Multiple testing for SNP-SNP interactions

Most genetic diseases are complex, i.e. associated to combinations of SNPs rather than individual SNPs. In the last few years, this topic has often been addressed in terms of SNP-SNP interaction patterns given as expressions linked by logical operators. Methods for multiple testing in high-dimensional settings can be applied when many SNPs are considered simultaneously. However, another less well-known multiple testing problem arises within a fixed subset of SNPs when the logic expression is chosen optimally. In this article, we propose a general asymptotic approach for deriving the distribution of the maximally selected chi-square statistic in various situations. We show how this result can be used for testing logic expressions - in particular SNP-SNP interaction patterns - while controlling for multiple comparisons. Simulations show that our method provides multiple testing adjustment when the logic expression is chosen such as to maximize the statistic. Its benefit is demonstrated through an application to a real dataset from a large population-based study considering allergy and asthma in KORA. An implementation of our method is available from the Comprehensive R Archive Network (CRAN) as R package 'SNPmaxsel'

GBS-SNP-CROP: a reference-optional pipeline for SNP discovery and plant germplasm characterization using variable length, paired-end genotyping-by-sequencing data

Background: With its simple library preparation and robust approach to genome reduction, genotyping-by-sequencing (GBS) is a flexible and cost-effective strategy for SNP discovery and genotyping, provided an appropriate reference genome is available. For resource-limited curation, research, and breeding programs of underutilized plant genetic resources, however, even low-depth references may not be within reach, despite declining sequencing costs. Such programs would find value in an open-source bioinformatics pipeline that can maximize GBS data usage and perform high-density SNP genotyping in the absence of a reference. Results: The GBS SNP-Calling Reference Optional Pipeline (GBS-SNP-CROP) developed and presented here adopts a clustering strategy to build a population-tailored “Mock Reference” from the same GBS data used for downstream SNP calling and genotyping. Designed for libraries of paired-end (PE) reads, GBS-SNP-CROP maximizes data usage by eliminating unnecessary data culling due to imposed read-length uniformity requirements. Using 150 bp PE reads from a GBS library of 48 accessions of tetraploid kiwiberry (Actinidia arguta), GBS-SNP-CROP yielded on average three times as many SNPs as TASSEL-GBS analyses (32 and 64 bp tag lengths) and over 18 times as many as TASSEL-UNEAK, with fewer genotyping errors in all cases, as evidenced by comparing the genotypic characterizations of biological replicates. Using the published reference genome of a related diploid species (A. chinensis), the reference-based version of GBS-SNP-CROP behaved similarly to TASSEL-GBS in terms of the number of SNPs called but had an improved read depth distribution and fewer genotyping errors. Our results also indicate that the sets of SNPs detected by the different pipelines above are largely orthogonal to one another; thus GBS-SNP-CROP may be used to augment the results of alternative analyses, whether or not a reference is available. Conclusions: By achieving high-density SNP genotyping in populations for which no reference genome is available, GBS-SNP-CROP is worth consideration by curators, researchers, and breeders of under-researched plant genetic resources. In cases where a reference is available, especially if from a related species or when the target population is particularly diverse, GBS-SNP-CROP may complement other reference-based pipelines by extracting more information per sequencing dollar spent. The current version of GBS-SNP-CROP is available at https://github.com/halelab/GBS-SNP-CROP.gi

Time After Time: Notes on Delays In Spiking Neural P Systems

Spiking Neural P systems, SNP systems for short, are biologically inspired computing devices based on how neurons perform computations. SNP systems use only one type of symbol, the spike, in the computations. Information is encoded in the time differences of spikes or the multiplicity of spikes produced at certain times. SNP systems with delays (associated with rules) and those without delays are two of several Turing complete SNP system variants in literature. In this work we investigate how restricted forms of SNP systems with delays can be simulated by SNP systems without delays. We show the simulations for the following spike routing constructs: sequential, iteration, join, and split.Comment: 11 pages, 9 figures, 4 lemmas, 1 theorem, preprint of Workshop on Computation: Theory and Practice 2012 at DLSU, Manila together with UP Diliman, DLSU, Tokyo Institute of Technology, and Osaka universit

Non-stationary self-similar Gaussian processes as scaling limits of power law shot noise processes and generalizations of fractional Brownian motion

We study shot noise processes with Poisson arrivals and non-stationary noises. The noises are conditionally independent given the arrival times, but the distribution of each noise does depend on its arrival time. We establish scaling limits for such shot noise processes in two situations: 1) the conditional variance functions of the noises have a power law and 2) the conditional noise distributions are piecewise. In both cases, the limit processes are self-similar Gaussian with nonstationary increments. Motivated by these processes, we introduce new classes of self-similar Gaussian processes with non-stationary increments, via the time-domain integral representation, which are natural generalizations of fractional Brownian motions.Published versio

Integrated probability of coronary heart disease subject to the -308 tumor necrosis factor-alpha SNP: a Bayesian meta-analysis

We present a meta-analysis of independent studies on the potential implication in the occurrence of coronary heart disease (CHD) of the single-nucleotide polymorphism (SNP) at the -308 position of the tumor necrosis factor alpha (TNF-alpha) gene. We use Bayesian analysis to integrate independent data sets and to infer statistically robust measurements of correlation. Bayesian hypothesis testing indicates that there is no preference for the hypothesis that the -308 TNF-alpha SNP is related to the occurrence of CHD, in the Caucasian or in the Asian population, over the null hypothesis. As a measure of correlation, we use the probability of occurrence of CHD conditional on the presence of the SNP, derived as the posterior probability of the Bayesian meta-analysis. The conditional probability indicates that CHD is not more likely to occur when the SNP is present, which suggests that the -308 TNF-alpha SNP is not implicated in the occurrence of CHD.Comment: 21 pages, 7 figures, Published in PeerJ (2015

Automated SNP genotype clustering algorithm to improve data completeness in high-throughput SNP genotyping datasets from custom arrays

High-throughput SNP genotyping platforms use automated genotype calling algorithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been optimized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be advisable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author

PTPN2 gene variants are associated with susceptibility to both Crohn's disease and ulcerative colitis supporting a common genetic disease background.

Genome-wide association studies identified PTPN2 (protein tyrosine phosphatase, non-receptor type 2) as susceptibility gene for inflammatory bowel diseases (IBD). However, the exact role of PTPN2 in Crohn's disease (CD) and ulcerative colitis (UC) and its phenotypic effect are unclear. We therefore performed a detailed genotype-phenotype and epistasis analysis of PTPN2 gene variants. Genomic DNA from 2131 individuals of Caucasian origin (905 patients with CD, 318 patients with UC, and 908 healthy, unrelated controls) was analyzed for two SNPs in the PTPN2 region (rs2542151, rs7234029) for which associations with IBD were found in previous studies in other cohorts. Our analysis revealed a significant association of PTPN2 SNP rs2542151 with both susceptibility to CD (p = 1.95×10⁻⁵; OR 1.49 [1.34-1.79]) and UC (p = 3.87×10⁻², OR 1.31 [1.02-1.68]). Moreover, PTPN2 SNP rs7234029 demonstrated a significant association with susceptibility to CD (p = 1.30×10⁻³; OR 1.35 [1.13-1.62]) and a trend towards association with UC (p = 7.53×10⁻²; OR 1.26 [0.98-1.62]). Genotype-phenotype analysis revealed an association of PTPN2 SNP rs7234029 with a stricturing disease phenotype (B2) in CD patients (p = 6.62×10⁻³). Epistasis analysis showed weak epistasis between the ATG16L1 SNP rs2241879 and PTPN2 SNP rs2542151 (p = 0.024) in CD and between ATG16L1 SNP rs4663396 and PTPN2 SNP rs7234029 (p = 4.68×10⁻³) in UC. There was no evidence of epistasis between PTPN2 and NOD2 and PTPN2 and IL23R. In silico analysis revealed that the SNP rs7234029 modulates potentially the binding sites of several transcription factors involved in inflammation including GATA-3, NF-κB, C/EBP, and E4BP4. Our data confirm the association of PTPN2 variants with susceptibility to both CD and UC, suggesting a common disease pathomechanism for these diseases. Given recent evidence that PTPN2 regulates autophagosome formation in intestinal epithelial cells, the potential link between PTPN2 and ATG16L1 should be further investigated

Identification of SNPs in TG and EDG1 genes and their relationships with carcass traits in Korean cattle (Hanwoo)

Thyroglobulin (TG) gene was known to be regulated fat cell growth and differentiation and the endothelial differentiation sphingolipid G-protein-coupled receptor 1 (EDG1) gene involves blood vessel formation and known to be affecting carcass traits in beef cattle. The aim of this study was to identify the single nucleotide polymorphisms (SNPs) in both TG and EDG1 genes and to analyze the association with carcass traits in Korean cattle (Hanwoo). The T354C SNP in TG gene located at the 3’ flanking region and c.-312A>G SNP located at 3’-UTR of EDG1 gene were used for genotyping the animals using PCR-RFLP method. Three genotypes were identified in T354C SNP in TG gene and only two AA and AG genotypes were observed for the c.-312A>G SNP in EDG1 gene. The results indicated that T354C SNP in TG gene was not significantly associated with carcass traits. However, the c.-312A>G SNP in EDG1 gene had significant effects on backfat thickness (BF) and yield index (YI). These results may provide valuable information for further candidate gene studies affecting carcass traits in Korean cattle and may use as marker assisted selection for improving the quality of meat in Hanwoo. Key words : TG, EDG1, Carcass traits, Hanwo