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John Woolliams

Bio: John Woolliams is an academic researcher from University of Edinburgh. The author has contributed to research in topics: Population & Selection (genetic algorithm). The author has an hindex of 59, co-authored 383 publications receiving 15274 citations. Previous affiliations of John Woolliams include Scottish Agricultural College & Norwegian University of Life Sciences.


Papers
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01 Jan 2007

1,925 citations

Journal ArticleDOI
Richard A. Gibbs1, Jeremy F. Taylor2, Curtis P. Van Tassell3, William Barendse4, William Barendse5, Kellye Eversole, Clare A. Gill6, Ronnie D. Green3, Debora L. Hamernik3, Steven M. Kappes3, Sigbjørn Lien7, Lakshmi K. Matukumalli3, Lakshmi K. Matukumalli8, John C. McEwan9, Lynne V. Nazareth1, Robert D. Schnabel2, George M. Weinstock1, David A. Wheeler1, Paolo Ajmone-Marsan10, Paul Boettcher11, Alexandre Rodrigues Caetano12, José Fernando Garcia13, José Fernando Garcia11, Olivier Hanotte14, Paola Mariani15, Loren C. Skow6, Tad S. Sonstegard3, John L. Williams15, John L. Williams16, Boubacar Diallo, Lemecha Hailemariam17, Mário Luiz Martinez12, C. A. Morris9, Luiz Otávio Campos da Silva12, Richard J. Spelman18, Woudyalew Mulatu14, Keyan Zhao19, Colette A. Abbey6, Morris Agaba14, Flábio R. Araújo12, Rowan J. Bunch5, Rowan J. Bunch4, James O. Burton16, C. Gorni15, Hanotte Olivier15, Blair E. Harrison4, Blair E. Harrison5, Bill Luff, Marco Antonio Machado12, Joel Mwakaya14, Graham Plastow20, Warren Sim4, Warren Sim5, Timothy P. L. Smith3, Merle B Thomas4, Merle B Thomas5, Alessio Valentini21, Paul D. Williams4, James E. Womack6, John Woolliams16, Yue Liu1, Xiang Qin1, Kim C. Worley1, Chuan Gao6, Huaiyang Jiang1, Stephen S. Moore20, Yanru Ren1, Xingzhi Song1, Carlos Bustamante19, Ryan D. Hernandez19, Donna M. Muzny1, Shobha Patil1, Anthony San Lucas1, Qing Fu1, Matthew Peter Kent7, Richard Vega1, Aruna Matukumalli3, Sean McWilliam5, Sean McWilliam4, Gert Sclep15, Katarzyna Bryc19, Jung-Woo Choi6, Hong Gao19, John J. Grefenstette8, Brenda M. Murdoch20, Alessandra Stella15, Rafael Villa-Angulo8, Mark G. Wright19, Jan Aerts16, Jan Aerts22, Oliver C. Jann16, Riccardo Negrini10, Michael E. Goddard23, Michael E. Goddard24, Ben J. Hayes24, Daniel G. Bradley25, Marcos V.B. da Silva12, Marcos V.B. da Silva3, Lilian P.L. Lau25, George E. Liu3, David J. Lynn25, David J. Lynn26, Francesca Panzitta15, Ken G. Dodds9 
24 Apr 2009-Science
TL;DR: Data show that cattle have undergone a rapid recent decrease in effective population size from a very large ancestral population, possibly due to bottlenecks associated with domestication, selection, and breed formation.
Abstract: The imprints of domestication and breed development on the genomes of livestock likely differ from those of companion animals. A deep draft sequence assembly of shotgun reads from a single Hereford female and comparative sequences sampled from six additional breeds were used to develop probes to interrogate 37,470 single-nucleotide polymorphisms (SNPs) in 497 cattle from 19 geographically and biologically diverse breeds. These data show that cattle have undergone a rapid recent decrease in effective population size from a very large ancestral population, possibly due to bottlenecks associated with domestication, selection, and breed formation. Domestication and artificial selection appear to have left detectable signatures of selection within the cattle genome, yet the current levels of diversity within breeds are at least as great as exists within humans.

769 citations

Journal ArticleDOI
01 Jul 2010-Genetics
TL;DR: The relative accuracy of GBLUP and BayesB for a given number of records and heritability are highly dependent on Me, which is a property of the target genome, as well as the architecture of the trait (NQTL).
Abstract: The rapid increase in high-throughput single-nucleotide polymorphism data has led to a great interest in applying genome-wide evaluation methods to identify an individual's genetic merit. Genome-wide evaluation combines statistical methods with genomic data to predict genetic values for complex traits. Considerable uncertainty currently exists in determining which genome-wide evaluation method is the most appropriate. We hypothesize that genome-wide methods deal differently with the genetic architecture of quantitative traits and genomes. A genomic linear method (GBLUP), and a genomic nonlinear Bayesian variable selection method (BayesB) are compared using stochastic simulation across three effective population sizes and a wide range of numbers of quantitative trait loci (NQTL). GBLUP had a constant accuracy, for a given heritability and sample size, regardless of NQTL. BayesB had a higher accuracy than GBLUP when NQTL was low, but this advantage diminished as NQTL increased and when NQTL became large, GBLUP slightly outperformed BayesB. In addition, deterministic equations are extended to predict the accuracy of both methods and to estimate the number of independent chromosome segments (Me) and NQTL. The predictions of accuracy and estimates of Me and NQTL were generally in good agreement with results from simulated data. We conclude that the relative accuracy of GBLUP and BayesB for a given number of records and heritability are highly dependent on Me, which is a property of the target genome, as well as the architecture of the trait (NQTL).

665 citations

Journal ArticleDOI
14 Oct 2008-PLOS ONE
TL;DR: This study derived simple deterministic formulae to predict the accuracy of predicted genetic risk from population or case control studies using a genome-wide approach and assuming a dichotomous disease phenotype with an underlying continuous liability.
Abstract: Background: The prediction of the genetic disease risk of an individual is a powerful public health tool. While predicting risk has been successful in diseases which follow simple Mendelian inheritance, it has proven challenging in complex diseases for which a large number of loci contribute to the genetic variance. The large numbers of single nucleotide polymorphisms now available provide new opportunities for predicting genetic risk of complex diseases with high accuracy. Methodology/Principal Findings: We have derived simple deterministic formulae to predict the accuracy of predicted genetic risk from population or case control studies using a genome-wide approach and assuming a dichotomous disease phenotype with an underlying continuous liability. We show that the prediction equations are special cases of the more general problem of predicting the accuracy of estimates of genetic values of a continuous phenotype. Our predictive equations are responsive to all parameters that affect accuracy and they are independent of allele frequency and effect distributions. Deterministic prediction errors when tested by simulation were generally small. The common link among the expressions for accuracy is that they are best summarized as the product of the ratio of number of phenotypic records per number of risk loci and the observed heritability. Conclusions/Significance: This study advances the understanding of the relative power of case control and population studies of disease. The predictions represent an upper bound of accuracy which may be achievable with improved effect estimation methods. The formulae derived will help researchers determine an appropriate sample size to attain a certain accuracy when predicting genetic risk.

658 citations

Journal ArticleDOI
TL;DR: A comparison of the results of the 1995 to 1998 trial with those of a previous milk progesterone database, which included 2503 lactations in British Friesian cows monitored using a similar milk sampling protocol, revealed a decline infertility between these periods.
Abstract: Reproductive performance of 714 Holstein Friesian dairy cows was monitored between October 1995 and June 1998 using thrice weekly milk progesterone determinations. Defined endocrine parameters such as interval to post-partum commencement of luteal activity, inter-ovulatory interval and length of luteal and inter-luteal intervals were used with a number of traditional measures of reproductive performance to investigate the current status of fertility in a sample of United Kingdom dairy herds. A comparison of the results of the 1995 to 1998 trial with those of a previous (1975 to 1982) milk progesterone database, which included 2503 lactations in British Friesian cows monitored using a similar milk sampling protocol, revealed a decline infertility between these periods. Between 1975-1982 and 1995-1998, pregnancy rate to first service declined from 55·6% to 39·7% (P < 0·001), at a derived average rate approaching 1% per year. This decline was associated with an increase (P < 0·001) in the proportion of animals with one or more atypical ovarian hormone patterns from 32% to 44%. There was a significant (P < 0·001) increase in the incidence of delayed luteolysis during the first cycle post partum (delayed luteolysis type I; 7·3% to 18·2%) and during subsequent cycles (delayed luteolysis type II; 6·4% to 16·8%), although the incidence of prolonged anovulation post partům (delayed ovulation type I; 10·9% to 12·9%) and prolonged inter-luteal intervals (delayed ovulation type II; 12·9% to 10·6%) did not alter significantly. These changes resulted in an increase in mean luteal phase length from 12·9 (s.e. 0·09) to 14·8 (s.e. 0·17) days and an increase in inter-ovulatory interval from 20·2 (s.e. 0·1) to 22·3 (s.e. 0·2) days. The decline infertility was also reflected in traditional measures of fertility since although interval to first service remained relatively unchanged (74·0 (s.e. 0·4) to 77·6 (s.e. 1·1) days) calving interval lengthened from 370 (s.e. 2·2) to 390 (s.e. 2·5) days. Collectively these changes may have contributed to the decline in pregnancy rates observed over the last 20 years.

601 citations


Cited by
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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal Article
TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

9,847 citations

Journal ArticleDOI
01 May 1981
TL;DR: This chapter discusses Detecting Influential Observations and Outliers, a method for assessing Collinearity, and its applications in medicine and science.
Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

4,948 citations

Journal ArticleDOI
TL;DR: Evidence is provided that the remaining heritability is due to incomplete linkage disequilibrium between causal variants and genotyped SNPs, exacerbated by causal variants having lower minor allele frequency than the SNPs explored to date.
Abstract: SNPs discovered by genome-wide association studies (GWASs) account for only a small fraction of the genetic variation of complex traits in human populations. Where is the remaining heritability? We estimated the proportion of variance for human height explained by 294,831 SNPs genotyped on 3,925 unrelated individuals using a linear model analysis, and validated the estimation method with simulations based on the observed genotype data. We show that 45% of variance can be explained by considering all SNPs simultaneously. Thus, most of the heritability is not missing but has not previously been detected because the individual effects are too small to pass stringent significance tests. We provide evidence that the remaining heritability is due to incomplete linkage disequilibrium between causal variants and genotyped SNPs, exacerbated by causal variants having lower minor allele frequency than the SNPs explored to date.

3,759 citations