Number Of Single Nucleotide Polymorphism Loci Research Articles

EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm.

In genome-wide association studies, detecting high-order epistasis is important for analyzing the occurrence of complex human diseases and explaining missing heritability. However, there are various challenges in the actual high-order epistasis detection process due to the large amount of data, “small sample size problem”, diversity of disease models, etc. This paper proposes a multi-objective genetic algorithm (EpiMOGA) for single nucleotide polymorphism (SNP) epistasis detection. The K2 score based on the Bayesian network criterion and the Gini index of the diversity of the binary classification problem were used to guide the search process of the genetic algorithm. Experiments were performed on 26 simulated datasets of different models and a real Alzheimer’s disease dataset. The results indicated that EpiMOGA was obviously superior to other related and competitive methods in both detection efficiency and accuracy, especially for small-sample-size datasets, and the performance of EpiMOGA remained stable across datasets of different disease models. At the same time, a number of SNP loci and 2-order epistasis associated with Alzheimer’s disease were identified by the EpiMOGA method, indicating that this method is capable of identifying high-order epistasis from genome-wide data and can be applied in the study of complex diseases.

BasePhasing: a highly efficient approach for preimplantation genetic haplotyping in clinical application of balanced translocation carriers

BackgroundPreimplantation genetic testing (PGT) has already been applied in chromosomally balanced translocation carriers to improve the clinical outcome of assisted reproduction. However, traditional methods could not further distinguish embryos carrying a translocation from those with a normal karyotype prior to implantation.MethodsTo solve this problem, we developed a method named “Chromosomal Phasing on Base level” (BasePhasing), which based on Infinium Asian Screening Array-24 v1.0 (ASA) and a specially phasing pipeline. Firstly, by comparing the number of single nucleotide polymorphism (SNP) loci in different minor allele frequencies (MAFs) and in 2Mbp continuous windows of ASA chip and karyomap-12 chip, we verified whether ASA could be adopted for genome-wide haplotype linkage analysis. Besides, the whole gene amplification (WGA) of 3–10 cells of GM16457 cell line was used to verify whether ASA chip could be used for testing of WGA products. Finally, two balanced translocation families were utilized to carry out BasePhasing and to validate the feasibility of its clinical application.ResultsThe average number of SNP loci in each window of ASA (473.2) was twice of that of Karyomap-12 (201.2). The coincidence rate of SNP loci in genomic DNA and WGA products was about 97%. The 5.3Mbp deletion was detected positively in cell line GM16457 of both genomic DNA and WGA products, and haplotype linkage analysis was performed in genome wide successfully. In the two balanced translocation families, 18 blastocysts were analyzed, in which 8 were unbalanced and the other 10 were balanced or normal chromosomes. Two embryos were transferred back to the patients successfully, and prenatal cytogenetic analysis of amniotic fluid was performed in the second trimester. The results predicted by BasePhasing and prenatal diagnosis were totally consistent.ConclusionsInfinium ASA bead chip based BasePhasing pipeline shows good performance in balanced translocation carrier testing. With the characteristics of simple operation procedure and accurate results, we demonstrate that BasePhasing is one of the most suitable methods to distinguish between balanced and structurally normal chromosome embryos from translocation carriers in PGT at present.

Inferring Selection Intensity and Allele Age from Multilocus Haplotype Structure

It is a challenging task to infer selection intensity and allele age from population genetic data. Here we present a method that can efficiently estimate selection intensity and allele age from the multilocus haplotype structure in the vicinity of a segregating mutant under positive selection. We use a structured-coalescent approach to model the effect of directional selection on the gene genealogies of neutral markers linked to the selected mutant. The frequency trajectory of the selected allele follows the Wright-Fisher model. Given the position of the selected mutant, we propose a simplified multilocus haplotype model that can efficiently model the dynamics of the ancestral haplotypes under the joint influence of selection and recombination. This model approximates the ancestral genealogies of the sample, which reduces the number of states from an exponential function of the number of single-nucleotide polymorphism loci to a quadratic function. That allows parameter inference from data covering DNA regions as large as several hundred kilo-bases. Importance sampling algorithms are adopted to evaluate the probability of a sample by exploring the space of both allele frequency trajectories of the selected mutation and gene genealogies of the linked sites. We demonstrate by simulation that the method can accurately estimate selection intensity for moderate and strong positive selection. We apply the method to a data set of the G6PD gene in an African population and obtain an estimate of 0.0456 (95% confidence interval 0.0144−0.0769) for the selection intensity. The proposed method is novel in jointly modeling the multilocus haplotype pattern caused by recombination and mutation, allowing the analysis of haplotype data in recombining regions. Moreover, the method is applicable to data from populations under exponential growth and a variety of other demographic histories.