Association of Common Genetic Variants in the CPSF7 and SDHAF2 Genes with Canine Idiopathic Pulmonary Fibrosis in the West Highland White Terrier

Piras, Ignazio S.; Bleul, Christiane; Siniard, Ashley L.; Wolfe, Amanda J.; Both, Matthew De; Hernandez, Alvaro G.; Huentelman, Matthew J.

doi:10.3390/genes11060609

Cited by 4 publications

(10 citation statements)

References 40 publications

(58 reference statements)

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“…haplo type-refer ence-conso rtium. org/) (McCarthy et al 2016;Mitt et al 2017;Yoo et al 2019;Zhou et al 2017), 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;Yoo et al 2019;Zhou et al 2017).…”

Section: Discussionmentioning

confidence: 99%

“…Due to its flexibility and scalability, low-pass sequencing and imputation has been applied to humans (Rubinacci et al 2021 ; Wasik et al 2021 ) and other mammalian species (Benjelloun et al 2019 ; Nosková et al 2021 ; Piras et al 2020 ; Snelling et al 2020 ). Results in humans demonstrate that low-pass WGS and imputation provide more accurate genotypes than those imputed using array data, leading to increased power for genome-wide association studies (GWAS) and more accurate polygenic risk score calculation.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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 rely 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 (> 15×) WGS datasets from 51 different breeds to approximately 1× 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 14 M 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: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…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%

“…Currently, available tools for imputation of low-pass WGS data include STITCH (Davies et al 2016), Beagle (Browning and Browning 2016), GeneImp (Spiliopoulou et al 2017), GLIMPSE (Rubinacci et al 2021), and loimpute (Wasik et al 2021). Our analyses used loimpute, as it had already been implemented with a dog reference panel and used by the canine genomics community (Piras et al 2020). However, if low-pass sequencing imputation approaches in dogs are going to improve, other algorithms need to be appropriately assessed with accuracy across a range of MAFs.…”

Section: Discussionmentioning

confidence: 99%

“…Due to its flexibility and scalability, low-pass sequencing and imputation has been applied to humans (Rubinacci et al 2021; Wasik et al 2021) and other mammalian species (Benjelloun et al 2019; Piras et al 2020; Snelling et al 2020; Nosková et al 2021). Results in humans demonstrate that low-pass WGS and imputation provide more accurate genotypes than those imputed using array data, leading to increased power for genome wide association studies (GWAS) and more accurate polygenic risk score calculation.…”

Section: Introductionmentioning

confidence: 99%

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

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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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