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The aftermath of the Human Genome Project has generated new revolutionary techniques and equipment such as high throughput measurement tools for collecting biological information. One notable tool is a microarray that can be used to genotype hundreds of thousands of single nucleotide polymorphisms (SNPs) in one run. This highthroughput SNP genotypes along with phenotypic measurements can be used in fine quantitative trait loci (QTL) mapping or genome-wide association studies (GWAS). The result of fine QTL mapping or GWAS is a set of statistically significant QTL regions or genetic markers such as SNPs. See Box 1 for SNP, QTL and GWAS explanation. The significant QTLs or SNPs from QTL mapping or GWAS are used subsequently in QTL or SNP – based selection of elite animals or plants for breeding in agriculture or used to predict disease risks in humans and animals (e.g. Burton et al. 2007, Mackay et al. 2009). GWAS relies on a natural phenomenon of linkage disequilibrium (LD) between genetic (SNP) markers and causal variants or quantitative trait nucleotide (QTN). For GWAS to be applied successfully there is a need to understand the extent and distribution of linkage disequilibrium (LD) across the entire genome in a population. In particular, we need to know how LD varies from one region (or population) to another. This need to know how LD (and haplotype diversity) varies from one region or population to another provided the motivation to develop SNPpattern, a generic bioinformatic tool for finding SNP allele patterns in populations.

SNPpattern: A Genetic Tool to Derive Haplotype Blocks and Measure Genomic Diversity in Populations Using SNP Genotypes