Biallelic Markers Research Articles

Estimation of the relatedness coefficients from biallelic markers, application in plant mating designs.

The problem of inferring the relatedness distribution between two individuals from biallelic marker data is considered. This problem can be cast as an estimation task in a mixture model: at each marker the latent variable is the relatedness state, and the observed variable is the genotype of the two individuals. In this model, only the prior proportions are unknown, and can be obtained via ML estimation using the EM algorithm. When the markers are biallelic and the data unphased, the identifiability of the model is known not to be guaranteed. In this article, model identifiability is investigated in the case of phased data generated from a crossing design, a classical situation in plant genetics. It is shown that identifiability can be guaranteed under some conditions on the crossing design. The adapted ML estimator is implemented in an R package called Relatedness. The performance of the ML estimator is evaluated and compared to that of the benchmark moment estimator, both on simulated and real data. Compared to its competitor, the ML estimator is shown to be more robust and to provide more realistic estimates.

Development of a SNP-based panel for human identification for Indian populations

The widely employed short tandem repeat (STR)-based panels for forensic human identification (HID) have limitations while dealing with challenging forensic samples involving DNA degradation, resulting in dropping-out of higher molecular weight alleles/loci. To address this issue, bialleic markers like single nucleotide polymorphisms (SNPs) and insertion-deletions (indels), which can be scored even when the template DNA is heavily degraded (<100bp), have been suggested as alternative markers for HID testing. Recent studies have highlighted their utility in forensic HID and several panels based on biallelic markers have been described for worldwide populations. However, there has been very little information about the behavior of such DNA markers in Indian populations, which is known to possess great genetic diversity. This study describes a two-step approach for designing a SNP-based panel consisting of 70 SNPs for HID testing in Indian populations. In the first step, candidate SNPs were shortlisted from public databases by screening them for several criteria including allelic distribution, genomic location, potential phenotypic expression or functionality and species specificity. The second step involved genotyping the shortlisted SNPs in various Indian populations followed by shortlisting of the best performers for identity-testing. Starting with 592,652 SNPs listed in Human660W-Quad Beadchip (Illumina Inc.), we shortlisted 275 candidate SNPs for identity-testing and genotyped them in 462 unrelated individuals from different population groups in India. Post genotyping and statistical analyses based on biogeographic regions, 206 SNPs demonstrated desired allelic distribution (Heterozygosity≥0.4 and FST≤0.02), from which 2–4 widely separated (>20 Mb apart) SNPs from each chromosome were finally selected to construct a panel of 70 SNPs. This panel on average possessed match probability 10e-29 and probability of paternity of 0.99999997, which was orders of magnitude higher than most of the currently employed STR-based chemistries and SNP-based panels that were proposed previously for HID testing. For comparison purpose, genotyping previously reported SNPs for HID in our samples led us to conclude that the panel developed in this study is much more efficient and robust and better suited for the Indian populations.

Accurate recapture identification for genetic mark-recapture studies with error-tolerant likelihood-based match calling and sample clustering.

Error-tolerant likelihood-based match calling presents a promising technique to accurately identify recapture events in genetic mark–recapture studies by combining probabilities of latent genotypes and probabilities of observed genotypes, which may contain genotyping errors. Combined with clustering algorithms to group samples into sets of recaptures based upon pairwise match calls, these tools can be used to reconstruct accurate capture histories for mark–recapture modelling. Here, we assess the performance of a recently introduced error-tolerant likelihood-based match-calling model and sample clustering algorithm for genetic mark–recapture studies. We assessed both biallelic (i.e. single nucleotide polymorphisms; SNP) and multiallelic (i.e. microsatellite; MSAT) markers using a combination of simulation analyses and case study data on Pacific walrus (Odobenus rosmarus divergens) and fishers (Pekania pennanti). A novel two-stage clustering approach is demonstrated for genetic mark–recapture applications. First, repeat captures within a sampling occasion are identified. Subsequently, recaptures across sampling occasions are identified. The likelihood-based matching protocol performed well in simulation trials, demonstrating utility for use in a wide range of genetic mark–recapture studies. Moderately sized SNP (64+) and MSAT (10–15) panels produced accurate match calls for recaptures and accurate non-match calls for samples from closely related individuals in the face of low to moderate genotyping error. Furthermore, matching performance remained stable or increased as the number of genetic markers increased, genotyping error notwithstanding.

PediHaplotyper: software for consistent assignment of marker haplotypes in pedigrees.

In the study of large outbred pedigrees with many founders, individual bi-allelic markers, such as SNP markers, carry little information. After phasing the marker genotypes, multi-allelic loci consisting of groups of closely linked markers can be identified, which are called “haploblocks”. Here, we describe PediHaplotyper, an R package capable of assigning consistent alleles to such haploblocks, allowing for missing and incorrect SNP data. These haploblock genotypes are much easier to interpret by the human investigator than the original SNP data and also allow more efficient QTL analyses that require less memory and computation time.Electronic supplementary materialThe online version of this article (doi:10.1007/s11032-016-0539-y) contains supplementary material, which is available to authorized users.

Does encoding matter? A novel view on the quantitative genetic trait prediction problem.

BackgroundGiven a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models which require quantitative encodings for the genotypes. There are lots of work on the prediction algorithms, but none of the existing work investigated the effects of the encodings on the genetic trait prediction problem.MethodsIn this work, we view the genetic trait prediction problem from a novel angle: a multiple regression on categorical data problem, which requires encoding the categorical data into numerical data. We further proposed two novel encoding methods and we show that they are able to generate numerical features with higher predictive power.Results and DiscussionOur experiments show that our methods are superior to the other encoding methods for both single marker model and epistasis model. We showed that the quantitative genetic trait prediction problem heavily depends on the encoding of genotypes, for both single marker model and epistasis model.ConclusionsWe conducted a detailed analysis on the performance of the hybrid encodings. To our knowledge, this is the first work that discusses the effects of encodings for genetic trait prediction problem.

Characterization of genome-wide SNPs for the water flea Daphnia pulicaria generated by genotyping-by-sequencing (GBS).

The keystone aquatic herbivore Daphnia has been studied for more than 150 years in the context of evolution, ecology and ecotoxicology. Although it is rapidly becoming an emergent model for environmental and population genomics, there have been limited genome-wide level studies in natural populations. We report a unique resource of novel Single Nucleotide Polymorphic (SNP) markers for Daphnia pulicaria using the reduction in genomic complexity with the restriction enzymes approach, genotyping-by-sequencing. Using the genome of D. pulex as a reference, SNPs were scored for 53 clones from five natural populations that varied in lake trophic status. Our analyses resulted in 32,313 highly confident and bi-allelic SNP markers. 1,364 outlier SNPs were mapped on the annotated D. pulex genome, which identified 2,335 genes, including 565 within functional genes. Out of 885 EuKaryotic Orthologous Groups that we found from outlier SNPs, 294 were involved in three metabolic and four regulatory pathways. Bayesian-clustering analyses showed two distinct population clusters representing the possible combined effects of geography and lake trophic status. Our results provide an invaluable tool for future population genomics surveys in Daphnia targeting informative regions related to physiological processes that can be linked to the ecology of this emerging eco-responsive taxon.

Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. Availability and implementation: The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request.Contact: dhe@us.ibm.com

GHap: an R package for genome-wide haplotyping.

The GHap R package was designed to call haplotypes from phased marker data. Given user-defined haplotype blocks (HapBlock), the package identifies the different haplotype alleles (HapAllele) present in the data and scores sample haplotype allele genotypes (HapGenotype) based on HapAllele dose (i.e. 0, 1 or 2 copies). The output is not only useful for analyses that can handle multi-allelic markers, but is also conveniently formatted for existing pipelines intended for bi-allelic markers. https://cran.r-project.org/package=GHap ytutsunomiya@gmail.com Supplementary data are available at Bioinformatics online.

ASAFE: ancestry-specific allele frequency estimation.

In a genome-wide association study (GWAS) of an admixed population, such as Hispanic Americans, ancestry-specific allele frequencies can inform the design of a replication GWAS. We derive an EM algorithm to estimate ancestry-specific allele frequencies for a bi-allelic marker given genotypes and local ancestries on a 3-way admixed population, when the phase of each admixed individual's genotype relative to the pair of local ancestries is unknown. We call our algorithm Ancestry Specific Allele Frequency Estimation (ASAFE). We demonstrate that ASAFE has low error on simulated data. The R source code for ASAFE is available for download at https://github.com/BiostatQian/ASAFE CONTACT: qszhang@uw.edu Supplementary data are available at Bioinformatics online.

Probabilistic Multilocus Haplotype Reconstruction in Outcrossing Tetraploids.

For both plant (e.g., potato) and animal (e.g., salmon) species, unveiling the genetic architecture of complex traits is key to the genetic improvement of polyploids in agriculture. F1 progenies of a biparental cross are often used for quantitative trait loci (QTL) mapping in outcrossing polyploids, where haplotype reconstruction by identifying the parental origins of marker alleles is necessary. In this paper, we build a novel and integrated statistical framework for multilocus haplotype reconstruction in a full-sib tetraploid family from biallelic marker dosage data collected from single-nucleotide polymorphism (SNP) arrays or next-generation sequencing technology given a genetic linkage map. Compared to diploids, in tetraploids, additional complexity needs to be addressed, including double reduction and possible preferential pairing of chromosomes. We divide haplotype reconstruction into two stages: parental linkage phasing for reconstructing the most probable parental haplotypes and ancestral inference for probabilistically reconstructing the offspring haplotypes conditional on the reconstructed parental haplotypes. The simulation studies and the application to real data from potato show that the parental linkage phasing is robust to, and that the subsequent ancestral inference is accurate for, complex chromosome pairing behaviors during meiosis, various marker segregation types, erroneous genetic maps except for long-range disturbances of marker ordering, various amounts of offspring dosage errors (up to ∼20%), and various fractions of missing data in parents and offspring dosages.

Testing for Hardy-Weinberg equilibrium at biallelic genetic markers on the X chromosome.

Testing genetic markers for Hardy–Weinberg equilibrium (HWE) is an important tool for detecting genotyping errors in large-scale genotyping studies. For markers at the X chromosome, typically the χ2 or exact test is applied to the females only, and the hemizygous males are considered to be uninformative. In this paper we show that the males are relevant, because a difference in allele frequency between males and females may indicate HWE not to hold. The testing of markers on the X chromosome has received little attention, and in this paper we lay down the foundation for testing biallelic X-chromosomal markers for HWE. We develop four frequentist statistical test procedures for X-linked markers that take both males and females into account: the χ2 test, likelihood ratio test, exact test and permutation test. Exact tests that include males are shown to have a better Type I error rate. Empirical data from the GENEVA project on venous thromboembolism is used to illustrate the proposed tests. Results obtained with the new tests differ substantially from tests that are based on female genotype counts only. The new tests detect differences in allele frequencies and seem able to uncover additional genotyping error that would have gone unnoticed in HWE tests based on females only.

SHEsisPlus, a toolset for genetic studies on polyploid species.

Currently, algorithms and softwares for genetic analysis of diploid organisms with bi-allelic markers are well-established, while those for polyploids are limited. Here, we present SHEsisPlus, the online algorithm toolset for both dichotomous and quantitative trait genetic analysis on polyploid species (compatible with haploids and diploids, too). SHEsisPlus is also optimized for handling multiple-allele datasets. It’s free, open source and also designed to perform a range of analyses, including haplotype inference, linkage disequilibrium analysis, epistasis detection, Hardy-Weinberg equilibrium and single locus association tests. Meanwhile, we developed an accurate and efficient haplotype inference algorithm for polyploids and proposed an entropy-based algorithm to detect epistasis in the context of quantitative traits. A study of both simulated and real datasets showed that our haplotype inference algorithm was much faster and more accurate than existing ones. Our epistasis detection algorithm was the first try to apply information theory to characterizing the gene interactions in quantitative trait datasets. Results showed that its statistical power was significantly higher than conventional approaches. SHEsisPlus is freely available on the web at http://shesisplus.bio-x.cn/. Source code is freely available for download at https://github.com/celaoforever/SHEsisPlus.

Alternative haplotype construction methods for genomic evaluation

Genomic evaluation methods today use single nucleotide polymorphism (SNP) as genomic markers to trace quantitative trait loci (QTL). Today most genomic prediction procedures use biallelic SNP markers. However, SNP can be combined into short, multiallelic haplotypes that can improve genomic prediction due to higher linkage disequilibrium between the haplotypes and the linked QTL. The aim of this study was to develop a method to identify the haplotypes, which can be expected to be superior in genomic evaluation, as compared with either SNP or other haplotypes of the same size. We first identified the SNP (termed as QTL-SNP) from the bovine 50K SNP chip that had the largest effect on the analyzed trait. It was assumed that these SNP were not the causative mutations and they merely indicated the approximate location of the QTL. Haplotypes of 3, 4, or 5 SNP were selected from short genomic windows surrounding these markers to capture the effect of the QTL. Two methods described in this paper aim at selecting the most optimal haplotype for genomic evaluation. They assumed that if an allele has a high frequency, its allele effect can be accurately predicted. These methods were tested in a classical validation study using a dairy cattle population of 2,235 bulls with genotypes from the bovine 50K SNP chip and daughter yield deviations (DYD) on 5 dairy cattle production traits. Combining the SNP into haplotypes was beneficial with all tested haplotypes, leading to an average increase of 2% in terms of correlations between DYD and genomic breeding value estimates compared with the analysis when the same SNP were used individually. Compared with haplotypes built by merging the QTL-SNP with its flanking SNP, the haplotypes selected with the proposed criteria carried less under- and over-represented alleles: the proportion of alleles with frequencies <1 or >40% decreased, on average, by 17.4 and 43.4%, respectively. The correlations between DYD and genomic breeding value estimates increased by 0.7 to 0.9 percentage points when the haplotypes were selected using any of the proposed methods compared with using the haplotypes built from the QTL-SNP and its flanking markers. We showed that the efficiency of genomic prediction could be improved at no extra costs, only by selecting the proper markers or combinations of markers for genomic prediction. One of the presented approaches was implemented in the new genomic evaluation procedure applied in dairy cattle in France in April 2015.

Exploring Durable Genetic Resistance against Leaf Rust through Phenotypic Characterization and Lr34Linked STS Marker in Wheat Germplasm

Present study was aimed to screening the population of 25 wheat genotypes from Baluchistan region of Pakistan along with five commercial cultivars for leaf rust adult plant resistance (APR) through gene postulation using natural inoculation of Puccinia triticina Erikss local pathotype. Infection severity was recorded on scale in comparison with susceptible control “Morroco” cultivar. On the basis of phenotypic score, seven accessions and four varieties (Zardana-89, Sariab-92, Zarlashta-99 and Raskoh-05) with AUDPC values up to 20% were characterized as resistant genotypes. Coefficient of infection (CI) score ranged from 0-10 for some accessions and cultivars showing high level of adult plant resistance. Furthermore, bi-allelic STS marker csLV34 having close linkage with Lr34 (0.4cM). This marker amplified one gene specific allele of 150bp in 21 genotypes, including 19 accessions and two commercial varieties (Sariab-92 and Zarghoon-79) which confirmed presence of Lr34 gene conferring adult plant resistance against leaf rust. The rust pathogenicity scale varied for accessions from resistant to moderately susceptible. However, beside Lr34 , phenotypic gene postulation, in combination with marker assisted selection for leaf rust resistance, has revealed presence of some other unknown resistance genes in local wheat germplasm which signified its use in wheat improvement programs both locally and abroad.

Frequency of TNFA, INFG, and IL10 Gene Polymorphisms and Their Association with Malaria Vivax and Genomic Ancestry.

Polymorphisms in cytokine genes can alter the production of these proteins and consequently affect the immune response. The trihybrid heterogeneity of the Brazilian population is characterized as a condition for the use of ancestry informative markers. The objective of this study was to evaluate the frequency of -1031T>C, -308G>A and -238G>A TNFA, +874 A>T IFNG and -819C>T, and -592C>A IL10 gene polymorphisms and their association with malaria vivax and genomic ancestry. Samples from 90 vivax malaria-infected individuals and 51 noninfected individuals from northern Brazil were evaluated. Genotyping was carried out by using ASO-PCR or PCR/RFLP. The genomic ancestry of the individuals was classified using 48 insertion/deletion polymorphism biallelic markers. There were no differences in the proportions of African, European, and Native American ancestry between men and women. No significant association was observed for the allele and genotype frequencies of the 6 SNPs between malaria-infected and noninfected individuals. However, there was a trend toward decreasing the frequency of individuals carrying the TNF-308A allele with the increasing proportion of European ancestry. No ethnic-specific SNPs were identified, and there was no allelic or genotype association with susceptibility or resistance to vivax malaria. Understanding the genomic mechanisms by which ancestry influences this association is critical and requires further study.

SNP discovery and Characterization from transcriptomes of Asian yellow pond turtle, Mauremys mutica

Asian yellow pond turtle (Mauremys mutica) is an endangered species, being listed in the IUCN red list of threatened species. In China, most living resources are kept in turtle farms. Insufficient molecular markers have hindered the effective management and artificial breeding programs of this important species. In this study, we discovered and validated 43 novel polymorphic bi-allelic SNP markers for M. mutica from the transcriptome of brain and gonad tissue. The observed and expected heterozygosity ranged from 0.0303 to 0.5758 and 0.0303–0.5072, respectively. These markers would be used in genetic studies helping conservation and management of M. mutica.

Developmental validation of an X-Insertion/Deletion polymorphism panel and application in HAN population of China.

InDels are short-length polymorphisms characterized by low mutation rates, high inter-population diversity, short amplicon strategy and simplicity of laboratory analysis. This work describes the developmental validation of an X-InDels panel amplifying 18 bi-allelic markers and Amelogenin in one single PCR system. Developmental validation indicated that this novel panel was reproducible, accurate, sensitive and robust for forensic application. Sensitivity testing of the panel was such that a full profile was obtainable even with 125 pg of human DNA with intra-locus balance above 70%. Specificity testing was demonstrated by the lack of cross-reactivity with a variety of commonly encountered animal species and microorganisms. For the stability testing in cases of PCR inhibition, full profiles have been obtained with hematin (≤1000 μM) and humic acid (≤150 ng/μL). For the forensic investigation of the 18 X-InDels in the HAN population of China, no locus deviated from the Hardy–Weinberg equilibrium and linkage disequilibrium. Since they are independent from each other, the CDPfemale was 0.999999726 and CDPmale was 0.999934223. The forensic parameters suggested that this X-Indel panel is polymorphic and informative, which provides valuable X-linked information for deficient relationship cases where autosomal markers are uninformative.

Y-Chromosomal Lineages of Latvians in the Context of the Genetic Variation of the Eastern-Baltic Region.

Variations of the nonrecombining Y-chromosomal region were investigated in 159 unrelated Baltic-speaking ethnic Latvians from four different geographic regions, using 28 biallelic markers and 12 short tandem repeats. Eleven different haplogroups (hgs) were detected in a regionally homogeneous Latvian population, among which N1c, R1a, and I1 cover more than 85% of its paternal lineages. When compared its closest geographic neighbors, the composition of the Latvian Y-chromosomal gene pool was found to be very similar to those of Lithuanians and Estonians. Despite the comparable frequency distribution of hg N1c in Latvians and Lithuanians with the Finno-Ugric-speaking populations from the Eastern coast of the Baltic Sea, the observed differences in allelic variances of N1c haplotypes between these two groups are in concordance with the previously stated hypothesis of different dispersal ways of this lineage in the region. More than a third of Latvian paternal lineages belong specifically to a recently defined R1a-M558 hg, indicating an influence from a common source within Eastern Slavic populations on the formation of the present-day Latvian Y-chromosome gene pool.

Typing of 71 biallelic markers on Y chromosome in Xinjiang Han population of China

71 Y chromosome-specific biallelic markers that are characteristic of major Asian haplogroups and subhaplogroups were selected. Genomic DNA was extracted from saliva samples of 202 unrelated Chinese Han male individuals in Xinjiang. Typing of 71 biallelic markers was performed using the MassARRAY MALDI-TOF MS platform (Sequenom Inc.). Allelic frequencies, gene diversities (GD) for each locus were then determined. Haplotype diversity (HD) was also calculated. Out of the 71 tested Y-chromosomal markers, five loci were not polymorphic (GD=0), 13 loci were found to be low informative (0.01<GD<0.2), 20 loci were found to be medium informative (0.2≤GD<0.3), 33 loci were found to be high informative (GD≥0.3). Of 76 observed haplotypes, 61 (80%) were unique and 15 (20%) were found more than once. The total polymorphic markers giving high haplotype diversity (0.9930) demonstrate the utility of these loci for forensic purpose.

SNPforID 52-plex in casework samples: “Cracking” bones and other difficult samples

Casework samples can present difficulties to forensic scientists in criminal and identification investigations. Some challenging samples like bones, teeth and crime scene samples, often contain low DNA quantity which can even be degraded. In these cases, obtained STR profiles are many times incomplete or even null. This is partly due to relative bigger size of commonly used STRs in forensic analysis. In order to bypass this problem, other strategies of analysis have been developed in the past based on mini-STRs and biallelic markers, such as Indels and SNPs. Although each marker type has its advantages and disadvantages, SNPs benefit from the fact of having smaller amplification products and its analysis can be realized analyzing simultaneously a great number of loci using large multiplexes. One of such multiplex is SNPforID 52-plex which analyzes 52 loci, providing good results, as reported by some authors. Taking this in consideration, we compared the amplification success of 53 real casework samples from our casuistic consisting of bones, teeth and other samples using the 52-plex and the Identifier® Plus kit. Mean amplification success rate by loci was of 73% and 43% respectively and 16 out of 36 samples in which STR profiles were not obtained or in which these were poor, generated complete or almost complete SNP profiles. We conclude that the 52-plex can be a valuable tool in the analysis of different types of challenging forensic samples when STRs fail to provide necessary genetic information for identification.