Association analysis of mild mental impairment using DNA pooling to screen 432 brain-expressed single-nucleotide polymorphisms

Interdisciplinary Approach to Biological and Health Implications in Selected Professional Competences

Everyday life’s hygiene and professional realities, especially in economically developed countries, indicate the need to modify the standards of pro-health programs as well as modern hygiene and work ergonomics programs. These observations are based on the problem of premature death caused by civilization diseases. The biological mechanisms associated with financial risk susceptibility are well described, but there is little data explaining the biological basis of neuroaccounting. Therefore, the aim of the study was to present relationships between personality traits, cognitive competences and biological factors shaping behavioral conditions in a multidisciplinary aspect. This critical review paper is an attempt to compile biological and psychological factors influencing the development of professional competences, especially decent in the area of accounting and finance. We analyzed existing literature from wide range of scientific disciplines (including economics, psychology, behavioral genetics) to create background to pursuit multidisciplinary research models in the field of neuroaccounting. This would help in pointing the best genetically based behavioral profile of future successful financial and accounting specialists.

Human cell adhesion molecules: annotated functional subtypes and overrepresentation of addiction-associated genes

Human cell adhesion molecules (CAMs) are essential for proper development, modulation, and maintenance of interactions between cells and cell-to-cell (and matrix-to-cell) communication about these interactions. Despite the differential functional significance of these roles, there have been surprisingly few systematic studies to enumerate the universe of CAMs and identify specific CAMs in distinct functions. In this paper, we update and review the set of human genes likely to encode CAMs with searches of databases, literature reviews, and annotations. We describe likely CAMs and functional subclasses, including CAMs that have a primary function in information exchange (iCAMs), CAMs involved in focal adhesions, CAM gene products that are preferentially involved with stereotyped and morphologically identifiable connections between cells (e.g., adherens junctions, gap junctions), and smaller numbers of CAM genes in other classes. We discuss a novel proposed mechanism involving selective anchoring of the constituents of iCAM-containing lipid rafts in zones of close neuronal apposition to membranes expressing iCAM binding partners. We also discuss data from genetic and genomic studies of addiction in humans and mouse models to highlight the ways in which CAM variation may contribute to a specific brain-based disorder such as addiction. Specific examples include changes in CAM mRNA splicing mediated by differences in the addiction-associated splicing regulator RBFOX1/A2BP1 and CAM expression in dopamine neurons.

Technological issues and experimental design of gene association studies

Genome-wide association studies (GWAS), in which thousands of single-nucleotide polymorphisms (SNPs) spanning the genome are genotyped in individuals who are phenotypically well characterized, -currently represent the most popular strategy for identifying gene regions associated with common -diseases and related quantitative traits. Improvements in technology and throughput capability, development of powerful statistical tools, and more widespread acceptance of pooling-based genotyping approaches have led to greater utilization of GWAS in human genetics research. However, important considerations for optimal experimental design, including selection of the most appropriate genotyping platform, can enhance the utility of the approach even further. This chapter reviews experimental and technological issues that may affect the success of GWAS findings and proposes strategies for developing the most comprehensive, logical, and cost-effective approaches for genotyping given the population of interest.

Genome-wide association study with DNA pooling identifies variants at CNTNAP2 associated with pseudoexfoliation syndrome

Genetic and nongenetic factors contribute to development of pseudoexfoliation (PEX) syndrome, a complex, age-related, generalized matrix process frequently associated with glaucoma. To identify specific genetic variants underlying its etiology, we performed a genome-wide association study (GWAS) using a DNA-pooling approach. Therefore, equimolar amounts of DNA samples of 80 subjects with PEX syndrome, 80 with PEX glaucoma (PEXG) and 80 controls were combined into separate pools and hybridized to 500K SNP arrays (Affymetrix). Array probe intensity data were analyzed and visualized with expressly developed software tools GPFrontend and GPGraphics in combination with GenePool software. For replication, independent German cohorts of 610 unrelated patients with PEX/PEXG and 364 controls as well as Italian cohorts of 249 patients and 190 controls were used. Of 19, 17 SNPs showing significant allele frequency difference in DNA pools were confirmed by individual genotyping. Further single genotyping at CNTNAP2 locus revealed association between PEX/PEXG for two SNPs, which was confirmed in an independent German but not the Italian cohort. Both SNPs remained significant in the combined German cohorts even after Bonferroni correction (rs2107856: P(c)=0.0108, rs2141388: P(c)=0.0072). CNTNAP2 was found to be ubiquitously expressed in all human ocular tissues, particularly in retina, and localized to cell membranes of epithelial, endothelial, smooth muscle, glial and neuronal cells. Confirming efficiency of GWAS with DNA-pooling approach by detection of the known LOXL1 locus, our study data show evidence for association of CNTNAP2 with PEX syndrome and PEXG in German patients.

A genome-wide association study identifies multiple loci associated with mathematics ability and disability

Numeracy is as important as literacy and exhibits a similar frequency of disability. Although its etiology is relatively poorly understood, quantitative genetic research has demonstrated mathematical ability to be moderately heritable. In this first genome-wide association study (GWAS) of mathematical ability and disability, 10 out of 43 single nucleotide polymorphism (SNP) associations nominated from two high- vs. low-ability (n = 600 10-year-olds each) scans of pooled DNA were validated (P < 0.05) in an individually genotyped sample of (*)2356 individuals spanning the entire distribution of mathematical ability, as assessed by teacher reports and online tests. Although the effects are of the modest sizes now expected for complex traits and require further replication, interesting candidate genes are implicated such as NRCAM which encodes a neuronal cell adhesion molecule. When combined into a set, the 10 SNPs account for 2.9% (F = 56.85; df = 1 and 1881; P = 7.277e-14) of the phenotypic variance. The association is linear across the distribution consistent with a quantitative trait locus (QTL) hypothesis; the third of children in our sample who harbour 10 or more of the 20 risk alleles identified are nearly twice as likely (OR = 1.96; df = 1; P = 3.696e-07) to be in the lowest performing 15% of the distribution. Our results correspond with those of quantitative genetic research in indicating that mathematical ability and disability are influenced by many genes generating small effects across the entire spectrum of ability, implying that more highly powered studies will be needed to detect and replicate these QTL associations.

Genome wide association for addiction: replicated results and comparisons of two analytic approaches

BACKGROUND

Vulnerabilities to dependence on addictive substances are substantially heritable complex disorders whose underlying genetic architecture is likely to be polygenic, with modest contributions from variants in many individual genes. "Nontemplate" genome wide association (GWA) approaches can identity groups of chromosomal regions and genes that, taken together, are much more likely to contain allelic variants that alter vulnerability to substance dependence than expected by chance.

METHODOLOGY/PRINCIPAL FINDINGS

We report pooled "nontemplate" genome-wide association studies of two independent samples of substance dependent vs control research volunteers (n = 1620), one European-American and the other African-American using 1 million SNP (single nucleotide polymorphism) Affymetrix genotyping arrays. We assess convergence between results from these two samples using two related methods that seek clustering of nominally-positive results and assess significance levels with Monte Carlo and permutation approaches. Both "converge then cluster" and "cluster then converge" analyses document convergence between the results obtained from these two independent datasets in ways that are virtually never found by chance. The genes identified in this fashion are also identified by individually-genotyped dbGAP data that compare allele frequencies in cocaine dependent vs control individuals.

CONCLUSIONS/SIGNIFICANCE

These overlapping results identify small chromosomal regions that are also identified by genome wide data from studies of other relevant samples to extents much greater than chance. These chromosomal regions contain more genes related to "cell adhesion" processes than expected by chance. They also contain a number of genes that encode potential targets for anti-addiction pharmacotherapeutics. "Nontemplate" GWA approaches that seek chromosomal regions in which nominally-positive associations are found in multiple independent samples are likely to complement classical, "template" GWA approaches in which "genome wide" levels of significance are sought for SNP data from single case vs control comparisons.

Validation of pooled genotyping on the Affymetrix 500 k and SNP6.0 genotyping platforms using the polynomial-based probe-specific correction

BACKGROUND

The use of pooled DNA on SNP microarrays (SNP-MaP) has been shown to be a cost effective and rapid manner to perform whole-genome association evaluations. While the accuracy of SNP-MaP was extensively evaluated on the early Affymetrix 10 k and 100 k platforms, there have not been as many similarly comprehensive studies on more recent platforms. In the present study, we used the data generated from the full Affymetrix 500 k SNP set together with the polynomial-based probe-specific correction (PPC) to derive allele frequency estimates. These estimates were compared to genotyping results of the same individuals on the same platform, as the basis to evaluate the reliability and accuracy of pooled genotyping on these high-throughput platforms. We subsequently extended this comparison to the new SNP6.0 platform capable of genotyping 1.8 million genetic variants.

RESULTS

We showed that pooled genotyping on the 500 k platform performed as well as those previously shown on the relatively lower throughput 10 k and 100 k array sets, with high levels of accuracy (correlation coefficient: 0.988) and low median error (0.036) in allele frequency estimates. Similar results were also obtained from the SNP6.0 array set. A novel pooling strategy of overlapping sub-pools was attempted and comparison of estimated allele frequencies showed this strategy to be as reliable as replicate pools. The importance of an appropriate reference genotyping data set for the application of the PPC algorithm was also evaluated; reference samples with similar ethnic background to the pooled samples were found to improve estimation of allele frequencies.

CONCLUSION

We conclude that use of the PPC algorithm to estimate allele frequencies obtained from pooled genotyping on the high throughput 500 k and SNP6.0 platforms is highly accurate and reproducible especially when a suitable reference sample set is used to estimate the beta values for PPC.

Molecular genetics of addiction and related heritable phenotypes: genome-wide association approaches identify "connectivity constellation" and drug target genes with pleiotropic effects

Genome-wide association (GWA) can elucidate molecular genetic bases for human individual differences in complex phenotypes that include vulnerability to addiction. Here, we review (a) evidence that supports polygenic models with (at least) modest heterogeneity for the genetic architectures of addiction and several related phenotypes; (b) technical and ethical aspects of importance for understanding GWA data, including genotyping in individual samples versus DNA pools, analytic approaches, power estimation, and ethical issues in genotyping individuals with illegal behaviors; (c) the samples and the data that shape our current understanding of the molecular genetics of individual differences in vulnerability to substance dependence and related phenotypes; (d) overlaps between GWA data sets for dependence on different substances; and (e) overlaps between GWA data for addictions versus other heritable, brain-based phenotypes that include bipolar disorder, cognitive ability, frontal lobe brain volume, the ability to successfully quit smoking, neuroticism, and Alzheimer's disease. These convergent results identify potential targets for drugs that might modify addictions and play roles in these other phenotypes. They add to evidence that individual differences in the quality and quantity of brain connections make pleiotropic contributions to individual differences in vulnerability to addictions and to related brain disorders and phenotypes. A "connectivity constellation" of brain phenotypes and disorders appears to receive substantial pathogenic contributions from individual differences in a constellation of genes whose variants provide individual differences in the specification of brain connectivities during development and in adulthood. Heritable brain differences that underlie addiction vulnerability thus lie squarely in the midst of the repertoire of heritable brain differences that underlie vulnerability to other common brain disorders and phenotypes.

OKCAM: an ontology-based, human-centered knowledgebase for cell adhesion molecules

‘Cell adhesion molecules’ (CAMs) are essential elements of cell/cell communication that are important for proper development and plasticity of a variety of organs and tissues. In the brain, appropriate assembly and tuning of neuronal connections is likely to require appropriate function of many cell adhesion processes. Genetic studies have linked and/or associated CAM variants with psychiatric, neurologic, neoplastic, immunologic and developmental phenotypes. However, despite increasing recognition of their functional and pathological significance, no systematic study has enumerated CAMs or documented their global features. We now report compilation of 496 human CAM genes in six gene families based on manual curation of protein domain structures, Gene Ontology annotations, and 1487 NCBI Entrez annotations. We map these genes onto a cell adhesion molecule ontology that contains 850 terms, up to seven levels of depth and provides a hierarchical description of these molecules and their functions. We develop OKCAM, a CAM knowledgebase that provides ready access to these data and ontologic system at http://okcam.cbi.pku.edu.cn. We identify global CAM properties that include: (i) functional enrichment, (ii) over-represented regulation modes and expression patterns and (iii) relationships to human Mendelian and complex diseases, and discuss the strengths and limitations of these data.

Testing replication of a 5-SNP set for general cognitive ability in six population samples

A 5-single nucleotide polymorphism (SNP) set has been associated with general cognitive ability in 5000 7-year-old children from the Twins Early Development Study (TEDS). Four of these SNPs were identified through a 10 K microarray analysis and one was identified through a targeted analysis of brain-expressed genes. The present study tested this association with general cognitive ability in six population samples of varying size and age from Australia, the UK (Scotland and England) and the Netherlands. Results from the largest sample (N=1310) approached significance (P=0.06) in the direction of the original finding, but results from the other samples (N=205-758) were mixed. A meta-analysis of the results--allowing for effect size heterogeneity between samples--yielded a non-significant correlation (r=-0.01, P=0.57), indicating that this SNP set was not associated with general cognitive ability in the populations studied.

The nature of nurture: a genomewide association scan for family chaos

Widely used measures of the environment, especially the family environment of children, show genetic influence in dozens of twin and adoption studies. This phenomenon is known as gene-environment correlation in which genetically driven influences of individuals affect their environments. We conducted the first genome-wide association (GWA) analysis of an environmental measure. We used a measure called CHAOS which assesses 'environmental confusion' in the home, a measure that is more strongly associated with cognitive development in childhood than any other environmental measure. CHAOS was assessed by parental report when the children were 3 years and again when the children were 4 years; a composite CHAOS measure was constructed across the 2 years. We screened 490,041 autosomal single-nucleotide polymorphisms (SNPs) in a two-stage design in which children in low chaos families (N = 469) versus high chaos families (N = 369) from 3,000 families of 4-year-old twins were screened in Stage 1 using pooled DNA. In Stage 2, following SNP quality control procedures, 41 nominated SNPs were tested for association with family chaos by individual genotyping an independent representative sample of 3,529. Despite having 99% power to detect associations that account for more than 0.5% of the variance, none of the 41 nominated SNPs met conservative criteria for replication. Similar to GWA analyses of other complex traits, it is likely that most of the heritable variation in environmental measures such as family chaos is due to many genes of very small effect size.

Complex aetiology of an apparently Mendelian form of mental retardation

Background

Mental Retardation is a common heterogeneous neurodevelopment condition, which causes are still largely elusive. It has been suggested that half of the phenotypic variation of intelligence is explained by genetic variation. And genetic or inherited factors indeed account for most of the cases of mental retardation with an identifiable cause. However, only a few autosomal genes have been mapped and identified to date. In this report, the genetic causes for an apparently recessive form of mental retardation, in a large nordern swedish pedigree, are investigated.

Methods

After extensive evaluation of the patients, which ruled out recognizable patterns of malformation and excluded known causes of MR, a comprehensive genome-wide linkage analysis, with 500 microsatellite markers, was performed in 24 members of this family. Additionally, a genome-wide copy number analysis, using an affimetrix 250 K SNP chip, was performed in this pedigree.

Results

No significant LOD score was found with either parametric and non-parametric linkage analysis. The highest scores are located at chromosomes 13, 15 and 17. Genome-wide copy number analysis identified no clear cause for the disorder; but rather, several variants were present in the family members, irrespective of their affected status.

Conclusion

These results suggest that mental retardation in this family, unlikely what was expected, has a heterogeneous aetiology; and that several lower effect genes variants might be involved. To demonstrate such effects, our family may be too small. This study also indicates that the ascertainment of the cause of MR may be challenging, and that a complex aetiology may be present even within a pedigree, constituting an additional obstacle for genetic counselling. Variants in genes involved in molecular mechanisms of cellular plasticity, in genes involved in the development of underlying neural architectures, and in genes involved in neurodevelopment and in the ongoing function of terminally differentiated neurons may underlie the phenotypic variation of intelligence and explain instances of intellectual impairment.

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.

"Higher order" addiction molecular genetics: convergent data from genome-wide association in humans and mice

Family, adoption and twin data each support substantial heritability for addictions. Most of this heritable influence is not substance-specific. The overlapping genetic vulnerability for developing dependence on a variety of addictive substances suggests large roles for "higher order" pharamacogenomics in addiction molecular genetics. We and others have now completed genome-wide association (GWA) studies of DNAs from individuals with dependence on a variety of addictive substances versus appropriate controls. Recently reported replicated GWA observations identify a number of genes based on comparisons between controls and European-American and African-American polysubstance abusers. Here we review the convergence between these results and data that compares control samples and (a) alcohol-dependent European-Americans, (b) methamphetamine-dependent Asians and (c) nicotine dependent samples from European backgrounds. We also compare these human data to quantitative trait locus (QTL) results from studies of addiction-related phenotypes in mice that focus on alcohol, methamphetamine and barbiturates. These comparisons support a genetic architecture built from largely polygenic contributions of common allelic variants to dependence on a variety of legal and illegal substances. Many of the gene variants identified in this way are likely to alter specification and maintenance of neuronal connections.

A genetic association analysis of cognitive ability and cognitive ageing using 325 markers for 109 genes associated with oxidative stress or cognition

Background

Non-pathological cognitive ageing is a distressing condition affecting an increasing number of people in our 'ageing society'. Oxidative stress is hypothesised to have a major role in cellular ageing, including brain ageing.

Results

Associations between cognitive ageing and 325 single nucleotide polymorphisms (SNPs), located in 109 genes implicated in oxidative stress and/or cognition, were examined in a unique cohort of relatively healthy older people, on whom we have cognitive ability scores at ages 11 and 79 years (LBC1921). SNPs showing a significant positive association were then genotyped in a second cohort for whom we have cognitive ability scores at the ages of 11 and 64 years (ABC1936). An intronic SNP in the APP gene (rs2830102) was significantly associated with cognitive ageing in both LBC1921 and a combined LBC1921/ABC1936 analysis (p < 0.01), but not in ABC1936 alone.

Conclusion

This study suggests a possible role for APP in normal cognitive ageing, in addition to its role in Alzheimer's disease.

Sex Differences in Childhood Associations between DNA Markers and General Cognitive Ability

Abstract. An association between intelligence at age 7 and a set of five single-nucleotide polymorphisms (SNPs) has been identified and replicated. We used this composite SNP set to investigate whether the associations differ between boys and girls for general cognitive ability at ages 2, 3, 4, 7, 9, and 10 years. In a longitudinal community sample of British twins aged 2-10 (n > 4,000 individuals), we found that the SNP set is more strongly associated with intelligence in males than in females at ages 7, 9, and 10 and the difference is significant at 10. If this finding replicates in other studies, these results will constitute the first evidence of the same autosomal genes acting differently on intelligence in the two sexes.

Calmodulin-binding transcription activator 1 ( CAMTA1 ) alleles predispose human episodic memory performance

Little is known about the genes and proteins involved in the process of human memory. To identify genetic factors related to human episodic memory performance, we conducted an ultra-high-density genome-wide screen at > 500 000 single nucleotide polymorphisms (SNPs) in a sample of normal young adults stratified for performance on an episodic recall memory test. Analysis of this data identified SNPs within the calmodulin-binding transcription activator 1 (CAMTA1) gene that were significantly associated with memory performance. A follow up study, focused on the CAMTA1 locus in an independent cohort consisting of cognitively normal young adults, singled out SNP rs4908449 with a P-value of 0.0002 as the most significant associated SNP in the region. These validated genetic findings were further supported by the identification of CAMTA1 transcript enrichment in memory-related human brain regions and through a functional magnetic resonance imaging experiment on individuals matched for memory performance that identified CAMTA1 allele-specific upregulation of medial temporal lobe brain activity in those individuals harboring the 'at-risk' allele for poorer memory performance. The CAMTA1 locus encodes a purported transcription factor that interfaces with the calcium-calmodulin system of the cell to alter gene expression patterns. Our validated genomic and functional biological findings described herein suggest a role for CAMTA1 in human episodic memory.

A generic research paradigm for identification and validation of early molecular diagnostics and new therapeutics in common disorders

Genetically complex disorders continue to confound investigators because of their many underlying factors, both genetic and environmental. In order to tease apart the heritable from the non-heritable contributions to disease, clinicians are relying on researchers in the rapidly expanding fields of high-throughput genomics to identify surrogate clinical endpoints, called biomarkers, that provide a measure of the probability that an individual will succumb to the disease in question. The goals of current biomedical research into complex disorders are to identify and utilize these biomarkers, not only for early detection, but also for personalized treatment with knowledge-guided therapeutics. As the identification of these biomarkers is basically a problem of discovery, we discuss new insights into biomarker detection utilizing the most current genomic technologies available. Additionally, we present here a generic paradigm for the validation of such molecular diagnostics as well as new treatment modalities for complex and increasingly common diseases. Lastly, we delve into the ways genomic biomarkers might be implemented in a clinical setting to allow the subsequent application of targeted therapeutics, which can help the ever expanding groups of individuals experiencing these insidious diseases.

Molecular genetics of nicotine dependence and abstinence: whole genome association using 520,000 SNPs

BACKGROUND

Classical genetic studies indicate that nicotine dependence is a substantially heritable complex disorder. Genetic vulnerabilities to nicotine dependence largely overlap with genetic vulnerabilities to dependence on other addictive substances. Successful abstinence from nicotine displays substantial heritable components as well. Some of the heritability for the ability to quit smoking appears to overlap with the genetics of nicotine dependence and some does not. We now report genome wide association studies of nicotine dependent individuals who were successful in abstaining from cigarette smoking, nicotine dependent individuals who were not successful in abstaining and ethnically-matched control subjects free from substantial lifetime use of any addictive substance.

RESULTS

These data, and their comparison with data that we have previously obtained from comparisons of four other substance dependent vs control samples support two main ideas: 1) Single nucleotide polymorphisms (SNPs) whose allele frequencies distinguish nicotine-dependent from control individuals identify a set of genes that overlaps significantly with the set of genes that contain markers whose allelic frequencies distinguish the four other substance dependent vs control groups (p < 0.018). 2) SNPs whose allelic frequencies distinguish successful vs unsuccessful abstainers cluster in small genomic regions in ways that are highly unlikely to be due to chance (Monte Carlo p < 0.00001).

CONCLUSION

These clustered SNPs nominate candidate genes for successful abstinence from smoking that are implicated in interesting functions: cell adhesion, enzymes, transcriptional regulators, neurotransmitters and receptors and regulation of DNA, RNA and proteins. As these observations are replicated, they will provide an increasingly-strong basis for understanding mechanisms of successful abstinence, for identifying individuals more or less likely to succeed in smoking cessation efforts and for tailoring therapies so that genotypes can help match smokers with the treatments that are most likely to benefit them.

Identification of PVT1 as a candidate gene for end-stage renal disease in type 2 diabetes using a pooling-based genome-wide single nucleotide polymorphism association study

To identify genetic variants contributing to end-stage renal disease (ESRD) in type 2 diabetes, we performed a genome-wide analysis of 115,352 single nucleotide polymorphisms (SNPs) in pools of 105 unrelated case subjects with ESRD and 102 unrelated control subjects who have had type 2 diabetes for > or =10 years without macroalbuminuria. Using a sliding window statistic of ranked SNPs, we identified a 200-kb region on 8q24 harboring three SNPs showing substantial differences in allelic frequency between case and control pools. These SNPs were genotyped in individuals comprising each pool, and strong evidence for association was found with rs2720709 (P = 0.000021; odds ratio 2.57 [95% CI 1.66-3.96]), which is located in the plasmacytoma variant translocation gene PVT1. We sequenced all exons, exon-intron boundaries, and the promoter of PVT1 and identified 47 variants, 11 of which represented nonredundant markers with minor allele frequency > or =0.05. We subsequently genotyped these 11 variants and an additional 87 SNPs identified through public databases in 319-kb flanking rs2720709 ( approximately 1 SNP/3.5 kb); 23 markers were associated with ESRD at P < 0.01. The strongest evidence for association was found for rs2648875 (P = 0.0000018; 2.97 [1.90-4.65]), which maps to intron 8 of PVT1. Together, these results suggest that PVT1 may contribute to ESRD susceptibility in diabetes.

Identification of the genetic basis for complex disorders by use of pooling-based genomewide single-nucleotide-polymorphism association studies

We report the development and validation of experimental methods, study designs, and analysis software for pooling-based genomewide association (GWA) studies that use high-throughput single-nucleotide-polymorphism (SNP) genotyping microarrays. We first describe a theoretical framework for establishing the effectiveness of pooling genomic DNA as a low-cost alternative to individually genotyping thousands of samples on high-density SNP microarrays. Next, we describe software called "GenePool," which directly analyzes SNP microarray probe intensity data and ranks SNPs by increased likelihood of being genetically associated with a trait or disorder. Finally, we apply these methods to experimental case-control data and demonstrate successful identification of published genetic susceptibility loci for a rare monogenic disease (sudden infant death with dysgenesis of the testes syndrome), a rare complex disease (progressive supranuclear palsy), and a common complex disease (Alzheimer disease) across multiple SNP genotyping platforms. On the basis of these theoretical calculations and their experimental validation, our results suggest that pooling-based GWA studies are a logical first step for determining whether major genetic associations exist in diseases with high heritability.

Addiction molecular genetics: 639,401 SNP whole genome association identifies many "cell adhesion" genes

Addictions are substantially heritable complex disorders. We now report whole genome association studies that identify 89 genes likely to contain variants that contribute to addiction vulnerability, using previously- and newly-validated microarray based pooling assays. Each gene contains clustered single nucleotide polymorphisms (SNPs) that display significant allele frequency differences between abusers and controls in each of the two samples studied with 639,401 SNP arrays and confirmatory SNPs from each of two other abuser/control samples. These genes are implicated in interesting functions, including "cell adhesion" processes that help to establish and maintain neuronal connections of special relevance to addiction's memory-like features.

Matrix-assisted laser desorption/ionisation, time-of-flight mass spectrometry in genomics research

The beginning of this millennium has seen dramatic advances in genomic research. Milestones such as the complete sequencing of the human genome and of many other species were achieved and complemented by the systematic discovery of variation at the single nucleotide (SNP) and whole segment (copy number polymorphism) level. Currently most genomics research efforts are concentrated on the production of whole genome functional annotations, as well as on mapping the epigenome by identifying the methylation status of CpGs, mainly in CpG islands, in different tissues. These recent advances have a major impact on the way genetic research is conducted and have accelerated the discovery of genetic factors contributing to disease. Technology was the critical driving force behind genomics projects: both the combination of Sanger sequencing with high-throughput capillary electrophoresis and the rapid advances in microarray technologies were keys to success. MALDI-TOF MS–based genome analysis represents a relative newcomer in this field. Can it establish itself as a long-term contributor to genetics research, or is it only suitable for niche areas and for laboratories with a passion for mass spectrometry? In this review, we will highlight the potential of MALDI-TOF MS–based tools for resequencing and for epigenetics research applications, as well as for classical complex genetic studies, allele quantification, and quantitative gene expression analysis. We will also identify the current limitations of this approach and attempt to place it in the context of other genome analysis technologies.

Analysis of pooled DNA samples on high density arrays without prior knowledge of differential hybridization rates

Array based DNA pooling techniques facilitate genome-wide scale genotyping of large samples. We describe a structured analysis method for pooled data using internal replication information in large scale genotyping sets. The method takes advantage of information from single nucleotide polymorphisms (SNPs) typed in parallel on a high density array to construct a test statistic with desirable statistical properties. We utilize a general linear model to appropriately account for the structured multiple measurements available with array data. The method does not require the use of additional arrays for the estimation of unequal hybridization rates and hence scales readily to accommodate arrays with several hundred thousand SNPs. Tests for differences between cases and controls can be conducted with very few arrays. We demonstrate the method on 384 endometriosis cases and controls, typed using Affymetrix Genechip© HindIII 50 K arrays. For a subset of this data there were accurate measures of hybridization rates available. Assuming equal hybridization rates is shown to have a negligible effect upon the results. With a total of only six arrays, the method extracted one-third of the information (in terms of equivalent sample size) available with individual genotyping (requiring 768 arrays). With 20 arrays (10 for cases, 10 for controls), over half of the information could be extracted from this sample.

Generalist genes and cognitive neuroscience

Multivariate genetic research suggests that a single set of genes affects most cognitive abilities and disabilities. This finding already has far-reaching implications for cognitive neuroscience, and will become even more revealing when this - presumably large - set of generalist genes is identified. Similar to other complex disorders and dimensions, molecular genetic research on cognitive abilities and disabilities is adopting genome-wide association strategies. These strategies involve very large samples to detect DNA associations of small effect size using microarrays that simultaneously assess hundreds of thousands of DNA markers. When this set of generalist genes is identified, it can be used to provide solid footholds in the climb towards a systems-level understanding of how genetically driven brain processes work together to affect diverse cognitive abilities and disabilities.

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.

A behavioural genomic analysis of DNA markers associated with general cognitive ability in 7-year-olds

BACKGROUND

Five DNA markers (single-nucleotide polymorphisms, SNPs) have recently been found to be associated with general cognitive ability ('g') in a sample of 7414 7-year-old twins. These children have also been studied at 2, 3, 4, and 7 years of age on measures of cognitive and language development and behaviour problems; family environment was also assessed.

METHODS

We used these data to conduct a behavioural genomic analysis of the five SNPs and a composite of them ('SNP set') that explored developmental, multivariate, and genotype-environment (GE) issues.

RESULTS

The 'g' SNP set identified at 7 years yielded significant associations with 'g' as early as 2 years. In multivariate analyses at 7 years, the 'g' SNP set was more strongly associated with verbal than nonverbal ability and with reading more than mathematics performance. GE correlations were found between the SNP set for 'g' at 7 years and preschool proximal measures of the family environment (chaos and discipline) rather than distal measures (maternal education and father's occupational class), suggesting evocative rather than passive GE correlation. Significant GE interactions were found for discipline, education and occupation in which the association between the SNP set and 'g' at 7 years is stronger in low-risk environments.

CONCLUSIONS

Although the effect sizes of the five SNP associations are very small, behavioural genomic analyses using a 'g' SNP set illustrate how developmental, multivariate and GE questions can be addressed as more DNA associations are identified for complex traits such as 'g'.

Identification of disease causing loci using an array-based genotyping approach on pooled DNA

BACKGROUND

Pooling genomic DNA samples within clinical classes of disease followed by genotyping on whole-genome SNP microarrays, allows for rapid and inexpensive genome-wide association studies. Key to the success of these studies is the accuracy of the allelic frequency calculations, the ability to identify false-positives arising from assay variability and the ability to better resolve association signals through analysis of neighbouring SNPs.

RESULTS

We report the accuracy of allelic frequency measurements on pooled genomic DNA samples by comparing these measurements to the known allelic frequencies as determined by individual genotyping. We describe modifications to the calculation of k-correction factors from relative allele signal (RAS) values that remove biases and result in more accurate allelic frequency predictions. Our results show that the least accurate SNPs, those most likely to give false-positives in an association study, are identifiable by comparing their frequencies to both those from a known database of individual genotypes and those of the pooled replicates. In a disease with a previously identified genetic mutation, we demonstrate that one can identify the disease locus through the comparison of the predicted allelic frequencies in case and control pools. Furthermore, we demonstrate improved resolution of association signals using the mean of individual test-statistics for consecutive SNPs windowed across the genome. A database of k-correction factors for predicting allelic frequencies for each SNP, derived from several thousand individually genotyped samples, is provided. Lastly, a Perl script for calculating RAS values for the Affymetrix platform is provided.

CONCLUSION

Our results illustrate that pooling of DNA samples is an effective initial strategy to identify a genetic locus. However, it is important to eliminate inaccurate SNPs prior to analysis by comparing them to a database of individually genotyped samples as well as by comparing them to replicates of the pool. Lastly, detection of association signals can be improved by incorporating data from neighbouring SNPs.

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.

A genomewide scan for intelligence identifies quantitative trait loci on 2q and 6p

Between 40% and 80% of the variation in human intelligence (IQ) is attributable to genetic factors. Except for many rare mutations resulting in severe cognitive dysfunction, attempts to identify these factors have not been successful. We report a genomewide linkage scan involving 634 sibling pairs designed to identify chromosomal regions that explain variation in IQ. Model-free multipoint linkage analysis revealed evidence of a significant quantitative-trait locus for performance IQ at 2q24.1-31.1 (LOD score 4.42), which overlaps the 2q21-33 region that has repeatedly shown linkage to autism. A second region revealed suggestive linkage for both full-scale and verbal IQs on 6p25.3-22.3 (LOD score 3.20 for full-scale IQ and 2.33 for verbal IQ), overlapping marginally with the 6p22.3-21.31 region implicated in reading disability and dyslexia.

Genotyping DNA pools on microarrays: tackling the QTL problem of large samples and large numbers of SNPs

Background

Quantitative trait locus (QTL) theory predicts that genetic influence on complex traits involves multiple genes of small effect size. To detect QTL associations of small effect size, large samples and systematic screens of thousands of DNA markers are required. An efficient solution is to genotype case and control DNA pools using SNP microarrays. We demonstrate that this is practical using DNA pools of 100 individuals.

Results

Using standard microarray protocols for the Affymetrix GeneChip^® Mapping 10 K Array Xba 131, we show that relative allele signal (RAS) values provide a quantitative index of allele frequencies in pooled DNA that correlate 0.986 with allele frequencies for 104 SNPs that were genotyped individually for 100 individuals. The sensitivity of the assay was demonstrated empirically in a spiking experiment in which 15% and 20% of one individual's DNA was added to a DNA pool.

Conclusion

We conclude that this approach, which we call SNP-MaP (SNP microarrays and pooling), is rapid, cost effective and promises to be a valuable initial screening method in the hunt for QTLs.

SNPs, microarrays and pooled DNA: identification of four loci associated with mild mental impairment in a sample of 6000 children

Mild mental impairment (MMI) represents the low extreme of the quantitative trait of general intelligence and is highly heritable. Quantitative trait loci (QTLs) conferring susceptibility to MMI, as for most complex traits, are likely to be of small effect size. Using a novel approach we call SNP-MaP (SNP Microarrays and Pooling), we have identified four loci associated with MMI. These four loci have been replicated in two SNP-MaP studies and verified by individual genotyping. The two SNP-MaP studies conducted were a case versus control comparison (n = 515 and n = 1028, respectively) and a low versus high general intelligence extremes group comparison (n = 503 and n = 505, respectively). Each of the four groups consisted of five independent 'subpools', with each subpool assayed on a separate microarray. Twelve loci showing the largest significant differences in both SNP-MaP studies were individually genotyped on 6154 children. Of the four loci positively associated with MMI, the minor allele of each conferred the greater risk for MMI. Two of the loci are close to known genes and may be in linkage disequilibrium with them. One of the loci is between the candidate genes KLF7 and CREB1, but given possible long-range effects on expression and the unknown importance of untranslated elements such as micro-RNAs, all four loci deserve attention as candidates. Although each SNP accounts for a small amount of variance, their effects are additive and they can be combined in a 'SNP set' that can be used as a genetic risk index for MMI in behavioral genomic analyses.

A central resource for accurate allele frequency estimation from pooled DNA genotyped on DNA microarrays

Analysing pooled DNA on microarrays is an efficient way to genotype hundreds of individuals for thousands of markers for genome-wide association. Although direct comparison of case and control fluorescence scores is possible, correction for differential hybridization of alleles is important, particularly for rare single nucleotide polymorphisms. Such correction relies on heterozygous fluorescence scores and requires the genotyping of hundreds of individuals to obtain sufficient estimates of the correction factor, completely negating any benefit gained by pooling samples. We explore the effect of differential hybridization on test statistics and provide a solution to this problem in the form of a central resource for the accumulation of heterozygous fluorescence scores, allowing accurate allele frequency estimation at no extra cost.