A genome-wide association study of social and non-social autistic-like traits in the general population using pooled DNA, 500 K SNP microarrays and both community and diagnosed autism replication samples.
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ABSTRACT:
SUBMITTER: Ronald A
PROVIDER: S-EPMC2797846 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND: Genotyping technologies for whole genome association studies are now available. To perform such studies to an affordable price, pooled DNA can be used. Recent studies have shown that GeneChip Human Mapping 10 K and 50 K arrays are suitable for the estimation of the allele frequency in pooled DNA. In the present study, we tested the accuracy of the 250 K Nsp array, which is part of the 500 K array set representing 500,568 SNPs. Furthermore, we compared different algorithms to estimate allele frequencies of pooled DNA. RESULTS: We could confirm that the polynomial based probe specific correction (PPC) was the most accurate method for allele frequency estimation. However, a simple k-correction, using the relative allele signal (RAS) of heterozygous individuals, performed only slightly worse and provided results for more SNPs. Using four replicates of the 250 K array and the k-correction using heterozygous RAS values, we obtained results for 104.141 SNPs. The correlation between estimated and real allele frequency was 0.983 and the average error was 0.046, which was comparable to the results obtained with the 10 K array. Furthermore, we could show how the estimation accuracy depended on the SNP type (average error for A/T SNPs: 0.043 and for G/C SNPs: 0.052). CONCLUSION: The combination of DNA pooling and analysis of single nucleotide polymorphisms (SNPs) on high density microarrays is a promising tool for whole genome association studies.
| S-EPMC1839100 | biostudies-literature
Project description:The identification of quantitative trait loci (QTLs) of small effect size that underlie complex traits poses a particular challenge for geneticists due to the large sample sizes and large numbers of genetic markers required for genomewide association scans. An efficient solution for screening purposes is to combine single nucleotide polymorphism (SNP) microarrays and DNA pooling (SNP-MaP), an approach that has been shown to be valid, reliable and accurate in deriving relative allele frequency estimates from pooled DNA for groups such as cases and controls for 10K SNP microarrays. However, in order to conduct a genomewide association study many more SNP markers are needed. To this end, we assessed the validity and reliability of the SNP-MaP method using Affymetrix GeneChip Mapping 100K Array set. Interpretable results emerged for 95% of the SNPs (nearly 110,000 SNPs). We found that SNP-MaP allele frequency estimates correlated 0.939 with allele frequencies for 97 605 SNPs that were genotyped individually in an independent population; the correlation was 0.971 for 26 SNPs that were genotyped individually for the 1028 individuals used to construct the DNA pools. We conclude that extending the SNP-MaP method to the Affymetrix GeneChip Mapping 100K Array set provides a useful screen of >100,000 SNP markers for QTL association scans.
| S-EPMC1368655 | biostudies-literature
Project description:We selected 125 candidate single nucleotide polymorphisms (SNPs) in genes belonging to the human type 1 interferon (IFN) gene family and the genes coding for proteins in the main type 1 IFN signalling pathway by screening databases and by in silico comparison of DNA sequences. Using quantitative analysis of pooled DNA samples by solid-phase mini-sequencing, we found that only 20% of the candidate SNPs were polymorphic in the Finnish and Swedish populations. To allow more effective validation of candidate SNPs, we developed a four-colour microarray-based mini-sequencing assay for multiplex, quantitative allele frequency determination in pooled DNA samples. We used cyclic mini-sequencing reactions with primers carrying 5'-tag sequences, followed by capture of the products on microarrays by hybridisation to complementary tag oligonucleotides. Standard curves prepared from mixtures of known amounts of SNP alleles demonstrate the applicability of the system to quantitative analysis, and showed that for about half of the tested SNPs the limit of detection for the minority allele was below 5%. The microarray-based genotyping system established here is universally applicable for genotyping and quantification of any SNP, and the validated system for SNPs in type 1 IFN-related genes should find many applications in genetic studies of this important immunoregulatory pathway.
| S-EPMC135771 | biostudies-literature
Project description:We have developed an accurate, yet inexpensive and high-throughput, method for determining the allele frequency of biallelic polymorphisms in pools of DNA samples. The assay combines kinetic (real-time quantitative) PCR with allele-specific amplification and requires no post-PCR processing. The relative amounts of each allele in a sample are quantified. This is performed by dividing equal aliquots of the pooled DNA between two separate PCR reactions, each of which contains a primer pair specific to one or the other allelic SNP variant. For pools with equal amounts of the two alleles, the two amplifications should reach a detectable level of fluorescence at the same cycle number. For pools that contain unequal ratios of the two alleles, the difference in cycle number between the two amplification reactions can be used to calculate the relative allele amounts. We demonstrate the accuracy and reliability of the assay on samples with known predetermined SNP allele frequencies from 5% to 95%, including pools of both human and mouse DNAs using eight different SNPs altogether. The accuracy of measuring known allele frequencies is very high, with the strength of correlation between measured and known frequencies having an r(2) = 0.997. The loss of sensitivity as a result of measurement error is typically minimal, compared with that due to sampling error alone, for population samples up to 1000. We believe that by providing a means for SNP genotyping up to thousands of samples simultaneously, inexpensively, and reproducibly, this method is a powerful strategy for detecting meaningful polymorphic differences in candidate gene association studies and genome-wide linkage disequilibrium scans.
| S-EPMC310828 | biostudies-literature
Project description:Copy number variants (CNVs) have repeatedly been found to cause or predispose to autism spectrum disorders (ASDs). For diagnostic purposes, we screened 194 individuals with ASDs for CNVs using Illumina SNP arrays. In several probands, we also analyzed candidate genes located in inherited deletions to unmask autosomal recessive variants. Three CNVs, a de novo triplication of chromosome 15q11-q12 of paternal origin, a deletion on chromosome 9p24 and a de novo 3q29 deletion, were identified as the cause of the disorder in one individual each. An autosomal recessive cause was considered possible in two patients: a homozygous 1p31.1 deletion encompassing PTGER3 and a deletion of the entire DOCK10 gene associated with a rare hemizygous missense variant. We also identified multiple private or recurrent CNVs, the majority of which were inherited from asymptomatic parents. Although highly penetrant CNVs or variants inherited in an autosomal recessive manner were detected in rare cases, our results mainly support the hypothesis that most CNVs contribute to ASDs in association with other CNVs or point variants located elsewhere in the genome. Identification of these genetic interactions in individuals with ASDs constitutes a formidable challenge.
| S-EPMC3865413 | biostudies-literature
Project description:BackgroundAutism can be diagnosed from 2 years of age, although most autistic people receive their diagnosis later than this after they have started education. Research is required to understand why some autistic children are diagnosed late, and the level and nature of unmet need prior to diagnosis for late-diagnosed children.MethodsWe examined trajectories of emotional, behavioural and social difficulties (EBSDs) across childhood and adolescence, comparing 'earlier-diagnosed' (diagnosed 7 years or younger) with 'late-diagnosed' (diagnosed between 8 and 14 years) autistic children. Data were from the Millennium Cohort Study, a population-based UK birth cohort. EBSDs were measured using the parent-report Strengths and Difficulties Questionnaire, at 3, 5, 7, 11 and 14 years. We used Growth Curve Modelling to investigate levels and rates of change in these difficulties, and to compare earlier- (n = 146) and late-diagnosed (n = 284) autistic children.ResultsAged 5, earlier-diagnosed autistic children had more emotional (i.e., internalising), conduct, hyperactivity and social difficulties; although clinical difficulties in these areas were nevertheless common in late-diagnosed children. There was a faster annual increase in scores for all domains for late-diagnosed children, and by age 14 years, they had higher levels of EBSDs. These results persisted when we ran adjusted models, to account for the late-diagnosed group having higher rates of late-diagnosed attention deficit/hyperactivity disorder, higher IQ, a higher proportion of females and older and more educated mothers.ConclusionsEmotional, behavioural and social difficulties are associated with, and may influence, the timing of autism diagnosis. Late-diagnosed autistic children often have high levels of mental health and social difficulties prior to their autism diagnosis, and tend to develop even more severe problems as they enter adolescence.
| S-EPMC9790627 | biostudies-literature
Project description:BackgroundThe social domain of autism has been studied in depth, but the relationship between the non-social traits of autism has received less attention. The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) outlines four criteria that make up the non-social domain including repetitive motor movements, insistence on sameness, restricted interests and sensory sensitivity. There is a lack of research into the relationship between these four criteria. This study aimed to evaluate the relationship between the non-social traits of autism in a large sample of autistic adults. It explored whether these traits are best conceptualised as four distinct factors, or exist along a single dimension.MethodsParticipants included autistic adults from the Netherlands Autism Register. The four components identified within the DSM-5 non-social domain were measured by items from the Adult Routines Inventory, the Autism Spectrum Quotient short and the Sensory Perception Quotient short. Confirmatory factor analysis, as well as exploratory factor analysis and exploratory structural equation modelling, was implemented to examine the relationship between these four criteria.ResultsResults indicated that a four-factor model provided the best fit, mapping onto the DSM-5 criteria. These four factors were moderately correlated, suggesting that four distinct, yet related factors best describe the non-social domain of autism. The one-factor model did not provide a good fit, highlighting that the non-social domain of autism is not a unitary construct.LimitationsThe study included autistic adults who were cognitively able to complete the self-report measures. This may limit the generalisability of the findings to those who are less able to do so.ConclusionsThis study provided evidence for the multidimensional nature of the non-social domain of autism. Given only two of the four criteria within the non-social domain need to be endorsed for a diagnosis of autism, there is room for substantial variation across individuals, who will have a unique profile within the non-social domain. The results have implications for our understanding of the heterogeneous nature of autistic traits, as well as for how we conceptualise autism as a diagnostic category. This is important for the provision of diagnosis and support within research and clinical practice.
| S-EPMC7931608 | biostudies-literature
Project description:Gene expression microarrays measure the levels of messenger ribonucleic acid (mRNA) in a sample using probe sequences that hybridize with transcribed regions. These probe sequences are designed using a reference genome for the relevant species. However, most model organisms and all humans have genomes that deviate from their reference. These variations, which include single nucleotide polymorphisms, insertions of additional nucleotides, and nucleotide deletions, can affect the microarray's performance. Genetic experiments comparing individuals bearing different population-associated single nucleotide polymorphisms that intersect microarray probes are therefore subject to systemic bias, as the reduction in binding efficiency due to a technical artifact is confounded with genetic differences between parental strains. This problem has been recognized for some time, and earlier methods of compensation have attempted to identify probes affected by genome variants using statistical models. These methods may require replicate microarray measurement of gene expression in the relevant tissue in inbred parental samples, which are not always available in model organisms and are never available in humans.By using sequence information for the genomes of organisms under investigation, potentially problematic probes can now be identified a priori. However, there is no published software tool that makes it easy to eliminate these probes from an annotation. I present equalizer, a software package that uses genome variant data to modify annotation files for the commonly used Affymetrix IVT and Gene/Exon platforms. These files can be used by any microarray normalization method for subsequent analysis. I demonstrate how use of equalizer on experiments mapping germline influence on gene expression in a genetic cross between two divergent mouse species and in human samples significantly reduces probe hybridization-induced bias, reducing false positive and false negative findings.The equalizer package reduces probe hybridization bias from experiments performed on the Affymetrix microarray platform, allowing accurate assessment of germline influence on gene expression.
| S-EPMC4518889 | biostudies-literature
Project description:BackgroundDNA extracted from historical samples is an important resource for understanding genetic consequences of anthropogenic influences and long-term environmental change. However, such samples generally yield DNA of a lower amount and quality, and the extent to which DNA degradation affects SNP genotyping success and allele frequency estimation is not well understood. We conducted high density SNP genotyping and allele frequency estimation in both individual DNA samples and pooled DNA samples extracted from dried Atlantic salmon (Salmo salar) scales stored at room temperature for up to 35 years, and assessed genotyping success, repeatability and accuracy of allele frequency estimation using a high density SNP genotyping array.ResultsIn individual DNA samples, genotyping success and repeatability was very high (> 0.973 and > 0.998, respectively) in samples stored for up to 35 years; both increased with the proportion of DNA of fragment size > 1000 bp. In pooled DNA samples, allele frequency estimation was highly repeatable (Repeatability = 0.986) and highly correlated with empirical allele frequency measures (Mean Adjusted R2 = 0.991); allele frequency could be accurately estimated in > 95% of pooled DNA samples with a reference group of at least 30 individuals. SNPs located in polyploid regions of the genome were more sensitive to DNA degradation: older samples had lower genotyping success at these loci, and a larger reference panel of individuals was required to accurately estimate allele frequencies.ConclusionsSNP genotyping was highly successful in degraded DNA samples, paving the way for the use of degraded samples in SNP genotyping projects. DNA pooling provides the potential for large scale population genetic studies with fewer assays, provided enough reference individuals are also genotyped and DNA quality is properly assessed beforehand. We provide recommendations for future studies intending to conduct high-throughput SNP genotyping and allele frequency estimation in historical samples.
| S-EPMC3716687 | biostudies-literature
Project description:BACKGROUND: Genetic variants in immune regulator genes have been associated with numerous diseases, including allergies and cancer. Increasing evidence suggests a substantially elevated disease risk in individuals who carry a combination of disease-relevant single nucleotide polymorphisms (SNPs). For the genotyping of immune regulator genes, such as cytokines, chemokines and transcription factors, an oligonucleotide microarray for the analysis of 99 relevant SNPs was established. Since the microarray design was based on a platform that permits flexible in situ oligonucleotide synthesis, a set of optimally performing probes could be defined by a selection approach that combined computational and experimental aspects. RESULTS: While the in silico process eliminated 9% of the initial probe set, which had been picked purely on the basis of potential association with disease, the subsequent experimental validation excluded more than twice as many. The performance of the optimized microarray was demonstrated in a pilot study. The genotypes of 19 hay-fever patients (aged 40-44) with high IgE levels against inhalant antigens were compared to the results obtained with 19 age- and sex-matched controls. For several variants, allele-frequency differences of more than 10% were identified. CONCLUSION: Based on the ability to improve empirically a chip design, the application of candidate-SNP typing represents a viable approach in the context of molecular epidemiological studies.
| S-EPMC1994959 | biostudies-literature