Initially thought to play a restricted role in calcium homeostasis, the pleiotropic actions of vitamin D in biology and their clinical significance are only now becoming apparent. However, the mode of action of vitamin D, through its cognate nuclear vitamin D receptor (VDR), and its contribution to diverse disorders, remain poorly understood. We determined VDR binding throughout the human genome using chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq). After calcitriol stimulation, we identified 2776 genomic positions occupied by the VDR and 229 genes with significant changes in expression in response to vitamin D. VDR binding sites were significantly enriched near autoimmune and cancer associated genes identified from genome-wide association (GWA) studies. Notable genes with VDR binding included IRF8, associated with MS, and PTPN2 associated with Crohn's disease and T1D. Furthermore, a number of single nucleotide polymorphism associations from GWA were located directly within VDR binding intervals, for example, rs13385731 associated with SLE and rs947474 associated with T1D. We also observed significant enrichment of VDR intervals within regions of positive selection among individuals of Asian and European descent. ChIP-seq determination of transcription factor binding, in combination with GWA data, provides a powerful approach to further understanding the molecular bases of complex diseases.

The immunoglobulin heavy-chain locus (IGH) encodes variable (IGHV), diversity (IGHD), joining (IGHJ), and constant (IGHC) genes and is responsible for antibody heavy-chain biosynthesis, which is vital to the adaptive immune response. Programmed V-(D)-J somatic rearrangement and the complex duplicated nature of the locus have impeded attempts to reconcile its genomic organization based on traditional B-lymphocyte derived genetic material. As a result, sequence descriptions of germline variation within IGHV are lacking, haplotype inference using traditional linkage disequilibrium methods has been difficult, and the human genome reference assembly is missing several expressed IGHV genes. By using a hydatidiform mole BAC clone resource, we present the most complete haplotype of IGHV, IGHD, and IGHJ gene regions derived from a single chromosome, representing an alternate assembly of ∼1 Mbp of high-quality finished sequence. From this we add 101 kbp of previously uncharacterized sequence, including functional IGHV genes, and characterize four large germline copy-number variants (CNVs). In addition to this germline reference, we identify and characterize eight CNV-containing haplotypes from a panel of nine diploid genomes of diverse ethnic origin, discovering previously unmapped IGHV genes and an additional 121 kbp of insertion sequence. We genotype four of these CNVs by using PCR in 425 individuals from nine human populations. We find that all four are highly polymorphic and show considerable evidence of stratification (Fst = 0.3-0.5), with the greatest differences observed between African and Asian populations. These CNVs exhibit weak linkage disequilibrium with SNPs from two commercial arrays in most of the populations tested.

Millions of open-source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an empirical study to assess the feasibility of using Neural Machine Translation techniques for learning bug-fixing patches for real defects. First, we mine millions of bug-fixes from the change histories of projects hosted on GitHub, in order to extract meaningful examples of such bug-fixes. Next, we abstract the buggy and corresponding fixed code, and use them to train an Encoder-Decoder model able to translate buggy code into its fixed version. In our empirical investigation we found that such a model is able to fix thousands of unique buggy methods in the wild. Overall, this model is capable of predicting fixed patches generated by developers in 9-50% of the cases, depending on the number of candidate patches we allow it to generate. Also, the model is able to emulate a variety of different Abstract Syntax Tree operations and generate candidate patches in a split second.

The immunoglobulin (IG) loci consist of repeated and highly homologous sets of genes of different types, variable (V), diversity (D) and junction (J), that rearrange in developing B cells to produce an individual's highly variable repertoire of expressed antibodies, designed to bind to a vast array of pathogens. This repeated structure makes these loci susceptible to a high frequency of insertion and deletion events through evolutionary time, and also makes them difficult to characterize at the genomic level or assay with high-throughput techniques. Given the central role of antibodies in the adaptive immune system, it is not surprising that early candidate gene approaches showed that germline polymorphisms in these regions correlated with susceptibility to both infectious and autoimmune diseases. However, more recent studies, particularly those using high-throughput genome-wide arrays, have failed to implicate these loci in disease. In this review of the IG heavy chain variable gene cluster (IGHV), we examine how poorly we understand the distribution of haplotype variation in this genomic region, and we argue that this lack of information may mask candidate loci in the IGHV gene cluster as causative factors for infectious and autoimmune diseases.