2009 Vienna - European Society of Human Genetics

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Genomics, Genomic technology and Epigenetics P11.020 Long range expression effects of copy number variation: insights from smith-magenis and Potocki-Lupski syndrome mouse models G. Ricard 1 , N. Gheldof 1 , J. Chrast 1 , J. Molina 2 , S. Pradervand 1 , J. Lupski 3 , K. Walz 2 , A. Reymond 1 ; 1 center for integrative genomics, lausanne, Switzerland, 2 Centro de Estudios Cientificos, Valdivia, Chile, 3 Baylor College of Medicine, houston, TX, United States. To study the effect of structural changes we assessed gene expression in genomic disorder mouse models. A microdeletion and its reciprocal microduplication, which model the rearrangements present in Smith- Magenis (SMS) and Potocki-Lupski (PTLS) syndromes, respectively, have been engineered. They show phenotypic features similar to those identified in human patients. We profiled the transcriptome of five different tissues affected in patients in mice with 1n (Deletion/+), 2n (+/+), 3n (Duplication/+) and uniallelic 2n (Deletion/Duplication) copies of the same region in an identical genetic background. The most differentially expressed transcripts were ranked. A highly significant propensity, are mapping to the engineered SMS/PTLS interval. A statistically significant overrepresentation of the genes mapping to the flanks of the engineered interval was also found in the top-ranked differentially expressed genes. A phenomenon efficient across multiple cell lineages and that extends along the entire chromosome, megabases from the breakpoints. These long-range effects are unidirectional and uncoupled from the number of copies of the CNV genes. Our results suggest that the assortment of genes mapping to a chromosome is not random. They also indicate that a structural change at a given position may cause the same perturbation in pathways regardless of gene dosage. An issue that should be considered in appreciating the contribution of this class of variation to phenotypic features. We will also discuss the molecular networks that are altered in the different models. This network analysis enables the identification of metabolic pathways that potentially play a function in the SMS/PTLS phenotypes. P11.021 Assessment of copy Number Variation on a Large Population- Based cohort, the Rotterdam study K. Estrada, M. Peters, B. Eussen, A. de Klein, A. de Klein, H. A. Pols, T. A. Knoch, J. M. van Meurs, A. G. Uitterlinden, F. Rivadeneira; Erasmus MC, Rotterdam, The Netherlands. Background: Copy number variants (CNVs) are a form of genetic variation where individuals have amplifications or deletions (> 1kb) in different regions of the genome. They occur commonly in the human genome, often affecting genes and potentially influencing human traits. Aim: Assess the prevalence of CNVs at the population level and their association with multiple traits and diseases. Methods: We obtained normalized intensity data of 5,824 individuals of the Rotterdam Study (RS), a population-based cohort of elderly men and women of Dutch origin, genotyped on the Illumina 550K array and applied two computational methods that use intensity signals (QuantiSNP) and deviations from Hardy-Weinberg equilibrium and information on LD patterns (Trityper) to detect both common and rare CNVs. Results: After quality control, Trityper identified 775 sites with evidence of a common deletion, 400 of which had a frequency > 0.05. QuantiSNP identified 49,229 events (mapping to 26,162 genomic locations) with an estimated false positive rate of 1 / 100,000. We performed a case-control association study in 5,287 participants of which 809 had clinical evidence of osteoporotic fractures. A 210 kb deletion located on the 6p25 chromosomal region was found present in seven individuals of the population (0.1%). The CNV is in the vicinity of the CYDL gene and was found significantly associated with the risk of osteoporotic fracture OR:32 ([95%CI 3.4-262]; p=3x10-7; p = 0.02 after correction for multiple testing). Conclusions: CNV association analyses for multiple cardiovascular, neurological, locomotor and opthalmological disease traits and conditions is currently underway. P11.022 copy Number Variation Analysis Using Quantitative taqman® copy Number Assays T. Hartshorne1 , K. Li1 , A. Broomer1 , Y. Wang1 , F. Wang1 , I. Casuga1 , E. Goley1 , W. Bi2 , S. Cheung2 , C. Chen1 ; 1 2 Applied Biosystems/Life Technologies, Foster City, CA, United States, Baylor College of Medicine, Houston, TX, United States. Recent whole-genome studies have identified 6225 Copy Number Variant (CNV) loci, which are large-sized deletion or duplication events. CNV regions can influence gene activity and certain CNVs have been associated with disease susceptibility. Copy number changes are also detected in microdeletion/microduplication syndromes that are associated with genomic disorders. Although array-based technologies are powerful for large-scale CNV and microdeletion/microduplication discoveries, more quantitative technologies with high accuracy, specificity and sample throughput are necessary to both validate microarray-identified copy number changes and to examine such changes in large samples sets. To meet these needs, Applied Biosystems has developed TaqMan® Copy Number Assays. Using a proprietary design pipeline, assays have been generated to high quality, genomewide targets. TaqMan Copy Number Assays are run with a reference assay, known to be present in two copies in a diploid genome, and gDNA in a duplex real-time PCR. Sample copy number is determined by relative quantitation analysis using Applied Biosystem’s CopyCaller Software tool. Here we show data generated from assays targeting X chromosome, CNV-associated OMIM genes, and chromosomal regions associated with genomic disorders. The assays were tested with different genomic DNAs including HapMap samples and samples with known deletions/duplications. These data sets demonstrate excellent TaqMan Copy Number Assay performance with high accuracy and specificity. We also applied TaqMan Copy Number Assays to array CGH validation and demonstrated concordance between platforms. P11.023 Deep surveying of whole transcriptome under cNV effect E. Ait Yahya Graison1 , C. N. Henrichsen1 , J. Thomas2 , S. Pradervand2 , G. Lefebvre3 , J. Rougemont3 , K. Harshman2 , A. Reymond1 ; 1 2 3 CIG, Lausanne, Switzerland, DAFL, Lausanne, Switzerland, EPFL, Lausanne, Switzerland. Copy number variation (CNV) of DNA segments has recently been identified as a major source of genetic diversity, but a comprehensive understanding of the phenotypic effect of these structural variations is only beginning to emerge. My host laboratory has generated an extensive map of CNV in wild mice and inbred strains. These variable regions cover ~11% of their autosomal genome. Tissue transcriptome data show that expression levels of genes within CNVs tend to correlate with copy number changes and that CNVs influence the expression of flanking genes. Genes within CNVs show lower expression levels and more specific spatial expression patterns than genes mapping elsewhere. These analyses reveal differential constraint on CNV genes expressed in different tissues. Dosage alterations of brain-expressed genes are less frequent than those of other genes. This study suggests that CNVs shape tissue transcriptomes on a global scale and thus represent a substantial source for within-species phenotypic variation. To unravel the effects of CNV on expression of both coding and non-coding RNA at the nucleotide rather than locus level I propose to use RNA-seq to monitor expression changes of transcripts that map to CNV regions and their flanks. Considering the multiple tissues we plan to investigate and the sequencing coverage we plan to achieve this work should (i) give an unprecedent global and precise view of the mouse transcriptome; (ii) produce the first transcriptome comparison of normal individuals of the population at the nucleotide level; and (iii) help gauge the influence of CNVs on the transcriptome. P11.024 indexed paired-end next-generation sequencing for medical resequencing demonstrated in patients with congenital hyperinsulinism (cHi) A. Benet-Pagès 1 , B. Lorenz-Depiereux 1 , S. Eck 1 , K. Mohnike 2 , O. Blankenstein 3 , T. Meitinger 1,4 , T. M. Strom 1,4 ; 1 Helmholtz Zentrum München, Institute of Human Genetics, Neuherberg, Germany, 2 Otto von Guericke University Magdeburg, Department of Pediatrics and Neonatology, Magdeburg, Germany, 3 Charité Campus Virchow, Department of
Genomics, Genomic technology and Epigenetics Pediatrics Endocrinology and Diabetes, Berlin, Germany, 4 Technische Universität München, Institute of Human Genetics, Munich, Germany. Capillary sequencing of the coding region of the ABCC8 and KCNJ11 genes in patients with diffuse Congenital Hyperinsulinism of Infancy (CHI) results in a low mutation detection rate. In 23 out of 44 patients no mutations were found and 9 patients had a heterozygous mutation. In order to detect further rare variants, we resequenced the entire genomic region of the ABCC8 and KCNJ11 genes (100 kb) on a GA II system in 24 samples and evaluated the sensitivity and specificity of variant detection. The region was amplified with 19 long-range PCR reactions. For library construction, a beta-version of the Illumina multiplexing oligonucleotide paired-end kit was used and up to 12 samples were processed in a single lane. Data were analyzed using the MAQ software. Coverage was between 300- and 5000-fold. Preliminary analysis revealed several false positive variants that were only found on either the forward or reverse strand and showed preference for specific neighboring nucleotides. After filtering for this systematic error, we detected 399 out of 402 previously sequenced coding SNPs/mutations. One of the three differing positions could be accounted for by low coverage in this region. The other two were unambiguous calls. Additionally to the known variants, we detected 88 new SNPs and 8 new indels in the intron- and intergenic regions. 59.1% SNPs and 62.5% indels were confined to a single patient. The other variants are encountered in at least two individuals. Population frequencies of these variants are being investigated. P11.025 Finding copy Number Polymorphism in the swiss Population A. Valsesia 1,2 , T. Johnson 2,3 , Z. Kutalik 2,3 , CoLaus Consortium, B. J. Stevenson 1,2 , C. V. Jongeneel 1,2 , J. S. Beckmann 4,3 , S. Bergmann 2,3 ; 1 Ludwig Institute for Cancer Research, Lausanne, Switzerland, 2 Swiss Institute of Bioinformatics, Lausanne, Switzerland, 3 Department of Medical Genetics UNIL, Lausanne, Switzerland, 4 Service of Medical Genetics, CHUV, Lausanne, Switzerland. Having technologies like microarrays and ultra-high throughput sequencing, facilitate the identification of genetic structural variations. SNPs are used to investigate susceptibility to common diseases, yet they explain but a small fraction of the phenotypic variance. Copy number variation (CNV) is the most frequent structural variation in the human genome and encompasses more nucleotides than SNPs. It is likely that CNVs explain at least some of phenotypic variance that cannot be attributed to SNPs, but the extent of this contribution remains unknown. We present an approach to detect CNVs from Affymetrix arrays and to combine these into Copy Number Polymorphic regions (CNPs). Using an HMM we attribute a copy number state to each SNP probed for a collection of individuals. We then perform a principal component analysis on a window of SNP data across individuals. Only components that explain most of the variance are used to cluster SNPs into CNPs. Based on this merging method, we report a comprehensive variation map on the CoLaus dataset (Cohort Lausannoise - a 6000 individual population). This map describes 1853 common events (>=1% frequency) and 5797 rare events (frequency between 0.1% and 1%) having respectively a median (mean) size of 140 (201) Kb and 81 (127) Kb. 17.1% of the rare CNVs and 17.97% of the common CNPs were already known. The remaining predicted variants are good candidates for being structural variations in European populations that should be replicated by independent studies. Our CNV map will facilitate association studies of clinical phenotypes with these variants. P11.026 Detection of copy number variations (cNV) in patients with mental retardation using high density sNP microarrays N. Rivera Brugués 1 , M. Hempel 2 , S. Spranger 3 , B. Kazmierczak 3 , T. Meitinger 1,2 , T. M. Strom 1,2 ; 1 Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany, 2 Institute of Human Genetics, Technische Univeristät München, Munich, Germany, 3 Praxis für Humangenetik, Bremen, Germany. Chromosomal abnormalities are a major cause of mental retardation. Whole-genome array-based technologies have increased the detection rate of cryptic aneusomies among these patients up to 10-20%. DNAs from 109 children with MR and normal G-banded chromosomes and 1 mentally retarded child with a de novo balanced reciprocal translocation were evaluated for rearrangements by Illumina Human610- Quadv1_B arrays. Data quality was assessed with standard deviation (mean SD: 0.19) and mean absolute deviation (mean MAD: 0.12) of the log2 intensity ratios. In male samples, a signal-to-noise ratio (mean SNR: 3.82) was calculated. CNVs were called using circular binary segmentation (DNAcopy). Candidate CNVs were compared with known polymorphisms and relevant regions were confirmed by qPCR. A total of 3,087 CNVs (2,436 losses, 651 gains) ranging in length from 118 to 13,386,172 base pairs (mean 85,224, median 24,331) were detected. 152 out of 3,087 candidate regions were investigated. Of these, 58 (38.2%) were confirmed, 94 (61.8%) were false-positive. 75 (79.8%) of the false positive were detected in regions defined by 20 SNP. This study led to the identification of 13 de novo CNVs and one maternally inherited Xq13.1 deletion in a male patient. The inheritance of 5 CNVs could not be established because of missing parental DNA. ID Chromosome Gain/Loss Position UCSC hg18 Number Number Length Known of SNPs of Genes (Mb) Syndrome 28181 4q28.3-q31.1 Loss 136,619,480- 140,515,101 770 7 3.895 no 28181 6q16.2-q21 Loss 98,465,339- 111,851,511 2628 >50 13.386 no 28181 6q24.1 Loss 141,216,188- 142,860,121 212 3 1.643 no 35858 2p12-p13.3 Loss 72,397,638- 77,632,757 1074 >50 5.235 no 35929 13q32.3 Loss 98,706,939- 98,709,985 6 1 0.003 no 37497 14q11.2 Loss 20,388,473- 21,164,794 222 21 0.776 no 38749 16p11.2 Loss 28,363,967- 28,549,743 34 8 0.185 16p11.2-p12.2 deletion 38749 16p11.2 Loss 28,733,550- 29,283,628 89 9 0.550 16p11.2-p12.2 deletion 39753 6p25.1 Loss 5,104,063- 5,406,969 92 2 0.302 no 39753 23p21.3 Gain 29,043,064- 29,392,222 60 1 0.349 no 40633 13q12.11 Gain 19,165,733- 19,942,459 200 7 0.776 no 43308 17q11.2 Loss 26,024,127- 27,392,540 211 14 1.368 NF1 type I 44289 2q23.3-q24.2 Loss 153,522,479- 161,822,306 1617 25 8.229 no 44399 23q13.1 Loss 69,341,389- 69,381,997 6 2 0.040 no 33361 4p16.3 Loss* 7,764- 1,504,781 289 24 1.497 no 33361 4p16.3-p15.33 Gain* 1,504,782- 13,259,183 2991 >50 13.251 no 32808 6q14.1 Loss* 79,576,561- 80,104,580 75 3 0.528 no 30921 2p25.3 Gain* 1,069,320- 1,752,354 195 3 0.683 no 31166 23p22.2 Gain* 15,952,583- 16,694,026 90 4 0.741 no P11.027 A genome-wide cpG island methylation analysis microarray A. Wong1 , R. Straussman2 , Z. Yakhini1 , I. Steinfeld1 , H. Cedar2 , A. Ashutosh1 , R. M. Saxena1 , D. Roberts1 ; 1 2 Agilent Technologies, Santa Clara, CA, United States, Hebrew University, Jerusalem, Israel. CpG islands are stretches of high GC content DNA containing multiple CpG dinucleotides. When CpG dinucleotides within these islands are methylated, especially in promoter regions, expression of the corresponding downstream genes is often repressed. Aberrant CpG island methylation is implicated in cancer. We have refined a protocol for methylated DNA immunoprecipitation (mDIP) and coupled it with microarray detection. DNA isolated by mDIP is fluorescently labeled and hybridized to an oligonucleotide microarray that specifically represents the unique CpG islands in the human genome. This microarray contains ~237,000 oligo probes tiling ~20,000 CpG islands, with an average spacing between probes of 95 base pairs. In addition, we have tiled CG rich promoter regions. As proof of concept we perform mDIP and microarray analysis of human genomic DNA samples from normal tissues. We compare our mDIP array data with bisulfite sequencing data. We demonstrate the ability of our array based methylation assay to distinguish probes corresponding to methylated regions from probes corresponding to unmethylated regions. We then apply the whole-genome assay to tumor and normal DNA.and identify differential methylation patterns.
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Volume 17 Supplement 2 May 2009 www
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European Society of Human Genetics
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Table of Contents spoken Presentati
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Plenary Lectures PL2.2 massive para
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Concurrent Symposia s01.1 Genome va
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Concurrent Symposia Monogenic diabe
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Concurrent Symposia invariable in t
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Concurrent Symposia s11.2 clinical,
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Concurrent Symposia s13.2 A novel g
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Concurrent Sessions c01.1 mRNA-seq
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Concurrent Sessions velopmental del
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Concurrent Sessions development of
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Concurrent Sessions c04.6 Duplicati
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Concurrent Sessions genes to be rec
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Concurrent Sessions c07.5 Prenatal
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Concurrent Sessions with familial h
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Concurrent Sessions University of W
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Concurrent Sessions with this, we f
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Concurrent Sessions had immotile ci
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Concurrent Sessions abnormal glycos
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Concurrent Sessions showers’ and
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Concurrent Sessions partial gene de
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Concurrent Sessions leads to distre
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Genetic counseling Genetics educati
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Clinical genetics and Dysmorphology
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Cytogenetics P03. cytogenetics Recu
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Cytogenetics use of metaphase analy
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Cytogenetics 2: mother’s age < fa
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Cytogenetics P03.028 HLA B27 allele
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Cytogenetics karyotype revealed a p
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Cytogenetics drome is estimated at
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Cytogenetics P03.057 Pathological c
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Cytogenetics grandmother and 2 mate
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Cytogenetics method used to detect
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Cytogenetics P03.082 High-resolutio
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Cytogenetics All detected anomalies
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Cytogenetics somy for chromosome 2.
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Cytogenetics cially in chromosome 4
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Cytogenetics (59.390.122 to 62.021.
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Cytogenetics For further characteri
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Cytogenetics 9p duplication. This c
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Cytogenetics regions with mental /d
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Cytogenetics donation program of po
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Cytogenetics P03.165 interpretation
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Cytogenetics P03.174 Deletion of th
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Cytogenetics P03.183 An atypical 7q
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Cytogenetics spermia, 11 oligosperm
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Reproductive genetics and social fa
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Reproductive genetics mosomal chang
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Reproductive genetics two cohorts w
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Reproductive genetics the 147 SC ch
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Prenatal and perinatal genetics P05
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Prenatal and perinatal genetics and
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Prenatal and perinatal genetics fro
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Prenatal and perinatal genetics dev
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Prenatal and perinatal genetics in
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Prenatal and perinatal genetics obj
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Prenatal and perinatal genetics P05
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Cancer genetics P06.005 tiling reso
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Cancer genetics tient group in whic
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Cancer genetics chronic inflammatio
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Cancer genetics licular thyroid can
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Cancer genetics also studied. In 31
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Cancer genetics tile polyposis. We
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Cancer genetics Upon recombinant ex
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Cancer genetics variant allele was
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Cancer genetics from patients with
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Cancer genetics tion (PAX5 and GATA
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Cancer genetics scribed underexpres
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Cancer genetics of the ZIC gene fam
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Cancer genetics Materials and metho
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Cancer genetics the past and it is
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Cancer genetics P06.130 spectrum an
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Cancer genetics P06.139 the role of
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Cancer genetics and MSH2 germline m
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Cancer genetics P06.155 New roles f
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Cancer genetics screening was perfo
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Cancer genetics val (DFI) than pati
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Cancer genetics is hTERC gene ampli
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Cancer genetics on chromosome regio
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Cancer genetics preferentially dupl
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Cancer cytogenetics mutations in co
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Cancer cytogenetics P07.08 molecula
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Statistical genetics, includes Mapp
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Complex traits and polygenic disord
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Complex traits and polygenic disord
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Molecular basis of Mendelian disord
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Metabolic disorders P12.167 Pattern
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Metabolic disorders PKU. BH4 challe
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Metabolic disorders catepe Universi
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Metabolic disorders respectively. G
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Metabolic disorders enzymaticaly co
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Metabolic disorders Normal Affected
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Therapy for genetic disorders in th
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Therapy for genetic disorders P14.0
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Therapy for genetic disorders of me
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Laboratory and quality management P
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Laboratory and quality management T
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Molecular and biochemical basis of
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Genetic analysis, linkage ans assoc
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Author Index Allanson, J.: P02.140
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Author Index 0 Barbacioru, C.: C01.
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Author Index 0 Bonneau, D.: C16.2,
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Author Index 0 Chakravarti, A.: C13
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Author Index 0 Dan, D.: P01.39, P14
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Author Index 0 Duskova, J.: P06.012
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Author Index Franke, A.: P09.056 Fr
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Author Index Grasso, R.: P02.025 Gr
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Author Index Holder-Espinasse, M.:
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Author Index Jurkiewicz, E.: C14.5
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Author Index Kooper, A. J. A.: P05.
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Author Index Li, K. J.: P11.086 Li,
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Author Index Martinet, D.: P03.095
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Author Index Moorman, A. F. M.: P16
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Author Index P10.57, P10.82 Oguzkan
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Author Index P09.054, P09.085, P09.
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Author Index P16.01 Renieri, A.: P0
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Author Index Santorelli, F.: P08.55
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Author Index Sinke, R. J.: P02.020
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Author Index Taheri, M.: P04.33, P0
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Author Index Valentino, P.: P02.064
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Author Index Wiemer-Kruel, A.: P14.
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Keyword Index 1 10q22 deletions: P0
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Keyword Index autosomal dominant re
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Keyword Index CLA: P06.073 CLCN1 ge
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Keyword Index DMD: P16.40, P16.41,
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Keyword Index gene networks: S12.2
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Keyword Index hydrocephaly: P05.11
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Keyword Index malignant hyperthermi
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Keyword Index NAT2: P12.118 natridi
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Keyword Index Polymalformations: P0
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Keyword Index Sardinia: C09.6 Sardi
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Keyword Index TNFalpha: P08.61, P09
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2009 Vienna - European Society of Human Genetics

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