An improved hidden Markov model for the characterization of homozygous-by-descent segments in individual genomes

Inbreeding results from the mating of related individuals and has negative consequences because it brings together deleterious variants in one individual. Genomic estimates of the inbreeding coefficients are preferred to pedigree-based estimators as they measure the realized inbreeding levels and they are more robust to pedigree errors. Several methods identifying homozygous-by-descent (HBD) segments with hidden Markov models (HMM) have been recently developed and are particularly valuable when the information is degraded or heterogeneous (e.g., low-fold sequencing, low marker density, heterogeneous genotype quality or variable marker spacing). We previously developed a multiple HBD class HMM where HBD segments are classified in different groups based on their length (e.g., recent versus old HBD segments) but we recently observed that for high inbreeding levels with many HBD segments, the estimated contributions might be biased towards more recent classes (i.e., associated with large HBD segments) although the overall estimated level of inbreeding remained unbiased. We herein propose an updated multiple HBD classes model in which the HBD classification is modeled in successive nested levels. In each level, the rate specifying the expected length of HBD segments, and that is directly related to the number of generations to the ancestors, is distinct. The non-HBD classes are now modeled as a mixture of HBD segments from later generations and shorter non-HBD segments (i.e., both with higher rates). The updated model had better statistical properties and performed better on simulated data compared to our previous version. We also show that the parameters of the model are easier to interpret and that the model is more robust to the choice of the number of classes. Overall, the new model results in an improved partitioning of inbreeding in different HBD classes and should be preferred in applications relying on the length of estimated HBD segments.

When a phenotype is not the genotype: Implications of phenotype misclassification and pedigree errors in genomics-assisted breeding of sweetpotato [Ipomoea batatas(L.) Lam.]

Experimental error, especially through genotype misclassification and pedigree errors, negatively affects breeding decisions by creating ‘noise’ that compounds the genetic signals for selection. Unlike genotype-by-environment interactions, for which different methods have been proposed to address, the effect of ‘noise’ due to pedigree errors and misclassification has not received much attention in most crops. We used two case studies in sweetpotato, based on data from the International Potato Center’s breeding program to estimate the level of phenotype misclassification and pedigree error and to demonstrate the consequences of such errors when combining phenotypes with the respective genotypes. In the first case study, 27.7% phenotype misclassification was observed when moving genotypes from a diversity panel throughin-vitro, screenhouse and field trialing. Additionally, 22.7% pedigree error was observed from misclassification between and within families. The second case study involving multi-environment testing of a full-sib population and quantitative trait loci (QTL) mapping showed reduced genetic correlations among pairs of environments in mega-environments with higher phenotype misclassification errors when compared to the mega-environments with lower phenotype misclassification errors. Additionally, no QTL could be identified in the low genetic correlation mega-environments. Simulation analysis indicated that phenotype misclassification was more detrimental to QTL detection when compared to missingness in data. The current information is important to inform current and future breeding activities involving genomic-assisted breeding decisions in sweetpotato, and to facilitate putting in place improved workflows that minimize phenotype misclassification and pedigree errors.

The simulation extrapolation technique meets ecology and evolution: A general and intuitive method to account for measurement error

Measurement error and other forms of uncertainty are commonplace in ecology and evolution and may bias estimates of parameters of interest. Although a variety of approaches to obtain unbiased estimators are available, these often require that errors are explicitly modeled and that a latent model for the unobserved error-free variable can be specified, which in practice is often difficult.Here we propose to generalize a heuristic approach to correct for measurement error, denoted as simulation extrapolation (SIMEX), to situations where explicit error modeling fails. We illustrate the application of SIMEX using the example of estimates of quantitative genetic parameters, e. g. inbreeding depression and heritability, in the presence of pedigree errors. Following the original SIMEX idea, the error in the pedigree is progressively increased to determine how the estimated quantities are affected by error. The observed trend is then extrapolated back to a hypothetical error-free pedigree, yielding unbiased estimates of inbreeding depression and heritability. We term this application of the SIMEX idea to pedigrees “PSIMEX”. We tested the method with simulated pedigrees with different pedigree structures and initial error proportions, and with real field data from a free-living population of song sparrows.The simulation study indicates that the accuracy and precision of the extrapolated error-free estimate for inbreeding depression and heritability are good. In the application to the song sparrow data, the error-corrected results could be validated against the actual values thanks to the availability of both an error-prone and an error-free pedigree, and the results indicate that the PSIMEX estimator is close to the actual value. For easy accessibility of the method, we provide the novel R-package PSIMEX.By transferring the SIMEX philosophy to error in pedigrees, we have illustrated how this heuristic approach can be generalized to situations where explicit latent models for the unobserved variables or for the error of the variables of interest are difficult to formulate. Thanks to the simplicity of the idea, many other error problems in ecology and evolution might be amenable to SIMEX-like error correction methods.

A pedigree-based analysis of mitochondrial DNA diversity in a dog population on the example of German Hovawarts

The purpose of the article is to illustrate the use of pedigree analysis to evaluate mtDNA diversity in a selected population of pedigree dogs, to describe the paths of mtDNA inheritance and to estimate the spread of potential pedigree errors or mutations that occurred in different generations of ancestors. Hovawart, old German breed, was used as an example. The number and frequencies of mtDNA haplotypes were calculated based on numbers of dam lines and their representatives. The scale of potential errors in calculations that can result from pedigree errors or from new mutations in ancestors from the 5th or 10th ancestral generation was evaluated. The analysis included 368 breeding bitches from four German kennel organizations. The bitches represented three dam lines, with the Ho1, Ho2 and HoU mtDNA haplotypes. Significant differences in the frequency of the haplotypes in the population, from 0.27 to 73.37 %, and among kennel organizations and regions of the country were recorded. Considerable differences in the scale of potential errors in calculations arising from mtDNA mutations or pedigree errors were noted between 0.27 and 28.69 %, depending on the number of representatives of the subline in which the error appeared and the generation taken into account in the simulations. The study revealed an interesting paradox: although the differences between the haplotypes are the result of events (mutations) from thousands of years ago, the number and the frequencies of the haplotypes in the population are the result of the modern history of the population and current breeding policy.

Male reproductive success and pedigree error in red spruce open-pollinated and polycross mating systems

Open-pollinated and polycross mating systems are widely used in forest genetics and breeding to quickly, simply, and inexpensively generate progenies assumed to be related as half-sibs (coefficient of relationship, r = 0.25) from a random mating population. However, nonrandom mating, such as unequal male reproductive success (RS) or selfing, can increase the genetic correlation among offspring, and thus, genetic variance and heritability are overestimated. Conversely, pedigree errors will cause additive genetic variance and heritability to be underestimated. Unequal male reproductive success and three types of potential pedigree errors (volunteers, mishandled maternal identities, and foreign pollen) were detected in operational open-pollinated and polycross red spruce ( Picea rubens Sarg.) progeny tests, through paternity testing using microsatellite (simple sequence repeat) DNA markers. The potential impact of unequal RS and pedigree errors on quantitative genetic parameters is discussed. Paternity and parentage analyses could be used to reconstruct the pedigree of any plantation consisting of sibships, where candidate parents (e.g., members of seed orchard) can be identified. This offers an alternative to traditional progeny testing for estimation of quantitative genetic parameters.