peter Donnelly |
| author_facet | Chris Spencer Eliana Hechter Damjan Vukcevic Peter Donnelly |
| author_sort | Chris Spencer |
| collection | DOAJ |
| description | Genome-wide association studies (GWAS) have identified hundreds of associated loci across many common diseases. Most risk variants identified by GWAS will merely be tags for as-yet-unknown causal variants. It is therefore possible that identification of the causal variant, by fine mapping, will identify alleles with larger effects on genetic risk than those currently estimated from GWAS replication studies. We show that under plausible assumptions, whilst the majority of the per-allele relative risks (RR) estimated from GWAS data will be close to the true risk at the causal variant, some could be considerable underestimates. For example, for an estimated RR in the range 1.2-1.3, there is approximately a 38% chance that it exceeds 1.4 and a 10% chance that it is over 2. We show how these probabilities can vary depending on the true effects associated with low-frequency variants and on the minor allele frequency (MAF) of the most associated SNP. We investigate the consequences of the underestimation of effect sizes for predictions of an individual's disease risk and interpret our results for the design of fine mapping experiments. Although these effects mean that the amount of heritability explained by known GWAS loci is expected to be larger than current projections, this increase is likely to explain a relatively small amount of the so-called "missing" heritability. |
| first_indexed | 2024-04-13T20:19:44Z |
| format | Article |
| id | doaj.art-0691003739494170bc646f0c3966fcd2 |
| institution | Directory Open Access Journal |
| issn | 1553-7390 1553-7404 |
| language | English |
| last_indexed | 2024-04-13T20:19:44Z |
| publishDate | 2011-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj.art-0691003739494170bc646f0c3966fcd22022-12-22T02:31:35ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042011-03-0173e100133710.1371/journal.pgen.1001337Quantifying the underestimation of relative risks from genome-wide association studies.Chris SpencerEliana HechterDamjan VukcevicPeter DonnellyGenome-wide association studies (GWAS) have identified hundreds of associated loci across many common diseases. Most risk variants identified by GWAS will merely be tags for as-yet-unknown causal variants. It is therefore possible that identification of the causal variant, by fine mapping, will identify alleles with larger effects on genetic risk than those currently estimated from GWAS replication studies. We show that under plausible assumptions, whilst the majority of the per-allele relative risks (RR) estimated from GWAS data will be close to the true risk at the causal variant, some could be considerable underestimates. For example, for an estimated RR in the range 1.2-1.3, there is approximately a 38% chance that it exceeds 1.4 and a 10% chance that it is over 2. We show how these probabilities can vary depending on the true effects associated with low-frequency variants and on the minor allele frequency (MAF) of the most associated SNP. We investigate the consequences of the underestimation of effect sizes for predictions of an individual's disease risk and interpret our results for the design of fine mapping experiments. Although these effects mean that the amount of heritability explained by known GWAS loci is expected to be larger than current projections, this increase is likely to explain a relatively small amount of the so-called "missing" heritability.http://europepmc.org/articles/PMC3060077?pdf=render |
| spellingShingle | Chris Spencer Eliana Hechter Damjan Vukcevic Peter Donnelly Quantifying the underestimation of relative risks from genome-wide association studies. PLoS Genetics |
| title | Quantifying the underestimation of relative risks from genome-wide association studies. |
| title_full | Quantifying the underestimation of relative risks from genome-wide association studies. |
| title_fullStr | Quantifying the underestimation of relative risks from genome-wide association studies. |
| title_full_unstemmed | Quantifying the underestimation of relative risks from genome-wide association studies. |
| title_short | Quantifying the underestimation of relative risks from genome-wide association studies. |
| title_sort | quantifying the underestimation of relative risks from genome wide association studies |
| url | http://europepmc.org/articles/PMC3060077?pdf=render |
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