Extension of variance components approach to incorporate temporal trends and longitudinal pedigree data analysis

2002 ◽  
Vol 22 (3) ◽  
pp. 221-232 ◽  
Author(s):  
Mariza de Andrade ◽  
René Guéguen ◽  
Sophie Visvikis ◽  
Catherine Sass ◽  
Gérard Siest ◽  
...  
Keyword(s):  
Data Analysis ◽  
Variance Components ◽  
Temporal Trends ◽  
Pedigree Data

Pedigree Data Analysis With Crossover Interference

Genetics ◽  
2003 ◽  
Vol 164 (4) ◽  
pp. 1561-1566
Author(s):  
Sharon Browning
Keyword(s):  
Data Analysis ◽  
Linkage Analysis ◽  
Importance Sampling ◽  
Genetic Data ◽  
Genotype Data ◽  
Pedigree Data ◽  
Chi Square ◽  
Crossover Interference ◽  
Map Construction ◽  
Interference Models

AbstractWe propose a new method for calculating probabilities for pedigree genetic data that incorporates crossover interference using the chi-square models. Applications include relationship inference, genetic map construction, and linkage analysis. The method is based on importance sampling of unobserved inheritance patterns conditional on the observed genotype data and takes advantage of fast algorithms for no-interference models while using reweighting to allow for interference. We show that the method is effective for arbitrarily many markers with small pedigrees.

Factors having effects on market value of Welsh Black Cattle in pedigree sales, variance components and estimation of breeding values of sale price

1998 ◽  
Vol 1998 ◽  
pp. 192-192 ◽  
Author(s):  
Z. Ulutas ◽  
I. Ap Dewi ◽  
M. Saatci
Keyword(s):  
Variance Components ◽  
Market Price ◽  
Market Value ◽  
Physical Characteristics ◽  
Pedigree Data ◽  
Sale Price ◽  
Breeding Values ◽  
Cattle Breeders

Few Welsh Black Cattle breeders record their animals as part of formal selection schemes. However there are sale and pedigree data available on Welsh Black Cattle as recorded by the breed society. The aim of the current project was to examine the use of market price as a basis for selection. This is justified on the basis that detailed production records are not available and that price could be considered as an indicator that summarises physical characteristics.

A Bayesian structural equation model in general pedigree data analysis

2019 ◽  
Vol 12 (5) ◽  
pp. 404-411
Author(s):  
Mahdi Akbarzadeh ◽  
Abbas Moghimbeigi ◽  
Nathan Morris ◽  
Maryam S. Daneshpour ◽  
Hossein Mahjub ◽  
...  
Keyword(s):  
Data Analysis ◽  
Structural Equation Model ◽  
Structural Equation ◽  
Equation Model ◽  
Pedigree Data ◽  
General Pedigree

Factors having effects on market value of Welsh Black Cattle in pedigree sales, variance components and estimation of breeding values of sale price

1998 ◽  
Vol 1998 ◽  
pp. 192-192
Author(s):  
Z. Ulutas ◽  
I. Ap Dewi ◽  
M. Saatci
Keyword(s):  
Variance Components ◽  
Market Price ◽  
Market Value ◽  
Physical Characteristics ◽  
Pedigree Data ◽  
Sale Price ◽  
Breeding Values ◽  
Cattle Breeders

Few Welsh Black Cattle breeders record their animals as part of formal selection schemes. However there are sale and pedigree data available on Welsh Black Cattle as recorded by the breed society. The aim of the current project was to examine the use of market price as a basis for selection. This is justified on the basis that detailed production records are not available and that price could be considered as an indicator that summarises physical characteristics.

Historical operational data analysis for defence preparedness planning

2016 ◽  
Vol 15 (2) ◽  
pp. 231-244
Author(s):  
Kamran Shafi ◽  
Essam Debie ◽  
David Oliver
Keyword(s):  
Data Analysis ◽  
Descriptive Analysis ◽  
Temporal Trends ◽  
Predictive Analysis ◽  
Data Set ◽  
Preparedness Planning ◽  
Australian Defence Force ◽  
Spatial And Temporal Trends ◽  
National Defence ◽  
Operational Data

Preparedness is an important function of defence planning that involves developing defence capabilities to deal with emergent situations relating to national defence and security. Preparedness planning relies on a number of inputs, including requirement analysis, to identify critical capability gaps. Modern data analysis can play an important role in identifying such future requirements. To this end, this paper presents an analytical study, consisting of both descriptive as well as predictive analysis, of historical defence operational data. The descriptive analysis component of the methodology focuses on identifying useful features in the collected data for building a predictive model. The predictive analysis investigates existing patterns in the data, including spatial and temporal trends. An artificial neural network based time series forecasting model is developed to predict future operations based on the identified features. The proposed methodology is applied to a defence operational data set, built from a number of unclassified sources relating to the historical operational deployments of the Australian Defence Force between 1885 and 2012. Implications are also discussed.

Missing phenotype data imputation in pedigree data analysis

2008 ◽  
Vol 32 (1) ◽  
pp. 52-60 ◽  
Author(s):  
Brooke L. Fridley ◽  
Mariza de Andrade
Keyword(s):  
Data Analysis ◽  
Data Imputation ◽  
Pedigree Data ◽  
Phenotype Data

A study of contemporary levels and temporal trends in inbreeding in the Tangier Island, Virginia, population using pedigree data and isonymy

2000 ◽  
Vol 112 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Rasika A. Mathias ◽  
Carol A. Bickel ◽  
Terri H. Beaty ◽  
Gloria M. Petersen ◽  
Jacqueline B. Hetmanski ◽  
...  
Keyword(s):  
Temporal Trends ◽  
Pedigree Data

Abstract MP59: Revisiting Heritability Accounting For Common Environmental Effects And Maternal Inheritance

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Chunyu Liu ◽  
Josée Dupuis ◽  
Martin G Larson ◽  
L. Adrienne Cupples ◽  
Jose M M Ordovas ◽  
...  
Keyword(s):  
Maternal Effects ◽  
Environmental Effects ◽  
Variance Components ◽  
Maternal Inheritance ◽  
Mitochondrial Inheritance ◽  
Pedigree Data ◽  
Extended Pedigrees ◽  
Heart Study ◽  
Heritability Estimates ◽  
Average Gene

Background: Heritability measures the proportion of phenotypic variation attributable to genetic factors. Reliable heritability estimates are used to determine the necessary sample size and power for subsequent genetic studies of susceptibility genes. In addition to a shared nuclear genetic component, common environmental factors and maternal or mitochondrial inheritance may have strong effects on some phenotypes. Failure to account for necessary variance components may give rise to biased heritability estimates. Aims: Our primary goal was to investigate how heritability estimates are biased in the presence of common environmental or/and maternal effects. The second goal was to estimate mitochondrial inheritance for a number of common phenotypes. Methods: We employed variance component methods to account for additional variance components using both simulated and Framingham Heart Study (FHS) pedigree data to revisit heritability estimates in the presence of common environmental and maternal effects. Results: Using both simulated families and actual FHS extended pedigrees, we demonstrated that heritability is greatly overestimated when key variance components are not properly accounted for. The inflation in heritability ranged from 9% to 214% across several anthropometric, metabolic and life-style phenotypes when we compared models that consider correct variance components and simple models that only consider familial relationships (Table 1). Maternal inheritance was observed in most phenotypes investigated. The estimated maternal inheritance ranged from ~1 to 5%, which is considerable because the mitochondrial genome is about the size of an average gene. Conclusion/Discussion: We systematically investigated the influence of common environmental effects and maternal inheritance in heritability estimates using extended pedigrees. Our findings may explain, in part, the missing heritability for some traits and may facilitate further collaborations in the genetic study of mitochondrial DNA in disease susceptibility for common phenotypes.

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