Accurate haplotype inference for multiple linked single-nucleotide polymorphisms using sibship data

Sibships are commonly used in genetic dissection of complex diseases, particularly for late-onset diseases. Haplotype-based association studies have been advocated as powerful tools for fine mapping and positional cloning of complex disease genes. Existing methods for haplotype inference using data from relatives were originally developed for pedigree data. In this study, we proposed a new statistical method for haplotype inference for multiple tightly linked single-nucleotide polymorphisms (SNPs), which is tailored for extensively accumulated sibship data. This new method was implemented via an expectation-maximization (EM) algorithm without the usual assumption of linkage equilibrium among markers. Our EM algorithm does not incur extra computational burden for haplotype inference using sibship data when compared with using unrelated parental data. Furthermore, its computational efficiency is not affected by increasing sibship size. We examined the robustness and statistical performance of our new method in simulated data created from an empirical haplotype data set of human growth hormone gene 1. The utility of our method was illustrated with an application to the analyses of haplotypes of three candidate genes for osteoporosis.     

The power of single-nucleotide polymorphisms for large-scale parentage inference

Likelihood-based parentage inference depends on the distribution of a likelihood-ratio statistic, which, in most cases of interest, cannot be exactly determined, but only approximated by Monte Carlo simulation. We provide importance-sampling algorithms for efficiently approximating very small tail probabilities in the distribution of the likelihood-ratio statistic. These importance-sampling methods allow the estimation of small false-positive rates and hence permit likelihood-based inference of parentage in large studies involving a great number of potential parents and many potential offspring. We investigate the performance of these importance-sampling algorithms in the context of parentage inference using single-nucleotide polymorphism (SNP) data and find that they may accelerate the computation of tail probabilities >1 millionfold. We subsequently use the importance-sampling algorithms to calculate the power available with SNPs for large-scale parentage studies, paying particular attention to the effect of genotyping errors and the occurrence of related individuals among the members of the putative mother-father-offspring trios. These simulations show that 60-100 SNPs may allow accurate pedigree reconstruction, even in situations involving thousands of potential mothers, fathers, and offspring. In addition, we compare the power of exclusion-based parentage inference to that of the likelihood-based method. Likelihood-based inference is much more powerful under many conditions; exclusion-based inference would require 40% more SNP loci to achieve the same accuracy as the likelihood-based approach in one common scenario. Our results demonstrate that SNPs are a powerful tool for parentage inference in large managed and/or natural populations.     

Accuracy of haplotype reconstruction from haplotype-tagging single-nucleotide polymorphisms

Many investigators are now using haplotype-tagging single-nucleotide polymorphism (htSNPs) as a way of screening regions of the genome for association with disease. A common approach is to genotype htSNPs in a study population and to use this information to draw inferences about each individual's haplotypic makeup, including SNPs that were not directly genotyped. To test the validity of this approach, we simulated the exercise of typing htSNPs in a large sample of individuals and compared the true and inferred haplotypes. The accuracy of haplotype inference varied, depending on the method of selecting htSNPs, the linkage-disequilibrium structure of the region, and the amount of missing data. At the stage of selection of htSNPs, haplotype-block-based methods required a larger number of htSNPs than did unstructured methods but gave lower levels of error in haplotype inference, particularly when there was a significant amount of missing data. We present a Web-based utility that allows investigators to compare the likely error rates of different sets of htSNPs and to arrive at an economical set of htSNPs that provides acceptable levels of accuracy in haplotype inference.     

Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure

Single-nucleotide polymorphisms (SNPs) are a class of attractive genetic markers for population genetic studies and for identifying genetic variations underlying complex traits. However, the usefulness and efficiency of SNPs in comparison to microsatellites in different scientific contexts, e.g., population structure inference or association analysis, still must be systematically evaluated through large empirical studies. In this article, we use the Collaborative Studies on Genetics of Alcoholism (COGA) data from Genetic Analysis Workshop 14 (GAW14) to compare the performance of microsatellites and SNPs in the whole human genome in the context of population structure inference. A total of 328 microsatellites and 15,840 SNPs are used to infer population structure in 236 unrelated individuals. We find that, on average, the informativeness of random microsatellites is four to twelve times that of random SNPs for various population comparisons, which is consistent with previous studies. Our results also indicate that for the combined set of microsatellites and SNPs, SNPs constitute the majority among the most informative markers and the use of these SNPs leads to better inference of population structure than the use of microsatellites. We also find that the inclusion of less informative markers may add noise and worsen the results.     

PCR-based genotyping and haplotype analysis of human TCRBV gene segment polymorphisms

There are at least 63 tandemly arranged human T-cell receptor (Tcr) -chain variable region (BV) gene segments, which have presumably arisen by repeated gene duplication events. The 5-most half of the TCRBV gene loci is particularly complex in organization due to the presence of multiple interspersed members of the largest BV subfamilies, BV5, BV6, and BV13. Polymorphism and linkage relationships among these genes has been poorly characterized in part due to the high similarity of these duplicands. Germline DNA polymorphisms were specifically examined in the exons and introns of these and other BV gene segments distributed across 240 kilobases (kb) in this 5-most region. Polymerase chain reaction restriction enzyme-based assays were used to genotype ten point mutations in seven of the BV gene segments. Eight of these polymorphisms altered an amino acid of the BV gene segment. In addition, length polymorphisms due to simple sequence repeats were noted in the introns of six BV6 subfamily members. Approximately 250 unrelated haplotypes were constructed by segregation analyses of fifteen of these TCRBV polymorphisms. Linkage disequilibrium analyses indicated that haplotypic relationships are not detectable over a distance of more than 55 kb in this genomic region. These TCRBV polymorphisms, and the haplotypic analysis, provide important resources and guidance for future attempts to associate Tcr germline DNA differences in the human population with immune response differences, such as might occur in some autoimmune diseases.     

A novel automated assay with dual-color hybridization for single-nucleotide polymorphisms genotyping on gold magnetic nanoparticle array

A high-throughput and cost-effective single-nucleotide polymorphism (SNP) genotyping method based on a gold magnetic nanoparticle (GMNP) array with dual-color hybridization has been designed. Biotinylated single-strand polymerase chain reaction (PCR) products containing the SNP locus were captured by the GMNPs that were coated with streptavidin. The GMNP array was fabricated by immobilizing single-stranded DNA (ssDNA)-GMNP complexes onto a glass slide using a magnetic field, and SNPs were identified with dual-color fluorescence hybridization. Three different SNP loci from 24 samples were genotyped successfully using this platform. This procedure allows the user to directly analyze the bead fluorescence to determine the SNP genotype, and it eliminates the need for background subtraction for signal determination. This method also bypasses tedious PCR purification and concentration procedures, and it facilitates large-scale SNP studies by using a method that is highly sensitive, simple, labor-saving, and potentially automatable.     

High-throughput microtiter well-based bioluminometric genotyping of two single-nucleotide polymorphisms in the toll-like receptor-4 gene

Toll-like receptors (TLRs) play a fundamental role in pathogen recognition and activation of innate immunity. Genetic variations in TLR have been associated with reduced host immune response to TLR ligands. We developed a rapid, simple and cost-effective method for identification of two common single-nucleotide polymorphisms (SNPs) within TLR4 gene in a high-throughput format. The method consists of a single polymerase chain reaction of the region spanning the A896G and C1196T polymorphic sites, followed by two primer extension reactions at each site using primers that carry a (dA)₂₄ segment at the 5′ end. A biotinylated nucleotide is incorporated in the extended primer. The products are captured in microtiter wells coated with streptavidin and detected using a (dT)₃₀-conjugated photoprotein aequorin. A total of 209 individuals were genotyped for each SNP. The A896G and C1196T polymorphisms were found to be in linkage disequilibrium; 186 individuals (89%) were wild-type homozygous (A/A or C/C), 22 (10.5%) were heterozygotes (A/G or C/T), and 1 (0.5%) was homozygous for the mutation (G/G or T/T). The accuracy of this method was confirmed by sequencing. The newly developed method may be useful for association studies of these two SNPs with several diseases.     

An improved heuristic for haplotype inference

Haplotypes include essential SNP information used for a variety of purposes such as investigating potential links between certain diseases and genetic variations. Given a set of genotypes, the haplotype inference problem based on pure parsimony is the problem of finding a minimum set of haplotypes that explains all the given genotypes. The problem is especially important because, while it is fairly inexpensive to obtain genotypes, other approaches to obtaining haplotypes are significantly expensive. There are two types of methods proposed for the problem, namely exact and inexact methods. Existing exact methods guarantee obtaining purely parsimonious solutions but have exponential time-complexities and are not practical for large number or length of genotypes. However, inexact methods are relatively fast but do not always obtain optimum solutions. In this paper, an improved heuristic is proposed, based on which new inexact and exact methods are provided. Experimental results indicate that the proposed methods replace the state-of-the-art inexact and exact methods for the problem.     

Rapid haplotype inference for nuclear families

Hapi is a new dynamic programming algorithm that ignores uninformative states and state transitions in order to efficiently compute minimum-recombinant and maximum likelihood haplotypes. When applied to a dataset containing 103 families, Hapi performs 3.8 and 320 times faster than state-of-the-art algorithms. Because Hapi infers both minimum-recombinant and maximum likelihood haplotypes and applies to related individuals, the haplotypes it infers are highly accurate over extended genomic distances.     

An improved heuristic for haplotype inference

We cloned a gene, kexD, that provides a multidrug-resistant phenotype from multidrug-resistant Klebsiella pneumoniae MGH78578. The deduced amino acid sequence of KexD is similar to that of the inner membrane protein, RND-type multidrug efflux pump. Introduction of the kexD gene into Escherichia coli KAM32 resulted in a MIC that was higher for erythromycin, novobiocin, rhodamine 6G, tetraphenylphosphonium chloride, and ethidium bromide than that of the control. Intracellular ethidium bromide levels in E. coli cells carrying the kexD gene were lower than that in the control cells under energized conditions, suggesting that KexD is a component of an energy-dependent efflux pump. RND-type pumps typically consist of three components: an inner membrane protein, a periplasmic protein, and an outer membrane protein. We discovered that KexD functions with a periplasmic protein, AcrA, from E. coli and K. pneumoniae, but not with the periplasmic proteins KexA and KexG from K. pneumoniae. KexD was able to utilize either TolC of E. coli or KocC of K. pneumoniae as an outer membrane component. kexD mRNA was not detected in K. pneumoniae MGH78578 or ATCC10031. We isolated erythromycin-resistant mutants from K. pneumoniae ATCC10031, and some showed a multidrug-resistant phenotype similar to the drug resistance pattern of KexD. Two strains of multidrug-resistant mutants were investigated for kexD expression; kexD mRNA levels were increased in these strains. We conclude that changing kexD expression can contribute to the occurrence of multidrug-resistant K. pneumoniae.     

New explicit expressions for relative frequencies of single-nucleotide polymorphisms with application to statistical inference on population growth

We present new methodology for calculating sampling distributions of single-nucleotide polymorphism (SNP) frequencies in populations with time-varying size. Our approach is based on deriving analytical expressions for frequencies of SNPs. Analytical expressions allow for computations that are faster and more accurate than Monte Carlo simulations. In contrast to other articles showing analytical formulas for frequencies of SNPs, we derive expressions that contain coefficients that do not explode when the genealogy size increases. We also provide analytical formulas to describe the way in which the ascertainment procedure modifies SNP distributions. Using our methods, we study the power to test the hypothesis of exponential population expansion vs. the hypothesis of evolution with constant population size. We also analyze some of the available SNP data and we compare our results of demographic parameters estimation to those obtained in previous studies in population genetics. The analyzed data seem consistent with the hypothesis of past population growth of modern humans. The analysis of the data also shows a very strong sensitivity of estimated demographic parameters to changes of the model of the ascertainment procedure.     

Microsatellite linkage analysis, single-nucleotide polymorphisms, and haplotype associations with ECB21 in the COGA data

This study, part of the Genetic Analysis Workshop 14 (GAW14), explored real Collaborative Study on the Genetics of Alcoholism data for linkage and association mapping between genetic polymorphisms (microsatellite and single-nucleotide polymorphisms (SNPs)) and beta (16.5-20 Hz) oscillations of the brain rhythms (ecb21). The ecb21 phenotype underwent the statistical adjustments for the age of participants, and for attaining a normal distribution. A total of 1,000 subjects' available phenotypes were included in linkage analysis with microsatellite markers. Linkage analysis was performed only for chromosome 4 where a quantitative trait locus with 5.01 LOD score had been previously reported. Previous findings related this location with the gamma-aminobutyric acid type A (GABAA) receptor. At the same location, our analysis showed a LOD score of 2.2. This decrease in the LOD score is the result of a drastic reduction (one-third) of the available GAW14 phenotypic data. We performed SNP and haplotype association analyses with the same phenotypic data under the linkage peak region on chromosome 4. Seven Affymetrix and two Illumina SNPs showed significant associations with ecb21 phenotype. A haplotype, a combination of SNPs TSC0044171 and TSC0551006 (the latter almost under the region of GABAA genes), showed a significant association with ecb21 (p = 0.015) and a relatively high frequency in the sample studied. Our results affirmed that the GABA region has potential of harboring genes that contribute quantitatively to the beta oscillation of the brain rhythms. The inclusion of the remaining 614 subjects, which in the GAW14 had missing data for the ecb21, can improve the strength of the associations as they have already shown that they contribute quite important information in the linkage analysis.     

A parsimonious tree-grow method for haplotype inference

MOTIVATION: Haplotype information has become increasingly important in analyzing fine-scale molecular genetics data, such as disease genes mapping and drug design. Parsimony haplotyping is one of haplotyping problems belonging to NP-hard class. RESULTS: In this paper, we aim to develop a novel algorithm for the haplotype inference problem with the parsimony criterion, based on a parsimonious tree-grow method (PTG). PTG is a heuristic algorithm that can find the minimum number of distinct haplotypes based on the criterion of keeping all genotypes resolved during tree-grow process. In addition, a block-partitioning method is also proposed to improve the computational efficiency. We show that the proposed approach is not only effective with a high accuracy, but also very efficient with the computational complexity in the order of O(m2n) time for n single nucleotide polymorphism sites in m individual genotypes. AVAILABILITY: The software is available upon request from the authors, or from http://zhangroup.aporc.org/bioinfo/ptg/ CONTACT: chen@elec.osaka-sandai.ac.jp SUPPLEMENTARY INFORMATION: Supporting materials is available from http://zhangroup.aporc.org/bioinfo/ptg/bti572supplementary.pdf     

A statistical framework for haplotype block inference

The existence of haplotype blocks transmitted from parents to offspring has been suggested recently. This has created an interest in the inference of the block structure and length. The motivation is that haplotype blocks that are characterized well will make it relatively easier to quickly map all the genes carrying human diseases. To study the inference of haplotype block systematically, we propose a statistical framework. In this framework, the optimal haplotype block partitioning is formulated as the problem of statistical model selection; missing data can be handled in a standard statistical way; population strata can be implemented; block structure inference/hypothesis testing can be performed; prior knowledge, if present, can be incorporated to perform a Bayesian inference. The algorithm is linear in the number of loci, instead of NP-hard for many such algorithms. We illustrate the applications of our method to both simulated and real data sets.     

Highly sensitive and selective colorimetric genotyping of single-nucleotide polymorphisms based on enzyme-amplified ligation on magnetic beads

Herein we report a new strategy for highly sensitive and selective colorimatric assay for genotyping of single-nucleotide polymorphisms (SNPs). It is based on the use of a specific gap ligation reaction, horseradish peroxidase (HRP) for signal amplification, and magnetic beads for the easy separation of the ligated product. Briefly, oligonucleotide capture probe functionalized magnetic beads are first hybridized to a target DNA. Biotinylated oligonucleotide detection probes are then allowed to hybridize to the already captured target DNA. A subsequent ligation at the mutation point joins the two probes together. The introduction of streptavidin-conjugated HRP and a simple magnetic separation allow colorimetric genotyping of SNPs. The assay is able to discriminate one copy of mutant in 1000 copies of wild-type KRAS oncogene at 30 picomolar. The detection limit of the assay is further improved to 1 femtomolar by incorporating a ligation chain reaction amplification step, offering an excellent opportunity for the development of a simple and highly sensitive diagnostic tool.     

Electrophoretic detection of single-nucleotide polymorphisms

Single-nucleotide polymorphisms (SNPs) represent the most prevalent class of genetic markers available for linkage disequilibrium or cladistic analyses. PCR primers may be labeled with fluorescent dyes and used to rapidly and accurately differentiate among alleles that are defined by a single-nucleotide differences. Here, we describe the primer-mediated detection of SNPs based on primer mismatch during allele-specific amplification of preamplified target sequences. Primers are labeled with different fluors at their 5' nucleotides, with their 3' termini at the transition mutation that defines allelic variation at the target locus. Each primer perfectly matches one of the two available alleles for each locus. Electrophoretic detection permits characterization of the product both by size and fluor. This report demonstrates some of the capabilities of this assay, including heterozygote determination and multiplexed analysis.     

A coalescence-guided hierarchical Bayesian method for haplotype inference

Haplotype inference from phase-ambiguous multilocus genotype data is an important task for both disease-gene mapping and studies of human evolution. We report a novel haplotype-inference method based on a coalescence-guided hierarchical Bayes model. In this model, a hierarchical structure is imposed on the prior haplotype frequency distributions to capture the similarities among modern-day haplotypes attributable to their common ancestry. As a consequence, the model both allows distinct haplotypes to have different a priori probabilities according to the inferred hierarchical ancestral structure and results in a proper joint posterior distribution for all the parameters of interest. A Markov chain-Monte Carlo scheme is designed to draw from this posterior distribution. By using coalescence-based simulation and empirically generated data sets (Whitehead Institute's inflammatory bowel disease data sets and HapMap data sets), we demonstrate the merits of the new method in comparison with HAPLOTYPER and PHASE, with or without the presence of recombination hotspots and missing genotypes.