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A Novel Stratification Method in Linkage Studies to Address Inter- and Intra-Family Heterogeneity in Autism.

ABSTRACT: Most genome linkage scans for autism spectrum disorders (ASDs) have failed to be replicated. Recently, a new ASD phenotypic sub-classification method was developed which employed cluster analyses of severity scores from the Autism Diagnostic Interview-Revised (ADI-R). Here, we performed linkage analysis for each of the four identified ADI-R stratified subgroups. Additional stratification was also applied to reduce intra-family heterogeneity and to investigate the impact of gender. For the purpose of replication, two independent sets of single nucleotide polymorphism markers for 392 families were used in our study. This deep subject stratification protocol resulted in 16 distinct group-specific datasets for linkage analysis. No locus reached significance for the combined non-stratified cohort. However, study-wide significant (P?=?0.02) linkage scores were reached for chromosomes 22q11 (LOD?=?4.43) and 13q21 (LOD?=?4.37) for two subsets representing the most severely language impaired individuals with ASD. Notably, 13q21 has been previously linked to autism with language impairment, and 22q11 has been separately associated with either autism or language disorders. Linkage analysis on chromosome 5p15 for a combination of two stratified female-containing subgroups demonstrated suggestive linkage (LOD?=?3.5), which replicates previous linkage result for female-containing pedigrees. A trend was also found for the association of previously reported 5p14-p15 SNPs in the same female-containing cohort. This study demonstrates a novel and effective method to address the heterogeneity in genetic studies of ASD. Moreover, the linkage results for the stratified subgroups provide evidence at the gene scan level for both inter- and intra-family heterogeneity as well as for gender-specific loci.

SUBMITTER: Talebizadeh Z

PROVIDER: S-EPMC3694043 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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A Novel Stratification Method in Linkage Studies to Address Inter- and Intra-Family Heterogeneity in Autism.

Talebizadeh Zohreh Z Arking Dan E DE Hu Valerie W VW

PloS one 20130626 6

Most genome linkage scans for autism spectrum disorders (ASDs) have failed to be replicated. Recently, a new ASD phenotypic sub-classification method was developed which employed cluster analyses of severity scores from the Autism Diagnostic Interview-Revised (ADI-R). Here, we performed linkage analysis for each of the four identified ADI-R stratified subgroups. Additional stratification was also applied to reduce intra-family heterogeneity and to investigate the impact of gender. For the purpos ...[more]

PMID: 23840741

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Project description:The poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is associated with the tumour heterogeneity. To explore intra- and inter-tumoural heterogeneity in PDAC, we analysed the multi-omics profiles of 61 PDAC lesion samples, along with the matched pancreatic normal tissue samples, from 19 PDAC patients. Haematoxylin and Eosin (H&E) staining revealed that diversely differentiated lesions coexisted both within and across individual tumours. Whole exome sequencing (WES) of samples from multi-region revealed diverse types of mutations in diverse genes between cancer cells within a tumour and between tumours from different individuals. The copy number variation (CNV) analysis also showed that PDAC exhibited intra- and inter-tumoural heterogeneity in CNV and that high average CNV burden was associated poor prognosis of the patients. Phylogenetic tree analysis and clonality/timing analysis of mutations displayed diverse evolutionary pathways and spatiotemporal characteristics of genomic alterations between different lesions from the same or different tumours. Hierarchical clustering analysis illustrated higher inter-tumoural heterogeneity than intra-tumoural heterogeneity of PDAC at the transcriptional levels as lesions from the same patients are grouped into a single cluster. Immune marker genes are differentially expressed in different regions and tumour samples as shown by tumour microenvironment (TME) analysis. TME appeared to be more heterogeneous than tumour cells in the same patient. Lesion-specific differentially methylated regions (DMRs) were identified by methylated DNA immunoprecipitation sequencing (MeDIP-seq). Furthermore, the integration analysis of multi-omics data showed that the mRNA levels of some genes, such as PLCB4, were significantly correlated with the gene copy numbers. The mRNA expressions of potential PDAC biomarkers ZNF521 and KDM6A were correlated with copy number alteration and methylation, respectively. Taken together, our results provide a comprehensive view of molecular heterogeneity and evolutionary trajectories of PDAC and may guide personalised treatment strategies in PDAC therapy.

Dataset Information

A Novel Stratification Method in Linkage Studies to Address Inter- and Intra-Family Heterogeneity in Autism.

Publications

A Novel Stratification Method in Linkage Studies to Address Inter- and Intra-Family Heterogeneity in Autism.

Similar Datasets

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