UNASSIGNED: Clinical manifestations of patients with Hemoglobin E/beta-thalassemia vary from mild to severe phenotypes despite exhibiting the same genotype. Studies have partially identified genetic modifiers. We aimed to study the association between rare variants in protein-coding regions and clinical severity in Thai patients.
UNASSIGNED: From April to November 2018, a case-control study was conducted based on clinical information and DNA samples collected from Thai patients with hemoglobin E/beta-thalassemia over the age of four years. Cases were patients with severe symptoms, while patients with mild symptoms acted as controls. Whole exome sequencing and rare variant association study were used to analyze the data.
UNASSIGNED: All 338 unrelated patients were classified into 165 severe and 173 mild cases. Genotypes comprised 81.4% of hemoglobin E/beta-thalassemia, 2.7% of homozygous or compound heterozygous beta-thalassemia, and 0.3% of (δβ)0 thalassemia Hb E while 15.7% of samples were not classified as beta-thalassemia. A novel cis heterozygotes of IVS I-7 (A > T) and codon 26 (G > A) was identified. Six genes (COL4A3, DLK1, FAM186A, PZP, THPO, and TRIM51) showed the strongest associations with severity (observed p-values of <0.05; significance lost after correction for multiplicity). Among known modifiers, KLF1 variants were found in four mild patients and one severe patient.
UNASSIGNED: No rare variants were identified as contributors to the clinical heterogeneity of hemoglobin E/beta-thalassemia. KLF1 mutations are potential genetic modifiers. Studies to identify genetic factors are still important and helpful for predicting severity and developing targeted therapy.

Genome-wide association studies have led to numerous genetic loci associated with Alzheimer\'s disease (AD). Whole-genome sequencing (WGS) now permits genome-wide analyses to identify rare variants contributing to AD risk.
We performed single-variant and spatial clustering-based testing on rare variants (minor allele frequency [MAF] ≤1%) in a family-based WGS-based association study of 2247 subjects from 605 multiplex AD families, followed by replication in 1669 unrelated individuals.
We identified 13 new AD candidate loci that yielded consistent rare-variant signals in discovery and replication cohorts (4 from single-variant, 9 from spatial-clustering), implicating these genes: FNBP1L, SEL1L, LINC00298, PRKCH, C15ORF41, C2CD3, KIF2A, APC, LHX9, NALCN, CTNNA2, SYTL3, and CLSTN2.
Downstream analyses of these novel loci highlight synaptic function, in contrast to common AD-associated variants, which implicate innate immunity and amyloid processing. These loci have not been associated previously with AD, emphasizing the ability of WGS to identify AD-associated rare variants, particularly outside of the exome.

Soon after the first genome-wide association study (GWAS) for type 2 diabetes (T2D) was published, it was hypothesized that rare and low-frequency variants might explain a substantial proportion of disease risk. Rare coding variants in particular were emphasized given their large expected role in disease. This review summarizes the extent to which recent T2D genetic studies provide evidence for or against this hypothesis.
Following a comprehensive study of T2D genetic architecture using three sequencing and genotyping technologies, four even larger studies have provided a yet higher resolution view of the role of rare and low-frequency coding variation in T2D susceptibility. Empirical evidence strongly suggests that common regulatory variants are the dominant contributor to T2D heritability. However, rare coding variants may nonetheless be pervasive across T2D-relevant genes. A strategy using common variants to map disease genes, and rare coding variants to link molecular gene perturbations to cellular and phenotypic effects, may be an effective means to investigate T2D pathogenesis and potential new therapies.

Sequencing of the protein-coding genome, the exome, has proven powerful to unravel links between genetic variation and disease for both Mendelian and complex conditions. Importantly, however, the increasing number of sequenced human exomes and mapping of disease-associated alleles is accompanied by a simultaneous, yet exponential increase in the overall number of rare and low frequency alleles identified. For most of these novel alleles, biological consequences remain unknown since reliable experimental approaches to better characterize their impact on protein function are only slowly emerging.Here we review a scalable, cell-based strategy that we have recently established to systematically profile the biological impact of rare and low frequency missense variants in vitro. By applying this approach to missense alleles identified through cohort-level exome sequencing in the low-density lipoprotein receptor (LDLR) we are able to distinguish rare alleles that predispose to familial hypercholesterolemia and myocardial infarction from alleles without obvious impact on LDLR levels or functions. We propose that systematic implementation of such and similar strategies will significantly advance our understanding of the protein-coding human genome and how rare and low frequency genetic variation impacts on health and disease.

Some common diseases are known to have an inherited component, however, their population- and familial-incidence patterns do not conform to any known monogenic Mendelian pattern of inheritance and instead they are currently much better explained if an underlying polygenic architecture is posited. Studies that have attempted to identify the causative genetic factors have been designed on this polygenic framework, but so far the yield has been largely unsatisfactory. Based on accumulating recent observations concerning the roles of somatic mosaicism in disease, in this article a second framework which posits a single gene-two hit model which can be modulated by a mutator/anti-mutator genetic background is suggested. I discuss whether such a model can be considered a viable alternative based on current knowledge, its advantages over the current polygenic framework, and describe practical routes via which the new framework can be investigated.