An overview of the genetic dissection of complex traits

GAWMerge expands GWAS sample size and diversity by combining array-based genotyping and whole-genome sequencing

Genome-wide association studies (GWAS) have made impactful discoveries for complex diseases, often by amassing very large sample sizes. Yet, GWAS of many diseases remain underpowered, especially for non-European ancestries. One cost-effective approach to increase sample size is to combine existing cohorts, which may have limited sample size or be case-only, with public controls, but this approach is limited by the need for a large overlap in variants across genotyping arrays and the scarcity of non-European controls. We developed and validated a protocol, Genotyping Array-WGS Merge (GAWMerge), for combining genotypes from arrays and whole-genome sequencing, ensuring complete variant overlap, and allowing for diverse samples like Trans-Omics for Precision Medicine to be used. Our protocol involves phasing, imputation, and filtering. We illustrated its ability to control technology driven artifacts and type-I error, as well as recover known disease-associated signals across technologies, independent datasets, and ancestries in smoking-related cohorts. GAWMerge enables genetic studies to leverage existing cohorts to validly increase sample size and enhance discovery for understudied traits and ancestries.

Structural Analysis of Glaucoma Brain and its Association With Ocular Parameters

PRECIS

Glaucoma patients presented a decreased occipital pole surface area in both hemispheres. Moreover, these parameters are independently correlated with functional and structural ocular parameters.

PURPOSE

The purpose of this study was to evaluate structural brain abnormalities in glaucoma patients using 3-Tesla magnetic resonance imaging and assess their correlation with associated structural and functional ocular findings.

PATIENTS AND METHODS

This cross-sectional prospective study included 30 glaucoma patients and 18 healthy volunteers. All participants underwent standard automated perimetry, spectral-domain optical coherence tomography, and 3.0-Tesla magnetic resonance imaging.

RESULTS

There was a significant difference between the surface area of the occipital pole in the left hemisphere of glaucoma patients (mean: 1253.9±149.3 mm) and that of control subjects (mean: 1341.9±129.8 mm), P=0.043. There was also a significant difference between the surface area of the occipital pole in the right hemisphere of glaucoma patients (mean: 1910.5±309.4 mm) and that of control subjects (mean: 2089.1±164.2 mm), P=0.029. There was no significant difference between the lingual, calcarine, superior frontal, and inferior frontal gyri of glaucoma patients and those of the control subjects (P>0.05 for all comparisons). The surface area of the occipital pole in the left hemisphere was significantly correlated with perimetry mean deviation values, visual acuity, age, and retinal nerve fiber layer thickness (P=0.001, <0.001, 0.010, and 0.006, respectively). The surface area of the occipital pole in the right hemisphere was significantly correlated with perimetry mean deviation values, visual field indices, visual acuity, age, and retinal nerve fiber layer thickness (P<0.001, 0.007, <0.001, 0.046, and <0.001, respectively).

CONCLUSION

Glaucoma patients presented a decreased occipital pole surface area in both hemispheres that independently correlated with functional and structural ocular parameters.

A Brief Review of Genetic Approaches to the Study of Food Preferences: Current Knowledge and Future Directions

Genetic variation plays a crucial role in individual differences in food preferences which ultimately influence food selection and health. Our current understanding of this pathway has been informed through twin studies (to assess the heritability of food preferences), candidate gene studies, and genome-wide association studies (GWAS). However, most of this literature is mainly focused on genes previously identified as having taste or smell functions. New data suggests that genes not associated with taste or smell perception may be involved in food preferences and contribute to health outcomes. This review highlights these emerging findings and suggests a polygenic risk assessment approach to explore new relationships between food preferences and health risks.

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.

An intersection network based on combining SNP coassociation and RNA coexpression networks for feed utilization traits in Japanese Black cattle

Genome-wide association studies (GWAS) of quantitative traits have detected numerous genetic associations, but they encounter difficulties in pinpointing prominent candidate genes and inferring gene networks. The present study used a systems genetics approach integrating GWAS results with external RNA-expression data to detect candidate gene networks in feed utilization and growth traits of Japanese Black cattle, which are matters of concern. A SNP coassociation network was derived from significant correlations between SNPs with effects estimated by GWAS across 7 phenotypic traits. The resulting network genes contained significant numbers of annotations related to the traits. Using bovine transcriptome data from a public database, an RNA coexpression network was inferred based on the similarity of expression patterns across different tissues. An intersection network was then generated by superimposing the SNP and RNA networks and extracting shared interactions. This intersection network contained 4 tissue-specific modules: nervous system, reproductive system, muscular system, and glands. To characterize the structure (topographical properties) of the 3 networks, their scale-free properties were evaluated, which revealed that the intersection network was the most scale-free. In the subnetwork containing the most connected transcription factors (URI1, ROCK2, and ETV6), most genes were widely expressed across tissues, and genes previously shown to be involved in the traits were found. Results indicated that the current approach might be used to construct a gene network that better reflects biological information, providing encouragement for the genetic dissection of economically important quantitative traits.

Understanding mutational effects in digenic diseases

To further our understanding of the complexity and genetic heterogeneity of rare diseases, it has become essential to shed light on how combinations of variants in different genes are responsible for a disease phenotype. With the appearance of a resource on digenic diseases, it has become possible to evaluate how digenic combinations differ in terms of the phenotypes they produce. All instances in this resource were assigned to two classes of digenic effects, annotated as true digenic and composite classes. Whereas in the true digenic class variants in both genes are required for developing the disease, in the composite class, a variant in one gene is sufficient to produce the phenotype, but an additional variant in a second gene impacts the disease phenotype or alters the age of onset. We show that a combination of variant, gene and higher-level features can differentiate between these two classes with high accuracy. Moreover, we show via the analysis of three digenic disorders that a digenic effect decision profile, extracted from the predictive model, motivates why an instance was assigned to either of the two classes. Together, our results show that digenic disease data generates novel insights, providing a glimpse into the oligogenic realm.

Expression-based GWAS identifies variants, gene interactions and key regulators affecting intramuscular fatty acid content and composition in porcine meat

The aim of this work is to better understand the genetic mechanisms determining two complex traits affecting porcine meat quality: intramuscular fat (IMF) content and its fatty acid (FA) composition. With this purpose, expression Genome-Wide Association Study (eGWAS) of 45 lipid-related genes associated with meat quality traits in swine muscle (Longissimus dorsi) of 114 Iberian × Landrace backcross animals was performed. The eGWAS identified 241 SNPs associated with 11 genes: ACSM5, CROT, FABP3, FOS, HIF1AN, IGF2, MGLL, NCOA1, PIK3R1, PLA2G12A and PPARA. Three expression Quantitative Trait Loci (eQTLs) for IGF2, ACSM5 and MGLL were identified, showing cis-acting effects, whereas 16 eQTLs had trans regulatory effects. A polymorphism in the ACSM5 promoter region associated with its expression was identified. In addition, strong candidate genes regulating ACSM5, FOS, PPARA, PIK3R1, PLA2G12A and HIF1AN gene expression were also seen. Notably, the analysis highlighted the NR3C1 transcription factor as a strong candidate gene involved in the regulation of the 45 genes analysed. Finally, the IGF2, MGLL, MC2R, ARHGAP6, and NR3C1 genes were identified as potential regulators co-localizing within QTLs for fatness and growth traits in the IBMAP population. The results obtained increase our knowledge in the functional regulatory mechanisms involved in these complex traits.

Linking Genes to Cardiovascular Diseases: Gene Action and Gene-Environment Interactions

A unique myocardial characteristic is its ability to grow/remodel in order to adapt; this is determined partly by genes and partly by the environment and the milieu intérieur. In the "post-genomic" era, a need is emerging to elucidate the physiologic functions of myocardial genes, as well as potential adaptive and maladaptive modulations induced by environmental/epigenetic factors. Genome sequencing and analysis advances have become exponential lately, with escalation of our knowledge concerning sometimes controversial genetic underpinnings of cardiovascular diseases. Current technologies can identify candidate genes variously involved in diverse normal/abnormal morphomechanical phenotypes, and offer insights into multiple genetic factors implicated in complex cardiovascular syndromes. The expression profiles of thousands of genes are regularly ascertained under diverse conditions. Global analyses of gene expression levels are useful for cataloging genes and correlated phenotypes, and for elucidating the role of genes in maladies. Comparative expression of gene networks coupled to complex disorders can contribute insights as to how "modifier genes" influence the expressed phenotypes. Increasingly, a more comprehensive and detailed systematic understanding of genetic abnormalities underlying, for example, various genetic cardiomyopathies is emerging. Implementing genomic findings in cardiology practice may well lead directly to better diagnosing and therapeutics. There is currently evolving a strong appreciation for the value of studying gene anomalies, and doing so in a non-disjointed, cohesive manner. However, it is challenging for many-practitioners and investigators-to comprehend, interpret, and utilize the clinically increasingly accessible and affordable cardiovascular genomics studies. This survey addresses the need for fundamental understanding in this vital area.

Association of Amine-Receptor DNA Sequence Variants with Associative Learning in the Honeybee

Octopamine- and dopamine-based neuromodulatory systems play a critical role in learning and learning-related behaviour in insects. To further our understanding of these systems and resulting phenotypes, we quantified DNA sequence variations at six loci coding octopamine-and dopamine-receptors and their association with aversive and appetitive learning traits in a population of honeybees. We identified 79 polymorphic sequence markers (mostly SNPs and a few insertions/deletions) located within or close to six candidate genes. Intriguingly, we found that levels of sequence variation in the protein-coding regions studied were low, indicating that sequence variation in the coding regions of receptor genes critical to learning and memory is strongly selected against. Non-coding and upstream regions of the same genes, however, were less conserved and sequence variations in these regions were weakly associated with between-individual differences in learning-related traits. While these associations do not directly imply a specific molecular mechanism, they suggest that the cross-talk between dopamine and octopamine signalling pathways may influence olfactory learning and memory in the honeybee.

Genetics in child and adolescent psychiatry: methodological advances and conceptual issues

Discovering the genetic basis of early-onset psychiatric disorders has been the aim of intensive research during the last decade. We will first selectively summarize results of genetic research in child and adolescent psychiatry by using examples from different disorders and discuss methodological issues, emerging questions and future directions. In the second part of this review, we will focus on how to link genetic causes of disorders with physiological pathways, discuss the impact of genetic findings on diagnostic systems, prevention and therapeutic interventions. Finally we will highlight some ethical aspects connected to genetic research in child and adolescent psychiatry. Advances in molecular genetic methods have led to insights into the genetic architecture of psychiatric disorders, but not yet provided definite pathways to pathophysiology. If replicated, promising findings from genetic studies might in some cases lead to personalized treatments. On the one hand, knowledge of the genetic basis of disorders may influence diagnostic categories. On the other hand, models also suggest studying the genetic architecture of psychiatric disorders across diagnoses and clinical groups.

A co-association network analysis of the genetic determination of pig conformation, growth and fatness

BACKGROUND

Several QTLs have been identified for major economically relevant traits in livestock, such as growth and meat quality, revealing the complex genetic architecture of these traits. The use of network approaches considering the interactions of multiple molecules and traits provides useful insights into the molecular underpinnings of complex traits. Here, a network based methodology, named Association Weight Matrix, was applied to study gene interactions and pathways affecting pig conformation, growth and fatness traits.

RESULTS

The co-association network analysis underpinned three transcription factors, PPARγ, ELF1, and PRDM16 involved in mesoderm tissue differentiation. Fifty-four genes in the network belonged to growth-related ontologies and 46 of them were common with a similar study for growth in cattle supporting our results. The functional analysis uncovered the lipid metabolism and the corticotrophin and gonadotrophin release hormone pathways among the most important pathways influencing these traits. Our results suggest that the genes and pathways here identified are important determining either the total body weight of the animal and the fat content. For instance, a switch in the mesoderm tissue differentiation may determinate the age-related preferred pathways being in the puberty stage those related with the miogenic and osteogenic lineages; on the contrary, in the maturity stage cells may be more prone to the adipocyte fate. Hence, our results demonstrate that an integrative genomic co-association analysis is a powerful approach for identifying new connections and interactions among genes.

CONCLUSIONS

This work provides insights about pathways and key regulators which may be important determining the animal growth, conformation and body proportions and fatness traits. Molecular information concerning genes and pathways here described may be crucial for the improvement of genetic breeding programs applied to pork meat production.

Whole exome sequencing of distant relatives in multiplex families implicates rare variants in candidate genes for oral clefts

A dozen genes/regions have been confirmed as genetic risk factors for oral clefts in human association and linkage studies, and animal models argue even more genes may be involved. Genomic sequencing studies should identify specific causal variants and may reveal additional genes as influencing risk to oral clefts, which have a complex and heterogeneous etiology. We conducted a whole exome sequencing (WES) study to search for potentially causal variants using affected relatives drawn from multiplex cleft families. Two or three affected second, third, and higher degree relatives from 55 multiplex families were sequenced. We examined rare single nucleotide variants (SNVs) shared by affected relatives in 348 recognized candidate genes. Exact probabilities that affected relatives would share these rare variants were calculated, given pedigree structures, and corrected for the number of variants tested. Five novel and potentially damaging SNVs shared by affected distant relatives were found and confirmed by Sanger sequencing. One damaging SNV in CDH1, shared by three affected second cousins from a single family, attained statistical significance (P = 0.02 after correcting for multiple tests). Family-based designs such as the one used in this WES study offer important advantages for identifying genes likely to be causing complex and heterogeneous disorders.

The St. Louis African American health-heart study: methodology for the study of cardiovascular disease and depression in young-old African Americans

Background

Coronary artery disease (CAD) is a major cause of death and disability worldwide. Depression has complex bidirectional adverse associations with CAD, although the mechanisms mediating these relationships remain unclear. Compared to European Americans, African Americans (AAs) have higher rates of morbidity and mortality from CAD. Although depression is common in AAs, its role in the development and features of CAD in this group has not been well examined. This project hypothesizes that the relationships between depression and CAD can be explained by common physiological pathways and gene-environment interactions. Thus, the primary aims of this ongoing project are to: a) determine the prevalence of CAD and depression phenotypes in a population-based sample of community-dwelling older AAs; b) examine the relationships between CAD and depression phenotypes in this population; and c) evaluate genetic variants from serotoninP and inflammatory pathways to discover potential gene-depression interactions that contribute significantly to the presence of CAD in AAs.

Methods/design

The St. Louis African American Health (AAH) cohort is a population-based panel study of community-dwelling AAs born in 1936–1950 (inclusive) who have been followed from 2000/2001 through 2010. The AAH-Heart study group is a subset of AAH participants recruited in 2009–11 to examine the inter-relationships between depression and CAD in this population. State-of-the-art CAD phenotyping is based on cardiovascular characterizations (coronary artery calcium, carotid intima-media thickness, cardiac structure and function, and autonomic function). Depression phenotyping is based on standardized questionnaires and detailed interviews. Single nucleotide polymorphisms of selected genes in inflammatory and serotonin-signaling pathways are being examined to provide information for investigating potential gene-depression interactions as modifiers of CAD traits. Information from the parent AAH study is being used to provide population-based prevalence estimates. Inflammatory and other biomarkers provide information about potential pathways.

Discussion

This population-based investigation will provide valuable information on the prevalence of both depression and CAD phenotypes in this population. The study will examine interactions between depression and genetic variants as modulators of CAD, with the intent of detecting mechanistic pathways linking these diseases to identify potential therapeutic targets. Analytic results will be reported as they become available.

Impact of changing drug treatment and malaria endemicity on the heritability of malaria phenotypes in a longitudinal family-based cohort study

Despite considerable success of genome wide association (GWA) studies in identifying causal variants for many human diseases, their success in unraveling the genetic basis to complex diseases has been more mitigated. Pathogen population structure may impact upon the infectious phenotype, especially with the intense short-term selective pressure that drug treatment exerts on pathogens. Rigorous analysis that accounts for repeated measures and disentangles the influence of genetic and environmental factors must be performed. Attempts should be made to consider whether pathogen diversity will impact upon host genetic responses to infection.We analyzed the heritability of two Plasmodium falciparum phenotypes, the number of clinical malaria episodes (PFA) and the proportion of these episodes positive for gametocytes (Pfgam), in a family-based cohort followed for 19 years, during which time there were four successive drug treatment regimes, with documented appearance of drug resistance. Repeated measures and variance components analyses were performed with fixed environmental, additive genetic, intra-individual and maternal effects for each drug period. Whilst there was a significant additive genetic effect underlying PFA during the first drug period of study, this was lost in subsequent periods. There was no additive genetic effect for Pfgam. The intra-individual effect increased significantly in the chloroquine period.The loss of an additive genetic effect following novel drug treatment may result in significant loss of power to detect genes in a GWA study. Prior genetic analysis must be a pre-requisite for more detailed GWA studies. The temporal changes in the individual genetic and the intra-individual estimates are consistent with those expected if there were specific host-parasite interactions. The complex basis to the human response to malaria parasite infection likely includes dominance/epistatic genetic effects encompassed within the intra-individual variance component. Evaluating their role in influencing the outcome of infection through host genotype by parasite genotype interactions warrants research effort.

Pharmacogenetics of osteoporosis-related bone fractures: moving towards the harmonization and validation of polymorphism diagnostic tools

Osteoporosis is one of the most common skeletal chronic conditions in developed countries, hip fracture being one of its major healthcare outcomes. There is considerable variation in the implementation of current pharmacological treatment and prevention, despite consistent recommendations and guidelines. Many studies have reported conflicting findings of genetic associations with bone density and turnover that might predict fracture risk. Moreover, it is not clear whether genetic differences exist in relation to the morbidity and efficiency of the pharmacotherapy treatments. Clinical response, including beneficial and adverse events associated with osteoporosis treatments, is highly variable among individuals. In this context, the present article intends to summarize putative candidate genes and genome-wide association studies that have been related with BMD and fracture risk, and to draw the attention to the need for pharmacogenetic methodology that could be applicable in clinical translational research after an adequate validation process. This article mainly compiles analysis of important polymorphisms in osteoporosis documented previously, and it describes the simple molecular biology tools for routine genotype acquisition. Validation of methods for the easy, fast and accessible identification of SNPs is necessary for evolving pharmacogenetic diagnostic tools in order to contribute to the discovery of clinically relevant genetic variation with an impact on osteoporosis and its personalized treatment.

Comparison of association mapping methods in a complex pedigreed population

Association mapping methods were compared using a simulation with a complex pedigree structure. The pedigree was simulated while keeping the present Danish Holstein population pedigree in view. A total of 15 quantitative trait loci (QTL) with varying effect sizes (10%, 5% and 2% of total genetic variance) were simulated. We compared the single-marker test, haplotype-based analysis, mixed model approach, and Bayesian analysis. The methods were compared for power, precision of location estimates, and type I error rates. Results found the best performance in a Bayesian method that included genetic background effects and simultaneously fitted all single-nucleotide polymorphisms (SNPs) with a variable selection method. A mixed model analysis that fitted genetic background effects and tested one SNP at a time performed nearly as well as the Bayesian method. For the Bayesian method, it proved necessary to collect SNP signals in intervals, to avoid the scattering of a QTL signal over multiple neighboring SNPs. Methods not accounting for genetic background (full pedigree information) performed worse, and methods using haplotypes were considerably worse with a high false-positive rate, probably due to the presence of low-frequency haplotypes. It was necessary to account for full relationships among individuals to avoid excess false discovery. Although the methods were tested on a cattle pedigree, the results are applicable to any population with a complex pedigree structure.

The impact of phenocopy on the genetic analysis of complex traits

A consistent debate is ongoing on genome-wide association studies (GWAs). A key point is the capability to identify low-penetrance variations across the human genome. Among the phenomena reducing the power of these analyses, phenocopy level (PE) hampers very seriously the investigation of complex diseases, as well known in neurological disorders, cancer, and likely of primary importance in human ageing. PE seems to be the norm, rather than the exception, especially when considering the role of epigenetics and environmental factors towards phenotype. Despite some attempts, no recognized solution has been proposed, particularly to estimate the effects of phenocopies on the study planning or its analysis design. We present a simulation, where we attempt to define more precisely how phenocopy impacts on different analytical methods under different scenarios. With our approach the critical role of phenocopy emerges, and the more the PE level increases the more the initial difficulty in detecting gene-gene interactions is amplified. In particular, our results show that strong main effects are not hampered by the presence of an increasing amount of phenocopy in the study sample, despite progressively reducing the significance of the association, if the study is sufficiently powered. On the opposite, when purely epistatic effects are simulated, the capability of identifying the association depends on several parameters, such as the strength of the interaction between the polymorphic variants, the penetrance of the polymorphism and the alleles (minor or major) which produce the combined effect and their frequency in the population. We conclude that the neglect of the possible presence of phenocopies in complex traits heavily affects the analysis of their genetic data.

Genetic pathway-based hierarchical clustering analysis of older adults with cognitive complaints and amnestic mild cognitive impairment using clinical and neuroimaging phenotypes

Hierarchical clustering is frequently used for grouping results in expression or haplotype analyses. These methods can elucidate patterns between measures that can then be applied to discerning their validity in discriminating between experimental conditions. Here a hierarchical clustering method is used to analyze the results of an imaging genetics study using multiple brain morphology and cognitive testing endpoints for older adults with amnestic mild cognitive impairment (MCI) or cognitive complaints (CC) compared to healthy controls (HC). The single nucleotide polymorphisms (SNPs) are a subset of those included on a larger array that are found in a reported Alzheimer's disease (AD) and neurodegeneration pathway. The results indicate that genetic models within the endpoints cluster together, while there are 4 distinct sets of SNPs that differentiate between the endpoints, with most significant results associated with morphology endpoints rather than cognitive testing of patients' reported symptoms. The genes found in at least one cluster are ABCB1, APBA1, BACE1, BACE2, BCL2, BCL2L1, CASP7, CHAT, CST3, DRD3, DRD5, IL6, LRP1, NAT1, and PSEN2. The greater associations with morphology endpoints suggests that changes in brain structure can be influenced by an individual's genetic background in the absence of dementia and in some cases (Cognitive Complaints group) even without those effects necessarily being detectable on commonly used clinical tests of cognition. The results are consistent with polygenic influences on early neurodegenerative changes and demonstrate the effectiveness of hierarchical clustering in identifying genetic associations among multiple related phenotypic endpoints.

A FTO variant and risk of acute coronary syndrome

BACKGROUND

The FTO gene plays an important role in the determination of body weight and BMI and it has been suspected of being associated with all-case mortality.

METHODS

We have analyzed the FTO rs17817449 variant in consecutive 1092 male patients with acute coronary syndrome (ACS) and in 1191 randomly selected Caucasian individuals (population controls).

RESULTS

The FTO variant was significantly associated with BMI both in controls (P<0.02) and ACS patients (P<0.01). In both groups, BMI was highest in GG homozygotes and lowest in TT homozygotes. There was a significant difference between the ACS patients and controls in the frequency of the FTO genotype GG (21.4% vs. 15.9%, P<0.005). FTO GG homozygotes had a significantly increased risk of ACS, compared with TT homozygotes which was independent of age and BMI (odds ratio 1.49, 95% confidence interval 1.16-1.93). The odds ratio of ACS patients for the GG genotype remained significant even after the exclusion of diabetics (100 controls and 339 ACS patients), with OR 1.32 (95% CI 1.01-1.72).

CONCLUSIONS

This study provides an evidence of an association between the FTO variant and risk of ACS in Caucasian males.

Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies

Choosing the appropriate neuroimaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroimaging methods provide numerous potential phenotypes, their role for imaging genetics studies is unclear. Here we examine the relationship between brain volume, grey matter volume, cortical thickness and surface area, from a genetic standpoint. Four hundred and eighty-six individuals from randomly ascertained extended pedigrees with high-quality T1-weighted neuroanatomic MRI images participated in the study. Surface-based and voxel-based representations of brain structure were derived, using automated methods, and these measurements were analysed using a variance-components method to identify the heritability of these traits and their genetic correlations. All neuroanatomic traits were significantly influenced by genetic factors. Cortical thickness and surface area measurements were found to be genetically and phenotypically independent. While both thickness and area influenced volume measurements of cortical grey matter, volume was more closely related to surface area than cortical thickness. This trend was observed for both the volume-based and surface-based techniques. The results suggest that surface area and cortical thickness measurements should be considered separately and preferred over gray matter volumes for imaging genetic studies.

Genetic polymorphisms associated with acute lung injury

Acute lung injury and acute respiratory distress syndrome are the result of intense inflammation in the lungs leading to respiratory failure. The causes of acute lung injury/acute respiratory distress syndrome are numerous (e.g., pneumonia, sepsis and trauma) but the reasons why certain individuals develop lung injury in response to these stimuli and others do not are not well understood. There is ample evidence in the literature that gene-host and gene-environment interactions may play a large role in the morbidity and mortality associated with this syndrome. In this review, we initially discuss methods for identification of candidate acute lung injury/acute respiratory distress syndrome susceptibility genes using a number of model systems including in vitro cell systems and inbred mice. We then describe examples of polymorphisms in genes that have been associated with the pathogenesis of acute lung injury/acute respiratory distress syndrome in human case-control studies. Systematic bench to bedside approaches to understand the genetic contribution to acute lung injury/acute respiratory distress syndrome have provided important insight to this complex disease and continuation of these investigations could lead to the development of novel prevention or intervention strategies.

Metabonomics--a new approach in ophthalmology

Metabonomics is a new technology providing broad information about dynamic metabolic responses in living systems to pathophysiological stimuli or genetic modification. Nuclear magnetic resonance (NMR) spectroscopy is one of the most powerful methods in metabonomics; it is utilized to establish the metabolic profiles of biofluids, and is practically the only method capable of examining intact tissue samples. Experience with the application of metabonomics in eye research is still limited, yet this method provides the possibility of exploring metabolic processes in the eye in vivo. This article presents a brief background to the usefulness of metabonomics, and the possible applications of an NMR-based technique in eye research and clinical practice.

Lack of replication of celiac disease risk variants reported in a Spanish population using an independent Spanish sample

Celiac disease (CD) is an inflammatory condition affecting small bowel and triggered by gluten (or related proteins) ingestion in genetic susceptible individuals. Polymorphisms in three genes, SERPINE2, PPP6C and PBX3, have recently been associated with CD in the Spanish population. However, this association could not be replicated in the UK population using imputed data. As this second study analyzed a different population, we aimed at reevaluating the role of those polymorphisms using an independent Spanish sample. We genotyped three single nucleotide polymorphisms: rs6747096 in SERPINE2, rs458046 in PPP6C and rs7040561 in PBX3, in 417 CD patients, 527 ethnically matched healthy controls and parents of 304 CD patients. A case-control study using the chi(2)-test and a familial study using the transmission disequilibrium test were performed. No association was detected in those analyses. Therefore, our results seem to discard the role of the previously described polymorphisms in SERPINE2, PPP6C and PBX3 in CD susceptibility.

The genetic contribution to non-syndromic human obesity

The last few years have seen major advances in common non-syndromic obesity research, much of it the result of genetic studies. This Review outlines the competing hypotheses about the mechanisms underlying the genetic and physiological basis of obesity, and then examines the recent explosion of genetic association studies that have yielded insights into obesity, both at the candidate gene level and the genome-wide level. With obesity genetics now entering the post-genome-wide association scan era, the obvious question is how to improve the results obtained so far using single nucleotide polymorphism markers and how to move successfully into the other areas of genomic variation that may be associated with common obesity.

The ABCB1 transporter gene and antidepressant response

P-glycoprotein, encoded by the ABCB1 gene, may modulate the brain concentration of several antidepressants. Functional genetic variation is thought to exist in this gene, and here we review several studies that have attempted to associate this variation with clinical response to antidepressant treatment.

Using epidemiologic methods to test hypotheses regarding causal influences on child and adolescent mental disorders

Epidemiology uses strong sampling methods and study designs to test refutable hypotheses regarding the causes of important health, mental health, and social outcomes. Epidemiologic methods are increasingly being used to move developmental psychopathology from studies that catalogue correlates of child and adolescent mental health to designs that can test rival hypotheses regarding causal genetic and environmental influences. A two-part strategy is proposed for the next phase of epidemiologic research. First, to facilitate the most informed tests of causal hypotheses, it is necessary to develop and test models of the structure of hypothesized genetic and environmental influences on mental health phenotypes. This will involve testing the related hypotheses that there are both (a) dimensions of psychopathology that are distinct in the sense of having at least some unique genetic and/or environmental influences, and (b) higher-order domains of correlated dimensions that are all apparently influenced in part by the same genetic and/or environmental factors. The resulting causal taxonomy would organize tests of causal hypotheses regarding both factors that may broadly increase risk for multiple dimensions of psychopathology and factors that may specifically increase risk for each individual dimension. Second, it is necessary to make greater use of a number of powerful epidemiologic designs that allow rigorous tests of rival hypotheses regarding genetic and environmental causes.

Tilting at quixotic trait loci (QTL): an evolutionary perspective on genetic causation

Recent years have seen great advances in generating and analyzing data to identify the genetic architecture of biological traits. Human disease has understandably received intense research focus, and the genes responsible for most Mendelian diseases have successfully been identified. However, the same advances have shown a consistent if less satisfying pattern, in which complex traits are affected by variation in large numbers of genes, most of which have individually minor or statistically elusive effects, leaving the bulk of genetic etiology unaccounted for. This pattern applies to diverse and unrelated traits, not just disease, in basically all species, and is consistent with evolutionary expectations, raising challenging questions about the best way to approach and understand biological complexity.