Motivation: A limitation of current methods used to declare significance in genome-wide association studies (GWAS) is that they do not provide clear information about the probability that GWAS findings are true of false. This lack of information increases the chance of false discoveries and may result in real effects being missed. Results: We propose a method to estimate the posterior probability that a marker has (no) effect given its test statistic value, also called the local false discovery rate (FDR), in the GWAS. A critical step involves the estimation the parameters of the distribution of the true alternative tests. For this, we derived and implemented the real maximum likelihood function, which turned out to provide us with significantly more accurate estimates than the widely used mixture model likelihood. Actual GWAS data are used to illustrate properties of the posterior probability estimates empirically. In addition to evaluating individual markers, a variety of applications are conceivable. For instance, posterior probability estimates can be used to control the FDR more precisely than Benjamini–Hochberg procedure. Availability: The codes are freely downloadable from the web site http://www.people.vcu.edu/~jbukszar.

Microsatellite loci are standard genetic markers for population genetic analysis, whereas single nucleotide polymorphisms (SNPs) are more recent tools that require assessment of neutrality and appropriate use in population genetics. Twelve SNP markers were used to describe the genetic structure of Diabrotica virgifera virgifera (LeConte; Coleoptera: Chrysomelidae) in the United States of America and revealed a high mean observed heterozygosity (0.40 ± 0.059) and low global FST (0.029). Pairwise FST estimates ranged from 0.007 to 0.045, and all but 2 populations showed significant levels of genetic differentiation (P ≤ 0.008). Population parameters and conclusions based on SNP markers were analogous to that obtained by use of microsatellite markers from the identical population samples. SNP-based FST estimates were 3-fold higher than corresponding estimates from microsatellites, wherein lower microsatellite FST estimates likely resulted from an overestimate of migration rates between subpopulations due to convergence of allele size (homoplasy). No significant difference was observed in the proportion of SNP or microsatellite markers loci that were nonneutral within populations. SNP markers provided estimates of population genetic parameters consistent with those from microsatellite data, and their low back mutation rates may result in reduced propensity for error in estimation of population parameters.

Background Analysis of allelic variation for relevant genes and monitoring chromosome segment transmission during selection are important approaches in plant breeding and ecology. To minimize the number of required molecular markers for this purpose is crucial due to cost and time constraints. To date, software for identification of the minimum number of required markers has been optimized for human genetics and is only partly matching the needs of plant scientists and breeders. In addition, different software packages with insufficient interoperability need to be combined to extract this information from available allele sequence data, resulting in an error-prone multi-step process of data handling. Results PolyMin, a computer program combining the detection of a minimum set of single nucleotide polymorphisms (SNPs) and / or insertions/deletions (INDELs) necessary for allele differentiation with the subsequent genotype differentiation in plant populations has been developed. Its efficiency in finding minimum sets of polymorphisms is comparable to other available program packages. Conclusions A computer program detecting the minimum number of SNPs for haplotype discrimination and subsequent genotype differentiation has been developed, and its performance compared to other relevant software. The main advantages of PolyMin, especially for plant scientists, is the integration of procedures from sequence analysis to polymorphism selection within a single program, including both haplotype and genotype differentiation.