Estimation of haplotype frequencies

Comparison of high-resolution human leukocyte antigen haplotype frequencies in different ethnic groups: Consequences of sampling fluctuation and haplotype frequency distribution tail truncation

High-resolution haplotype frequency estimations and descriptive metrics are becoming increasingly popular for accurately describing human leukocyte antigen diversity. In this study, we compared sample sets of publically available haplotype frequencies from different populations to characterize the consequences of unequal sample size on haplotype frequency estimation. We found that for low samples sizes (a few thousand), haplotype frequencies were overestimated, affecting all descriptive metrics of the underlying distribution, such as most frequent haplotype, the number of haplotypes, and the mean/median frequency. This overestimation was a result of random sample fluctuation and truncation of the tail end of the frequency distribution that comprises the least frequent haplotypes. Finally, we simulated balanced datasets through resampling and contrasted the disparities of descriptive metrics among equal and unequal datasets. This simulation resulted in the global description of the most frequent human leukocyte antigen haplotypes worldwide.

Comparative validation of computer programs for haplotype frequency estimation from donor registry data

Estimation of human leukocyte antigen (HLA) haplotype frequencies from unrelated stem cell donor registries presents a challenge because of large sample sizes and heterogeneity of HLA typing data. For the 14th International HLA and Immunogenetics Workshop, five bioinformatics groups initiated the 'Registry Diversity Component' aiming to cross-validate and improve current haplotype estimation tools. Five datasets were derived from different donor registries and then used as input for five different computer programs for haplotype frequency estimation. Because of issues related to heterogeneity and complexity of HLA typing data identified in the initial phase, the same five implementations, and two new ones, were used on simulated datasets in a controlled experiment where the correct results were known a priori. These datasets contained various fractions of missing HLA-DR modeled after European haplotype frequencies. We measured the contribution of sampling fluctuation and estimation error to the deviation of the frequencies from their true values, finding equivalent contributions of each for the chosen samples. Because of patient-directed activities, selective prospective typing strategies and the variety and evolution of typing technology, some donors have more complete and better HLA data. In this setting, we show that restricting estimation to fully typed individuals introduces biases that could be overcome by including all donors in frequency estimation. Our study underlines the importance of critical review and validation of tools in registry-related activity and provides a sustainable framework for validating the computational tools used. Accurate frequencies are essential for match prediction to improve registry operations and to help more patients identify suitably matched donors.

16(th) IHIW: global analysis of registry HLA haplotypes from 20 million individuals: report from the IHIW Registry Diversity Group

This project has the goal to validate bioinformatics methods and tools for HLA haplotype frequency analysis specifically addressing unique issues of haematopoietic stem cell registry data sets. In addition to generating new methods and tools for the analysis of registry data sets, the intent is to produce a comprehensive analysis of HLA data from 20 million donors from the Bone Marrow Donors Worldwide (BMDW) database. This report summarizes the activity on this project as of the 16IHIW meeting in Liverpool.

Analytical methods for immunogenetic population data

In this chapter, we describe analyses commonly applied to immunogenetic population data, along with software tools that are currently available to perform those analyses. Where possible, we focus on tools that have been developed specifically for the analysis of highly polymorphic immunogenetic data. These analytical methods serve both as a means to examine the appropriateness of a dataset for testing a specific hypothesis, as well as a means of testing hypotheses. Rather than treat this chapter as a protocol for analyzing any population dataset, each researcher and analyst should first consider their data, the possible analyses, and any available tools in light of the hypothesis being tested. The extent to which the data and analyses are appropriate to each other should be determined before any analyses are performed.

Inferred HLA Haplotype Information for Donors From Hematopoietic Stem Cells Donor Registries

Human leukocyte antigen (HLA) matching remains a key issue in the outcome of transplantation. In hematopoietic stem cell transplantation with unrelated donors, the matching for compatible donors is based on the HLA phenotype information. In familial transplantation, the matching is achieved at the haplotype level because donor and recipient share the block-transmitted major histocompatibility complex region. We present a statistical method based on the HLA haplotype inference to refine the HLA information available in an unrelated situation. We implement a systematic statistical inference of the haplotype combinations at the individual level. It computes the most likely haplotype pair given the phenotype and its probability. The method is validated on 301 phase-known phenotypes from CEPH families (Centre d'Etude du Polymorphisme Humain). The method is further applied to 85,933 HLA-A B DR typed unrelated donors from the French Registry of hematopoietic stem cells donors (France Greffe de Moelle). The average value of prediction probability is 0.761 (SD 0.199) ranging from 0.26 to 1. Correlations between phenotype characteristics and predictions are also given. Homozygosity (OR = 2.08; [2.02-2.14] p <10(-3)) and linkage disequilibrium (p <10(-3)) are the major factors influencing the quality of prediction. Limits and relevance of the method are related to limits of haplotype estimation. Relevance of the method is discussed in the context of HLA matching refinement.

Handling missing values in population data: consequences for maximum likelihood estimation of haplotype frequencies

Haplotype frequency estimation in population data is an important problem in genetics and different methods including expectation maximisation (EM) methods have been proposed. The statistical properties of EM methods have been extensively assessed for data sets with no missing values. When numerous markers and/or individuals are tested, however, it is likely that some genotypes will be missing. Thus, it is of interest to investigate the behaviour of the method in the presence of incomplete genotype observations. We propose an extension of the EM method to handle missing genotypes, and we compare it with commonly used methods (such as ignoring individuals with incomplete genotype information or treating a missing allele as any other allele). Simulations were performed, starting from data sets of haematopoietic stem cell donors genotyped at three HLA loci. We deleted some data to create incomplete genotype observations in various proportions. We then compared the haplotype frequencies obtained on these incomplete data sets using the different methods to those obtained on the complete data. We found that the method proposed here provides better estimations, both qualitatively and quantitatively, but increases the computation time required. We discuss the influence of missing values on the algorithm's efficiency and the advantages and disadvantages of deleting incomplete genotypes. We propose guidelines for missing data handling in routine analysis.

Gene and haplotype frequencies for the loci hLA-A, hLA-B, and hLA-DR based on over 13,000 german blood donors

Numerous applications in clinical medicine and forensic sciences depend on reliable data concerning the frequencies of human leukocyte antigen (HLA) genes and haplotypes. Assuming a Hardy-Weinberg equilibrium of the underlying population, these frequencies can be estimated from phenotype data using an expectation-maximization-algorithm also known under the name "gene counting." We have refined this algorithm in order to cope with the heterogeneous resolution of HLA phenotypes frequently occurring in large datasets due to the structure of the HLA nomenclature. This was a prerequisite to analyze a set of 13,386 blood donors contributed by over 40 blood banks who were tested for HLA-DR when they volunteered to become marrow donors. This data set is still unique in the German national donor registry because their HLA-DR-typing was not biased by patient oriented searches or other strategies for selective typing. As a consequence of the size of the sample, the frequency estimates for the genes and the two- and three-locus haplotypes of HLA-A, HLA-B, and HLA-DR are of unprecedented precision and allow interesting projections concerning the efficiency and economic aspects of the development of a large donor registry in Germany.

Estimation of myriad haplotype frequencies

The present paper demonstrates algorithms for applying gene counting estimation of haplotype frequencies in very large genetic systems. A factor union representation of phenotypes is used which conveniently yields the sets of potential haplotypes and diplotypes for each phenotype. Methods for storing and rapidly retrieving the relevant haplotypes are given. An example with several hundred frequencies is given which required a few seconds computing time for each estimation iteration on a small computer. A computer program employing the methods described has been written in Fortran-77 and is available to any investigator on request.

Kinship mapping of multilocus systems

The linear order of loci in a complex system can be determined by recombination, physical assignment, and correspondence of rare haplotypes with double recombinants. An independent approach is based on the concept of kinship between pairs of loci which gives the apparently correct order for the Rhesus system. Noise due to drift, selection, recent hybridization, and errors of haplotype frequency estimation can be controlled by averaging over populations.