Alberto Magi

EXCAVATOR: detecting copy number variants from whole-exome sequencing data.

AbstractWe developed a novel software tool, EXCAVATOR, for the detection of copy number variants (CNVs) from whole-exome sequencing data. EXCAVATOR combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies genomic regions into five copy number states. We validate EXCAVATOR on three datasets and compare the results with three other methods. These analyses show that EXCAVATOR outperforms the other methods and is therefore a valuable tool for the investigation of CNVs in largescale projects, as well as in clinical research and diagnostics. EXCAVATOR is freely available at http://sourceforge.net/projects/excavatortool/.

Detection of Genomic Structural Variants from Next-Generation Sequencing Data.

Structural variants are genomic rearrangements larger than 50 bp accounting for around 1% of the variation among human genomes. They impact on phenotypic diversity and play a role in various diseases including neurological/neurocognitive disorders and cancer development and progression. Dissecting structural variants from next-generation sequencing data presents several challenges and a number of approaches have been proposed in the literature. In this mini review, we describe and summarize the latest tools – and their underlying algorithms – designed for the analysis of whole-genome sequencing, whole-exome sequencing, custom captures, and amplicon sequencing data, pointing out the major advantages/drawbacks. We also report a summary of the most recent applications of third-generation sequencing platforms. This assessment provides a guided indication – with particular emphasis on human genetics and copy number variants – for researchers involved in the investigation of these genomic events.

Bioinformatics for Next Generation Sequencing Data

The emergence of next-generation sequencing (NGS) platforms imposes increasing demands on statistical methods and bioinformatic tools for the analysis and the management of the huge amounts of data generated by these technologies. Even at the early stages of their commercial availability, a large number of softwares already exist for analyzing NGS data. These tools can be fit into many general categories including alignment of sequence reads to a reference, base-calling and/or polymorphism detection, de novo assembly from paired or unpaired reads, structural variant detection and genome browsing. This manuscript aims to guide readers in the choice of the available computational tools that can be used to face the several steps of the data analysis workflow.

Genetic analysis of 56 polymorphisms in 17 genes involved in methionine metabolism in patients with abdominal aortic aneurysm

Background: Previous studies suggested an association between abdominal aortic aneurysm (AAA) and hyperhomocysteinaemia, a complex trait determined by genetic and environmental factors. Our hypothesis was that polymorphisms in genes directly or indirectly involved in methionine metabolism may contribute to AAA susceptibility. Method: We studied 56 polymorphisms in MTHFR, MTR, MTRR, CBS, MTHFD1, SLC19A1, NNMT, TCN2, AHCY, BHMT, BHMT2, FOLH1, TYMS, ENOSF1, SHMT1, PON1, PON2 genes according to their demonstrated/putative function, localisation in promoter or regulatory and coding regions and/or heterozygosity values >0.300. Polymorphisms were evaluated by using a primer extension based microarray technology in 423 AAA patients and 423 matched controls. Results: All polymorphisms resulted in Hardy–Weinberg equilibrium in patients and controls. At the multiple logistic regression analysis adjusted for traditional cardiovascular risk factors (sex, age, hypertension, smoking habit, dyslipidaemia, diabetes) and chronic obstructive pulmonary disease (COPD), rs8003379 MTHFD1 (odds ratio (OR) 0.41, 95% confidence interval (CI) 0.26 to 0.65) and rs326118 MTRR (OR 0.47, 95% CI 0.29 to 0.77) polymorphisms resulted in independent susceptibility factor for AAA. Conclusions: After haplotype reconstruction, logistic regression analyses adjusted for traditional risk factors and COPD showed a significant association among AAA and AHCY, FOLH1, MTHFD1, MTR, NNMT, PON1 and TYMS haplotypes. Our findings offer new insights into the pathogenesis of AAA.

Read count approach for DNA copy number variants detection

MOTIVATION The advent of high-throughput sequencing technologies is revolutionizing our ability in discovering and genotyping DNA copy number variants (CNVs). Read count-based approaches are able to detect CNV regions with an unprecedented resolution. Although this computational strategy has been recently introduced in literature, much work has been already done for the preparation, normalization and analysis of this kind of data. RESULTS Here we face the many aspects that cover the detection of CNVs by using read count approach. We first study the characteristics and systematic biases of read count distributions, focusing on the normalization methods designed for removing these biases. Subsequently, we compare the algorithms designed to detect the boundaries of CNVs and we investigate the ability of read count data to predict the exact number of DNA copy. Finally, we review the tools publicly available for analysing read count data. To better understand the state of the art of read count approaches, we compare the performance of the three most widely used sequencing technologies (Illumina Genome Analyzer, Roche 454 and Life Technologies SOLiD) in all the analyses that we perform.

Gene expression profiling of peripheral blood in patients with abdominal aortic aneurysm.

OBJECT Abdominal aortic aneurysm (AAA) pathogenesis remains poorly understood. This study investigated the gene expression profile of peripheral blood from patients with AAA using microarray technology. METHODS AND RESULTS We determined gene expression profiles in pooled RNA from 10 AAA patients and 10 matched controls with arrays representing 14,000 transcripts. Microarray data for selected genes were confirmed by real-time PCR in two different AAA (n=36) and control (n=36) populations and integrated with biochemical data. We identified 91 genes which were differentially expressed in AAA patients. Gene Ontology analysis indicated a significant alteration of oxygen transport (increased hemoglobin gene expression) and lipid metabolism [including monoglyceride lipase and low density lipoprotein receptor-related protein 5 (LRP5) gene]. LRP5 expression was associated inversely with serum lipoprotein(a) [Lp(a)] concentration. CONCLUSIONS Increased expression of hemoglobin chain genes as well as of genes involved in erythrocyte mechanical stability were observed in the AAA RNA pools. The association between low levels of LRP5 gene expression and increased levels of Lp(a) in AAA patients suggests a potential role of LRP5 in Lp(a) catabolism. Our data underline the power of microarrays in identifying further molecular perturbations associated with AAA.

H3M2: detection of runs of homozygosity from whole-exome sequencing data

MOTIVATION Runs of homozygosity (ROH) are sizable chromosomal stretches of homozygous genotypes, ranging in length from tens of kilobases to megabases. ROHs can be relevant for population and medical genetics, playing a role in predisposition to both rare and common disorders. ROHs are commonly detected by single nucleotide polymorphism (SNP) microarrays, but attempts have been made to use whole-exome sequencing (WES) data. Currently available methods developed for the analysis of uniformly spaced SNP-array maps do not fit easily to the analysis of the sparse and non-uniform distribution of the WES target design. RESULTS To meet the need of an approach specifically tailored to WES data, we developed [Formula: see text], an original algorithm based on heterogeneous hidden Markov model that incorporates inter-marker distances to detect ROH from WES data. We evaluated the performance of [Formula: see text] to correctly identify ROHs on synthetic chromosomes and examined its accuracy in detecting ROHs of different length (short, medium and long) from real 1000 genomes project data. [Formula: see text] turned out to be more accurate than GERMLINE and PLINK, two state-of-the-art algorithms, especially in the detection of short and medium ROHs. AVAILABILITY AND IMPLEMENTATION [Formula: see text] is a collection of bash, R and Fortran scripts and codes and is freely available at https://sourceforge.net/projects/h3m2/. CONTACT albertomagi@gmail.com SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

EX-HOM (EXome HOMozygosity): A Proof of Principle

Objective: We provide the proof of principle that exome sequencing of only two affected siblings born to first-cousin parents is capable of directly identifying a single candidate gene for an autosomal recessive disorder. This strategy, which we call EX-HOM (EXome HOMozygosity), combines in a single step the capacity of exome sequencing to identify all the coding variants present in a genome with the property of homozygosity mapping to limit the search for candidate genes to specific chromosomal regions. Methods: We sequenced the exomes of two siblings born to first-cousin parents affected with dysmyelinating leukodystrophy and spastic paraparesis caused by a mutation in FA2H. We used exome sequencing data to identify homozygous regions shared by the two affected siblings (EX-HOM regions), compared them with the regions of maximum LOD score obtained with SNP genotyping, and selected the candidate variants within. Results: We identified regions of shared homozygosity (>1 Mb) accounting for about 290 Mb, containing only 3 candidate variants. Among these, the FA2H mutation remained the only plausible one. Conclusion: In single consanguineous pedigrees with a few affected sibs, EX-HOM can be a one-step approach to identify the candidate genetic defect, bypassing obstacles such as genetic heterogeneity and the need for large pedigrees.

372 kb microdeletion in 18q12.3 causing SETBP1 haploinsufficiency associated with mild mental retardation and expressive speech impairment.

Several cases of interstitial deletion encompassing band 18q12.3 are described in patients with mild dysmorphic features, mental retardation and impairment of expressive language. The critical deleted region contains SETBP1 gene (SET binding protein 1). Missense heterozygous mutations in this gene cause Schinzel-Giedion syndrome (SGS, MIM#269150), characterized by profound mental retardation and multiple congenital malformations. Recently, a 18q12.3 microdeletion causing SETBP1 haploinsufficiency has been described in two patients that show expressive speech impairment, moderate developmental delay and peculiar facial features. The phenotype of individual with partial chromosome 18q deletions does not resemble SGS. The deletion defines a critical region in which SETBP1 is the major candidate gene for expressive speech defect. We describe an additional patient with the smallest 18q12.3 microdeletion never reported that causes the disruption of SETBP1. The patient shows mild mental retardation and expressive speech impairment with striking discrepancy between expressive and receptive language skills. He is able to communicate using gestures and mimic expression of face and body with surprising efficacy. The significant phenotypic overlap between this patient and the cases previously reported enforce the hypothesis that SETBP1 haploinsufficiency may have a role in expressive language development.

Early-onset ischaemic stroke: Analysis of 58 polymorphisms in 17 genes involved in methionine metabolism

The hypothesis underlying this study is that variations in genes involved in methionine metabolism may contribute to genetic susceptibility for early-onset ischaemic stroke. We investigated 58 polymorphisms in AHCY, BHMT, BHMT2, CBS, ENOSF1, FOLH1, MTHFD1, MTHFR, MTR, MTRR, NNMT, PON1, PON2, SLC19A1, SHMT1, TCN2, TYMS genes on genomic DNA from 501 young patients who survived ischaemic stroke and 1,211 sex and age comparable controls. Genotype distribution was significantly different between patients and controls for the following SNPs: rs10037045 BHMT, rs682985 BHMT2, rs1051319 CBS, rs202680 FOLH1, rs2274976 MTHFR, rs1979277 SHMT1, rs20721958 TCN2. On multiple logistic regression analysis adjusted for traditional risk factors, rs10037045 BHMT, rs682985 BHMT2, rs1051319 CBS, and rs202680 FOLH1 remained independent risk factors for stroke. After haplotype reconstruction, generalised linear model analyses adjusted for traditional risk factors and using the FDR multiple testing correction showed significant associations between ischaemic stroke and BHMT, CBS, FOLH1, MTR, PON2, TCN2 and TYMS haplotypes. This study identifies significant genetic associations between premature ischaemic stroke and haplotypes in BHMT, CBS, FOLH1, MTR, PON2, TCN2 and TYMS genes involved in methionine metabolism.