A comprehensive imputation-based evaluation of tag SNP selection strategies

Nguyen, Dat; Dinh, Hieu; Vu, Giang Minh; Nguyen, Duong Thuy; Vo, Nam S

doi:10.1109/kse53942.2021.9648614

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…Finally, the imputed genotyping data of 10 batches were combined to estimate imputation and PGS performance according to their populations, including 504, 503, 489, 661, 347, and 1,008 individuals in East Asian (EAS), European (EUR), South Asian (SAS), African (AFR), American (AMR) and Vietnamese (VNP) populations, respectively. This approach is similar to the strategy used previously to estimate imputation-based genomic coverage (Lindquist et al, 2013; Nelson et al, 2013; Nguyen et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

Imputation and polygenic score performances of human genotyping arrays in diverse populations

Nguyen¹,

Tran²,

Tran³

et al. 2022

Preprint

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Regardless of the overwhelming use of next-generation sequencing technologies, microarray- based genotyping combined with the imputation of untyped variants remains a cost-effective means to interrogate genetic variations across the human genome. This technology is widely used in genome-wide association studies (GWAS) at bio-bank scales, and more recently, in polygenic score (PGS) analysis to predict and to stratify disease risk. Over the last decade, human genotyping arrays have undergone a tremendous growth in both number, and content making a comprehensive evaluation of their performances became more important. Here, we performed a comprehensive performance assessment for 23 available human genotyping arrays in 6 ancestry groups using diverse public, and in-house datasets. The analyses focus on performance estimation of derived imputation (in terms of accuracy and coverage) and PGS (in term of concordance to PGS estimated from whole genome sequencing data) in three different traits and diseases. We found that the arrays with a higher number of SNPs are not necessarily the ones with higher imputation performance, but the arrays that are well-optimized for the targeted population could provide very good imputation performance. In addition, PGS estimated by imputed SNP array data is highly correlated to PGS estimated by whole genome sequencing data in most of cases. When optimal arrays are used, the correlations of key PGS metrics between two types of data can be higher than 0.97, but interestingly, arrays with high density can result in lower PGS performance. Our results suggest the importance of properly selecting a suitable genotyping array for PGS applications. Finally, we developed a web tool that provides interactive analyses of tag SNP contents and imputation performance based on population and genomic regions of interest. This study would act as a practical guide for researchers to design their genotyping arrays-based studies. The tool is available at: https://genome.vinbigdata.org/tools/saa/ .

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Section: Methodsmentioning

confidence: 99%

Imputation and polygenic score performances of human genotyping arrays in diverse populations

Nguyen¹,

Tran²,

Tran³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

A comprehensive evaluation of polygenic score and genotype imputation performances of human SNP arrays in diverse populations

Nguyen

Tran²,

Tran³

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

Regardless of the overwhelming use of next-generation sequencing technologies, microarray-based genotyping combined with the imputation of untyped variants remains a cost-effective means to interrogate genetic variations across the human genome. This technology is widely used in genome-wide association studies (GWAS) at bio-bank scales, and more recently, in polygenic score (PGS) analysis to predict and stratify disease risk. Over the last decade, human genotyping arrays have undergone a tremendous growth in both number and content making a comprehensive evaluation of their performances became more important. Here, we performed a comprehensive performance assessment for 23 available human genotyping arrays in 6 ancestry groups using diverse public and in-house datasets. The analyses focus on performance estimation of derived imputation (in terms of accuracy and coverage) and PGS (in terms of concordance to PGS estimated from whole-genome sequencing data) in three different traits and diseases. We found that the arrays with a higher number of SNPs are not necessarily the ones with higher imputation performance, but the arrays that are well-optimized for the targeted population could provide very good imputation performance. In addition, PGS estimated by imputed SNP array data is highly correlated to PGS estimated by whole-genome sequencing data in most cases. When optimal arrays are used, the correlations of PGS between two types of data are higher than 0.97, but interestingly, arrays with high density can result in lower PGS performance. Our results suggest the importance of properly selecting a suitable genotyping array for PGS applications. Finally, we developed a web tool that provides interactive analyses of tag SNP contents and imputation performance based on population and genomic regions of interest. This study would act as a practical guide for researchers to design their genotyping arrays-based studies. The tool is available at: https://genome.vinbigdata.org/tools/saa/.

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LmTag: functional-enrichment and imputation-aware tag SNP selection for population-specific genotyping arrays

Nguyen¹,

Nguyen

et al. 2022

Preprint

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Despite the rapid development of sequencing technology, single-nucleotide polymorphism (SNP) array is still the most cost-effective genotyping solutions for large-scale genomic research and applications. Recent years have witnessed the rapid development of numerous genotyping platforms of different sizes and designs, but population-specific platforms are still lacking, especially for those in developing countries. We aim to develop methods to design SNP arrays for thse countries, so the arrays should be cost-effective (small size), yet can still generate key information needed to associate genotypes with traits. A key design principle for most current platforms is to improve genome-wide imputation so that more SNPs (imputed tag SNPs) not included in the array can be predicted. However, current tag SNP selection methods mostly focus on imputation accuracy and coverage, but not the functional content of the measured and imputed SNPs. It is those functional SNPs that are most likely associated to traits. Here, we propose LmTag, a novel method for tag SNP selection that not only improves imputation performance but also prioritizes highly functional SNP markers. We apply LmTag on a wide range of populations using both public and in-house whole genome sequencing databases. Our results showed that LmTag improved both functional marker prioritization and genome-wide imputation accuracy compared to existing methods. This novel approach could contribute to the next generation genotyping arrays that provide excellent imputation capability as well as facilitate array-based functional genetic studies. Such arrays are particularly suitable for under-represented populations in developing countries or non-model species, where little genomics data are available while investment in genome sequencing or high-density SNP arrays is limited.

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A comprehensive imputation-based evaluation of tag SNP selection strategies

Cited by 3 publications

References 32 publications

Imputation and polygenic score performances of human genotyping arrays in diverse populations

Imputation and polygenic score performances of human genotyping arrays in diverse populations

A comprehensive evaluation of polygenic score and genotype imputation performances of human SNP arrays in diverse populations

LmTag: functional-enrichment and imputation-aware tag SNP selection for population-specific genotyping arrays

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