Abstract
The human genome contains an estimated 100,000 to 300,000 DNA variants that alter an amino acid in an encoded protein. However, our ability to predict which of these variants are functionally significant is limited. We used a bioinformatics approach to define the functional significance of genetic variation in the ABCA1 gene, a cholesterol transporter crucial for the metabolism of high density lipoprotein cholesterol. To predict the functional consequence of each coding single nucleotide polymorphism and mutation in this gene, we calculated a substitution position-specific evolutionary conservation score for each variant, which considers site-specific variation among evolutionarily related proteins. To test the bioinformatics predictions experimentally, we evaluated the biochemical consequence of these sequence variants by examining the ability of cell lines stably transfected with the ABCA1 alleles to elicit cholesterol efflux. Our bioinformatics approach correctly predicted the functional impact of greater than 94% of the naturally occurring variants we assessed. The bioinformatics predictions were significantly correlated with the degree of functional impairment of ABCA1 mutations (r 2 = 0.62, p = 0.0008). These results have allowed us to define the impact of genetic variation on ABCA1 function and to suggest that the in silico evolutionary approach we used may be a useful tool in general for predicting the effects of DNA variation on gene function. In addition, our data suggest that considering patterns of positive selection, along with patterns of negative selection such as evolutionary conservation, may improve our ability to predict the functional effects of amino acid variation.
Highlights
The ATP-binding cassette transporter A1 (ABCA1) is a cholesterol and phospholipid transporter, and mutations in ABCA1 cause Tangier disease (TD) [1,2,3], a rare disorder characterized by reduced levels of plasma high density lipoprotein (HDL) cholesterol and increased risk for coronary artery disease [4]
We predicted the functional consequence of each variant in ABCA1 using PANTHER [6], a collection of protein families and subfamilies that allows one to ask the question, how often does a given amino acid occur at a given position in a family of evolutionarily related proteins across different species? PANTHER uses as its dataset the natural experiment of evolution, in which over time, random mutation will test every amino acid–coding nucleotide sequence in the genome, with those variants that do not impair protein function being represented in the dataset of extant proteins
The majority of ABCA1 mutations are predicted to impair the function of the ABCA1 protein on the basis of the variability of the particular amino acid positions at which the variants occur in evolutionarily related proteins, compared to only a small fraction of coding single nucleotide polymorphism (cSNP)
Summary
The ATP-binding cassette transporter A1 (ABCA1) is a cholesterol and phospholipid transporter, and mutations in ABCA1 cause Tangier disease (TD) [1,2,3], a rare disorder characterized by reduced levels of plasma high density lipoprotein (HDL) cholesterol and increased risk for coronary artery disease [4]. Without functional testing of individual variants, it has not been possible to determine which of these variants directly affect ABCA1 protein function. This is a fundamental problem in human genetics, in which most DNA variants are not functionally tested and the number of individuals with any given mutation is often small, making statistical assessment difficult or impossible. We used an evolutionary model to predict the functional consequence of genetic variation in the ABCA1 gene and tested these predictions through in vitro assessments of protein function. The probability that a given coding variant will cause a deleterious functional change is estimated by the substitution position-specific evolutionary conservation (subPSEC) score, derived from the probabilities of observing the variant amino acids in a PANTHER hidden Markov model (HMM)
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