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. 2015 Oct;1(1):a000422.
doi: 10.1101/mcs.a000422.

Genome-wide Variant Analysis of Simplex Autism Families With an Integrative Clinical-Bioinformatics Pipeline

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Free PMC article

Genome-wide Variant Analysis of Simplex Autism Families With an Integrative Clinical-Bioinformatics Pipeline

Laura T Jiménez-Barrón et al. Cold Spring Harb Mol Case Stud. .
Free PMC article

Abstract

Autism spectrum disorders (ASDs) are a group of developmental disabilities that affect social interaction and communication and are characterized by repetitive behaviors. There is now a large body of evidence that suggests a complex role of genetics in ASDs, in which many different loci are involved. Although many current population-scale genomic studies have been demonstrably fruitful, these studies generally focus on analyzing a limited part of the genome or use a limited set of bioinformatics tools. These limitations preclude the analysis of genome-wide perturbations that may contribute to the development and severity of ASD-related phenotypes. To overcome these limitations, we have developed and utilized an integrative clinical and bioinformatics pipeline for generating a more complete and reliable set of genomic variants for downstream analyses. Our study focuses on the analysis of three simplex autism families consisting of one affected child, unaffected parents, and one unaffected sibling. All members were clinically evaluated and widely phenotyped. Genotyping arrays and whole-genome sequencing were performed on each member, and the resulting sequencing data were analyzed using a variety of available bioinformatics tools. We searched for rare variants of putative functional impact that were found to be segregating according to de novo, autosomal recessive, X-linked, mitochondrial, and compound heterozygote transmission models. The resulting candidate variants included three small heterozygous copy-number variations (CNVs), a rare heterozygous de novo nonsense mutation in MYBBP1A located within exon 1, and a novel de novo missense variant in LAMB3. Our work demonstrates how more comprehensive analyses that include rich clinical data and whole-genome sequencing data can generate reliable results for use in downstream investigations.

Keywords: autism.

Figures

Figure 1.
Figure 1.
(A) Pedigree structure of a simplex autism family. For a family to be classified as a simplex autism family, it has to be composed of one affected child and at least one unaffected sibling, and both parents should not have obvious autism. Probands and siblings can be either males or females. (B) K21 proband showing no dysmorphology. (C) Analyzed pedigrees. Two of the families have male probands and unaffected male siblings (K21 and SSC_12605), whereas the third family has a male proband and a female unaffected sibling (SSC_12596).
Figure 2.
Figure 2.
A conceptual map of human sequence variation. Here, we show approximate sizes, as well as the associated signature, of the various different types of human sequence variation that can be currently detected with whole-genome sequencing (WGS), microarray data, and informatics technologies used in this work. The frequency axis shows the approximate frequency of the various genetic variation types that are currently detectable via germline WGS combined with microarray data. Above the visual signatures of the different types of human sequence variation, the general names of the different informatics software tools for detecting the variation are noted which include, the Genome Analysis Toolkit (GATK), Scalpel, PennCNV, the estimation by read depth with single-nucleotide variants (ERDS) CNV caller, and the FreeBayes caller.
Figure 3.
Figure 3.
Genome Browser Screen view for the read depths in the MYBBP1A stop gain (Chr17:4458481) mutation in the K21 family.
Figure 4.
Figure 4.
Genome Browser Screen view for the read depths in the LAMB3 missense mutation (Chr1:209823359), showing 34 reads supporting the variant for the proband in SSC_12605 family.
Figure 5.
Figure 5.
(A) Genome browser screen view for the read depths in the K21 CNV 3q22.1 region of 16 kb. (B) B allele frequencies (BAF) for Illumina Omni2.5 markers on 3q22.1 region including the markers belonging to the CNV region detected by ERDS in red. (C) Log R ratio (LRR) values for Illumina Omni2.5 markers on 3q22.1 region including the markers belonging to the CNV region detected by ERDS in red.
Figure 6.
Figure 6.
(A) Genome browser screen view for the read depths in the K21 CNV 16p12.3 region of 22 kb. (B) B allele frequencies (BAF) for Illumina Omni2.5 markers on K21 16p12.3 region including the markers belonging to the CNV region detected by ERDS in red. (C) Log R ratio (LRR) values for Illumina Omni2.5 markers on K21 16p12.3 region including the markers belonging to the CNV region detected by ERDS in red.
Figure 7.
Figure 7.
Genome Browser Screen view for the read depths in the SSC_12596 CNV 4p16.3 region of 50 kb. The highlighted region is where the four people bear either a homozygous or heterozygous deletion, only the proband has a homozygous deletion including the highlighted area plus the two regions indicated by the red arrows, which could have been generated by inheriting the deleted copy from both parents.

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