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.