title: Single nucleotide polymorphism (SNP)-strings: an alternative method for assessing genetic associations.
abstract: BACKGROUND
  Genome-wide association studies (GWAS) identify disease-associations for single-nucleotide-polymorphisms (SNPs) from scattered genomic-locations. However, SNPs frequently reside on several different SNP-haplotypes, only some of which may be disease-associated. This circumstance lowers the observed odds-ratio for disease-association.
  
  
  RESULTS
  Here we develop a method to identify the two SNP-haplotypes, which combine to produce each person's SNP-genotype over specified chromosomal segments. Two multiple sclerosis (MS)-associated genetic regions were modeled; DRB1 (a Class II molecule of the major histocompatibility complex) and MMEL1 (an endopeptidase that degrades both neuropeptides and β-amyloid). For each locus, we considered sets of eleven adjacent SNPs, surrounding the putative disease-associated gene and spanning ∼200 kb of DNA. The SNP-information was converted into an ordered-set of eleven-numbers (subject-vectors) based on whether a person had zero, one, or two copies of particular SNP-variant at each sequential SNP-location. SNP-strings were defined as those ordered-combinations of eleven-numbers (0 or 1), representing a haplotype, two of which combined to form the observed subject-vector. Subject-vectors were resolved using probabilistic methods. In both regions, only a small number of SNP-strings were present. We compared our method to the SHAPEIT-2 phasing-algorithm. When the SNP-information spanning 200 kb was used, SHAPEIT-2 was inaccurate. When the SHAPEIT-2 window was increased to 2,000 kb, the concordance between the two methods, in both of these eleven-SNP regions, was over 99%, suggesting that, in these regions, both methods were quite accurate. Nevertheless, correspondence was not uniformly high over the entire DNA-span but, rather, was characterized by alternating peaks and valleys of concordance. Moreover, in the valleys of poor-correspondence, SHAPEIT-2 was also inconsistent with itself, suggesting that the SNP-string method is more accurate across the entire region.
  
  
  CONCLUSIONS
  Accurate haplotype identification will enhance the detection of genetic-associations. The SNP-string method provides a simple means to accomplish this and can be extended to cover larger genomic regions, thereby improving a GWAS's power, even for those published previously.
authors.affiliation: Department of Neurology, University of California San Francisco, San Francisco, California, United States of America; UCSF Multiple Sclerosis Center, University of California San Francisco, San Francisco, California, United States of America.
authors.forename: Douglas S
authors.initials: DS
authors.lastname: Goodin
authors.affiliation: Department of Neurology, University of California San Francisco, San Francisco, California, United States of America; UCSF Multiple Sclerosis Center, University of California San Francisco, San Francisco, California, United States of America.
authors.forename: Pouya
authors.initials: P
authors.lastname: Khankhanian
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping; D005260:Female; D020022:Genetic Predisposition to Disease / Q000235:genetics; D055106:Genome-Wide Association Study; D005838:Genotype; D006239:Haplotypes / Q000235:genetics; D006801:Humans; D015810:Linkage Disequilibrium; D008297:Male; D009103:Multiple Sclerosis / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D011446:Prospective Studies
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