204 research outputs found
Shedding new light on genetic dark matter
Discoveries from genome-wide association studies have contributed to our knowledge of the genetic etiology of many complex diseases. However, these account for only a small fraction of each disease's heritability. Here, we comment on approaches currently available to uncover more of the genetic 'dark matter,' including an approach introduced recently by Naukkarinen and colleagues. These authors propose a method for distinguishing between gene expression driven by genetic variation and that driven by non-genetic factors. This dichotomy allows investigators to focus statistical tests and further molecular analyses on a smaller set of genes, thereby discovering new genetic variation affecting risk for disease. We need more methods like this one if we are to shed a powerful light on dark matter. By enhancing our understanding of molecular genetic etiology, such methods will help us to understand disease processes better and will advance the promise of personalized medicine
GemTools: A fast and efficient approach to estimating genetic ancestry
To uncover the genetic basis of complex disease, individuals are often
measured at a large number of genetic variants (usually SNPs) across the
genome. GemTools provides computationally efficient tools for modeling genetic
ancestry based on SNP genotypes. The main algorithm creates an eigenmap based
on genetic similarities, and then clusters subjects based on their map
position. This process is continued iteratively until each cluster is
relatively homogeneous. For genetic association studies, GemTools matches cases
and controls based on genetic similarity.Comment: 5 pages, 1 figur
Genetics in psychiatry: common variant association studies.
Many psychiatric conditions and traits are associated with significant heritability. Genetic risk for psychiatric conditions encompass rare variants, identified due to major effect, as well as common variants, the latter analyzed by association analyses. We review guidelines for common variant association analyses, undertaking after assessing evidence of heritability. We highlight the importance of: suitably large sample sizes; an experimental design that controls for ancestry; careful data cleaning; correction for multiple testing; small P values for positive findings; assessment of effect size for positive findings; and, inclusion of an independent replication sample. We also note the importance of a critical discussion of any prior findings, biological follow-up where possible, and a means of accessing the raw data.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are
Refining genetically inferred relationships using treelet covariance smoothing
Recent technological advances coupled with large sample sets have uncovered
many factors underlying the genetic basis of traits and the predisposition to
complex disease, but much is left to discover. A common thread to most genetic
investigations is familial relationships. Close relatives can be identified
from family records, and more distant relatives can be inferred from large
panels of genetic markers. Unfortunately these empirical estimates can be
noisy, especially regarding distant relatives. We propose a new method for
denoising genetically - inferred relationship matrices by exploiting the
underlying structure due to hierarchical groupings of correlated individuals.
The approach, which we call Treelet Covariance Smoothing, employs a multiscale
decomposition of covariance matrices to improve estimates of pairwise
relationships. On both simulated and real data, we show that smoothing leads to
better estimates of the relatedness amongst distantly related individuals. We
illustrate our method with a large genome-wide association study and estimate
the "heritability" of body mass index quite accurately. Traditionally
heritability, defined as the fraction of the total trait variance attributable
to additive genetic effects, is estimated from samples of closely related
individuals using random effects models. We show that by using smoothed
relationship matrices we can estimate heritability using population-based
samples. Finally, while our methods have been developed for refining genetic
relationship matrices and improving estimates of heritability, they have much
broader potential application in statistics. Most notably, for
error-in-variables random effects models and settings that require
regularization of matrices with block or hierarchical structure.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS598 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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Amino Acid Position 11 of HLA-DRβ1 is a Major Determinant of Chromosome 6p Association with Ulcerative Colitis
The major histocompatibility complex (MHC) on chromosome 6p is an established risk locus for ulcerative colitis (UC) and Crohn’s disease (CD). We aimed to better define MHC association signals in UC and CD by combining data from dense single nucleotide polymorphism (SNP) genotyping and from imputation of classical HLA types, their constituent SNPs and corresponding amino acids in 562 UC, 611 CD, and 1,428 control subjects. Univariate and multivariate association analyses were performed, controlling for ancestry. In univariate analyses, absence of the rs9269955 C allele was strongly associated with risk for UC (P = 2.67×). rs9269955 is a SNP in the codon for amino acid position 11 of HLA-DRβ1, located in the P6 pocket of the HLA-DR antigen binding cleft. This amino acid position was also the most significantly UC-associated amino acid in omnibus tests (P = 2.68×). Multivariate modeling identified rs9269955-C and 13 other variants in best predicting UC versus control status. In contrast, there was only suggestive association evidence between the MHC and CD. Taken together, these data demonstrate that variation at HLA-DRβ1, amino acid 11 in the P6 pocket of the HLA-DR complex antigen binding cleft is a major determinant of chromosome 6p association with ulcerative colitis
The unfolded protein response is activated in disease-affected brain regions in progressive supranuclear palsy and Alzheimer’s disease
Abstract Background Progressive supranuclear palsy (PSP) is a neurodegenerative disorder pathologically characterized by intracellular tangles of hyperphosphorylated tau protein distributed throughout the neocortex, basal ganglia, and brainstem. A genome-wide association study identified EIF2AK3 as a risk factor for PSP. EIF2AK3 encodes PERK, part of the endoplasmic reticulum’s (ER) unfolded protein response (UPR). PERK is an ER membrane protein that senses unfolded protein accumulation within the ER lumen. Recently, several groups noted UPR activation in Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis, multiple system atrophy, and in the hippocampus and substantia nigra of PSP subjects. Here, we evaluate UPR PERK activation in the pons, medulla, midbrain, hippocampus, frontal cortex and cerebellum in subjects with PSP, AD, and in normal controls. Results We found UPR activation primarily in disease-affected brain regions in both disorders. In PSP, the UPR was primarily activated in the pons and medulla and to a much lesser extent in the hippocampus. In AD, the UPR was extensively activated in the hippocampus. We also observed UPR activation in the hippocampus of some elderly normal controls, severity of which positively correlated with both age and tau pathology but not with Aβ plaque burden. Finally, we evaluated EIF2AK3 coding variants that influence PERK activation. We show that a haplotype associated with increased PERK activation is genetically associated with increased PSP risk. Conclusions The UPR is activated in disease affected regions in PSP and the genetic evidence shows that this activation increases risk for PSP and is not a protective response. </jats:sec
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