Genome-wide quantitative trait locus association scan of general cognitive ability using pooled DNA and 500K single nucleotide polymorphism microarrays

General cognitive ability ( g ), which refers to what cognitive abilities have in common, is an important target for molecular genetic research because multivariate quantitative genetic analyses have shown that the same set of genes affects diverse cognitive abilities as well as learning disabilities. In this first autosomal genome-wide association scan of g , we used a two-stage quantitative trait locus (QTL) design with pooled DNA to screen more than 500 000 single nucleotide polymorphisms (SNPs) on microarrays, selecting from a sample of 7000 7-year-old children. In stage 1, we screened for allele frequency differences between groups pooled for low and high g . In stage 2, 47 SNPs nominated in stage 1 were tested by individually genotyping an independent sample of 3195 individuals, representative of the entire distribution of g scores in the full 7000 7-year-old children. Six SNPs yielded significant associations across the normal distribution of g , although only one SNP remained significant after a false discovery rate of 0.05 was imposed. However, none of these SNPs accounted for more than 0.4% of the variance of g , despite 95% power to detect associations of that size. It is likely that QTL effect sizes, even for highly heritable traits such as cognitive abilities and disabilities, are much smaller than previously assumed. Nonetheless, an aggregated 'SNP set' of the six SNPs correlated 0.11 ( P  < 0.00000003) with g . This shows that future SNP sets that will incorporate many more SNPs could be useful for predicting genetic risk and for investigating functional systems of effects from genes to brain to behavior.     

Recent developments in genomewide association scans: a workshop summary and review

With the imminent availability of ultra-high-volume genotyping platforms (on the order of 100,000-1,000,000 genotypes per sample) at a manageable cost, there is growing interest in the possibility of conducting genomewide association studies for a variety of diseases but, so far, little consensus on methods to design and analyze them. In April 2005, an international group of >100 investigators convened at the University of Southern California over the course of 2 days to compare notes on planned or ongoing studies and to debate alternative technologies, study designs, and statistical methods. This report summarizes these discussions in the context of the relevant literature. A broad consensus emerged that the time was now ripe for launching such studies, and several common themes were identified--most notably the considerable efficiency gains of multistage sampling design, specifically those made by testing only a portion of the subjects with a high-density genomewide technology, followed by testing additional subjects and/or additional SNPs at regions identified by this initial scan.     

Candidate genes for non-diabetic ESRD in African Americans: a genome-wide association study using pooled DNA

African Americans have increased susceptibility to non-diabetic (non-DM) forms of end-stage renal disease (ESRD) and extensive evidence supports a genetic contribution. A genome-wide association study (GWAS) using pooled DNA was performed in 1,000 African Americans to detect associated genes. DNA from 500 non-DM ESRD cases and 500 non-nephropathy controls was quantified using gel electrophoresis and spectrophotometric analysis and pools of 50 case and 50 control DNA samples were created. DNA pools were genotyped in duplicate on the Illumina HumanHap550-Duo BeadChip. Normalization methods were developed and applied to array intensity values to reduce inter-array variance. Allele frequencies were calculated from normalized channel intensities and compared between case and control pools. Three SNPs had p values of <1.0E−6: rs4462445 (ch 13), rs4821469 (ch 22) and rs8077346 (ch 17). After normalization, top scoring SNPs (n = 65) were genotyped individually in 464 of the original cases and 478 of the controls, with replication in 336 non-DM ESRD cases and 363 non-nephropathy controls. Sixteen SNPs were associated with non-DM ESRD (p < 7.7E−4, Bonferroni corrected). Twelve of these SNPs are in or near the MYH9 gene. The four non-MYH9 SNPs that were associated with non-DM ESRD in the pooled samples were not associated in the replication set. Five SNPs that were modestly associated in the pooled samples were more strongly associated in the replication and/or combined samples. This is the first GWAS for non-DM ESRD in African Americans using pooled DNA. We demonstrate strong association between non-DM ESRD in African Americans with MYH9, and have identified additional candidate loci.     

Multiplex SNP genotyping in pooled DNA samples by a four-colour microarray system

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.     

Quantitative trait loci for IQ and other complex traits: single-nucleotide polymorphism genotyping using pooled DNA and microarrays

Similar to other complex traits, it is likely that many DNA polymorphisms of small effect size [quantitative trait loci (QTLs)] are responsible for the high heritability of intelligence, in addition to many rare monogenic disorders known to contribute to lowered intelligence. We review the current status of approaches to identify QTLs associations for intelligence employing genome-wide strategies using pooled DNA from many individuals and evaluate the innovative approach of microarray analysis to genotype DNA pools for large numbers of single nucleotide polymorphisms.     

Highly cost-efficient genome-wide association studies using DNA pools and dense SNP arrays

Genome-wide association (GWA) studies to map genes for complex traits are powerful yet costly. DNA-pooling strategies have the potential to dramatically reduce the cost of GWA studies. Pooling using Affymetrix arrays has been proposed and used but the efficiency of these arrays has not been quantified. We compared and contrasted Affymetrix Genechip HindIII and Illumina HumanHap300 arrays on the same DNA pools and showed that the HumanHap300 arrays are substantially more efficient. In terms of effective sample size, HumanHap300-based pooling extracts >80% of the information available with individual genotyping (IG). In contrast, Genechip HindIII-based pooling only extracts ~30% of the available information. With HumanHap300 arrays concordance with IG data is excellent. Guidance is given on best study design and it is shown that even after taking into account pooling error, one stage scans can be performed for >100-fold reduced cost compared with IG. With appropriately designed two stage studies, IG can provide confirmation of pooling results whilst still providing ~20-fold reduction in total cost compared with IG-based alternatives. The large cost savings with Illumina HumanHap300-based pooling imply that future studies need only be limited by the availability of samples and not cost.     

Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays

We use high-density single nucleotide polymorphism (SNP) genotyping microarrays to demonstrate the ability to accurately and robustly determine whether individuals are in a complex genomic DNA mixture. We first develop a theoretical framework for detecting an individual's presence within a mixture, then show, through simulations, the limits associated with our method, and finally demonstrate experimentally the identification of the presence of genomic DNA of specific individuals within a series of highly complex genomic mixtures, including mixtures where an individual contributes less than 0.1% of the total genomic DNA. These findings shift the perceived utility of SNPs for identifying individual trace contributors within a forensics mixture, and suggest future research efforts into assessing the viability of previously sub-optimal DNA sources due to sample contamination. These findings also suggest that composite statistics across cohorts, such as allele frequency or genotype counts, do not mask identity within genome-wide association studies. The implications of these findings are discussed.     

高保真酶特异性检测大鼠SNP基因型新方法

目的应用高保真酶（Pfu）和3’末端修饰引物在单管双向等位基因特异性扩增（SB-ASA）中区分SNP基因型,建立高保真酶特异性检测SNP基因型的新方法。方法选取近交系大鼠SNP位点,以RS8149053为例,设计两个外部引物和两个等位基因特异性引物,四引物3’末端进行硫代磷酸化修饰,应用高保真聚合酶（Pfu）进行特异性扩增,扩增结果测序验证其可靠性。结果在RS8149053 SNP位点（C/T）上,等位基因型CC扩增出179 bp目的片段,基因型TT扩增出597 bp目的片段,基因型不同则扩增出分子量不同的片段,目的条带测序结果与Rat Genome Database数据库基因型结果一致,高保真酶扩增结果稳定且特异性强。结论高保真酶等位基因特异性扩增技术能有效降低假阳性率,是一种快速、特异的SNP基因分型新方法。     

ALOHOMORA: a tool for linkage analysis using 10K SNP array data

SUMMARY: ALOHOMORA is a software tool designed to facilitate genome-wide linkage studies performed with high-density single nucleotide polymorphism (SNP) marker panels such as the Affymetrix GeneChip(R) Human Mapping 10K Array. Genotype data are converted into appropriate formats for a number of common linkage programs and subjected to standard quality control routines before linkage runs are started. ALOHOMORA is written in Perl and may be used to perform state-of-the-art linkage scans in small and large families with any genetic model. Options for using different genetic maps or ethnicity-specific allele frequencies are implemented. Graphic outputs of whole-genome multipoint LOD score values are provided for the entire dataset as well as for individual families. AVAILABILITY: ALOHOMORA is available free of charge for non-commercial research institutions. For more details, see http://gmc.mdc-berlin.de/alohomora/     

Exploring the sequence space of a DNA aptamer using microarrays

The relationship between sequence and binding properties of an aptamer for immunoglobulin E (IgE) was investigated using custom DNA microarrays. Single, double and some triple mutations of the aptamer sequence were created to evaluate the importance of specific base composition on aptamer binding. The majority of the positions in the aptamer sequence were found to be immutable, with changes at these positions resulting in more than a 100-fold decrease in binding affinity. Improvements in binding were observed by altering the stem region of the aptamer, suggesting that it plays a significant role in binding. Results obtained for the various mutations were used to estimate the information content and the probability of finding a functional aptamer sequence by selection from a random library. For the IgE-binding aptamer, this probability is on the order of 10⁻¹⁰ to 10⁻⁹. Results obtained for the double and triple mutations also show that there are no compensatory mutations within the space defined by those mutations. Apparently, at least for this particular aptamer, the functional sequence space can be represented as a rugged landscape with sharp peaks defined by highly constrained base compositions. This makes the rational optimization of aptamer sequences using step-wise mutagenesis approaches very challenging.     

Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis

For pedigree-based quantitative trait loci (QTL) association analysis, a range of methods utilizing within-family variation such as transmission-disequilibrium test (TDT)-based methods have been developed. In scenarios where stratification is not a concern, methods exploiting between-family variation in addition to within-family variation, such as the measured genotype (MG) approach, have greater power. Application of MG methods can be computationally demanding (especially for large pedigrees), making genomewide scans practically infeasible. Here we suggest a novel approach for genomewide pedigree-based quantitative trait loci (QTL) association analysis: genomewide rapid association using mixed model and regression (GRAMMAR). The method first obtains residuals adjusted for family effects and subsequently analyzes the association between these residuals and genetic polymorphisms using rapid least-squares methods. At the final step, the selected polymorphisms may be followed up with the full measured genotype (MG) analysis. In a simulation study, we compared type 1 error, power, and operational characteristics of the proposed method with those of MG and TDT-based approaches. For moderately heritable (30%) traits in human pedigrees the power of the GRAMMAR and the MG approaches is similar and is much higher than that of TDT-based approaches. When using tabulated thresholds, the proposed method is less powerful than MG for very high heritabilities and pedigrees including large sibships like those observed in livestock pedigrees. However, there is little or no difference in empirical power of MG and the proposed method. In any scenario, GRAMMAR is much faster than MG and enables rapid analysis of hundreds of thousands of markers.     

PPC: an algorithm for accurate estimation of SNP allele frequencies in small equimolar pools of DNA using data from high density microarrays

Robust estimation of allele frequencies in pools of DNA has the potential to reduce genotyping costs and/or increase the number of individuals contributing to a study where hundreds of thousands of genetic markers need to be genotyped in very large populations sample sets, such as genome wide association studies. In order to make accurate allele frequency estimations from pooled samples a correction for unequal allele representation must be applied. We have developed the polynomial based probe specific correction (PPC) which is a novel correction algorithm for accurate estimation of allele frequencies in data from high-density microarrays. This algorithm was validated through comparison of allele frequencies from a set of 10 individually genotyped DNA's and frequencies estimated from pools of these 10 DNAs using GeneChip 10K Mapping Xba 131 arrays. Our results demonstrate that when using the PPC to correct for allelic biases the accuracy of the allele frequency estimates increases dramatically.     

SNP cherry picker: maximizing the chance of finding an association with a disease SNP

SUMMARY: The high cost of genotyping single nucleotide polymorphisms (SNPs) generally prohibits the systematic mapping of entire genetic linkage regions in order to find the polymorphisms associated with increased risk of disease. In practice, SNPs are selected at approximately equal spacing across the linkage region to try to locate a SNP lying in the haplotype block of the disease SNP. The size of the haplotype block may not be known, however, and SNPs taken from public domain sources may not in fact be polymorphic. Our program will choose a subset of the SNPs in a linkage region so as to maximize the expected proportion of the sequence that lies within a given distance of a real SNP. AVAILABILITY: The software is available, free of charge, for academic use on request from the authors. SUPPLEMENTARY INFORMATION: www.oxagen.co.uk     

SNP discovery using Paired‐End RAD‐tag sequencing on pooled genomic DNA of Sisymbrium austriacum (Brassicaceae)

Single nucleotide polymorphisms SNPs are rapidly replacing anonymous markers in population genomic studies, but their use in non model organisms is hampered by the scarcity of cost‐effective approaches to uncover genome‐wide variation in a comprehensive subset of individuals. The screening of one or only a few individuals induces ascertainment bias. To discover SNPs for a population genomic study of the Pyrenean rocket (Sisymbrium austriacum subsp. chrysanthum), we undertook a pooled RAD‐PE (Restriction site Associated DNA Paired‐End sequencing) approach. RAD tags were generated from the PstI‐digested pooled genomic DNA of 12 individuals sampled across the species distribution range and paired‐end sequenced using Illumina technology to produce ~24.5 Mb of sequences, covering ~7% of the specie's genome. Sequences were assembled into ~76 000 contigs with a mean length of 323 bp (N₅₀ = 357 bp, sequencing depth = 24x). In all, >15 000 SNPs were called, of which 47% were annotated in putative genic regions based on homology with the Arabidopsis thaliana genome. Gene ontology (GO) slim categorization demonstrated that the identified SNPs covered extant genic variation well. The validation of 300 SNPs on a larger set of individuals using a KASPar assay underpinned the utility of pooled RAD‐PE as an inexpensive genome‐wide SNP discovery technique (success rate: 87%). In addition to SNPs, we discovered >600 putative SSR markers.     

Copy number variations identified in the chicken using a 60K SNP BeadChip

Copy number variation (CNV) is considered an important genetic variation, contributing to many economically important traits in the chicken. Although CNVs can be detected using a comparative genomic hybridization array, the high‐density SNP array has provided an alternative way to identify CNVs in the chicken. In the current study, a chicken 60K SNP BeadChip was used to identify CNVs in two distinct chicken genetic lines (White Leghorn and dwarf) using the penncnv program. A total of 209 CNV regions were identified, distributing on chromosomes 1–22 and 24–28 and encompassing 13.55 Mb (1.42%) of chicken autosomal genome area. Three of seven selected CNVs (73.2% individuals) were completely validated by quantitative PCR. To our knowledge, this is the first report in the chicken identifying CNVs using a SNP array. Identification of 190 new identified CNVs illustrates the feasibility of the chicken 60K SNP BeadChip to detect CNVs in the chicken, which lays a solid foundation for future analyses of associations of CNVs with economically important phenotypes in chickens.     

A genome-wide study of cytogenetic changes in colorectal cancer using SNP microarrays: opportunities for future personalized treatment

In colorectal cancer (CRC), chromosomal instability (CIN) is typically studied using comparative-genomic hybridization (CGH) arrays. We studied paired (tumor and surrounding healthy) fresh frozen tissue from 86 CRC patients using Illumina's Infinium-based SNP array. This method allowed us to study CIN in CRC, with simultaneous analysis of copy number (CN) and B-allele frequency (BAF)--a representation of allelic composition. These data helped us to detect mono-allelic and bi-allelic amplifications/deletion, copy neutral loss of heterozygosity, and levels of mosaicism for mixed cell populations, some of which can not be assessed with other methods that do not measure BAF. We identified associations between CN abnormalities and different CRC phenotypes (histological diagnosis, location, tumor grade, stage, MSI and presence of lymph node metastasis). We showed commonalities between regions of CN change observed in CRC and the regions reported in previous studies of other solid cancers (e.g. amplifications of 20q, 13q, 8q, 5p and deletions of 18q, 17p and 8p). From Therapeutic Target Database, we identified relevant drugs, targeted to the genes located in these regions with CN changes, approved or in trials for other cancers and common diseases. These drugs may be considered for future therapeutic trials in CRC, based on personalized cytogenetic diagnosis. We also found many regions, harboring genes, which are not currently targeted by any relevant drugs that may be considered for future drug discovery studies. Our study shows the application of high density SNP arrays for cytogenetic study in CRC and its potential utility for personalized treatment.