On Distinguishing Unique Combinations in Biological Sequences

Salamon, Hugh; Tarhio, Jorma; Rønningen, Kjersti S.; Thomson, Glenys

doi:10.1089/cmb.1996.3.407

Cited by 7 publications

(8 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the original Unique Combinations algorithm of Salamon et al [3], two categories of sequences are defined by the user: those in the “check” category are compared against those in the “group” category in order to identify combinations of sites that are unique between these two sets of sequences. However, when there are two or more sequences in the “check” category, sites that are polymorphic between these “check” sequences are excluded from consideration.…”

Section: Resultsmentioning

confidence: 99%

“…The Unique Combinations algorithm developed in [3] efficiently identifies combinations of AAs that distinguish a sequence or set of sequences from a set of other sequences. See Section 3.4 in Results for more details.…”

Section: Methodsmentioning

confidence: 99%

“…Other analysis methods have been applied to type 1 diabetes and other diseases, and have successfully identified AAs important in disease risk heterogeneity, e.g., with type 1 diabetes, the Unique Combinations algorithm of Salamon et al [3], and the Conditional Haplotype Method in Valdes et al [4]. The aim with Sequence Feature Variant Type (SFVT) analysis [6] is to systematically perform association tests focusing on variation (VTs) at biologically relevant SFs, which are based on structural and functional features of the protein.…”

Section: Introductionmentioning

confidence: 99%

“…We have extended SFVT analysis by creating an automated pipeline to include additional complementary and informative analyses (see Figure 1 and the Methods and Results Sections): including pairwise comparisons of HLA DRB1 alleles, and our extension of the Salamon Unique Combinations algorithm [3], to detect single AAs and combinations thereof that uniquely define different risk categories of alleles. AAs implicated from these analyses are now additionally combined with those identified by the first round of SFVT analysis in the construction of tSFs.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Sequence Feature Variant Type (Sfvt) Analysis of the Hla Genetic Association in Juvenile Idiopathic Arthritis

Thomson¹,

Marthandan²,

Hollenbach³

et al. 2009

Biocomputing 2010

Self Cite

View full text Add to dashboard Cite

The immune response HLA class II DRB1 gene provides the major genetic contribution to Juvenile Idiopathic Arthritis (JIA), with a hierarchy of predisposing through intermediate to protective effects. With JIA, and the many other HLA associated diseases, it is difficult to identify the combinations of biologically relevant amino acid (AA) residues directly involved in disease due to the high level of HLA polymorphism, the pattern of AA variability, including varying degrees of linkage disequilibrium (LD), and the fact that most HLA variation occurs at functionally important sites. In a subset of JIA patients with the clinical phenotype oligoarticularpersistent (OP), we have applied a recently developed novel approach to genetic association analyses with genes/proteins sub-divided into biologically relevant smaller sequence features (SFs), and their "alleles" which are called variant types (VTs). With SFVT analysis, association tests are performed on variation at biologically relevant SFs based on structural (e.g., beta-strand 1) and functional (e.g., peptide binding site) features of the protein. We have extended the SFVT analysis pipeline to additionally include pairwise comparisons of DRB1 alleles within serogroup classes, our extension of the Salamon Unique Combinations algorithm, and LD patterns of AA variability to evaluate the SFVT results; all of which contributed additional complementary information. With JIA-OP, we identified a set of single AA SFs, and SFs in which they occur, particularly pockets of the peptide binding site, that account for the major disease risk attributable † corresponding author: glenys@berkeley.edu. NIH Public Access Author ManuscriptPac Symp Biocomput. Author manuscript; available in PMC 2010 October 20. to HLA DRB1. These are (in numeric order): AAs 13 (pockets 4 and 6), 37 and 57 (both pocket 9), 67 (pocket 7), 74 (pocket 4), and 86 (pocket 1), and to a lesser extent 30 (pockets 6 and 7) and 71 (pockets 4, 5, and 7).

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sequence Feature Variant Type (Sfvt) Analysis of the Hla Genetic Association in Juvenile Idiopathic Arthritis

Thomson¹,

Marthandan²,

Hollenbach³

et al. 2009

Biocomputing 2010

Self Cite

View full text Add to dashboard Cite

show abstract

“…This association is also stronger than other potentially biologically relevant combinations of AAs defined under sequence feature variant-type (SFVT) analysis (Karp et al 2010;Thomson et al 2010). AA13 is also identified as potentially causative in disease using an extension of Salamon's unique combinations algorithm (Salamon et al 1996;Thomson et al 2010). The overall AA LD (W n ) patterns are quite complex for each of the classical HLA loci, with DRB1 control data for JIA-OP shown in Figure 5.…”

mentioning

confidence: 97%

Conditional Asymmetric Linkage Disequilibrium (ALD): Extending the Biallelic r2 Measure

Thomson

Single

2014

Genetics

Self Cite

View full text Add to dashboard Cite

For multiallelic loci, standard measures of linkage disequilibrium provide an incomplete description of the correlation of variation at two loci, especially when there are different numbers of alleles at the two loci. We have developed a complementary pair of conditional asymmetric linkage disequilibrium (ALD) measures. Since these measures do not assume symmetry, they more accurately describe the correlation between two loci and can identify heterogeneity in genetic variation not captured by other symmetric measures. For biallelic loci the ALD are symmetric and equivalent to the correlation coefficient r. The ALD measures are particularly relevant for disease-association studies to identify cases in which an analysis can be stratified by one of more loci. A stratified analysis can aid in detecting primary disease-predisposing genes and additional disease genes in a genetic region. The ALD measures are also informative for detecting selection acting independently on loci in high linkage disequilibrium or on specific amino acids within genes. For SNP data, the ALD statistics provide a measure of linkage disequilibrium on the same scale for comparisons among SNPs, among SNPs and more polymorphic loci, among haplotype blocks of SNPs, and for fine mapping of disease genes. The ALD measures, combined with haplotype-specific homozygosity, will be increasingly useful as next-generation sequencing methods identify additional allelic variation throughout the genome.T HE definition of the linkage disequilibrium (LD) parameter D ij of nonrandom association between a pair of alleles A i and B j at two loci (A and B) is straightforward and unequivocal. It is the difference between the observed (or estimated) haplotype (chromosomal or gametic) frequency ( f ij ) and that expected under random association of the two allele frequencies ðp Ai and p Bj Þ : D ij ¼ f ij 2 p Ai p Bj . While this is the base of all other measures of LD, defining the strength of any observed nonrandom association is complicated by the fact that the maximum value D ij can take is a function of the observed allele frequencies. A number of normalized measures to reflect the strength of LD have been proposed; both for bi-and multiallelic data (Hedrick 1987;Lewontin 1988). However, since these are all a single summary of multidimensional data, no proposed measure of the strength of LD can be perfect; although each may have strengths and weaknesses with respect to the question being addressed.The two most common measures of the strength of LD are: (1) the normalized measure of the individual LD values (Lewontin 1964), D ij 9 = D ij /D max (see Supporting Information, File S1 for details) and (2) the correlation coefficient r for biallelic data, which is most often reported as Hedrick (1987) extended the D9 measure for multiallelic data as a weighted average over all alleles at each locus of the individual normalized LD values: D9 = S i S j p Ai p Bj |D ij 9| . The multiallelic extension of the r 2 measure iswhere k A and k B indicate the number of ...

show abstract