Dissecting heterogeneity in GWAS meta-analysis
<p>Statistical heterogeneity refers to differences among results of studies combined in a meta-analysis beyond that expected by chance. On the one hand, excessive heterogeneity can diminish power to discover genetic signals; on the other, moderate heterogeneity can reveal important biological...
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| Format: | Thesis |
| Language: | English |
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2017
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| _version_ | 1797094324227276800 |
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| author | Magosi, L |
| author2 | Farrall, M |
| author_facet | Farrall, M Magosi, L |
| author_sort | Magosi, L |
| collection | OXFORD |
| description | <p>Statistical heterogeneity refers to differences among results of studies combined in a meta-analysis beyond that expected by chance. On the one hand, excessive heterogeneity can diminish power to discover genetic signals; on the other, moderate heterogeneity can reveal important biological differences among studies. Given its double-edged nature, this thesis dissects heterogeneity in genetic association meta-analyses from three vantage points. First, a novel multi-variant statistic, M is proposed to detect genome-wide (systematic) heterogeneity patterns in genetic association meta-analyses. This was motivated by the limited availability of appropriate methodology to measure the impact of heterogeneity across genetic signals, since traditional metrics (Q, I<sup>2</sup> and τ<sup>2</sup>) measure heterogeneity at individual variants. Second, given that meta-analyses comprising small numbers of studies typically report imprecise summary effect estimates; GWAS-derived empirical heterogeneity priors are used to improve precision in estimation of average genetic effects and heterogeneity in smaller meta-analyses (e.g. ≤ 10 studies). Third, a critical evaluation of the Han-Eskin random-effects model shows how it can identify small effect heterogeneous loci overlooked by traditional fixed and random-effects methods.</p> <p>This work draws attention to the existence of genome-wide heterogeneity patterns, to reveal systematic differences among the ascertainment criteria of participating studies in a meta-analysis of coronary disease (CAD) risk. Furthermore, simulation studies with the Han-Eskin random-effects model revealed inflated genetic signals at small effect loci when heterogeneity levels were high. However, it did reveal an additional CAD risk variant overlooked by traditional meta-analysis methods.</p> <p>We therefore recommend a holistic approach to exploring heterogeneity in meta-analyses which assesses heterogeneity of genetic effects both at individual variants with traditional statistics and across multiple genetic signals with the <em>M</em> statistic. Furthermore, it is critically important to review forest plots for small effect loci identified using the Han-Eskin random-effects model amidst moderate-to-high heterogeneity (<em>I</em><sup>2</sup> ≥ 40%).</p> |
| first_indexed | 2024-03-07T04:12:33Z |
| format | Thesis |
| id | oxford-uuid:c853f7e7-93de-440c-b57c-fcfc03d3bb86 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T04:12:33Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | oxford-uuid:c853f7e7-93de-440c-b57c-fcfc03d3bb862022-03-27T06:51:24ZDissecting heterogeneity in GWAS meta-analysisThesishttp://purl.org/coar/resource_type/c_db06uuid:c853f7e7-93de-440c-b57c-fcfc03d3bb86cardiovascular geneticsmeta-analysisstatistical geneticsheterogeneityprior distributionscoronary artery diseasegenetic association meta-analysesBayesian methodsgenetic epidemiologyLPAcoronary heart diseasegenome-wide association studiesEnglishORA Deposit2017Magosi, LFarrall, MHopewell, J<p>Statistical heterogeneity refers to differences among results of studies combined in a meta-analysis beyond that expected by chance. On the one hand, excessive heterogeneity can diminish power to discover genetic signals; on the other, moderate heterogeneity can reveal important biological differences among studies. Given its double-edged nature, this thesis dissects heterogeneity in genetic association meta-analyses from three vantage points. First, a novel multi-variant statistic, M is proposed to detect genome-wide (systematic) heterogeneity patterns in genetic association meta-analyses. This was motivated by the limited availability of appropriate methodology to measure the impact of heterogeneity across genetic signals, since traditional metrics (Q, I<sup>2</sup> and τ<sup>2</sup>) measure heterogeneity at individual variants. Second, given that meta-analyses comprising small numbers of studies typically report imprecise summary effect estimates; GWAS-derived empirical heterogeneity priors are used to improve precision in estimation of average genetic effects and heterogeneity in smaller meta-analyses (e.g. ≤ 10 studies). Third, a critical evaluation of the Han-Eskin random-effects model shows how it can identify small effect heterogeneous loci overlooked by traditional fixed and random-effects methods.</p> <p>This work draws attention to the existence of genome-wide heterogeneity patterns, to reveal systematic differences among the ascertainment criteria of participating studies in a meta-analysis of coronary disease (CAD) risk. Furthermore, simulation studies with the Han-Eskin random-effects model revealed inflated genetic signals at small effect loci when heterogeneity levels were high. However, it did reveal an additional CAD risk variant overlooked by traditional meta-analysis methods.</p> <p>We therefore recommend a holistic approach to exploring heterogeneity in meta-analyses which assesses heterogeneity of genetic effects both at individual variants with traditional statistics and across multiple genetic signals with the <em>M</em> statistic. Furthermore, it is critically important to review forest plots for small effect loci identified using the Han-Eskin random-effects model amidst moderate-to-high heterogeneity (<em>I</em><sup>2</sup> ≥ 40%).</p> |
| spellingShingle | cardiovascular genetics meta-analysis statistical genetics heterogeneity prior distributions coronary artery disease genetic association meta-analyses Bayesian methods genetic epidemiology LPA coronary heart disease genome-wide association studies Magosi, L Dissecting heterogeneity in GWAS meta-analysis |
| title | Dissecting heterogeneity in GWAS meta-analysis |
| title_full | Dissecting heterogeneity in GWAS meta-analysis |
| title_fullStr | Dissecting heterogeneity in GWAS meta-analysis |
| title_full_unstemmed | Dissecting heterogeneity in GWAS meta-analysis |
| title_short | Dissecting heterogeneity in GWAS meta-analysis |
| title_sort | dissecting heterogeneity in gwas meta analysis |
| topic | cardiovascular genetics meta-analysis statistical genetics heterogeneity prior distributions coronary artery disease genetic association meta-analyses Bayesian methods genetic epidemiology LPA coronary heart disease genome-wide association studies |
| work_keys_str_mv | AT magosil dissectingheterogeneityingwasmetaanalysis |