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Hierarchical Generalized Linear Models for Multiple Groups of Rare and Common Variants: Jointly Estimating Group and Individual-Variant Effects

DOI: 
info:doi/10.1371/journal.pgen.1002382

Complex diseases and traits are likely influenced by many common and rare genetic variants and environmental factors. Detecting disease susceptibility variants is a challenging task, especially when their frequencies are low and/or their effects are small or moderate. We propose here a comprehensive hierarchical generalized linear model framework for simultaneously analyzing multiple groups of rare and common variants and relevant covariates.

Gene-Based Tests of Association

DOI: 
info:doi/10.1371/journal.pgen.1002177

Genome-wide association studies (GWAS) are now used routinely to identify SNPs associated with complex human phenotypes. In several cases, multiple variants within a gene contribute independently to disease risk. Here we introduce a novel Gene-Wide Significance (GWiS) test that uses greedy Bayesian model selection to identify the independent effects within a gene, which are combined to generate a stronger statistical signal.

Beyond Missing Heritability: Prediction of Complex Traits

DOI: 
10.1371/journal.pgen.1002051

Despite rapid advances in genomic technology, our ability to account for phenotypic variation using genetic information remains limited for many traits. This has unfortunately resulted in limited application of genetic data towards preventive and personalized medicine, one of the primary impetuses of genome-wide association studies. Recently, a large proportion of the “missing heritability” for human height was statistically explained by modeling thousands of single nucleotide polymorphisms concurrently.

A Population Genetic Approach to Mapping Neurological Disorder Genes Using Deep Resequencing

DOI: 
10.1371/journal.pgen.1001318

Author Summary

Genome-Wide Association Study SNPs in the Human Genome Diversity Project Populations: Does Selection Affect Unlinked SNPs with Shared Trait Associations?

DOI: 
10.1371/journal.pgen.1001266

Natural selection exerts its influence by changing allele frequencies at genomic polymorphisms. Alleles associated with harmful traits decrease in frequency while those associated with beneficial traits become more common. In a simple case, selection acts on a trait controlled by a single polymorphism; a large change in allele frequency at this polymorphism can eliminate a deleterious phenotype from a population or fix a beneficial one.

An Evolutionary Framework for Association Testing in Resequencing Studies

DOI: 
10.1371/journal.pgen.1001202

Author Summary

The Next Generation Becomes the Now Generation

DOI: 
info:doi/10.1371/journal.pgen.1000906

News item in PLoS Genetics:

In recent years, several so-called next-generation DNA sequencing platforms have begun to challenge the well-established Sanger sequencing method. In two important ways—cost and speed—these next-gen technologies provide improvements over Sanger sequencing...

The Limits of Individual Identification from Sample Allele Frequencies: Theory and Statistical Analysis

DOI: 
info:doi/10.1371/journal.pgen.1000628

It was shown recently by Homer and colleagues that it may be possible to determine whether a person with known genotypes at a number of markers was part of a pool of DNA from which only frequencies of alleles at the markers are known. In this study, we quantify how well such identification can work in practice. The larger the size of the sample from which the allele frequencies are available, the more independent genetic markers are required to allow individual identification.

Public Access to Genome-Wide Data: Five Views on Balancing Research with Privacy and Protection

DOI: 
info:doi/10.1371/journal.pgen.1000665

PLoS Genetics today has published five viewpoints relating to the safe use of GWAS study data.

Needles in the Haystack: Identifying Individuals Present in Pooled Genomic Data

DOI: 
10.1371/journal.pgen.1000668
In this report, we evaluate a recently-published method for resolving whether individuals are present in a complex genomic DNA mixture. Based on the intuition that an individual will be genetically “closer” to a sample containing him than to a sample not, the method investigated here uses a distance metric to quantify the similarity of an individual relative to two population samples.
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