Summix: A method for detecting and adjusting for population structure in genetic summary data

Arriaga-MacKenzie, Ian S.; Matesi, G.; Chen, Samuel; Ronco, A.; Marker, Katie M.; Hall, Jordan R.; Scherenberg, R.; Khajeh-Sharafabadi, Mobin; Wu, Yinfei; Gignoux, Christopher R.; Null, Megan; Hendricks, Audrey E.

doi:10.1016/j.ajhg.2021.05.016

Cited by 9 publications

(13 citation statements)

References 57 publications

(63 reference statements)

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“…summary statistics). Arriaga-MacKenzie et al (2021) previously proposed method Summix, which finds the convex combination of ancestry proportions α k (positive and sum to 1) which minimizes the following problem:

where M is the number of variants, K the number of reference populations,

is the frequency of variant j in population k and

is the frequency of variant j in the cohort of interest. Arriaga-MacKenzie et al (2021) used the five continental 1KG populations as reference.…”

Section: Methodsmentioning

confidence: 99%

Using the UK Biobank as a global reference of worldwide populations: application to measuring ancestry diversity from GWAS summary statistics

Privé

2022

Bioinformatics

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Motivation Measuring genetic diversity is an important problem because increasing genetic diversity is key to making new genetic discoveries, while also being a major source of confounding to be aware of in genetics studies. Results Using the UK Biobank data, a prospective cohort study with deep genetic and phenotypic data collected on almost 500,000 individuals from across the United Kingdom, we carefully define 21 distinct ancestry groups from all four corners of the world. These ancestry groups can serve as a global reference of worldwide populations, with a handful of applications. Here we develop a method that uses allele frequencies and principal components derived from these ancestry groups to effectively measure ancestry proportions from allele frequencies of any genetic dataset. Availability This method is implemented in function snp_ancestry_summary of R package bigsnpr. Supplementary information Supplementary data are available at Bioinformatics online.

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where M is the number of variants, K the number of reference populations,

is the frequency of variant j in population k and

is the frequency of variant j in the cohort of interest. Arriaga-MacKenzie et al (2021) used the five continental 1KG populations as reference.…”

Section: Methodsmentioning

confidence: 99%

Using the UK Biobank as a global reference of worldwide populations: application to measuring ancestry diversity from GWAS summary statistics

Privé

2022

Bioinformatics

View full text Add to dashboard Cite

show abstract

“…(2021) previously proposed method Summix, which finds the convex combination of ancestry proportions α k (positive and sum to 1) which minimizes the following problem: , where M is the number of variants, K the number of reference populations, is the frequency of variant j in population k , and is the frequency of variant j in the cohort of interest. Arriaga-MacKenzie et al . (2021) used the five continental 1KG populations as reference.…”

Section: Methodsmentioning

confidence: 99%

Using the UK Biobank as a global reference of worldwide populations: application to measuring ancestry diversity from GWAS summary statistics

Privé

2021

Preprint

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The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on almost 500,000 individuals from across the United Kingdom. Within this dataset, we carefully define 21 distinct ancestry groups from all four corners of the world. These ancestry groups can serve as a global reference of worldwide populations, with a handful of applications. As an example application here, we use allele frequencies derived from these ancestry groups to effectively measure diversity from summary statistics of any genetic dataset. Measuring genetic diversity is an important problem because increasing genetic diversity is key to making new genetic discoveries, while also being a major source of confounding to be aware of in genetics studies.

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“…Summix 36 employs a mixture model to estimate substructure proportions in genetic summary data by minimizing the least-squares (LS) difference between a vector of observed sample AFs and vectors of reference group AFs. Using the estimated substructure proportions, Summix can adjust observed AFs to match target sample subgroup proportions, supporting harmonization of summary data.…”

Section: Summix2 Overviewmentioning

confidence: 99%

Characterizing substructure via mixture modeling in large-scale genetic summary statistics

Stoneman,

Price,

Scribner Trout

et al. 2024

Preprint

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Genetic summary data are both broadly accessible and highly useful including for risk prediction, causal inference, fine mapping, and the incorporation of external controls. Nevertheless, collapsing individual-level data into groups masks intra- and inter-sample heterogeneity (e.g., population structure), leading to confounding, reduced power, and bias. Unaccounted for substructure limits summary data usability, especially for understudied or admixed populations. Here, we present Summix2, a comprehensive set of methods and software based on a computationally efficient mixture model of genetic similarity to estimate and adjust for substructure in genetic summary data. In extensive simulations and application to public data, Summix2 provides accurate and precise estimates detecting regions of selection, identifying fine-scale population structure, and disentangling latent genetic disease risk. Summix2 increases the robust use of diverse publicly available summary data resulting in improved and more equitable research to address population structure, disease etiology, and risk prediction.

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Summix: A method for detecting and adjusting for population structure in genetic summary data

Cited by 9 publications

References 57 publications

Using the UK Biobank as a global reference of worldwide populations: application to measuring ancestry diversity from GWAS summary statistics

Using the UK Biobank as a global reference of worldwide populations: application to measuring ancestry diversity from GWAS summary statistics

Using the UK Biobank as a global reference of worldwide populations: application to measuring ancestry diversity from GWAS summary statistics

Characterizing substructure via mixture modeling in large-scale genetic summary statistics

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