Fine-mapping from summary data with the “Sum of Single Effects” model

In recent work, Wang et al introduced the “Sum of Single Effects” ( SuSiE ) model, and showed that it provides a simple and efficient approach to fine-mapping genetic variants from individual-level data. Here we present new methods for fitting the SuSiE model to summary data, for example to single-S...

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Vydáno v:	PLoS genetics Ročník 18; číslo 7; s. e1010299
Hlavní autoři:	Zou, Yuxin, Carbonetto, Peter, Wang, Gao, Stephens, Matthew
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 19.07.2022 Public Library of Science (PLoS)
Témata:	Algorithms Approximation Biology and Life Sciences Feature selection Gene mapping Genetic diversity Genetic research Genetic screening Identification and classification Likelihood Functions Linkage Disequilibrium Methods Models, Genetic Physical Sciences Polymorphism, Single Nucleotide - genetics Regression analysis Reproducibility of Results Research and Analysis Methods Sample variance Single nucleotide polymorphisms Single-nucleotide polymorphism United States
ISSN:	1553-7404, 1553-7390, 1553-7404
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Popis
Shrnutí:	In recent work, Wang et al introduced the “Sum of Single Effects” ( SuSiE ) model, and showed that it provides a simple and efficient approach to fine-mapping genetic variants from individual-level data. Here we present new methods for fitting the SuSiE model to summary data, for example to single-SNP z -scores from an association study and linkage disequilibrium (LD) values estimated from a suitable reference panel. To develop these new methods, we first describe a simple, generic strategy for extending any individual-level data method to deal with summary data. The key idea is to replace the usual regression likelihood with an analogous likelihood based on summary data. We show that existing fine-mapping methods such as FINEMAP and CAVIAR also (implicitly) use this strategy, but in different ways, and so this provides a common framework for understanding different methods for fine-mapping. We investigate other common practical issues in fine-mapping with summary data, including problems caused by inconsistencies between the z -scores and LD estimates, and we develop diagnostics to identify these inconsistencies. We also present a new refinement procedure that improves model fits in some data sets, and hence improves overall reliability of the SuSiE fine-mapping results. Detailed evaluations of fine-mapping methods in a range of simulated data sets show that SuSiE applied to summary data is competitive, in both speed and accuracy, with the best available fine-mapping methods for summary data.
Bibliografie:	new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 The authors have declared that no competing interests exist.
ISSN:	1553-7404 1553-7390 1553-7404
DOI:	10.1371/journal.pgen.1010299