Replication of CNTNAP2 association with nonword repetition and support for FOXP2 association with timed reading and motor activities in a dyslexia family sample

Two functionally related genes, FOXP2 and CNTNAP2, influence language abilities in families with rare syndromic and common nonsyndromic forms of impaired language, respectively. We investigated whether these genes are associated with component phenotypes of dyslexia and measures of sequential motor ability. Quantitative transmission disequilibrium testing (QTDT) and linear association modeling were used to evaluate associations with measures of phonological memory (nonword repetition, NWR), expressive language (sentence repetition), reading (real word reading efficiency, RWRE; word attack, WATT), and timed sequential motor activities (rapid alternating place of articulation, RAPA; finger succession in the dominant hand, FS-D) in 188 family trios with a child with dyslexia. Consistent with a prior study of language impairment, QTDT in dyslexia showed evidence of CNTNAP2 single nucleotide polymorphism (SNP) association with NWR. For FOXP2, we provide the first evidence for SNP association with component phenotypes of dyslexia, specifically NWR and RWRE but not WATT. In addition, FOXP2 SNP associations with both RAPA and FS-D were observed. Our results confirm the role of CNTNAP2 in NWR in a dyslexia sample and motivate new questions about the effects of FOXP2 in neurodevelopmental disorders

Human Whole-Exome Genotype Data For alzheimer\u27s Disease

The heterogeneity of the whole-exome sequencing (WES) data generation methods present a challenge to a joint analysis. Here we present a bioinformatics strategy for joint-calling 20,504 WES samples collected across nine studies and sequenced using ten capture kits in fourteen sequencing centers in the Alzheimer\u27s Disease Sequencing Project. The joint-genotype called variant-called format (VCF) file contains only positions within the union of capture kits. The VCF was then processed specifically to account for the batch effects arising from the use of different capture kits from different studies. We identified 8.2 million autosomal variants. 96.82% of the variants are high-quality, and are located in 28,579 Ensembl transcripts. 41% of the variants are intronic and 1.8% of the variants are with CADD \u3e 30, indicating they are of high predicted pathogenicity. Here we show our new strategy can generate high-quality data from processing these diversely generated WES samples. The improved ability to combine data sequenced in different batches benefits the whole genomics research community

The Effect of Algorithms on Copy Number Variant Detection

BACKGROUND: The detection of copy number variants (CNVs) and the results of CNV-disease association studies rely on how CNVs are defined, and because array-based technologies can only infer CNVs, CNV-calling algorithms can produce vastly different findings. Several authors have noted the large-scale variability between CNV-detection methods, as well as the substantial false positive and false negative rates associated with those methods. In this study, we use variations of four common algorithms for CNV detection (PennCNV, QuantiSNP, HMMSeg, and cnvPartition) and two definitions of overlap (any overlap and an overlap of at least 40% of the smaller CNV) to illustrate the effects of varying algorithms and definitions of overlap on CNV discovery. METHODOLOGY AND PRINCIPAL FINDINGS: We used a 56 K Illumina genotyping array enriched for CNV regions to generate hybridization intensities and allele frequencies for 48 Caucasian schizophrenia cases and 48 age-, ethnicity-, and gender-matched control subjects. No algorithm found a difference in CNV burden between the two groups. However, the total number of CNVs called ranged from 102 to 3,765 across algorithms. The mean CNV size ranged from 46 kb to 787 kb, and the average number of CNVs per subject ranged from 1 to 39. The number of novel CNVs not previously reported in normal subjects ranged from 0 to 212. CONCLUSIONS AND SIGNIFICANCE: Motivated by the availability of multiple publicly available genome-wide SNP arrays, investigators are conducting numerous analyses to identify putative additional CNVs in complex genetic disorders. However, the number of CNVs identified in array-based studies, and whether these CNVs are novel or valid, will depend on the algorithm(s) used. Thus, given the variety of methods used, there will be many false positives and false negatives. Both guidelines for the identification of CNVs inferred from high-density arrays and the establishment of a gold standard for validation of CNVs are needed

Evidence for involvement of GNB1L in autism

Structural variations in the chromosome 22q11.2 region mediated by nonallelic homologous recombination result in 22q11.2 deletion (del22q11.2) and 22q11.2 duplication (dup22q11.2) syndromes. The majority of del22q11.2 cases have facial and cardiac malformations, immunologic impairments, specific cognitive profile and increased risk for schizophrenia and autism spectrum disorders (ASDs). The phenotype of dup22q11.2 is frequently without physical features but includes the spectrum of neurocognitive abnormalities. Although there is substantial evidence that haploinsufficiency for TBX1 plays a role in the physical features of del22q11.2, it is not known which gene(s) in the critical 1.5 Mb region are responsible for the observed spectrum of behavioral phenotypes. We identified an individual with a balanced translocation 46,XY,t(1;22)(p36.1;q11.2) and a behavioral phenotype characterized by cognitive impairment, autism, and schizophrenia in the absence of congenital malformations. Using somatic cell hybrids and comparative genomic hybridization (CGH) we mapped the chromosome-22 breakpoint within intron 7 of the GNB1L gene. Copy number evaluations and direct DNA sequencing of GNB1L in 271 schizophrenia and 513 autism cases revealed dup22q11.2 in two families with autism and private GNB1L missense variants in conserved residues in three families (P = 0.036). The identified missense variants affect residues in the WD40 repeat domains and are predicted to have deleterious effects on the protein. Prior studies provided evidence that GNB1L may have a role in schizophrenia. Our findings support involvement of GNB1L in ASDs as well. © 2011 Wiley Periodicals, Inc

Relative Burden of Large CNVs on a Range of Neurodevelopmental Phenotypes

While numerous studies have implicated copy number variants (CNVs) in a range of neurological phenotypes, the impact relative to disease severity has been difficult to ascertain due to small sample sizes, lack of phenotypic details, and heterogeneity in platforms used for discovery. Using a customized microarray enriched for genomic hotspots, we assayed for large CNVs among 1,227 individuals with various neurological deficits including dyslexia (376), sporadic autism (350), and intellectual disability (ID) (501), as well as 337 controls. We show that the frequency of large CNVs (>1 Mbp) is significantly greater for ID–associated phenotypes compared to autism (p = 9.58×10−11, odds ratio = 4.59), dyslexia (p = 3.81×10−18, odds ratio = 14.45), or controls (p = 2.75×10−17, odds ratio = 13.71). There is a striking difference in the frequency of rare CNVs (>50 kbp) in autism (10%, p = 2.4×10−6, odds ratio = 6) or ID (16%, p = 3.55×10−12, odds ratio = 10) compared to dyslexia (2%) with essentially no difference in large CNV burden among dyslexia patients compared to controls. Rare CNVs were more likely to arise de novo (64%) in ID when compared to autism (40%) or dyslexia (0%). We observed a significantly increased large CNV burden in individuals with ID and multiple congenital anomalies (MCA) compared to ID alone (p = 0.001, odds ratio = 2.54). Our data suggest that large CNV burden positively correlates with the severity of childhood disability: ID with MCA being most severely affected and dyslexics being indistinguishable from controls. When autism without ID was considered separately, the increase in CNV burden was modest compared to controls (p = 0.07, odds ratio = 2.33)

Schallmo_etal_2017_SuppressionFacilitation

This file contains the data for the manuscript entitled "Suppression and facilitation of human neural responses" Authors: Schallmo, M-P., Kale, A.M., Millin, R., Flevaris, A.V., Brkanac, Z., Edden, R.A.E., Bernier, R.A., Murray, S.O

Schallmo_etal_2017_SuppressionFacilitation

This file contains the data for the manuscript entitled "Suppression and facilitation of human neural responses" Authors: Schallmo, M-P., Kale, A.M., Millin, R., Flevaris, A.V., Brkanac, Z., Edden, R.A.E., Bernier, R.A., Murray, S.O

PBAP: a pipeline for file processing and quality control of pedigree data with dense genetic markers

Motivation: Huge genetic datasets with dense marker panels are now common. With the availability of sequence data and recognition of importance of rare variants, smaller studies based on pedigrees are again also common. Pedigree-based samples often start with a dense marker panel, a subset of which may be used for linkage analysis to reduce computational burden and to limit linkage disequilibrium between single-nucleotide polymorphisms (SNPs). Programs attempting to select markers for linkage panels exist but lack flexibility. Results: We developed a pedigree-based analysis pipeline (PBAP) suite of programs geared towards SNPs and sequence data. PBAP performs quality control, marker selection and file preparation. PBAP sets up files for MORGAN, which can handle analyses for small and large pedigrees, typically human, and results can be used with other programs and for downstream analyses. We evaluate and illustrate its features with two real datasets