Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel

Mitt, Mario; Kals, Mart; Pärn, Kalle; Gabriel, Stacey; Lander, Eric S.; Palotie, Aarno; Ripatti, Samuli; Morris, Andrew P.; Metspalu, Andres; Esko, Tõnu; Mägi, Reedik; Palta, Priit

doi:10.1038/ejhg.2017.51

Cited by 203 publications

(216 citation statements)

References 52 publications

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“…Interestingly, we observed that the HUNT WGS with 500 samples outperforms 1000G(Auton et al, 2015) for variants with MAF > 0.5%. These results are consistent with other studies with population specific reference panels(Mitt et al, 2017; Pistis et al, 2015). The subset of 1000 samples provides better imputation accuracy than 1000G(Auton et al, 2015) even for variants with MAF as low as 0.1% and comparable imputation accuracy to HRC(McCarthy et al, 2016) for variants with MAF > 0.5%.…”

Section: Resultssupporting

confidence: 93%

“…Although panel sizes are similar, the population-specific reference panel results in higher imputation accuracy than the mixed-ancestry 1000G panel (Auton et al, 2015) for variants with MAF ≥ 0.05%. This has also been observed by a recently published study on Estonians(Mitt et al, 2017). …”

Section: Discussionsupporting

confidence: 82%

“…H. Huang & Tseng, 2014; J. Huang et al, 2015; Low-Kam et al, 2016; Mitt et al, 2017; Okada, Momozawa, Ashikawa, Kanai, & Matsuda, 2015; Pistis et al, 2015; Roshyara & Scholz, 2015; Walter et al, 2015). On the other hand, the imputation accuracy increases when larger reference panels are used, especially for lower-frequency variants(Browning & Browning, 2009; B.…”

Section: Introductionmentioning

confidence: 99%

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Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels

Zhou

Fritsche

et al. 2017

Genetic Epidemiology

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The accuracy of genotype imputation depends upon two factors: the sample size of the reference panel and the genetic similarity between the reference panel and the target samples. When multiple reference panels are not consented to combine together, it is unclear how to combine the imputation results to optimize the power of genetic association studies. We compared the accuracy of 9,265 Norwegian genomes imputed from three reference panels-1000 Genomes phase 3 (1000G), Haplotype Reference Consortium (HRC), and a reference panel containing 2,201 Norwegian participants from the population-based Nord Trøndelag Health Study (HUNT) from low-pass genome sequencing. We observed that the population-matched reference panel allowed for imputation of more population-specific variants with lower frequency (minor allele frequency (MAF) between 0.05% and 0.5%). The overall imputation accuracy from the population-specific panel was substantially higher than 1000G and was comparable with HRC, despite HRC being 15-fold larger. These results recapitulate the value of population-specific reference panels for genotype imputation. We also evaluated different strategies to utilize multiple sets of imputed genotypes to increase the power of association studies. We observed that testing association for all variants imputed from any panel results in higher power to detect association than the alternative strategy of including only one version of each genetic variant, selected for having the highest imputation quality metric. This was particularly true for lower frequency variants (MAF < 1%), even after adjusting for the additional multiple testing burden.

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Section: Resultssupporting

confidence: 93%

Section: Discussionsupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

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Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels

Zhou

Fritsche

et al. 2017

Genetic Epidemiology

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“…Our comparisons on imputation strategies agree with recent literature (Deelen et al., ; Mitt et al., ; Pistis et al., ) in terms of the improvement in accuracy brought by a reference panel specific to the population under study. Mitt et al.…”

Section: Discussionsupporting

confidence: 90%

“…Mitt et al. () concluded that for certain outbred populations, such a panel can outperform an order of magnitude larger and more diverse reference panel (the HRC). We show that for a population isolate, an SSP can be far smaller and still outperform the HRC.…”

Section: Discussionmentioning

confidence: 99%

Strategies for phasing and imputation in a population isolate

Herzig

Nutile

Babron

et al. 2018

Genetic Epidemiology

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In the search for genetic associations with complex traits, population isolates offer the advantage of reduced genetic and environmental heterogeneity. In addition, cost-efficient next-generation association approaches have been proposed in these populations where only a subsample of representative individuals is sequenced and then genotypes are imputed into the rest of the population. Gene mapping in such populations thus requires high-quality genetic imputation and preliminary phasing. To identify an effective study design, we compare by simulation a range of phasing and imputation software and strategies. We simulated 1,115,604 variants on chromosome 10 for 477 members of the large complex pedigree of Campora, a village within the established isolate of Cilento in southern Italy. We assessed the phasing performance of identical by descent based software ALPHAPHASE and SLRP, LD-based software SHAPEIT2, SHAPEIT3, and BEAGLE, and new software EAGLE that combines both methodologies. For imputation we compared IMPUTE2, IMPUTE4, MINIMAC3, BEAGLE, and new software PBWT. Genotyping errors and missing genotypes were simulated to observe their effects on the performance of each software. Highly accurate phased data were achieved by all software with SHAPEIT2, SHAPEIT3, and EAGLE2 providing the most accurate results. MINIMAC3, IMPUTE4, and IMPUTE2 all performed strongly as imputation software and our study highlights the considerable gain in imputation accuracy provided by a genome sequenced reference panel specific to the population isolate.

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Overlap of Genetic Loci for Central Serous Chorioretinopathy With Age-Related Macular Degeneration

et al. 2023

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ImportanceCentral serous chorioretinopathy (CSC) is a serous maculopathy of unknown etiology. Two of 3 previously reported CSC genetic risk loci are also associated with AMD. Improved understanding of CSC genetics may broaden our understanding of this genetic overlap and unveil mechanisms in both diseases.ObjectiveTo identify novel genetic risk factors for CSC and compare genetic risk factors for CSC and AMD.Design, Setting, and ParticipantsUsing International Classification of Diseases, Ninth (ICD-9) and Tenth (ICD-10) Revision code-based inclusion and exclusion criteria, patients with CSC and controls were identified in both the FinnGen study and the Estonian Biobank (EstBB). Also included in a meta-analysis were previously reported patients with chronic CSC and controls. Data were analyzed from March 1 to September 31, 2022.Main Outcomes and MeasuresGenome-wide association studies (GWASs) were performed in the biobank-based cohorts followed by a meta-analysis of all cohorts. The expression of genes prioritized by the polygenic priority score and nearest-gene methods were assessed in cultured choroidal endothelial cells and public ocular single-cell RNA sequencing data sets. The predictive utility of polygenic scores (PGSs) for CSC and AMD were evaluated in the FinnGen study.ResultsA total of 1176 patients with CSC and 526 787 controls (312 162 female [59.3%]) were included in this analysis: 552 patients with CSC and 343 461 controls were identified in the FinnGen study, 103 patients with CSC and 178 573 controls were identified in the EstBB, and 521 patients with chronic CSC and 3577 controls were included in a meta-analysis. Two previously reported CSC risk loci were replicated (near CFH and GATA5) and 3 novel loci were identified (near CD34/46, NOTCH4, and PREX1). The CFH and NOTCH4 loci were associated with AMD but in the opposite direction. Prioritized genes showed increased expression in cultured choroidal endothelial cells compared with other genes in the loci (median [IQR] of log 2 [counts per million], 7.3 [0.6] vs 4.7 [3.7]; P = .004) and were differentially expressed in choroidal vascular endothelial cells in single-cell RNA sequencing data (mean [SD] fold change, 2.05 [0.38] compared with other cell types; P &lt; 7.1 × 10−20). A PGS for AMD was predictive of reduced CSC risk (odds ratio, 0.76; 95% CI, 0.70-0.83 per +1 SD in AMD-PGS; P = 7.4 × 10−10). This association may have been mediated by loci containing complement genes.Conclusions and RelevanceIn this 3-cohort genetic association study, 5 genetic risk loci for CSC were identified, highlighting a likely role for genes involved in choroidal vascular function and complement regulation. Results suggest that polygenic AMD risk was associated with reduced risk of CSC and that this genetic overlap was largely due to loci containing complement genes.

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Improved imputation accuracy of rare and low-frequency variants using population-specific high-coverage WGS-based imputation reference panel

Cited by 203 publications

References 52 publications

Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels

Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels

Strategies for phasing and imputation in a population isolate

Overlap of Genetic Loci for Central Serous Chorioretinopathy With Age-Related Macular Degeneration

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