Frank Dudbridge

Association of the paternally transmitted copy of common Valine allele of the Val66Met polymorphism of the brain-derived neurotrophic factor (BDNF) gene with susceptibility to ADHD

Attention deficit hyperactivity disorder (ADHD) is a common, highly heritable, neurodevelopmental disorder with onset in early childhood. Genes involved in neuronal development and growth are, thus, important etiological candidates and brain-derived neurotrophic factor (BDNF), has been hypothesized to play a role in the pathogenesis of ADHD. BDNF is a member of the neurotrophin family and is involved in the survival and differentiation of dopaminergic neurons in the developing brain (of relevance because drugs that block the dopamine transporter can be effective therapeutically). The common Val66Met functional polymorphism in the human BDNF gene (rs 6265) was genotyped in a collaborative family-based sample of 341 white UK or Irish ADHD probands and their parents. We found evidence for preferential transmission of the valine (G) allele of BDNF (odds ratio, OR=1.6, P=0.02) with a strong paternal effect (paternal transmissions: OR=3.2, P=0.0005; maternal transmissions: OR=1.00; P=1.00). Our findings support the hypothesis that BDNF is involved in the pathogenesis of ADHD. The transmission difference between parents raises the possibility that an epigenetic process may be involved.

Associations of FTO and MC4R Variants with Obesity Traits in Indians and the Role of Rural/Urban Environment as a Possible Effect Modifier.

Few studies have investigated the association between genetic variation and obesity traits in Indian populations or the role of environmental factors as modifiers of these relationships. In the context of rapid urbanisation, resulting in significant lifestyle changes, understanding the aetiology of obesity is important. We investigated associations of FTO and MC4R variants with obesity traits in 3390 sibling pairs from four Indian cities, most of whom were discordant for current dwelling (rural or urban). The FTO variant rs9939609 predicted increased weight (0.09 Z-scores, 95% CI: 0.03, 0.15) and BMI (0.08 Z-scores, 95% CI: 0.02, 0.14). The MC4R variant rs17782313 was weakly associated with weight and hip circumference (P < .05). There was some indication that the association between FTO and weight was stronger in urban than that in rural dwellers (P for interaction = .03), but no evidence for effect modification by diet or physical activity. Further studies are needed to investigate ways in which urban environment may modify genetic risk of obesity.

Using genetic data to strengthen causal inference in observational research

Causal inference is essential across the biomedical, behavioural and social sciences.By progressing from confounded statistical associations to evidence of causal relationships, causal inference can reveal complex pathways underlying traits and diseases and help to prioritize targets for intervention. Recent progress in genetic epidemiology — including statistical innovation, massive genotyped data sets and novel computational tools for deep data mining — has fostered the intense development of methods exploiting genetic data and relatedness to strengthen causal inference in observational research. In this Review, we describe how such genetically informed methods differ in their rationale, applicability and inherent limitations and outline how they should be integrated in the future to offer a rich causal inference toolbox.Various types of observational studies can provide statistical associations between factors, such as between an environmental exposure and a disease state. This Review discusses the various genetics-focused statistical methodologies that can move beyond mere associations to identify (or refute) various mechanisms of causality, with implications for responsibly managing risk factors in health care and the behavioural and social sciences.

Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference

Detailed phenotyping is required to deepen our understanding of the biological mechanisms behind genetic associations. In addition, the impact of potentially modifiable risk factors on disease requires analytical frameworks that allow causal inference. Here, we discuss the characteristics of Recall-by-Genotype (RbG) as a study design aimed at addressing both these needs. We describe two broad scenarios for the application of RbG: studies using single variants and those using multiple variants. We consider the efficacy and practicality of the RbG approach, provide a catalogue of UK-based resources for such studies and present an online RbG study planner.Recall-by-Genotype (RbG) is an approach to recall participants from genetic studies based on their specific genotype for further, more extensive phenotyping. Here, the authors discuss examples of RbG as well as practical and ethical considerations and provide an online tool to aid in designing RbG studies.

Bone structural effects of variation in the TNFRSF1B gene encoding the tumor necrosis factor receptor 2

SummaryThe 1p36 region of the human genome has been identified as containing a QTL for BMD in multiple studies. We analysed the TNFRSF1B gene from this region, which encodes the TNF receptor 2, in two large population-based cohorts. Our results suggest that variation in TNFRSF1B is associated with BMD.IntroductionThe TNFRSF1B gene, encoding the TNF receptor 2, is a strong positional and functional candidate gene for impaired bone structure through the role that TNF has in bone cells. The aims of this study were to evaluate the role of variations in the TNFRSF1B gene on bone structure and osteoporotic fracture risk in postmenopausal women.MethodsSix SNPs in TNFRSF1B were analysed in a cohort of 1,190 postmenopausal Australian women, three of which were also genotyped in an independent cohort of 811 UK postmenopausal women. Differences in phenotypic means for genotype groups were examined using one-way ANOVA and ANCOVA.ResultsSignificant associations were seen for IVS1+5580A>G with BMD and QUS parameters in the Australian population (P = 0.008 − 0.034) and with hip BMD parameters in the UK population (P = 0.005 − 0.029). Significant associations were also observed between IVS1+6528G>A and hip BMD parameters in the UK cohort (P = 0.0002 − 0.003). We then combined the data from the two cohorts and observed significant associations between both IVS1+5580A>G and IVS1+6528G>A and hip BMD parameters (P = 0.002 − 0.033).ConclusionsGenetic variation in TNFRSF1B plays a role in the determination of bone structure in Caucasian postmenopausal women, possibly through effects on osteoblast and osteoclast differentiation.

Causal Analyses, Statistical Efficiency And Phenotypic Precision Through Recall-By-Genotype Study Design

Genome-wide association studies have been useful in identifying common genetic variants related to a variety of complex traits and diseases; however, they are often limited in their ability to inform about underlying biology. Whilst bioinformatics analyses, studies of cells, animal models and applied genetic epidemiology have provided some understanding of genetic associations or causal pathways, there is a need for new genetic studies that elucidate causal relationships and mechanisms in a cost-effective, precise and statistically efficient fashion. We discuss the motivation for and the characteristics of the Recall-by-Genotype (RbG) study design, an approach that enables genotype-directed deep-phenotyping and improvement in drawing causal inferences. Specifically, we present RbG designs using single and multiple variants and discuss the inferential properties, analytical approaches and applications of both. We consider the efficiency of the RbG approach, the likely value of RbG studies for the causal investigation of disease aetiology and the practicalities of incorporating genotypic data into population studies. Finally, we provide a catalogue of the UK-based resources for such studies, an online tool to aid the design of new RbG studies and discuss future developments of this approach.

Adjustment for index event bias in genome-wide association studies of subsequent events

Following numerous genome-wide association studies of disease susceptibility, there is increasing interest in genetic associations with prognosis, survival or other subsequent events. Such associations are vulnerable to index event bias, by which selection of subjects according to disease status creates biased associations if common causes of incidence and prognosis are not accounted for. We propose an adjustment for index event bias using the residuals from the regression of genetic effects on prognosis on genetic effects on incidence. Our approach eliminates this bias when direct genetic effects on incidence and prognosis are independent, and otherwise reduces bias in realistic situations. In a study of idiopathic pulmonary fibrosis, we reverse a paradoxical association of the strong susceptibility gene MUCSB with increased survival, suggesting instead a significant association with decreased survival. In re-analysis of a study of Crohn’s disease prognosis, four regions remain associated at genome-wide significance but with increased standard errors.

Expression quantitative trait locus study of bone mineral density GWAS variants in human osteoclasts

Osteoporosis is a complex disease with a strong genetic component. Genomewide association studies (GWAS) have been very successful at identifying common genetic variants associated with bone parameters. A recently published study documented the results of the largest GWAS for bone mineral density (BMD) performed to date (n = 142,487), identifying 307 conditionally independent single‐nucleotide polymorphisms (SNPs) as associated with estimated BMD (eBMD) at the genomewide significance level. The vast majority of these variants are non‐coding SNPs. Expression quantitative trait locus (eQTL) studies using disease‐specific cell types have increasingly been integrated with the results from GWAS to identify genes through which the observed GWAS associations are likely mediated. We generated a unique human osteoclast‐specific eQTL data set using cells differentiated in vitro from 158 participants. We then used this resource to characterize the 307 recently identified BMD GWAS SNPs for association with nearby genes (±500 kb). After correction for multiple testing, 24 variants were found to be significantly associated with the expression of 32 genes in the osteoclast‐like cells. Bioinformatics analysis suggested that these variants and those in strong linkage disequilibrium with them are enriched in regulatory regions. Several of the eQTL associations identified are relevant to genes that present strongly as having a role in bone, particularly IQGAP1, CYP19A1, CTNNB1, and COL6A3. Supporting evidence for many of the associations was obtained from publicly available eQTL data sets. We have also generated strong evidence for the presence of a regulatory region on chromosome 15q21.2 relevant to both the GLDN and CYP19A1 genes. In conclusion, we have generated a unique osteoclast‐specific eQTL resource and have used this to identify 32 eQTL associations for recently identified BMD GWAS loci, which should inform functional studies of osteoclast biology.