NARD: whole-genome reference panel of 1779 Northeast Asians improves imputation accuracy of rare and low-frequency variants

Kim, Chang Uk; Kim, Hie Lim; Kim, Sung-Jae; Shin, Jong Yeon; Kim, Namcheol; Yang, Joshua Sung Woo; Lo, Kwok Wai; Cho, Belong; Matsuda, Fumihiko; Schuster, Stephan C.; Kim, Changhoon; Kim, Jong‐Il; Seo, Jeong Sun

doi:10.1186/s13073-019-0677-z

Cited by 41 publications

(43 citation statements)

References 55 publications

(85 reference statements)

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“…There are two strategies for developing reference panels, the first is to use a population with closely matched ancestry to that of the group under study, and the second is to use as many samples as possible. While not evaluated in the context of low-pass sequencing imputation, analysis of DNA arrays shows that reference panels matched to the population of interest outperform diverse reference panels of similar sizes (Mitt et al 2017; Zhou et al 2017; Bai et al 2019; Yoo et al 2019). This would suggest that larger refence panels are preferable as long as they contain sufficient representation of the study population.…”

Section: Discussionmentioning

confidence: 99%

“…These observed effects were for initial population-matched reference panels of ~100 samples, with additional diverse samples increasing the reference panels to over 860 samples (Bai et al 2019). Other analyses compared references panels of > 1500 samples to the Haplotype Reference Consortium (HRC) reference panel (http://www.haplotype-reference-consortium.org/) (McCarthy et al 2016; Mitt et al 2017; Zhou et al 2017; Yoo et al 2019), which consists of 32,611 samples, indicating the potential for increased resolution in human studies compared to canine studies, which used a panel of just 676 samples from 91 breeds (Piras et al 2020). Altogether, at MAFs > 0.05, human imputation studies conducted using DNA array genotypes show non-reference concordance rates > 97.5% and mean r 2 values > 0.95 (Mitt et al 2017; Zhou et al 2017; Yoo et al 2019).…”

Section: Discussionmentioning

confidence: 99%

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Best practices for analyzing imputed genotypes from low-pass sequencing in dogs

Buckley

Harris

Whitaker

et al. 2021

Preprint

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Although DNA array-based approaches for genome wide association studies (GWAS) permit the collection of thousands of low-cost genotypes, it is often at the expense of resolution and completeness, as SNP chip technologies are ultimately limited by SNPs chosen during array development. An alternative low-cost approach is low-pass whole genome sequencing (WGS) followed by imputation. Rather than relying on high levels of genotype confidence at a set of select loci, low-pass WGS and imputation relies on the combined information from millions of randomly sampled low confidence genotypes. To investigate low-pass WGS and imputation in the dog, we assessed accuracy and performance by downsampling 97 high-coverage (>15x) WGS datasets from 51 different breeds to approximately 1x coverage, simulating low-pass WGS. Using a reference panel of 676 dogs from 91 breeds, genotypes were imputed from the downsampled data and compared to a truth set of genotypes generated from high coverage WGS. Using our truth set, we optimized a variant quality filtering strategy that retained approximately 80% of 14M imputed sites and lowered the imputation error rate from 3.0% to 1.5%. Seven million sites remained with a MAF > 5% and an average imputation quality score of 0.95. Finally, we simulated the impact of imputation errors on outcomes for case-control GWAS, where small effect sizes were most impacted and medium to large effect sizes were minorly impacted. These analyses provide best practice guidelines for study design and data post-processing of low-pass WGS imputed genotypes in dogs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Best practices for analyzing imputed genotypes from low-pass sequencing in dogs

Buckley

Harris

Whitaker

et al. 2021

Preprint

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“…Asian Reference Database (NARD) has a haplotype reference panel comprised of 1,779 northeast Asian individuals, but the haplotype panel is not publicly available, and thus can be used only in the NARD imputation server [8]. Thus, in order to use publicly unavailable haplotypes for more accurate imputation, we must send the input genotype data to other research institutes having their own closed haplotype data.…”

Section: Introductionmentioning

confidence: 99%

“…Although the imputation methods based on the Li and Stephens model require a haplotype reference panel as in an explicit form, the accessibility of haplotype data is often limited, due to the requirement of agreements from the donors for public use. For example, the Northeast Asian Reference Database (NARD) has a haplotype reference panel comprised of 1,779 northeast Asian individuals, but the haplotype panel is not publicly available, and thus can be used only in the NARD imputation server [ 8 ]. Thus, in order to use publicly unavailable haplotypes for more accurate imputation, we must send the input genotype data to other research institutes having their own closed haplotype data.…”

Section: Introductionmentioning

confidence: 99%

A genotype imputation method for de-identified haplotype reference information by using recurrent neural network

et al. 2020

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Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel. In general, more accurate imputation results were obtained using a larger size of haplotype reference panel. Most of the existing genotype imputation methods explicitly require the haplotype reference panel in precise form, but the accessibility of haplotype data is often limited, due to the requirement of agreements from the donors. Since de-identified information such as summary statistics or model parameters can be used publicly, imputation methods using de-identified haplotype reference information might be useful to enhance the quality of imputation results under the condition where the access of the haplotype data is limited. In this study, we proposed a novel imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network (RNN). The model parameters are presented in the form of de-identified information from which the restoration of the genotype data at the individual-level is almost impossible. We demonstrated that the proposed method provides comparable imputation accuracy when compared with the existing imputation methods using haplotype datasets from the 1000 Genomes Project (1KGP) and the Haplotype Reference Consortium. We also considered a scenario where a subset of haplotypes is made available only in de-identified form for the haplotype reference panel. In the evaluation using the 1KGP dataset under the scenario, the imputation accuracy of the proposed method is much higher than that of the existing imputation methods. We therefore conclude that our RNN-based method is quite promising to further promote the datasharing of sensitive genome data under the recent movement for the protection of individuals' privacy.

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“…In coordination with our packages VCFTools.jl (handling VCF files) and SnpArrays.jl (handling PLINK files), OpenMendel powers a streamlined pipeline for endto-end data analysis. In an era where the cost of genotyping arrays continues to drop faster than Moore's law and telomere-to-telomere reference panels are within reach [23,28], MendelImpute offers a compelling mix of excellent speed, small memory footprint, and simplicity of use.…”

Section: Introductionmentioning

confidence: 99%

A Fast Data-Driven Method for Genotype Imputation, Phasing, and Local Ancestry Inference: MendelImpute.jl

Chu¹,

Sobel²,

Wasiolek³

et al. 2020

Preprint

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Current methods for genotype imputation and phasing exploit the sheer volume of data in haplotype reference panels and rely on hidden Markov models. Existing programs all have essentially the same imputation accuracy, are computationally intensive, and generally require pre-phasing the typed markers. We propose a novel data-mining method for genotype imputation and phasing that substitutes highly efficient linear algebra routines for hidden Markov model calculations. This strategy, embodied in our Julia program MendelImpute.jl, avoids explicit assumptions about recombination and population structure while delivering similar prediction accuracy, better memory usage, and an order of magnitude or better run-times compared to the fastest competing method. MendelImpute operates on both dosage data and unphased genotype data and simultaneously imputes missing genotypes and phase at both the typed and untyped SNPs. Finally, MendelImpute naturally extends to global and local ancestry estimation and lends itself to new strategies for data compression and hence faster data transport and sharing.

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NARD: whole-genome reference panel of 1779 Northeast Asians improves imputation accuracy of rare and low-frequency variants

Cited by 41 publications

References 55 publications

Best practices for analyzing imputed genotypes from low-pass sequencing in dogs

Best practices for analyzing imputed genotypes from low-pass sequencing in dogs

A genotype imputation method for de-identified haplotype reference information by using recurrent neural network

A Fast Data-Driven Method for Genotype Imputation, Phasing, and Local Ancestry Inference: MendelImpute.jl

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