Estimating the Effective Number of Breeders From Heterozygote Excess in Progeny

Abstract The heterozygote-excess method is a recently published method for estimating the effective population size (Ne). It is based on the following principle: When the effective number of breeders (Neb) in a population is small, the allele frequencies will (by chance) be different in males and females, which causes an excess of heterozygotes in the progeny with respect to Hardy-Weinberg equilibrium expectations. We evaluate the accuracy and precision of the heterozygote-excess method using empirical and simulated data sets from polygamous, polygynous, and monogamous mating systems and by using realistic sample sizes of individuals (15-120) and loci (5-30) with varying levels of polymorphism. The method gave nearly unbiased estimates of Neb under all three mating systems. However, the confidence intervals on the point estimates of Neb were sufficiently small (and hence the heterozygote-excess method useful) only in polygamous and polygynous populations that were produced by <10 effective breeders, unless samples included > ∼60 individuals and 20 multiallelic loci.

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The Impact of Site Sample Size on the Reconstruction of Culture Histories

American Antiquity ◽

10.7183/0002-7316.76.3.547 ◽

2011 ◽

Vol 76 (3) ◽

pp. 547-572 ◽

Cited By ~ 3

Author(s):

Charles Perreault

Keyword(s):

Simulated Data ◽

Archaeological Sites ◽

Cultural Tradition ◽

Data Sets ◽

Data Set ◽

Rate Of Spread ◽

Accuracy And Precision ◽

Historical Processes ◽

The Impact ◽

Simulated Data Sets

I examine how our capacity to produce accurate culture-historical reconstructions changes as more archaeological sites are discovered, dated, and added to a data set. More precisely, I describe, using simulated data sets, how increases in the number of known sites impact the accuracy and precision of our estimations of (1) the earliest and (2) latest date of a cultural tradition, (3) the date and (4) magnitude of its peak popularity, as well as (5) its rate of spread and (6) disappearance in a population. I show that the accuracy and precision of inferences about these six historical processes are not affected in the same fashion by changes in the number of known sites. I also consider the impact of two simple taphonomic site destruction scenarios on the results. Overall, the results presented in this paper indicate that unless we are in possession of near-total samples of sites, and can be certain that there are no taphonomic biases in the universe of sites to be sampled, we will make inferences of varying precision and accuracy depending on the aspect of a cultural trait’s history in question.

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On the Potential for Estimating the Effective Number of Breeders From Heterozygote-Excess in Progeny

Genetics ◽

10.1093/genetics/144.1.383 ◽

1996 ◽

Vol 144 (1) ◽

pp. 383-387 ◽

Cited By ~ 11

Author(s):

A I Pudovkin ◽

D V Zaykin ◽

D Hedgecock

Keyword(s):

Population Size ◽

Effective Population Size ◽

Sampling Error ◽

Demographic Data ◽

Rank Correlation ◽

Effective Number ◽

Effective Population ◽

Heterozygote Excess ◽

Effective Number Of Breeders ◽

Age Structured

Abstract The important parameter of effective population size is rarely estimable directly from demographic data. Indirect estimates of effective population size may be made from genetic data such as temporal variation of allelic frequencies or linkage disequilibrium in cohorts. We suggest here that an indirect estimate of the effective number of breeders might be based on the excess of heterozygosity expected in a cohort of progeny produced by a limited number of males and females. In computer simulations, heterozygote excesses for 30 unlinked loci having various numbers of alleles and allele-frequency profiles were obtained for cohorts produced by samples of breeders drawn from an age-structured population and having known variance in reproductive success and effective number. The 95% confidence limits around the estimate contained the true effective population size in 70 of 72 trials and the Spearman rank correlation of estimated and actual values was 0.991. An estimate based on heterozygote excess might have certain advantages over the previous estimates, requiring only single-locus and single-cohort data, but the sampling error among individuals and the effect of departures from random union of gametes still need-to be explored.

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Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽

10.3390/e22090949 ◽

2020 ◽

Vol 22 (9) ◽

pp. 949

Author(s):

Jiangyi Wang ◽

Min Liu ◽

Xinwu Zeng ◽

Xiaoqiang Hua

Keyword(s):

Neural Network ◽

Signal Detection ◽

Convolutional Neural Network ◽

Deep Neural Network ◽

Detection Method ◽

Learning Algorithm ◽

Simulated Data ◽

Data Sets ◽

Feature Maps ◽

Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

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Benchmarking Statistical Multiple Sequence Alignment

10.1101/304659 ◽

2018 ◽

Cited By ~ 1

Author(s):

Michael Nute ◽

Ehsan Saleh ◽

Tandy Warnow

Keyword(s):

Sequence Alignment ◽

Multiple Sequence Alignment ◽

Structural Alignment ◽

Estimation Method ◽

Simulated Data ◽

Protein Sequences ◽

Data Sets ◽

Sequence Alignments ◽

Multiple Sequence ◽

Simulated Data Sets

AbstractThe estimation of multiple sequence alignments of protein sequences is a basic step in many bioinformatics pipelines, including protein structure prediction, protein family identification, and phylogeny estimation. Statistical co-estimation of alignments and trees under stochastic models of sequence evolution has long been considered the most rigorous technique for estimating alignments and trees, but little is known about the accuracy of such methods on biological benchmarks. We report the results of an extensive study evaluating the most popular protein alignment methods as well as the statistical co-estimation method BAli-Phy on 1192 protein data sets from established benchmarks as well as on 120 simulated data sets. Our study (which used more than 230 CPU years for the BAli-Phy analyses alone) shows that BAli-Phy is dramatically more accurate than the other alignment methods on the simulated data sets, but is among the least accurate on the biological benchmarks. There are several potential causes for this discordance, including model misspecification, errors in the reference alignments, and conflicts between structural alignment and evolutionary alignments; future research is needed to understand the most likely explanation for our observations. multiple sequence alignment, BAli-Phy, protein sequences, structural alignment, homology

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Bayesian Planet Searches for the 10 cm/s Radial Velocity Era

Proceedings of the International Astronomical Union ◽

10.1017/s1743921316002817 ◽

2015 ◽

Vol 11 (A29A) ◽

pp. 205-207

Author(s):

Philip C. Gregory

Keyword(s):

Radial Velocity ◽

State Of The Art ◽

Simulated Data ◽

Model Parameters ◽

Data Sets ◽

Stellar Activity ◽

Bayesian Fusion ◽

Multiple State ◽

Simulated Data Sets ◽

Apodization Function

AbstractA new apodized Keplerian model is proposed for the analysis of precision radial velocity (RV) data to model both planetary and stellar activity (SA) induced RV signals. A symmetrical Gaussian apodization function with unknown width and center can distinguish planetary signals from SA signals on the basis of the width of the apodization function. The general model for m apodized Keplerian signals also includes a linear regression term between RV and the stellar activity diagnostic In (R'hk), as well as an extra Gaussian noise term with unknown standard deviation. The model parameters are explored using a Bayesian fusion MCMC code. A differential version of the Generalized Lomb-Scargle periodogram provides an additional way of distinguishing SA signals and helps guide the choice of new periods. Sample results are reported for a recent international RV blind challenge which included multiple state of the art simulated data sets supported by a variety of stellar activity diagnostics.

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