Tandem repeat copy-number variation in protein-coding regions of human genes
© O'Dushlaine et al.; licensee BioMed Central Ltd. 2005
Received: 11 February 2005
Accepted: 13 July 2005
Published: 28 July 2005
Tandem repeat variation in protein-coding regions will alter protein length and may introduce frameshifts. Tandem repeat variants are associated with variation in pathogenicity in bacteria and with human disease. We characterized tandem repeat polymorphism in human proteins, using the UniGene database, and tested whether these were associated with host defense roles.
Protein-coding tandem repeat copy-number polymorphisms were detected in 249 tandem repeats found in 218 UniGene clusters; observed length differences ranged from 2 to 144 nucleotides, with unit copy lengths ranging from 2 to 57. This corresponded to 1.59% (218/13,749) of proteins investigated carrying detectable polymorphisms in the copy-number of protein-coding tandem repeats. We found no evidence that tandem repeat copy-number polymorphism was significantly elevated in defense-response proteins (p = 0.882). An association with the Gene Ontology term 'protein-binding' remained significant after covariate adjustment and correction for multiple testing. Combining this analysis with previous experimental evaluations of tandem repeat polymorphism, we estimate the approximate mean frequency of tandem repeat polymorphisms in human proteins to be 6%. Because 13.9% of the polymorphisms were not a multiple of three nucleotides, up to 1% of proteins may contain frameshifting tandem repeat polymorphisms.
Around 1 in 20 human proteins are likely to contain tandem repeat copy-number polymorphisms within coding regions. Such polymorphisms are not more frequent among defense-response proteins; their prevalence among protein-binding proteins may reflect lower selective constraints on their structural modification. The impact of frameshifting and longer copy-number variants on protein function and disease merits further investigation.
DNA tandem repeats are two or more adjacent and approximate copies of a sequence of nucleotides. The presence of tandem repeats has been associated with a number of diseases and phenotypic conditions. For instance, repeat polymorphisms in 5' and 3' regions are known to cause diseases such as Huntington's disease  and certain forms of Fragile X syndrome . Other tandem repeat polymorphisms in noncoding regions are known to modify function through their impact on gene regulation [3, 4]. These polymorphisms can arise from events such as unequal crossover, replication slippage or double-strand break repair [5–7].
Polymorphism of tandem repeats within protein-coding sequences is known to modulate disease risks and can effect changes in the protein products of genes, leading to diseases such as myotonic dystrophy . A number of diseases caused by repeat polymorphism arise from the expansion of trinucleotide repeats . Other longer repeat polymorphisms have been postulated to modify disease risk (for example, platelet glycoprotein Ib-α (GP1BA) repeat , the kringle repeat in apolipoprotein(a) (LPA) , and P-selectin ligand (SELPLG) repeat ).
While single-nucleotide polymorphisms (SNPs) are currently the subject of extensive research, tandem repeats can exhibit high levels of length polymorphism that will potentially alter protein function. In addition, the comparatively greater mutability of certain classes of tandem repeats may lead to a different spectrum of effects on function, as mildly deleterious variants of recent origin may not have had time to be eliminated. Previous studies [13, 14] have predicted polymorphism using a minimum threshold of repeating units and a minimum homogeneity criteria. The threshold refers to the minimum number of repeat units needed for a locus to be scored as likely to contain polymorphism, and the homogeneity refers to percentage of nucleotides within a repeat that may deviate from the core repetitive unit. The criteria depended on the length of the repeat unit and were drawn from the literature on repeat polymorphisms. For instance, for a dinucleotide repeat block to be scored as a likely polymorphism, a threshold number of eight repeat units and a minimum homogeneity of 0.9 was required.
This approach was used to predict 11,265 potentially polymorphic tandem repeats and led to the proposal that 22% of UniGene  clusters contain at least one potentially polymorphic locus . Of these, 8% were predicted to be in coding regions. If polymorphic, these loci could cause frameshift mutations, which would be likely to significantly alter the protein product. However, these studies only analyzed a single representative sequence from each UniGene cluster, and did not investigate the observed variability among all sequences within the cluster. Additional studies predicting potentially polymorphic repeats have focused on minisatellite repeats. For instance, Denoeud and colleagues  were more interested in highly polymorphic minisatellites and only used strict definitions of minisatellites (unit length greater than 17 nucleotides, for instance). Naslund and co-workers  used a logistic regression approach to predict potentially polymorphic repeats. However, they were specifically interested in minisatellites with a minimum repeat unit length of six nucleotides and not the full spectrum of repeat unit lengths. Denoeud and Vergnaud have carried out genomic comparisons of related bacteria to observe tandem repeat sequence length differences . However, no such analysis has been carried out to detect human repeat polymorphism.
It has been hypothesized that an excess diversity of coding tandem repeats contributes to antigenic variation within the prokaryotic pathogen Neisseria . Variations in the numbers of repeats within the collagen-like region in Bacillus anthracis correlated with variation of filament length on the spore surface and have been proposed to affect the properties of the spores in response to various environments . Indeed, repeat-mediated variation may form an integral part of the ability of many pathogens to adapt and remain adapted to their hosts and environments  and has been proposed as a molecular basis for the rapid adaptation of both prokaryotes and eukaryotes to environmental changes . Our investigations sought to find evidence of the existence of this in humans. We proposed that repeat polymorphism within host-defense proteins in the human population might be advantageous, as previously postulated , and thus we would expect higher levels of tandem repeat sequence length variation in these genes. Such proteins exhibit rapid rates of evolution in interspecies comparisons, consistent with positive selection for changes in response to pathogen selection pressures [23, 24].
Here we report an investigation into the level of apparent polymorphism in human genes within the UniGene database, and examine whether such polymorphism is elevated in host-defense genes.
Results and discussion
Protein-coding repeat distribution
Of the 106,937 UniGene  sequence clusters, 14,953 (14%) contained coding sequence annotation. Of these, a total of 13,749 (13%) clusters had more than one sequence overlapping a repeat block, enabling a search for tandem repeat copy-number variants.
Range of tandem repeat copy-number variation
Detected variants were screened to ensure that they represented length variation arising as copy-number differences in genomic DNA, rather than intron retention or alternative splicing. Only length variations that corresponded to a length difference that was a multiple of the repeat unit were selected. This reduced the number of clusters with variation from 4,458 (16,483 query/hit pairs) to 623 (3,111 query/hit pairs). For this set, tandem repeats were detected in the variant sequence and checked to ensure that the observed copy-number was in agreement with the expected one, given the length of the hit block and the length of the repeat unit, further reducing the dataset to 218 clusters with observations of length variation (753 query/hit pairs).
In total, 249 unique repeat blocks (spanning 218 clusters) showed variation that was consistent with a change in repeat copy-number (Figure 1). We found 295 allelic variants that differed from the UniGene representative sequence (Additional data file 1) and 85.8% of these variants were a multiple of three nucleotides (253/295). Nearly 70% of variations that were a multiple of both three nucleotides and the repeat unit length arose within blocks of trinucleotide tandem repeats (Figure 1). Although some of the invariant repeats were imperfect, all the variant repeats were 100% homogenous (that is, every repeat unit was identical), and a large proportion were short (48% of variant repeat arrays were less than 20 nucleotides in length). The mean percentage match for repeats with array length less than 20 nucleotides was 98.52%. The mean percentage match for repeats with array length equal to or greater than 20 nucleotides was 90.50%.
As an independent check for the completeness of our observations, the Human Gene Mutation Database (HGMD)  was queried with a set of all official HUGO gene symbols. A total of 18 contained coding-sequence repeat polymorphisms. Of these, eight (or 44%) were detected in our analysis - HD, ATXN1, ATXN2, AR, CACNA1A, TBP, SELPLG, and ATN1. Four of the remaining ten lacked coding-sequence annotation in the Hs.seq.uniq representative precluding the use of our method. One of the remaining six was a polymorphic mononucleotide repeat - these repeats were not included in our analysis. Two further genes contained cryptic GCN repeats. The last three had no variant hits in UniGene, either because of small cluster size (13, 170, 56), sequence error reducing the amount of hits (within-cluster alignments), or a lack of sufficient sequence coverage over the repeat region. Thus, in total, seven repeat variations were 'missed' either because of a lack of UniGene coding sequence annotation (4) or as a result of cluster size/sequence quality limitations (3), and three did not conform to the types of repeats considered in our analysis. Therefore, in relation to repeat variations previously associated with disease and considered in our analysis, we detected variations in 53% (8/15) of the associated genes.
This analysis highlights that fact that, while UniGene is a useful resource for looking at polymorphism, it has its limitations, specifically in relation to sample size, sequence quality and annotation. Of the 218 gene clusters with repeat variation, 34 had entries on the HGMD, eight of which - HD, ATXN1, ATXN2, AR, CACNA1A, TBP, SELPLG, and ATN1 - had coding-region repeat polymorphisms that were detected in our analysis. One further gene - VWF - was annotated as having a small deletion that corresponded to one of our repeat variants. Another gene - TWIST1 - was annotated as having a small deletion in the Saethre-Chotzen syndrome phenotype, which was detected in our analysis as a 12-nucleotide indel for a three-nucleotide repeat (GGC). While the variation observed in VWF may have arisen from a repeat slippage event, the variant for TWIST1 is unlikely to have done so. In addition to these variants, three genes - NUMBL, E2F4 and NOTCH4 - were annotated by Online Mendelian Inheritance in Man (OMIM)  as exhibiting trinucleotide repeat variation. Thus, 13 variants detected in our analysis were previously identified.
Frequency of repeat variants
Given the likely sampling errors and biases, we did not expect frequencies of repeat variants to closely reflect true population frequencies. However, for known repeat variations from the literature that were also detected in our analysis, we compared heterozygosities by querying the GDB database . For a set of five genes that had heterozygosity information and existed in the GDB database (HD, AR, TBP, ATN1, HRC), the heterozygosity in GDB was broadly similar (values of 0.8, 0.63, 0.81, 0.79 and 0.55, respectively) to that estimated from this dataset (Additional data file 2).
Repeat copy-number and extent of variation
Origin of variation
Interestingly, the vast majority of dimer, tetramer and pentamer copy-number variants resulted in a length difference that was not divisible by three (Figure 1b). Given the preference for repeat variation that is a multiple of three nucleotides, we had anticipated that there would be a greater proportion of copy-number variants that expand or contract dimer and tetramer repeats by exactly three copies (for example, we expected to see a larger number of dimer tandem variants that differed in length by six nucleotides). The observation that such variants are very rare (Figure 1b), even though they do not disrupt the reading frame, strongly supports the stepwise mutation model for microsatellite repeats [40, 41], and suggests that insertion/deletion mutations of more than one unit at a time are quite unusual. It is probable that the frameshifting copy-number variants are mainly recent mutations that are selectively deleterious, reducing the chance of gradual expansion of the tandem array variant over time; trimer repeat variants could typically be much older. Thus, the majority of copy-number mutations in tandem arrays with short unit sizes are likely to arise by slippage , which occurs most often in homogenous repeats [43, 44]. Consistent with this, the majority of observed variants for these repeats differ by a single unit.
In contrast, for a number of the larger tandem repeats (unit size of 12 and above) the observed variants in some cases differ by more than one copy, with no sampling of an intermediate allele (Figure 2). Such longer repeat variants may potentially arise through recombination, rather than slippage mechanisms, giving the potential for the gain or loss of more than one unit at a time. It should also be mentioned that the use of UniGene to detect variation precludes the ability to determine if the variation exists at DNA or transcriptional level. Our requirement that observed length variations had to be consistent with a change in repeat copy-number minimized the likelihood of detecting variation resulting from an alternative splice site arising within a repeat block. This did not, however, rule out inclusion of alternative splices where the splice sites might coincide with boundaries of tandem repeat units. Inspection of the intron/exon structure of genes in our results using EnsEMBL  revealed no such examples (data not shown).
Frameshifting copy-number variation
This dataset is likely to underestimate the frequency of frameshifting repeat variants, as a large number of frameshifts stimulate nonsense-mediated RNA decay, biasing against their chance of being detected in UniGene. Messages carrying stop codons more than 50 nucleotides upstream of an intron are typically subject to rapid mRNA decay . Secondly, nonsense polymorphisms typically occur at a low frequency in human proteins , reflecting selection against deleterious alleles, and it is possible that frameshifting tandem copy-number variants may similarly be at a lower frequency. Given the small sample size for many of the UniGene clusters, the incidence of frameshifting polymorphisms is probably strongly under-represented.
A few of the observed variants may not be true frameshifts, however, owing either to errors in coding-sequence annotation, sequencing errors, transcriptional errors or transcribed pseudogenes in the database. While we cannot definitively rule these out, the validation of repeat variants to ensure that they represent a change in repeat copy-number would reduce that possibility of some of these errors arising. Nevertheless, for the two reasons outlined above, we believe that the observation of one frameshifting tandem repeat polymorphism per 404 (34 out of 13,749) proteins surveyed (0.25%) represents a likely lower bound of the frequency. Wren et al.  predicted that 0.5% of proteins are likely to contain frameshifting tandem repeat polymorphisms.
It is of course possible that frameshifting tandem repeats can arise from sequencing errors, transcription errors or pseudogene transcripts. We inspected the 34 sequences containing frameshifting dinucleotide variants, and found that, in all but one sequence, the percentage of bases that were ambiguous (denoted by base 'N') was less than 1% (the outlier was 4%). We also searched the 51 frameshifting sequences and the representative allele against the human genome, and in each case both alleles hit the same sequence; that is, there was no evidence for the existence of a pseudogene with greater similarity to the frameshifted allele.
We cannot rule out the possibility of occasional transcriptional slippage giving rise to a small proportion of the observed variation: an experimental screen for such transcriptional errors estimated their frequency at approximately 1 in 5,000 transcripts in dinucleotide tandem repeats : in our survey of 5,304 sequences containing 8,449 dinucleotide repeats, we found an incidence of 36 frameshifting dinucleotide mutations, compared with an expectation of less than two, arising from transcriptional errors. Secondly, two of the tetramer frameshifting repeats, and four of the dimer repeats, were observed in more than one sequence, which is a strong indication of a DNA, rather than a transcriptional, difference. None of the variants detected involved complete deletion of the repeat, with the lowest copy-number in the variant being 1.8 (see Additional data file 1).
Association of copy-number variation and host-defense functions
GO analysis of repeat variants
(a) Primary hypothesis
Chi-squared test 0.98
Logistic regression 0.88
(b) All level 4
Most significant terms
Transcription cofactor activity
RNA polymerase II transcription factor activity
Protein serine/threonine phosphatase complex
Structural constituent of epidermis
Regulation of physiological process
The ability to detect repeat variation within a given cluster is partially dependent on both the number of sequences in which we detected tandem repeats, and the number of repeat blocks in the sequence. These are highly correlated with the number of sequences in the cluster and sequence length, respectively (data not shown). It is possible that these two variables - cluster size and sequence length - might relate to protein groupings with certain functions. In addition, cluster size may be affected by ascertainment bias for certain genes highly expressed in well sampled tissues, and there may be an ascertainment bias towards variant sequences that have been preferentially selected for sequencing. Therefore, we performed a logistic regression where the dependent categorical variable described whether or not the cluster contained a variant repeat population, and tested this against the categorical 'defense response' variable (describing whether the cluster linked to the GO term). We considered as covariates the number of sequences within each cluster as well as the length of the protein. We found that variation was not dependent on the defense-response classification when both the number of sequences and the length of the protein were considered as covariates (p = 0.882) (Table 1).
Thus, we find no evidence that human host-defense proteins have an excess of tandem repeat variation. It is possible that the large size of human gene promoters and their innate variability (in SNPs, tandem repeats, indels and other polymorphisms) provides ample opportunity in response to pathogen challenges for rapid selection of variants modulating gene function. There may therefore be no strong long-term selection pressure to develop an innate reservoir of potential variation within the protein sequences themselves. We anticipate that it may be more likely that such advantageous tandem repeat polymorphisms would arise in host-defense proteins of organisms that lack the adaptive immune system and have much larger population sizes.
Association of tandem repeat copy-number variation and Gene Ontology (GO) terms
We investigated whether the occurrence of copy-number polymorphisms was associated with any other GO terms. Of the 362 level-4 terms in GO , 167 terms could be linked to our dataset and had at least one cluster linking to the term. We tested whether or not variation was significantly associated with any of these terms using a Fisher's exact test. This found 13 terms to be significant, of which only the term 'protein-binding' (GO:0005515) remained significant after Bonferroni correction for multiple testing. Again, we wished to ensure that the UniGene cluster size and the sequence length were not confounding the associations between variability and GO terms. Therefore, we performed the logistic regression described above, for which 67 of the 167 terms had a sufficiently large sample size to be tested. Twelve of these terms were significant, one of which remained significant after correcting for multiple testing. Again, this term was 'protein binding'. To ensure that the observed significance could not be largely attributed to differences in repeat copy-number between variants and non-variants (Figure 3) we performed the logistic regression with the mean repeat copy-number per cluster as an additional covariate. The significance remained the same under this model (p < 0.00001).
Length changes in repeats involved in protein-protein interactions may affect the evolution of cellular signaling pathways . This process may be facilitated by an absence of selective constraint on the repeat if there are no deleterious effects on the phenotype. An elevation of sequence variability at the population level in these proteins is similarly consistent with lack of evolutionary constraint on the protein regions. Previous work has shown that for polyglutamine repeats between human and mouse, there is an association between new repeats and a high nonsynonymous sequence divergence rate, corresponding to regions of low purifying selection . Further investigation of the classes of repeats that are polymorphic in different groups of genes is of interest  but sample sizes are too limited to draw strong inferences.
We investigated in more detail the 45 variant clusters linked to 'protein-binding'. Investigation of the daughter GO terms did not reveal any striking association with any subcategory (data not shown). A number of clusters corresponding to this category have previously been described to be associated with disease, particularly trinucleotide-repeat expansion diseases [54, 55]. The existence of repeats in protein- and DNA-binding proteins has been linked to their functional roles [56–60]. The question is whether the polymorphisms in these repeats are likely to have a functional impact. There are two models that may explain the higher level of polymorphism. One is that these proteins are typically under low selective constraint, as repetitive regions in protein- and DNA-binding proteins are often substantially structurally disordered  and expansion is unlikely to destabilize the protein's overall folding. Supporting this is the observation that new repeats emerge in regions of proteins that are subject to lower-than-average levels of purifying selection . The second model is that such polymorphisms are promoted by balancing selection or recent selection for adaptive change. In the dog, evidence has been found of repeat conservation across mammalian orders despite high mutation rates, suggesting strong stabilizing selection acting on these loci. In addition, it has been found that morphological differences between breeds of dog correlated with variations in repeat number . Thus, in the presence of strong selection, significant repeat polymorphism can arise.
Overall incidence of tandem repeat polymorphism
We noted that our estimate of polymorphism was higher when only clusters with a larger sample size were used (for example, 3.06% among 3,331 tandem repeats for which the UniGene cluster size was at least 200 sequences), indicating that our overall estimate is a lower estimate of the true frequency. Wren et al.  predicted that around 92% of polymorphic repeats in protein-coding regions would be a multiple of three nucleotides, which is concordant with the observation seen in Figure 1b. They experimentally confirmed 40% (17/42) of their predicted polymorphic protein-coding repeats within a sample of at least 60 chromosomes. Of the 249 unique repeat polymorphisms detected in our analysis, 56% were below the minimum threshold used by Wren et al. to predict polymorphism. Thus, while the method of Wren et al. is a useful prediction algorithm, it fails to predict many observed polymorphisms in shorter tandem arrays. Predicted polymorphism reflects the consequences of mutation, while actual polymorphism reflects the combination of mutation and subsequent selection pressures, and therefore the two approaches may well lead to different conclusions.
It is not surprising that a purely computational prediction will have false negatives, as it must protect against the problem of predicting too many false positives. We make the following assumptions: first, the Wren et al. prediction method only provides coverage of 44% (standard error 0.03) of tandem repeat polymorphisms, given that 56% of our variants were below their thresholds for polymorphism prediction; second, only 40% (standard error 0.08) of predicted repeats are actually polymorphic; third, there is one computationally predicted polymorphic tandem repeat per 23,000 nucleotides of protein-coding DNA ; and fourth, the average length of protein-coding DNA is 1,666 nucleotides (based on the UniGene dataset analyzed here). This then implies a revised estimate of estimated polymorphic tandem repeat copy-number variation to 1 in 25,000 nucleotides (with a 95% confidence interval of 17,911-43,066) , and that the average frequency of polymorphic tandem repeats in human proteins is 6%. The existence of annotation and experimental error may bias this upwards, while the existence of nonsense-mediated RNA decay may bias the estimate downwards.
Since 14.24% (42/295) of the polymorphisms were not a multiple of three nucleotides, up to 1% of proteins may contain frameshifting tandem repeat polymorphisms. It is likely that a much greater number of genes contain rarer frameshifting copy-number variants below the 1% frequency threshold used to define polymorphisms .
Our analysis confirms that tandem repeat variation is an important source of variation in many proteins. Much of this variation is of potential relevance to protein function and disease. A more thorough evaluation of the frequency of coding-sequence tandem repeat polymorphism will be possible once the resequencing of human exons from a panel of individuals becomes available. This will allow an unbiased assessment of the extent of common frameshifting tandem repeat variants. However, characterization of the frequency of rarer frameshifting tandem repeats will require larger sample sizes than typical current resequencing projects, as many repeats with large biological effects, such as frameshifts, are likely to occur at low frequencies. Thus, extensive resequencing or genotyping through large cohorts of individuals will be required in order to define their true incidence and to provide a clearer picture of the balance of mutational and selection pressures acting on the generation, fixation and elimination of tandem repeat copy-number variants in human genes.
Materials and methods
Detection of tandem repeats
Two files, Hs.seq.uniq and Hs.seq.all, from the UniGene database  build 172 were downloaded. Hs.seq.uniq was used as the template for tandem repeat detection and consisted of one sequence per UniGene cluster that contained the longest region of high-quality sequence data. Hs.seq.all consisted of a redundant set of gene-orientated sequences - that is, multiple sequences can correspond to the same gene cluster identifier. Tandem repeats detected in Hs.seq.uniq were defined as the queries. Tandem repeat blocks detected in Hs.seq.all using the queries were defined as the hits.
To ensure that there was no significant bias arising from expressed sequence tags (ESTs) of cancerous origin, we eliminated these sequences from our results by using the TissueInfo  classification of EST libraries (December 2002).
Tandem repeats are often complex patterns and it was found that repeats were often detected as smaller sub-patterns when using a lower minimum score to report a repeat. This occurred for the 69-nucleotide repeat in MUC2 for instance, where the repeat unit was detected as a series of six- and three-nucleotide repeat units. As we wanted to detect the largest range of repeats possible while retaining repeat patterns that were correct, we decided to retain all repeats detected under default parameter settings and then to search for repeats using more sensitive parameters. Only repeats detected in the latter search that did not overlap with those in the former were included.
Tandem repeats were first detected in Hs.seq.uniq using the Tandem Repeats Finder (TRF) program version 3.21  with default parameters for repeat detection. A minscore of 12 instead of 50 was used the second time round, which corresponds to a minimum of three copies of a 2-nucleotide repeat as an example. The TRF detection cutoff of 12 was deliberately chosen to be low: this was motivated by the desire to determine the level of repeat variation in all repeats, regardless of their mutational origin. Thus, of the repeats we investigated, 98% (87,787/89,243) had scores below the TRF default score of 50. Of the variants detected, 67% (167/249) had a TRF score below 50. Thus, searches for variant tandem repeats need to consider low copy-number repeats, as well as those high copy-number repeats which are more likely to be variant. For shorter arrays to be reported by TRF, they will need to be 100% homogeneous to be detectable. Clearly, there may be other insertions or deletions among short inexact repeat arrays that we have not detected. Sequences lacking 25 nucleotides of flanking sequence on both sides of the detected tandem repeat block were omitted from further analysis.
We restricted our analysis to variability among protein-coding repeat sequences. Definitions of coding sequence (CDS) start and stop points were taken from the sequence header of the Hs.seq.uniq sequences in UniGene. Sequences lacking CDS information and tandem repeat sequences that did not lie exclusively within coding regions were not included. Mononucleotide tandem repeats were excluded from the analysis, as we considered the probability of detecting sequence errors too great .
Detection of tandem repeat variation
Similarity of the tandem repeat region within the Hs.seq.uniq representative to the same region within other sequences within the cluster was assessed by matching up the corresponding sequences using their 25-nucleotide flanks. Length differences were detected by comparing the length of the representative tandem repeat block to that of the other sequences in the cluster.
Detected repeat blocks thus have the following properties: a 25-nucleotide flanking sequence on both sides (which is used to align repeat blocks from different sequences in the cluster), and they belong to a cluster containing more than one sequence overlapping the tandem repeat sequence block and its 25-nucleotide flanks.
Detected variants were screened to ensure that they represented length variation arising as copy-number differences in genomic DNA rather than intron retention or alternative splicing: Only length variations that corresponded to a length difference that was a multiple of the repeat unit were selected. For this set, tandem repeats were detected in the variant sequence and checked to ensure that the observed copy-number agreed with the expected one, given the length of the hit block and the length of the repeat unit.
We calculated the gene diversity (or heterozygosity) as
where Pi is the frequency of the ith of k repeat lengths at a locus ( and see Additional data file 2).
Gene Ontology (GO) data
To test the hypothesis that the number of genes with tandem repeat variation is elevated in genes involved in defense-related processes, the term 'defense response' (GO:0006952) was selected from GO. Human UniGene clusters linked to GO terms and their hierarchies were obtained by linking LocusLink to both UniGene and GO and also by linking UniGene to EMBL and then linking, via the EMBL accessions, to UniProt and thence to GO. Links were subsequently completed by adding links to all parent GO terms for each GO term using the GO_GRAPH_PATH and GO_TERM tables from the Gene Ontology database (dated 1 July 2004). By cross-referencing our GO term of interest with the file linking GO to UniGene, we were able to assign a binary classification (yes/no related to our GO term of interest) to each UniGene cluster. This allowed us to statistically assess the differences in the levels of variation between genes related and not related to the defense response. Significant terms were corrected for multiple testing using the Bonferroni method. Statistical analysis was carried out in STATA 8.
Additional data files
The following additional data is available with the online version of this paper. Additional data file 1 is a table listing the 295 repeat variants (spanning 218 UniGene clusters) detected in our analysis, with information on the repeats and a description of the cluster representative sequence. Additional data file 2 contains block lengths of repeats grouped into 249 unique repeat loci. For each locus, the heterozygosity of the repeat length allele frequencies has been calculated. Additional data file 3 contains data used for Figure 3. Counts of variant and invariant repeats of different unit lengths and copy-numbers are tabulated.
We thank Philip Cotter and Kate Johnston for comments and suggestions during the preparation of this manuscript, and Patrick Dicker for advice on statistical tests used. The work was supported by the Higher Education Authority of Ireland.
- A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington's disease chromosomes. The Huntington's Disease Collaborative Research Group. Cell. 1993, 72: 971-983. 10.1016/0092-8674(93)90585-E.
- Verkerk AJ, Pieretti M, Sutcliffe JS, Fu YH, Kuhl DP, Pizzuti A, Reiner O, Richards S, Victoria MF, Zhang FP, et al: Identification of a gene (FMR-1) containing a CGG repeat coincident with a breakpoint cluster region exhibiting length variation in fragile X syndrome. Cell. 1991, 65: 905-914. 10.1016/0092-8674(91)90397-H.PubMedView ArticleGoogle Scholar
- Hui J, Stangl K, Lane WS, Bindereif A: HnRNP L stimulates splicing of the eNOS gene by binding to variable-length CA repeats. Nat Struct Biol. 2003, 10: 33-37. 10.1038/nsb875.PubMedView ArticleGoogle Scholar
- Gebhardt F, Zanker KS, Brandt B: Modulation of epidermal growth factor receptor gene transcription by a polymorphic dinucleotide repeat in intron 1. J Biol Chem. 1999, 274: 13176-13180. 10.1074/jbc.274.19.13176.PubMedView ArticleGoogle Scholar
- Jeffreys AJ, Royle NJ, Wilson V, Wong Z: Spontaneous mutation rates to new length alleles at tandem-repetitive hypervariable loci in human DNA. Nature. 1988, 332: 278-281. 10.1038/332278a0.PubMedView ArticleGoogle Scholar
- Jakupciak JP, Wells RD: Genetic instabilities in (CTG.CAG) repeats occur by recombination. J Biol Chem. 1999, 274: 23468-23479. 10.1074/jbc.274.33.23468.PubMedView ArticleGoogle Scholar
- Richard GF, Dujon B, Haber JE: Double-strand break repair can lead to high frequencies of deletions within short CAG/CTG trinucleotide repeats. Mol Gen Genet. 1999, 261: 871-882. 10.1007/s004380050031.PubMedView ArticleGoogle Scholar
- La Spada AR, Wilson EM, Lubahn DB, Harding AE, Fischbeck KH: Androgen receptor gene mutations in X-linked spinal and bulbar muscular atrophy. Nature. 1991, 352: 77-79. 10.1038/352077a0.PubMedView ArticleGoogle Scholar
- Sutherland GR, Richards RI: Simple tandem DNA repeats and human genetic disease. Proc Natl Acad Sci USA. 1995, 92: 3636-3641.PubMedPubMed CentralView ArticleGoogle Scholar
- Kenny D, Muckian C, Fitzgerald DJ, Cannon CP, Shields DC: Platelet glycoprotein Ib alpha receptor polymorphisms and recurrent ischaemic events in acute coronary syndrome patients. J Thromb Thrombolysis. 2002, 13: 13-19. 10.1023/A:1015307823578.PubMedView ArticleGoogle Scholar
- Holmer SR, Hengstenberg C, Kraft HG, Mayer B, Poll M, Kurzinger S, Fischer M, Lowel H, Klein G, Riegger GA, Schunkert H: Association of polymorphisms of the apolipoprotein(a) gene with lipoprotein(a) levels and myocardial infarction. Circulation. 2003, 107: 696-701. 10.1161/01.CIR.0000048125.79640.77.PubMedView ArticleGoogle Scholar
- Bugert P, Hoffmann MM, Winkelmann BR, Vosberg M, Jahn J, Entelmann M, Katus HA, Marz W, Mansmann U, Boehm BO, et al: The variable number of tandem repeat polymorphism in the P-selectin glycoprotein ligand-1 gene is not associated with coronary heart disease. J Mol Med. 2003, 81: 495-501. 10.1007/s00109-003-0459-2.PubMedView ArticleGoogle Scholar
- Fondon JW, Mele GM, Brezinschek RI, Cummings D, Pande A, Wren J, O'Brien KM, Kupfer KC, Wei MH, Lerman M, et al: Computerized polymorphic marker identification: experimental validation and a predicted human polymorphism catalog. Proc Natl Acad Sci USA. 1998, 95: 7514-7519. 10.1073/pnas.95.13.7514.PubMedPubMed CentralView ArticleGoogle Scholar
- Wren JD, Forgacs E, Fondon JW, Pertsemlidis A, Cheng SY, Gallardo T, Williams RS, Shohet RV, Minna JD, Garner HR: Repeat polymorphisms within gene regions: phenotypic and evolutionary implications. Am J Hum Genet. 2000, 67: 345-356. 10.1086/303013.PubMedPubMed CentralView ArticleGoogle Scholar
- Schuler GD, Boguski MS, Stewart EA, Stein LD, Gyapay G, Rice K, White RE, Rodriguez-Tome P, Aggarwal A, Bajorek E, et al: A gene map of the human genome. Science. 1996, 274: 540-546. 10.1126/science.274.5287.540.PubMedView ArticleGoogle Scholar
- Denoeud F, Vergnaud G, Benson G: Predicting human minisatellite polymorphism. Genome Res. 2003, 13: 856-867. 10.1101/gr.574403.PubMedPubMed CentralView ArticleGoogle Scholar
- Naslund K, Saetre P, von Salome J, Bergstrom TF, Jareborg N, Jazin E: Genome-wide prediction of human VNTRs. Genomics. 2005, 85: 24-35. 10.1016/j.ygeno.2004.10.009.PubMedView ArticleGoogle Scholar
- Denoeud F, Vergnaud G: Identification of polymorphic tandem repeats by direct comparison of genome sequence from different bacterial strains: a web-based resource. BMC Bioinformatics. 2004, 5: 4-10.1186/1471-2105-5-4.PubMedPubMed CentralView ArticleGoogle Scholar
- Jordon P, Snyder LA, Saunders NJ: Diversity in coding tandem repeats in related Neisseria spp. BMC Microbiol. 2003, 3: 23-10.1186/1471-2180-3-23.View ArticleGoogle Scholar
- Sylvestre P, Couture-Tosi E, Mock M: Polymorphism in the collagen-like region of the Bacillus anthracis BclA protein leads to variation in exosporium filament length. J Bacteriol. 2003, 185: 1555-1563. 10.1128/JB.185.5.1555-1563.2003.PubMedPubMed CentralView ArticleGoogle Scholar
- van Belkum A, Scherer S, van Alphen L, Verbrugh H: Short-sequence DNA repeats in prokaryotic genomes. Microbiol Mol Biol Rev. 1998, 62: 275-293.PubMedPubMed CentralGoogle Scholar
- Li YC, Korol AB, Fahima T, Nevo E: Microsatellites within genes: structure, function, and evolution. Mol Biol Evol. 2004, 21: 991-1007. 10.1093/molbev/msh073.PubMedView ArticleGoogle Scholar
- Murphy PM: Molecular mimicry and the generation of host defense protein diversity. Cell. 1993, 72: 823-826. 10.1016/0092-8674(93)90571-7.PubMedView ArticleGoogle Scholar
- Shields DC, Harmon DL, Whitehead AS: Evolution of hemopoietic ligands and their receptors. Influence of positive selection on correlated replacements throughout ligand and receptor proteins. J Immunol . 1996, 156: 1062-1070.PubMedGoogle Scholar
- Metzgar D, Bytof J, Wills C: Selection against frameshift mutations limits microsatellite expansion in coding DNA. Genome Res. 2000, 10: 72-80.PubMedPubMed CentralGoogle Scholar
- Dokholyan NV, Buldyrev SV, Havlin S, Stanley HE: Distributions of dimeric tandem repeats in noncoding and coding DNA sequences. J Theor Biol. 2000, 202: 273-282. 10.1006/jtbi.1999.1052.PubMedView ArticleGoogle Scholar
- Subramanian S, Mishra RK, Singh L: Genome-wide analysis of microsatellite repeats in humans: their abundance and density in specific genomic regions. Genome Biol. 2003, 4: R13-10.1186/gb-2003-4-2-r13.PubMedPubMed CentralView ArticleGoogle Scholar
- Chang FM, Kidd JR, Livak KJ, Pakstis AJ, Kidd KK: The world-wide distribution of allele frequencies at the human dopamine D4 receptor locus. Hum Genet. 1996, 98: 91-101. 10.1007/s004390050166.PubMedView ArticleGoogle Scholar
- Afshar-Kharghan V, Diz-Kucukkaya R, Ludwig EH, Marian AJ, Lopez JA: Human polymorphism of P-selectin glycoprotein ligand 1 attributable to variable numbers of tandem decameric repeats in the mucinlike region. Blood. 2001, 97: 3306-3307. 10.1182/blood.V97.10.3306.PubMedView ArticleGoogle Scholar
- Toribara NW, Gum JR, Culhane PJ, Lagace RE, Hicks JW, Petersen GM, Kim YS: MUC-2 human small intestinal mucin gene structure. Repeated arrays and polymorphism. J Clin Invest. 1991, 88: 1005-1013.PubMedPubMed CentralView ArticleGoogle Scholar
- Muckian C, Hillmann A, Kenny D, Shields DC: A novel variant of the platelet glycoprotein Ibalpha macroglycopeptide region lacks any copies of the 'perfect' 13 amino acid repeat. Thromb Haemost. 2000, 83: 513-514.PubMedGoogle Scholar
- Matsuyama Z, Kawakami H, Maruyama H, Izumi Y, Komure O, Udaka F, Kameyama M, Nishio T, Kuroda Y, Nishimura M, Nakamura S: Molecular features of the CAG repeats of spinocerebellar ataxia 6 (SCA6). Hum Mol Genet. 1997, 6: 1283-1287. 10.1093/hmg/6.8.1283.PubMedView ArticleGoogle Scholar
- Koide R, Kobayashi S, Shimohata T, Ikeuchi T, Maruyama M, Saito M, Yamada M, Takahashi H, Tsuji S: A neurological disease caused by an expanded CAG trinucleotide repeat in the TATA-binding protein gene: a new polyglutamine disease?. Hum Mol Genet. 1999, 8: 2047-2053. 10.1093/hmg/8.11.2047.PubMedView ArticleGoogle Scholar
- Zuhlke C, Hellenbroich Y, Dalski A, Kononowa N, Hagenah J, Vieregge P, Riess O, Klein C, Schwinger E: Different types of repeat expansion in the TATA-binding protein gene are associated with a new form of inherited ataxia. Eur J Hum Genet. 2001, 9: 160-164. 10.1038/sj.ejhg.5200617.PubMedView ArticleGoogle Scholar
- Dean M, Park M, Le Beau MM, Robins TS, Diaz MO, Rowley JD, Blair DG, Vande Woude GF: The human met oncogene is related to the tyrosine kinase oncogenes. Nature. 1985, 318: 385-388. 10.1038/318385a0.PubMedView ArticleGoogle Scholar
- Stenson PD, Ball EV, Mort M, Phillips AD, Shiel JA, Thomas NS, Abeysinghe S, Krawczak M, Cooper DN: Human Gene Mutation Database (HGMD): 2003 update. Hum Mutat. 2003, 21: 577-581. 10.1002/humu.10212.PubMedView ArticleGoogle Scholar
- Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA: Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005, 33 (Database issue): D514-D517. 10.1093/nar/gki033.PubMedPubMed CentralView ArticleGoogle Scholar
- Letovsky SI, Cottingham RW, Porter CJ, Li PW: GDB: the Human Genome Database. Nucleic Acids Res. 1998, 26: 94-99. 10.1093/nar/26.1.94.PubMedPubMed CentralView ArticleGoogle Scholar
- Charmley P, Concannon P, Hood L, Rowen L: Frequency and polymorphism of simple sequence repeats in a contiguous 685-kb DNA sequence containing the human T-cell receptor beta-chain gene complex. Genomics. 1995, 29: 760-765. 10.1006/geno.1995.9940.PubMedView ArticleGoogle Scholar
- Kimmel M, Chakraborty R, Stivers DN, Deka R: Dynamics of repeat polymorphisms under a forward-backward mutation model: within- and between-population variability at microsatellite loci. Genetics. 1996, 143: 549-555.PubMedPubMed CentralGoogle Scholar
- Ota T, Kimura M: A model of mutation appropriate to estimate the number of electrophoretically detectable alleles in a finite population. Genet Res. 1973, 22: 201-204.View ArticleGoogle Scholar
- Schlotterer C, Tautz D: Slippage synthesis of simple sequence DNA. Nucleic Acids Res. 1992, 20: 211-215.PubMedPubMed CentralView ArticleGoogle Scholar
- Weber JL: Informativeness of human (dC-dA)n.(dG-dT)n polymorphisms. Genomics. 1990, 7: 524-530. 10.1016/0888-7543(90)90195-Z.PubMedView ArticleGoogle Scholar
- Kunst CB, Leeflang EP, Iber JC, Arnheim N, Warren ST: The effect of FMR1 CGG repeat interruptions on mutation frequency as measured by sperm typing. J Med Genet. 1997, 34: 627-631.PubMedPubMed CentralView ArticleGoogle Scholar
- Hubbard T, Andrews D, Caccamo M, Cameron G, Chen Y, Clamp M, Clarke L, Coates G, Cox T, Cunningham F, et al: Ensembl 2005. Nucleic Acids Res. 2005, 33 (Database issue): D447-D453. 10.1093/nar/gki138.PubMedPubMed CentralView ArticleGoogle Scholar
- Lykke-Andersen J, Shu MD, Steitz JA: Human Upf proteins target an mRNA for nonsense-mediated decay when bound downstream of a termination codon. Cell. 2000, 103: 1121-1131. 10.1016/S0092-8674(00)00214-2.PubMedView ArticleGoogle Scholar
- Hughes AL, Packer B, Welch R, Bergen AW, Chanock SJ, Yeager M: Widespread purifying selection at polymorphic sites in human protein-coding loci. Proc Natl Acad Sci USA. 2003, 100: 15754-15757. 10.1073/pnas.2536718100.PubMedPubMed CentralView ArticleGoogle Scholar
- van Den Hurk WH, Willems HJ, Bloemen M, Martens GJ: Novel frameshift mutations near short simple repeats. J Biol Chem. 2001, 276: 11496-11498. 10.1074/jbc.M011040200.PubMedView ArticleGoogle Scholar
- Karlin S, Burge C: Trinucleotide repeats and long homopeptides in genes and proteins associated with nervous system disease and development. Proc Natl Acad Sci USA. 1996, 93: 1560-1565. 10.1073/pnas.93.4.1560.PubMedPubMed CentralView ArticleGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25: 25-29. 10.1038/75556.PubMedPubMed CentralView ArticleGoogle Scholar
- Hancock JM, Simon M: Simple sequence repeats in proteins and their significance for network evolution. Gene. 2005, 345: 113-118. 10.1016/j.gene.2004.11.023.PubMedView ArticleGoogle Scholar
- Hancock JM, Worthey EA, Santibanez-Koref MF: A role for selection in regulating the evolutionary emergence of disease-causing and other coding CAG repeats in humans and mice. Mol Biol Evol. 2001, 18: 1014-1023.PubMedView ArticleGoogle Scholar
- Alba MM, Laskowski RA, Hancock JM: Detecting cryptically simple protein sequences using the SIMPLE algorithm. Bioinformatics. 2002, 18: 672-678. 10.1093/bioinformatics/18.5.672.PubMedView ArticleGoogle Scholar
- Koide R, Ikeuchi T, Onodera O, Tanaka H, Igarashi S, Endo K, Takahashi H, Kondo R, Ishikawa A, Hayashi T, et al: Unstable expansion of CAG repeat in hereditary dentatorubral-pallidoluysian atrophy (DRPLA). Nat Genet. 1994, 6: 9-13. 10.1038/ng0194-9.PubMedView ArticleGoogle Scholar
- Kennedy WR, Alter M, Sung JH: Progressive proximal spinal and bulbar muscular atrophy of late onset. A sex-linked recessive trait. Neurology. 1968, 18: 671-680.PubMedView ArticleGoogle Scholar
- Hamada H, Seidman M, Howard BH, Gorman CM: Enhanced gene expression by the poly(dT-dG).poly(dC-dA) sequence. Mol Cell Biol . 1984, 4: 2622-2630.PubMedPubMed CentralView ArticleGoogle Scholar
- Lu Q, Wallrath LL, Granok H, Elgin SC: (CT)n (GA)n repeats and heat shock elements have distinct roles in chromatin structure and transcriptional activation of the Drosophila hsp26 gene. Mol Cell Biol. 1993, 13: 2802-2814.PubMedPubMed CentralView ArticleGoogle Scholar
- Yee HA, Wong AK, van de Sande JH, Rattner JB: Identification of novel single-stranded d(TC)n binding proteins in several mammalian species. Nucleic Acids Res. 1991, 19: 949-953.PubMedPubMed CentralView ArticleGoogle Scholar
- Richards RI, Holman K, Yu S, Sutherland GR: Fragile X syndrome unstable element, p(CCG)n, and other simple tandem repeat sequences are binding sites for specific nuclear proteins. Hum Mol Genet. 1993, 2: 1429-1435.PubMedView ArticleGoogle Scholar
- Colafranceschi M, Colosimo A, Zbilut JP, Uversky VN, Giuliani A: Structure-related statistical singularities along protein sequences: a correlation study. J Chem Inf Model. 2005, 45: 183-189.PubMedView ArticleGoogle Scholar
- Fondon JW, Garner HR: Molecular origins of rapid and continuous morphological evolution. Proc Natl Acad Sci USA. 2004, 101: 18058-18063. 10.1073/pnas.0408118101.PubMedPubMed CentralView ArticleGoogle Scholar
- Armitage P, Berry G: Statistical Methods in Medical Research. 1994, Oxford, UK: Blackwell Science, 3Google Scholar
- Day IN, Alharbi KK, Smith M, Aldahmesh MA, Chen X, Lotery AJ, Pante-de-Sousa G, Hou G, Ye S, Eccles D, et al: Paucimorphic alleles versus polymorphic alleles and rare mutations in disease causation: theory, observation and detection. Curr Genomics. 2004, 5: 431-438. 10.2174/1389202043349156.View ArticleGoogle Scholar
- Skrabanek L, Campagne F: TissueInfo: high-throughput identification of tissue expression profiles and specificity. Nucleic Acids Res. 2001, 29: E102-10.1093/nar/29.21.e102.PubMedPubMed CentralView ArticleGoogle Scholar
- Benson G: Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999, 27: 573-580. 10.1093/nar/27.2.573.PubMedPubMed CentralView ArticleGoogle Scholar
- Weber JL, David D, Heil J, Fan Y, Zhao C, Marth G: Human diallelic insertion/deletion polymorphisms. Am J Hum Genet. 2002, 71: 854-862. 10.1086/342727.PubMedPubMed CentralView ArticleGoogle Scholar
- Weir BS: Genetic Data Analysis II: Methods for Discrete Population Genetic Data. 1996, Sunderland, MA: Sinauer, 2Google Scholar
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