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

A digital coding combination analysis for mutational genotyping using pyrosequencing

In the present study, we developed a novel digital coding combination analysis (DCCA) to analyze the gene mutation based on the sample combination principle. The principle is that any numerically named sample is divided into two groups, any two samples are not grouped in the same two groups, and any sample can be tested within the detection limit. Therefore, we proposed a specific combination that N samples were divided into M groups. Then N samples were analyzed, which could obtain the mutation results of M mixed groups. If only two groups showed positive (mutant type) signals, the same sample number from two positive signal groups would be the positive sample, and the remaining samples were negative (wild type). If three groups or more exhibited positive results, the same sample number from three positive signal groups would be the positive sample. If some samples remained uncertain, individual samples could be analyzed on a small scale. In the present study, we used the two genotypes of a mutation site (A5301G) to verify whether it was a useful and promising method. The results showed that we could quantitatively detect mutations and demonstrate 100% consistent results against a panel of defined mixtures with the detection limit using pyrosequencing. This method was suitable, sensitive, and reproducible for screening and analyzing low-frequency mutation samples, which could reduce reagent consumption and cost by approximately 70-80% compared with conventional clinical methods.

Evolution, genes, and inter‐disciplinary personality research

Most commentaries welcomed an evolutionary genetic approach to personality, but several raised concerns about our integrative model. In response, we clarify the scientific status of evolutionary genetic theory and explain the plausibility and value of our evolutionary genetic model of personality, despite some shortcomings with the currently available theories and data. We also have a closer look at mate choice for personality traits, point to promising ways to assess evolutionarily relevant environmental factors and defend higher‐order personality domains and the g‐factor as the best units for evolutionary genetic analyses. Finally, we discuss which extensions of and alternatives to our model appear most fruitful, and end with a call for more inter‐disciplinary personality research grounded in evolutionary theory. Copyright © 2007 John Wiley & Sons, Ltd.

A genome-wide association study for extremely high intelligence

We used a case-control genome-wide association (GWA) design with cases consisting of 1238 individuals from the top 0.0003 (~170 mean IQ) of the population distribution of intelligence and 8172 unselected population-based controls. The single-nucleotide polymorphism heritability for the extreme IQ trait was 0.33 (0.02), which is the highest so far for a cognitive phenotype, and significant genome-wide genetic correlations of 0.78 were observed with educational attainment and 0.86 with population IQ. Three variants in locus ADAM12 achieved genome-wide significance, although they did not replicate with published GWA analyses of normal-range IQ or educational attainment. A genome-wide polygenic score constructed from the GWA results accounted for 1.6% of the variance of intelligence in the normal range in an unselected sample of 3414 individuals, which is comparable to the variance explained by GWA studies of intelligence with substantially larger sample sizes. The gene family plexins, members of which are mutated in several monogenic neurodevelopmental disorders, was significantly enriched for associations with high IQ. This study shows the utility of extreme trait selection for genetic study of intelligence and suggests that extremely high intelligence is continuous genetically with normal-range intelligence in the population.

A Pooled Genome-Wide Association Study of Asperger Syndrome

Asperger Syndrome (AS) is a neurodevelopmental condition characterized by impairments in social interaction and communication, alongside the presence of unusually repetitive, restricted interests and stereotyped behaviour. Individuals with AS have no delay in cognitive and language development. It is a subset of Autism Spectrum Conditions (ASC), which are highly heritable and has a population prevalence of approximately 1%. Few studies have investigated the genetic basis of AS. To address this gap in the literature, we performed a genome-wide pooled DNA association study to identify candidate loci in 612 individuals (294 cases and 318 controls) of Caucasian ancestry, using the Affymetrix GeneChip Human Mapping version 6.0 array. We identified 11 SNPs that had a p-value below 1x10-5. These SNPs were independently genotyped in the same sample. Three of the SNPs (rs1268055, rs7785891 and rs2782448) were nominally significant, though none remained significant after Bonferroni correction. Two of our top three SNPs (rs7785891 and rs2782448) lie in loci previously implicated in ASC. However, investigation of the three SNPs in the ASC genome-wide association dataset from the Psychiatric Genomics Consortium indicated that these three SNPs were not significantly associated with ASC. The effect sizes of the variants were modest, indicating that our study was not sufficiently powered to identify causal variants with precision.

Commentary on "A Role for the X Chromosome in Sex Differences in Variability in General Intelligence?" (Johnson et al., 2009)

Johnson et al.'s (2009) article highlights the role of X-chromosomal genes in general intelligence and draws attention to their potential role in explaining the observed greater variance for this trait in males and their excess at both extremes of the distribution. We note that this would result from a simple additive effect of X-linked intelligence genes and also discuss the potentially important contribution of recessive deleterious loci. The buffering effect of heterozygosity in females will be partly constrained by the skewing of X-inactivation patterns increasing the variance of females beyond what is expected. Furthermore, escape of some X-linked genes from in-activation may also be relevant to male-female variance comparisons. We also comment on the difficulty of establishing the extent to which the X chromosome is enriched for intelligence genes and point out that their estimates of the proportion of genes influencing general intelligence that might be located on the X chromosome rely on some doubtful premises, especially concerning the likely equivalence of X-linked gene action in males and females. Finally, we discuss the increasingly compelling evidence for the accumulation of genes on the X chromosome that have selective benefit to males, including those implicated infertility and some manifestations of intelligence.

Comparison of genotyping using pooled DNA samples (allelotyping) and individual genotyping using the affymetrix genome-wide human SNP array 6.0

Background

Genome-wide association studies (GWAS) using array-based genotyping technology are widely used to identify genetic loci associated with complex diseases or other phenotypes. The costs of GWAS projects based on individual genotyping are still comparatively high and increase with the size of study populations. Genotyping using pooled DNA samples, as also being referred as to allelotyping approach, offers an alternative at affordable costs. In the present study, data from 100 DNA samples individually genotyped with the Affymetrix Genome-Wide Human SNP Array 6.0 were used to estimate the error of the pooling approach by comparing the results with those obtained using the same array type but DNA pools each composed of 50 of the same samples. Newly developed and established methods for signal intensity correction were applied. Furthermore, the relative allele intensity signals (RAS) obtained by allelotyping were compared to the corresponding values derived from individual genotyping. Similarly, differences in RAS values between pools were determined and compared.

Results

Regardless of the intensity correction method applied, the pooling-specific error of the pool intensity values was larger for single pools than for the comparison of the intensity values of two pools, which reflects the scenario of a case–control study. Using 50 pooled samples and analyzing 10,000 SNPs with a minor allele frequency of >1% and applying the best correction method for the corresponding type of comparison, the 90% quantile (median) of the pooling-specific absolute error of the RAS values for single sub-pools and the SNP-specific difference in allele frequency comparing two pools was 0.064 (0.026) and 0.056 (0.021), respectively.

Conclusions

Correction of the RAS values reduced the error of the RAS values when analyzing single pool intensities. We developed a new correction method with high accuracy but low computational costs. Correction of RAS, however, only marginally reduced the error of true differences between two sample groups and those obtained by allelotyping. Exclusion of SNPs with a minor allele frequency of ≤1% notably reduced the pooling-specific error. Our findings allow for improving the estimation of the pooling-specific error and may help in designing allelotyping studies using the Affymetrix Genome-Wide Human SNP Array 6.0.

Shared genetic factors for age at natural menopause in Iranian and European women

STUDY QUESTION

Do differences in heritable genetic factors explain some of the difference in age at natural menopause (ANM) among populations?

SUMMARY ANSWER

One single nucleotide polymorphism (SNP)-ANM association (rs16991615) detected in European women was replicated in Iranian women.

WHAT IS KNOWN ALREADY

Genetics plays an important role in ANM, and well-powered genome-wide association studies (GWAS) of ANM performed in European women have discovered many statistically significant SNP-ANM associations. Average ANM varies by ethnicity, and population-specific differences in ANM-associated alleles may in part explain these differences.

STUDY DESIGN, SIZE, DURATION

After quality control procedures, 97 SNPs were analyzed in genotype data of 828 Iranian women who experienced natural menopause. SNP genotyping data were used to perform linear regression analyses with ANM as a quantitative trait. Study participants were drawn from the population-based Tehran Lipid and Glucose Study based in Tehran, Iran. This study was performed between February 2009 and March 2012.

PARTICIPANTS/MATERIALS, SETTING AND METHODS

Based on an ANM-GWAS literature review, eight SNPs at four loci previously associated with ANM in European women were tested for replication in Iranian women. Linear regression analyses were performed including (n = 828) and excluding (n = 783) women who experience premature ovarian failure (ANM before 40 years of age). In addition, to search for novel population-specific ANM risk alleles, a pool-based GWAS was performed using this collection of Iranian women. Two DNA pools were constructed and compared: an 'early' ANM pool (lower 20(th) percentile of menopause ages, 40-45 years, n = 165) and a 'late' ANM pool (upper 20(th) percentile of menopause ages, 54-65 years, n = 187). Each DNA pool was assayed on four Illumina Human1M-Duo arrays, and allele-based tests of association were used to rank SNPs. One hundred and two highly ranked SNPs were chosen for individual genotyping by Sequenom MassARRAY and association analysis in the Iranian women.

MAIN RESULTS AND THE ROLE OF CHANCE

One SNP-ANM association previously detected in European women was replicated in Iranian women (rs16991615; β = 1.07, standard error (SE): 0.49, P = 0.02). SNPs at the previously reported 19q13.42 and 6p24.2 loci also approached statistical significance and had consistent SNP effects (magnitude and direction) in Iranian women (rs1172822; β = -0.39, SE: 0.22, P = 0.08; and rs2153157, β = 0.41, SE: 0.21, P = 0.05). We found little evidence for novel SNP-ANM associations in Iranian women; no SNP selected based on the pool-based GWAS achieved genome-wide significance.

LIMITATIONS, REASONS FOR CAUTION

Due to small sample size this study was powered to reliably detect only moderate-to-large SNP effect sizes. This limited our ability to replicate many of the previously reported SNP-ANM risk alleles and to discover novel SNP-ANM associations' specific to the Iranian population. In performing our pool-based GWAS, a reduction in power was introduced relative to a conventional GWAS.

WIDER IMPLICATIONS OF THE FINDINGS

Our results imply that European and Iranian women share ANM-associated genetic variants. Our study was underpowered but for all SNPs tested the direction of the effect was consistent with data from the European study. Therefore, we anticipate that many (if not all) of the ANM-associated SNPs discovered in European women will replicate in Iranian women upon genotyping a sufficient number of women. Our data do not support the hypothesis that population-specific SNP-ANM associations explain population-specific differences in the mean ANM.

Genome-wide DNA methylation and gene expression analyses of monozygotic twins discordant for intelligence levels

Human intelligence, as measured by intelligence quotient (IQ) tests, demonstrates one of the highest heritabilities among human quantitative traits. Nevertheless, studies to identify quantitative trait loci responsible for intelligence face challenges because of the small effect sizes of individual genes. Phenotypically discordant monozygotic (MZ) twins provide a feasible way to minimize the effects of irrelevant genetic and environmental factors, and should yield more interpretable results by finding epigenetic or gene expression differences between twins. Here we conducted array-based genome-wide DNA methylation and gene expression analyses using 17 pairs of healthy MZ twins discordant intelligently. ARHGAP18, related to Rho GTPase, was identified in pair-wise methylation status analysis and validated via direct bisulfite sequencing and quantitative RT-PCR. To perform expression profile analysis, gene set enrichment analysis (GSEA) between the groups of twins with higher IQ and their co-twins revealed up-regulated expression of several ribosome-related genes and DNA replication-related genes in the group with higher IQ. To focus more on individual pairs, we conducted pair-wise GSEA and leading edge analysis, which indicated up-regulated expression of several ion channel-related genes in twins with lower IQ. Our findings implied that these groups of genes may be related to IQ and should shed light on the mechanism underlying human intelligence.

Twins' Early Development Study (TEDS): A Multivariate, Longitudinal Genetic Investigation of Language, Cognition and Behavior Problems from Childhood Through Adolescence

The Twins' Early Development Study (TEDS) is a large-scale longitudinal study of twins from early childhood through adolescence. Since its conception, TEDS has had as its focus the study of problematic development within the context of normal variation, mainly in the development of language, cognitive and academic abilities and behavior problems from multivariate quantitative and molecular genetic perspectives. TEDS twins have been assessed at 2, 3, 4, 7, 9, 10 and (currently) 12 years of age, and DNA collected from more than 12,000 children. Identified from birth records of twins born in the United Kingdom between 1994 and 1996, more than 15,000 pairs of twins originally enrolled in TEDS, and well over 13,000 pairs — representative of the UK population — remain involved in the study to date. Similar to many other twin and adoption studies, TEDS data indicate that both genetic and environmental influences are important in nearly all areas of behavioral development. Multivariate genetic analyses allow researchers to go beyond this basic nature–nurture question, and TEDS results suggest that, especially in the area of learning abilities and disabilities, genes are generalists and environments are specialists. That is, genes largely contribute to similarity in performance within and between learning abilities and disabilities and across age, whereas the environment contributes to differences in performance. Quantitative genetic findings such as these chart the course for molecular genetic research. The TEDS dataset is proving valuable in genome-wide association research that tries to identify some of the many genes responsible for the ubiquitous heritability of behavior.

The efficacy of detecting variants with small effects on the Affymetrix 6.0 platform using pooled DNA

Genome-wide genotyping of a cohort using pools rather than individual samples has long been proposed as a cost-saving alternative for performing genome-wide association (GWA) studies. However, successful disease gene mapping using pooled genotyping has thus far been limited to detecting common variants with large effect sizes, which tend not to exist for many complex common diseases or traits. Therefore, for DNA pooling to be a viable strategy for conducting GWA studies, it is important to determine whether commonly used genome-wide SNP array platforms such as the Affymetrix 6.0 array can reliably detect common variants of small effect sizes using pooled DNA. Taking obesity and age at menarche as examples of human complex traits, we assessed the feasibility of genome-wide genotyping of pooled DNA as a single-stage design for phenotype association. By individually genotyping the top associations identified by pooling, we obtained a 14- to 16-fold enrichment of SNPs nominally associated with the phenotype, but we likely missed the top true associations. In addition, we assessed whether genotyping pooled DNA can serve as an inexpensive screen as the second stage of a multi-stage design with a large number of samples by comparing the most cost-effective 3-stage designs with 80% power to detect common variants with genotypic relative risk of 1.1, with and without pooling. Given the current state of the specific technology we employed and the associated genotyping costs, we showed through simulation that a design involving pooling would be 1.07 times more expensive than a design without pooling. Thus, while a significant amount of information exists within the data from pooled DNA, our analysis does not support genotyping pooled DNA as a means to efficiently identify common variants contributing small effects to phenotypes of interest. While our conclusions were based on the specific technology and study design we employed, the approach presented here will be useful for evaluating the utility of other or future genome-wide genotyping platforms in pooled DNA studies.

The ATXN1 and TRIM31 genes are related to intelligence in an ADHD background: evidence from a large collaborative study totaling 4,963 subjects

Intelligence is a highly heritable trait for which it has proven difficult to identify the actual genes. In the past decade, five whole-genome linkage scans have suggested genomic regions important to human intelligence; however, so far none of the responsible genes or variants in those regions have been identified. Apart from these regions, a handful of candidate genes have been identified, although most of these are in need of replication. The recent growth in publicly available data sets that contain both whole genome association data and a wealth of phenotypic data, serves as an excellent resource for fine mapping and candidate gene replication. We used the publicly available data of 947 families participating in the International Multi-Centre ADHD Genetics (IMAGE) study to conduct an in silico fine mapping study of previously associated genomic locations, and to attempt replication of previously reported candidate genes for intelligence. Although this sample was ascertained for attention deficit/hyperactivity disorder (ADHD), intelligence quotient (IQ) scores were distributed normally. We tested 667 single nucleotide polymorphisms (SNPs) within 15 previously reported candidate genes for intelligence and 29451 SNPs in five genomic loci previously identified through whole genome linkage and association analyses. Significant SNPs were tested in four independent samples (4,357 subjects), one ascertained for ADHD, and three population-based samples. Associations between intelligence and SNPs in the ATXN1 and TRIM31 genes and in three genomic locations showed replicated association, but only in the samples ascertained for ADHD, suggesting that these genetic variants become particularly relevant to IQ on the background of a psychiatric disorder.

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.

A simple method using PyrosequencingTM to identify de novo SNPs in pooled DNA samples

A practical way to reduce the cost of surveying single-nucleotide polymorphism (SNP) in a large number of individuals is to measure the allele frequencies in pooled DNA samples. Pyrosequencing^TM has been frequently used for this application because signals generated by this approach are proportional to the amount of DNA templates. The Pyrosequencing^TM pyrogram is determined by the dispensing order of dNTPs, which is usually designed based on the known SNPs to avoid asynchronistic extensions of heterozygous sequences. Therefore, utilizing the pyrogram signals to identify de novo SNPs in DNA pools has never been undertook. Here, in this study we developed an algorithm to address this issue. With the sequence and pyrogram of the wild-type allele known in advance, we could use the pyrogram obtained from the pooled DNA sample to predict the sequence of the unknown mutant allele (de novo SNP) and estimate its allele frequency. Both computational simulation and experimental Pyrosequencing^TM test results suggested that our method performs well. The web interface of our method is available at http://life.nctu.edu.tw/∼yslin/PSM/.

An alternative to the search for single polymorphisms: toward molecular personality scales for the five-factor model

There is growing evidence that personality traits are affected by many genes, all of which have very small effects. As an alternative to the largely unsuccessful search for individual polymorphisms associated with personality traits, the authors identified large sets of potentially related single nucleotide polymorphisms (SNPs) and summed them to form molecular personality scales (MPSs) with from 4 to 2,497 SNPs. Scales were derived from two thirds of a large (N = 3,972) sample of individuals from Sardinia who completed the Revised NEO Personality Inventory (P. T. Costa, Jr., & R. R. McCrae, 1992) and were assessed in a genomewide association scan. When MPSs were correlated with the phenotype in the remaining one third of the sample, very small but significant associations were found for 4 of the 5e personality factors when the longest scales were examined. These data suggest that MPSs for Neuroticism, Openness to Experience, Agreeableness, and Conscientiousness (but not Extraversion) contain genetic information that can be refined in future studies, and the procedures described here should be applicable to other quantitative traits.

Utility of the pooling approach as applied to whole genome association scans with high-density Affymetrix microarrays

Background

We report an attempt to extend the previously successful approach of combining SNP (single nucleotide polymorphism) microarrays and DNA pooling (SNP-MaP) employing high-density microarrays. Whereas earlier studies employed a range of Affymetrix SNP microarrays comprising from 10 K to 500 K SNPs, this most recent investigation used the 6.0 chip which displays 906,600 SNP probes and 946,000 probes for the interrogation of CNVs (copy number variations). The genotyping assay using the Affymetrix SNP 6.0 array is highly demanding on sample quality due to the small feature size, low redundancy, and lack of mismatch probes.

Findings

In the first study published so far using this microarray on pooled DNA, we found that pooled cheek swab DNA could not accurately predict real allele frequencies of the samples that comprised the pools. In contrast, the allele frequency estimates using blood DNA pools were reasonable, although inferior compared to those obtained with previously employed Affymetrix microarrays. However, it might be possible to improve performance by developing improved analysis methods.

Conclusions

Despite the decreasing costs of genome-wide individual genotyping, the pooling approach may have applications in very large-scale case-control association studies. In such cases, our study suggests that high-quality DNA preparations and lower density platforms should be preferred.

GPFrontend and GPGraphics: graphical analysis tools for genetic association studies

Background

Most software packages for whole genome association studies are non-graphical, purely text based programs originally designed to run with UNIX-like operating systems. Graphical output is often not intended or supposed to be performed with other command line tools, e.g. gnuplot.

Results

Using the Microsoft .NET 2.0 platform and Visual Studio 2005, we have created a graphical software package to analyze data from microarray whole genome association studies, both for a DNA-pooling based approach as well as regular single sample data. Part of this package was made to integrate with GenePool 0.8.2, a previously existing software suite for GNU/Linux systems, which we have modified to run in a Microsoft Windows environment. Further modifications cause it to generate some additional data. This enables GenePool to interact with the .NET parts created by us. The programs we developed are GPFrontend, a graphical user interface and frontend to use GenePool and create metadata files for it, and GPGraphics, a program to further analyze and graphically evaluate output of different WGA analysis programs, among them also GenePool.

Conclusions

Our programs enable regular MS Windows users without much experience in bioinformatics to easily visualize whole genome data from a variety of sources.

Human intelligence and polymorphisms in the DNA methyltransferase genes involved in epigenetic marking

Epigenetic mechanisms have been implicated in syndromes associated with mental impairment but little is known about the role of epigenetics in determining the normal variation in human intelligence. We measured polymorphisms in four DNA methyltransferases (DNMT1, DNMT3A, DNMT3B and DNMT3L) involved in epigenetic marking and related these to childhood and adult general intelligence in a population (n = 1542) consisting of two Scottish cohorts born in 1936 and residing in Lothian (n = 1075) or Aberdeen (n = 467). All subjects had taken the same test of intelligence at age 11yrs. The Lothian cohort took the test again at age 70yrs. The minor T allele of DNMT3L SNP 11330C>T (rs7354779) allele was associated with a higher standardised childhood intelligence score; greatest effect in the dominant analysis but also significant in the additive model (coefficient = 1.40_additive; 95%CI 0.22,2.59; p = 0.020 and 1.99_dominant; 95%CI 0.55,3.43; p = 0.007). The DNMT3L C allele was associated with an increased risk of being below average intelligence (OR 1.25_additive; 95%CI 1.05,1.51; p = 0.011 and OR 1.37_dominant; 95%CI 1.11,1.68; p = 0.003), and being in the lowest 40^th (p_additive = 0.009; p_dominant = 0.002) and lowest 30^th (p_additive = 0.004; p_dominant = 0.002) centiles for intelligence. After Bonferroni correction for the number variants tested the link between DNMT3L 11330C>T and childhood intelligence remained significant by linear regression and centile analysis; only the additive regression model was borderline significant. Adult intelligence was similarly linked to the DNMT3L variant but this analysis was limited by the numbers studied and nature of the test and the association was not significant after Bonferroni correction. We believe that the role of epigenetics in the normal variation in human intelligence merits further study and that this novel finding should be tested in other cohorts.

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.

Genetics and Cognition

How genes contribute to cognition is a perennial question for psychologists and geneticists. In the early 21st century, familial studies, including twin studies, supported the theory that genetic variations contribute to differences in cognition, but have been of little practical use to clinical and educational practitioners as no individual predictions can be made using such data; heritability cannot predict the impact of environmental factors or intervention programs. With the sequencing of animal genomes and the development of molecular genetics, new methodologies have been developed: gene targeting (replacing a functional gene with a neutral gene by homologous recombination), transgenesis (overexpressing one gene or a set of genes from one species in another species), and genome-wide scans and quantitative trait loci mapping (a strategy for identifying chromosomal regions involved in complex traits). Association studies can be performed to find associations between allelic forms and variations in IQ. Genes linked to “normal” variations in cognition have been detected but for the moment such discoveries have had no direct applications in a clinical setting; the number of genes identified as being linked to intellectual impairment has increased rapidly. Links have been reported between chromosomal deletions and triplications and behavioral phenotypes. The identification of mechanisms involved in genetic diseases should have long-term consequences on educational and/or psychological support programs as well as on health care. Psychologists need to keep up to date on advances in research establishing relationships between genetics and intellectual disability and will thus be able to refer children with cognitive impairments to specialized care services.

Challenges in phenotype definition in the whole-genome era: multivariate models of memory and intelligence

Refining phenotypes for the study of neuropsychiatric disorders is of paramount importance in neuroscience. Poor phenotype definition provides the greatest obstacle for making progress in disorders like schizophrenia, bipolar disorder, Attention Deficit/Hyperactivity Disorder (ADHD), and autism. Using freely available informatics tools developed by the Consortium for Neuropsychiatric Phenomics (CNP), we provide a framework for defining and refining latent constructs used in neuroscience research and then apply this strategy to review known genetic contributions to memory and intelligence in healthy individuals. This approach can help us begin to build multi-level phenotype models that express the interactions between constructs necessary to understand complex neuropsychiatric diseases. These results are available online through the http://www.phenowiki.org database. Further work needs to be done in order to provide consensus-building applications for the broadly defined constructs used in neuroscience research.

A comparison of association statistics between pooled and individual genotypes

BACKGROUND

Markers for individual genotyping can be selected using quantitative genotyping of pooled DNA. This strategy saves time and money.

METHODS

To determine the efficacy of this approach, we investigated the bivariate distribution of association test statistics from pooled and individual genotypes. We used a sample of approximately 1,000 samples with individual and pooled genotyping on 40,000 SNPs.

RESULTS AND CONCLUSIONS

We found that the distribution of the joint test statistics can be modelled as a mixture of two bivariate normal distributions. One distribution has a correlation of zero, and is probably due to SNPs whose pooled genotyping was unsuccessful. The other distribution has a correlation of approximately 0.65 in our data. This latter distribution is probably accounted for by SNPs whose pooled genotyping accurately predicts the underlying allele frequency. Approximately 87% of the data belongs to this distribution. We also derived a method to investigate the effect of both the correlation and selection cut-off on the relative power of pooling studies. We demonstrate that pooled genotyping has good power to detect SNPs that are truly associated with disease-causing variants for SNPs showing good correlation between pooled and individual genotyping. Therefore, this approach is a cost effective tool for association studies.

Phenomics: the systematic study of phenotypes on a genome-wide scale

Phenomics is an emerging transdiscipline dedicated to the systematic study of phenotypes on a genome-wide scale. New methods for high-throughput genotyping have changed the priority for biomedical research to phenotyping, but the human phenome is vast and its dimensionality remains unknown. Phenomics research strategies capable of linking genetic variation to public health concerns need to prioritize development of mechanistic frameworks that relate neural systems functioning to human behavior. New approaches to phenotype definition will benefit from crossing neuropsychiatric syndromal boundaries, and defining phenotypic features across multiple levels of expression from proteome to syndrome. The demand for high throughput phenotyping may stimulate a migration from conventional laboratory to web-based assessment of behavior, and this offers the promise of dynamic phenotyping-the iterative refinement of phenotype assays based on prior genotype-phenotype associations. Phenotypes that can be studied across species may provide greatest traction, particularly given rapid development in transgenic modeling. Phenomics research demands vertically integrated research teams, novel analytic strategies and informatics infrastructure to help manage complexity. The Consortium for Neuropsychiatric Phenomics at UCLA has been supported by the National Institutes of Health Roadmap Initiative to illustrate these principles, and is developing applications that may help investigators assemble, visualize, and ultimately test multi-level phenomics hypotheses. As the transdiscipline of phenomics matures, and work is extended to large-scale international collaborations, there is promise that systematic new knowledge bases will help fulfill the promise of personalized medicine and the rational diagnosis and treatment of neuropsychiatric syndromes.

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.

Quantitative trait locus association scan of early reading disability and ability using pooled DNA and 100K SNP microarrays in a sample of 5760 children

Quantitative genetic research suggests that reading disability is the quantitative extreme of the same genetic and environmental factors responsible for normal variation in reading ability. This finding warrants a quantitative trait locus (QTL) strategy that compares low versus high extremes of the normal distribution of reading in the search for QTLs associated with variation throughout the distribution. A low reading ability group (N=755) and a high reading group (N=747) were selected from a representative UK sample of 7-year-olds assessed on two measures of reading that we have shown to be highly heritable and highly genetically correlated. The low and high reading ability groups were each divided into 10 independent DNA pools and the 20 pools were assayed on 100 K single nucleotide polymorphism (SNP) microarrays to screen for the largest allele frequency differences between the low and high reading ability groups. Seventy five of these nominated SNPs were individually genotyped in an independent sample of low (N=452) and high (N=452) reading ability children selected from a second sample of 4258 7-year-olds. Nine of the seventy-five SNPs were nominally significant (P<0.05) in the predicted direction. These 9 SNPs and 14 other SNPs showing low versus high allele frequency differences in the predicted direction were genotyped in the rest of the second sample to test the QTL hypothesis. Ten SNPs yielded nominally significant linear associations in the expected direction across the distribution of reading ability. However, none of these SNP associations accounted for more than 0.5% of the variance of reading ability, despite 99% power to detect them. We conclude that QTL effect sizes, even for highly heritable common disorders and quantitative traits such as early reading disability and ability, might be much smaller than previously considered.

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.

A whole genome association study of neuroticism using DNA pooling

We describe a multistage approach to identify single nucleotide polymorphisms (SNPs) associated with neuroticism, a personality trait that shares genetic determinants with major depression and anxiety disorders. Whole genome association with 452 574 SNPs was performed on DNA pools from approximately 2000 individuals selected on extremes of neuroticism scores from a cohort of 88 142 people from southwest England. The most significant SNPs were then genotyped on independent samples to replicate findings. We were able to replicate association of one SNP within the PDE4D gene in a second sample collected by our laboratory and in a family-based test in an independent sample; however, the SNP was not significantly associated with neuroticism in two other independent samples. We also observed an enrichment of low P-values in known regions of copy number variations. Simulation indicates that our study had approximately 80% power to identify neuroticism loci in the genome with odds ratio (OR)>2, and approximately 50% power to identify small effects (OR=1.5). Since we failed to find any loci accounting for more than 1% of the variance, the heritability of neuroticism probably arises from many loci each explaining much less than 1%. Our findings argue the need for much larger samples than anticipated in genetic association studies and that the biological basis of emotional disorders is extremely complex.

Neuroimaging findings in children with rare or novel de novo chromosomal anomalies

BACKGROUND

De novo constitutional chromosomal anomalies provide important insights into the genetic loci responsible for congenital neurological disorders. However, most phenotypic descriptions of patients with rare chromosomal abnormalities are published as individual case reports or small group studies, making genotype-phenotype correlations unclear. Moreover, many clinical genetic reports do not include neuroimaging.

METHODS

We conducted a retrospective case series study of all children who had genetic testing done at Children's Memorial Hospital in Chicago, Illinois between 1985 and 2006. The case series was selected from a database containing all chromosomal testing results, clinical data, and neuroimaging. Clinical examination results were assigned by board certified geneticists and/or neurologists and neuroimages were reviewed by both a neurologist or neuroradiologist and a blinded neurologist.

RESULTS

Of the 28,108 children in the series, we identified 34 children with novel or apparently novel de novo chromosomal abnormalities. Several of the cases represent potentially new genetic loci for neurological malformations and novel syndromic conditions.

CONCLUSIONS

This study demonstrates the utility of large clinical databases in assessing genotype-phenotype correlations and mapping loci for congenital neurological disorders. We describe a case-series strategy to analyze existing databases to reveal new genotype-phenotype correlations.

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 approximately 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 approximately 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.

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.

Generalist genes and the Internet generation: etiology of learning abilities by web testing at age 10

A key translational issue for neuroscience is to understand how genes affect individual differences in brain function. Although it is reasonable to suppose that genetic effects on specific learning abilities, such as reading and mathematics, as well as general cognitive ability (g), will overlap very little, the counterintuitive finding emerging from multivariate genetic studies is that the same genes affect these diverse learning abilities: a Generalist Genes hypothesis. To conclusively test this hypothesis, we exploited the widespread access to inexpensive and fast Internet connections in the UK to assess 2541 pairs of 10-year-old twins for reading, mathematics and g, using a web-based test battery. Heritabilities were 0.38 for reading, 0.49 for mathematics and 0.44 for g. Multivariate genetic analysis showed substantial genetic correlations between learning abilities: 0.57 between reading and mathematics, 0.61 between reading and g, and 0.75 between mathematics and g, providing strong support for the Generalist Genes hypothesis. If genetic effects on cognition are so general, the effects of these genes on the brain are also likely to be general. In this way, generalist genes may prove invaluable in integrating top-down and bottom-up approaches to the systems biology of the brain.

Association mapping using pooled DNA

The genetic dissection of complex disorders via genetic marker data has gained popularity in the postgenome era. Methods for typing genetic markers on human chromosomes continue to improve. Compared with the popular individual genotyping experiment, a pooled-DNA experiment (alleotyping experiment) is more cost effective when carrying out genetic typing. This chapter provides an overview of association mapping using pooled DNA and describes a five-stage study design including the preliminary calibration of peak intensities, estimation of allele frequency, single-locus association mapping, multilocus association mapping, and a confirmation study. Software and an analysis of authentic data are presented. The strengths and weaknesses of pooled-DNA analyses, as well as possible future applications for this method, are discussed.

"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.

Applicability of DNA pools on 500 K SNP microarrays for cost-effective initial screens in genomewide association studies

Background

Genetic influences underpinning complex traits are thought to involve multiple quantitative trait loci (QTLs) of small effect size. Detection of such QTL associations requires systematic screening of large numbers of DNA markers within large sample populations. Using pooled DNA on SNP microarrays to screen for allelic frequency differences between groups such as cases and controls (called SNP Microarray and Pooling, or SNP-MaP) has been validated as an efficient solution on both 10 k and 100 k platforms. We demonstrate that this approach can be effectively applied to the truly genomewide Affymetrix GeneChip^® Mapping 500 K Array.

Results

In comparisons between five independent DNA pools (N ~200 per pool) on separate Affymetrix GeneChip^® Mapping 500 K Array sets, we show that, for SNPs with minor allele frequencies > 0.05, the reliability of the rank order of estimated allele frequencies, assessed as the average correlation between allele frequency estimates across the DNA pools, was 0.948 (average mean difference across the five pools = 0.069). Similarly, validity of the SNP-MaP approach was demonstrated by a rank-order correlation of 0.937 (average mean difference = 0.095) between the average DNA pool allele frequency estimates and the allele frequencies of an independent (CEPH) sample of 60 unrelated individually genotyped subjects.

Conclusion

We conclude that SNP-MaP can be extended for use on the Affymetrix GeneChip^® Mapping 500 K Array, providing a cost-effective, reliable and valid initial screen of 500 K SNP microarrays in genomewide association scans.

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.

Allelotyping of pooled DNA with 250 K SNP microarrays

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.

Microarrays

Microarrays are revolutionizing genetics by making it possible to genotype hundreds of thousands of DNA markers and to assess the expression (RNA transcripts) of all of the genes in the genome. Microarrays are slides the size of a postage stamp that contain millions of DNA sequences to which single-stranded DNA or RNA can hybridize. This miniaturization requires little DNA or RNA and makes the method fast and inexpensive; multiple assays of each target make the method highly accurate. DNA microarrays with hundreds of thousands of DNA markers have made it possible to conduct systematic scans of the entire genome to identify genetic associations with complex disorders or dimensions likely to be influenced by many genes of small effect size. RNA microarrays can provide snapshots of gene expression across all of the genes in the genome at any time in any tissue, which has far-reaching applications such as structural and functional 'genetic neuroimaging' and providing a biological basis for understanding environmental influence.

SNP-based chromosomal copy number ascertainment following multiple displacement whole-genome amplification

Whole genome amplification by multiple displacement amplification (MDA) offers investigators using precious genomic DNA samples a high fidelity method for amplifying nanogram quantities of DNA several thousandfold. This becomes especially important for the modemrn day genomics researcher who more and more commonly is applying today's genome scanning technologies to patient cohort samples collected years ago that are irrecoverable and invariably in short supply. We present evidence here that MDA-prepared genomic DNA includes artifacts of chromosomal copy number that resemble copy number polymorphisms (CNPs) upon analysis of the DNA on the Affymetrix 10K GeneChip. The study of CNPs in both health and disease is a rapidly growing area of research, however our current understanding of the relevance of CNPs is incomplete. Our data indicate that utilization of whole genome-amplified samples for analysis heavily reliant on accurate copy number retention could be confounded if the genomic DNA sample was subjected to MDA. We recommend that small amounts of patient cohort DNA stocks be set aside and not subjected to whole genome amplification in order to facilitate the unbiased determination of chromosomal copy numbers when desired.

Genetic regulation of plasma von Willebrand factor levels: quantitative trait loci analysis in a mouse model

BACKGROUND

The genetic factors responsible for the wide variation in plasma von Willebrand factor (VWF) levels observed among individuals are largely unknown, although these genes are also likely to contribute to variability in the severity of von Willebrand disease (VWD) and other bleeding and thrombotic disorders. We have previously mapped two genes contributing to the regulation of plasma VWF levels in mice (Mvwf1 on chromosome 11 and Mvwf2 on chromosome 6).

OBJECTIVE

To identify additional quantitative trait loci (QTL) contributing to the genetic regulation of murine plasma VWF levels.

METHODS

To map genetic loci contributing to the > 7-fold difference in plasma VWF levels between two mouse strains (A/J and CASA/RkJ), high-density individual genotyping and R/qtl analyses were applied to a previously generated set of approximately 200 F2 mice obtained from an intercross of these two inbred lines.

RESULTS

Genomic loci for two additional candidate VWF modifier genes were identified: Mvwf3 on chromosome 4 and Mvwf4 on chromosome 13. These loci demonstrate primarily epistatic effects when co-inherited with two CASA/RkJ Vwf alleles, although Mvwf4 may also exert a small, independent, additive effect.

CONCLUSIONS

Mvwf3 and Mvwf4, combined with the effect of Mvwf2, explain approximately 45% of the genetic variation in plasma VWF level among the A/J and CASA/RkJ strains. Mvwf3 and Mvwf4 exhibit homology of synteny to three human chromosomal segments (on chromosomes 1, 5 and 6) previously reported by the Genetic Analysis of Idiopathic Thrombophilia (GAIT) study, suggesting that orthologs of Mvwf3 and Mvwf4 may also encode important VWF modifier genes in humans.

Design and analysis of association studies using pooled DNA from large twin samples

Evidence is mounting that multiple genes are involved in complex traits and that these each account for very small proportions of the overall phenotypic variance. Association studies of many markers in 1000s of individuals will be required to identify such genes. A number of large twin cohorts have already been collected and provide a valuable resource for carrying out studies that are robust to the effect of population stratification. Technologies based on microarrays will soon allow 1.000,000 SNPs to be typed at one time, however financial considerations prevent most researchers from using these approaches to genotype all individuals. Recently, microarrays have been shown to give accurate allele frequency measurements in pooled DNA samples and provide a simple way to select the best markers for individual genotyping. This drastically reduces the cost and workload of large scale association studies. One limitation of this methodology relates to the analytical procedures which have only been developed to allow comparison of two pools e.g. case/control pools. In this paper we use meta-regression to analyze pooled DNA data allowing the allele frequency in each pool to be related to the average quantitative phenotypic measure of the individuals whose DNA were used to construct the pools. Alongside this we describe a technique that can be used to determine the power for such studies. We present results from some preliminary investigations of different pooling strategies that can be applied to large twin samples and demonstrate that the method retains a large proportion of the power available from individual genotyping.