Using probabilistic genotypes in linkage analysis of polyploids.
Ontology highlight
ABSTRACT: Key message
In polyploids, linkage mapping is carried out using genotyping with discrete dosage scores. Here, we use probabilistic genotypes and we validate it for the construction of polyploid linkage maps. Marker genotypes are generally called as discrete values: homozygous versus heterozygous in the case of diploids, or an integer allele dosage in the case of polyploids. Software for linkage map construction and/or QTL analysis usually relies on such discrete genotypes. However, it may not always be possible, or desirable, to assign definite values to genotype observations in the presence of uncertainty in the genotype calling. Here, we present an approach that uses probabilistic marker dosages for linkage map construction in polyploids. We compare our method to an approach based on discrete dosages, using simulated SNP array and sequence reads data with varying levels of data quality. We validate our approach using experimental data from a potato (Solanum tuberosum L.) SNP array applied to an F1 mapping population. In comparison to the approach based on discrete dosages, we mapped an additional 562 markers. All but three of these were mapped to the expected chromosome and marker position. For the remaining three markers, no physical position was known. The use of dosage probabilities is of particular relevance for map construction in polyploids using sequencing data, as these often result in a higher level of uncertainty regarding allele dosage.
SUBMITTER: Liao Y
PROVIDER: S-EPMC8277618 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Studies involving the use of probabilistic record linkage are becoming increasingly common. However, the methods underpinning probabilistic record linkage are not widely taught or understood, and therefore these studies can appear to be a 'black box' research tool. In this article, we aim to describe the process of probabilistic record linkage through a simple exemplar. We first introduce the concept of deterministic linkage and contrast this with probabilistic linkage. We illustrate each step of the process using a simple exemplar and describe the data structure required to perform a probabilistic linkage. We describe the process of calculating and interpreting matched weights and how to convert matched weights into posterior probabilities of a match using Bayes theorem. We conclude this article with a brief discussion of some of the computational demands of record linkage, how you might assess the quality of your linkage algorithm, and how epidemiologists can maximize the value of their record-linked research using robust record linkage methods.
| S-EPMC5005943 | biostudies-literature
Project description:Motivation:Polyploid species carry more than two copies of each chromosome, a condition found in many of the world's most important crops. Genetic mapping in polyploids is more complex than in diploid species, resulting in a lack of available software tools. These are needed if we are to realize all the opportunities offered by modern genotyping platforms for genetic research and breeding in polyploid crops. Results:polymapR is an R package for genetic linkage analysis and integrated genetic map construction from bi-parental populations of outcrossing autopolyploids. It can currently analyse triploid, tetraploid and hexaploid marker datasets and is applicable to various crops including potato, leek, alfalfa, blueberry, chrysanthemum, sweet potato or kiwifruit. It can detect, estimate and correct for preferential chromosome pairing, and has been tested on high-density marker datasets from potato, rose and chrysanthemum, generating high-density integrated linkage maps in all of these crops. Availability and implementation:polymapR is freely available under the general public license from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/package=polymapR. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC6184683 | biostudies-literature
Project description:Many exome sequencing studies of Mendelian disorders fail to optimally exploit family information. Classical genetic linkage analysis is an effective method for eliminating a large fraction of the candidate causal variants discovered, even in small families that lack a unique linkage peak. We demonstrate that accurate genetic linkage mapping can be performed using SNP genotypes extracted from exome data, removing the need for separate array-based genotyping. We provide software to facilitate such analyses.
| S-EPMC3308048 | biostudies-literature
Project description:We develop an algorithm for probabilistic linkage of de-identified research datasets at the patient level, when only diagnosis codes with discrepancies and no personal health identifiers such as name or date of birth are available. It relies on Bayesian modelling of binarized diagnosis codes, and provides a posterior probability of matching for each patient pair, while considering all the data at once. Both in our simulation study (using an administrative claims dataset for data generation) and in two real use-cases linking patient electronic health records from a large tertiary care network, our method exhibits good performance and compares favourably to the standard baseline Fellegi-Sunter algorithm. We propose a scalable, fast and efficient open-source implementation in the ludic R package available on CRAN, which also includes the anonymized diagnosis code data from our real use-case. This work suggests it is possible to link de-identified research databases stripped of any personal health identifiers using only diagnosis codes, provided sufficient information is shared between the data sources.
| S-EPMC6326114 | biostudies-literature
Project description:ObjectiveThe disjointed healthcare system and the nonexistence of a universal patient identifier across systems necessitates accurate record linkage (RL). We aim to describe the implementation and evaluation of a hybrid record linkage method in a statewide surveillance system for congenital heart disease.Materials and methodsClear-text personally identifiable information on individuals in the Colorado Congenital Heart Disease surveillance system was obtained from 5 electronic health record and medical claims data sources. Two deterministic methods and 1 probabilistic RL method using first name, last name, social security number, date of birth, and house number were initially implemented independently and then sequentially in a hybrid approach to assess RL performance.Results16 480 nonunique individuals with congenital heart disease were ascertained. Deterministic linkage methods, when performed independently, yielded 4505 linked pairs (consisting of 2 records linked together within or across data sources). Probabilistic RL, using 3 initial characters of last name and gender for blocking, yielded 6294 linked pairs when executed independently. Using a hybrid linkage routine resulted in 6451 linkages and an additional 18%-24% correct linked pairs as compared to the independent methods. A hybrid linkage routine resulted in higher recall and F-measure scores compared to probabilistic and deterministic methods performed independently.DiscussionThe hybrid approach resulted in increased linkage accuracy and identified pairs of linked record that would have otherwise been missed when using any independent linkage technique.ConclusionWhen performing RL within and across disparate data sources, the hybrid RL routine outperformed independent deterministic and probabilistic methods.
| S-EPMC7647290 | biostudies-literature
Project description:BackgroundThe Enhanced Matching System (EMS) is a probabilistic record linkage program developed by the tuberculosis section at Public Health England to match data for individuals across two datasets. This paper outlines how EMS works and investigates its accuracy for linkage across public health datasets.MethodsEMS is a configurable Microsoft SQL Server database program. To examine the accuracy of EMS, two public health databases were matched using National Health Service (NHS) numbers as a gold standard unique identifier. Probabilistic linkage was then performed on the same two datasets without inclusion of NHS number. Sensitivity analyses were carried out to examine the effect of varying matching process parameters.ResultsExact matching using NHS number between two datasets (containing 5931 and 1759 records) identified 1071 matched pairs. EMS probabilistic linkage identified 1068 record pairs. The sensitivity of probabilistic linkage was calculated as 99.5% (95%CI: 98.9, 99.8), specificity 100.0% (95%CI: 99.9, 100.0), positive predictive value 99.8% (95%CI: 99.3, 100.0), and negative predictive value 99.9% (95%CI: 99.8, 100.0). Probabilistic matching was most accurate when including address variables and using the automatically generated threshold for determining links with manual review.ConclusionWith the establishment of national electronic datasets across health and social care, EMS enables previously unanswerable research questions to be tackled with confidence in the accuracy of the linkage process. In scenarios where a small sample is being matched into a very large database (such as national records of hospital attendance) then, compared to results presented in this analysis, the positive predictive value or sensitivity may drop according to the prevalence of matches between databases. Despite this possible limitation, probabilistic linkage has great potential to be used where exact matching using a common identifier is not possible, including in low-income settings, and for vulnerable groups such as homeless populations, where the absence of unique identifiers and lower data quality has historically hindered the ability to identify individuals across datasets.
| S-EPMC4547731 | biostudies-literature
Project description:In retrospective cohort studies of interventions disseminated to communities, it is challenging to find comparison groups with high-quality data for evaluation. We present one methodological approach as part of our study of birth outcomes of second-born children in a home visiting (HV) program targeting first-time mothers. We used probabilistic record linkage to link Connecticut's Nurturing Families Network (NFN) HV program and birth-certificate data for children born from 2005 to 2015. We identified two potential comparison groups: a propensity-score-matched group from the remaining birth certificate sample and eligible-but-unenrolled families. An analysis of interpregnancy interval (IPI) is presented to exemplify the approach. We identified the birth certificates of 4822 NFN families. The propensity-score-matched group had 14,219 families (3-to-1 matching) and we identified 1101 eligible-but-unenrolled families. Covariates were well balanced for the propensity-score-matched group, but poorly balanced for the eligible-but-unenrolled group. No program effect on IPI was found. By combining propensity-score matching and probabilistic record linkage, we were able to retrospectively identify relatively large comparison groups for quasi-experimental research. Using birth certificate data, we accessed outcomes for all of these individuals from a single data source. Multiple comparison groups allow us to confirm findings when each method has some limitations. Other researchers seeking community-based comparison groups could consider a similar approach.
| S-EPMC9064948 | biostudies-literature
Project description:BACKGROUND: As the use of next-generation sequencing technologies is becoming more widespread, the need for robust software to help with the analysis is growing as well. A key challenge when analyzing sequencing data is the prediction of genotypes from the reads, i.e. correct inference of the underlying DNA sequences that gave rise to the sequenced fragments. For diploid organisms, the genotyper should be able to predict both alleles in the individual. Variations between the individual and the population can then be analyzed by looking for SNPs (single nucleotide polymorphisms) in order to investigate diseases or phenotypic features. To perform robust and high confidence genotyping and SNP calling, methods are needed that take the technology specific limitations into account and can model different sources of error. As an example, ancient DNA poses special challenges as the data is often shallow and subject to errors induced by post mortem damage. FINDINGS: We present a novel approach to the genotyping problem where a probabilistic framework describing the process from sampling to sequencing is implemented as a graphical model. This makes it possible to model technology specific errors and other sources of variation that can affect the result. The inferred genotype is given a posterior probability to signify the confidence in the result. SNPest has already been used to genotype large scale projects such as the first ancient human genome published in 2010. CONCLUSIONS: We compare the performance of SNPest to a number of other widely used genotypers on both real and simulated data, covering both haploid and diploid genomes. We investigate the effects of read depth, of removing adapters before mapping and genotyping, of using different mapping tools, and of using the correct model in the genotyping process. We show that the performance of SNPest is comparable to existing methods, and we also illustrate cases where SNPest has an advantage over other methods, e.g. when dealing with simulated ancient DNA.
| S-EPMC4203901 | biostudies-literature
Project description:Probabilistic record linkage is a process used to bring together person-based records from within the same dataset (de-duplication) or from disparate datasets using pairwise comparisons and matching probabilities. The linkage strategy and associated match probabilities are often estimated through investigations into data quality and manual inspection. However, as privacy-preserved datasets comprise encrypted data, such methods are not possible. In this paper, we present a method for estimating the probabilities and threshold values for probabilistic privacy-preserved record linkage using Bloom filters.Our method was tested through a simulation study using synthetic data, followed by an application using real-world administrative data. Synthetic datasets were generated with error rates from zero to 20% error. Our method was used to estimate parameters (probabilities and thresholds) for de-duplication linkages. Linkage quality was determined by F-measure. Each dataset was privacy-preserved using separate Bloom filters for each field. Match probabilities were estimated using the expectation-maximisation (EM) algorithm on the privacy-preserved data. Threshold cut-off values were determined by an extension to the EM algorithm allowing linkage quality to be estimated for each possible threshold. De-duplication linkages of each privacy-preserved dataset were performed using both estimated and calculated probabilities. Linkage quality using the F-measure at the estimated threshold values was also compared to the highest F-measure. Three large administrative datasets were used to demonstrate the applicability of the probability and threshold estimation technique on real-world data.Linkage of the synthetic datasets using the estimated probabilities produced an F-measure that was comparable to the F-measure using calculated probabilities, even with up to 20% error. Linkage of the administrative datasets using estimated probabilities produced an F-measure that was higher than the F-measure using calculated probabilities. Further, the threshold estimation yielded results for F-measure that were only slightly below the highest possible for those probabilities.The method appears highly accurate across a spectrum of datasets with varying degrees of error. As there are few alternatives for parameter estimation, the approach is a major step towards providing a complete operational approach for probabilistic linkage of privacy-preserved datasets.
| S-EPMC5504757 | biostudies-other
Project description:BackgroundThe extent of linkage disequilibrium (LD) between molecular markers impacts genome-wide association studies and implementation of genomic selection. The availability of high-density single nucleotide polymorphism (SNP) genotyping platforms makes it possible to investigate LD at an unprecedented resolution. In this work, we characterised LD decay in breeds of beef cattle of taurine, indicine and composite origins and explored its variation across autosomes and the X chromosome.FindingsIn each breed, LD decayed rapidly and r2 was less than 0.2 for marker pairs separated by 50 kb. The LD decay curves clustered into three groups of similar LD decay that distinguished the three main cattle types. At short distances between markers (<10 kb), taurine breeds showed higher LD (r2=0.45) than their indicine (r2=0.25) and composite (r2=0.32) counterparts. This higher LD in taurine breeds was attributed to a smaller effective population size and a stronger bottleneck during breed formation. Using all SNPs on only the X chromosome, the three cattle types could still be distinguished. However for taurine breeds, the LD decay on the X chromosome was much faster and the background level much lower than for indicine breeds and composite populations. When using only SNPs that were polymorphic in all breeds, the analysis of the X chromosome mimicked that of the autosomes.ConclusionsThe pattern of LD mirrored some aspects of the history of breed populations and showed a sharp decay with increasing physical distance between markers. We conclude that the availability of the HD chip can be used to detect association signals that remained hidden when using lower density genotyping platforms, since LD dropped below 0.2 at distances of 50 kb.
| S-EPMC4021229 | biostudies-literature