Study of Regions of Homozygosity (ROH) Patterns to Evaluate the Use of Dogs’ Genome in Human Drug Development

Sandra Smieszek, Mihael H. Polymeropoulos

Abstract


Animals are used as preclinical models for human diseases in drug development. Dogs, especially, are used in preclinical research to support clinical safety evaluations during drug development. Comparisons of patterns of regions of homozygosity (ROH) and phenotypes between dogs and humans are not well known. We conducted a genome-wide homozygosity analysis (GWHA) on the human and dog genomes. We calculated ROH patterns across distinct human cohorts, including the Amish, the 1000 genomes, Vanda 1 k genomes, and the Alzheimer’s cohort. The Amish provided a large cohort of extended kinships, allowing for in-depth family-oriented analyses. The remaining human cohorts served as statistical references. We then calculated ROH across different dog breeds, with emphasis on the beagle—the preferred breed used in drug development. Out of five studied human cohorts, we reported the highest mean ROH in the Amish population. We calculated the extent of the genome covered by ROH (FROH) (human 3.2 Gb, dog 2.5 Gb). Overall, FROH differed significantly between the Amish and the 1000 genomes, and between the human and the beagle genomes. The mean FROHper 1 Mb was ~ 16 kb for Amish, ~ 0.6 kb for Vanda 1 k, and ~ 128 kb for beagles. This result demonstrated the highest degree of inbreeding in beagles, far above that of the Amish, one of the most inbred human populations. ROH can contribute to inbreeding depression if it contains deleterious variants that are fully or partially recessive. The differences in ROH characteristics between human and dog genomes question the applicability of dog models in preclinical research, especially when the goal is to gauge the subtle effects on the organism’s physiology produced by candidate therapeutic agents. Importantly, there are huge differences in a subset of ADME genes, specifically the cytochrome P450 family (CYPs), which constitute major enzymes involved in drug metabolism. We should use caution when generalizing from dog to human, even if human and beagle are relatively close species phylogenetically.

 

Doi: 10.28991/HEF-SP2022-01-02

Full Text: PDF


Keywords


ROH; Genomics; Consanguinity; Dog Genome; Deleterious Variants; CYP2D6; ADME Genes.

References


Bailey, J., Thew, M., & Balls, M. (2014). An analysis of the use of animal models in predicting human toxicology and drug safety. ATLA Alternatives to Laboratory Animals, 42(3), 181–199. doi:10.1177/026119291404200306.

Sams, A. J., & Boyko, A. R. (2019). Fine-scale resolution of runs of homozygosity reveal patterns of inbreeding and substantial overlap with recessive disease genotypes in domestic dogs. G3: Genes, Genomes, Genetics, 9(1), 117–123. doi:10.1534/g3.118.200836.

Szpiech, Z. A., Xu, J., Pemberton, T. J., Peng, W., Zöllner, S., Rosenberg, N. A., & Li, J. Z. (2013). Long runs of homozygosity are enriched for deleterious variation. American Journal of Human Genetics, 93(1), 90–102. doi:10.1016/j.ajhg.2013.05.003.

Dyer, W. T. T. (1877). The Effects of Cross and Self-Fertilisation in the Vegetable Kingdom. Nature, 15(381), 329–332. doi:10.1038/015329a0.

Berra, T. M., Alvarez, G., & Ceballos, F. C. (2010). Was the darwin/wedgwood dynasty adversely affected by consanguinity? BioScience, 60(5), 376–383. doi:10.1525/bio.2010.60.5.7.

Garrod, A. E. (1902). The Incidence of Alkaptonuria : a Study in Chemical Individuality. The Lancet 160(4137), 1616–1620. doi:10.1016/S0140-6736(01)41972-6.

Ceballos, F. C., Joshi, P. K., Clark, D. W., Ramsay, M., & Wilson, J. F. (2018). Runs of homozygosity: Windows into population history and trait architecture. Nature Reviews Genetics, 19(4), 220–234. doi:10.1038/nrg.2017.109.

Robinson, J. A., Räikkönen, J., Vucetich, L. M., Vucetich, J. A., Peterson, R. O., Lohmueller, K. E., & Wayne1, R. K. (2019). Genomic signatures of extensive inbreeding in Isle Royale wolves, a population on the threshold of extinction. Science Advances, 5(5), 757. doi:10.1126/sciadv.aau0757.

Clarke, L., Zheng-Bradley, X., Smith, R., Kulesha, E., Xiao, C., Toneva, I., Vaughan, B., Preuss, D., Leinonen, R., Shumway, M., Sherry, S., & Flicek, P. (2012). The 1000 Genomes Pproject: Data management and community access. Nature Methods, 9(5), 459–462. doi:10.1038/nmeth.1974.

Erikson, G. A., Bodian, D. L., Rueda, M., Molparia, B., Scott, E. R., Scott-Van Zeeland, A. A., … Torkamani, A. (2016). Whole-Genome Sequencing of a Healthy Aging Cohort. Cell, 165(4), 1002–1011. doi:10.1016/j.cell.2016.03.022.

Agarwala, R., Biesecker, L. G., & Schäffer, A. A. (2003). Anabaptist genealogy database. American Journal of Medical Genetics - Seminars in Medical Genetics, 121 C(1), 32–37. doi:10.1002/ajmg.c.20004.

Wang, J.-F., & Chou, K.-C. (2010). Molecular Modeling of Cytochrome P450 and Drug Metabolism. Current Drug Metabolism, 11(4), 342–346. doi:10.2174/138920010791514180.

Wang, K., Li, M., & Hakonarson, H. (2010). ANNOVAR: Functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Research, 38(16), 164– 164. doi:10.1093/nar/gkq603.

Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., … Sham, P. C. (2007). PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. The American Journal of Human Genetics, 81(3), 559–575. doi:10.1086/519795.

Smedley, D., Haider, S., Durinck, S., Pandini, L., Provero, P., Allen, J., … Barbiera, G. (2015). The BioMart community portal: an innovative alternative to large, centralized data repositories. Nucleic Acids Research, 43(W1), W589–W598. doi:10.1093/nar/gkv350.

Sund, K. L., Zimmerman, S. L., Thomas, C., Mitchell, A. L., Prada, C. E., Grote, L., Bao, L., Martin, L. J., & Smolarek, T. A. (2013). Regions of homozygosity identified by SNP microarray analysis aid in the diagnosis of autosomal recessive disease and incidentally detect parental blood relationships. Genetics in Medicine, 15(1), 70–78. doi:10.1038/gim.2012.94.

Pembroke, W. G., Babbs, A., Davies, K. E., Ponting, C. P., & Oliver, P. L. (2015). Temporal transcriptomics suggest that twin-peaking genes reset the clock. ELife, 4(NOVEMBER2015), 1–15. doi:10.7554/eLife.10518.001.

Pemberton, T. J., Absher, D., Feldman, M. W., Myers, R. M., Rosenberg, N. A., & Li, J. Z. (2012). Genomic patterns of homozygosity in worldwide human populations. American Journal of Human Genetics, 91(2), 275–292. doi:10.1016/j.ajhg.2012.06.014.

Škarić-Jurić, T., Tomas, Ž., Petranović, M. Z., Božina, N., Narančić, N. S., Janićijević, B., & Salihović, M. P. (2018). Characterization of ADME genes variation in Roma and 20 populations worldwide. PLoS ONE, 13(11), 207671. doi:10.1371/journal.pone.0207671.

Gaedigk, A., Whirl-Carrillo, M., Pratt, V. M., Miller, N. A., & Klein, T. E. (2020). PharmVar and the Landscape of Pharmacogenetic Resources. Clinical Pharmacology and Therapeutics, 107(1), 43–46. doi:10.1002/cpt.1654.

Van der Lee, M., Allard, W. G., Vossen, R. H. A. M., Baak-Pablo, R. F., Menafra, R., Deiman, B. A. L. M., … Anvar, S. Y. (2021). Toward predicting CYP2D6-mediated variable drug response from CYP2D6 gene sequencing data. Science Translational Medicine, 13(603). doi:10.1126/scitranslmed.abf3637.

Taylor, C., Crosby, I., Yip, V., Maguire, P., Pirmohamed, M., & Turner, R. M. (2020). A Review of the Important Role of CYP2D6 in Pharmacogenomics. Genes, 11(11), 1295. doi:10.3390/genes11111295.

Petrović, J., Pešić, V., & Lauschke, V. M. (2019). Frequencies of clinically important CYP2C19 and CYP2D6 alleles are graded across Europe. European Journal of Human Genetics, 28(1), 88–94. doi:10.1038/s41431-019-0480-8.


Full Text: PDF

DOI: 10.28991/HEF-SP2022-01-02

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Sandra Paulina Smieszek