Single Nucleotide Polymorphism (SNP)-Strings: An Alternative Method for Assessing Genetic Associations

• Published in: PloS one Vol. 9; no. 4; p. e90034
• Main Authors: Goodin, Douglas S., Khankhanian, Pouya
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2014; Public Library of Science (PLoS)
• Subjects: Algorithms; Alzheimer's disease; Analysis; Biology and Life Sciences; Chromosome Mapping; Deoxyribonucleic acid; Diagnosis; DNA; Drb1 protein; Endopeptidase; Endopeptidases; Female; Gene loci; Gene polymorphism; Genetic aspects; Genetic Predisposition to Disease - genetics; Genetics; Genome-Wide Association Study; Genomes; Genotype; Haplotypes; Haplotypes - genetics; Humans; Linkage Disequilibrium; Major histocompatibility complex; Male; Medicine and Health Sciences; Methods; Multiple sclerosis; Multiple Sclerosis - genetics; Neurology; Neuropeptides; Patient outcomes; Polymorphism; Polymorphism, Single Nucleotide - genetics; Population; Probabilistic methods; Prospective Studies; Quality control; Single nucleotide polymorphisms; Single-nucleotide polymorphism; Statistical analysis; Strings; Studies; Valleys; β-Amyloid; United States; United States > US; San Francisco California; California
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0090034

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. 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. 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.