The genetic architecture of autism spectrum disorders(ASDs) and the potential importance of common regulatory genetic variants

Currently, there is great interest in identifying genetic variants that contribute to the risk of developing autism spectrum disorders(ASDs), due in part to recent increases in the frequency of diagnosis of these disorders worldwide. While there is nearly universal agreement that ASDs are complex diseases, with multiple genetic and environmental contributing factors, there is less agreement concerning the relative importance of common vs rare genetic variants in ASD liability. Recent observations that rare mutations and copy number variants(CNVs) are frequently associated with ASDs, combined with reduced fecundity of individuals with these disorders, has led to the hypothesis that ASDs are caused primarily by de novo or rare genetic mutations. Based on this model, large-scale whole-genome DNA sequencing has been proposed as the most appropriate method for discovering ASD liability genes. While this approach will undoubtedly identify many novel candidate genes and produce important new insights concerning the genetic causes of these disorders, a full accounting of the genetics of ASDs will be incomplete absent an understanding of the contributions of common regulatory variants, which are likely to influence ASD liability by modifying the effects of rare variants or, by assuming unfavorable combinations, directly produce these disorders. Because it is not yet possible to identify regulatory genetic variants by examination of DNA sequences alone, their identification will require experimentation. In this essay, I discuss these issues and describe the advantages of measurements of allelic expression imbalance(AEI) of m RNA expression for identifying cis-acting regulatory variants that contribute to ASDs.     

Gene-based Genomewide Association Analysis: A Comparison Study

The study of gene-based genetic associations has gained conceptual popularity recently. Biologic insight into the etiology of a complex disease can be gained by focusing on genes as testing units. Several gene-based methods (e.g., minimum p-value (or maximum test statistic) or entropy-based method) have been developed and have more power than a single nucleotide polymorphism (SNP)-based analysis. The objective of this study is to compare the performance of the entropy-based method with the minimum p-value and single SNP–based analysis and to explore their strengths and weaknesses. Simulation studies show that: 1) all three methods can reasonably control the false-positive rate; 2) the minimum p-value method outperforms the entropy-based and the single SNP–based method when only one disease-related SNP occurs within the gene; 3) the entropy-based method outperforms the other methods when there are more than two disease-related SNPs in the gene; and 4) the entropy-based method is computationally more efficient than the minimum p-value method. Application to a real data set shows that more significant genes were identified by the entropy-based method than by the other two methods.     

Suicidal Behavior and Haplotypes of the Dopamine Receptor Gene (DRD2) and ANKK1 Gene Polymorphisms in Patients with Alcohol Dependence – Preliminary Report

Suicide is a significant public health issue and a major cause of death throughout the world. According to WHO it accounts for almost 2% of deaths worldwide. The etiology of suicidal behavior is complex but the results of many studies suggest that genetic determinants are of significant importance. In our study,- we have analyzed selected SNPs polymorphisms in the DRD2 and ANKK1 genes in patients with alcohol dependence syndrome (169 Caucasian subjects) including a subgroup of individuals (n = 61) who have experienced at least one suicide attempt. The aim of the study was to verify if various haplotypes of selected genes, comprising Taq1A, Taq1B, and Taq1D single nucleotide polymorphisms (SNP), play any role in the development of alcohol dependence and suicidal behavior. The control group comprised 157 unrelated individuals matched for ethnicity, gender,- and age and included no individuals with mental disorders. All subjects were recruited in the North West region of Poland. The study showed that alcohol dependent subjects with a history of at least one suicidal attempt were characterized by a significantly higher frequency of the T-G-A2 haplotype when compared to individuals in whom alcohol dependence was not associated with suicidal behavior (p = 0.006). It appears that studies based on identifying correlation between SNPs is the future for research on genetic risk factors that contribute to the development of alcohol addiction and other associated disorders. To sum up, there is a necessity to perform further research to explain dependencies between the dopaminergic system, alcohol use disorders and suicidal behavior.     

Analysis of genome-wide association study data using the protein knowledge base

Background

Genome-wide association studies (GWAS) aim to identify causal variants and genes for complex disease by independently testing a large number of SNP markers for disease association. Although genes have been implicated in these studies, few utilise the multiple-hit model of complex disease to identify causal candidates. A major benefit of multi-locus comparison is that it compensates for some shortcomings of current statistical analyses that test the frequency of each SNP in isolation for the phenotype population versus control.

Results

Here we developed and benchmarked several protocols for GWAS data analysis using different in-silico gene prediction and prioritisation methodologies. We adopted a high sensitivity approach to the data, using less conservative statistical SNP associations. Multiple gene search spaces, either of fixed-widths or proximity-based, were generated around each SNP marker. We used the candidate disease gene prediction system Gentrepid to identify candidates based on shared biomolecular pathways or domain-based protein homology. Predictions were made either with phenotype-specific known disease genes as input; or without a priori knowledge, by exhaustive comparison of genes in distinct loci. Because Gentrepid uses biomolecular data to find interactions and common features between genes in distinct loci of the search spaces, it takes advantage of the multi-locus aspect of the data.

Conclusions

Results suggest testing multiple SNP-to-gene search spaces compensates for differences in phenotypes, populations and SNP platforms. Surprisingly, domain-based homology information was more informative when benchmarked against gene candidates reported by GWA studies compared to previously determined disease genes, possibly suggesting a larger contribution of gene homologs to complex diseases than Mendelian diseases.     

Disease and Polygenic Architecture: Avoid Trio Design and Appropriately Account for Unscreened Control Subjects for Common Disease

Genome-wide association studies (GWASs) are an optimal design for discovery of disease risk loci for diseases whose underlying genetic architecture includes many common causal loci of small effect (a polygenic architecture). We consider two designs that deserve careful consideration if the true underlying genetic architecture of the trait is polygenic: parent-offspring trios and unscreened control subjects. We assess these designs in terms of quantification of the total contribution of genome-wide genetic markers to disease risk (SNP heritability) and power to detect an associated risk allele. First, we show that trio designs should be avoided when: (1) the disease has a lifetime risk > 1%; (2) trio probands are ascertained from families with more than one affected sibling under which scenario the SNP heritability can drop by more than 50% and power can drop as much as from 0.9 to 0.15 for a sample of 20,000 subjects; or (3) assortative mating occurs (spouse correlation of the underlying liability to the disorder), which decreases the SNP heritability but not the power to detect a single locus in the trio design. Some studies use unscreened rather than screened control subjects because these can be easier to collect; we show that the estimated SNP heritability should then be scaled by dividing by (1 − K × u)² for disorders with population prevalence K and proportion of unscreened control subjects u. When omitting to scale appropriately, the SNP heritability of, for example, major depressive disorder (K = 0.15) would be underestimated by 28% when none of the control subjects are screened.     

SNP identification in unamplified human genomic DNA with gold nanoparticle probes

Single nucleotide polymorphisms (SNPs) comprise the most abundant source of genetic variation in the human genome. SNPs may be linked to genetic predispositions, frank disorders or adverse drug responses, or they may serve as genetic markers in linkage disequilibrium analysis. Thus far, established SNP detection techniques have utilized enzymes to meet the sensitivity and specificity requirements needed to overcome the high complexity of the human genome. Herein, we present for the first time a microarray-based method that allows multiplex SNP genotyping in total human genomic DNA without the need for target amplification or complexity reduction. This direct SNP genotyping methodology requires no enzymes and relies on the high sensitivity of the gold nanoparticle probes. Specificity is derived from two sequential oligonucleotide hybridizations to the target by allele-specific surface-immobilized capture probes and gene-specific oligonucleotide-functionalized gold nanoparticle probes. Reproducible multiplex SNP detection is demonstrated with unamplified human genomic DNA samples representing all possible genotypes for three genes involved in thrombotic disorders. The assay format is simple, rapid and robust pointing to its suitability for multiplex SNP profiling at the ‘point of care’.     

Disease-driven detection of differential inherited SNP modules from SNP network

Detection of the synergetic effects between variants, such as single-nucleotide polymorphisms (SNPs), is crucial for understanding the genetic characters of complex diseases. Here, we proposed a two-step approach to detect differentially inherited SNP modules (synergetic SNP units) from a SNP network. First, SNP-SNP interactions are identified based on prior biological knowledge, such as their adjacency on the chromosome or degree of relatedness between the functional relationships of their genes. These interactions form SNP networks. Second, disease-risk SNP modules (or sub-networks) are prioritised by their differentially inherited properties in IBD (Identity by Descent) profiles of affected and unaffected sibpairs. The search process is driven by the disease information and follows the structure of a SNP network. Simulation studies have indicated that this approach achieves high accuracy and a low false-positive rate in the identification of known disease-susceptible SNPs. Applying this method to an alcoholism dataset, we found that flexible patterns of susceptible SNP combinations do play a role in complex diseases, and some known genes were detected through these risk SNP modules. One example is GRM7, a known alcoholism gene successfully detected by a SNP module comprised of two SNPs, but neither of the two SNPs was significantly associated with the disease in single-locus analysis. These identified genes are also enriched in some pathways associated with alcoholism, including the calcium signalling pathway, axon guidance and neuroactive ligand-receptor interaction. The integration of network biology and genetic analysis provides putative functional bridges between genetic variants and candidate genes or pathways, thereby providing new insight into the aetiology of complex diseases.     

Current limitations of SNP data from the public domain for studies of complex disorders: a test for ten candidate genes for obesity and osteoporosis

Background

Public SNP databases are frequently used to choose SNPs for candidate genes in the association and linkage studies of complex disorders. However, their utility for such studies of diseases with ethnic-dependent background has never been evaluated.

Results

To estimate the accuracy and completeness of SNP public databases, we analyzed the allele frequencies of 41 SNPs in 10 candidate genes for obesity and/or osteoporosis in a large American-Caucasian sample (1,873 individuals from 405 nuclear families) by PCR-invader assay. We compared our results with those from the databases and other published studies. Of the 41 SNPs, 8 were monomorphic in our sample. Twelve were reported for the first time for Caucasians and the other 29 SNPs in our sample essentially confirmed the respective allele frequencies for Caucasians in the databases and previous studies. The comparison of our data with other ethnic groups showed significant differentiation between the three major world ethnic groups at some SNPs (Caucasians and Africans differed at 3 of the 18 shared SNPs, and Caucasians and Asians differed at 13 of the 22 shared SNPs). This genetic differentiation may have an important implication for studying the well-known ethnic differences in the prevalence of obesity and osteoporosis, and complex disorders in general.

Conclusion

A comparative analysis of the SNP data of the candidate genes obtained in the present study, as well as those retrieved from the public domain, suggests that the databases may currently have serious limitations for studying complex disorders with an ethnic-dependent background due to the incomplete and uneven representation of the candidate SNPs in the databases for the major ethnic groups. This conclusion attests to the imperative necessity of large-scale and accurate characterization of these SNPs in different ethnic groups.     

Combining gene expression and molecular marker information for mapping complex trait genes: a simulation study

A method for mapping complex trait genes using cDNA microarray and molecular marker data jointly is presented and illustrated via simulation. We introduce a novel approach for simulating phenotypes and genotypes conditionally on real, publicly available, microarray data. The model assumes an underlying continuous latent variable (liability) related to some measured cDNA expression levels. Partial least-squares logistic regression is used to estimate the liability under several scenarios where the level of gene interaction, the gene effect, and the number of cDNA levels affecting liability are varied. The results suggest that: (1) the usefulness of microarray data for gene mapping increases when both the number of cDNA levels in the underlying liability and the QTL effect decrease and when genes are coexpressed; (2) the correlation between estimated and true liability is large, at least under our simulation settings; (3) it is unlikely that cDNA clones identified as significant with partial least squares (or with some other technique) are the true responsible cDNAs, especially as the number of clones in the liability increases; (4) the number of putatively significant cDNA levels increases critically if cDNAs are coexpressed in a cluster (however, the proportion of true causal cDNAs within the significant ones is similar to that in a no-coexpression scenario); and (5) data reduction is needed to smooth out the variability encountered in expression levels when these are analyzed individually.     

Designing Genome-Wide Association Studies: Sample Size, Power, Imputation, and the Choice of Genotyping Chip

Genome-wide association studies are revolutionizing the search for the genes underlying human complex diseases. The main decisions to be made at the design stage of these studies are the choice of the commercial genotyping chip to be used and the numbers of case and control samples to be genotyped. The most common method of comparing different chips is using a measure of coverage, but this fails to properly account for the effects of sample size, the genetic model of the disease, and linkage disequilibrium between SNPs. In this paper, we argue that the statistical power to detect a causative variant should be the major criterion in study design. Because of the complicated pattern of linkage disequilibrium (LD) in the human genome, power cannot be calculated analytically and must instead be assessed by simulation. We describe in detail a method of simulating case-control samples at a set of linked SNPs that replicates the patterns of LD in human populations, and we used it to assess power for a comprehensive set of available genotyping chips. Our results allow us to compare the performance of the chips to detect variants with different effect sizes and allele frequencies, look at how power changes with sample size in different populations or when using multi-marker tags and genotype imputation approaches, and how performance compares to a hypothetical chip that contains every SNP in HapMap. A main conclusion of this study is that marked differences in genome coverage may not translate into appreciable differences in power and that, when taking budgetary considerations into account, the most powerful design may not always correspond to the chip with the highest coverage. We also show that genotype imputation can be used to boost the power of many chips up to the level obtained from a hypothetical “complete” chip containing all the SNPs in HapMap. Our results have been encapsulated into an R software package that allows users to design future association studies and our methods provide a framework with which new chip sets can be evaluated.     

Gene disruption in mice: Models of development and disease

Gene targeting technology in mice by homologous recombination has become an important method to generate loss-of-function of genes in a predetermined locus. Although the inactivation is limited to irreversible alteration of chromosomal DNA and a surprising variety of genes have given unexpected and disappointing results, modification of the basic technology now provides additional choices for a more specific and variety of manipulations of the mouse genome. This includes conditional cell-type specific gene targeting, knockin technique and the induction of the specific balanced chromosomal translocations. In the past decade this technique not only generated a wealth of knowledge concerning the roles of growth factors, oncogenes, hormone receptors and Hox genes but also helped to produce animal models for several human genetic disorders. In the future it may provide more powerful and necessary tools to dissect the psychiatric disorders, understanding the complex central nervous system and to correct the inherited disorders.     

FunctSNP: an R package to link SNPs to functional knowledge and dbAutoMaker: a suite of Perl scripts to build SNP databases

Background  

Whole genome association studies using highly dense single nucleotide polymorphisms (SNPs) are a set of methods to identify DNA markers associated with variation in a particular complex trait of interest. One of the main outcomes from these studies is a subset of statistically significant SNPs. Finding the potential biological functions of such SNPs can be an important step towards further use in human and agricultural populations (e.g., for identifying genes related to susceptibility to complex diseases or genes playing key roles in development or performance). The current challenge is that the information holding the clues to SNP functions is distributed across many different databases. Efficient bioinformatics tools are therefore needed to seamlessly integrate up-to-date functional information on SNPs. Many web services have arisen to meet the challenge but most work only within the framework of human medical research. Although we acknowledge the importance of human research, we identify there is a need for SNP annotation tools for other organisms.     

Comparison of Family History and SNPs for Predicting Risk of Complex Disease

The clinical utility of family history and genetic tests is generally well understood for simple Mendelian disorders and rare subforms of complex diseases that are directly attributable to highly penetrant genetic variants. However, little is presently known regarding the performance of these methods in situations where disease susceptibility depends on the cumulative contribution of multiple genetic factors of moderate or low penetrance. Using quantitative genetic theory, we develop a model for studying the predictive ability of family history and single nucleotide polymorphism (SNP)–based methods for assessing risk of polygenic disorders. We show that family history is most useful for highly common, heritable conditions (e.g., coronary artery disease), where it explains roughly 20%–30% of disease heritability, on par with the most successful SNP models based on associations discovered to date. In contrast, we find that for diseases of moderate or low frequency (e.g., Crohn disease) family history accounts for less than 4% of disease heritability, substantially lagging behind SNPs in almost all cases. These results indicate that, for a broad range of diseases, already identified SNP associations may be better predictors of risk than their family history–based counterparts, despite the large fraction of missing heritability that remains to be explained. Our model illustrates the difficulty of using either family history or SNPs for standalone disease prediction. On the other hand, we show that, unlike family history, SNP–based tests can reveal extreme likelihood ratios for a relatively large percentage of individuals, thus providing potentially valuable adjunctive evidence in a differential diagnosis.     

Semiparametric maximum likelihood methods for analyzing genetic and environmental effects with case-control mother-child pair data

Summary Case-control mother-child pair design represents a unique advantage for dissecting genetic susceptibility of complex traits because it allows the assessment of both maternal and offspring genetic compositions. This design has been widely adopted in studies of obstetric complications and neonatal outcomes. In this work, we developed an efficient statistical method for evaluating joint genetic and environmental effects on a binary phenotype. Using a logistic regression model to describe the relationship between the phenotype and maternal and offspring genetic and environmental risk factors, we developed a semiparametric maximum likelihood method for the estimation of odds ratio association parameters. Our method is novel because it exploits two unique features of the study data for the parameter estimation. First, the correlation between maternal and offspring SNP genotypes can be specified under the assumptions of random mating, Hardy-Weinberg equilibrium, and Mendelian inheritance. Second, environmental exposures are often not affected by offspring genes conditional on maternal genes. Our method yields more efficient estimates compared with the standard prospective method for fitting logistic regression models to case-control data. We demonstrated the performance of our method through extensive simulation studies and the analysis of data from the Jerusalem Perinatal Study.     

Mapping of complex traits by single-nucleotide polymorphisms.

Molecular geneticists are developing the third-generation human genome map with single-nucleotide polymorphisms (SNPs), which can be assayed via chip-based microarrays. One use of these SNP markers is the ability to locate loci that may be responsible for complex traits, via linkage/linkage-disequilibrium analysis. In this communication, we describe a semiparametric method for combined linkage/linkage-disequilibrium analysis using SNP markers. Asymptotic results are obtained for the estimated parameters, and the finite-sample properties are evaluated via a simulation study. We also applied this technique to a simulated genome-scan experiment for mapping a complex trait with two major genes. This experiment shows that separate linkage and linkage-disequilibrium analyses correctly detected the signals of both major genes; but the rates of false-positive signals seem high. When linkage and linkage-disequilibrium signals were combined, the analysis yielded much stronger and clearer signals for the presence of two major genes than did two separate analyses.     

A penalized structural equation modeling method accounting for secondary phenotypes for variable selection on genetically regulated expression from PrediXcan for Alzheimer's disease

As the global burden of mental illness is estimated to become a severe issue in the near future, it demands the development of more effective treatments. Most psychiatric diseases are moderately to highly heritable and believed to involve many genes. Development of new treatment options demands more knowledge on the molecular basis of psychiatric diseases. Toward this end, we propose to develop new statistical methods with improved sensitivity and accuracy to identify disease‐related genes specialized for psychiatric diseases. The qualitative psychiatric diagnoses such as case control often suffer from high rates of misdiagnosis and oversimplify the disease phenotypes. Our proposed method utilizes endophenotypes, the quantitative traits hypothesized to underlie disease syndromes, to better characterize the heterogeneous phenotypes of psychiatric diseases. We employ the structural equation modeling using the liability‐index model to link multiple genetically regulated expressions from PrediXcan and the manifest variables including endophenotypes and case‐control status. The proposed method can be considered as a general method for multivariate regression, which is particularly helpful for psychiatric diseases. We derive penalized retrospective likelihood estimators to deal with the typical small sample size issue. Simulation results demonstrate the advantages of the proposed method and the real data analysis of Alzheimer's disease illustrates the practical utility of the techniques. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative database.     

Bipolar I disorder and schizophrenia: a 440-single-nucleotide polymorphism screen of 64 candidate genes among Ashkenazi Jewish case-parent trios

Bipolar, schizophrenia, and schizoaffective disorders are common, highly heritable psychiatric disorders, for which familial coaggregation, as well as epidemiological and genetic evidence, suggests overlapping etiologies. No definitive susceptibility genes have yet been identified for any of these disorders. Genetic heterogeneity, combined with phenotypic imprecision and poor marker coverage, has contributed to the difficulty in defining risk variants. We focused on families of Ashkenazi Jewish descent, to reduce genetic heterogeneity, and, as a precursor to genomewide association studies, we undertook a single-nucleotide polymorphism (SNP) genotyping screen of 64 candidate genes (440 SNPs) chosen on the basis of previous linkage or of association and/or biological relevance. We genotyped an average of 6.9 SNPs per gene, with an average density of 1 SNP per 11.9 kb in 323 bipolar I disorder and 274 schizophrenia or schizoaffective Ashkenazi case-parent trios. Using single-SNP and haplotype-based transmission/disequilibrium tests, we ranked genes on the basis of strength of association (P<.01). Six genes (DAO, GRM3, GRM4, GRIN2B, IL2RB, and TUBA8) met this criterion for bipolar I disorder; only DAO has been previously associated with bipolar disorder. Six genes (RGS4, SCA1, GRM4, DPYSL2, NOS1, and GRID1) met this criterion for schizophrenia or schizoaffective disorder; five replicate previous associations, and one, GRID1, shows a novel association with schizophrenia. In addition, six genes (DPYSL2, DTNBP1, G30/G72, GRID1, GRM4, and NOS1) showed overlapping suggestive evidence of association in both disorders. These results may help to prioritize candidate genes for future study from among the many suspected/proposed for schizophrenia and bipolar disorders. They provide further support for shared genetic susceptibility between these two disorders that involve glutamate-signaling pathways.