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

Buckley, R.; Harris, Alex C.; Whitaker, D. Thad; Zhang, Yaping; Ostrander, Elaine A.

doi:10.1007/s00335-021-09914-z

Cited by 12 publications

(10 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the contrary, the PCC and IQS of the SCDA model increased when MAF was increased. This phenomenon is consistent with previous studies (Buckley et al, 2022;Kai-li et al, 2022). This is because the CR does not consider the correct genotype imputation with a random guess, especially for the rare variants.…”

Section: Imputation Quality With DI Erent Mafssupporting

confidence: 94%

An autoencoder-based deep learning method for genotype imputation

Song

Greenbaum²,

Luttrell³

et al. 2022

Front. Artif. Intell.

View full text Add to dashboard Cite

Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a single batch loss rather than the average loss over batches. This modified AE imputation model was evaluated using a yeast dataset, the human leukocyte antigen (HLA) data from the 1,000 Genomes Project (1KGP), and our in-house genotype data from the Louisiana Osteoporosis Study (LOS). Our modified AE imputation model has achieved comparable or better performance than the existing SCDA model in terms of evaluation metrics such as the concordance rate (CR), the Hellinger score, the scaled Euclidean norm (SEN) score, and the imputation quality score (IQS) in all three datasets. Taking the imputation results from the HLA data as an example, the AE model achieved an average CR of 0.9468 and 0.9459, Hellinger score of 0.9765 and 0.9518, SEN score of 0.9977 and 0.9953, and IQS of 0.9515 and 0.9044 at missing ratios of 10% and 20%, respectively. As for the results of LOS data, it achieved an average CR of 0.9005, Hellinger score of 0.9384, SEN score of 0.9940, and IQS of 0.8681 at the missing ratio of 20%. In summary, our proposed method for genotype imputation has a great potential to increase the statistical power of GWAS and improve downstream post-GWAS analyses.

show abstract

Section: Imputation Quality With DI Erent Mafssupporting

confidence: 94%

An autoencoder-based deep learning method for genotype imputation

Song

Greenbaum²,

Luttrell³

et al. 2022

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…Our analyses demonstrate the utility of the Dog10K variant dataset as a reference panel for use in genotype imputation [ 111 ], an approach which has been shown to be effective in making use of low-pass or poor-quality sequence data [ 112 , 113 ]. Canine studies have successfully incorporated this approach [ 114 – 117 ], particularly for disease GWAS, leading to identification of a risk haplotype for congenital laryngeal paralysis in Alaska sled dogs [ 118 ], and a locus for canine idiopathic pulmonary fibrosis in West Highland white terriers [ 114 ], among others.…”

Section: Discussionmentioning

confidence: 99%

Genome sequencing of 2000 canids by the Dog10K consortium advances the understanding of demography, genome function and architecture

Meadows,

Kidd,

Wang

et al. 2023

Genome Biol

Self Cite

View full text Add to dashboard Cite

Background The international Dog10K project aims to sequence and analyze several thousand canine genomes. Incorporating 20 × data from 1987 individuals, including 1611 dogs (321 breeds), 309 village dogs, 63 wolves, and four coyotes, we identify genomic variation across the canid family, setting the stage for detailed studies of domestication, behavior, morphology, disease susceptibility, and genome architecture and function. Results We report the analysis of > 48 M single-nucleotide, indel, and structural variants spanning the autosomes, X chromosome, and mitochondria. We discover more than 75% of variation for 239 sampled breeds. Allele sharing analysis indicates that 94.9% of breeds form monophyletic clusters and 25 major clades. German Shepherd Dogs and related breeds show the highest allele sharing with independent breeds from multiple clades. On average, each breed dog differs from the UU_Cfam_GSD_1.0 reference at 26,960 deletions and 14,034 insertions greater than 50 bp, with wolves having 14% more variants. Discovered variants include retrogene insertions from 926 parent genes. To aid functional prioritization, single-nucleotide variants were annotated with SnpEff and Zoonomia phyloP constraint scores. Constrained positions were negatively correlated with allele frequency. Finally, the utility of the Dog10K data as an imputation reference panel is assessed, generating high-confidence calls across varied genotyping platform densities including for breeds not included in the Dog10K collection. Conclusions We have developed a dense dataset of 1987 sequenced canids that reveals patterns of allele sharing, identifies likely functional variants, informs breed structure, and enables accurate imputation. Dog10K data are publicly available.

show abstract

“…Low coverage genome sequencing (~1x) is a cost-effective approach to sequence a sample at a low coverage depth, typically between 1x and 3x, which can significantly reduce sequencing costs while still providing complete genome coverage and the ability to detect a greater amount of genetic variation. However, it is important to note that this approach is known to produce higher levels of missing data compared to higher coverage sequencing methods [ 24 , 33 ]. To overcome this, high missing calls were imputed to determine the missing genotype calls or unobserved data in the samples using the haplotype profile.…”

Section: Discussionmentioning

confidence: 99%

Skim resequencing finely maps the downy mildew resistance loci RPF2 and RPF3 in spinach cultivars whale and Lazio

Bhattarai

Shi

Mou

et al. 2023

Horticulture Research

View full text Add to dashboard Cite

Commercial production of spinach (Spinacia oleracea L.) is centered in California and Arizona in the US, where downy mildew caused by Peronospora effusa is the most destructive disease. Nineteen typical races of P. effusa have been established to infect spinach, with 16 identified after 1990. The regular appearance of new pathogen races breaks the resistance gene introgressed in spinach. We attempted to map and delineate the RPF2 locus at a finer resolution, identify linked single nucleotide polymorphism (SNP) markers, and report candidate downy mildew resistance (R) genes. Progeny populations segregating for RPF2 locus derived from resistant differential line Lazio were infected using race 5 of P. effusa and were used to study for genetic transmission and mapping analysis in this study. Association analysis performed with low coverage whole genome resequencing-generated SNP markers mapped the RPF2 locus between 0.47 to 1.46 Mb of chromosome 3 with peak SNP (Chr3_1221009) showing a LOD value of 61.6 in the GLM model in TASSEL, which was within 1.08 Kb from Spo12821, a gene that encodes CC-NBS-LRR plant disease resistance protein. In addition, a combined analysis of progeny panels of Lazio and Whale segregating for RPF2 and RPF3 loci delineated the resistance section in chromosome 3 between 1.18-1.23 and 1.75-1.76 Mb. This study provides valuable information on the RPF2 resistance region in the spinach cultivar Lazio compared to RPF3 loci in the cultivar Whale. The RPF2 and RPF3 specific SNP markers, plus the resistant genes reported here, could add value to breeding efforts to develop downy mildew resistant cultivars in the future.

show abstract

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

Cited by 12 publications

References 47 publications

An autoencoder-based deep learning method for genotype imputation

An autoencoder-based deep learning method for genotype imputation

Genome sequencing of 2000 canids by the Dog10K consortium advances the understanding of demography, genome function and architecture

Skim resequencing finely maps the downy mildew resistance loci RPF2 and RPF3 in spinach cultivars whale and Lazio

Contact Info

Product

Resources

About