Association analysis of 94 candidate genes and schizophrenia-related endophenotypes

While it is clear that schizophrenia is highly heritable, the genetic basis of this heritability is complex. Human genetic, brain imaging, and model organism studies have met with only modest gains. A complementary research tactic is to evaluate the genetic substrates of quantitative endophenotypes with demonstrated deficits in schizophrenia patients. We used an Illumina custom 1,536-SNP array to interrogate 94 functionally relevant candidate genes for schizophrenia and evaluate association with both the qualitative diagnosis of schizophrenia and quantitative endophenotypes for schizophrenia. Subjects included 219 schizophrenia patients and normal comparison subjects of European ancestry and 76 schizophrenia patients and normal comparison subjects of African ancestry, all ascertained by the UCSD Schizophrenia Research Program. Six neurophysiological and neurocognitive endophenotype test paradigms were assessed: prepulse inhibition (PPI), P50 suppression, the antisaccade oculomotor task, the Letter-Number Span Test, the California Verbal Learning Test-II, and the Wisconsin Card Sorting Test-64 Card Version. These endophenotype test paradigms yielded six primary endophenotypes with prior evidence of heritability and demonstrated schizophrenia-related impairments, as well as eight secondary measures investigated as candidate endophenotypes. Schizophrenia patients showed significant deficits on ten of the endophenotypic measures, replicating prior studies and facilitating genetic analyses of these phenotypes. A total of 38 genes were found to be associated with at least one endophenotypic measure or schizophrenia with an empirical p-value<0.01. Many of these genes have been shown to interact on a molecular level, and eleven genes displayed evidence for pleiotropy, revealing associations with three or more endophenotypic measures. Among these genes were ERBB4 and NRG1, providing further support for a role of these genes in schizophrenia susceptibility. The observation of extensive pleiotropy for some genes and singular associations for others in our data may suggest both converging and independent genetic (and neural) pathways mediating schizophrenia risk and pathogenesis.     

Walking the interactome for prioritization of candidate disease genes

The identification of genes associated with hereditary disorders has contributed to improving medical care and to a better understanding of gene functions, interactions, and pathways. However, there are well over 1500 Mendelian disorders whose molecular basis remains unknown. At present, methods such as linkage analysis can identify the chromosomal region in which unknown disease genes are located, but the regions could contain up to hundreds of candidate genes. In this work, we present a method for prioritization of candidate genes by use of a global network distance measure, random walk analysis, for definition of similarities in protein-protein interaction networks. We tested our method on 110 disease-gene families with a total of 783 genes and achieved an area under the ROC curve of up to 98% on simulated linkage intervals of 100 genes surrounding the disease gene, significantly outperforming previous methods based on local distance measures. Our results not only provide an improved tool for positional-cloning projects but also add weight to the assumption that phenotypically similar diseases are associated with disturbances of subnetworks within the larger protein interactome that extend beyond the disease proteins themselves.     

Association analysis of 33 lipoprotein candidate genes in multi-generational families of African ancestry

African ancestry individuals have a more favorable lipoprotein profile than Caucasians, although the mechanisms for these differences remain unclear. We measured fasting serum lipoproteins and genotyped 768 tagging or potentially functional single nucleotide polymorphisms (SNPs) across 33 candidate gene regions in 401 Afro-Caribbeans older than 18 years belonging to 7 multi-generational pedigrees (mean family size 51, range 21–113, 3,426 relative pairs). All lipoproteins were significantly heritable (P < 0.05). Gender-specific analysis showed that heritability for triglycerides was much higher (P < 0.01) in women than in men (women, 0.62 ± 0.18, P < 0.01; men, 0.13 ± 0.17, P > 0.10), but the heritability for LDL cholesterol (LDL-C) was higher (P < 0.05) in men than in women (men, 0.79 ± 0.21, P < 0.01; women, 0.39 ± 0.12, P < 0.01). The top 14 SNPs that passed the false discovery rate threshold in the families were then tested for replication in an independent population-based sample of 1,750 Afro-Caribbean men aged 40+ years. Our results revealed significant associations for three SNPs in two genes (rs5929 and rs6511720 in LDLR and rs7517090 in PCSK9) and LDL-C in both the family study and in the replication study. Our findings suggest that LDLR and PCSK9 variants may contribute to a variation in LDL-C among African ancestry individuals. Future sequencing and functional studies of these loci may advance our understanding of genetic factors contributing to LDL-C in African ancestry populations.Lipoprotein abnormalities, characterized by elevated levels of LDL cholesterol (LDL-C) and triglycerides (TRIG) and low levels of HDL cholesterol (HDL-C), have a central role in the development of atherosclerotic coronary heart disease (CHD). A recent meta-analysis, including 3,000 individuals with CHD-related deaths, showed that HDL-C and LDL-C are independently associated with CHD risk (1). There is also considerable evidence that high levels of TRIG are an additional, independent risk factor for CHD (2, 3), although this is still controversial (4).Individuals of African ancestry have a more favorable lipoprotein profile than Caucasians, characterized by lower levels of TRIG and higher levels of HDL-C (5–8). The mechanisms responsible for these ethnic differences remain to be defined. In particular, the differences in TRIG levels are independent of the greater degree of obesity among individuals of African ancestry and several other risk factors and appear to be consistent across African populations in different environments (9), indicating a possible role of genetic factors. Although genetic factors are important in determining lipoprotein levels, little data exists regarding the importance of heredity and specific genetic factors in determining lipoprotein levels in populations of African ancestry, especially outside the US, and the findings from previous studies in African-Americans may not necessarily apply to other African ancestry populations. Recently, several genome-wide association studies identified a number of loci contributing to inter-individual variation in lipoprotein levels (10, 11). However, the majority of these studies were restricted to Caucasian populations. Given the ethnic differences in lifestyle and environmental factors, as well as in genetic background, it is important to examine genes related to lipoprotein metabolism in different ethnic groups. Therefore, we examined the heritability of fasting, serum levels of HDL-C, LDL-C, and TRIG and systematically screened for association with 33 positional and biological candidate genes in large, multigenerational families of African ancestry.     

Computational tools for prioritizing candidate genes: boosting disease gene discovery

At different stages of any research project, molecular biologists need to choose - often somewhat arbitrarily, even after careful statistical data analysis - which genes or proteins to investigate further experimentally and which to leave out because of limited resources. Computational methods that integrate complex, heterogeneous data sets - such as expression data, sequence information, functional annotation and the biomedical literature - allow prioritizing genes for future study in a more informed way. Such methods can substantially increase the yield of downstream studies and are becoming invaluable to researchers.     

Computational analysis of candidate disease genes and variants for salt-sensitive hypertension in indigenous Southern Africans

Multiple factors underlie susceptibility to essential hypertension, including a significant genetic and ethnic component, and environmental effects. Blood pressure response of hypertensive individuals to salt is heterogeneous, but salt sensitivity appears more prevalent in people of indigenous African origin. The underlying genetics of salt-sensitive hypertension, however, are poorly understood. In this study, computational methods including text- and data-mining have been used to select and prioritize candidate aetiological genes for salt-sensitive hypertension. Additionally, we have compared allele frequencies and copy number variation for single nucleotide polymorphisms in candidate genes between indigenous Southern African and Caucasian populations, with the aim of identifying candidate genes with significant variability between the population groups: identifying genetic variability between population groups can exploit ethnic differences in disease prevalence to aid with prioritisation of good candidate genes. Our top-ranking candidate genes include parathyroid hormone precursor (PTH) and type-1 angiotensin II receptor (AGTR1). We propose that the candidate genes identified in this study warrant further investigation as potential aetiological genes for salt-sensitive hypertension.     

Evolution of disease response genes in loblolly pine: insights from candidate genes

Background

Host-pathogen interactions that may lead to a competitive co-evolution of virulence and resistance mechanisms present an attractive system to study molecular evolution because strong, recent (or even current) selective pressure is expected at many genomic loci. However, it is unclear whether these selective forces would act to preserve existing diversity, promote novel diversity, or reduce linked neutral diversity during rapid fixation of advantageous alleles. In plants, the lack of adaptive immunity places a larger burden on genetic diversity to ensure survival of plant populations. This burden is even greater if the generation time of the plant is much longer than the generation time of the pathogen.

Methodology/Principal Findings

Here, we present nucleotide polymorphism and substitution data for 41 candidate genes from the long-lived forest tree loblolly pine, selected primarily for their prospective influences on host-pathogen interactions. This dataset is analyzed together with 15 drought-tolerance and 13 wood-quality genes from previous studies. A wide range of neutrality tests were performed and tested against expectations from realistic demographic models.

Conclusions/Significance

Collectively, our analyses found that axr (auxin response factor), caf1 (chromatin assembly factor) and gatabp1 (gata binding protein 1) candidate genes carry patterns consistent with directional selection and erd3 (early response to drought 3) displays patterns suggestive of a selective sweep, both of which are consistent with the arm-race model of disease response evolution. Furthermore, we have identified patterns consistent with diversifying selection at erf1-like (ethylene responsive factor 1), ccoaoemt (caffeoyl-CoA-O-methyltransferase), cyp450-like (cytochrome p450-like) and pr4.3 (pathogen response 4.3), expected under the trench-warfare evolution model. Finally, a drought-tolerance candidate related to the plant cell wall, lp5, displayed patterns consistent with balancing selection. In conclusion, both arms-race and trench-warfare models seem compatible with patterns of polymorphism found in different disease-response candidate genes, indicating a mixed strategy of disease tolerance evolution for loblolly pine, a major tree crop in southeastern United States.     

Finding disease candidate genes by liquid association

A novel approach to finding candidate genes by using gene expression data through liquid association is developed and used to identify multiple sclerosis susceptibility candidate genes.     

Comprehensive analysis of photoreceptor gene expression and the identification of candidate retinal disease genes.

To identify the full set of genes expressed by mammalian rods, we conducted serial analysis of gene expression (SAGE) by using libraries generated from mature and developing mouse retina. We identified 264 uncharacterized genes that were specific to or highly enriched in rods. Nearly half of all cloned human retinal disease genes are selectively expressed in rod photoreceptors. In silico mapping of the human orthologs of genes identified in our screen revealed that 86 map within intervals containing uncloned retinal disease genes, representing 37 different loci. We expect these data will allow identification of many disease genes, and that this approach may be useful for cloning genes involved in classes of disease where cell type-specific expression of disease genes is observed.     

Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease

Background

Alzheimer’s disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling.

Results

Our approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (S_R) based method followed by prioritization using clusters derived from PPI network. S_R for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes.

Conclusions

With the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers than candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-199) contains supplementary material, which is available to authorized users.     

Association studies in candidate genes: strategies to select SNPs to be tested

OBJECTIVE: When numerous single nucleotide polymorphisms (SNPs) have been identified in a candidate gene, a relevant and still unanswered question is to determine how many and which of these SNPs should be optimally tested to detect an association with the disease. Testing them all is expensive and often unnecessary. Alleles at different SNPs may be associated in the population because of the existence of linkage disequilibrium, so that knowing the alleles carried at one SNP could provide exact or partial knowledge of alleles carried at a second SNP. We present here a method to select the most appropriate subset of SNPs in a candidate gene based on the pairwise linkage disequilibrium between the different SNPs. METHOD: The best subset is identified through power computations performed under different genetic models, assuming that one of the SNPs identified is the disease susceptibility variant. RESULTS: We applied the method on two data sets, an empirical study of the APOE gene region and a simulated study concerning one of the major genes (MG1) from the Genetic Analysis Workshop 12. For these two genes, the sets of SNPs selected were compared to the ones obtained using two other methods that need the reconstruction of multilocus haplotypes in order to identify haplotype-tag SNPs (htSNPs). We showed that with both data sets, our method performed better than the other selection methods.     

Linkage analysis with candidate genes: the Taiwan young-onset hypertension genetic study

A genetic linkage study of young-onset hypertension was performed on data from 59 nucleus families of Han Chinese residing in Taiwan. Thirty seven microsatellite markers near 18 hypertension candidate genes were genotyped. In a nonparametric identity-by-descent sibpair analysis, a positive linkage signal (defined as P<0.05) was found for four microsatellite markers, viz., D1S1612 (P=0.0162), D1S547 (P=0.0263), D8S 1145 (P= 0.0284), and D17S2193 (P=0.0256), which were located near genes for atrial natriuretic peptide (NPPA)/glucose transporter 5 (SLC2A5), angiotensinogen (AGT), lipoprotein lipase (LPL), and angiotensin-conveting enzyme (DCP1), respectively. Marker D5S1480 located near beta-2-adrenergic receptor (ADRB2) had a borderline P value (P=0.0785) for the positive signal. Comprehensive genotyping with further markers in these regions is underway to confirm whether these genes are linked to young-onset hypertension.     

Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease

Objective

Candidate genes for non-alcoholic fatty liver disease (NAFLD) identified by a bioinformatics approach were examined for variant associations to quantitative traits of NAFLD-related phenotypes.

Research Design and Methods

By integrating public database text mining, trans-organism protein-protein interaction transferal, and information on liver protein expression a protein-protein interaction network was constructed and from this a smaller isolated interactome was identified. Five genes from this interactome were selected for genetic analysis. Twenty-one tag single-nucleotide polymorphisms (SNPs) which captured all common variation in these genes were genotyped in 10,196 Danes, and analyzed for association with NAFLD-related quantitative traits, type 2 diabetes (T2D), central obesity, and WHO-defined metabolic syndrome (MetS).

Results

273 genes were included in the protein-protein interaction analysis and EHHADH, ECHS1, HADHA, HADHB, and ACADL were selected for further examination. A total of 10 nominal statistical significant associations (P<0.05) to quantitative metabolic traits were identified. Also, the case-control study showed associations between variation in the five genes and T2D, central obesity, and MetS, respectively. Bonferroni adjustments for multiple testing negated all associations.

Conclusions

Using a bioinformatics approach we identified five candidate genes for NAFLD. However, we failed to provide evidence of associations with major effects between SNPs in these five genes and NAFLD-related quantitative traits, T2D, central obesity, and MetS.     

Microarray analysis for identification of Plasmodium-refractoriness candidate genes in mosquitoes.

The identification and cloning of genes conferring mosquito refractoriness to the malaria parasite is critical for understanding malaria transmission mechanisms and holds great promise for developing novel approaches to malaria control. The mosquito midgut is the first major site of interaction between the parasite and the mosquito. Failure of the parasite to negotiate this environment can be a barrier for development and is likely the main cause of mosquito refractoriness. This paper reports a study on Aedes aegypti midgut expressed sequence tag (EST) identification and the determination of genes differentially expressed in mosquito populations susceptible and refractory to the avian malaria parasite Plasmodium gallinaceum. We sequenced a total of 1200 cDNA clones and obtained 1183 high-quality mosquito midgut ESTs that were computationally collapsed into 105 contigs and 251 singlets. All 1200 midgut cDNA clones, together with an additional 102 genetically or physically mapped Ae. aegypti clones, were spotted on single arrays with 12 replicates. Of those interrogated microarray elements, 28 (2.3%) were differentially expressed between the susceptible and refractory mosquito populations. Twenty-seven elements showed at least a two-fold increase in expression in the susceptible population level relative to the refractory population and one clone showed reduced expression. Sequence analysis of these differentially expressed genes revealed that 10 showed no significant similarity to any known genes, 6 clones had matches with unannotated genes of Anopheles gambiae, and 12 clones exhibited significant similarity to known genes. Real-time quantitative RT-PCR of selected clones confirmed the mRNA expression profiles from the microarray analysis.     

Confirmation of the chromosomal localization of human lamp genes and their exclusion as candidate genes for Salla disease

Summary Salla disease is an inherited lysosomal storage disorder caused by accumulation of free sialic acid in the lysosomes. Lamp genes, lamp A and lamp B (lysosome associated membrane proteins), are the first known genes encoding for human lysosomal membrane proteins. Absence of linkage in a large group of families shows that lamp genes are not involved in Salla disease. The lamp genes were localized, using Southern hybridization in hamster — human hybrid cell panels, to chromosomes 13 (lamp A) and X (lamp B).