Joint Linkage and Association Analysis Using GENEHUNTER-MODSCORE with an Application to Familial Pancreatic Cancer.

INTRODUCTION: Joint linkage and association (JLA) analysis combines two disease gene mapping strategies: linkage information contained in families and association information contained in populations. Such a JLA analysis can increase mapping power, especially when the evidence for both linkage and association is low to moderate. Similarly, an association analysis based on haplotypes instead of single markers can increase mapping power when the association pattern is complex. METHODS: In this paper, we present an extension to the GENEHUNTER-MODSCORE software package that enables a JLA analysis based on haplotypes and uses information from arbitrary pedigree types and unrelated individuals. Our new JLA method is an extension of the MOD score approach for linkage analysis, which allows the estimation of trait-model and linkage disequilibrium (LD) parameters, i.e., penetrance, disease-allele frequency, and haplotype frequencies. LD is modeled between alleles at a single diallelic disease locus and up to three diallelic test markers. Linkage information is contributed by additional multi-allelic flanking markers. We investigated the statistical properties of our JLA implementation using extensive simulations, and we compared our approach to another commonly used single-marker JLA test. To demonstrate the applicability of our new method in practice, we analyzed pedigree data from the German National Case Collection for Familial Pancreatic Cancer (FaPaCa). RESULTS: Based on the simulated data, we demonstrated the validity of our JLA-MOD score analysis implementation and identified scenarios in which haplotype-based tests outperformed the single-marker test. The estimated trait-model and LD parameters were in good accordance with the simulated values. Our method outperformed another commonly used JLA single-marker test when the LD pattern was complex. The exploratory analysis of the FaPaCa families led to the identification of a promising genetic region on chromosome 22q13.33, which can serve as a starting point for future mutation analysis and molecular research in pancreatic cancer. CONCLUSION: Our newly proposed JLA-MOD score method proves to be a valuable gene mapping and characterization tool, especially when either linkage or association information alone provide insufficient power to identify the disease-causing genetic variants.

A spatio-temporal model of multi-marker antimalarial resistance.

The emergence and spread of drug-resistant Plasmodium falciparum parasites have hindered efforts to eliminate malaria. Monitoring the spread of drug resistance is vital, as drug resistance can lead to widespread treatment failure. We develop a Bayesian model to produce spatio-temporal maps that depict the spread of drug resistance, and apply our methods for the antimalarial sulfadoxine-pyrimethamine. We infer from genetic count data the prevalences over space and time of various malaria parasite haplotypes associated with drug resistance. Previous work has focused on inferring the prevalence of individual molecular markers. In reality, combinations of mutations at multiple markers confer varying degrees of drug resistance to the parasite, indicating that multiple markers should be modelled together. However, the reporting of genetic count data is often inconsistent as some studies report haplotype counts, whereas some studies report mutation counts of individual markers separately. In response, we introduce a latent multinomial Gaussian process model to handle partially reported spatio-temporal count data. As drug-resistant mutations are often used as a proxy for treatment efficacy, point estimates from our spatio-temporal maps can help inform antimalarial drug policies, whereas the uncertainties from our maps can help with optimizing sampling strategies for future monitoring of drug resistance.

Common and well-documented HLA alleles of German stem cell donors by haplotype frequency estimation.

We present a catalog of common and well-documented (CWD) alleles of the German population for the six HLA loci A, B, C, DRB1, DQB1, and DPB1. This study is based on a sample of over 5 million volunteer adult hematopoietic stem cell donors from the 26 German donor centers. To establish the catalog, allele and haplotype frequencies were estimated with a validated implementation of the expectation-maximization algorithm. CWD criteria similar to existing CWD catalogs were applied in order to be able to put our findings into the context of relevant existing references. Overall, 2155 HLA-A, -B, -C, -DRB1, -DQB1, and -DPB1 alleles were identified as CWD in the German donor population representing about 20% of the HLA alleles at two-field resolution in the IPD-IMGT/HLA Database release v3.25.0 from July 2016 for these six loci. We found a substantial concordance of CWD alleles between the three catalogs and showed the contribution of the German donor population to the CWD alleles domain. In conclusion, the definition of CWD criteria that allow interoperability, scalability, and flexibility will be crucial for the development of a worldwide CWD catalog.

Probability of success in the search for a related bone marrow donor in Cologne, Germany using HLA-A, -B and -DRB1 haplotype frequencies.

Between 2004 and 2013, 603 patients and their relatives (n = 1297) were typed as part of the search for a suitable HLA-matched donor in their nuclear and extended families at the central service provider for transfusion medicine at the University Hospital of Cologne. The high success rate in finding donors over the years at our center (38.1%) led us to examine our database retrospectively in order to evaluate the donor search and haplotype frequencies (HFs) in the sample. Our goal was to identify the factors contributing to this high success rate and also to compare the HFs we observed with other reported haplotype frequency estimations (HFE) for the Cologne area. Probability estimations for a successful donor search were constructed based on the HFEs for the sample.

How to optimize the precision of allele and haplotype frequency estimates using pooled-sequencing data.

Sequencing pools of individuals rather than individuals separately reduces the costs of estimating allele frequencies at many loci in many populations. Theoretical and empirical studies show that sequencing pools comprising a limited number of individuals (typically fewer than 50) provides reliable allele frequency estimates, provided that the DNA pooling and DNA sequencing steps are carefully controlled. Unequal contributions of different individuals to the DNA pool and the mean and variance in sequencing depth both can affect the standard error of allele frequency estimates. To our knowledge, no study separately investigated the effect of these two factors on allele frequency estimates; so that there is currently no method to a priori estimate the relative importance of unequal individual DNA contributions independently of sequencing depth. We develop a new analytical model for allele frequency estimation that explicitly distinguishes these two effects. Our model shows that the DNA pooling variance in a pooled sequencing experiment depends solely on two factors: the number of individuals within the pool and the coefficient of variation of individual DNA contributions to the pool. We present a new method to experimentally estimate this coefficient of variation when planning a pooled sequencing design where samples are either pooled before or after DNA extraction. Using this analytical and experimental framework, we provide guidelines to optimize the design of pooled sequencing experiments. Finally, we sequence replicated pools of inbred lines of the plant Medicago truncatula and show that the predictions from our model generally hold true when estimating the frequency of known multilocus haplotypes using pooled sequencing.

Distribution of HLA-A, -B and -DRB1 antigenic groups and haplotypes from the Brazilian bone marrow donor registry (REDOME).

To improve assistance for patients awaiting a bone marrow transplant from an unrelated donor, it is important to genetically characterize the Brazilian volunteer bone marrow donors registry (REDOME). Our objective was to describe the antigenic groups and haplotype frequencies of HLA-A, HLA-B and HLA-DRB1 in the five regions of Brazil and by self-reported ethnicity groups using the REDOME data. Our study included 3,038,286 individuals. HLA antigenic groups and haplotype frequencies were estimated using an Expectation-Maximization (EM) algorithm. All described HLA-A*, HLA-B* and HLA-DRB1* groups were identified in this study. A*02 (25.9%), B*35 (11.8%) and DRB1*13 (13.4%) are the most frequent antigenic groups in REDOME, and the A*01-B*08-DRB1*03 haplotype is the most frequent in the registry. The antigenic group and haplotype frequency data obtained in this study could be helpful for national donor recruitment strategies across the country.

Ehapp2: Estimate haplotype frequencies from pooled sequencing data with prior database information.

To reduce the cost of large-scale re-sequencing, multiple individuals are pooled together and sequenced called pooled sequencing. Pooled sequencing could provide a cost-effective alternative to sequencing individuals separately. To facilitate the application of pooled sequencing in haplotype-based diseases association analysis, the critical procedure is to accurately estimate haplotype frequencies from pooled samples. Here we present Ehapp2 for estimating haplotype frequencies from pooled sequencing data by utilizing a database which provides prior information of known haplotypes. We first translate the problem of estimating frequency for each haplotype into finding a sparse solution for a system of linear equations, where the NNREG algorithm is employed to achieve the solution. Simulation experiments reveal that Ehapp2 is robust to sequencing errors and able to estimate the frequencies of haplotypes with less than 3% average relative difference for pooled sequencing of mixture of real Drosophila haplotypes with 50× total coverage even when the sequencing error rate is as high as 0.05. Owing to the strategy that proportions for local haplotypes spanning multiple SNPs are accurately calculated first, Ehapp2 retains excellent estimation for recombinant haplotypes resulting from chromosomal crossover. Comparisons with present methods reveal that Ehapp2 is state-of-the-art for many sequencing study designs and more suitable for current massive parallel sequencing.

The Impact of HLA-C Matching on Donor Identification Rates in a European-Caucasian Population.

The degree of HLA concordance with the patient has long been known to be the major donor-related prediction factor for the success of hematopoietic stem cell transplantations and, with the progress of HLA typing technology, selection criteria became more stringent with regard to the recommended loci and resolution. A late refinement was HLA-C matching, which gained broader acceptance only after the turn of the millennium. The enormous HLA polymorphism has always necessitated registries with a large number of donors in order to be able to provide well-matched donors to a substantial fraction of patients. Using a biostatistical approach, we investigated the impact of adding HLA-C at low or high resolution as a supplementary matching criterion on some key parameters in donor provision for a European-Caucasian population. Starting point is donor selection based on allele level matching for HLA-A, -B, -DRB1, and, optionally, HLA-DQB1. Without typing for HLA-C, 68% of the donors selected based on matching for HLA-A, -B, -DRB1, and -DQB1 at high resolution will also match for HLA-C, 29% will have a single and only 3% will have two HLA-C alleles different from the patient. In order to provide the same fraction of patients with a fully matched donor, a registry would have to be about twice the size if HLA-C is considered in addition to the four other loci, with the exact factor increasing with the registry's size. If the provision of donors with up to a single allele mismatch is considered, this factor doubles due to the strong linkage between HLA-B and -C. These figures only change slightly when HLA-DQB1 is completely ignored or HLA-C matching is only considered at low resolution. Our results contribute to quantifying the medical and economic impact of the progress in donor selection algorithms.

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.