566 research outputs found
Comparison of Clustering Methods for Time Course Genomic Data: Applications to Aging Effects
Time course microarray data provide insight about dynamic biological
processes. While several clustering methods have been proposed for the analysis
of these data structures, comparison and selection of appropriate clustering
methods are seldom discussed. We compared probabilistic based clustering
methods and distance based clustering methods for time course microarray
data. Among probabilistic methods, we considered: smoothing spline clustering
also known as model based functional data analysis (MFDA), functional
clustering models for sparsely sampled data (FCM) and model-based clustering
(MCLUST). Among distance based methods, we considered: weighted gene
co-expression network analysis (WGCNA), clustering with dynamic time warping
distance (DTW) and clustering with autocorrelation based distance (ACF). We
studied these algorithms in both simulated settings and case study data. Our
investigations showed that FCM performed very well when gene curves were short
and sparse. DTW and WGCNA performed well when gene curves were medium or long
( observations). SSC performed very well when there were clusters of gene
curves similar to one another. Overall, ACF performed poorly in these
applications. In terms of computation time, FCM, SSC and DTW were considerably
slower than MCLUST and WGCNA. WGCNA outperformed MCLUST by generating more
accurate and biological meaningful clustering results. WGCNA and MCLUST are the
best methods among the 6 methods compared, when performance and computation
time are both taken into account. WGCNA outperforms MCLUST, but MCLUST provides
model based inference and uncertainty measure of clustering results
A genome-wide survey of segmental duplications that mediate common human genetic variation of chromosomal architecture.
Recent studies have identified a small number of genomic rearrangements that occur frequently in the general population. Bioinformatics tools are now available for systematic genome-wide surveys of higher-order structures predisposing to such common variations in genomic architecture. Segmental duplications (SDs) constitute up to 5 per cent of the genome and play an important role in generating additional rearrangements and in disease aetiology. We conducted a genome-wide database search for a form of SD, palindromic segmental duplications (PSDs), which consist of paired, inverted duplications, and which predispose to inversions, duplications and deletions. The survey was complemented by a search for SDs in tandem orientation (TSDs) that can mediate duplications and deletions but not inversions. We found more than 230 distinct loci with higher-order genomic structure that can mediate genomic variation, of these about 180 contained a PSD. A number of these sites were previously identified as harbouring common inversions or as being associated with specific genomic diseases characterised by duplication, deletions or inversions. Most of the regions, however, were previously unidentified; their characterisation should identify further common rearrangements and may indicate localisations for additional genomic disorders. The widespread distribution of complex chromosomal architecture suggests a potentially high degree of plasticity of the human genome and could uncover another level of genetic variation within human populations
Reconstructing DNA copy number by penalized estimation and imputation
Recent advances in genomics have underscored the surprising ubiquity of DNA
copy number variation (CNV). Fortunately, modern genotyping platforms also
detect CNVs with fairly high reliability. Hidden Markov models and algorithms
have played a dominant role in the interpretation of CNV data. Here we explore
CNV reconstruction via estimation with a fused-lasso penalty as suggested by
Tibshirani and Wang [Biostatistics 9 (2008) 18--29]. We mount a fresh attack on
this difficult optimization problem by the following: (a) changing the penalty
terms slightly by substituting a smooth approximation to the absolute value
function, (b) designing and implementing a new MM (majorization--minimization)
algorithm, and (c) applying a fast version of Newton's method to jointly update
all model parameters. Together these changes enable us to minimize the
fused-lasso criterion in a highly effective way. We also reframe the
reconstruction problem in terms of imputation via discrete optimization. This
approach is easier and more accurate than parameter estimation because it
relies on the fact that only a handful of possible copy number states exist at
each SNP. The dynamic programming framework has the added bonus of exploiting
information that the current fused-lasso approach ignores. The accuracy of our
imputations is comparable to that of hidden Markov models at a substantially
lower computational cost.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS357 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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Methylation age acceleration does not predict mortality in schizophrenia.
Schizophrenia (SCZ) is associated with high mortality. DNA methylation levels vary over the life course, and pre-selected combinations of methylation array probes can be used to estimate "methylation age" (mAge). mAge correlates highly with chronological age but when it differs, termed mAge acceleration, it has been previously associated with all-cause mortality. We tested the association between mAge acceleration and mortality in SCZ and controls. We selected 190 SCZ cases and 190 controls from the Sweden Schizophrenia Study. Cases were identified from the Swedish Hospital Discharge Register with â„5 specialist treatment contacts and â„5 antipsychotic prescriptions. Controls had no psychotic disorder or antipsychotics. Subjects were selected if they had died or survived during follow-up (2:1 oversampling). Extracted DNA was assayed on the Illumina MethylationEPIC array. mAge was regressed on age at sampling to obtain mAge acceleration. Using Cox proportional hazards regression, the association between mAge acceleration and mortality was tested. After quality control, the following were available: nâ=â126 SCZ died, 63 SCZ alive, 127 controls died, 62 controls alive. In the primary analyses, we did not find a significant association between mAge acceleration and SCZ mortality (adjusted pâ>â0.005). Sensitivity analyses excluding SCZ cases with pre-existing cancer demonstrated a significant association between the Hannum mAge acceleration and mortality (hazard ratioâ=â1.13, 95% confidence intervalâ=â1.04-1.22, pâ=â0.005). Per our pre-specified criteria, we did not confirm our primary hypothesis that mAge acceleration would predict subsequent mortality in people with SCZ, but we cannot rule out smaller effects or effects in patient subsets
Large-Scale Whole Genome Sequence Analysis of >22,000 Subjects Provides no Evidence of FMR1 Premutation Allele Involvement in Autism Spectrum Disorder
Expansion of a CGG repeat in the Fragile X Messenger Ribonucleoprotein 1 (FMR1) gene on the X chromosome is the cause of Fragile X Syndrome (FXS). The repeat length of unaffected individuals varies between 5â40 repeats, whereas >200 repeats are observed in cases of FXS. The intermediate range between 55â200 repeats is considered the premutation range and is observed in roughly 1:300 females and 1:900 males in the general population. With the availability of large-scale whole genome sequence (WGS) data and the development of computational tools to detect repeat expansions, we systematically examined the role of FMR1 premutation alleles in autism spectrum disorder (ASD) susceptibility, assess the prevalence, and consider the allelic stability between parents and offspring. We analyzed the WGS data of 22,053 subjects, including 32 FXS positive controls, 1359 population controls, and 5467 ASD families. We observed no FMR1 full mutation range repeats among the ASD parent-offspring families but identified 180 family members with premutation range alleles, which represents a higher prevalence compared to the independent WGS control sample and previous reports in the literature. A sex-specific analysis between probands and unaffected siblings did not reveal a significant increase in the burden of premutation alleles in either males or females with ASD. PCR validation, however, suggests an overestimation of the frequency of FMR1 premutation range alleles through computational analysis of WGS data. Overall, we show the utility of large-scale repeat expansion screening in WGS data and conclude that there is no apparent evidence of FMR1 premutation alleles contributing to ASD susceptibility.</p
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