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10 results for “pedigree reconstruction”
Forward selection in a maritime pine polycross progeny trial using pedigree reconstruction.
<p>These two excel files gather genotyping data used in the following publication:</p> <p>Vidal M, Plomion C, Raffin A, Harvengt L, Bouffier L (2017) Forward selection in a maritime pine polycross progeny trial using pedigree reconstruction. Annals of Forest Science, 74(1). DOI 10.1007/s13595-016-0596-8</p> <p>The dataset describes genotyping profiles (with 56 or 63 SNPs) for the G1 and G2 individuals sampled in this paper. For each individual, the following information is mentioned: identity, preselection option (only for G2 individuals), the generation to which the individual belongs, pedigree (only for G2 individuals), alleles for each SNP.</p>
Estimation of breeding population size using DNA-based pedigree reconstruction in brown bears
Robust estimates of demographic parameters are critical for effective wildlife conservation and management, but are difficult to obtain for elusive species. We estimated the breeding and adult population sizes, as well as the minimum population size, in a high-density brown bear population on the Shiretoko Peninsula, in Hokkaido, Japan, using DNA-based pedigree reconstruction. A total of 1,288 individuals, collected in and around the Shiretoko Peninsula between 1998 and 2020, were genotyped at 21 microsatellite loci. Among them, 499 individuals were identified by intensive genetic sampling conducted in two consecutive years (2019 and 2020) mainly by noninvasive methods (e.g., hair and fecal DNA). Among them, both parents were assigned for 330 bears, and either maternity or paternity was assigned to 47 and 76 individuals, respectively. The subsequent pedigree reconstruction indicated a range of breeding and adult (≥4 years old) population sizes: 128–173 for female breeders and 66–91 male breeders, and 155–200 for female adults and 84–109 male adults. The minimum population size was estimated to be 449 (252 females and 197 males) in 2019. Long-term continuous genetic sampling prior to a short-term intensive survey would enable parentage to be identified in a population with a high probability, thus enabling reliable estimates of breeding population size for elusive species. --
Results of quantitative genetic sensitivity analysis performed on reconstructed pedigrees based on large-scale genealogies
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Estimation of breeding population size using DNA-based pedigree reconstruction in brown bears
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Data from: Genomic pedigree reconstruction identifies predictors of mating and reproductive success in an invasive vertebrate
The persistence of an invasive species is influenced by its reproductive ecology, and a successful control program must operate on this premise. However, the reproductive ecology of invasive species may be enigmatic due to factors that also limit their management, such as cryptic coloration and behavior. We explored the mating and reproductive ecology of the invasive Brown Treesnake (BTS: <i>Boiga irregularis</i>) by reconstructing a multigenerational genomic pedigree based on 654 single nucleotide polymorphisms for a geographically-closed population established in 2004 on Guam (N=426). The pedigree allowed annual estimates of individual mating and reproductive success to be inferred for snakes in the study population over a 14-year period. We then employed generalized linear mixed models to gauge how well phenotypic and genomic data could predict sex-specific annual mating and reproductive success. Average snout-vent length (SVL), average body condition index (BCI), and trappability were significantly related to annual mating success for males, with average SVL also related to annual mating success for females. Male and female annual reproductive success were positively affected by SVL, BCI, and trappability. Surprisingly, the degree to which individuals were inbred had no effect on annual mating or reproductive success. When juxtaposed with current control methods, these results indicate that baited traps, a common interdiction tool, may target fecund BTS in some regards but not others. Our study emphasizes the importance of reproductive ecology as a focus for improving BTS control and promotes genomic pedigree reconstruction for such an endeavor in this invasive species and others.
Data from: Genomic pedigree reconstruction identifies predictors of mating and reproductive success in an invasive vertebrate
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Genomic SSR and EST-SSR markers for phylogenetic and pedigree reconstruction: A comparison in sea buckthorn
<p>Simple sequence repeat (SSR) markers play an important role in genetic and molecular breeding programs, especially for non-model species. We identified SSRs from expressed sequence tags (EST-SSRs) and whole genome sequences (gSSRs), by analyzing the transcriptome and genome of the Mongolian sea buckthorn (<i>Hippophae rhamnoides</i> subsp. <i>Mongolica</i>) cultivar 'Sunny'<i>.</i> Using the same number of loci, EST-SSRs showed higher transferability to <i>Hippophae </i>than gSSRs, and phylogenetic trees based on EST-SSRs demonstrated higher resolution for classifying <i>Hippophae </i>species and subspecies into clades, possibly due to high conservatism and/or existence of adaptive divergence between species/subspecies in coding sequence regions. In contrast, parentage analyses showed that gSSRs were more effective at resolving parentage than EST-SSRs due to higher level of polymorphism in gSSRs. The markers developed in this study can be used in the identification and pedigree reconstruction of species/subspecies of sea buckthorn, while their usage in clarifying phylogenetic relationship among species remains to be discussed. The comparison between EST-SSRs and gSSRs would provide insight into the trade-off between differentiation and polymorphism in marker selection for breeding programs.</p>
Genomic SSR and EST-SSR markers for phylogenetic and pedigree reconstruction: A comparison in sea buckthorn
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Data from: Estimating genotyping errors from genotype and reconstructed pedigree data
1. Genotyping errors are rules rather than exceptions in reality, and are found in virtually all but very small datasets. These errors, even when occurring at an extremely low rate, can derail many genetic analyses such as parentage/sibship assignments and linkage/association studies. 2. Nonetheless, few robust and accurate methods are available for estimating the rate of occurrence of genotyping errors and for identifying individual erroneous genotypes at a locus. Methods based on duplicate genotyping are expensive, and estimate genotype inconsistency rather than error rate at a locus. Methods based on Hardy-Weinberg equilibrium tests have low robustness and low power, and apply only to those particular errors that cause excessive homozygosity. Methods based on pedigrees are powerful, robust and accurate. However, they rely on known and complete pedigrees that are unfortunately rarely available from natural populations in the wild. 3. I proposed a maximum likelihood method to reconstruct pedigrees from genotype data with errors occurring at a roughly estimated (presumed) rate. In this paper, I describe how to use the method and inferred pedigree in estimating allelic dropout (or null allele) rate and false allele rate jointly at each marker locus, in identifying the erroneous genotypes, and in inferring the most likely genotypes at each locus of each individual. I examine the power, accuracy and robustness of the method by extensive simulations, and demonstrate the usefulness of the method by analysing three empirical datasets. 4. It is concluded that, both pedigrees and the rates of genotyping errors at each locus can be reliably estimated from the same genotype data by the same likelihood method, when marker information is sufficient and some sampled individuals are first-degree relatives. The erroneous genotypes are however inferred conservatively, and are reliably detected only when they occur in large families and/or at highly polymorphic loci. Estimation of genotyping error rates per locus and identification of erroneous genotypes of each individual at each locus should be routinely conducted to assess and improve data quality, to highlight markers for optimization of genotyping protocols or for replacement, and to enable the integration of genotyping errors in a robust statistical analysis.
Data from: Estimating genotyping errors from genotype and reconstructed pedigree data
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