scholarly journals Unbiased estimation of linkage disequilibrium from unphased data

2019 ◽  
Author(s):  
Aaron P. Ragsdale ◽  
Simon Gravel
Keyword(s):  
Linkage Disequilibrium ◽  
Population Size ◽  
Evolutionary History ◽  
Unbiased Estimation ◽  
Sequencing Data ◽  
Unbiased Estimators ◽  
Population Size Estimates ◽  
Wide Range ◽  
The Common ◽  
Size Estimates

AbstractLinkage disequilibrium is used to infer evolutionary history and to identify regions under selection or associated with a given trait. In each case, we require accurate estimates of linkage disequilibrium from sequencing data. Unphased data presents a challenge because the co-occurrence of alleles at different loci is ambiguous. Commonly used estimators for the common statistics r2 and D2 exhibit large and variable upward biases that complicate interpretation and comparison across cohorts. Here, we show how to find unbiased estimators for a wide range of two-locus statistics, including D2, for both single and multiple randomly mating populations. These provide accurate estimates over three orders of magnitude in LD. We also use these estimators to construct an estimator for r2 that is less biased than commonly used estimators, but nevertheless argue for using rather than r2 for population size estimates.

scholarly journals Unbiased Estimation of Linkage Disequilibrium from Unphased Data

2019 ◽  
Vol 37 (3) ◽  
pp. 923-932 ◽  
Author(s):  
Aaron P Ragsdale ◽  
Simon Gravel
Keyword(s):  
Linkage Disequilibrium ◽  
Unbiased Estimation ◽  
Sequencing Data ◽  
Effective Population ◽  
Island Fox ◽  
Wide Range ◽  
Population Sizes ◽  
The Common ◽  
Genomic Regions ◽  
The Relationship

Abstract Linkage disequilibrium (LD) is used to infer evolutionary history, to identify genomic regions under selection, and to dissect the relationship between genotype and phenotype. In each case, we require accurate estimates of LD statistics from sequencing data. Unphased data present a challenge because multilocus haplotypes cannot be inferred exactly. Widely used estimators for the common statistics r2 and D2 exhibit large and variable upward biases that complicate interpretation and comparison across cohorts. Here, we show how to find unbiased estimators for a wide range of two-locus statistics, including D2, for both single and multiple randomly mating populations. These unbiased statistics are particularly well suited to estimate effective population sizes from unlinked loci in small populations. We develop a simple inference pipeline and use it to refine estimates of recent effective population sizes of the threatened Channel Island Fox populations.

scholarly journals Linkage disequilibrium, persistence of phase and effective population size estimates in Hereford and Braford cattle

BMC Genetics ◽  
2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Patrícia Biegelmeyer ◽  
Claudia C. Gulias-Gomes ◽  
Alexandre R. Caetano ◽  
Juan P. Steibel ◽  
Fernando F. Cardoso
Keyword(s):  
Linkage Disequilibrium ◽  
Population Size ◽  
Effective Population Size ◽  
Effective Population ◽  
Population Size Estimates ◽  
Size Estimates

Estimating the population size of people who inject drugs in Ho Chi Minh City, Vietnam: A two-survey capture-recapture exercise (Preprint)

2019 ◽  
Author(s):  
Abu Abdul-Quader
Keyword(s):  
Population Size ◽  
Illicit Drugs ◽  
People Who Inject Drugs ◽  
Care Service ◽  
Ho Chi Minh City ◽  
Size Estimation ◽  
Population Size Estimates ◽  
Capture Recapture ◽  
The City ◽  
Size Estimates

BACKGROUND Population size estimation of people who inject drugs (PWID) in Ho Chi Minh City (HCMC), Vietnam relied on the UNAIDS Estimation and Projection Package and reports from the city police department. The two estimates vary widely. OBJECTIVE To estimate the population size of people who inject drugs in Ho Chi Minh City, Vietnam METHODS Using Respondent-driven sampling (RDS), we implemented two-source capture-recapture method to estimate the population size of PWID in HCMC in 2017 in 7 out of 24 districts. The study included men or women aged at least 18 years who reported injecting illicit drugs in the last 90 days and who had lived in the city the past six months. We calculated two sets of size estimates, the first assumed that all participants in each survey round resided in the district where the survey was conducted, the second, used the district of residence as reported by the participant. District estimates were summed to obtain an aggregate estimate for the seven districts. To calculate the city total, we weighted the population size estimates for each district by the inverse of the stratum specific sampling probabilities. RESULTS The first estimate resulted in a population size of 19,155 (95% CI: 17,006–25,039). The second one generated a smaller population size estimate of 12,867 (95% CI: 11,312–17,393). CONCLUSIONS The two-survey capture-recapture exercise provided two disparate estimates of PWID in HCMC. For planning HIV prevention and care service needs among PWID in HCMC, both estimates may need to be taken into consideration together with size estimates from other sources.

scholarly journals Microsatellite Development and First Population Size Estimates for the Groundwater Isopod Proasellus walteri

PLoS ONE ◽  
2013 ◽  
Vol 8 (9) ◽  
pp. e76213 ◽  
Author(s):  
Cécile Capderrey ◽  
Bernard Kaufmann ◽  
Pauline Jean ◽  
Florian Malard ◽  
Lara Konecny-Dupré ◽  
...  
Keyword(s):  
Population Size ◽  
Microsatellite Development ◽  
Population Size Estimates ◽  
Size Estimates

Estimation of Catchabilities and Population Sizes of Lobsters

1963 ◽  
Vol 20 (1) ◽  
pp. 59-88 ◽  
Author(s):  
J. E. Paloheimo
Keyword(s):  
Environmental Factors ◽  
Population Size ◽  
Atlantic Coast ◽  
Temperature Data ◽  
New Method ◽  
Bottom Temperature ◽  
Population Size Estimates ◽  
Population Sizes ◽  
Estimate Population Size ◽  
Size Estimates

Techniques of estimating population size, level of fishing, and the degree of dependence of fishing success on environmental factors are examined on the basis of tagging, catch and effort data. A new method is developed to estimate population size from catch, effort, and temperature data when the catchability varies with temperature.The methods of estimation discussed are applied to data collected from a number of lobster fisheries on Canada's Atlantic coast. Analysis confirms a relationship between the catchability of lobsters and bottom temperature. Differences in this relationship are found between areas and between tagged and untagged lobsters within areas. It is suggested that these differences are attributable to the differences in densities as well as to aggregations of lobsters and fishing. The effect of these aggregations on population size estimates is considered.Calculated average catchabilities at comparable temperatures are different for different areas. These differences are correlated with the numbers of trap hauls per day per square miles fished. It is suggested that the differences in the catchabilities might be due to interactions between units of gear not predicted by the customary relationship between catch and effort.

scholarly journals Estimating population size of the cave shrimp Troglocaris anophthalmus (Crustacea, Decapoda, Caridea) using mark–release–recapture data

2015 ◽  
Vol 38 (1) ◽  
pp. 77-86
Author(s):  
J. Jugovic ◽  
◽  
E. Praprotnik ◽  
E. V. Buzan ◽  
M. Luznik ◽  
...  
Keyword(s):  
Population Size ◽  
Aquatic Invertebrates ◽  
Anthropogenic Activities ◽  
Age Distribution ◽  
Mark Release Recapture ◽  
Population Size Estimates ◽  
Cave Ecosystems ◽  
Size Estimates ◽  
Abundant Population ◽  
Population Numbers

Population size estimates are lacking for many small cave–dwelling aquatic invertebrates that are vulnerable to groundwater contamination from anthropogenic activities. Here we estimated the population size of freshwater shrimp Troglocaris anophthalmus sontica (Crustacea, Decapoda, Caridea) based on mark–release–recapture techniques. The subspecies was investigated in Vipavska jama (Vipava cave), Slovenia, with estimates of sex ratio and age distribution. A high abundance of shrimps was found even after considering the lower limit of the confidence intervals. However, we found no evidence of differences in shrimp abundances between summer and winter. The population was dominated by females. Ease of capture and abundant population numbers indicate that these cave shrimps may be useful as a bioindicator in cave ecosystems.

scholarly journals Software Application Profile: The Anchored Multiplier calculator—a Bayesian tool to synthesize population size estimates

2019 ◽  
Vol 48 (6) ◽  
pp. 1744-1749
Author(s):  
Paul D Wesson ◽  
Willi McFarland ◽  
Cong Charlie Qin ◽  
Ali Mirzazadeh
Keyword(s):  
Population Size ◽  
Web Application ◽  
Probability Distributions ◽  
Public Health Research ◽  
Forest Plot ◽  
Hidden Populations ◽  
Software Application ◽  
Posterior Probability Distribution ◽  
Population Size Estimates ◽  
Size Estimates

Abstract Estimating the number of people in hidden populations is needed for public health research, yet available methods produce highly variable and uncertain results. The Anchored Multiplier calculator uses a Bayesian framework to synthesize multiple population size estimates to generate a consensus estimate. Users submit point estimates and lower/upper bounds which are converted to beta probability distributions and combined to form a single posterior probability distribution. The Anchored Multiplier calculator is available as a web browser-based application. The software allows for unlimited empirical population size estimates to be submitted and combined according to Bayes Theorem to form a single estimate. The software returns output as a forest plot (to visually compare data inputs and the final Anchored Multiplier estimate) and a table that displays results as population percentages and counts. The web application ‘Anchored Multiplier Calculator’ is free software and is available at [http://globalhealthsciences.ucsf.edu/resources/tools] or directly at [http://anchoredmultiplier.ucsf.edu/].

scholarly journals Challenges of ecological monitoring: estimating population abundance from sparse trap counts

2011 ◽  
Vol 9 (68) ◽  
pp. 420-435 ◽  
Author(s):  
Natalia Petrovskaya ◽  
Sergei Petrovskii ◽  
Archie K. Murchie
Keyword(s):  
Population Size ◽  
Numerical Integration ◽  
Spatial Data ◽  
Population Distribution ◽  
Species Abundance ◽  
Ecological Monitoring ◽  
Field Studies ◽  
Pest Species ◽  
Population Size Estimates ◽  
Size Estimates

Ecological monitoring aims to provide estimates of pest species abundance—this information being then used for making decisions about means of control. For invertebrate species, population size estimates are often based on trap counts which provide the value of the population density at the traps' location. However, the use of traps in large numbers is problematic as it is costly and may also be disruptive to agricultural procedures. Therefore, the challenge is to obtain a reliable population size estimate from sparse spatial data. The approach we develop in this paper is based on the ideas of numerical integration on a coarse grid. We investigate several methods of numerical integration in order to understand how badly the lack of spatial data can affect the accuracy of results. We first test our approach on simulation data mimicking spatial population distributions of different complexity. We show that, rather counterintuitively, a robust estimate of the population size can be obtained from just a few traps, even when the population distribution has a highly complicated spatial structure. We obtain an estimate of the minimum number of traps required to calculate the population size with good accuracy. We then apply our approach to field data to confirm that the number of trap/sampling locations can be much fewer than has been used in many monitoring programmes. We also show that the accuracy of our approach is greater that that of the statistical method commonly used in field studies. Finally, we discuss the implications of our findings for ecological monitoring practice and show that the use of trap numbers ‘smaller than minimum’ may still be possible but it would result in a paradigm shift: the population size estimates should be treated probabilistically and the arising uncertainty may introduce additional risk in decision-making.

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