Deep resequencing identifies candidate functional genes in leprosy GWAS loci

Leprosy is the second most prevalent mycobacterial disease globally. Despite the existence of an effective therapy, leprosy incidence has consistently remained above 200,000 cases per year since 2010. Numerous host genetic factors have been identified for leprosy that contribute to the persistently high case numbers. In the past decade, genetic epidemiology approaches, including genome-wide association studies (GWAS), identified more than 30 loci contributing to leprosy susceptibility. However, GWAS loci commonly encompass multiple genes, which poses a challenge to define causal candidates for each locus. To address this problem, we hypothesized that genes contributing to leprosy susceptibility differ in their frequencies of rare protein-altering variants between cases and controls. Using deep resequencing we assessed protein-coding variants for 34 genes located in GWAS or linkage loci in 555 Vietnamese leprosy cases and 500 healthy controls. We observed 234 nonsynonymous mutations in the targeted genes. A significant depletion of protein-altering variants was detected for the IL18R1 and BCL10 genes in leprosy cases. The IL18R1 gene is clustered with IL18RAP and IL1RL1 in the leprosy GWAS locus on chromosome 2q12.1. Moreover, in a recent GWAS we identified an HLA-independent signal of association with leprosy on chromosome 6p21. Here, we report amino acid changes in the CDSN and PSORS1C2 genes depleted in leprosy cases, indicating them as candidate genes in the chromosome 6p21 locus. Our results show that deep resequencing can identify leprosy candidate susceptibility genes that had been missed by classic linkage and association approaches.

Genetic epidemiology of autoinflammatory disease variants in Indian population from 1029 whole genomes

Background Autoinflammatory disorders are the group of inherited inflammatory disorders caused due to the genetic defect in the genes that regulates innate immune systems. These have been clinically characterized based on the duration and occurrence of unprovoked fever, skin rash, and patient’s ancestry. There are several autoinflammatory disorders that are found to be prevalent in a specific population and whose disease genetic epidemiology within the population has been well understood. However, India has a limited number of genetic studies reported for autoinflammatory disorders till date. The whole genome sequencing and analysis of 1029 Indian individuals performed under the IndiGen project persuaded us to perform the genetic epidemiology of the autoinflammatory disorders in India. Results We have systematically annotated the genetic variants of 56 genes implicated in autoinflammatory disorder. These genetic variants were reclassified into five categories (i.e., pathogenic, likely pathogenic, benign, likely benign, and variant of uncertain significance (VUS)) according to the American College of Medical Genetics and Association of Molecular pathology (ACMG-AMP) guidelines. Our analysis revealed 20 pathogenic and likely pathogenic variants with significant differences in the allele frequency compared with the global population. We also found six causal founder variants in the IndiGen dataset belonging to different ancestry. We have performed haplotype prediction analysis for founder mutations haplotype that reveals the admixture of the South Asian population with other populations. The cumulative carrier frequency of the autoinflammatory disorder in India was found to be 3.5% which is much higher than reported. Conclusion With such frequency in the Indian population, there is a great need for awareness among clinicians as well as the general public regarding the autoinflammatory disorder. To the best of our knowledge, this is the first and most comprehensive population scale genetic epidemiological study being reported from India.

Genetic epidemiology

The integration of epidemiological methods with genome-wide technologies has provided unprecedented insights into the complex biological mechanisms of traits and diseases in human populations. These advances have revolutionized the scope and scale of what can be done, ranging from studies of single gene variants measured in small samples through to the emergence of high-throughput genotyping, genome-wide association studies, and next-generation whole genome sequencing technologies which produce information on millions of genetic variants in thousands of individuals. In parallel, as these genomic technologies provide new opportunities to better understand disease biology, it is becoming increasingly apparent that a knowledge of genomic medicine will be essential for public health practitioners in meeting the public’s future healthcare needs. As our understanding of disease aetiology and its underlying biological mechanisms increases, there is the potential for new drug development and improved therapeutic strategies to manage disease at the population level. At the same time, there is growing interest in the use of these genetic variants to predict individual disease risk over and above classical risk factors, and to develop stratified and personalized approaches to diagnosis and disease management. The public health community has a central role to play in critically and systematically evaluating these discoveries for their potential use in disease management. Thus, in this 21st century era of genetic epidemiology and genomic science, public health practitioners cannot overlook the global relevance of genetic epidemiology on our understanding of models of disease, personalized medicine, and the relationship between individuals and populations, treatment, and prevention strategies.

The Opportunities and Challenges of Integrating Population Histories Into Genetic Studies for Diverse Populations: A Motivating Example From Native Hawaiians

There is a well-recognized need to include diverse populations in genetic studies, but several obstacles continue to be prohibitive, including (but are not limited to) the difficulty of recruiting individuals from diverse populations in large numbers and the lack of representation in available genomic references. These obstacles notwithstanding, studying multiple diverse populations would provide informative, population-specific insights. Using Native Hawaiians as an example of an understudied population with a unique evolutionary history, I will argue that by developing key genomic resources and integrating evolutionary thinking into genetic epidemiology, we will have the opportunity to efficiently advance our knowledge of the genetic risk factors, ameliorate health disparity, and improve healthcare in this underserved population.

1057Addressing challenges in life-course epidemiology: established and novel approaches using big data and twin/family studies

Focus and outcomes for participants Long periods between exposures and outcomes pose a number of challenges for life course epidemiological research, including unmeasured confounding factors (e.g.; familial factors) and mediation by other covariates, which make it difficult to unequivocally establish associations let alone causality. In this symposium we will present a number of different studies based on big data utilizing a variety of methods to overcome some of the issues encountered in research across long time frames or generations. Our focus will be on the different methods, the solutions they provide as well as their limitations. The methods presented were applied in the context of life course epidemiology and include: mediation analyses; genetic epidemiology; well-established and novel family designs including twins, siblings and cousins, and a method comparable to Mendelian randomization - ICE FALCON (Inference on Causation from Examination of Familial Confounding) which is part of a more general approach called ICE CRiSTAL (Inference on Causation from Examining Changes in Regression coefficients in STatistical AnaLsyes). The intended outcomes for the audience are to increase awareness of the challenges imposed by the data frequently used in this field of research and present possible solutions to (at least partly) address those. It is our intention to generate discussion and encourage other researchers to share their experiences and solutions to increase knowledge collectively. Rationale for the symposium, including for its inclusion in the Congress The main theme of the conference is ‘Methodological Innovations in Epidemiology’. Our symposium includes six different methods to strengthen causal inferences in epidemiology. While some of the presented methods are well established in classic epidemiology research (i.e. mediation analyses), others are more commonly found in different disciplines such as the expanding genetic epidemiology field (e.g. alternative twin designs and measured genetic risk approaches). In addition, we are presenting a new method for making inference about causation developed by Prof. John Hopper and Dr Shuai Li and co-workers called ICE FALCON, which applies to twin and family data and is part of a more general approach called ICE CRiSTAL. These methods use observational data to infer or rebut causality between measured variables, similar to Mendelian randomization (without relying on genetic information or strong assumptions). All the work presented is either nearing publication or has been published in the last two years and each presenter is intimately involved with the analysis and methods they will be presenting. Beyond a range of methods and study designs we have also a diversity of researchers and research questions in our symposium by including: researchers at different stages in their career and from around the world (ranging from early Postdoctoral Fellows over Senior Research Fellows/Assistant professors to Professors); a variety of research questions and diseases; and a range of population context including Indigenous Australians, babies, children, and adults, in order to appeal to a wider audience. Presentation program 6 talks of 8 minutes each with 2 minutes for questions followed by a general discussion facilitated by the chair. Names of presenters Dr Miriam A Mosing1,2