MOTIVATION: Next-generation genotyping microarrays have been designed with insights from 1000 Genomes Project and whole-exome sequencing studies. These arrays additionally include variants that are typically present at lower frequencies. Determining the genotypes of these variants from hybridization intensities is challenging because there is less support to locate the presence of the minor alleles when the allele counts are low. Existing algorithms are mainly designed for calling common variants and are notorious for failing to generate accurate calls for low-frequency and rare variants. Here, we introduce a new calling algorithm, iCall, to call genotypes for variants across the whole spectrum of allele frequencies. RESULTS: We benchmarked iCall against four of the most commonly used algorithms, GenCall, optiCall, illuminus and GenoSNP, as well as a post-processing caller zCall that adopted a two-stage calling design. Normalized hybridization intensities for 12 370 individuals genotyped on the Illumina HumanExome BeadChip were considered, of which 81 individuals were also whole-genome sequenced. The sequence calls were used to benchmark the accuracy of the genotype calling, and our comparisons indicated that iCall outperforms all four single-stage calling algorithms in terms of call rates and concordance, particularly in the calling accuracy of minor alleles, which is the principal concern for rare and low-frequency variants. The application of zCall to post-process the output from iCall also produced marginally improved performance to the combination of zCall and GenCall. AVAILABILITY AND IMPLEMENTATION: iCall is implemented in C++ for use on Linux operating systems and is available for download at http://www.statgen.nus.edu.sg/∼software/icall.html.

BACKGROUND: The advent of genotype data from large-scale efforts that catalog the genetic variants of different populations have given rise to new avenues for multifactorial disease association studies. Recent work shows that genotype data from the International HapMap Project have a high degree of transferability to the wider population. This implies that the design of genotyping studies on local populations may be facilitated through inferences drawn from information contained in HapMap populations. RESULTS: To facilitate analysis of HapMap data for characterizing the haplotype structure of genes or any chromosomal regions, we have developed an integrated web-based resource, iHAP. In addition to incorporating genotype and haplotype data from the International HapMap Project and gene information from the UCSC Genome Browser Database, iHAP also provides capabilities for inferring haplotype blocks and selecting tag SNPs that are representative of haplotype patterns. These include block partitioning algorithms, block definitions, tag SNP definitions, as well as SNPs to be "force included" as tags. Based on the parameters defined at the input stage, iHAP performs on-the-fly analysis and displays the result graphically as a webpage. To facilitate analysis, intermediate and final result files can be downloaded. CONCLUSION: The iHAP resource, available at http://ihap.bii.a-star.edu.sg, provides a convenient yet flexible approach for the user community to analyze HapMap data and identify candidate targets for genotyping studies.