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Chris Haley

Bio: Chris Haley is an academic researcher from University of Edinburgh. The author has contributed to research in topics: Quantitative trait locus & Population. The author has an hindex of 71, co-authored 410 publications receiving 23592 citations. Previous affiliations of Chris Haley include Medical Research Council & The Roslin Institute.


Papers
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Journal ArticleDOI
01 Oct 1992-Heredity
TL;DR: Methods for mapping QTL based on multiple regression which can be applied using any general statistical package are developed and it is shown that these regression methods produce very similar results to those obtained using maximum likelihood.
Abstract: The use of flanking marker methods has proved to be a powerful tool for the mapping of quantitative trait loci (QTL) in the segregating generations derived from crosses between inbred lines. Methods to analyse these data, based on maximum-likelihood, have been developed and provide good estimates of QTL effects in some situations. Maximum-likelihood methods are, however, relatively complex and can be computationally slow. In this paper we develop methods for mapping QTL based on multiple regression which can be applied using any general statistical package. We use the example of mapping in an F(2) population and show that these regression methods produce very similar results to those obtained using maximum likelihood. The relative simplicity of the regression methods means that models with more than a single QTL can be explored and we give examples of two lined loci and of two interacting loci. Other models, for example with more than two QTL, with environmental fixed effects, with between family variance or for threshold traits, could be fitted in a similar way. The ease, speed of application and generality of regression methods for flanking marker analysis, and the good estimates they obtain, suggest that they should provide the method of choice for the analysis of QTL mapping data from inbred line crosses.

2,079 citations

Journal ArticleDOI
Jennifer E. Huffman1, Eva Albrecht, Alexander Teumer2, Massimo Mangino3, Karen Kapur, Toby Johnson4, Z. Kutalik, Nicola Pirastu5, Giorgio Pistis6, Lorna M. Lopez1, Toomas Haller7, Perttu Salo8, Anuj Goel9, Man Li10, Toshiko Tanaka8, Abbas Dehghan11, Daniela Ruggiero, Giovanni Malerba12, Albert V. Smith13, Ilja M. Nolte, Laura Portas, Amanda Phipps-Green14, Lora Boteva1, Pau Navarro1, Åsa Johansson15, Andrew A. Hicks16, Ozren Polasek17, Tõnu Esko18, John F. Peden9, Sarah E. Harris1, Federico Murgia, Sarah H. Wild1, Albert Tenesa1, Adrienne Tin10, Evelin Mihailov7, Anne Grotevendt2, Gauti Kjartan Gislason, Josef Coresh10, Pio D'Adamo5, Sheila Ulivi, Peter Vollenweider19, Gérard Waeber19, Susan Campbell1, Ivana Kolcic17, Krista Fisher7, Margus Viigimaa, Jeffrey Metter8, Corrado Masciullo6, Elisabetta Trabetti12, Cristina Bombieri12, Rossella Sorice, Angela Doering, Eva Reischl, Konstantin Strauch20, Albert Hofman11, André G. Uitterlinden11, Melanie Waldenberger, H-Erich Wichmann20, Gail Davies1, Alan J. Gow1, Nicola Dalbeth21, Lisa K. Stamp14, Johannes H. Smit22, Mirna Kirin1, Ramaiah Nagaraja8, Matthias Nauck2, Claudia Schurmann2, Kathrin Budde2, Susan M. Farrington1, Evropi Theodoratou1, Antti Jula8, Veikko Salomaa8, Cinzia Sala6, Christian Hengstenberg23, Michel Burnier19, R Maegi7, Norman Klopp20, Stefan Kloiber24, Sabine Schipf25, Samuli Ripatti26, Stefano Cabras27, Nicole Soranzo28, Georg Homuth2, Teresa Nutile, Patricia B. Munroe4, Nicholas D. Hastie1, Harry Campbell1, Igor Rudan1, Claudia P. Cabrera29, Chris Haley1, Oscar H. Franco11, Tony R. Merriman14, Vilmundur Gudnason13, Mario Pirastu, Brenda W.J.H. Penninx30, Brenda W.J.H. Penninx11, Harold Snieder, Andres Metspalu7, Marina Ciullo, Peter P. Pramstaller16, Cornelia M. van Duijn11, Luigi Ferrucci8, Giovanni Gambaro31, Ian J. Deary1, Malcolm G. Dunlop1, James F. Wilson1, Paolo Gasparini5, Ulf Gyllensten15, Tim D. Spector3, Alan F. Wright1, Caroline Hayward1, Hugh Watkins9, Markus Perola8, Murielle Bochud32, W. H. Linda Kao10, Mark J. Caulfield4, Daniela Toniolo6, Henry Voelzke25, Christian Gieger, Anna Koettgen33, Veronique Vitart1 
26 Mar 2015-PLOS ONE
TL;DR: Interactions between body mass index (BMI) and common genetic variants affecting serum urate levels, genome-wide, and regression-type analyses in a non BMI-stratified overall sample suggested a role for N-glycan biosynthesis as a prominent urate-associated pathway in the lean stratum.
Abstract: We tested for interactions between body mass index (BMI) and common genetic variants affecting serum urate levels, genome-wide, in up to 42569 participants. Both stratified genome-wide association (GWAS) analyses, in lean, overweight and obese individuals, and regression-type analyses in a non BMI-stratified overall sample were performed. The former did not uncover any novel locus with a major main effect, but supported modulation of effects for some known and potentially new urate loci. The latter highlighted a SNP at RBFOX3 reaching genome-wide significant level (effect size 0.014, 95% CI 0.008-0.02, Pinter= 2.6 x 10-8). Two top loci in interaction term analyses, RBFOX3 and ERO1LB-EDARADD, also displayed suggestive differences in main effect size between the lean and obese strata. All top ranking loci for urate effect differences between BMI categories were novel and most had small magnitude but opposite direction effects between strata. They include the locus RBMS1-TANK (men, Pdifflean-overweight= 4.7 x 10-8), a region that has been associated with several obesity related traits, and TSPYL5 (men, Pdifflean-overweight= 9.1 x 10-8), regulating adipocytes-produced estradiol. The top-ranking known urate loci was ABCG2, the strongest known gout risk locus, with an effect halved in obese compared to lean men (Pdifflean-obese= 2 x 10-4). Finally, pathway analysis suggested a role for N-glycan biosynthesis as a prominent urate-associated pathway in the lean stratum. These results illustrate a potentially powerful way to monitor changes occurring in obesogenic environment.

1,293 citations

Journal ArticleDOI
04 Mar 2021-Nature
TL;DR: The GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2244 critically ill Covid-19 patients from 208 UK intensive care units is reported, finding evidence in support of a causal link from low expression of IFNAR2, and high expression of TYK2, to life-threatening disease.
Abstract: Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10−8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10−8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 × 10−12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10−8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte–macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice. A genome-wide association study of critically ill patients with COVID-19 identifies genetic signals that relate to important host antiviral defence mechanisms and mediators of inflammatory organ damage that may be targeted by repurposing drug treatments.

941 citations

Journal ArticleDOI
TL;DR: It is argued that epistasis should be accounted for in complex trait studies; current study designs for detecting epistasis are critically assessed and how these might be adapted for use in additional populations, including humans.
Abstract: Interactions among loci or between genes and environmental factors make a substantial contribution to variation in complex traits such as disease susceptibility. Nonetheless, many studies that attempt to identify the genetic basis of complex traits ignore the possibility that loci interact. We argue that epistasis should be accounted for in complex trait studies; we critically assess current study designs for detecting epistasis and discuss how these might be adapted for use in additional populations, including humans.

936 citations

Journal ArticleDOI
23 Oct 2003-Nature
TL;DR: This study establishes a causal relationship between a single-base-pair substitution in a non-coding region and a QTL effect, and supports the long-held view that regulatory mutations are important for controlling phenotypic variation.
Abstract: Most traits and disorders have a multifactorial background indicating that they are controlled by environmental factors as well as an unknown number of quantitative trait loci (QTLs). The identification of mutations underlying QTLs is a challenge because each locus explains only a fraction of the phenotypic variation. A paternally expressed QTL affecting muscle growth, fat deposition and size of the heart in pigs maps to the IGF2 (insulin-like growth factor 2) region. Here we show that this QTL is caused by a nucleotide substitution in intron 3 of IGF2. The mutation occurs in an evolutionarily conserved CpG island that is hypomethylated in skeletal muscle. The mutation abrogates in vitro interaction with a nuclear factor, probably a repressor, and pigs inheriting the mutation from their sire have a threefold increase in IGF2 messenger RNA expression in postnatal muscle. Our study establishes a causal relationship between a single-base-pair substitution in a non-coding region and a QTL effect. The result supports the long-held view that regulatory mutations are important for controlling phenotypic variation.

885 citations


Cited by
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Journal Article
Fumio Tajima1
30 Oct 1989-Genomics
TL;DR: It is suggested that the natural selection against large insertion/deletion is so weak that a large amount of variation is maintained in a population.

11,521 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 Apr 2001-Genetics
TL;DR: It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.
Abstract: Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of ∼50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size (Ne = 100), the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.

6,036 citations

Journal ArticleDOI
01 Nov 1994-Genetics
TL;DR: An empirical method is described, based on the concept of a permutation test, for estimating threshold values that are tailored to the experimental data at hand, and is demonstrated using two real data sets derived from F(2) and recombinant inbred plant populations.
Abstract: The detection of genes that control quantitative characters is a problem of great interest to the genetic mapping community. Methods for locating these quantitative trait loci (QTL) relative to maps of genetic markers are now widely used. This paper addresses an issue common to all QTL mapping methods, that of determining an appropriate threshold value for declaring significant QTL effects. An empirical method is described, based on the concept of a permutation test, for estimating threshold values that are tailored to the experimental data at hand. The method is demonstrated using two real data sets derived from F(2) and recombinant inbred plant populations. An example using simulated data from a backcross design illustrates the effect of marker density on threshold values.

4,964 citations

Journal ArticleDOI
11 Oct 2018-Nature
TL;DR: Deep phenotype and genome-wide genetic data from 500,000 individuals from the UK Biobank is described, describing population structure and relatedness in the cohort, and imputation to increase the number of testable variants to 96 million.
Abstract: The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.

4,489 citations