Sharon R Browning

Summary

Affiliation: University of Auckland
Country: New Zealand

Publications

  1. pmc A groupwise association test for rare mutations using a weighted sum statistic
    Bo Eskerod Madsen
    Bioinformatics Research Center, University of Aarhus, Aarhus C, Denmark
    PLoS Genet 5:e1000384. 2009
  2. pmc Missing data imputation and haplotype phase inference for genome-wide association studies
    Sharon R Browning
    Department of Statistics, The University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand
    Hum Genet 124:439-50. 2008
  3. pmc High-resolution detection of identity by descent in unrelated individuals
    Sharon R Browning
    Department of Statistics, University of Auckland, Auckland, New Zealand
    Am J Hum Genet 86:526-39. 2010
  4. pmc Estimation of pairwise identity by descent from dense genetic marker data in a population sample of haplotypes
    Sharon R Browning
    Department of Statistics, The University of Auckland, Auckland 1142, New Zealand
    Genetics 178:2123-32. 2008
  5. pmc Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering
    Sharon R Browning
    Department of Statistics, The University of Auckland, Auckland, New Zealand
    Am J Hum Genet 81:1084-97. 2007
  6. pmc Population structure with localized haplotype clusters
    Sharon R Browning
    Department of Statistics, University of Auckland, Auckland 1142, New Zealand
    Genetics 185:1337-44. 2010
  7. pmc Haplotypic analysis of Wellcome Trust Case Control Consortium data
    Brian L Browning
    Department of Statistics, The University of Auckland, Private Bag 92019, Auckland, New Zealand
    Hum Genet 123:273-80. 2008
  8. ncbi request reprint Efficient multilocus association testing for whole genome association studies using localized haplotype clustering
    Brian L Browning
    Department of Statistics, The University of Auckland, Auckland, New Zealand
    Genet Epidemiol 31:365-75. 2007
  9. pmc Efficient clustering of identity-by-descent between multiple individuals
    Yu Qian
    Bioinformatics Research Center, Aarhus Universitet, 8000C Aarhus, Denmark, Department of Biostatistics and Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA
    Bioinformatics 30:915-22. 2014
  10. pmc A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals
    Brian L Browning
    Department of Statistics, University of Auckland, Auckland 1142, New Zealand
    Am J Hum Genet 84:210-23. 2009

Collaborators

Detail Information

Publications14

  1. pmc A groupwise association test for rare mutations using a weighted sum statistic
    Bo Eskerod Madsen
    Bioinformatics Research Center, University of Aarhus, Aarhus C, Denmark
    PLoS Genet 5:e1000384. 2009
    ..This study thus demonstrates that resequencing studies can identify important genetic associations, provided that specialised analysis methods, such as the weighted-sum method, are used...
  2. pmc Missing data imputation and haplotype phase inference for genome-wide association studies
    Sharon R Browning
    Department of Statistics, The University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand
    Hum Genet 124:439-50. 2008
    ....
  3. pmc High-resolution detection of identity by descent in unrelated individuals
    Sharon R Browning
    Department of Statistics, University of Auckland, Auckland, New Zealand
    Am J Hum Genet 86:526-39. 2010
    ..We detect HBD in 4.7 individuals per 10,000 on average at a given location. Our methodology is implemented in the freely available BEAGLE software package...
  4. pmc Estimation of pairwise identity by descent from dense genetic marker data in a population sample of haplotypes
    Sharon R Browning
    Department of Statistics, The University of Auckland, Auckland 1142, New Zealand
    Genetics 178:2123-32. 2008
    ..This enables detection of pairwise IBD between haplotypes from individuals whose most recent common ancestor lived up to 50 generations ago...
  5. pmc Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering
    Sharon R Browning
    Department of Statistics, The University of Auckland, Auckland, New Zealand
    Am J Hum Genet 81:1084-97. 2007
    ..1 days of computing time, with 99% of masked alleles imputed correctly. Our method is implemented in the Beagle software package, which is freely available...
  6. pmc Population structure with localized haplotype clusters
    Sharon R Browning
    Department of Statistics, University of Auckland, Auckland 1142, New Zealand
    Genetics 185:1337-44. 2010
    ..Thus, these new measures of F(ST) and haplotype-cluster diversity provide an important new tool for population genetic analysis of high-density SNP data...
  7. pmc Haplotypic analysis of Wellcome Trust Case Control Consortium data
    Brian L Browning
    Department of Statistics, The University of Auckland, Private Bag 92019, Auckland, New Zealand
    Hum Genet 123:273-80. 2008
    ..In addition, we demonstrate that it is possible to simultaneously phase 16,000 individuals genotyped on genome-wide data (450 K markers) using the Beagle software package...
  8. ncbi request reprint Efficient multilocus association testing for whole genome association studies using localized haplotype clustering
    Brian L Browning
    Department of Statistics, The University of Auckland, Auckland, New Zealand
    Genet Epidemiol 31:365-75. 2007
    ....
  9. pmc Efficient clustering of identity-by-descent between multiple individuals
    Yu Qian
    Bioinformatics Research Center, Aarhus Universitet, 8000C Aarhus, Denmark, Department of Biostatistics and Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA
    Bioinformatics 30:915-22. 2014
    ..Existing methods for detecting multiple-haplotype IBD clusters are often computationally expensive and unable to handle large samples with thousands of haplotypes...
  10. pmc A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals
    Brian L Browning
    Department of Statistics, University of Auckland, Auckland 1142, New Zealand
    Am J Hum Genet 84:210-23. 2009
    ..We present a useful measure of imputation accuracy, allelic R(2), and show that this measure can be estimated accurately from posterior genotype probabilities. Our methods are implemented in version 3.0 of the BEAGLE software package...
  11. pmc Multilocus analysis of GAW15 NARAC chromosome 18 case-control data
    Sharon R Browning
    Department of Statistics, The University of Auckland, Private Bag 92019, Auckland, New Zealand
    BMC Proc 1:S11. 2007
    ..This haplotype was located less than 500 base pairs upstream of the CCBE1 gene. The association was not detected using single-marker tests, but could be found using a variety of multilocus tests...
  12. doi request reprint A canine model of inherited myopia: familial aggregation of refractive error in labrador retrievers
    Joanna Black
    Department of Optometry and Vision Science, University of Auckland, Auckland, New Zealand
    Invest Ophthalmol Vis Sci 49:4784-9. 2008
    ..To determine whether the distribution of naturally occurring myopia in Labrador Retrievers has a genetic component...
  13. pmc Multilocus association mapping using variable-length Markov chains
    Sharon R Browning
    Department of Statistics, The University of Auckland, Auckland 92019, New Zealand
    Am J Hum Genet 78:903-13. 2006
    ..I present analyses of two published data sets that show that this approach can have better power than single-marker tests or sliding-window haplotypic tests...
  14. ncbi request reprint Case-control single-marker and haplotypic association analysis of pedigree data
    Sharon R Browning
    Genetics Research, GlaxoSmithKline, Research Triangle Park, North Carolina 27709, USA
    Genet Epidemiol 28:110-22. 2005
    ..We demonstrate that our method has power at least as great as that of several competing methods, while offering advantages in the ability to handle missing data and perform haplotypic analysis...