Common statistical issues in genome-wide association studies: a review on power, data quality control, genotype calling and population structure

A statistical genetic investigation of psychiatric resilience

Background: Although trauma exposure (TE) is a transdiagnostic risk factor for many psychiatric disorders, not everyone who experiences TE develops a psychiatric disorder. Resilience may explain this heterogeneity; thus, it is critical to understand the etiologic underpinnings of resilience.Objective: The present study sought to examine the genetic underpinnings of psychiatric resilience using genome-wide association studies (GWAS), genome-wide complex trait analysis (GCTA), and polygenic risk score (PRS) analyses.Method: Participants were 6,634 trauma exposed college students attending a diverse, public university in the Mid Atlantic. GWAS and GCTA analyses were conducted, and using GWAS summary statistics from large genetic consortia, PRS analyses examined the shared genetic risk between resilience and various phenotypes.Results: Results demonstrate that nine single-nucleotide polymorphisms (SNPs) met the suggestive of significance threshold, heritability estimates for resilience were non-significant, and that there is genetic overlap between resilience and AD, as well as resilience and PTSD.Conclusion: Mixed findings from the present study suggest additional research to elucidate the etiological underpinnings of resilience, ideally with larger samples less biased by variables such as heterogeneity (i.e. clinical vs. population based) and population stratification. Genetic investigations of resilience have the potential to elucidate the molecular bases of stress-related psychopathology, suggesting new avenues for prevention and intervention efforts.

Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle

Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.

The influence of genetic structure on phenotypic diversity in the Australian mango (Mangifera indica) gene pool

Genomic selection is a promising breeding technique for tree crops to accelerate the development of new cultivars. However, factors such as genetic structure can create spurious associations between genotype and phenotype due to the shared history between populations with different trait values. Genetic structure can therefore reduce the accuracy of the genotype to phenotype map, a fundamental requirement of genomic selection models. Here, we employed 272 single nucleotide polymorphisms from 208 Mangifera indica accessions to explore whether the genetic structure of the Australian mango gene pool explained variation in trunk circumference, fruit blush colour and intensity. Multiple population genetic analyses indicate the presence of four genetic clusters and show that the most genetically differentiated cluster contains accessions imported from Southeast Asia (mainly those from Thailand). We find that genetic structure was strongly associated with three traits: trunk circumference, fruit blush colour and intensity in M. indica. This suggests that the history of these accessions could drive spurious associations between loci and key mango phenotypes in the Australian mango gene pool. Incorporating such genetic structure in associations between genotype and phenotype can improve the accuracy of genomic selection, which can assist the future development of new cultivars.

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.

Preparation and Curation of Omics Data for Genome-Wide Association Studies

With the development of large-scale molecular phenotyping platforms, genome-wide association studies have greatly developed, being no longer limited to the analysis of classical agronomic traits, such as yield or flowering time, but also embracing the dissection of the genetic basis of molecular traits. Data generated by omics platforms, however, pose some technical and statistical challenges to the classical methodology and assumptions of an association study. Although genotyping data are subject to strict filtering procedures, and several advanced statistical approaches are now available to adjust for population structure, less attention has been instead devoted to the preparation of omics data prior to GWAS. In the present chapter, we briefly present the methods to acquire profiling data from transcripts, proteins, and small molecules, and discuss the tools and possibilities to clean, normalize, and remove the unwanted variation from large datasets of molecular phenotypic traits prior to their use in GWAS.

Suitability of GWAS as a Tool to Discover SNPs Associated with Tick Resistance in Cattle: A Review

Understanding the biological mechanisms underlying tick resistance in cattle holds the potential to facilitate genetic improvement through selective breeding. Genome wide association studies (GWAS) are popular in research on unraveling genetic determinants underlying complex traits such as tick resistance. To date, various studies have been published on single nucleotide polymorphisms (SNPs) associated with tick resistance in cattle. The discovery of SNPs related to tick resistance has led to the mapping of associated candidate genes. Despite the success of these studies, information on genetic determinants associated with tick resistance in cattle is still limited. This warrants the need for more studies to be conducted. In Africa, the cost of genotyping is still relatively expensive; thus, conducting GWAS is a challenge, as the minimum number of animals recommended cannot be genotyped. These population size and genotype cost challenges may be overcome through the establishment of collaborations. Thus, the current review discusses GWAS as a tool to uncover SNPs associated with tick resistance, by focusing on the study design, association analysis, factors influencing the success of GWAS, and the progress on cattle tick resistance studies.

The Association Between rs1748195 and rs11207997 Variants of the ANGPTL3 Gene and Susceptibility to Cardiovascular Disease in the MASHAD Cohort Study

There is a strong genetic predisposition to cardiovascular disease (CVD). Loss-of-function variants of the angiopoietin-like 3 (ANGPTL3) gene have been reported to be associated with several lipid-related CVD risk factors that include serum high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG) level, and total cholesterol (TC). We aimed to determine the association of two genetic variants, rs1748195 and rs11207997, of the ANGPTL3 locus and CVD risk in the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) cohort study. The participants were 1002 individuals in the MASHAD cohort, with or without CVD, during the 6 years of follow-up. The subjects were categorized into two groups according to serum HDL concentration. DNA was extracted by the routine salting-out method, and genotyping of rs1748195 and rs11207997 variants of the ANGPTL3 gene was performed using the ARMS PCR method. Univariate and multivariate statistical analysis was used to assess the two gene variants' association with incident CVD and baseline lipid profile. There was a significant relationship between rs1748195 GG genotype and CVD risk in the individuals with a normal serum HDL-C. There was a significant association between the CT genotype of the rs11207997 polymorphism and CVD risk in individuals with a low serum HDL-C. Furthermore, carriers of the GG genotype of the rs1748195 and CT genotype of rs11207997 variant of ANGPTL3 had a higher risk of developing CVD disease. We have shown that the 1748195(GG) and 11207997(CT) gene variants of the ANGPTL3 locus are associated with an increased risk of CVD in an Iranian population sample.

Effects of Starch Synthesis-Related Genes Polymorphism on Quality of Glutinous Rice

Glutinous rice (Oryza sativa L.) quality includes thermal properties, retrogradation and pasting viscosity properties, and so on, which have little or no amylose. However, the genetic network regulation of different quality indices has not been systematically studied. The aim was to investigate the relationship between starch synthesis-related genes (SSRGs) and the physicochemical properties of glutinous rice by targeted-gene association analysis (TGAS). The genotypes of 17 SSRGs were analyzed using 46 gene-specific molecular markers in 63 glutinous rice accessions. TGAS and gene interactions analysis indicated that soluble starch synthase (SS) IIa, SSI, starch branching enzyme (BE) IIa, and pullulanase (PUL) had significant genetic effects on glutinous rice quality. SSI and SSIIa were the major genes that regulated thermal properties and retrogradation properties (RP). PUL was central in the regulation of gel consistency (GC), and it participated in the regulation of pasting viscosity parameters (PVP) except for the pasting time and the pasting temperature. BEIIb, ISA1, SSIVb, BEIIa, SSIVa, and their interactions with SSIIa regulated gelatinization temperature (GT) and PVP. The starch properties of glutinous rice are mainly controlled by SSIIa, SSI, PUL, and their interactions, but SSIIa is central among them. These findings indicate that starch properties in glutinous rice have a complex genetic system. It provides crucial information for promoting glutinous rice quality.

snpQT: flexible, reproducible, and comprehensive quality control and imputation of genomic data

Quality control of genomic data is an essential but complicated multi-step procedure, often requiring separate installation and expert familiarity with a combination of different bioinformatics tools. Software incompatibilities, and inconsistencies across computing environments, are recurrent challenges, leading to poor reproducibility. Existing semi-automated or automated solutions lack comprehensive quality checks, flexible workflow architecture, and user control. To address these challenges, we have developed snpQT: a scalable, stand-alone software pipeline using nextflow and BioContainers, for comprehensive, reproducible and interactive quality control of human genomic data. snpQT offers some 36 discrete quality filters or correction steps in a complete standardised pipeline, producing graphical reports to demonstrate the state of data before and after each quality control procedure. This includes human genome build conversion, population stratification against data from the 1,000 Genomes Project, automated population outlier removal, and built-in imputation with its own pre- and post- quality controls. Common input formats are used, and a synthetic dataset and comprehensive online tutorial are provided for testing, educational purposes, and demonstration. The snpQT pipeline is designed to run with minimal user input and coding experience; quality control steps are implemented with numerous user-modifiable thresholds, and workflows can be flexibly combined in custom combinations. snpQT is open source and freely available at https://github.com/nebfield/snpQT. A comprehensive online tutorial and installation guide is provided through to GWAS (https://snpqt.readthedocs.io/en/latest/), introducing snpQT using a synthetic demonstration dataset and a real-world Amyotrophic Lateral Sclerosis SNP-array dataset.

snpQT: flexible, reproducible, and comprehensive quality control and imputation of genomic data

Quality control of genomic data is an essential but complicated multi-step procedure, often requiring separate installation and expert familiarity with a combination of different bioinformatics tools. Dependency hell and reproducibility are recurrent challenges. Existing semi-automated or automated solutions lack comprehensive quality checks, flexible workflow architecture, and user control. To address these challenges, we have developed snpQT: a scalable, stand-alone software pipeline using nextflow and BioContainers, for comprehensive, reproducible and interactive quality control of human genomic data. snpQT offers some 36 discrete quality filters or correction steps in a complete standardised pipeline, producing graphical reports to demonstrate the state of data before and after each quality control procedure. This includes human genome build conversion, population stratification against data from the 1,000 Genomes Project, automated population outlier removal, and built-in imputation with its own pre- and post- quality controls. Common input formats are used, and a synthetic dataset and comprehensive online tutorial are provided for testing, educational purposes, and demonstration. The snpQT pipeline is designed to run with minimal user input and coding experience; quality control steps are implemented with default thresholds which can be modified by the user, and workflows can be flexibly combined in custom combinations. snpQT is open source and freely available at https://github.com/nebfield/snpQT. A comprehensive online tutorial and installation guide is provided through to GWAS (https://snpqt.readthedocs.io/en/latest/), introducing snpQT using a synthetic demonstration dataset and a real-world Amyotrophic Lateral Sclerosis SNP-array dataset.

Does geographic variation in thermal tolerance in Daphnia represent trade-offs or conditional neutrality?

Geographic variation in thermal tolerance in Daphnia seems to represent genetic load at the loci specifically responsible for heat tolerance resulting from conditional neutrality. We see no evidence of trade-offs between fitness-related traits at 25 °C vs. 10 °C or between two algal diets across Daphnia magna clones from a variety of locations representing the opposite ends of the distribution of long-term heat tolerance. Likewise, we found no evidence of within-environment trade-offs between heat tolerance and fitness-related traits in any of the environments. Neither short-term and long-term heat tolerance shows any consistent relationship with lipid fluorescence polarization and lipid peroxidation across clones or environments. Pervasive positive correlations between fitness-related traits indicate differences in genetic load rather than trade-off based local adaptation or thermal specialization. For heat tolerance such differences may be caused by either relaxation of stabilizing selection due to lower exposure to high temperature extremes, i.e., conditional neutrality, or by small effective population size followed by the recent range expansion.

Integrated Genomic Strategies for Cereal Genetic Enhancement: Combining QTL and Association Mapping

Identification of genetic basis for important agronomic traits is essential for marker-assisted crop improvement. Linkage mapping is one of the most popular approaches utilized for identification of major quantitative trait loci (QTLs) governing important agronomic traits in cereals. However, the identified QTLs usually span large genomic intervals and very few of these are subsequently fine mapped to single major effect gene. This hinders application of these QTLs in marker-aided breeding and crop genetic enhancement. On the contrary, association mapping, another popular approach for identification of QTLs, provides very high resolution but suffers from high level of false positives. Joint linkage-association analysis provides a way to combine advantages and avoid the pitfalls associated with both these methods. In this context, we recently developed MetaQTL specific regional association analysis and demonstrated its utility to rapidly narrow down previously identified QTL intervals to few candidate genes. Here, we describe the detailed step-by-step guide for performing MetaQTL specific regional association analysis to identify important genomic regions and underlying potential major effect genes governing traits of agronomic importance in cereals.

A Genome-Wide Association Study of Bisphosphonate-Associated Atypical Femoral Fracture

Atypical femoral fracture is a well-documented adverse reaction to bisphosphonates. It is strongly related to duration of bisphosphonate use, and the risk declines rapidly after drug withdrawal. The mechanism behind bisphosphonate-associated atypical femoral fracture is unclear, but a genetic predisposition has been suggested. With the aim to identify common genetic variants that could be used for preemptive genetic testing, we performed a genome-wide association study. Cases were recruited mainly through reports of adverse drug reactions sent to the Swedish Medical Products Agency on a nation-wide basis. We compared atypical femoral fracture cases (n = 51) with population-based controls (n = 4891), and to reduce the possibility of confounding by indication, we also compared with bisphosphonate-treated controls without a current diagnosis of cancer (n = 324). The total number of single-nucleotide polymorphisms after imputation was 7,585,874. A genome-wide significance threshold of p < 5 × 10^-8 was used to correct for multiple testing. In addition, we performed candidate gene analyses for a panel of 29 genes previously implicated in atypical femoral fractures (significance threshold of p < 5.7 × 10^-6). Compared with population controls, bisphosphonate-associated atypical femoral fracture was associated with four isolated, uncommon single-nucleotide polymorphisms. When cases were compared with bisphosphonate-treated controls, no statistically significant genome-wide association remained. We conclude that the detected associations were either false positives or related to the underlying disease, i.e., treatment indication. Furthermore, there was no significant association with single-nucleotide polymorphisms in the 29 candidate genes. In conclusion, this study found no evidence of a common genetic predisposition for bisphosphonate-associated atypical femoral fracture. Further studies of larger sample size to identify possible weakly associated genetic traits, as well as whole exome or whole-genome sequencing studies to identify possible rare genetic variation conferring a risk are warranted.

Genomics of radiation sensitivity in squamous cell carcinomas

Radiotherapy is an important modality in the management of squamous cell cancers with 50% of patients receiving radiotherapy at some point. Despite technological advances, the risk of severe toxicity in a proportion of radiosensitive patients limits radiation doses that can be safely prescribed affecting the potential for cure. While comorbidities, lifestyle and treatment factors can influence interindividual variations, genetic factors are thought to play a major role, accounting for approximately 80% of the variance observed. Over the last decade, substantial progress has been made in the field of radiogenomics, with compelling associations for SNPs identified in genes involved in DNA-damage response, cell-cycle control, apoptosis, antioxidant defenses and cytokine production. Future research efforts should be collaborative, focused on validating and broadening their clinical applicability. Numerous obstacles exist to the clinical application of this knowledge, which need to be overcome before personalized radiation therapy becomes a routine component of oncologic care.

Multivariate Methods for Meta-Analysis of Genetic Association Studies

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

Multiple Testing in the Context of Gene Discovery in Sickle Cell Disease Using Genome-Wide Association Studies

The issue of multiple testing, also termed multiplicity, is ubiquitous in studies where multiple hypotheses are tested simultaneously. Genome-wide association study (GWAS), a type of genetic association study that has gained popularity in the past decade, is most susceptible to the issue of multiple testing. Different methodologies have been employed to address the issue of multiple testing in GWAS. The purpose of the review is to examine the methodologies employed in dealing with multiple testing in the context of gene discovery using GWAS in sickle cell disease complications.

Genetic Approaches to Understanding Psychiatric Disease

Human genetic studies have been the driving force in bringing to light the underlying biology of psychiatric conditions. As these studies fill in the gaps in our knowledge of the mechanisms at play, we will be better equipped to design therapies in rational and targeted ways, or repurpose existing therapies in previously unanticipated ways. This review is intended for those unfamiliar with psychiatric genetics as a field and provides a primer on different modes of genetic variation, the technologies currently used to probe them, and concepts that provide context for interpreting the gene-phenotype relationship. Like other subfields in human genetics, psychiatric genetics is moving from microarray technology to sequencing-based approaches as barriers of cost and expertise are removed, and the ramifications of this transition are discussed here. A summary is then given of recent genetic discoveries in a number of neuropsychiatric conditions, with particular emphasis on neurodevelopmental conditions. The general impact of genetics on drug development has been to underscore the extensive etiological heterogeneity in seemingly cohesive diagnostic categories. Consequently, the path forward is not in therapies hoping to reach large swaths of patients sharing a clinically defined diagnosis, but rather in targeting patients belonging to specific "biotypes" defined through a combination of objective, quantifiable data, including genotype.

Rethinking the Epigenetic Framework to Unravel the Molecular Pathology of Schizophrenia

Schizophrenia is a complex mental disorder whose causes are still far from being known. Although researchers have focused on genetic or environmental contributions to the disease, we still lack a scientific framework that joins molecular and clinical findings. Epigenetic can explain how environmental variables may affect gene expression without modifying the DNA sequence. In fact, neuroepigenomics represents an effort to unify the research available on the molecular pathology of mental diseases, which has been carried out through several approaches ranging from interrogating single DNA methylation events and hydroxymethylation patterns, to epigenome-wide association studies, as well as studying post-translational modifications of histones, or nucleosomal positioning. The high dependence on tissues with epigenetic marks compels scientists to refine their sampling procedures, and in this review, we will focus on findings obtained from brain tissue. Despite our efforts, we still need to refine our hypothesis generation process to obtain real knowledge from a neuroepigenomic framework, to avoid the creation of more noise on this innovative point of view; this may help us to definitively unravel the molecular pathology of severe mental illnesses, such as schizophrenia.

Network analysis of genes and their association with diseases

A plethora of network-based approaches within the Systems Biology universe have been applied, to date, to investigate the underlying molecular mechanisms of various human diseases. In the present study, we perform a bipartite, topological and clustering graph analysis in order to gain a better understanding of the relationships between human genetic diseases and the relationships between the genes that are implicated in them. For this purpose, disease-disease and gene-gene networks were constructed from combined gene-disease association networks. The latter, were created by collecting and integrating data from three diverse resources, each one with different content covering from rare monogenic disorders to common complex diseases. This data pluralism enabled us to uncover important associations between diseases with unrelated phenotypic manifestations but with common genetic origin. For our analysis, the topological attributes and the functional implications of the individual networks were taken into account and are shortly discussed. We believe that some observations of this study could advance our understanding regarding the etiology of a disease with distinct pathological manifestations, and simultaneously provide the springboard for the development of preventive and therapeutic strategies and its underlying genetic mechanisms.

Genetic underpinnings of left superior temporal gyrus thickness in patients with schizophrenia

OBJECTIVES

Schizophrenia is a highly disabling psychiatric disorder with a heterogeneous phenotypic appearance. We aimed to further the understanding of some of the underlying genetics of schizophrenia, using left superior temporal gyrus (STG) grey matter thickness reduction as an endophenoptype in a genome-wide association (GWA) study.

METHODS

Structural magnetic resonance imaging (MRI) and genetic data of the Mind Clinical Imaging Consortium (MCIC) study of schizophrenia were used to analyse the interaction effects between 1,067,955 single nucleotide polymorphisms (SNPs) and disease status on left STG thickness in 126 healthy controls and 113 patients with schizophrenia. We next used a pathway approach to detect underlying pathophysiological pathways that may be related to schizophrenia.

RESULTS

No SNP by diagnosis interaction effect reached genome-wide significance (5 × 10-8) in our GWA study, but 10 SNPs reached P-values less than 10-6. The most prominent pathways included those involved in insulin, calcium, PI3K-Akt and MAPK signalling.

CONCLUSIONS

Our strongest findings in the GWA study and pathway analysis point towards an involvement of glucose metabolism in left STG thickness reduction in patients with schizophrenia only. These results are in line with recently published studies, which showed an increased prevalence of psychosis among patients with metabolic syndrome-related illnesses including diabetes.

Pathway Analysis of Metabolic Syndrome Using a Genome-Wide Association Study of Korea Associated Resource (KARE) Cohorts

Metabolic syndrome (MetS) is a complex disorder related to insulin resistance, obesity, and inflammation. Genetic and environmental factors also contribute to the development of MetS, and through genome-wide association studies (GWASs), important susceptibility loci have been identified. However, GWASs focus more on individual single-nucleotide polymorphisms (SNPs), explaining only a small portion of genetic heritability. To overcome this limitation, pathway analyses are being applied to GWAS datasets. The aim of this study is to elucidate the biological pathways involved in the pathogenesis of MetS through pathway analysis. Cohort data from the Korea Associated Resource (KARE) was used for analysis, which include 8,842 individuals (age, 52.2 ± 8.9 years; body mass index, 24.6 ± 3.2 kg/m(2)). A total of 312,121 autosomal SNPs were obtained after quality control. Pathway analysis was conducted using Meta-analysis Gene-Set Enrichment of Variant Associations (MAGENTA) to discover the biological pathways associated with MetS. In the discovery phase, SNPs from chromosome 12, including rs11066280, rs2074356, and rs12229654, were associated with MetS (p < 5 × 10(-6)), and rs11066280 satisfied the Bonferroni-corrected cutoff (unadjusted p < 1.38 × 10(-7), Bonferroni-adjusted p < 0.05). Through pathway analysis, biological pathways, including electron carrier activity, signaling by platelet-derived growth factor (PDGF), the mitogen-activated protein kinase kinase kinase cascade, PDGF binding, peroxisome proliferator-activated receptor (PPAR) signaling, and DNA repair, were associated with MetS. Through pathway analysis of MetS, pathways related with PDGF, mitogen-activated protein kinase, and PPAR signaling, as well as nucleic acid binding, protein secretion, and DNA repair, were identified. Further studies will be needed to clarify the genetic pathogenesis leading to MetS.

SNPQC--an R pipeline for quality control of Illumina SNP genotyping array data

In genome-wide association studies, quality control (QC) of genotypes is important to avoid spurious results. It is also important to maintain long-term data integrity, particularly in settings with ongoing genotyping (e.g. estimation of genomic breeding values). Here we discuss SNPQc, a fully automated pipeline to perform QC analyses of Illumina SNP array data. It applies a wide range of common quality metrics with user-defined filtering thresholds to generate a comprehensive QC report and a filtered dataset, including a genomic relationship matrix, ready for further downstream analyses which make it amenable for integration in high-throughput environments. SNPQC also builds a database to store genotypic, phenotypic and quality metrics to ensure data integrity and the option of integrating more samples from subsequent runs. The program is generic across species and array designs, providing a convenient interface between the genotyping laboratory and downstream genome-wide association study or genomic prediction.

Genetic Addiction Risk Score (GARS): molecular neurogenetic evidence for predisposition to Reward Deficiency Syndrome (RDS)

We have published extensively on the neurogenetics of brain reward systems with reference to the genes related to dopaminergic function in particular. In 1996, we coined “Reward Deficiency Syndrome” (RDS), to portray behaviors found to have gene-based association with hypodopaminergic function. RDS as a useful concept has been embraced in many subsequent studies, to increase our understanding of Substance Use Disorder (SUD), addictions, and other obsessive, compulsive, and impulsive behaviors. Interestingly, albeit others, in one published study, we were able to describe lifetime RDS behaviors in a recovering addict (17 years sober) blindly by assessing resultant Genetic Addiction Risk Score (GARS™) data only. We hypothesize that genetic testing at an early age may be an effective preventive strategy to reduce or eliminate pathological substance and behavioral seeking activity. Here, we consider a select number of genes, their polymorphisms, and associated risks for RDS whereby, utilizing GWAS, there is evidence for convergence to reward candidate genes. The evidence presented serves as a plausible brain-print providing relevant genetic information that will reinforce targeted therapies, to improve recovery and prevent relapse on an individualized basis. The primary driver of RDS is a hypodopaminergic trait (genes) as well as epigenetic states (methylation and deacetylation on chromatin structure). We now have entered a new era in addiction medicine that embraces the neuroscience of addiction and RDS as a pathological condition in brain reward circuitry that calls for appropriate evidence-based therapy and early genetic diagnosis and that requires further intensive investigation.

Genetic and functional evidence for a locus controlling otitis media at chromosome 10q26.3

BACKGROUND

Otitis media (OM) is a common childhood disease characterised by middle ear effusion and inflammation. Susceptibility to recurrent acute OM and chronic OM with effusion is 40-70% heritable. Linkage studies provide evidence for multiple putative OM susceptibility loci. This study attempts to replicate these linkages in a Western Australian (WA) population, and to identify the etiological gene(s) in a replicated region.

METHODS

Microsatellites were genotyped in 468 individuals from 101 multicase families (208 OM cases) from the WA Family Study of OM (WAFSOM) and non-parametric linkage analysis carried out in ALLEGRO. Association mapping utilized dense single nucleotide polymorphism (SNP) data extracted from Illumina 660 W-Quad analysis of 256 OM cases and 575 controls from the WA Pregnancy Cohort (Raine) Study. Logistic regression analysis was undertaken in ProbABEL. RT-PCR was used to compare gene expression in paired adenoid and tonsil samples, and in epithelial and macrophage cell lines. Comparative genomics methods were used to identify putative regulatory elements and transcription factor binding sites potentially affected by associated SNPs.

RESULTS

Evidence for linkage was observed at 10q26.3 (Zlr = 2.69; P = 0.0036; D10S1770) with borderline evidence for linkage at 10q22.3 (Zlr = 1.64; P = 0.05; D10S206). No evidence for linkage was seen at 3p25.3, 17q12, or 19q13.43. Peak association at 10q26.3 was in the intergenic region between TCERG1L and PPP2R2D (rs7922424; P = 9.47 × 10-6), immediately under the peak of linkage. Independent associations were observed at DOCK1 (rs9418832; P = 7.48 × 10-5) and ADAM12 (rs7902734; P = 8.04 × 10-4). RT-PCR analysis confirmed expression of all 4 genes in adenoid samples. ADAM12, DOCK1 and PPP2R2D, but not TCERG1L, were expressed in respiratory epithelial and macrophage cell lines. A significantly associated polymorphism (rs7087384) in strong LD with the top SNP (rs7922424; r2 = 0.97) alters a transcription factor binding site (CREB/CREBP) in the intergenic region between TCERG1L and PPP2R2D.

CONCLUSIONS

OM linkage was replicated at 10q26.3. Whilst multiple genes could contribute to this linkage, the weight of evidence supports PPP2R2D, a TGF-β/Activin/Nodal pathway modulator, as the more likely functional candidate lying immediately under the linkage peak for OM susceptibility at chromosome 10q26.3.

Testing gene-gene interactions in genome wide association studies

Detection of gene-gene interaction has become increasingly popular over the past decade in genome wide association studies (GWAS). Besides traditional logistic regression analysis for detecting interactions between two markers, new methods have been developed in recent years such as comparing linkage disequilibrium (LD) in case and control groups. All these methods form the building blocks of most screening strategies for disease susceptibility loci in GWAS. In this paper, we are interested in comparing the competing methods and providing practical guidelines for selecting appropriate testing methods for interaction in GWAS. We first review a series of existing statistical methods to detect interactions, and then examine different definitions of interactions to gain insight into the theoretical relationship between the existing testing methods. Lastly, we perform extensive simulations to compare powers of various methods to detect either interaction between two markers at two unlinked loci or the overall association allowing for both interaction and main effects. This investigation reveals informative characteristics of various methods that are helpful to GWAS investigators.

Defining a comprehensive verotype using electronic health records for personalized medicine

The burgeoning adoption of electronic health records (EHR) introduces a golden opportunity for studying individual manifestations of myriad diseases, which is called 'EHR phenotyping'. In this paper, we break down this concept by: relating it to phenotype definitions from Johannsen; comparing it to cohort identification and disease subtyping; introducing a new concept called 'verotype' (Latin: vere = true, actually) to represent the 'true' population of similar patients for treatment purposes through the integration of genotype, phenotype, and disease subtype (eg, specific glucose value pattern in patients with diabetes) information; analyzing the value of the 'verotype' concept for personalized medicine; and outlining the potential for using network-based approaches to reverse engineer clinical disease subtypes.

Perspective: a systems approach to diabetes research

We review here the status of human type 2 diabetes studies from a genetic, epidemiological, and clinical (intervention) perspective. Most studies limit analyses to one or a few omic technologies providing data of components of physiological processes. Since all chronic diseases are multifactorial and arise from complex interactions between genetic makeup and environment, type 2 diabetes mellitus (T2DM) is a collection of sub-phenotypes resulting in high fasting glucose. The underlying gene–environment interactions that produce these classes of T2DM are imperfectly characterized. Based on assessments of the complexity of T2DM, we propose a systems biology approach to advance the understanding of origin, onset, development, prevention, and treatment of this complex disease. This systems-based strategy is based on new study design principles and the integrated application of omics technologies: we pursue longitudinal studies in which each subject is analyzed at both homeostasis and after (healthy and safe) challenges. Each enrolled subject functions thereby as their own case and control and this design avoids assigning the subjects a priori to case and control groups based on limited phenotyping. Analyses at different time points along this longitudinal investigation are performed with a comprehensive set of omics platforms. These data sets are generated in a biological context, rather than biochemical compound class-driven manner, which we term “systems omics.”

A Genome-Wide Association Study Suggests Novel Loci Associated with a Schizophrenia-Related Brain-Based Phenotype

Patients with schizophrenia and their siblings typically show subtle changes of brain structures, such as a reduction of hippocampal volume. Hippocampal volume is heritable, may explain a variety of cognitive symptoms of schizophrenia and is thus considered an intermediate phenotype for this mental illness. The aim of our analyses was to identify single-nucleotide polymorphisms (SNP) related to hippocampal volume without making prior assumptions about possible candidate genes. In this study, we combined genetics, imaging and neuropsychological data obtained from the Mind Clinical Imaging Consortium study of schizophrenia (n = 328). A total of 743,591 SNPs were tested for association with hippocampal volume in a genome-wide association study. Gene expression profiles of human hippocampal tissue were investigated for gene regions of significantly associated SNPs. None of the genetic markers reached genome-wide significance. However, six highly correlated SNPs (rs4808611, rs35686037, rs12982178, rs1042178, rs10406920, rs8170) on chromosome 19p13.11, located within or in close proximity to the genes NR2F6, USHBP1, and BABAM1, as well as four SNPs in three other genomic regions (chromosome 1, 2 and 10) had p-values between 6.75×10(-6) and 8.3×10(-7). Using existing data of a very recently published GWAS of hippocampal volume and additional data of a multicentre study in a large cohort of adolescents of European ancestry, we found supporting evidence for our results. Furthermore, allelic differences in rs4808611 and rs8170 were highly associated with differential mRNA expression in the cis-acting region. Associations with memory functioning indicate a possible functional importance of the identified risk variants. Our findings provide new insights into the genetic architecture of a brain structure closely linked to schizophrenia. In silico replication, mRNA expression and cognitive data provide additional support for the relevance of our findings. Identification of causal variants and their functional effects may unveil yet unknown players in the neurodevelopment and the pathogenesis of neuropsychiatric disorders.

Cumulative genetic risk and prefrontal activity in patients with schizophrenia

The lack of consistency of genetic associations in highly heritable mental illnesses, such as schizophrenia, remains a challenge in molecular psychiatry. Because clinical phenotypes for psychiatric disorders are often ill defined, considerable effort has been made to relate genetic polymorphisms to underlying physiological aspects of schizophrenia (so called intermediate phenotypes), that may be more reliable. Given the polygenic etiology of schizophrenia, the aim of this work was to form a measure of cumulative genetic risk and study its effect on neural activity during working memory (WM) using functional magnetic resonance imaging. Neural activity during the Sternberg Item Recognition Paradigm was measured in 79 schizophrenia patients and 99 healthy controls. Participants were genotyped, and a genetic risk score (GRS), which combined the additive effects of 41 single-nucleotide polymorphisms (SNPs) from 34 risk genes for schizophrenia, was calculated. These risk SNPs were chosen according to the continuously updated meta-analysis of genetic studies on schizophrenia available at www.schizophreniaresearchforum.org. We found a positive relationship between GRS and left dorsolateral prefrontal cortex inefficiency during WM processing. GRS was not correlated with age, performance, intelligence, or medication effects and did not differ between acquisition sites, gender, or diagnostic groups. Our study suggests that cumulative genetic risk, combining the impact of many genes with small effects, is associated with a known brain-based intermediate phenotype for schizophrenia. The GRS approach could provide an advantage over studying single genes in studies focusing on the genetic basis of polygenic conditions such as neuropsychiatric disorders.

SNPflow: a lightweight application for the processing, storing and automatic quality checking of genotyping assays

Single nucleotide polymorphisms (SNPs) play a prominent role in modern genetics. Current genotyping technologies such as Sequenom iPLEX, ABI TaqMan and KBioscience KASPar made the genotyping of huge SNP sets in large populations straightforward and allow the generation of hundreds of thousands of genotypes even in medium sized labs. While data generation is straightforward, the subsequent data conversion, storage and quality control steps are time-consuming, error-prone and require extensive bioinformatic support. In order to ease this tedious process, we developed SNPflow. SNPflow is a lightweight, intuitive and easily deployable application, which processes genotype data from Sequenom MassARRAY (iPLEX) and ABI 7900HT (TaqMan, KASPar) systems and is extendible to other genotyping methods as well. SNPflow automatically converts the raw output files to ready-to-use genotype lists, calculates all standard quality control values such as call rate, expected and real amount of replicates, minor allele frequency, absolute number of discordant replicates, discordance rate and the p-value of the HWE test, checks the plausibility of the observed genotype frequencies by comparing them to HapMap/1000-Genomes, provides a module for the processing of SNPs, which allow sex determination for DNA quality control purposes and, finally, stores all data in a relational database. SNPflow runs on all common operating systems and comes as both stand-alone version and multi-user version for laboratory-wide use. The software, a user manual, screenshots and a screencast illustrating the main features are available at http://genepi-snpflow.i-med.ac.at.

Marbled inflation from population structure in gene-based association studies with rare variants

Accurate genetic association studies are crucial for the detection and the validation of disease determinants. One of the main confounding factors that affect accuracy is population stratification, and great efforts have been extended for the past decade to detect and to adjust for it. We have now efficient solutions for population stratification adjustment for single-SNP (where SNP is single-nucleotide polymorphisms) inference in genome-wide association studies, but it is unclear whether these solutions can be effectively applied to rare variation studies and in particular gene-based (or set-based) association methods that jointly analyze multiple rare and common variants. We examine here, both theoretically and empirically, the performance of two commonly used approaches for population stratification adjustment-genomic control and principal component analysis-when used on gene-based association tests. We show that, different from single-SNP inference, genes with diverse composition of rare and common variants may suffer from population stratification to various extent. The inflation in gene-level statistics could be impacted by the number and the allele frequency spectrum of SNPs in the gene, and by the gene-based testing method used in the analysis. As a consequence, using a universal inflation factor as a genomic control should be avoided in gene-based inference with sequencing data. We also demonstrate that caution needs to be exercised when using principal component adjustment because the accuracy of the adjusted analyses depends on the underlying population substructure, on the way the principal components are constructed, and on the number of principal components used to recover the substructure.

Quality control for genome-wide association studies

This chapter overviews the quality control (QC) issues for SNP-based genotyping methods used in genome-wide association studies. The main metrics for evaluating the quality of the genotypes are discussed followed by a worked out example of QC pipeline starting with raw data and finishing with a fully filtered dataset ready for downstream analysis. The emphasis is on automation of data storage, filtering, and manipulation to ensure data integrity throughput the process and on how to extract a global summary from these high dimensional datasets to allow better-informed downstream analytical decisions. All examples will be run using the R statistical programming language followed by a practical example using a fully automated QC pipeline for the Illumina platform.

Predicting outcomes in radiation oncology--multifactorial decision support systems

With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome-including survival, recurrence patterns and toxicity-in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner.

Genome-wide association study heterogeneous cohort homogenization via subject weight knock-down

Population structure can be a source of both false-positive and false-negative findings in a genome-wide association study. This article proposes an approach that helps to reduce the false-positives. It consists of homogenizing the diseased/healthy phenotype ratio across the cohort, by decreasing the statistical weight of selected individuals. After homogenization, the cohort is statistically handled as if originating from a single well-mixed population. The method was applied to homogenize a Parkinson's disease genome-wide association study cohort.

IPGWAS: an integrated pipeline for rational quality control and association analysis of genome-wide genetic studies

Large numbers of samples and marker loci were tested for association in genome-wide association studies (GWAS). Hence, quality control (QC) by removing individuals or markers with low genotyping quality is of utmost importance to minimize potential false positive associations. IPGWAS was developed to facilitate the identification of the rational thresholds in QC of GWAS datasets, association analysis, Manhattan plot, quantile-quantile (QQ) plot, and format conversion for genetic analyses, such as meta-analysis, genotype phasing, and imputation. IPGWAS is a multiplatform application written in Perl with a graphical user interface (GUI) and available for free at http://sourceforge.net/projects/ipgwas/.

Genetics and genomics of radiotherapy toxicity: towards prediction

Radiotherapy is involved in many curative treatments of cancer; millions of survivors live with the consequences of treatment, and toxicity in a minority limits the radiation doses that can be safely prescribed to the majority. Radiogenomics is the whole genome application of radiogenetics, which studies the influence of genetic variation on radiation response. Work in the area focuses on uncovering the underlying genetic causes of individual variation in sensitivity to radiation, which is important for effective, safe treatment. In this review, we highlight recent advances in radiotherapy and discuss results from four genome-wide studies of radiotoxicity.

FBXO11, a regulator of the TGFβ pathway, is associated with severe otitis media in Western Australian children

Otitis media (OM) is a common childhood disease characterised by middle ear inflammation following infection. Susceptibility to recurrent acute OM (rAOM) and chronic OM with effusion (COME) is highly heritable. Two murine mutants, Junbo and Jeff, spontaneously develop severe OM with similar phenotypes to human disease. Fine-mapping of these mutants identified two genes (Evi1 and Fbxo11) that interact with the transforming growth factor β (TGFβ) signalling pathway. We investigated these genes, as well as four Sma- and Mad-related (SMAD) genes of the TGFβ pathway, as candidate rAOM/COME susceptibility genes in two predominantly Caucasian populations. Single-nucleotide polymorphisms (SNPs) within FBXO11 (family-based association testing Z-Score=2.61; P(best)=0.009) were associated with severe OM in family-based analysis of 434 families (561 affected individuals) from the Western Australian Family Study of OM. The FBXO11 association was replicated by directed analysis of Illumina 660W-Quad Beadchip data available for 253 cases and 866 controls (OR=1.55 (95% CI 1.28-1.89); P(best)=6.9 × 10(-6)) available within the Western Australian Pregnancy Cohort (Raine) Study. Combined primary and replication results show P(combined)=2.98 × 10(-6). Neither cohort showed an association with EVI1 variants. Family-based associations at SMAD2 (P=0.038) and SMAD4 (P=0.048) were not replicated. Together, these data provide strong evidence for FBXO11 as a susceptibility gene for severe OM.

Genome-wide Association: From Confounded to Confident

Genome-wide association studies (GWAS) allow for a large number of samples to be assayed simultaneously, using a genome-wide tagging single nucleotide polymorphism (SNP) approach. The initial boon of success from disease studies such as macular degeneration and inflammatory bowel disease has been mitigated by lack of genome-wide significance for psychiatric disorders and related traits, despite evaluations of large populations. In addition to SNP genotypes, which are common variants typically attributing small or modest relative risk, copy number variations can be detected based on the same data set. Several rare recurrent copy number variations have been associated with psychiatric diseases in genome-wide analyses. Proper and responsible study design, followed by rigorous data quality assessment of genomic matching of cases and controls, is most likely to uncover regions of significant association that replicate in independent cohorts, thereby maximizing the chance of significant and confident association.

Toward failure analyses in systems biology

Parallels between designed and biological systems with respect to formal failure analyses have been presented. Failure analysis in designed systems depends on an identified, limited set of parameters or operation variables with high predictive value. In contrast, the biological systems pose problems in identification of operation variables and the identified variables may not be accurate predictors of failure. The difficulty in parameter identification is because of large numbers of components and the inability to envelope variables at each compartment or contour level. Contour level maps for biological systems are currently non-existent, and most failure models are based on very limited, unilateral operation variables (a mutant gene). Operation variable identification within each contour level will enhance failure analyses of complex biological systems.

Evaluating variations of genotype calling: a potential source of spurious associations in genome-wide association studies

Genome-wide association studies (GWAS) examine the entire human genome with the goal of identifying genetic variants (usually single nucleotide polymorphisms (SNPs)) that are associated with phenotypic traits such as disease status and drug response. The discordance of significantly associated SNPs for the same disease identified from different GWAS indicates that false associations exist in such results. In addition to the possible sources of spurious associations that have been investigated and discussed intensively, such as sample size and population stratification, an accurate and reproducible genotype calling algorithm is required for concordant GWAS results from different studies. However, variations of genotype calling of an algorithm and their effects on significantly associated SNPs identified in downstream association analyses have not been systematically investigated. In this paper, the variations of genotype calling using the Bayesian Robust Linear Model with Mahalanobis distance classifier (BRLMM) algorithm and the resulting influence on the lists of significantly associated SNPs were evaluated using the raw data of 270 HapMap samples analysed with the Affymetrix Human Mapping 500K Array Set (Affy500K) by changing algorithmic parameters. Modified were the Dynamic Model (DM) call confidence threshold (threshold) and the number of randomly selected SNPs (size). Comparative analysis of the calling results and the corresponding lists of significantly associated SNPs identified through association analysis revealed that algorithmic parameters used in BRLMM affected the genotype calls and the significantly associated SNPs. Both the threshold and the size affected the called genotypes and the lists of significantly associated SNPs in association analysis. The effect of the threshold was much larger than the effect of the size. Moreover, the heterozygous calls had lower consistency compared to the homozygous calls.

Genome-wide association studies in nephrology research

Kidney diseases constitute a serious public health burden worldwide, with substantial associated morbidity and mortality. The role of a genetic contribution to kidney disease is supported by heritability studies of kidney function measures, the presence of monogenic diseases with renal manifestations, and familial aggregation studies of complex kidney diseases, such as chronic kidney disease. Because complex diseases arise from the combination of multiple genetic and environmental risk factors, the identification of underlying genetic susceptibility variants has been challenging. Recently, genome-wide association studies have emerged as a method to conduct searches for such susceptibility variants. They have successfully identified genomic loci that contain variants associated with kidney diseases and measures of kidney function. For example, common variants in the UMOD and PRKAG2 genes are associated with risk of chronic kidney disease; variants in CLDN14 with risk of kidney stone disease; and variants in or near SHROOM3, STC1, LASS2, GCKR, NAT8/ALMS1, TFDP2, DAB2, SLC34A1, VEGFA, FAM122A/PIP5K1B, ATXN2, DACH1, UBE2Q2/FBXO22, and SLC7A9, with differences in glomerular filtration rate. The purpose of this review is to provide an overview of the genome-wide association study method as it relates to nephrology research and summarize recent findings in the field. Results from genome-wide association studies of renal phenotypes represent a first step toward improving our knowledge about underlying mechanisms of kidney function and disease and ultimately may aid in the improved treatment and prevention of kidney diseases.

Identifying gene regulatory networks in schizophrenia

The imaging genetics approach to studying the genetic basis of disease leverages the individual strengths of both neuroimaging and genetic studies by visualizing and quantifying the brain activation patterns in the context of genetic background. Brain imaging as an intermediate phenotype can help clarify the functional link among genes, the molecular networks in which they participate, and brain circuitry and function. Integrating genetic data from a genome-wide association study (GWAS) with brain imaging as a quantitative trait (QT) phenotype can increase the statistical power to identify risk genes. A QT analysis using brain imaging (DLPFC activation during a working memory task) as a quantitative trait has identified unanticipated risk genes for schizophrenia. Several of these genes (RSRC1, ARHGAP18, ROBO1-ROBO2, GPC1, TNIK, and CTXN3-SLC12A2) have functions related to progenitor cell proliferation, migration, and differentiation, cytoskeleton reorganization, axonal connectivity, and development of forebrain structures. These genes, however, do not function in isolation but rather through gene regulatory networks. To obtain a deeper understanding how the GWAS-identified genes participate in larger gene regulatory networks, we measured correlations among transcript levels in the mouse and human postmortem tissue and performed a gene set enrichment analysis (GSEA) that identified several microRNA associated with schizophrenia (448, 218, 137). The results of such computational approaches can be further validated in animal experiments in which the networks are experimentally studied and perturbed with specific compounds. Glypican 1 and FGF17 mouse models for example, can be used to study such gene regulatory networks. The model demonstrates epistatic interactions between FGF and glypican on brain development and may be a useful model of negative symptom schizophrenia.

A quality control algorithm for filtering SNPs in genome-wide association studies

MOTIVATION

The quality control (QC) filtering of single nucleotide polymorphisms (SNPs) is an important step in genome-wide association studies to minimize potential false findings. SNP QC commonly uses expert-guided filters based on QC variables [e.g. Hardy-Weinberg equilibrium, missing proportion (MSP) and minor allele frequency (MAF)] to remove SNPs with insufficient genotyping quality. The rationale of the expert filters is sensible and concrete, but its implementation requires arbitrary thresholds and does not jointly consider all QC features.

RESULTS

We propose an algorithm that is based on principal component analysis and clustering analysis to identify low-quality SNPs. The method minimizes the use of arbitrary cutoff values, allows a collective consideration of the QC features and provides conditional thresholds contingent on other QC variables (e.g. different MSP thresholds for different MAFs). We apply our method to the seven studies from the Wellcome Trust Case Control Consortium and the major depressive disorder study from the Genetic Association Information Network. We measured the performance of our method compared to the expert filters based on the following criteria: (i) percentage of SNPs excluded due to low quality; (ii) inflation factor of the test statistics (lambda); (iii) number of false associations found in the filtered dataset; and (iv) number of true associations missed in the filtered dataset. The results suggest that with the same or fewer SNPs excluded, the proposed algorithm tends to give a similar or lower value of lambda, a reduced number of false associations, and retains all true associations.

AVAILABILITY

The algorithm is available at http://www4.stat.ncsu.edu/jytzeng/software.php

Quality control for genome-wide association studies

This chapter is a comprehensive review of quality control (QC) methods for SNP-based genotyping panels used in genome-wide association studies. These include QC on individuals for missingness, gender checks, duplicates and cryptic relatedness, population outliers, heterozygosity and inbreeding, and QC on SNPs for missingness, minor allele frequency and Hardy-Weinberg equilibrium. The emphasis is on the reasons behind each QC step and on the use of intelligent approaches rather than arbitrary QC thresholds. Scripts and code for performing these QC steps are available at www.kcl.ac.uk/mmg/gwascode/ .

Genetics research in systemic lupus erythematosus for clinicians: methodology, progress, and controversies

PURPOSE OF REVIEW

Clinical journals are reporting genetic associations with systemic lupus erythematosus (SLE) with increasing frequency. Interpreting these studies is difficult for clinicians without rigorous training in epidemiology, statistics, and genetics. In this review, we discuss basic issues important to understanding and contextualizing new genetic association studies. We, therefore, highlight literature related to methodology as well as recent genetic discoveries in SLE.

RECENT FINDINGS

Functional single nucleotide polymorphisms (SNPs) and/or haplotypes have now been identified for ITGAM, PTPN22, and IRF5, and several additional loci have been highlighted in recent genome-wide association studies in SLE. Recent work also indicates that several regions within the extended major histocompatibility complex contribute independently to SLE risk. Evidence of additive statistical interaction has been found between IRF5 and TYK2, IRF5, and STAT4, and between NAT2 and exposure to tobacco smoke.

SUMMARY

Many new genes have been associated with SLE susceptibility, revealing insight into SLE pathophysiology. Current research is focusing on further refining the initial genetic association results and extending this work to non-European populations. Research is also expanding beyond SNP associations to investigate the contribution of copy number variants (CNVs) and DNA methylation to SLE risk.

Host genetic and epigenetic factors in toxoplasmosis

Analysing human genetic variation provides a powerful tool in understanding risk factors for disease. Toxoplasma gondii acquired by the mother can be transmitted to the fetus. Infants with the most severe clinical signs in brain and eye are those infected early in pregnancy when fetal immunity is least well developed. Genetic analysis could provide unique insight into events in utero that are otherwise difficult to determine. We tested the hypothesis that propensity for T. gondii to cause eye disease is associated with genes previously implicated in congenital or juvenile onset ocular disease. Using mother-child pairs from Europe (EMSCOT) and child/parent trios from North America (NCCCTS), we demonstrated that ocular and brain disease in congenital toxoplasmosis associate with polymorphisms in ABCA4 encoding ATP-binding cassette transporter, subfamily A, member 4 previously associated with juvenile onset retinal dystrophies including Stargardt's disease. Polymorphisms at COL2A1 encoding type II collagen, previously associated with Stickler syndrome, associated only with ocular disease in congenital toxoplasmosis. Experimental studies showed that both ABCA4 and COL2A1 show isoform-specific epigenetic modifications consistent with imprinting, which provided an explanation for the patterns of inheritance observed. These genetic and epigenetic risk factors provide unique insight into molecular pathways in the pathogenesis of disease.