Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering

Whole-genome association studies present many new statistical and computational challenges due to the large quantity of data obtained. One of these challenges is haplotype inference; methods for haplotype inference designed for small data sets from candidate-gene studies do not scale well to the large number of individuals genotyped in whole-genome association studies. We present a new method and software for inference of haplotype phase and missing data that can accurately phase data from whole-genome association studies, and we present the first comparison of haplotype-inference methods for real and simulated data sets with thousands of genotyped individuals. We find that our method outperforms existing methods in terms of both speed and accuracy for large data sets with thousands of individuals and densely spaced genetic markers, and we use our method to phase a real data set of 3,002 individuals genotyped for 490,032 markers in 3.1 days of computing time, with 99% of masked alleles imputed correctly. Our method is implemented in the Beagle software package, which is freely available.     

snp.plotter: an R-based SNP/haplotype association and linkage disequilibrium plotting package

snp.plotter is a newly developed R package which produces high-quality plots of results from genetic association studies. The main features of the package include options to display a linkage disequilibrium (LD) plot below the P-value plot using either the r2 or D' LD metric, to set the X-axis to equal spacing or to use the physical map of markers, and to specify plot labels, colors, symbols and LD heatmap color scheme. snp.plotter can plot single SNP and/or haplotype data and simultaneously plot multiple sets of results. R is a free software environment for statistical computing and graphics available for most platforms. The proposed package provides a simple way to convey both association and LD information in a single appealing graphic for genetic association studies. AVAILABILITY: Downloadable R package and example datasets are available at http://cbdb.nimh.nih.gov/~kristin/snp.plotter.html and http://www.r-project.org.     

Genotype calling and mapping of multisite variants using an Atlantic salmon iSelect SNP array

MOTIVATION: Due to a genome duplication event in the recent history of salmonids, modern Atlantic salmon (Salmo salar) have a mosaic genome with roughly one-third being tetraploid. This is a complicating factor in genotyping and genetic mapping since polymorphisms within duplicated regions (multisite variants; MSVs) are challenging to call and to assign to the correct paralogue. Standard genotyping software offered by Illumina has not been written to interpret MSVs and will either fail or miscall these polymorphisms. For the purpose of mapping, linkage or association studies in non-diploid species, there is a pressing need for software that includes analysis of MSVs in addition to regular single nucleotide polymorphism (SNP) markers. RESULTS: A software package is presented for the analysis of partially tetraploid genomes genotyped using Illumina Infinium BeadArrays (Illumina Inc.) that includes pre-processing, clustering, plotting and validation routines. More than 3000 salmon from an aquacultural strain in Norway, distributed among 266 full-sib families, were genotyped on a 15K BeadArray including both SNP- and MSV-markers. A total of 4268 SNPs and 1471 MSVs were identified, with average call accuracies of 0.97 and 0.86, respectively. A total of 150 MSVs polymorphic in both paralogs were dissected and mapped to their respective chromosomes, yielding insights about the salmon genome reversion to diploidy and improving marker genome coverage. Several retained homologies were found and are reported. Availability and implementation: R-package beadarrayMSV freely available on the web at http://cran.r-project.org/.     

PPC: an algorithm for accurate estimation of SNP allele frequencies in small equimolar pools of DNA using data from high density microarrays

Robust estimation of allele frequencies in pools of DNA has the potential to reduce genotyping costs and/or increase the number of individuals contributing to a study where hundreds of thousands of genetic markers need to be genotyped in very large populations sample sets, such as genome wide association studies. In order to make accurate allele frequency estimations from pooled samples a correction for unequal allele representation must be applied. We have developed the polynomial based probe specific correction (PPC) which is a novel correction algorithm for accurate estimation of allele frequencies in data from high-density microarrays. This algorithm was validated through comparison of allele frequencies from a set of 10 individually genotyped DNA's and frequencies estimated from pools of these 10 DNAs using GeneChip 10K Mapping Xba 131 arrays. Our results demonstrate that when using the PPC to correct for allelic biases the accuracy of the allele frequency estimates increases dramatically.     

Inferring missing genotypes in large SNP panels using fast nearest-neighbor searches over sliding windows

MOTIVATION: Typical high-throughput genotyping techniques produce numerous missing calls that confound subsequent analyses, such as disease association studies. Common remedies for this problem include removing affected markers and/or samples or, otherwise, imputing the missing data. On small marker sets imputation is frequently based on a vote of the K-nearest-neighbor (KNN) haplotypes, but this technique is neither practical nor justifiable for large datasets. RESULTS: We describe a data structure that supports efficient KNN queries over arbitrarily sized, sliding haplotype windows, and evaluate its use for genotype imputation. The performance of our method enables exhaustive exploration over all window sizes and known sites in large (150K, 8.3M) SNP panels. We also compare the accuracy and performance of our methods with competing imputation approaches. AVAILABILITY: A free open source software package, NPUTE, is available at http://compgen.unc.edu/software, for non-commercial uses.     

P2BAT: a massive parallel implementation of PBAT for genome-wide association studies in R

The software tool P2BAT provides a massive parallel and user friendly implementation of the PBAT-analysis tools for family-based association tests (FBATs) in large-scale studies, including genome-wide association studies with several thousand subjects. Built on the original PBAT-implementation of the Lange-Van Steen algorithm to bypass the multiple testing problem in family-based association studies, P2BAT integrates all PBAT-analysis tools for binary and complex traits into R and makes them accessible through a user-friendly GUI. The genome-wide analysis tools are fully automated and can be ran massively parallel directly through the GUI. P2BAT is fully documented and contains graphical output tools for time-to-onset analysis. P2BAT also features the ability to test for gene and environment/drug interaction. AVAILABILITY: The P2BAT package is available as the R package 'pbatR' which can be downloaded from http://cran.r-project.org/. The PBAT-software is available at http://www.biostat.harvard.edu/~clange/.     

Bait-ER: A Bayesian method to detect targets of selection in Evolve-and-Resequence experiments

For over a decade, experimental evolution has been combined with high-throughput sequencing techniques. In so-called Evolve-and-Resequence (E&R) experiments, populations are kept in the laboratory under controlled experimental conditions where their genomes are sampled and allele frequencies monitored. However, identifying signatures of adaptation in E&R datasets is far from trivial, and it is still necessary to develop more efficient and statistically sound methods for detecting selection in genome-wide data. Here, we present Bait-ER – a fully Bayesian approach based on the Moran model of allele evolution to estimate selection coefficients from E&R experiments. The model has overlapping generations, a feature that describes several experimental designs found in the literature. We tested our method under several different demographic and experimental conditions to assess its accuracy and precision, and it performs well in most scenarios. Nevertheless, some care must be taken when analysing trajectories where drift largely dominates and starting frequencies are low. We compare our method with other available software and report that ours has generally high accuracy even for trajectories whose complexity goes beyond a classical sweep model. Furthermore, our approach avoids the computational burden of simulating an empirical null distribution, outperforming available software in terms of computational time and facilitating its use on genome-wide data. We implemented and released our method in a new open-source software package that can be accessed at https://doi.org/10.5281/zenodo.7351736 .     

DNA pooling: a comprehensive, multi-stage association analysis of ACSL6 and SIRT5 polymorphisms in schizophrenia

Many candidate gene association studies have evaluated incomplete, unrepresentative sets of single nucleotide polymorphisms (SNPs), producing non-significant results that are difficult to interpret. Using a rapid, efficient strategy designed to investigate all common SNPs, we tested associations between schizophrenia and two positional candidate genes: ACSL6 (Acyl-Coenzyme A synthetase long-chain family member 6) and SIRT5 (silent mating type information regulation 2 homologue 5). We initially evaluated the utility of DNA sequencing traces to estimate SNP allele frequencies in pooled DNA samples. The mean variances for the DNA sequencing estimates were acceptable and were comparable to other published methods (mean variance: 0.0008, range 0-0.0119). Using pooled DNA samples from cases with schizophrenia/schizoaffective disorder (Diagnostic and Statistical Manual of Mental Disorders edition IV criteria) and controls (n=200, each group), we next sequenced all exons, introns and flanking upstream/downstream sequences for ACSL6 and SIRT5. Among 69 identified SNPs, case-control allele frequency comparisons revealed nine suggestive associations (P<0.2). Each of these SNPs was next genotyped in the individual samples composing the pools. A suggestive association with rs 11743803 at ACSL6 remained (allele-wise P=0.02), with diminished evidence in an extended sample (448 cases, 554 controls, P=0.062). In conclusion, we propose a multi-stage method for comprehensive, rapid, efficient and economical genetic association analysis that enables simultaneous SNP detection and allele frequency estimation in large samples. This strategy may be particularly useful for research groups lacking access to high throughput genotyping facilities. Our analyses did not yield convincing evidence for associations of schizophrenia with ACSL6 or SIRT5.     

BaSAR-A tool in R for frequency detection

Many biological processes are periodic, for example cell cycle expression, circadian rhythms and calcium oscillations. However, measured time series from these processes are commonly short and noisy, and finding frequencies in such data can be challenging. Here we present BaSAR, Bayesian Spectrum Analysis in R, a package for extracting frequency information from time series data. The software uses advanced techniques of Bayesian inference that are well suited for handling typical biological data. The core functions are designed for detecting a single key frequency, without the need for data pre-processing such as detrending. The package is freely available at CRAN - The Comprehensive R Archive Network: http://cran.r-project.org/web/packages/BaSAR.     

Multitrait Bayesian shrinkage and variable selection models with the BGLR-R package

The BGLR-R package implements various types of single-trait shrinkage/variable selection Bayesian regressions. The package was first released in 2014, since then it has become a software very often used in genomic studies. We recently develop functionality for multitrait models. The implementation allows users to include an arbitrary number of random-effects terms. For each set of predictors, users can choose diffuse, Gaussian, and Gaussian–spike–slab multivariate priors. Unlike other software packages for multitrait genomic regressions, BGLR offers many specifications for (co)variance parameters (unstructured, diagonal, factor analytic, and recursive). Samples from the posterior distribution of the models implemented in the multitrait function are generated using a Gibbs sampler, which is implemented by combining code written in the R and C programming languages. In this article, we provide an overview of the models and methods implemented BGLR’s multitrait function, present examples that illustrate the use of the package, and benchmark the performance of the software.     

Genotyping pooled DNA using 100K SNP microarrays: a step towards genomewide association scans

The identification of quantitative trait loci (QTLs) of small effect size that underlie complex traits poses a particular challenge for geneticists due to the large sample sizes and large numbers of genetic markers required for genomewide association scans. An efficient solution for screening purposes is to combine single nucleotide polymorphism (SNP) microarrays and DNA pooling (SNP-MaP), an approach that has been shown to be valid, reliable and accurate in deriving relative allele frequency estimates from pooled DNA for groups such as cases and controls for 10K SNP microarrays. However, in order to conduct a genomewide association study many more SNP markers are needed. To this end, we assessed the validity and reliability of the SNP-MaP method using Affymetrix GeneChip^® Mapping 100K Array set. Interpretable results emerged for 95% of the SNPs (nearly 110000 SNPs). We found that SNP-MaP allele frequency estimates correlated 0.939 with allele frequencies for 97605 SNPs that were genotyped individually in an independent population; the correlation was 0.971 for 26 SNPs that were genotyped individually for the 1028 individuals used to construct the DNA pools. We conclude that extending the SNP-MaP method to the Affymetrix GeneChip^® Mapping 100K Array set provides a useful screen of >100000 SNP markers for QTL association scans.     

HAPLORE: a program for haplotype reconstruction in general pedigrees without recombination

MOTIVATION: Haplotype reconstruction is an essential step in genetic linkage and association studies. Although many methods have been developed to estimate haplotype frequencies and reconstruct haplotypes for a sample of unrelated individuals, haplotype reconstruction in large pedigrees with a large number of genetic markers remains a challenging problem. METHODS: We have developed an efficient computer program, HAPLORE (HAPLOtype REconstruction), to identify all haplotype sets that are compatible with the observed genotypes in a pedigree for tightly linked genetic markers. HAPLORE consists of three steps that can serve different needs in applications. In the first step, a set of logic rules is used to reduce the number of compatible haplotypes of each individual in the pedigree as much as possible. After this step, the haplotypes of all individuals in the pedigree can be completely or partially determined. These logic rules are applicable to completely linked markers and they can be used to impute missing data and check genotyping errors. In the second step, a haplotype-elimination algorithm similar to the genotype-elimination algorithms used in linkage analysis is applied to delete incompatible haplotypes derived from the first step. All superfluous haplotypes of the pedigree members will be excluded after this step. In the third step, the expectation-maximization (EM) algorithm combined with the partition and ligation technique is used to estimate haplotype frequencies based on the inferred haplotype configurations through the first two steps. Only compatible haplotype configurations with haplotypes having frequencies greater than a threshold are retained. RESULTS: We test the effectiveness and the efficiency of HAPLORE using both simulated and real datasets. Our results show that, the rule-based algorithm is very efficient for completely genotyped pedigree. In this case, almost all of the families have one unique haplotype configuration. In the presence of missing data, the number of compatible haplotypes can be substantially reduced by HAPLORE, and the program will provide all possible haplotype configurations of a pedigree under different circumstances, if such multiple configurations exist. These inferred haplotype configurations, as well as the haplotype frequencies estimated by the EM algorithm, can be used in genetic linkage and association studies. AVAILABILITY: The program can be downloaded from http://bioinformatics.med.yale.edu.     

ExactFDR: exact computation of false discovery rate estimate in case-control association studies

Genome-wide association studies require accurate and fast statistical methods to identify relevant signals from the background noise generated by a huge number of simultaneously tested hypotheses. It is now commonly accepted that exact computations of association probability value (P-value) are preferred to chi(2) and permutation-based approximations. Following the same principle, the ExactFDR software package improves speed and accuracy of the permutation-based false discovery rate (FDR) estimation method by replacing the permutation-based estimation of the null distribution by the generalization of the algorithm used for computing individual exact P-values. It provides a quick and accurate non-conservative estimator of the proportion of false positives in a given selection of markers, and is therefore an efficient and pragmatic tool for the analysis of genome-wide association studies.     

白介素10和白介素17基因启动子区多态性位点与儿童哮喘的相关性研究

目的：探讨IL-10基因启动子区-627A/C和IL-17基因启动子-152A/G位点多态性与儿童哮喘发生的相关性。方法：采用聚合酶链反应-限制性片段长度多态性分析（PCR-PFLP）方法检测186名哮喘儿童、198名健康儿童各个多态性位点的基因型,采用SPSS13.0进行统计学分析。结果：IL-17基因-152A/G位点的基因型及等位基因频率分布在哮喘组与正常对照组均存在显著性差异（p〈0.05）,哮喘组-152A/G位点等位基因A频率显著高于正常对照组（x2=6.077,p=0.014,OR=1.430,95%CI=1.076-1.902）。结论：IL-17基因-152A/G位点可能与儿童哮喘的发病存在关系,其中A等位基因可能是易感基因,携带A的个体可能更易患有哮喘。     

MLR-tagging: informative SNP selection for unphased genotypes based on multiple linear regression

The search for the association between complex diseases and single nucleotide polymorphisms (SNPs) or haplotypes has recently received great attention. For these studies, it is essential to use a small subset of informative SNPs accurately representing the rest of the SNPs. Informative SNP selection can achieve (1) considerable budget savings by genotyping only a limited number of SNPs and computationally inferring all other SNPs or (2) necessary reduction of the huge SNP sets (obtained, e.g. from Affymetrix) for further fine haplotype analysis. A novel informative SNP selection method for unphased genotype data based on multiple linear regression (MLR) is implemented in the software package MLR-tagging. This software can be used for informative SNP (tag) selection and genotype prediction. The stepwise tag selection algorithm (STSA) selects positions of the given number of informative SNPs based on a genotype sample population. The MLR SNP prediction algorithm predicts a complete genotype based on the values of its informative SNPs, their positions among all SNPs, and a sample of complete genotypes. An extensive experimental study on various datasets including 10 regions from HapMap shows that the MLR prediction combined with stepwise tag selection uses fewer tags than the state-of-the-art method of Halperin et al. (2005). AVAILABILITY: MLR-Tagging software package is publicly available at http://alla.cs.gsu.edu/~software/tagging/tagging.html     

A friendly statistics package for microarray analysis

SUMMARY: The friendly statistics package for microarray analysis (FSPMA) is a tool that aims to fill the gap between simple to use and powerful analysis. FSPMA is a platform-independent R-package that allows efficient exploration of microarray data without the need for computer programming. Analysis is based on a mixed model ANOVA library (YASMA) that was extended to allow more flexible comparisons and other useful operations like k nearest neighbour imputing and spike-based normalization. Processing is controlled by a definition file that specifies all the steps necessary to derive analysis results from quantified microarray data. In addition to providing analysis without programming, the definition file also serves as exact documentation of all the analysis steps. AVAILABILITY: The library is available under GPL 2 license and, together with additional information, provided at http://www.ccbi.cam.ac.uk/software/psyk/software.html#fspma     

大口黑鲈转录组SNPs筛选及其与生长的关联分析

为开发人工饲料代替冰鲜杂鱼养殖大口黑鲈的分子标记,以食用冰鲜鱼和配合饲料的同批大口黑鲈为研究材料,利用RNA-Seq（RNA sequencing）技术挖掘SNPs（Single nucleotide polymorphisms）标记,并以关联分析筛选可用于育种的候选标记。转录组进行测序共获得174 M数据,8681个SNPs位点。挑选其中具有表达差异的50个SNPs位点进行SNaPshot分型,结果39个分型成功,其中有4个为假阳性,通过转录组技术开发出SNPs标记35个,成功率为70.0%。为进一步检验这些标记是否可用于评估驯食饲料的大口黑鲈选育研究,研究以327尾摄食人工配合饲料的大口黑鲈为试验材料,SPSS软件进行一般线性模型分析SNPs的不同基因型与生长性状的相关性,结果显示有2个SNPs位点与体质量、全长和体高等生长性状存在显著相关性（P<0.05）,可作为候选标记用于大口黑鲈的分子辅助育种。由于转录组数据直接反应基因的表达情况,从中挖掘与性状相关的优势基因型与分子标记的成功率高,效果较好。同时也为解决大口黑鲈选育研究中标记缺乏提供了有效途径,为选育提供遗传依据、加速育种进程。     

Exploring massive, genome scale datasets with the GenometriCorr package

We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features), for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets. AVAILABILITY AND IMPLEMENTATION: The package, GenometriCorr, can be freely downloaded at http://genometricorr.sourceforge.net/. Installation guidelines and examples are available from the sourceforge repository. The package is pending submission to Bioconductor.