Yujun Shao

Fine Mapping of Autistic Disorder to Chromosome 15q11-q13 by Use of Phenotypic Subtypes

Autistic disorder (AutD) is a complex genetic disease. Available evidence suggests that several genes contribute to the underlying genetic risk for the development of AutD. However, both etiologic heterogeneity and genetic heterogeneity confound the discovery of AutD-susceptibility genes. Chromosome 15q11-q13 has been identified as a strong candidate region on the basis of both the frequent occurrence of chromosomal abnormalities in that region and numerous suggestive linkage and association findings. Ordered-subset analysis (OSA) is a novel statistical method to identify a homogeneous subset of families that contribute to overall linkage at a given chromosomal location and thus to potentially help in the fine mapping and localization of the susceptibility gene within a chromosomal area. For the present analysis, a factor that represents insistence on sameness (IS)--derived from a principal-component factor analysis using data on 221 patients with AutD from the repetitive behaviors/stereotyped patterns domain in the Autism Diagnostic Interview-Revised--was used as a covariate in OSA. Analysis of families sharing high scores on the IS factor increased linkage evidence for the 15q11-q13 region, at the GABRB3 locus, from a LOD score of 1.45 to a LOD score of 4.71. These results narrow our region of interest on chromosome 15 to an area surrounding the gamma-aminobutyric acid-receptor subunit genes, in AutD, and support the hypothesis that the analysis of phenotypic homogeneous subtypes may be a powerful tool for the mapping of disease-susceptibility genes in complex traits.

Analysis of the RELN gene as a genetic risk factor for autism

Several genome-wide screens have indicated the presence of an autism susceptibility locus within the distal long arm of chromosome 7 (7q). Mapping at 7q22 within this region is the candidate gene reelin (RELN). RELN encodes a signaling protein that plays a pivotal role in the migration of several neuronal cell types and in the development of neural connections. Given these neurodevelopmental functions, recent reports that RELN influences genetic risk for autism are of significant interest. The total data set consists of 218 Caucasian families collected by our group, 85 Caucasian families collected by AGRE, and 68 Caucasian families collected at Tufts University were tested for genetic association of RELN variants to autism. Markers included five single-nucleotide polymorphisms (SNPs) and a repeat in the 5′-untranslated region (5′-UTR). Tests for association in Duke and AGRE families were also performed on four additional SNPs in the genes PSMC2 and ORC5L, which flank RELN. Family-based association analyses (PDT, Geno-PDT, and FBAT) were used to test for association of single-locus markers and multilocus haplotypes with autism. The most significant association identified from this combined data set was for the 5′-UTR repeat (PDT P-value=0.002). These analyses show the potential of RELN as an important contributor to genetic risk in autism.

Phenotypic Homogeneity Provides Increased Support for Linkage on Chromosome 2 in Autistic Disorder

Autistic disorder (AutD) is a neurodevelopmental disorder characterized by significant disturbances in social, communicative, and behavioral functioning. A two-stage genomic screen analysis of 99 families with AutD revealed suggestive evidence for linkage to chromosome 2q (D2S116 nonparametric sib-pair LOD score [MLS] 1.12 at 198 cM). In addition, analysis of linkage disequilibrium for D2S116 showed an allele-specific P value of <.01. Recently, linkage to the same region of 2q was reported in an independent genome screen. This evidence for linkage increased when analysis was restricted to the subset of patients with AutD who had delayed onset (>36 mo) of phrase speech (PSD). We similarly classified our data set of 82 sib pairs with AutD, identifying 45 families with AutD and PSD. Analysis of this PSD subset increased our support for linkage to 2q (MLS 2.86 and HLOD 2.12 for marker D2S116). These data support evidence for a gene on chromosome 2 contributing to risk of AutD, and they suggest that phenotypic homogeneity increases the power to find susceptibility genes for AutD.

Association Analysis of Chromosome 15 GABAA Receptor Subunit Genes in Autistic Disorder

Gamma-aminobutyric acid (GABA) is the major inhibitory neurotransmitter in the brain, acting via the GABAA receptors. The GABAA receptors are comprised of several different homologous subunits, forming a group of receptors that are both structurally and functionally diverse. Three of the GABAA receptor subunit genes (GABRB3, GABRA5 and GABRG3) form a cluster on chromosome 15q11-q13, in a region that has been genetically associated with autistic disorder (AutD). Based on these data, we examined 16 single nucleotide polymorphisms (SNPs) located within GABRB3, GABRA5 and GABRG3 for linkage disequilibrium (LD) in 226 AutD families (AutD patients and parents). Genotyping was performed using either OLA (oligonucleotide ligation assay), or SSCP (single strand conformation polymorphism) followed by DNA sequencing. We tested for LD using the Pedigree Disequilibrium Test (PDT). PDT results gave significant evidence that AutD is associated with two SNPs located within the GABRG3 gene (exon5_539T/C, p = 0.02 and intron5_687T/C, p = 0.03), suggesting that the GABRG3 gene or a gene nearby contributes to genetic risk in AutD.

Behavioral comparisons in autistic individuals from multiplex and singleton families.

Autistic disorder (AD) is a complex neurodevelopmental disorder. The role of genetics in AD etiology is well established, and it is postulated that anywhere from 2 to 10 genes could be involved. As part of a larger study to identify these genetic effects we have ascertained a series of AD families: Sporadic (SP, 1 known AD case per family and no known history of AD) and multiplex (MP, ≥2 cases per family). The underlying etiology of both family types is unknown. It is possible that MP families may constitute a unique subset of families in which the disease phenotype is more likely due to genetic factors. Clinical differences between the two family types could represent underlying genetic heterogeneity. We examined ADI-R data for 69 probands from MP families and 88 from SP families in order to compare and contrast the clinical phenotypes for each group as a function of verbal versus nonverbal status. Multivariate analysis controlling for covariates of age at examination, gender, and race (MANCOVA) revealed no differences between either the verbal or nonverbal MP and SP groups for the three ADI-R area scores: social interaction, communication, and restricted/repetitive interests or behaviors. These data failed to find clinical heterogeneity between MP and SP family types. This supports previous work that indicated that autism features are not useful as tools to index genetic heterogeneity. Thus, although there may be different underlying etiologic mechanisms in the SP and MP probands, there are no distinct behavioral patterns associated with probands from MP families versus SP families. These results suggests the possibility that common etiologic mechanisms, either genetic and/or environmental, could underlie all of AD.

No association between the APOE gene and autism

Autism is a neurodevelopmental disorder characterized by stereotypic and repetitive behavior and interests, together with social and communicative deficiencies. The results of several genomic screens suggest the presence of an autism susceptibility locus on chromosome 19p13.2‐q13.4. The apolipoprotein E (APOE) gene on chromosome 19 encodes for a protein, apoE, whose different isoforms (E2, E3, E4) influence neuronal growth. APOE participates in lipid transport and metabolism, repair, growth, and maintenance of axons and myelin during neuronal development. The APOE protein competes with the Reelin protein for VLDL/APOER2 receptor binding. Several studies have reported evidence for an association between autism and the Reelin gene. Based on these data we tested for association between APOE and autism using family‐based association methods in a data set of 322 autism families. Three promoter, one intronic, and one 3′ UTR single nucleotide polymorphisms (SNPs) in the APOE gene (−491a/t, −427c/t, −219g/t, 113c/g, and 5361c/t) as well as the APOE functional polymorphism (E2, E3, E4) were examined and failed to reveal significant evidence that autism is associated with APOE.

Life after the screen: making sense of many P-values.

A multiple analytic approach may be useful for analyzing complex traits since different methods extract both similar and distinct, but complementary pieces of information from genome screen data on extended pedigrees. We examined the usefulness of combining p‐values both across methods and across adjacent markers, taking into account the observed correlation structure among these p‐values. To this end, we employed the recently proposed truncated product method [Zaykin et al., Genet Epidemiol, in press]. It appears that this approach is helpful for visualizing priority regions for follow‐up analysis and reducing the number of false‐positive linkage signals.

Adjusting for covariates on a slippery slope: linkage analysis of change over time.

BackgroundWe analyzed the Genetic Analysis Workshop 13 (GAW13) simulated data to contrast and compare different methods for the genetic linkage analysis of hypertension and change in blood pressure over time. We also examined methods for incorporating covariates into the linkage analysis. We used methods for quantitative trait loci (QTL) linkage analysis with and without covariates and affected sib-pair (ASP) analysis of hypertension followed by ordered subset analysis (OSA), using variables associated with change in blood pressure over time.ResultsFour of the five baseline genes and one of the three slope genes were not detected by any method using conventional criteria. OSA detected baseline gene b35 on chromosome 13 when using the slope in blood pressure to adjust for change over time. Slope gene s10 was detected by the ASP analysis and slope gene s11 was detected by QTL linkage analysis as well as by OSA analysis. Analysis of null chromosomes, i.e., chromosomes without genes, did not reveal significant increases in type I error. However, there were a number of genes indirectly related to blood pressure detected by a variety of methods.ConclusionsWe noted that there is no obvious first choice of analysis software for analyzing a complicated model, such as the one underlying the GAW13 simulated data. Inclusion of covariates and longitudinal data can improve localization of genes for complex traits but it is not always clear how best to do this. It remains a worthwhile task to apply several different approaches since one method is not always the best.