How Developers Copy

Copy-paste programming is dangerous as it may lead to hidden dependencies between different parts of the system. Modifying clones is not always straight forward, because we might not know all the places that need modification. This is even more of a problem when several developers need to know about how to change the clones. In this paper, we correlate the code clones with the time of the modification and with the developer that performed the modification to detect patterns of how developers copy from one another. We develop a visualization, named Clone Evolution View, to represent the evolution of the duplicated code. We show the relevance of our approach on several large case studies and we distill our experience in forms of interesting copy patterns.

A MULTILEVEL MODEL TO ADDRESS BATCH EFFECTS IN COPY NUMBER ESTIMATION USING SNP ARRAYS

Submicroscopic changes in chromosomal DNA copy number dosage are common and have been implicated in many heritable diseases and cancers. Recent high-throughput technologies have a resolution that permits the detection of segmental changes in DNA copy number that span thousands of basepairs across the genome. Genome-wide association studies (GWAS) may simultaneously screen for copy number-phenotype and SNP-phenotype associations as part of the analytic strategy. However, genome-wide array analyses are particularly susceptible to batch effects as the logistics of preparing DNA and processing thousands of arrays often involves multiple laboratories and technicians, or changes over calendar time to the reagents and laboratory equipment. Failure to adjust for batch effects can lead to incorrect inference and requires inefficient post-hoc quality control procedures that exclude regions that are associated with batch. Our work extends previous model-based approaches for copy number estimation by explicitly modeling batch effects and using shrinkage to improve locus-specific estimates of copy number uncertainty. Key features of this approach include the use of diallelic genotype calls from experimental data to estimate batch- and locus-specific parameters of background and signal without the requirement of training data. We illustrate these ideas using a study of bipolar disease and a study of chromosome 21 trisomy. The former has batch effects that dominate much of the observed variation in quantile-normalized intensities, while the latter illustrates the robustness of our approach to datasets where as many as 25% of the samples have altered copy number. Locus-specific estimates of copy number can be plotted on the copy-number scale to investigate mosaicism and guide the choice of appropriate downstream approaches for smoothing the copy number as a function of physical position. The software is open source and implemented in the R package CRLMM available at Bioconductor (http:www.bioconductor.org).

A HIDDEN MARKOV MODEL FOR JOINT ESTIMATION OF GENOTYPE AND COPY NUMBER IN HIGH-THROUGHPUT SNP CHIPS

Amplifications and deletions of chromosomal DNA, as well as copy-neutral loss of heterozygosity have been associated with diseases processes. High-throughput single nucleotide polymorphism (SNP) arrays are useful for making genome-wide estimates of copy number and genotype calls. Because neighboring SNPs in high throughput SNP arrays are likely to have dependent copy number and genotype due to the underlying haplotype structure and linkage disequilibrium, hidden Markov models (HMM) may be useful for improving genotype calls and copy number estimates that do not incorporate information from nearby SNPs. We improve previous approaches that utilize a HMM framework for inference in high throughput SNP arrays by integrating copy number, genotype calls, and the corresponding confidence scores when available. Using simulated data, we demonstrate how confidence scores control smoothing in a probabilistic framework. Software for fitting HMMs to SNP array data is available in the R package ICE.

USING THE R PACKAGE crlmm FOR GENOTYPING AND COPY NUMBER ESTIMATION

Genotyping platforms such as Affymetrix can be used to assess genotype-phenotype as well as copy number-phenotype associations at millions of markers. While genotyping algorithms are largely concordant when assessed on HapMap samples, tools to assess copy number changes are more variable and often discordant. One explanation for the discordance is that copy number estimates are susceptible to systematic differences between groups of samples that were processed at different times or by different labs. Analysis algorithms that do not adjust for batch effects are prone to spurious measures of association. The R package crlmm implements a multilevel model that adjusts for batch effects and provides allele-specific estimates of copy number. This paper illustrates a workflow for the estimation of allele-specific copy number, develops markerand study-level summaries of batch effects, and demonstrates how the marker-level estimates can be integrated with complimentary Bioconductor software for inferring regions of copy number gain or loss. All analyses are performed in the statistical environment R. A compendium for reproducing the analysis is available from the author’s website (http://www.biostat.jhsph.edu/~rscharpf/crlmmCompendium/index.html).

Large deletions in the CFTR gene: clinics and genetics in Swiss patients with CF

Cystic fibrosis (CF) is the most common life-shortening autosomal recessive disorder in Caucasians, and is associated with at least one mutation on each CF transmembrane conductance regulator (CFTR) allele. Some patients, however, with only one identifiable point mutation carry on the other allele, a large deletion that is not detected by conventional screening methods. The overall frequency of large deletions in patients with CF is estimated to be 1-3%. Using the CFTR Multiplex Ligation dependent Probe Amplification Kit (MRC-Holland, Amsterdam, Netherlands) that allows the exact detection of copy numbers from all 27 exons in the CFTR gene, we screened 50 patients with only one identified mutation for large deletions in the CFTR gene. Each detected deletion was confirmed using our real-time polymerase chain reaction (PCR) assay and deletion-specific PCR reactions using junction fragment primers. We detected large deletions in eight patients (16%). These eight CF alleles belong to four different deletion types (CFTRindel2, CFTRdele14b-17b, CFTRdele17a-17b and CFTRdele 2-9) whereof the last is novel. Comparing detailed clinical data of all these patients with CF and the molecular genetic findings, we were able to elaborate criteria for deletion screenings and possible genotype-phenotype associations. In conclusion, we agree with other authors that deletion screenings should be implemented in routine genetic diagnostics of CF.