Vitamin D deficiency is a candidate risk factor for a range of adverse health outcomes. In a genome-wide association study of 25 hydroxyvitamin D (25OHD) concentration in 417,580 Europeans we identify 143 independent loci in 112 1-Mb regions, providing insights into the physiology of vitamin D and implicating genes involved in lipid and lipoprotein metabolism, dermal tissue properties, and the sulphonation and glucuronidation of 25OHD. Mendelian randomization models find no robust evidence that 25OHD concentration has causal effects on candidate phenotypes (e.g. BMI, psychiatric disorders), but many phenotypes have (direct or indirect) causal effects on 25OHD concentration, clarifying the epidemiological relationship between 25OHD status and the health outcomes examined in this study.

Accurate prediction of an individual’s phenotype from their DNA sequence is one of the great promises of genomics and precision medicine. We extend a powerful individual-level data Bayesian multiple regression model (BayesR) to one that utilises summary statistics from genome-wide association studies (GWAS), SBayesR. In simulation and cross-validation using 12 real traits and 1.1 million variants on 350,000 individuals from the UK Biobank, SBayesR improves prediction accuracy relative to commonly used state-of-the-art summary statistics methods at a fraction of the computational resources. Furthermore, using summary statistics for variants from the largest GWAS meta-analysis (n ≈ 700, 000) on height and BMI, we show that on average across traits and two independent data sets that SBayesR improves prediction R2 by 5.2% relative to LDpred and by 26.5% relative to clumping and p value thresholding.

The capacity to accurately predict an individual's phenotype from their DNA sequence is one of the great promises of genomics and precision medicine. Recently, Bayesian methods for generating polygenic predictors have been successfully applied in human genomics but require the individual level data, which are often limited in their access due to privacy or logistical concerns, and are computationally very intensive. This has motivated methodological frameworks that utilise publicly available genome-wide association studies (GWAS) summary data, which now for some traits include results from greater than a million individuals. In this study, we extend the established summary statistics methodological framework to include a class of point-normal mixture prior Bayesian regression models, which have been shown to generate optimal genetic predictions and can perform heritability estimation, variant mapping and estimate the distribution of the genetic effects. In a wide range of simulations and cross-validation using 10 real quantitative traits and 1.1 million variants on 350,000 individuals from the UK Biobank (UKB), we establish that our summary based method, SBayesR, performs similarly to methods that use the individual level data and outperforms other state-of-the-art summary statistics methods in terms of prediction accuracy and heritability estimation at a fraction of the computational resources. We generate polygenic predictors for body mass index and height in two independent data sets and show that by exploiting summary statistics on 1.1 million variants from the largest GWAS meta-analysis (n ≈ 700, 000) that the SBayesR prediction R 2 improved on average across traits by 6.8% relative to that estimated from an individual-level data BayesR analysis of data from the UKB (n ≈ 450, 000). Compared with commonly used state-of-the-art summarybased methods, SBayesR improved the prediction R 2 by 4.1% relative to LDpred and by 28.7% relative to clumping and p-value thresholding. SBayesR gave comparable prediction accuracy to the recent RSS method, which has a similar model, but at a computational time that is two orders of magnitude smaller. The methodology is implemented in a very efficient and user-friendly software tool titled GCTB. Introduction 1The capacity to accurately predict an individual's phenotype from their DNA sequence 2 is one of the great promises of genomics and precision medicine 1-5 , recognising that the 3 accuracy of a genetic risk predictor is dependent on the genetic contribution to variation 4 in the trait. It is anticipated that genetic risk prediction will be useful for informing early 5 disease intervention and aiding diagnosis by identifying individuals with an increased 6 genetic risk of disease 5-7 . Accurate genetic predictors for complex traits and disorders are 7 currently limited, due mainly to an incomplete understanding of complex genetic varia-8 tion, small training sample sizes and suboptimal modelling 4,8,9 . Through large consortia 9 and biobank initiatives, sample sizes for gen...

Genotype-by-environment interaction (GEI) is a fundamental component in understanding complex trait variation. However, it remains challenging to identify genetic variants with GEI effects in humans largely because of the small effect sizes and the difficulty of monitoring environmental fluctuations. Here, we demonstrate that GEI can be inferred from genetic variants associated with phenotypic variability in a large sample without the need of measuring environmental factors. We performed a genome-wide variance quantitative trait locus (vQTL) analysis of ~5.6 million variants on 348,501 unrelated individuals of European ancestry for 13 quantitative traits in the UK Biobank and identified 75 significant vQTLs with P < 2.0 × 10−9 for 9 traits, especially for those related to obesity. Direct GEI analysis with five environmental factors showed that the vQTLs were strongly enriched with GEI effects. Our results indicate pervasive GEI effects for obesity-related traits and demonstrate the detection of GEI without environmental data.

AbstractVitamin D deficiency is a candidate risk factor for a range of adverse health outcomes. In a genome-wide association study of 25 hydroxyvitamin D (25OHD) concentration in 417,580 Europeans we identified 143 independent loci in 112 1-Mb regions providing new insights into the physiology of vitamin D and implicating genes involved in (a) lipid and lipoprotein metabolism, (b) dermal tissue properties, and (c) the sulphonation and glucuronidation of 25OHD. Mendelian randomization models found no robust evidence that 25OHD concentration had causal effects on candidate phenotypes (e.g. BMI, psychiatric disorders), but many phenotypes had (direct or indirect) causal effects on 25OHD concentration, clarifying the relationship between 25OHD status and health.

Understanding how natural selection has shaped genetic architecture of complex traits is of importance in medical and evolutionary genetics. Bayesian methods have been developed using individual-level GWAS data to estimate multiple genetic architecture parameters including selection signature. Here, we present a method (SBayesS) that only requires GWAS summary statistics. We analyse data for 155 complex traits (n = 27k–547k) and project the estimates onto those obtained from evolutionary simulations. We estimate that, on average across traits, about 1% of human genome sequence are mutational targets with a mean selection coefficient of ~0.001. Common diseases, on average, show a smaller number of mutational targets and have been under stronger selection, compared to other traits. SBayesS analyses incorporating functional annotations reveal that selection signatures vary across genomic regions, among which coding regions have the strongest selection signature and are enriched for both the number of associated variants and the magnitude of effect sizes.

Key Points• Wild-type AML1 and AML1/ ETO form a complex on chromatin via binding to adjacent different motifs and interacting through the runt homology domain.• The relative binding signals of AML1/ETO and AML1 and AP-1 recruitment determine whether AML1/ETO activates or represses its targets.The AML1/ETO fusion protein is essential to the development of t(8;21) acute myeloid leukemia (AML) and is well recognized for its dominant-negative effect on the coexisting wild-type protein AML1. However, the genome-wide interplay between AML1/ETO and wild-type AML1 remains elusive in the leukemogenesis of t(8;21) AML. Through chromatin immunoprecipitation sequencing and computational analysis, followed by a series of experimental validations, we report here that wild-type AML1 is able to orchestrate the expression of AML1/ETO targets regardless of being activated or repressed; this is achieved via forming a complex with AML1/ETO and via recruiting the cofactor AP-1 on chromatin. On chromatin occupancy, AML1/ETO and wild-type AML1 largely overlap and preferentially bind to adjacent and distinct short and long AML1 motifs on the colocalized regions, respectively. On physical interaction, AML1/ETO can form a complex with wild-type AML1 on chromatin, and the runt homology domain of both proteins are responsible for their interactions. More importantly, the relative binding signals of AML1 and AML1/ETO on chromatin determine which genes are repressed or activated by AML1/ETO. Further analysis of coregulators indicates that AML1/ETO transactivates gene expression through recruiting AP-1 to the AML1/ETO-AML1 complex. These findings enrich our knowledge of understanding the significance of the interplay between the wild-type protein and the oncogenic fusion protein in the development of leukemia. (Blood. 2016;127(2):233-242) IntroductionMany oncogenic fusion proteins generated by chromosomal translocations play a causal role in the development of leukemia. The oncogenic lesion almost exclusively occurs in a single allele within an individual leukemia, whereas the wild-type protein produced from the nontranslocated allele generally still exists.1,2 The coexistence of the wild-type protein with the oncogenic fusion protein raises the question about their significance in leukemogenesis. To address this question, acute myeloid leukemia (AML) with the t(8;21) translocation and resultant AML1/ETO fusion gene is an ideal model, particularly through understanding the interplay between the AML1/ETO fusion protein and the AML1 wild-type protein.AML1 (also known as RUNX1 or core-binding factor a [CBFa]), a critical regulator of normal hematopoiesis, 3 is widely expressed in multiple hematopoietic lineages and regulates the expression of a variety of hematopoietic genes through recognizing the motif TGTGGT.4 AML1 is frequently involved in chromosomal abnormalities in AML, [5][6][7] among which AML1/ETO is the most common fusion protein resulted from the t(8;21) translocation. 7 Structurally, AML1/ETO consists of the N-terminal portion o...

Leukemia stem cells (LSCs) play important roles in leukemia initiation, progression, and relapse, and thus represent a critical target for therapeutic intervention. However, relatively few agents have been shown to target LSCs, slowing progress in the treatment of acute myelogenous leukemia (AML). Based on in vitro and in vivo evidence, we report here that fenretinide, a well-tolerated vitamin A derivative, is capable of eradicating LSCs but not normal hematopoietic progenitor/stem cells at physiologically achievable concentrations. Fenretinide exerted a selective cytotoxic effect on primary AML CD34 + cells, especially the LSC-enriched CD34 + CD38 − subpopulation, whereas no significant effect was observed on normal counterparts. Methylcellulose colony formation assays further showed that fenretinide significantly suppressed the formation of colonies derived from AML CD34 + cells but not those from normal CD34 + cells. Moreover, fenretinide significantly reduced the in vivo engraftment of AML stem cells but not normal hematopoietic stem cells in a nonobese diabetic/SCID mouse xenotransplantation model. Mechanistic studies revealed that fenretinide-induced cell death was linked to a series of characteristic events, including the rapid generation of reactive oxygen species, induction of genes associated with stress responses and apoptosis, and repression of genes involved in NF-κB and Wnt signaling. Further bioinformatic analysis revealed that the fenretinide–down-regulated genes were significantly correlated with the existing poor-prognosis signatures in AML patients. Based on these findings, we propose that fenretinide is a potent agent that selectively targets LSCs, and may be of value in the treatment of AML.