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Title: Genome-Wide Analysis of Human Disease Alleles Reveals That Their Locations Are Correlated in Paralogous Proteins}
Article:
Abstract:
The millions of mutations and polymorphisms that occur in human populations are potential predictors of disease, of our reactions to drugs, of predisposition to microbial infections, and of age-related conditions such as impaired brain and cardiovascular functions. However, predicting the phenotypic consequences and eventual clinical significance of a sequence variant is not an easy task. Computational approaches have found perturbation of conserved amino acids to be a useful criterion for identifying variants likely to have phenotypic consequences. To our knowledge, however, no study to date has explored the potential of variants that occur at homologous positions within paralogous human proteins as a means of identifying polymorphisms with likely phenotypic consequences. In order to investigate the potential of this approach, we have assembled a unique collection of known disease-causing variants from OMIM and the Human Genome Mutation Database( HGMD) and used them to identify and characterize pairs of sequence variants that occur at homologous positions within paralogous human proteins. Our analyses demonstrate that the locations of variants are correlated in paralogous proteins. Moreover, if one member of a variant-pair is disease-causing, its partner is likely to be disease-causing as well. Thus, information about variant-pairs can be used to identify potentially disease-causing variants, extend existing procedures for polymorphism prioritization, and provide a suite of candidates for further diagnostic and therapeutic purposes.
Introduction:
The publicly available dbSNP database[1] contains approximately 12 million unique human sequence variants, few of which are associated with any particular phenotype or disease. Large-scale association studies often produce hundreds of first “hits” in the form of individual SNPs or haplotypes that then need to be “characterized” for a potential role in a disease phenotype; see[2]–[4]. Currently, there exist few methods for prioritizing variations in a locus for use in association studies or to determine post-facto whether an association found in a genome wide association study is likely to hold up to further testing. The large number of uncharacterized SNPs has driven the development of computational methods aimed at identifying those variations likely to cause disease. To date, the most successful approaches to in silico SNP characterization have been protein-based and comparative in nature, namely SIFT[5]–[7] and PolyPhen[8]–[9]. These approaches, often collectively referred to as Amino Acid Substitution, or AAS, approaches( for a review, see[4]), examine non-synonymous changes in human proteins in the context of multiple alignments of homologous proteins from other organisms. The assumption is that variants effecting amino acid replacements rarely seen in a given column of the multiple alignment will adversely impact protein function and perhaps cause disease[4]. However, the complex relationship between sequence conservation, protein function and disease poses a difficult problem for comparative approaches. For some proteins, conservative changes in poorly conserved regions may be the only tolerated changes, and even these may have severe phenotypic consequences; in other cases, even non-conservative changes that destroy protein function may not be disease-causing, especially for non-essential, redundant genes. These considerations suggest that algorithms for the identification of disease-causing variations based upon trends in protein sequence conservation will miss many particular instances. Given these issues, we wondered whether paralogous human genes might provide a source of additional information for in silico variation characterization, one complementary to AAS approaches such as SIFT[5]–[7] and PolyPhen[8],[9]. By ‘paralogous genes’ we mean “Genes that have arisen by gene duplication events in an organism and are transmitted to offspring as a gene family”[10]. By this criterion, over half of human genes have at least one paralog. The advantage of using paralogous genes is that information from them is uncomplicated by issues that surround attempts to compare allelic data between multiple organisms. In order to explore the utility of paralogous genes for purposes of phenotypic in silico variation characterization and prioritization, we have systematically examined the genome-wide distribution of sequence variants along the lengths of paralogous proteins. To further test the clinical relevance of these data, we have also assembled a collection of known disease-causing variations drawn from OMIM[11] and HGMD[12]; both of these databases provide extensive documentation of disease-causing sequence variations. We then mapped each of these variations to their gene annotations and protein sequences. This dataset has allowed us to identify and characterize pairs of variations that occur at homologous positions within human disease genes( Figure 1). We find that sequence variants co-occur at aligned amino acid pairs more frequently than expected by chance, suggesting that similar functional constraints on paralogous protein sequences result in coordinated distributions of both disease and non–disease-causing variants along their lengths. Moreover, our disease-gene analyses demonstrate that if one member of a variant-pair is disease-causing, its partner is likely to be disease-causing as well. Thus, knowledge of a sequence variant's paralogous relationships is useful for purposes of in-silico identification of novel disease-causing alleles.
Discussion:
We have performed the first analysis of the distribution of DNA sequence variants within the protein coding portions of paralogous genes. Our data show that( 1) protein sequence variants, both synonymous and non-synonymous, tend to occur with high frequency at homologous positions within paralogous proteins;( 2) that different subclasses of variants have distinct distributions along the aligned proteins; and( 3) that disease-causing variants also tend to pair with one another. Overall, the magnitude of the correlation in variant positions is correlated with the sequence similarity of the two proteins. These facts suggest that similar patterns of codon usage and functional constraints combine to produce correlations in the locations of variants along the lengths of paralogous proteins. This coordination includes not only common( MAF>2%), synonymous variants, without phenotypic consequences, but also extends to rare, disease-causing alleles. We also discovered that different subclasses of variant have distinct distributions along the lengths of paralogous proteins. Two facts support this conclusion. First, we observed differing tendencies of variant sub-classes to pair with one another( Table 1). Among best-hit paralogous proteins, the ODDs scores for synonymous, non-synonymous, conservative, and non-conservative variants are 32. 2, 31. 3, 62. 1 and 89. 3 respectively. Second, combining classes always depresses the ODDs score. Thus it appears that each subclass of variant occurs in a specific pattern along the lengths of paralogous proteins, with non-conservative variants having the most highly correlated distribution. One possible explanation of the different distributions is that purifying selection acts to restrict non-synonymous substitutions to a subset of positions in the two proteins, while synonymous variants are free to occur at a greater number of positions; hence the lower ODDS score for synonymous pairs. Disease-causing variants also tend to align with one another. Moreover, they do so to the exclusion of phenotypically uncharacterized variants in dbSNP. Overall, disease-causing variants are 6. 6-fold( P<1×10−4) more likely to pair with one another than with non-synonymous dbSNP variants. When disease-causing variants producing conservative and non-conservative amino acid changes are considered separately, the enrichment is even more pronounced: 14- and 19-fold, respectively. As the dbSNP database presumably contains some undiscovered disease-causing variants, these odds ratios are likely lower bounds; thus the trend is quite robust. Though speculative, one possible explanation of these facts is that similar functional constraints in paralogous proteins restrict rare, disease-causing variants to a few homologous positions where particular amino acid substitutions produce the disease phenotype. In any case, like other classes of variant, rare, disease-causing variants in paralogous proteins also tend to pair. Alignments of paralogous proteins and their variations provide a novel resource for functional genomics. Consider that aligned variation pairs can be divided into three basic classes depending on whether their members are known to be disease-causing or not; Figure 2 provides a summary of this classification system. Each class 1 pair, for example, relates a pair of known diseases, both caused by mutations at equivalent positions in two paralogous proteins. Table 3 shows a sample set of class 1 pairs. Similar changes in similar proteins suggest similar biochemical etiologies, and in some cases overlapping disease phenotypes. Patients with Menkes and Wilson disease, for example, both suffer from abnormalities in copper metabolism[17]–[19]. Likewise Alagille and Marfan syndrome Type I are both associated with spinal, vision, and circulatory abnormalities[20]–[25]. These facts demonstrate how variant pairs can be used as starting points in the search for latent knowledge in disease literature and databases. In other words, therapies and drugs used to treat disease symptoms caused by mutations in one member of a pair might prove efficacious in treating the other disease as well. No doubt, a myriad of issues including time and place of gene expression will complicate such simple conclusions. Nonetheless, these data show how paralogous disease genes and their variant-pairs can be used for hypothesis generation and as points of departure for further clinical research. Similarly, extending this procedure to include paralogs of known disease genes, that are not themselves yet associated with any disease could be used to identify new disease gene candidates, and to identify uncharacterized variants within them likely to have phenotypic consequences. Another use for variant-pairs is in variation prioritization for association studies and clinical validation studies. Current AAS methodologies[4] such as SIFT[5] and PolyPhen[8] look for those sequence changes likely to disrupt conserved regions. Obviously, many disease alleles do result in violent changes to conserved portions of proteins—frameshift, and nonsense mutations for example. Nevertheless, some alleles are more subtle: For some proteins, conservative changes in poorly conserved regions may be the only tolerated changes; in other cases, even non-conservative changes that destroy protein function may not be disease-causing, especially for non-essential, redundant genes. These considerations suggest that a hybrid approach to variant prioritization might prove effective, one that used conservation together with information about the locations of uncharacterized variants relative to other disease-causing alleles, e. g. Class 2 variants in Figure 2. Our analyses suggest that such an approach might improve the performance of AAS approaches, especially for genes with a closely related paralog known to be a human disease gene. Our results naturally raise questions as to the origins of variant pairs. One possibility is “common descent”, but this seems unlikely. Widely accepted models for gene duplication[26]–[28] generally assume that paralogous genes arise from one or a few ancestral duplication events, which are later fixed in the population as a result of positive selection. No matter how numerous the variations at each position in the progenitor protein, a duplication event will fix a single variant at each position in the new paralog. Thus, it seems more likely that variants in the duplicated gene will have arisen after the duplication event, especially for more ancient duplications—and that the correspondence in their locations is a product of similar selective pressures acting on both paralogs. Another potential explanation for the trends we have observed is gene conversion[26],[29],[30], which may provide another source of coordinated variation among highly conserved paralogous genes. Repeated recombination between the new duplicate and its ancestor within a population might furnish the newer gene with many of the same variants as its parent. Novel variants in the duplicate might also be passed to its ancestor as well. In principle, disequilibrium and/or similar variant frequency spectra at aligned amino acids might be used to distinguish this model from the one proposed above, with disequilibrium and similar variant frequency spectra construed as supporting the gene conversion model. However, such analyses lie outside the scope of the present paper. Whatever the ultimate cause of the phenomenon, our results clearly demonstrate that variations tend to occur at equivalent positions in paralogous proteins. This fact provides new avenues for DNA variant prioritization and for clinical studies.
There exists a superabundance of human sequence variations . Testing every sequence variant for association with human disease is often infeasible , as studies must be very large—and hence expensive—to overcome the statistical penalties used to control for multiple tests . A common alternative is to assay only a subset of sequence variants for which there are prior reasons to believe they may be disease-causing . Sequence variants that change conserved amino acids , for example , are often disease-causing . As an adjunct to this approach , we have explored the potential of variants that occur at homologous positions within paralogous human proteins as a means of identifying disease-causing DNA sequence variations . We find that DNA sequence variants co-occur at aligned amino acid pairs more frequently than expected by chance , suggesting that similar functional constraints on paralogous proteins result in coordinated distributions of variants along their lengths . Moreover , if one member of a variant-pair is disease-causing , its partner is likely to be disease-causing as well . These facts provide new avenues for the identification of disease-causing sequence variations .