We’ve highlighted some examples of our 100+ collaborations below.
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The relationship between allergy-related disorders (asthma, allergy and eczema) and infectious diseases is complex and incompletely understood. In epidemiological studies there are reports of infectious diseases both being protective and a risk factor for later development of allergy-related diseases. A previous study conducted in collaboration with 23andMe showed evidence of shared genetic loci between allergy related diseases and infectious diseases. The aim of the current proposal is to study in more details this genetic relationship by a focused analysis involving a larger dataset. The analyses will focus on direction of effect of shared loci and shared genetic pathways. The project will involve data from 23andMe as well as genetic data from Danish studies, including the Copenhagen Prospective Studies on Asthma in Childhood (COPSAC) birth cohorts. The study is expected to increase our understanding of the genetic background and thereby the pathogenesis of allergy-related and infectious disease. This might provide possibilities for improved prevention and treatment of these diseases.
Major depressive disorder (MDD) is a genetically complex trait. The lifetime prevalence of MDD is approximately 15% (Hasin, 2005; Kessler, 2003). As a recurrent course is most common (Mueller, 1999), MDD is accompanied by considerable morbidity (Judd, 1997; Lopez, 2006; Wittchen, 2011), excess mortality (Lopez, 2006; Angst, 2002) and substantial costs (Gustavsson, 2011). The World Health Organization projects MDD to be the second leading cause of disability by 2020 (Murray, 1996). The heritability of MDD is 31-42% (Sullivan, 2000) although certain subsets may be more heritable (e.g., recurrent, early-onset or clinically ascertained MDD) (Levinson, 2006; McGuffin, 1996). This modest heritability could reasonably be expected to complicate attempts to identify genetic loci that confer risk or protection. However, heritability is not necessarily key for the identification of strong and replicable genetic associations (Chanock, 2007). For example, there have been notable successes in genome-wide searches (Hindorff, 2009) for susceptibility loci for breast cancer (heritability ~25%), lung cancer (26%), type 2 diabetes mellitus (26%), and Parkinson’s disease (34%) (Lichtenstein, 2000; Wirdefeldt, 2011; Hawkes, 2009; Poulsen, 1999; Seddon, 2005; Wang, 2007). We propose a joint analysis of case-control MDD from the Psychiatric Genomics Consortium (PGC) dataset and the 23andMe cohort, with the most recent analyses of self-reported diagnosis or treatment of clinical depression.
The comorbidity between several of the major psychiatric disorders (Schizophrenia (SZ), Bipolar Affective Disorder (BPD), Autism Spectrum Disorder (ASD), Major Depressive Disorder (MDD) and (ADHD)) is subject to broad variety of studies, both in epidemiology and genetics. Several studies find evidence of close relationship of several of the disorders. However, little is known about the genetic overlap of these disorders other than that they are genetically correlated. One of the potential reasons might be a lack in power for the recently published studies looking into cross disorder associations for these traits. With the emergence of new large-scale genetic datasets in psychiatric genetics, including the contribution of samples from the UK Biobank, 23andMe and the Lundbeck Initiative for Integrative Psychiatric research (iPSYCH) other traits either already have published robust association with their trait under study (in case of major depression) or are close to submit their work for publication. We therefore believe that it is time to study the genetic overlap of MDD and multiple correlated traits on the basis of the latest available datasets (including 23andme samples and the Danish bloodspot samples from iPSYCH). Our aims are to identify cross-disorder associations between MDD and SZ, BPD, ASD, ADHD and assess the nature of these associations, capitalizing on the comprehensive Danish register information. As part of this effort we aim to identify evidence for biological processes involved in these five disorders. For each pairwise comparison with MDD, we will also search for genetic regions that show cross disorder association that are based on the association of one trait with the genetic regions and an indirect association of the second trait due to a causal relationship of the two traits (mediation effects). A specific focus of our analyses will be on individuals with co-occurrence of MDD and each of the other four disorders. Using established approaches that make use of (genotypes and) summary statistics from GWAS we will characterize these groups of individuals in terms of their genetic architecture.
Major depressive disorder (MDD) is a genetically complex trait. The lifetime prevalence of MDD is approximately 15% (Hasin, 2005; Kessler, 2003). As a recurrent course is most common (Mueller, 1999), MDD is accompanied by considerable morbidity (Judd, 1997; Lopez, 2006; Wittchen, 2011), excess mortality (Lopez, 2006; Angst, 2002) and substantial costs (Gustavsson, 2011). The World Health Organization projects MDD to be the second leading cause of disability by 2020 (Murray, 1996). The heritability of MDD is 31-42% (Sullivan, 2000) although certain subsets may be more heritable (e.g., recurrent, early-onset or clinically ascertained MDD) (Levinson, 2006; McGuffin, 1996). This modest heritability could reasonably be expected to complicate attempts to identify genetic loci that confer risk or protection. We propose a joint and comprehensive analysis of case-control MDD from the Psychiatric Genomics Consortium (PGC) dataset and the 23andMe cohort, with the most recent analyses of self-reported diagnosis or treatment of clinical depression.
Birth weight and gestational age at delivery are closely correlated complex traits which are observationally associated with diseases and adverse traits in adulthood such as type 2 diabetes and high blood pressure. The underlying causes are not well understood. Extremes of both birth weight and gestational duration also have immediate implications for maternal and fetal morbidity/mortality. We are using genetic analyses to enhance the understanding maternal and fetal influence on birth weight. We are analysing data from the UK Biobank and meta-analysing with existing data from the Early Growth Genetics (EGG) consortium to identify robust genetic associations. Many of our contributing studies do not have gestational duration measured. Therefore, we aim to use the summary statistics from 23andMe to examine the overlap between genetic associations with birth weight and gestational duration.
Genome-wide association studies are an effective approach to link genetic variants to complex traits and disease. However, they do not provide direct information of the biological perturbations that leads to a genetic variant to affect complex physiological traits. One important approach to bridge this gap is to analyze how genetic variants in these associated loci influence cellular traits such as expression of genes. In this study, we used statistical methods to integrate 23andMe data of genetic associations to disease to several data sets of genetic associations to gene expression in multiple tissues, cell types, and conditions. This work provides insights into biological mechanisms behind human traits, and identifies potential molecular targets at individual disease-associated loci.
The objective of our study is to identify rare variants contained within or near the coding regions of genes that explain a significant portion of the risk for preterm birth (PTB). Women of European ancestry who had previously experienced at least on PTB less than 36 weeks were recruited from Denmark. Whole exome sequencing was performed using the Complete Genomics platform (BGI, Shenzhen, China) on 192 samples representing 93 sister pairs and 2 sister triads. We have a promising list of rare (<10% minor allele frequency) variants that are found at a higher frequency in our preterm birth sister-pairs compared to the general population. We propose examining the results from recently published genome wide association study by Zhang et al. to determine if any of the variants identified by whole exome sequencing are also associated in the 23andMe population.
Asthma is a respiratory disease characterized by recurrent respiratory symptoms, reversible variable airway obstruction and airway inflammation. Estimates suggest that 100-150 million people worldwide have asthma. 5.4 million UK people are currently receiving treatment for asthma: 1.1 million children and 4.3 million adults. Currently, there is no cure for asthma available, only treatments designed to manage the symptoms, and treatment options are limited for moderate-severe asthmatics that remain uncontrolled despite maximal standard therapy. Asthma involves the interaction of genetic and environmental components leading to disease, therefore genetics may help us understand disease mechanisms and provide new targets for drug development. This study aims to use Genome Wide Association (GWA) to investigate the genetic basis of moderate-severe asthma using patient we recruited from the UK. The approach is to see if genetic changes are found in patients more or less frequently than in people without disease. This will help us identify genetic changes that are risk factors for the development of moderate-severe asthma. Similarly, we also want to compare the genetic risk factors that we find in moderate-severe asthma to those found in mild asthma to see if i) they are the same and/or ii) these risk factors are more or less important based on the severity of the asthma.
We have completed a large GWA study involving 5,135 European ancestry individuals with moderate-severe asthma and 25,675 controls free from lung disease, allergic rhinitis and atopic dermatitis. We have identified several genetic changes that appear to be risk factors for the development of moderate-severe asthma and now want to investigate the contribution of these risk factors to mild asthma using the data generated by 23andMe which ran a GWA study of mild asthma involving 28,399 people with asthma and 128,843 people without. Overall, this research has the potential to identify novel genetic risk factors for the development of moderate-severe asthma which will provide greater understanding of this difficult to treat patient population.
To investigate differential gene expression between preterm and term birth, we performed a transcriptomic meta-analysis using data from three independent publicly available studies. We identified three datasets which were comprised of whole blood gene expression profiles from women who delivered preterm and term respectively leveraging the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. These three studies included 339 maternal whole blood samples, 134 from women who delivered preterm and 205 from women who delivered at term. Imposing an FDR cutoff of 0.1 and Fold Change of 1.3, we identify 210 genes that are significantly differentially expressed and observe clustering of preterm samples and term samples based on this signature, which fall into immune pathways. Specifically, the downregulated genes were highly involved in the adaptive immune response, the upregulated genes were specifically involved in the innate immune response. We would like to combine the data that we aggregated together with the genetics results from the 23andMe study in order to compare the genomic and transcriptomic signals and carry out integrative analysis to identify pathways associated with preterm birth.
We will use ADHD GWAS summary statistics to conduct analyses with independent genetic data sets to further our understanding of the relationship between ADHD and other disorders and traits.Publication:
Present-day selection is often assessed with phenotypic correlations between traits and reproductive success. However, selection should be measured with genetic covariance between traits and fitness. Reproductive success is a good proxy for fitness in modern societies, but phenotypic correlations are not always good proxies for genetic correlations. Genome-wide association summary statistics allow high-powered tests of genetic correlations of reproductive success with various traits of interest and using LD-score regression. We will use summary statistics from a large fertility genome-wide association study and determine its genetic correlation with a wide range of other phenotypes of interest (N>50). For this purpose we need summary statistics of well-powered genome wide association studies. We would like to include personality traits and depression, but the publicly available summary statistics are not sufficiently powered to perform our analyses. Therefore, we would like to obtain access to the summary statistics from 23andMe for the Big Five personality dimensions.