Evaluating the Accuracy of Imputation Methods in a Five-Way Admixed Population

Schurz, Haiko; Muller, Stéphanie; Helden, Paul D. van; Tromp, Gerard; Hoal, Eileen G.; Kinnear, Craig; Möller, Marlo

doi:10.3389/fgene.2019.00034

Cited by 55 publications

(53 citation statements)

References 35 publications

(57 reference statements)

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“…Since imputation accuracy from genotyping array data is known to depend heavily on the size and composition of the haplotype reference panel used [29], it will be interesting to replicate these results for low-pass sequence across different panels, particularly as extremely large panels (e.g., HRC and TOPmed [30,31]) as well as panels comprising currently underrepresented populations (e.g., NeuroGAP-Psychosis [32]) come online.…”

Section: Discussionmentioning

confidence: 99%

Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arrays

Mazur

Berisa

et al. 2020

Preprint

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Low-pass sequencing (sequencing a genome to an average depth less than 1x coverage) combined with genotype imputation has been proposed as an alternative to genotyping arrays for trait mapping and calculation of polygenic scores; however, the current literature is largely limited to simulation-and downsampling-based approaches. To empirically assess the relative performance of these technologies for different applications, we performed low-pass sequencing (targeting coverage levels of 0.5x and 1x) and array genotyping (using the Illumina Global Screening Array) on 120 DNA samples derived from African and European-ancestry individuals that are part of the 1000 Genomes Project. We then imputed both the sequencing data and the genotyping array data to the 1000 Genomes Phase 3 haplotype reference panel using a leaveone-out design. First, we evaluated overall imputation accuracy from these different assays as measured by genotype concordance; we introduce the concept of effective coverage that accounts for evenness of sequencing and show that this metric is a better predictor of imputation accuracy than nominal mapped coverage for low-pass sequencing data. Next, we evaluated overall power for genome-wide association studies (GWAS) as measured by the squared correlation between imputed and true genotypes. In the African individuals, at common variants (> 5% minor allele frequency), imputation r 2 averaged 0.83 for the array data and ranged from 0.89 to 0.95 for the low-pass sequencing data, corresponding to an effective 7 − 15% increase in GWAS discovery power. For the same variants in the European individuals, imputation r 2 averaged 0.91 for the array data and ranged from 0.92-0.96 for the low-pass sequencing data, corresponding to an effective 1-6% increase in GWAS discovery power. Finally, we computed polygenic risk scores for breast cancer and coronary artery disease from the different assays. We observed consistently lower measurement error for risk scores computed from low-pass sequencing data above an effective coverage of ∼ 0.5x. The mean squared error of the array-based estimates was three to four times that of the estimates from samples sequenced at an effective coverage of ∼ 1.2x for coronary artery disease, with qualitatively similar results for breast cancer. We conclude that low-pass sequencing plus imputation, in addition to providing a substantial increase in statistical power for genome wide association studies, provides increased accuracy for polygenic risk prediction at effective coverages of ∼ 0.5x and higher.

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Section: Discussionmentioning

confidence: 99%

Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arrays

Mazur

Berisa

et al. 2020

Preprint

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“…Notably, imputation using the AGR and the 1000 Genomes reference panel was shown to achieve high quality and accuracy even in the presence of five-way admixture in a South African population. 75 Additionally, it is anticipated that data from H3Africa will be made available as an imputation resource. 76 As the price of sequencing technologies continue to decrease 77 and bioinformatic advances reduce the time and expertise burdens associated with processing these data, it is possible that sequencing may become more common.…”

Section: Overcoming Technological Challengesmentioning

confidence: 99%

Evaluating the promise of inclusion of African ancestry populations in genomics

2020

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The lack of representation of diverse ancestral backgrounds in genomic research is well-known, and the resultant scientific and ethical limitations are becoming increasingly appreciated. The paucity of data on individuals with African ancestry is especially noteworthy as Africa is the birthplace of modern humans and harbors the greatest genetic diversity. It is expected that greater representation of those with African ancestry in genomic research will bring novel insights into human biology, and lead to improvements in clinical care and improved understanding of health disparities. Now that major efforts have been undertaken to address this failing, is there evidence of these anticipated advances? Here, we evaluate the promise of including diverse individuals in genomic research in the context of recent literature on individuals of African ancestry. In addition, we discuss progress and achievements on related technological challenges and diversity among scientists conducting genomic research.

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“…For the South African cohort, the SIS workflow using the AGR resource imputed the highest proportion of SNPs, whereas for the Ghanaian cohort, the MIS workflow using the CAAPA resource imputed the greatest proportion of SNPs with a quality metric greater than 0.45 (Table 3). For both chromosomes 1 and X, imputation using either the 1000G or the CAAPA resource with the MIS performed the worst for the South African cohort (Schurz et al, 2019b) with the maximum median quality score only reaching 0.82 at a MAF of 50%. In comparison, the SIS-AGR workflow outperformed all other workflows, and the result correlated with the AGR imputing the highest SNP density for chromosome 1 and chromosome X (Schurz et al, 2019b).…”

Section: Selection Of High-quality Imputed Genotype Datamentioning

confidence: 99%

“…For both chromosomes 1 and X, imputation using either the 1000G or the CAAPA resource with the MIS performed the worst for the South African cohort (Schurz et al, 2019b) with the maximum median quality score only reaching 0.82 at a MAF of 50%. In comparison, the SIS-AGR workflow outperformed all other workflows, and the result correlated with the AGR imputing the highest SNP density for chromosome 1 and chromosome X (Schurz et al, 2019b).…”

Section: Selection Of High-quality Imputed Genotype Datamentioning

confidence: 99%

“…The quality control procedure for the imputed data was implemented as described in Schurz et al (2019). Briefly, following five imputation protocols, imputed data were filtered using a genotype calling threshold of 0.7, and the internal quality metric produced by the imputation process (Schurz et al, 2019b). SNPs with an INFO or R-squared (Rsq) value greater than 0.45 were prioritised for the association analysis and filtered iteratively for a maximum of 2% SNP genotype missingness, 10% sample missingness, and 5% SNP MAF using PLINK.…”

Section: Selection Of High-quality Imputed Genotype Datamentioning

confidence: 99%

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A multi-phenotype genome-wide association study of clades causing tuberculosis in a Ghanaian- and South African cohort

Muller

Schurz

Tromp

et al. 2020

Preprint

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Despite decades of research and advancements in diagnostics and treatment, tuberculosis remains a major public health concern, particularly in low- and middle-income countries. New bioinformatics and computational methods are needed to interrogate the intersection of host- and bacterial genomes and identify novel targets for anti-tuberculosis drugs. Host genotype datum and paired infecting bacterial isolate information were analysed for associations using a multinomial logistic regression framework implemented in SNPTest. Two geographically distinct cohorts were evaluated: a cohort of 947 participants self-identifying as belonging to a five-way admixed South African population and a Ghanaian cohort consisting of 3 311 participants. We report potential associations between host genetic variants and multiple members of the Mycobacterium tuberculosis complex (MTBC). Although none of the variants analyzed in the South African cohort passed the GWAS cut off for significance, 32 single nucleotide polymorphisms were identified in the Ghanaian cohort as being statistically significantly associated with risk for infection with strains of different members of the MTBC. Further analysis revealed that two of these SNPs were directly genotyped, and the rest were imputed using the 1000 Genomes Phase 3 reference panel. The availability of paired host pathogen data is imperative for investigating strain-specific interactions between MTBC and its host. As demonstrated by this study, the implementation of a multinomial logistic regression using paired host pathogen data may prove valuable for further research investigating the complex relationships driving infectious disease.

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Evaluating the Accuracy of Imputation Methods in a Five-Way Admixed Population

Cited by 55 publications

References 35 publications

Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arrays

Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arrays

Evaluating the promise of inclusion of African ancestry populations in genomics

A multi-phenotype genome-wide association study of clades causing tuberculosis in a Ghanaian- and South African cohort

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