Systems genetics: From GWAS to disease pathways

Most common diseases are complex, involving multiple genetic and environmental factors and their interactions. In the past decade, genome-wide association studies (GWAS) have successfully identified thousands of genetic variants underlying susceptibility to complex diseases. However, the results from these studies often do not provide evidence on how the variants affect downstream pathways and lead to the disease. Therefore, in the post-GWAS era the greatest challenge lies in combining GWAS findings with additional molecular data to functionally characterize the associations. The advances in various ~omics techniques have made it possible to investigate the effect of risk variants on intermediate molecular levels, such as gene expression, methylation, protein abundance or metabolite levels. As disease aetiology is complex, no single molecular analysis is expected to fully unravel the disease mechanism. Multiple molecular levels can interact and also show plasticity in different physiological conditions, cell types and disease stages. There is therefore a great need for new integrative approaches that can combine data from different molecular levels and can help construct the causal inference from genotype to phenotype. Systems genetics is such an approach; it is used to study genetic effects within the larger scope of systems biology by integrating genotype information with various ~omics datasets as well as with environmental and physiological variables. In this review, we describe this approach and discuss how it can help us unravel the molecular mechanisms through which genetic variation causes disease. This article is part of a Special Issue entitled: From Genome to Function.     

Trait-Associated SNPs Are More Likely to Be eQTLs: Annotation to Enhance Discovery from GWAS

Although genome-wide association studies (GWAS) of complex traits have yielded more reproducible associations than had been discovered using any other approach, the loci characterized to date do not account for much of the heritability to such traits and, in general, have not led to improved understanding of the biology underlying complex phenotypes. Using a web site we developed to serve results of expression quantitative trait locus (eQTL) studies in lymphoblastoid cell lines from HapMap samples (http://www.scandb.org), we show that single nucleotide polymorphisms (SNPs) associated with complex traits (from http://www.genome.gov/gwastudies/) are significantly more likely to be eQTLs than minor-allele-frequency–matched SNPs chosen from high-throughput GWAS platforms. These findings are robust across a range of thresholds for establishing eQTLs (p-values from 10⁻⁴–10⁻⁸), and a broad spectrum of human complex traits. Analyses of GWAS data from the Wellcome Trust studies confirm that annotating SNPs with a score reflecting the strength of the evidence that the SNP is an eQTL can improve the ability to discover true associations and clarify the nature of the mechanism driving the associations. Our results showing that trait-associated SNPs are more likely to be eQTLs and that application of this information can enhance discovery of trait-associated SNPs for complex phenotypes raise the possibility that we can utilize this information both to increase the heritability explained by identifiable genetic factors and to gain a better understanding of the biology underlying complex traits.     

Bioinspiration: applying mechanical design to experimental biology

The production of bioinspired and biomimetic constructs has fostered much collaboration between biologists and engineers, although the extent of biological accuracy employed in the designs produced has not always been a priority. Even the exact definitions of "bioinspired" and "biomimetic" differ among biologists, engineers, and industrial designers, leading to confusion regarding the level of integration and replication of biological principles and physiology. By any name, biologically-inspired mechanical constructs have become an increasingly important research tool in experimental biology, offering the opportunity to focus research by creating model organisms that can be easily manipulated to fill a desired parameter space of structural and functional repertoires. Innovative researchers with both biological and engineering backgrounds have found ways to use bioinspired models to explore the biomechanics of organisms from all kingdoms to answer a variety of different questions. Bringing together these biologists and engineers will hopefully result in an open discourse of techniques and fruitful collaborations for experimental and industrial endeavors.     

The psychiatric GWAS consortium: big science comes to psychiatry

The Psychiatric GWAS Consortium was founded with the aim of conducting statistically rigorous and comprehensive GWAS meta-analyses for five major psychiatric disorders: ADHD, autism, bipolar disorder, major depressive disorder, and schizophrenia. In the era of GWAS and high-throughput genomics, a major trend has been the emergence of collaborative, consortia approaches. Taking advantage of the scale that collaborative consortia approaches can bring to a problem, the PGC has been a major driver in psychiatric genetics and provides a model for how similar approaches may be applied to other disease communities.     

Common mistakes in infant feeding: survey from London borough.

A survey of infant-feeding practices among 265 mothers of 12-week-old infants in a London borough showed that only 18 gave breast milk alone, 236 giving bottle feeds, and 197 solids. Serious mistakes in feed preparation were common, and only 51 mothers followed manufacturers'' instructions exactly. Most mothers (239) had attended a well-baby clinic, but clinic staff gave conflicting advice on infant feeding. Health workers must pay greater attention to educating mothers on the details of infant feeding if the risks of hypernatraemia and obesity are to be reduced.     

Mapping Allele-Specific DNA Methylation: A New Tool for Maximizing Information from GWAS

In this issue of The Journal, an article by Schalkwyk et al.1 shows the landscape of allele-specific DNA methylation (ASM) in the human genome. ASM has long been studied as a hallmark of imprinted genes, and a chromosome-wide version of this phenomenon occurs, in a random fashion, during X chromosome inactivation in female cells. But the type of ASM motivating the study by Schalkwyk et al. is different. They used a high-resolution, methylation-sensitive SNP array (MSNP) method for genome-wide profiling of ASM in total peripheral-blood leukocytes (PBL) and buccal cells from a series of monozygotic twin pairs. Their data bring a new level of detail to our knowledge of a newly recognized phenomenon—nonimprinted, sequence-dependent ASM. They document the widespread occurrence of this phenomenon among human genes and discuss its basic implications for gene regulation and genetic-epigenetic interactions. But this paper and recent work from other laboratories2,3 raises the possibility of a more immediate and practical application for ASM mapping, namely to help extract maximum information from genome-wide association studies.     

Network-assisted analysis to prioritize GWAS results: principles, methods and perspectives

Genome-wide association studies (GWAS) have rapidly become a powerful tool in genetic studies of complex diseases and traits. Traditionally, single marker-based tests have been used prevalently in GWAS and have uncovered tens of thousands of disease-associated SNPs. Network-assisted analysis (NAA) of GWAS data is an emerging area in which network-related approaches are developed and utilized to perform advanced analyses of GWAS data in order to study various human diseases or traits. Progress has been made in both methodology development and applications of NAA in GWAS data, and it has already been demonstrated that NAA results may enhance our interpretation and prioritization of candidate genes and markers. Inspired by the strong interest in and high demand for advanced GWAS data analysis, in this review article, we discuss the methodologies and strategies that have been reported for the NAA of GWAS data. Many NAA approaches search for subnetworks and assess the combined effects of multiple genes participating in the resultant subnetworks through a gene set analysis. With no restriction to pre-defined canonical pathways, NAA has the advantage of defining subnetworks with the guidance of the GWAS data under investigation. In addition, some NAA methods prioritize genes from GWAS data based on their interconnections in the reference network. Here, we summarize NAA applications to various diseases and discuss the available options and potential caveats related to their practical usage. Additionally, we provide perspectives regarding this rapidly growing research area.     

Monocyte separation from whole blood : A method for experimental animals

A large supply of monocytes can be obtained from whole blood of rats. The principle is to stimulate a monocytosis, separate the lymphocyte/monocyte series on a Ficoll/sodium metrizoate gradient and eliminate lymphocytes with anti-lymphocyte serum. A simple method of labelling monocytes with tritiated thymidine is described. Some possible situations for studying monocytes in syngeneic animals in experimental situations are mentioned.     

Core-directed protein design. I. An experimental method for selecting stable proteins from combinatorial libraries.

The design of proteins represents a significant challenge to modern-day structural biology. A major obstacle here is the specification of well-packed hydrophobic cores to drive the folding and stabilization of the target. Computational approaches have been used to alleviate this by testing alternate sequences prior to the production and characterization of a few proteins. Here we present the experimental counterpart of this approach. We selected stable variants from a library of ubiquitin hydrophobic-core mutants as follows. Hexahistidine-tagged proteins were displayed on the surface of phage. These protein-phage were immobilized onto Ni-coated surfaces. The bound fusion-phage were treated with protease to remove unstable or poorly folded proteins. Stable phage fusions were eluted and infected into Escherichia coli, which allowed amplification for further selection, sequencing, or protein expression. Two Ni-derivatized supports were tested: Ni-NTA chips for surface plasmon resonance (SPR) and Ni-NTA agarose beads. SPR had an advantage in that the selection process could be monitored directly. This allowed individual clones and experimental conditions to be tested rapidly prior to preparative panning of the library, which was carried out using Ni-NTA agarose beads. We demonstrate the method by selecting stable core mutants of ubiquitin, the characterization of which is described in the following paper [Finucane, M. D., and Woolfson, D. N. (1999) Biochemistry 38, XXXXX-XXXXX]. As our method selects only on the basis of structure and stability, it will be of use in improving the stabilities and structural specificities of proteins of de novo design, and in establishing rules that link sequence and structure.     

Extended genetic effects of ADH cluster genes on the risk of alcohol dependence: from GWAS to replication

Alcohol dependence (AD) is a multifactorial and polygenic disorder involving complex gene-to-gene and gene-to-environment interactions. Several genome-wide association studies have reported numerous risk factors for AD, but replication results following these studies have been controversial. To identify new candidate genes, the present study used GWAS and replication studies in a Korean cohort with AD. Genome-wide association analysis revealed that two chromosome regions on Chr. 4q22-q23 (ADH gene cluster, including ADH5, ADH4, ADH6, ADH1A, ADH1B, and ADH7) and Chr. 12q24 (ALDH2) showed multiple association signals for the risk of AD. To investigate detailed genetic effects of these ADH genes on AD, a follow-up study of the ADH gene cluster on 4q22-q23 was performed. A total of 90 SNPs, including ADH1B rs1229984 (H47R), were genotyped in an additional 975 Korean subjects. In case–control analysis, ADH1B rs1229984 (H47R) showed the most significant association with the risk of AD (p = 2.63 × 10^?21, OR = 2.35). Moreover, subsequent conditional analyses revealed that all positive associations of other ADH genes in the cluster disappeared, which suggested that ADH1B rs1229984 (H47R) might be the sole functional genetic marker across the ADH gene cluster. Our findings could provide additional information on the ADH gene cluster regarding the risk of AD, as well as a new and important insight into the genetic factors associated with AD.