10.1186/1471-2105-11-147BMC Bioinformatics1

Calza, Stefano

Mei, Teo Shu

Pawitan, Yudi

Salim, Agus

Seng, Chia Kee

Seng, Ku Chee

BMC Bioinformatics

English

PubMed

Teo Shu Mei

Agus Salim

Stefano Calza

Ku Chee Seng

Chia Kee Seng

Yudi Pawitan

Springer - Publisher Connector

Identification of recurrent regions of copy-number variants across multiple individuals

Mei, T.S.

Salim, A.

Seng, C.K.

Calza, S.

Pawitan, Y.

ScholarBank@NUS

Crossref

TEO SHU MEI

Statistical Methods for the Detection and Analyses of Structural Variants in the Human Genome

BACKGROUND: Algorithms and software for CNV detection have been developed, but they detect the CNV regions sample-by-sample with individual-specific breakpoints, while common CNV regions are likely to occur at the same genomic locations across different individuals in a homogenous population. Current algorithms to detect common CNV regions do not account for the varying reliability of the individual CNVs, typically reported as confidence scores by SNP-based CNV detection algorithms. General methodologies for identifying these recurrent regions, especially those directed at SNP arrays, are still needed. RESULTS: In this paper, we describe two new approaches for identifying common CNV regions based on (i) the frequency of occurrence of reliable CNVs, where reliability is determined by high confidence scores, and (ii) a weighted frequency of occurrence of CNVs, where the weights are determined by the confidence scores. In addition, motivated by the fact that we often observe partially overlapping CNV regions as a mixture of two or more distinct subregions, regions identified using the two approaches can be fine-tuned to smaller sub-regions using a clustering algorithm. We compared the performance of the methods with sequencing-based results in terms of discordance rates, rates of departure from Hardy-Weinberg equilibrium (HWE) and average frequency and size of the identified regions. The discordance rates as well as the rates of departure from HWE decrease when we select CNVs with higher confidence scores. We also performed comparisons with two previously published methods, STAC and GISTIC, and showed that the methods we consider are better at identifying low-frequency but high-confidence CNV regions. CONCLUSIONS: The proposed methods for identifying common CNV regions in multiple individuals perform well compared to existing methods. The identified common regions can be used for downstream analyses such as group comparisons in association studies

Mei, TS

Salim, A

Calza, S

Seng, KC

Seng, CK

Pawitan, Y

University of Melbourne Institutional Repository

Structural variations (SVs) are an important and abundant source of variation in the human genome, encompassing a greater proportion of the genome as compared to single nucleotide polymorphisms (SNPs). This thesis investigates different aspects of SV analysis, focusing on copy number variations (CNVs) and regions of homozygosity (ROHs). It is divided into four main studies, each focusing on a different set of aims.



In Study I, Identification of recurrent regions of copy-number variation across multiple individuals, we develop an algorithm and software to identify common CNV regions using individually segmented data. The identified common regions allow us to investigate population characteristics of CNVs, as well as to perform association studies.



In Study II, Multi-platform segmentation for joint detection of copy number variants, we develop an algorithm to identify CNVs using intensity data from more than one platform. The algorithm is useful when researchers have data from multiple platforms on the same individual.



In Study III, Regions of homozygosity in three Southeast-Asian populations, we identify ROHs in three Singapore populations, namely the Chinese, Malays and Indians. We characterize the regions and provide population summary statistics. We also investigate the relationship between the occurrence of ROHs and haplotype frequency, regional linkage disequilibrium (LD) and positive selection. The results show that frequency of occurrence of ROHs is positively associated with haplotype frequency and regional LD. The majority of regions detected for recent positive selection and regions with differential LD between populations overlap with the ROH loci. When we consider both the location of the ROHs and the allelic form of the ROHs, we are able to separate the populations by principal component analysis, demonstrating that ROHs contain information on population structure and the demographic history of a population.



Last but not least, in Study IV, Statistical challenges associated with detecting copy number variants with next-generation sequencing technology, we describe and discuss areas of potential biases in CNV detection for each of four commonly used methods. In particular, we focus on issues pertaining to (1) mappability, (2) GC-content bias, (3) quality-control measures of reads, and (4) difficulties in identifying duplications. To gain insights to some of the issues discussed, we download real data from the 1000 Genomes Project and analyze it in terms of depth of coverage (DOC). We show examples of how reads in repeated regions can affect CNV detection, demonstrate current GC correction algorithms, investigate sensitivity of DOC algorithm before and after quality-control of reads and discuss reasons for which duplications are harder to detect than deletions

Teo, Shu Mei

Publications from Karolinska Institutet

Statistical methods for the detection and analyses of structural variants in the human genome

T. S. Mei

A. Salim

K. C. Seng

C. K. Seng

Y. Pawitan

CALZA, Stefano

Archivio istituzionale della ricerca - Università di Brescia

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http://scholarbank.nus.edu.sg/bitstream/10635/24623/2/2010-identification_recurrent_regions_copy_number-pub.pdf

Identification of recurrent regions of copy-number variants across multiple individuals

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ScholarBank@NUS

University of Melbourne Institutional Repository

Publications from Karolinska Institutet

Archivio istituzionale della ricerca - Università di Brescia