64,173 research outputs found
Genome-wide association study identifies multiple susceptibility loci for diffuse large B-cell lymphoma
Diffuse large B cell lymphoma (DLBCL) is the most common lymphoma subtype and is clinically aggressive. To identify genetic susceptibility loci for DLBCL, we conducted a meta-analysis of 3 new genome-wide association studies (GWAS) and 1 previous scan, totaling 3,857 cases and 7,666 controls of European ancestry, with additional genotyping of 9 promising SNPs in 1,359 cases and 4,557 controls. In our multi-stage analysis, five independent SNPs in four loci achieved genome-wide significance marked by rs116446171 at 6p25.3 (EXOC2; P = 2.33 x 10(-21)), rs2523607 at 6p21.33 (HLA-B; P = 2.40 x 10(-10)), rs79480871 at 2p23.3 (NCOA1; P = 4.23 x 10(-8)) and two independent SNPs, rs13255292 and rs4733601, at 8q24.21 (PVT1; P = 9.98 x 10(-13) and 3.63 x 10(-11), respectively). These data provide substantial new evidence for genetic susceptibility to this B cell malignancy and point to pathways involved in immune recognition and immune function in the pathogenesis of DLBCL
Replication in Genome-Wide Association Studies
Replication helps ensure that a genotype-phenotype association observed in a
genome-wide association (GWA) study represents a credible association and is
not a chance finding or an artifact due to uncontrolled biases. We discuss
prerequisites for exact replication, issues of heterogeneity, advantages and
disadvantages of different methods of data synthesis across multiple studies,
frequentist vs. Bayesian inferences for replication, and challenges that arise
from multi-team collaborations. While consistent replication can greatly
improve the credibility of a genotype-phenotype association, it may not
eliminate spurious associations due to biases shared by many studies.
Conversely, lack of replication in well-powered follow-up studies usually
invalidates the initially proposed association, although occasionally it may
point to differences in linkage disequilibrium or effect modifiers across
studies.Comment: Published in at http://dx.doi.org/10.1214/09-STS290 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Methodological Issues in Multistage Genome-Wide Association Studies
Because of the high cost of commercial genotyping chip technologies, many
investigations have used a two-stage design for genome-wide association
studies, using part of the sample for an initial discovery of ``promising''
SNPs at a less stringent significance level and the remainder in a joint
analysis of just these SNPs using custom genotyping. Typical cost savings of
about 50% are possible with this design to obtain comparable levels of overall
type I error and power by using about half the sample for stage I and carrying
about 0.1% of SNPs forward to the second stage, the optimal design depending
primarily upon the ratio of costs per genotype for stages I and II. However,
with the rapidly declining costs of the commercial panels, the generally low
observed ORs of current studies, and many studies aiming to test multiple
hypotheses and multiple endpoints, many investigators are abandoning the
two-stage design in favor of simply genotyping all available subjects using a
standard high-density panel. Concern is sometimes raised about the absence of a
``replication'' panel in this approach, as required by some high-profile
journals, but it must be appreciated that the two-stage design is not a
discovery/replication design but simply a more efficient design for discovery
using a joint analysis of the data from both stages. Once a subset of
highly-significant associations has been discovered, a truly independent
``exact replication'' study is needed in a similar population of the same
promising SNPs using similar methods.Comment: Published in at http://dx.doi.org/10.1214/09-STS288 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Biological networks and epistasis in genome-wide association studies
Over the last few years, technological improvements have made possible the genotyping of hundreds of thousands of SNPs, enabling whole-genome association studies. The first genome-wide association studies have recently been completed to detect causal variant for complex traits. Although increasing evidence suggests that interaction between loci, such as epistasis between two loci, should be considered, most of these studies proceed by considering each SNP independently. One reason for this choice is that looking at all pairs of SNPs increases dramatically the number of tests (approximatively 50 billions of tests for a 300,000 SNPs data set) that faces with computational limitation and strong multiple testing correction.
We proposed to reduce the number of tests by focusing on pairs of SNPs that belong to genes known to interact in some metabolic network. Although some interactions might be missed, these pairs of genes are good candidates for epistasis. Furthermore the use of protein interaction databases (such as the STRING database) may reduce the number of tests by a factor of 5,000.
Results using this approach will be presented on simulated data sets and on public data sets.

Introduction to the Special Issue: Genome-Wide Association Studies
Introduction to the Special Issue: Genome-Wide Association StudiesComment: Published in at http://dx.doi.org/10.1214/09-STS310 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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