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Alexandra I. F. Blakemore

dblp:43/8091 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2011
0000-0003-0661-564XORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › statistical genetics › association analysis
copy number variant association
0.112011
famCNV: copy number variant association for quantitative traits in families · Bioinform. 2011
Bioinformatics and computational biology › genomics
genome-wide association study
0.112011
famCNV: copy number variant association for quantitative traits in families · Bioinform. 2011
Bioinformatics and computational biology
genomics
0.112010
Inferring combined CNV/SNP haplotypes from genotype data · Bioinform. 2010
Bioinformatics and computational biology › genomics
haplotype inference
0.112010
Inferring combined CNV/SNP haplotypes from genotype data · Bioinform. 2010
Bioinformatics and computational biology › genomics › structural variation
copy number variation
0.012010
Inferring combined CNV/SNP haplotypes from genotype data · Bioinform. 2010

Methods — techniques the papers use, named apart from their topics

variance component model · 0.1likelihood testing · 0.1sampling algorithm · 0.1hidden markov model · 0.1
YearPublicationVenuePosition
2011 famCNV: copy number variant association for quantitative traits in families
abstract
UNLABELLED: A program package to enable genome-wide association of copy number variants (CNVs) with quantitative phenotypes in families of arbitrary size and complexity. Intensity signals that act as proxies for the number of copies are modeled in a variance component framework and association with traits is assessed through formal likelihood testing. AVAILABILITY AND IMPLEMENTATION: The Java package is made available at www.imperial.ac.uk/medicine/people/m.falchi/. CONTACT: [email protected].
Hariklia Eleftherohorinou, Johanna C. Andersson-Assarsson, Robin G. Walters, Julia S. El-Sayed Moustafa, Lachlan James M. Coin, Peter Jacobson, Lena M. S. Carlsson, Alexandra I. F. Blakemore, Philippe Froguel, Andrew J. Walley, Mario Falchi
Bioinform.8
2010 Inferring combined CNV/SNP haplotypes from genotype data
abstract
MOTIVATION: Copy number variations (CNVs) are increasingly recognized as an substantial source of individual genetic variation, and hence there is a growing interest in investigating the evolutionary history of CNVs as well as their impact on complex disease susceptibility. CNV/SNP haplotypes are critical for this research, but although many methods have been proposed for inferring integer copy number, few have been designed for inferring CNV haplotypic phase and none of these are applicable at genome-wide scale. Here, we present a method for inferring missing CNV genotypes, predicting CNV allelic configuration and for inferring CNV haplotypic phase from SNP/CNV genotype data. Our method, implemented in the software polyHap v2.0, is based on a hidden Markov model, which models the joint haplotype structure between CNVs and SNPs. Thus, haplotypic phase of CNVs and SNPs are inferred simultaneously. A sampling algorithm is employed to obtain a measure of confidence/credibility of each estimate. RESULTS: We generated diploid phase-known CNV-SNP genotype datasets by pairing male X chromosome CNV-SNP haplotypes. We show that polyHap provides accurate estimates of missing CNV genotypes, allelic configuration and CNV haplotypic phase on these datasets. We applied our method to a non-simulated dataset-a region on Chromosome 2 encompassing a short deletion. The results confirm that polyHap's accuracy extends to real-life datasets. AVAILABILITY: Our method is implemented in version 2.0 of the polyHap software package and can be downloaded from http://www.imperial.ac.uk/medicine/people/l.coin.
Shu-Yi Su, Julian E. Asher, Marjo-Riitta Järvelin, Philippe Froguel, Alexandra I. F. Blakemore, David J. Balding, Lachlan James M. Coin
Bioinform.5