Alan Medlar

University College London, London, ENG, United Kingdom

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Publications (3)62.16 Total impact

  • Article: SwiftLink: Parallel MCMC linkage analysis utilising multicore CPU and GPU.
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    ABSTRACT: MOTIVATION: Linkage analysis remains an important tool in elucidating the genetic component of disease and has become even more important with the advent of whole exome sequencing, enabling the user to focus on only those genomic regions co-segregating with Mendelian traits. Unfortunately, methods to perform multipoint linkage analysis scale poorly with either the number of markers or with the size of the pedigree. Large pedigrees with many markers can only be evaluated with Markov chain Monte Carlo (MCMC) methods that are slow to converge and, as no attempts have been made to exploit parallelism, massively under utilise available processing power. Here, we describe SWIFTLINK, a novel application that performs MCMC linkage analysis by spreading the computational burden between multiple processor cores and a graphics processing unit (GPU) simultaneously. SWIFTLINK was designed around the concept of explicitly matching the characteristics of an algorithm with the underlying computer architecture to maximise performance. RESULTS: We implement our approach using existing Gibbs samplers redesigned for parallel hardware. We applied SWIFTLINK to a real-world dataset, performing parametric multipoint linkage analysis on a highly consanguineous pedigree with EAST syndrome, containing 28 members, where a subset of individuals were genotyped with single nucleotide polymorphisms (SNPs). In our experiments with a four core CPU and GPU, SWIFTLINK achieves a 8.5× speed up over the single-threaded version and a 109× speed up over the popular linkage analysis program SIMWALK. AVAILABILITY: SWIFTLINK is available at https://github.com/ajm/swiftlink All source code is licenced under GPLv3. CONTACT: alan.j.medlar@helsinki.fi.
    Bioinformatics 12/2012; · 5.47 Impact Factor
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    Article: Risk HLA-DQA1 and PLA(2)R1 alleles in idiopathic membranous nephropathy.
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    ABSTRACT: Idiopathic membranous nephropathy is a major cause of the nephrotic syndrome in adults, but its etiologic basis is not fully understood. We investigated the genetic basis of biopsy-proven cases of idiopathic membranous nephropathy in a white population. We performed independent genomewide association studies of single-nucleotide polymorphisms (SNPs) in patients with idiopathic membranous nephropathy from three populations of white ancestry (75 French, 146 Dutch, and 335 British patients). The patients were compared with racially matched control subjects; population stratification and quality controls were carried out according to standard criteria. Associations were calculated by means of a chi-square basic allele test; the threshold for significance was adjusted for multiple comparisons (with the Bonferroni method). In a joint analysis of data from the 556 patients studied (398 men), we identified significant alleles at two genomic loci associated with idiopathic membranous nephropathy. Chromosome 2q24 contains the gene encoding M-type phospholipase A(2) receptor (PLA(2)R1) (SNP rs4664308, P=8.6×10(-29)), previously shown to be the target of an autoimmune response. Chromosome 6p21 contains the gene encoding HLA complex class II HLA-DQ alpha chain 1 (HLA-DQA1) (SNP rs2187668, P=8.0×10(-93)). The association with HLA-DQA1 was significant in all three populations (P=1.8×10(-9), P=5.6×10(-27), and P=5.2×10(-36) in the French, Dutch, and British groups, respectively). The odds ratio for idiopathic membranous nephropathy with homozygosity for both risk alleles was 78.5 (95% confidence interval, 34.6 to 178.2). An HLA-DQA1 allele on chromosome 6p21 is most closely associated with idiopathic membranous nephropathy in persons of white ancestry. This allele may facilitate an autoimmune response against targets such as variants of PLA2R1. Our findings suggest a basis for understanding this disease and illuminate how adaptive immunity is regulated by HLA.
    New England Journal of Medicine 02/2011; 364(7):616-26. · 53.30 Impact Factor
  • Article: Cystinosis and mickey mouse.
    Alan Medlar, Robert Kleta
    Nephrology Dialysis Transplantation 12/2009; 25(4):1032-3. · 3.40 Impact Factor