Large SNP Datasets Research Articles

Insights into the structural and functional analysis of impact of the missense mutations on α-synuclein: an in silico study

BackgroundAlpha synuclein (α-synuclein) is coded by SNCA gene and found in a helical form with phospholipids or in an unfolded arrangement in the cytosol and belongs to the synuclein family other than beta synuclein and gamma synuclein. It is a short protein made of 140 amino acids with three domains: an N-terminal lipid binding helix, a non-amyloid-ß component (NAC), and an acidic tail at the C-terminus. α-Synuclein is present in aggregated and fibrillar form in Lewy bodies and its association has been related to multiple system atrophy (MSA), Parkinson’s disease (PD), and Dementia with Lewy bodies (DLB). Our objective is to investigate and prioritise the possible nsSNPs in the α-synuclein protein that have been potentially connected to human neurodegenerative diseases.ResultsWe used the series of computational tools to predict the mutation's harmful effect on three-dimensional structure of α-synuclein based on consensus approach. Our findings pointed to a significant computational blueprint for discovering nsSNPs connected to neurodegenerative illnesses from a large SNP data set while also minimising the expenses of experimentally showing harmful nsSNPs.ConclusionsThe prioritised G25S (rs1433622151), V66E (rs1261243630), and V77D (rs745815563) mutations can be employed in additional experimental studies to assess the α-synuclein protein mutation in relation to neurodegenerative illnesses and develop a therapeutics against them.Graphical

Unmasking microsatellite deceptiveness and debunking hybridization with SNPs in four marine copepod species of Calanus.

Interspecific hybridization events are on the rise in natural systems due to climate change disrupting species barriers. Across taxa, microsatellites have long been the molecular markers of choice to identify admixed individuals. However, with the advent of high-throughput sequencing easing the generation of genome-wide datasets, incorrect reports of hybridization resulting from microsatellite technical artefacts have been uncovered in a growing number of taxa. In the marine zooplankton genus Calanus (Copepoda), whose species are used as climate change indicators, microsatellite markers have suggested hybridization between C. finmarchicus and C. glacialis, while other nuclear markers (InDels) never detected any admixed individuals, leaving the scientific community divided. Here, for the first time, we investigated the potential for hybridization among C. finmarchicus, C. glacialis, C. helgolandicus and C. hyperboreus using two large and independent SNP datasets. These were derived firstly from a protocol of target-capture applied to 179 individuals collected from 17 sites across the North Atlantic and Arctic Oceans, including sympatric areas, and second from published RNA sequences. All SNP-based analyses were congruent in showing that Calanus species are distinct and do not appear to hybridize. We then thoroughly re-assessed the microsatellites showing hybrids, with the support of published transcriptomes, and identified technical issues plaguing eight out of 10 microsatellites, including size homoplasy, paralogy, potential for null alleles and even two primer pairs targeting the same locus. Our study illustrates how deceptive microsatellites can be when applied to the investigation of hybridization.

The value of a large Pisum SNP dataset

The value of a large Pisum SNP dataset

The effects of climate and demographic history in shaping genomic variation across populations of the Desert Horned Lizard (Phrynosoma platyrhinos).

Species often experience spatial environmental heterogeneity across their range, and populations may exhibit signatures of adaptation to local environmental characteristics. Other population genetic processes, such as migration and genetic drift, can impede the effects of local adaptation. Genetic drift in particular can have a pronounced effect on population genetic structure during large-scale geographic expansions, where a series of founder effects leads to decreases in genetic variation in the direction of the expansion. Here, we explore the genetic diversity of a desert lizard that occupies a wide range of environmental conditions and that has experienced post-glacial expansion northwards along two colonization routes. Based on our analyses of a large SNP data set, we find evidence that both climate and demographic history have shaped the genetic structure of populations. Pronounced genetic differentiation was evident between populations occupying cold versus hot deserts, and we detected numerous loci with significant associations with climate. The genetic signal of founder effects, however, is still present in the genomes of the recently expanded populations, which comprise subsets of genetic variation found in the southern populations.

Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest.

Simulation‐based methods such as approximate Bayesian computation (ABC) are well‐adapted to the analysis of complex scenarios of populations and species genetic history. In this context, supervised machine learning (SML) methods provide attractive statistical solutions to conduct efficient inferences about scenario choice and parameter estimation. The Random Forest methodology (RF) is a powerful ensemble of SML algorithms used for classification or regression problems. Random Forest allows conducting inferences at a low computational cost, without preliminary selection of the relevant components of the ABC summary statistics, and bypassing the derivation of ABC tolerance levels. We have implemented a set of RF algorithms to process inferences using simulated data sets generated from an extended version of the population genetic simulator implemented in DIYABC v2.1.0. The resulting computer package, named DIYABC Random Forest v1.0, integrates two functionalities into a user‐friendly interface: the simulation under custom evolutionary scenarios of different types of molecular data (microsatellites, DNA sequences or SNPs) and RF treatments including statistical tools to evaluate the power and accuracy of inferences. We illustrate the functionalities of DIYABC Random Forest v1.0 for both scenario choice and parameter estimation through the analysis of pseudo‐observed and real data sets corresponding to pool‐sequencing and individual‐sequencing SNP data sets. Because of the properties inherent to the implemented RF methods and the large feature vector (including various summary statistics and their linear combinations) available for SNP data, DIYABC Random Forest v1.0 can efficiently contribute to the analysis of large SNP data sets to make inferences about complex population genetic histories.

An empirical comparison of population genetic analyses using microsatellite and SNP data for a species of conservation concern

BackgroundUse of genomic tools to characterize wildlife populations has increased in recent years. In the past, genetic characterization has been accomplished with more traditional genetic tools (e.g., microsatellites). The explosion of genomic methods and the subsequent creation of large SNP datasets has led to the promise of increased precision in population genetic parameter estimates and identification of demographically and evolutionarily independent groups, as well as questions about the future usefulness of the more traditional genetic tools. At present, few empirical comparisons of population genetic parameters and clustering analyses performed with microsatellites and SNPs have been conducted.ResultsHere we used microsatellite and SNP data generated from Gunnison sage-grouse (Centrocercus minimus) samples to evaluate concordance of the results obtained from each dataset for common metrics of genetic diversity (HO, HE, FIS, AR) and differentiation (FST, GST, DJost). Additionally, we evaluated clustering of individuals using putatively neutral (SNPs and microsatellites), putatively adaptive, and a combined dataset of putatively neutral and adaptive loci. We took particular interest in the conservation implications of any differences. Generally, we found high concordance between microsatellites and SNPs for HE, FIS, AR, and all differentiation estimates. Although there was strong correlation between metrics from SNPs and microsatellites, the magnitude of the diversity and differentiation metrics were quite different in some cases. Clustering analyses also showed similar patterns, though SNP data was able to cluster individuals into more distinct groups. Importantly, clustering analyses with SNP data suggest strong demographic independence among the six distinct populations of Gunnison sage-grouse with some indication of evolutionary independence in two or three populations; a finding that was not revealed by microsatellite data.ConclusionWe demonstrate that SNPs have three main advantages over microsatellites: more precise estimates of population-level diversity, higher power to identify groups in clustering methods, and the ability to consider local adaptation. This study adds to a growing body of work comparing the use of SNPs and microsatellites to evaluate genetic diversity and differentiation for a species of conservation concern with relatively high population structure and using the most common method of obtaining SNP genotypes for non-model organisms.

FGTpartitioner: A rapid method for parsimonious delimitation of ancestry breakpoints in large genome-wide SNP datasets

Abstract Partitioning large (e.g. chromosomal) alignments into ancestry blocks is a common step in phylogenomic analyses. However, current solutions require complicated analytical assumptions, or are difficult to implement due to excessive runtimes and unintuitive documentation. Additionally, most methods require haplotype phasing, which is often intractable for non-model studies. Here, I present an efficient and rapid solution for partitioning large genome alignments into ancestry blocks, which better accommodates non-model diploid organisms in that phasing information is not required a priori. FGTpartitioner processes a full-chromosome alignment orders of magnitude faster than alternative solutions, while recovering identical results, both via algorithmic improvements and the use of native parallelization. FGTpartitioner provides a means for simple and rapid block delimitation in genome-wide datasets as a pretext for phylogenomic analysis. It thus widens the potential uses for researchers studying phylogenetic processes across large, non-model genomes. Complete code and documentation for FGTpartitioner are available as an open-source repository on GitHub: https://github.com/tkchafin/FGTpartitioner

LD-annot: A Bioinformatics Tool to Automatically Provide Candidate SNPs With Annotations for Genetically Linked Genes.

A multitude of model and non-model species studies have now taken full advantage of powerful high-throughput genotyping advances such as SNP arrays and genotyping-by-sequencing (GBS) technology to investigate the genetic basis of trait variation. However, due to incomplete genome coverage by these technologies, the identified SNPs are likely in linkage disequilibrium (LD) with the causal polymorphisms, rather than be causal themselves. In addition, researchers could benefit from annotations for the identified candidate SNPs and, simultaneously, for all neighboring genes in genetic linkage. In such case, LD extent estimation surrounding the candidate SNPs is required to determine the regions encompassing genes of interest. We describe here an automated pipeline, “LD-annot,” designed to delineate specific regions of interest for a given experiment and candidate polymorphisms on the basis of LD extent, and furthermore, provide annotations for all genes within such regions. LD-annot uses standard file formats, bioinformatics tools, and languages to provide identifiers, coordinates, and annotations for genes in genetic linkage with each candidate polymorphism. Although the focus lies upon SNP arrays and GBS data as they are being routinely deployed, this pipeline can be applied to a variety of datasets as long as genotypic data are available for a high number of polymorphisms and formatted into a vcf file. A checkpoint procedure in the pipeline allows to test several threshold values for linkage without having to rerun the entire pipeline, thus saving the user computational time and resources. We applied this new pipeline to four different sample sets: two breeding populations GBS datasets, one within-pedigree SNP set coming from whole genome sequencing (WGS), and a very large multi-varieties SNP dataset obtained from WGS, representing variable sample sizes, and numbers of polymorphisms. LD-annot performed within minutes, even when very high numbers of polymorphisms are investigated and thus will efficiently assist research efforts aimed at identifying biologically meaningful genetic polymorphisms underlying phenotypic variation. LD-annot tool is available under a GPL license from https://github.com/ArnaudDroitLab/LD-annot.

Single and Multi-trait GWAS Identify Genetic Factors Associated with Production Traits in Common Bean Under Abiotic Stress Environments.

The genetic improvement of economically important production traits of dry bean (Phaseolus vulgaris L.), for geographic regions where production is threatened by drought and high temperature stress, is challenging because of the complex genetic nature of these traits. Large scale SNP data sets for the two major gene pools of bean, Andean and Middle American, were developed by mapping multiple pools of genotype-by-sequencing reads and identifying over 200k SNPs for each gene pool against the most recent assembly of the P. vulgaris genome sequence. Moderately sized Bean Abiotic Stress Evaluation (BASE) panels, consisting of genotypes appropriate for production in Central America and Africa, were assembled. Phylogenetic analyses demonstrated the BASE populations represented broad genetic diversity for the appropriate races within the two gene pools. Joint mixed linear model genome-wide association studies with data from multiple locations discovered genetic factors associated with four production traits in both heat and drought stress environments using the BASE panels. Pleiotropic genetic factors were discovered using a multi-trait mixed model analysis. SNPs within or near candidate genes associated with hormone signaling, epigenetic regulation, and ROS detoxification under stress conditions were identified and can be used as genetic markers in dry bean breeding programs.

Analyses of African common bean (Phaseolus vulgaris L.) germplasm using a SNP fingerprinting platform: diversity, quality control and molecular breeding

Common bean (Phaseolus vulgaris L.) is an important staple crop for smallholder farmers, particularly in Eastern and Southern Africa. To support common bean breeding and seed dissemination, a high throughput SNP genotyping platform with 1500 established SNP assays has been developed at a genotyping service provider which allows breeders without their own genotyping infrastructure to outsource such service. A set of 708 genotypes mainly composed of germplasm from African breeders and CIAT breeding program were assembled and genotyped with over 800 SNPs. Diversity analysis revealed that both Mesoamerican and Andean gene pools are in use, with an emphasis on large seeded Andean genotypes, which represents the known regional preferences. The analysis of genetic similarities among germplasm entries revealed duplicated lines with different names as well as distinct SNP patterns in identically named samples. Overall, a worrying number of inconsistencies was identified in this data set of very diverse origins. This exemplifies the necessity to develop and use a cost-effective fingerprinting platform to ensure germplasm purity for research, sharing and seed dissemination. The genetic data also allows to visualize introgressions, to identify heterozygous regions to evaluate hybridization success and to employ marker-assisted selection. This study presents a new resource for the common bean community, a SNP genotyping platform, a large SNP data set and a number of applications on how to utilize this information to improve the efficiency and quality of seed handling activities, breeding, and seed dissemination through molecular tools.

Quantitative analysis of connectivity in populations of a semi-aquatic mammal using kinship categories and network assortativity.

Analysing the impact of anthropogenic and natural river barriers on the dispersal of aquatic and semi-aquatic species may be critical for their conservation. Knowledge of kinship relationships between individuals and reconstructions of pedigrees obtained using genomic data can be extremely useful, not only for studying the social organization of animals, but also inferring contemporary dispersal and quantifying the effect of specific barriers on current connectivity. In this study, we used kinship data to analyse connectivity patterns in a small semi-aquatic mammal, the Pyrenean desman (Galemys pyrenaicus), in an area comprising two river systems with close headwaters and dams of various heights and types. Using a large SNP dataset from 70 specimens, we obtained kinship categories and reconstructed pedigrees. To quantify the barrier effect of specific obstacles, we built kinship networks and devised a method based on the assortativity coefficient, which measures the proportion between observed and expected kinship relationships across a barrier. The estimation of this parameter enabled us to infer that the most important barrier in the area was the watershed divide between the rivers, followed by a dam on one of the rivers. Other barriers did not significantly reduce the expected number of kinship relationships across them. This strategy and the information obtained with it may be crucial in determining the most important connectivity problems in an area and help develop conservation plans aimed at improving genetic exchange between populations of threatened species.

Genotyping-by-sequencing reveals the origin of the Tunisian relatives of cultivated carrot (Daucus carota)

Tunisia, other countries in northwestern Africa, and the Iberian Peninsula, represent a center of diversity of Daucus. The genus traditionally has included about 20–25 species worldwide, but a recent molecular study redefined and expanded Daucus to include representatives from nine other genera. By this classification, Daucus now contains about 40 species, with some of them having winged fruits in addition to its traditionally recognized spiny fruits. The taxonomy of Daucus in Tunisia has recently been studied with morphological data, concluding that D. carota subsp. capillifolius is a subspecies that co-occurs and crosses with subsp. carota. The present study extends these findings with additional data from Genotyping-by-Sequencing (GBS) from 33 Tunisian accessions of D. carota (93 individuals with two to three replicates per accession), including the first collections of subsp. gummifer from Tunisia. Placed in the context of additional GBS data, D. carota subsp. gummifer has separate origins from other collections of subsp. carota in Tunisia and/or immediately adjacent areas to the north in Italy or its surrounding islands. Our results add support to the utility of large SNP datasets for species-level phylogenetic studies in Daucus.

Genetic sex assignment in wild populations using genotyping-by-sequencing data: A statistical threshold approach.

Establishing the sex of individuals in wild systems can be challenging and often requires genetic testing. Genotyping-by-sequencing (GBS) and other reduced-representation DNA sequencing (RRS) protocols (e.g., RADseq, ddRAD) have enabled the analysis of genetic data on an unprecedented scale. Here, we present a novel approach for the discovery and statistical validation of sex-specific loci in GBS data sets. We used GBS to genotype 166 New Zealand fur seals (NZFS, Arctocephalus forsteri) of known sex. We retained monomorphic loci as potential sex-specific markers in the locus discovery phase. We then used (i) a sex-specific locus threshold (SSLT) to identify significantly male-specific loci within our data set; and (ii) a significant sex-assignment threshold (SSAT) to confidently assign sex in silico the presence or absence of significantly male-specific loci to individuals in our data set treated as unknowns (98.9% accuracy for females; 95.8% for males, estimated via cross-validation). Furthermore, we assigned sex to 86 individuals of true unknown sex using our SSAT and assessed the effect of SSLT adjustments on these assignments. From 90 verified sex-specific loci, we developed a panel of three sex-specific PCR primers that we used to ascertain sex independently of our GBS data, which we show amplify reliably in at least two other pinniped species. Using monomorphic loci normally discarded from large SNP data sets is an effective way to identify robust sex-linked markers for nonmodel species. Our novel pipeline can be used to identify and statistically validate monomorphic and polymorphic sex-specific markers across a range of species and RRS data sets.

Demographic model selection using random forests and the site frequency spectrum.

Phylogeographic data sets have grown from tens to thousands of loci in recent years, but extant statistical methods do not take full advantage of these large data sets. For example, approximate Bayesian computation (ABC) is a commonly used method for the explicit comparison of alternate demographic histories, but it is limited by the "curse of dimensionality" and issues related to the simulation and summarization of data when applied to next-generation sequencing (NGS) data sets. We implement here several improvements to overcome these difficulties. We use a Random Forest (RF) classifier for model selection to circumvent the curse of dimensionality and apply a binned representation of the multidimensional site frequency spectrum (mSFS) to address issues related to the simulation and summarization of large SNP data sets. We evaluate the performance of these improvements using simulation and find low overall error rates (~7%). We then apply the approach to data from Haplotrema vancouverense, a land snail endemic to the Pacific Northwest of North America. Fifteen demographic models were compared, and our results support a model of recent dispersal from coastal to inland rainforests. Our results demonstrate that binning is an effective strategy for the construction of a mSFS and imply that the statistical power of RF when applied to demographic model selection is at least comparable to traditional ABC algorithms. Importantly, by combining these strategies, large sets of models with differing numbers of populations can be evaluated.

A fast and accurate method for detection of IBD shared haplotypes in genome-wide SNP data.

Identical by descent (IBD) segments are used to understand a number of fundamental issues in genetics. IBD segments are typically detected using long stretches of identical alleles between haplotypes in phased, whole-genome SNP data. Phase or SNP call errors in genomic data can degrade accuracy of IBD detection and lead to false-positive/negative calls and to under/overextension of true IBD segments. Furthermore, the number of comparisons increases quadratically with sample size, requiring high computational efficiency. We developed a new IBD segment detection program, FISHR (Find IBD Shared Haplotypes Rapidly), in an attempt to accurately detect IBD segments and to better estimate their endpoints using an algorithm that is fast enough to be deployed on very large whole-genome SNP data sets. We compared the performance of FISHR to three leading IBD segment detection programs: GERMLINE, refined IBD, and HaploScore. Using simulated and real genomic sequence data, we show that FISHR is slightly more accurate than all programs at detecting long (>3 cm) IBD segments but slightly less accurate than refined IBD at detecting short (~1 cm) IBD segments. More centrally, FISHR outperforms all programs in determining the true endpoints of IBD segments, which is crucial for several applications of IBD information. FISHR takes two to three times longer than GERMLINE to run, whereas both GERMLINE and FISHR were orders of magnitude faster than refined IBD and HaploScore. Overall, FISHR provides accurate IBD detection in unrelated individuals and is computationally efficient enough to be utilized on large SNP data sets >60 000 individuals.

InbreedR: an R package for the analysis of inbreeding based on genetic markers

Summary Heterozygosity–fitness correlations (HFCs) have been widely used to explore the impact of inbreeding on individual fitness. Initially, most studies used small panels of microsatellites, but more recently with the advent of next‐generation sequencing, large SNP datasets are becoming increasingly available and these provide greater power and precision to quantify the impact of inbreeding on fitness. Despite the popularity of HFC studies, effect sizes tend to be rather small. One reason for this may be low variation in inbreeding levels among individuals. Using genetic markers, it is possible to measure variance in inbreeding through the strength of correlation in heterozygosity across marker loci, termed identity disequilibrium (ID). ID can be quantified using the measure , which is also a central parameter in HFC theory that can be used within a wider framework to estimate the direct impact of inbreeding on both marker heterozygosity and fitness. However, no software exists to calculate for large SNP datasets nor to implement this framework. inbreedR is an R package that provides functions to calculate based on microsatellite and SNP markers with associated P‐values and confidence intervals. Within the framework of HFC theory, inbreedR also estimates the impact of inbreeding on marker heterozygosity and fitness. Finally, inbreedR implements user‐friendly simulations to explore the precision and magnitude of estimates based on different numbers of genetic markers. We hope this package will facilitate good practice in the analysis of HFCs and help to deepen our understanding of inbreeding effects in natural populations.

Inferring the Dynamics of Effective Population Size Using Autosomal Genomes.

Next-generation sequencing technology has provided a great opportunity for inferring human demographic history by investigating changes in the effective population size (Ne). In this report, we introduce a strategy for estimating Ne dynamics, allowing the exploration of large multi-locus SNP datasets. We applied this strategy to the Phase 1 Han Chinese samples from the 1000 Genomes Project. The Han Chinese population has undergone a continuous expansion since 25,000 years ago, at first slowly from about 7,300 to 9,800 (at the end of the last glacial maximum about 15,000 YBP), then more quickly to about 46,000 (at the beginning of the Neolithic about 8,000 YBP), and then even more quickly to reach a population size of about 140,000 (recently).

Hybridization promotes speciation in Coenonympha butterflies

Hybridization has become a central element in theories of animal evolution during the last decade. New methods in population genomics and statistical model testing now allow the disentangling of the complexity that hybridization brings into key evolutionary processes such as local adaptation, colonization of new environments, species diversification and extinction. We evaluated the consequences of hybridization in a complex of three alpine butterflies in the genus Coenonympha, by combining morphological, genetic and ecological analyses. A series of approximate Bayesian computation procedures based on a large SNP data set strongly suggest that the Darwin's Heath (Coenonympha darwiniana) originated through hybridization between the Pearly Heath (Coenonympha arcania) and the Alpine Heath (Coenonympha gardetta) with different parental contributions. As a result of hybridization, the Darwin's Heath presents an intermediate morphology between the parental species, while its climatic niche seems more similar to the Alpine Heath. Our results also reveal a substantial genetic and morphologic differentiation between the two geographically disjoint Darwin's Heath lineages leading us to propose the splitting of this taxon into two different species.

HEXT, a software supporting tree-based screens for hybrid taxa in multilocus data sets, and an evaluation of the homoplasy excess test.

Summary The homoplasy excess test (HET) is a tree‐based screen for hybrid taxa in multilocus nuclear phylogenies. Homoplasy between a hybrid taxon and the clades containing the parental taxa reduces bootstrap support in the tree. The HET is based on the expectation that excluding the hybrid taxon from the data set increases the bootstrap support for the parental clades, whereas excluding non‐hybrid taxa has little effect on statistical node support. To carry out a HET, bootstrap trees are calculated with taxon‐jackknife data sets, that is excluding one taxon (species, population) at a time. Excess increase in bootstrap support for certain nodes upon exclusion of a particular taxon indicates the hybrid (the excluded taxon) and its parents (the clades with increased support).We introduce a new software program, hext, which generates the taxon‐jackknife data sets, runs the bootstrap tree calculations, and identifies excess bootstrap increases as outlier values in boxplot graphs. hext is written in r language and accepts binary data (0/1; e.g. AFLP) as well as co‐dominant SNP and genotype data.We demonstrate the usefulness of hext in large SNP data sets containing putative hybrids and their parents. For instance, using published data of the genus Vitis (˜6,000 SNP loci), hext output supports V. × champinii as a hybrid between V. rupestris and V. mustangensis.With simulated SNP and AFLP data sets, excess increases in bootstrap support were not always connected with the hybrid taxon (false positives), whereas the expected bootstrap signal failed to appear on several occasions (false negatives). Potential causes for both types of spurious results are discussed.With both empirical and simulated data sets, the taxon‐jackknife output generated by hext provided additional signatures of hybrid taxa, including changes in tree topology across trees, consistent effects of exclusions of the hybrid and the parent taxa, and moderate (rather than excessive) increases in bootstrap support. hext significantly facilitates the taxon‐jackknife approach to hybrid taxon detection, even though the simple test for excess bootstrap increase may not reliably identify hybrid taxa in all applications.

A fast method for evaluating opposing homozygosity in large SNP data sets

Optimized algorithms are indispensable for analyzing large SNP data sets. To date, research has focused on the development of methods for calculating genomic relationship matrices. However, little attention has been given to algorithms for calculating the number of opposing homozygous SNP loci (OH) between genotyped individuals, where this parameter can be used to detect pedigree errors, genotyping errors, mixing of DNA samples, or for paternity tests. A recently proposed approach (LOOP) is sufficient for small data sets but not applicable to larger data sets in terms of number of SNPs and genotyped individuals. In this paper we propose a fast method for the calculation of OH in matrix format (OHM). This method is very fast and easy to implement. For example, it can create the OHM for 12,000 individuals genotyped for 40,000 SNPs with only 12% of the real time used by the LOOP approach. Thus, calculation of OHM from a sequence of matrix manipulations substantially increased the speed for determining the number of opposing homozygous SNP loci between all genotyped individuals of a data set. Given the availability of packages facilitating parallel processing this holds even when using R, and therefore allows inference from OHM even for large data sets.