From single genes to gene networks: high-throughput-high-content screening for neurological disease

Neuronal development, function, and the subsequent degeneration of the brain are still an enigma in both the normal and pathologic states, and there is an urgent need to find better targets for developing therapeutic intervention. Current techniques to deconstruct the architecture of brain and disease-related pathways are best suited for following up on single genes but would take an impractical amount of time for the leads from the current wave of genetic and genomic data. New technical developments have made combined high-throughput-high-content (HT-HC) cellular screens possible, which have the potential to contextualize the information, gathered from a combination of genetic and genomic approaches, into networks and functional biology and can be utilized for the identification of therapeutic targets. Herein we discuss the potential impact of HT-HC cellular screens on medical neuroscience.     

MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study

Background  

The interactions of multiple single nucleotide polymorphisms (SNPs) are highly hypothesized to affect an individual's susceptibility to complex diseases. Although many works have been done to identify and quantify the importance of multi-SNP interactions, few of them could handle the genome wide data due to the combinatorial explosive search space and the difficulty to statistically evaluate the high-order interactions given limited samples.     

From genome-wide association studies to etiology: probing autoimmunity genes by RNAi

Autoimmunity cannot yet be prevented or cured, in large part due to our poor understanding of disease etiology. Remarkable advances in genomic technology have recently enabled the discovery of a large number of disease-associated gene variations by genome-wide association studies. The next step towards understanding autoimmune disorders entails the functional study of susceptibility genes within experimental disease models. RNA interference (RNAi) is a promising tool for such investigations. Several features of RNAi, including its specificity, versatility and reversible nature, allow experimental systems to be tailored to relevant gene variations. This review discusses how the experimental use of RNAi is invaluable in bridging the gap between the identification of susceptibility genes and the elucidation of their functional contribution to autoimmune disease.     

Evolutionary conservation and disease gene association of the human genes composing pseudogenes

Pseudogenes, the 'genomic fossils' present portrayal of evolutionary history of human genome. The human genes configuring pseudogenes are also now coming forth as important resources in the study of human protein evolution. In this communication, we explored evolutionary conservation of the genes forming pseudogenes over the genes lacking any pseudogene and delving deeper, we probed an evolutionary rate difference between the disease genes in the two groups. We illustrated this differential evolutionary pattern by gene expressivity, number of regulatory miRNA targeting per gene, abundance of protein complex forming genes and lesser percentage of protein intrinsic disorderness. Furthermore, pseudogenes are observed to harbor sequence variations, over their entirety, those become degenerative disease-causing mutations though the disease involvement of their progenitors is still unexplored. Here, we unveiled an immense association of disease genes in the genes casting pseudogenes in human. We interpreted the issue by disease associated miRNA targeting, genes containing polymorphisms in miRNA target sites, abundance of genes having disease causing non-synonymous mutations, disease gene specific network properties, presence of genes having repeat regions, affluence of dosage sensitive genes and the presence of intrinsically unstructured protein regions.     

SNPs3D: Candidate gene and SNP selection for association studies

Background  

The relationship between disease susceptibility and genetic variation is complex, and many different types of data are relevant. We describe a web resource and database that provides and integrates as much information as possible on disease/gene relationships at the molecular level.     

Scanning the genome for gene SNPs related to climate adaptation and estimating selection at the molecular level in boreal black spruce

Outlier detection methods were used to scan the genome of the boreal conifer black spruce (Picea mariana [Mill.] B.S.P.) for gene single-nucleotide polymorphisms (SNPs) potentially involved in adaptations to temperature and precipitation variations. The scan involved 583 SNPs from 313 genes potentially playing adaptive roles. Differentiation estimates among population groups defined following variation in temperature and precipitation were moderately high for adaptive quantitative characters such as the timing of budset or tree height (Q(ST) = 0.189-0.314). Average differentiation estimates for gene SNPs were null, with F(ST) values of 0.005 and 0.006, respectively, among temperature and precipitation population groups. Using two detection approaches, a total of 26 SNPs from 25 genes distributed among 11 of the 12 linkage groups of black spruce were detected as outliers with F(ST) as high as 0.078. Nearly half of the outlier SNPs were located in exons and half of those were nonsynonymous. The functional annotations of genes carrying outlier SNPs and regression analyses between the frequencies of these SNPs and climatic variables supported their implication in adaptive processes. Several genes carrying outlier SNPs belonged to gene families previously found to harbour outlier SNPs in a reproductively isolated but largely sympatric congeneric species, suggesting differential subfunctionalization of gene duplicates. Selection coefficient estimates (S) were moderate but well above the magnitude of drift (>1/N(e)), indicating that the signature of natural selection could be detected at the nucleotide level despite the recent establishment of these populations during the Holocene.     

Mapping disease genes: family-based association studies.

With recent rapid advances in mapping of the human genome, including highly polymorphic and closely linked markers, studies of marker associations with disease are increasingly relevant for mapping disease genes. The use of nuclear-family data in association studies was initially developed to avoid possible ethnic mismatching between patients and randomly ascertained controls. The parental marker alleles not transmitted to an affected child or never transmitted to an affected sib pair form the so-called AFBAC (affected family-based controls) population. In this paper, the theoretical foundation of the AFBAC method is proved for any single-locus model of disease and for any nuclear family-based ascertainment scheme. In a random-mating population, when there is a marker association with disease, the AFBAC population provides an unbiased estimate of the overall population (control) marker alleles when the recombination fraction (theta) between the marker and disease genes is sufficiently small that it can be taken as zero (theta = 0). With population stratification, only marker associations present in the subpopulations will be detected with family-based analyses. Of more importance, however, is the fact that, when theta not equal to 0, differences between transmitted parental (patient) marker allele frequencies and non- or never-transmitted parental marker allele frequencies (implying a marker association with disease) can only be observed for marker genes linked to a disease gene (theta < 1/2). Thus, associations of unlinked marker loci with disease at the population level, caused by population stratification, migration, or admixture, are eliminated. This validates the use of family-based association tests as an appropriate strategy for mapping disease genes.     

Molecular basis for population variation: From SNPs to SAPs

Single nucleotide polymorphisms (SNPs) are one type of genomic DNA variations in a population. Correspondingly, single amino-acid polymorphisms (SAPs) derived from non-synonymous SNPs represent protein variations in a population. Recently, using proteomic approaches, SAPs in the plasma proteomes of an Asian population were systematically identified for the first time. That study showed that heterozygous and homozygous proteins with various SAPs have different associations with particular traits in the population. Recent discoveries of widespread differences between RNA and DNA sequences indicate that RNA editing is also a source of SAPs - one that is independent of genomic SNPs. Furthermore, we argue that there are de novo SAPs that are not encoded by either DNA or RNA sequences.     

Genome-wide association analysis identified SNPs closely linked to a gene resistant to Soil-borne wheat mosaic virus

Key message

Using association and linkage mapping, two SNP markers closely linked to the SBWMV resistance gene on chromosome 5D were identified and can be used to select the gene in breeding.

Abstract

Soil-borne wheat mosaic virus (SBWMV) disease is a serious viral disease of winter wheat growing areas worldwide. SBWMV infection can significantly reduce grain yield up to 80 %. Developing resistant wheat cultivars is the only feasible strategy to reduce the losses. In this study, wheat Infinium iSelect Beadchips with 9 K wheat SNPs were used to genotype an association mapping population of 205 wheat accessions. Six new SNPs from two genes were identified to be significantly associated with the gene for SBWMV resistance on chromosome 5D. The SNPs and Xgwm469, an SSR marker that has been reported to be associated with the gene, were mapped close to the gene using F₆-derived recombinant inbred lines from the cross between a resistant parent ‘Heyne’ and a susceptible parent ‘Trego’. Two representative SNPs, wsnp_CAP11_c209_198467 and wsnp_JD_c4438_5568170, from the two linked genes in wheat were converted into KBioscience Competitive Allele-Specific Polymerase assays and can be easily used in marker-assisted selection to improve wheat resistance to SBWMV in breeding.     

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.     

Biological invasions at the gene level

Despite several recent contributions of population and evolutionary biology to the rapidly developing field of invasion biology, integration is far from perfect. I argue here that invasion and native status are sometimes best discussed at the level of the gene rather than at the level of the species. This, and the need to consider both natural (e.g. postglacial) and human‐induced invasions, suggests that a more integrative view of invasion biology is required.     

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.     

Programmable technologies to manipulate gene expression at the RNA level

RNA has long been an enticing therapeutic target, but is now garnering increased attention, largely driven by clinical successes of RNA interference–based drugs. While gene knockdown by well-established RNA interference– and other oligonucleotide-based strategies continues to advance in the clinic, the repertoire of targetable effectors capable of altering gene expression at the RNA level is also rapidly expanding. In this review, we focus on several recently developed bifunctional molecular technologies that both interact with and act upon a target RNA. These new approaches for programmable RNA knockdown, editing, splicing, translation, and chemical modifications stand to provide impactful new modalities for therapeutic development in the coming decades.