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. 2022 Jun 16:9:862414.
doi: 10.3389/fvets.2022.862414. eCollection 2022.

Complex Feline Disease Mapping Using a Dense Genotyping Array

Isabel Hernandez  1 , Jessica J Hayward  2 , Jeff A Brockman  3 , Michelle E White  4   5 , Lara Mouttham  6 , Elizabeth A Wilcox  6 , Susan Garrison  6 , Marta G Castelhano  6 , John P Loftus  1 , Filipe Espinheira Gomes  1 , Cheryl Balkman  1 , Marjory B Brooks  7 , Nadine Fiani  1 , Marnin Forman  8 , Tom Kern  1 , Bruce Kornreich  1 , Eric C Ledbetter  1 , Santiago Peralta  1 , Angela M Struble  1 , Lisa Caligiuri  1 , Elizabeth Corey  6 , Lin Lin  6 , Julie Jordan  6 , Danny Sack  1 , Adam R Boyko  2 , Leslie A Lyons  9 , Rory J Todhunter  1
Affiliations

Complex Feline Disease Mapping Using a Dense Genotyping Array

Isabel Hernandez et al. Front Vet Sci. .

Abstract

The current feline genotyping array of 63 k single nucleotide polymorphisms has proven its utility for mapping within breeds, and its use has led to the identification of variants associated with Mendelian traits in purebred cats. However, compared to single gene disorders, association studies of complex diseases, especially with the inclusion of random bred cats with relatively low linkage disequilibrium, require a denser genotyping array and an increased sample size to provide statistically significant associations. Here, we undertook a multi-breed study of 1,122 cats, most of which were admitted and phenotyped for nine common complex feline diseases at the Cornell University Hospital for Animals. Using a proprietary 340 k single nucleotide polymorphism mapping array, we identified significant genome-wide associations with hyperthyroidism, diabetes mellitus, and eosinophilic keratoconjunctivitis. These results provide genomic locations for variant discovery and candidate gene screening for these important complex feline diseases, which are relevant not only to feline health, but also to the development of disease models for comparative studies.

Keywords: Felis catus; biobank; complex disease; genome-wide association study; genotyping.

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Conflict of interest statement

Hill's Pet Nutrition Inc., United States, was affiliated with this study, employing author JB and funding the costs of the arrays. The funder was not involved in study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Principal component analysis of cat genetic structure. Dimensions PC1 and PC2 are shown. (A) All 1,122 cats that passed QC, color-coded by genotyping plate (1–11), showing the absence of a batch effect. PC1 shows the eastern-western breed distribution. The cluster of cats that separate on PC2 is from a local colony that were genotyped on plates 2 (dark blue), 7 (brown), and 11 (orange). (B) 221 purebred cats color-coded by breed, showing the eastern-western breed distribution on PC1. The Devon Rex breed (dark green) separates on PC2.
Figure 2
Figure 2
Manhattan and quantile-quantile (QQ) plots for GWAS positive controls. X axis represents the chromosomal SNP position and Y axis represents the -log10(P-value). The QQ plots show observed vs. expected P-values for each SNP. (A) Orange coat color locus, showing the significant association on chromosome X (P = 1.8 × 10−102). (B) Factor XII deficiency, showing the significant associations on chromosomes A1 and C2. The red line on the Manhattan plots shows the Bonferroni-corrected significance threshold and the blue line on the Manhattan plot in B shows the Bonferroni-corrected significance threshold calculated using unlinked SNPs. The genomic inflation factor (λ) is shown on each QQ plot.
Figure 3
Figure 3
Manhattan, quantile-quantile (QQ), and LD plots for case-control disease significant associations, using the LMM GWAS results. X axis represents the chromosomal SNP position and Y axis represents the -log10 (P-value). The QQ plots show observed vs. expected P-values for each SNP. (A) Hyperthyroidism, showing the significant association on chr B2. (B) DM, showing the significant association on chr D4. (C) FEK, showing the significant association on chr E3. On Manhattan plots, the red line is Bonferroni-corrected significance threshold, and the blue line is Bonferroni-corrected significance threshold calculated using unlinked SNPs. Inflation factors (λ) are shown on QQ plots. On LD plots, the colors indicate the amount of LD (r2) with the most significant SNP, ranging from black (r2 < 0.2) to red (r2 > 0.8).
Figure 4
Figure 4
Manhattan, quantile-quantile (QQ), and LD plots for case-control disease suggestive associations, using the LMM GWAS results. X axis represents the chromosomal SNP position and Y axis represents the -log10 (P-value). The QQ plots show observed vs. expected P-values for each SNP. (A) HCM, showing the suggestive association on chr E3. (B) Hypercalcemia, showing the suggestive association on chr C1. On Manhattan plots, the red line is Bonferroni-corrected significance threshold, and the blue line is Bonferroni-corrected significance threshold calculated using unlinked SNPs. Inflation factors (λ) are shown on QQ plots. On LD plots, the colors indicate the amount of LD (r2) with the most significant SNP, ranging from black (r2 < 0.2) to red (r2 > 0.8).
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