Losses of human disease-associated genes in placental mammals

Abstract We systematically investigate whether losses of human disease-associated genes occurred in other mammals during evolution. We first show that genes lost in any of 62 non-human mammals generally have a lower degree of pleiotropy, and are highly depleted in essential and disease-associated genes. Despite this under-representation, we discovered multiple genes implicated in human disease that are truly lost in non-human mammals. In most cases, traits resembling human disease symptoms are present but not deleterious in gene-loss species, exemplified by losses of genes causing human eye or teeth disorders in poor-vision or enamel-less mammals. We also found widespread losses of PCSK9 and CETP genes, where loss-of-function mutations in humans protect from atherosclerosis. Unexpectedly, we discovered losses of disease genes (TYMP, TBX22, ABCG5, ABCG8, MEFV, CTSE) where deleterious phenotypes do not manifest in the respective species. A remarkable example is the uric acid-degrading enzyme UOX, which we found to be inactivated in elephants and manatees. While UOX loss in hominoids led to high serum uric acid levels and a predisposition for gout, elephants and manatees exhibit low uric acid levels, suggesting alternative ways of metabolizing uric acid. Together, our results highlight numerous mammals that are ‘natural knockouts’ of human disease genes.

Download Full-text

How much do model organism phenotypes contribute to the computational identification of human disease genes?

10.1101/2021.12.24.474099 ◽

2021 ◽

Author(s):

Sarah M Alghamdi ◽

Paul N Schofield ◽

Robert Hoehndorf

Keyword(s):

Human Gene ◽

Model Organism ◽

Model Organisms ◽

Disease Genes ◽

Loss Of Function ◽

Relative Contribution ◽

Phenotype Data ◽

Disease Associations ◽

Disease Associated Genes ◽

Human Disease Genes

Computing phenotypic similarity has been shown to be useful in identification of new disease genes and for rare disease diagnostic support. Genotype--phenotype data from orthologous genes in model organisms can compensate for lack of human data to greatly increase genome coverage. Work over the past decade has demonstrated the power of cross-species phenotype comparisons, and several cross-species phenotype ontologies have been developed for this purpose. The relative contribution of different model organisms to identifying disease-associated genes using computational approaches is not yet fully explored. We use methods based on phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in different model organisms to disease-associated phenotypes in humans. Semantic machine learning methods are used to measure how much different model organisms contribute to the identification of known human gene--disease associations. We find that only mouse phenotypes can accurately predict human gene--disease associations. Our work has implications for the future development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation.

Download Full-text