Population Genetics of Rare Variants and Complex Diseases

Identifying drivers of complex traits from the noisy signals of genetic variation obtained from high throughput genome sequencing technologies is a central challenge faced by human geneticists today. We hypothesize that the variants involved in complex diseases are likely to exhibit non-neutral evolutionary signatures. Uncovering the evolutionary history of all variants is therefore of intrinsic interest for complex disease research. However, doing so necessitates the simultaneous elucidation of the targets of natural selection and population-specific demographic history. Here we characterize the action of natural selection operating across complex disease categories, and use population genetic simulations to evaluate the expected patterns of genetic variation in large samples. We focus on populations that have experienced historical bottlenecks followed by explosive growth (consistent with most human populations), and describe the differences between evolutionarily deleterious mutations and those that are neutral. Genes associated with several complex disease categories exhibit stronger signatures of purifying selection than non-disease genes. In addition, loci identified through genome-wide association studies of complex traits also exhibit signatures consistent with being in regions recurrently targeted by purifying selection. Through simulations, we show that population bottlenecks and rapid growth enables deleterious rare variants to persist at low frequencies just as long as neutral variants, but low frequency and common variants tend to be much younger than neutral variants. This has resulted in a large proportion of modern-day rare alleles that have a deleterious effect on function, and that potentially contribute to disease susceptibility.Comment: 36 pages, 7 figure

Sialendoscopic management of autoimmune sialadenitis: a review of literature

Autoimmune diseases of major salivary glands include Sjögren's syndrome and a complex of disorders classified as immunoglobulin G4-related diseases. These pathologies are characterised by an autoimmune reaction mediated by T-helper lymphocytes that targets the ducts of exocrine glands in Sjögren's syndrome and glandular parenchyma in immunoglobulin G4-related diseases. Immunoglobulin G4-related diseases represent recently introduced multi-organ diseases that also involve the salivary glands. However, the morbid conditions once known as Mikulicz's disease and Kuttner's tumour were recently considered as two variants of immunoglobulin G4-related diseases affecting the major salivary glands ( immunoglobulin G4-related sialadenitis). This review briefly summarises the pathogenesis and clinical features of autoimmune diseases of the major salivary glands, focusing on the diagnostic and therapeutic role of sialendoscopy

Modeling and Testing for Joint Association Using a Genetic Random Field Model

Substantial progress has been made in identifying single genetic variants predisposing to common complex diseases. Nonetheless, the genetic etiology of human diseases remains largely unknown. Human complex diseases are likely influenced by the joint effect of a large number of genetic variants instead of a single variant. The joint analysis of multiple genetic variants considering linkage disequilibrium (LD) and potential interactions can further enhance the discovery process, leading to the identification of new disease-susceptibility genetic variants. Motivated by the recent development in spatial statistics, we propose a new statistical model based on the random field theory, referred to as a genetic random field model (GenRF), for joint association analysis with the consideration of possible gene-gene interactions and LD. Using a pseudo-likelihood approach, a GenRF test for the joint association of multiple genetic variants is developed, which has the following advantages: 1. considering complex interactions for improved performance; 2. natural dimension reduction; 3. boosting power in the presence of LD; 4. computationally efficient. Simulation studies are conducted under various scenarios. Compared with a commonly adopted kernel machine approach, SKAT, GenRF shows overall comparable performance and better performance in the presence of complex interactions. The method is further illustrated by an application to the Dallas Heart Study.Comment: 17 pages, 4 tables, the paper has been published on Biometric