The human X chromosome has a unique biology that was shaped by its evolution as the sex chromosome shared by males and females. We have determined 99.3% of the euchromatic sequence of the X chromosome. Our analysis illustrates the autosomal origin of the mammalian sex chromosomes, the stepwise process that led to the progressive loss of recombination between X and Y, and the extent of subsequent degradation of the Y chromosome. LINE1 repeat elements cover one-third of the X chromosome, with a distribution that is consistent with their proposed role as way stations in the process of X-chromosome inactivation. We found 1,098 genes in the sequence, of which 99 encode proteins expressed in testis and in various tumour types. A disproportionately high number of mendelian diseases are documented for the X chromosome. Of this number, 168 have been explained by mutations in 113 X-linked genes, which in many cases were characterized with the aid of the DNA sequence.

Knowledge of the complete genomic DNA sequence of an organism allows a systematic approach to defining its genetic components. The genomic sequence provides access to the complete structures of all genes, including those without known function, their control elements, and, by inference, the proteins they encode, as well as all other biologically important sequences. Furthermore, the sequence is a rich and permanent source of information for the design of further biological studies of the organism and for the study of evolution through cross-species sequence comparison. The power of this approach has been amply demonstrated by the determination of the sequences of a number of microbial and model organisms. The next step is to obtain the complete sequence of the entire human genome. Here we report the sequence of the euchromatic part of human chromosome 22. The sequence obtained consists of 12 contiguous segments spanning 33.4 megabases, contains at least 545 genes and 134 pseudogenes, and provides the first view of the complex chromosomal landscapes that will be found in the rest of the genome.

Alzheimer’s disease (AD) is a public health priority for the 21st century. Risk reduction currently revolves around lifestyle changes with much research trying to elucidate the biological underpinnings. We show that self-report of parental history of Alzheimer’s dementia for case ascertainment in a genome-wide association study of 314,278 participants from UK Biobank (27,696 maternal cases, 14,338 paternal cases) is a valid proxy for an AD genetic study. After meta-analysing with published consortium data (n = 74,046 with 25,580 cases across the discovery and replication analyses), three new AD-associated loci (P < 5 × 10−8) are identified. These contain genes relevant for AD and neurodegeneration: ADAM10, BCKDK/KAT8 and ACE. Novel gene-based loci include drug targets such as VKORC1 (warfarin dose). We report evidence that the association of SNPs in the TOMM40 gene with AD is potentially mediated by both gene expression and DNA methylation in the prefrontal cortex. However, it is likely that multiple variants are affecting the trait and gene methylation/expression. Our discovered loci may help to elucidate the biological mechanisms underlying AD and, as they contain genes that are drug targets for other diseases and disorders, warrant further exploration for potential precision medicine applications.

Background DNA methylation changes with age. Chronological age predictors built from DNA methylation are termed ‘epigenetic clocks’. The deviation of predicted age from the actual age (‘age acceleration residual’, AAR) has been reported to be associated with death. However, it is currently unclear how a better prediction of chronological age affects such association. Methods In this study, we build multiple predictors based on training DNA methylation samples selected from 13,661 samples (13,402 from blood and 259 from saliva). We use the Lothian Birth Cohorts of 1921 (LBC1921) and 1936 (LBC1936) to examine whether the association between AAR (from these predictors) and death is affected by (1) improving prediction accuracy of an age predictor as its training sample size increases (from 335 to 12,710) and (2) additionally correcting for confounders (i.e., cellular compositions). In addition, we investigated the performance of our predictor in non-blood tissues. Results We found that in principle, a near-perfect age predictor could be developed when the training sample size is sufficiently large. The association between AAR and mortality attenuates as prediction accuracy increases. AAR from our best predictor (based on Elastic Net, https://github.com/qzhang314/DNAm-based-age-predictor ) exhibits no association with mortality in both LBC1921 (hazard ratio = 1.08, 95% CI 0.91–1.27) and LBC1936 (hazard ratio = 1.00, 95% CI 0.79–1.28). Predictors based on small sample size are prone to confounding by cellular compositions relative to those from large sample size. We observed comparable performance of our predictor in non-blood tissues with a multi-tissue-based predictor. Conclusions This study indicates that the epigenetic clock can be improved by increasing the training sample size and that its association with mortality attenuates with increased prediction of chronological age. Electronic supplementary material The online version of this article (10.1186/s13073-019-0667-1) contains supplementary material, which is available to authorized users.

Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.(Extended Data Fig. 5). These results show the value of large sample sizes in blood to detect trans-mQTLs regardless of the tissue. Trans-mQTL SNPs and DNAm exhibit patterned TF binding.Recent studies have uncovered multiple types of transcription factor (TF)-DNA interactions influenced by DNAm, including the binding of DNAm-sensitive TFs [26][27][28] and cooperativity between TFs 27,29 . To gain insights into how SNPs induce long-range DNAm changes, we mapped enrichments for DNAm sites and SNPs across binding sites for 171 TFs in 27 cell types 30,31 . We found strong enrichments for most TFs and cell types among DNAm sites with a trans association (cis + trans: 55%; trans only: 80%; cis only: 18%) and among cis-acting SNPs (cis only: 96%, cis + trans: 91%, trans only: 1%; Fig. 2b, Supplementary Tables 7 and 8, and Supplementary Figs. 22 and 23). Consistent with the observation that trans-only DNAm sites are enriched for CpG islands (Supplementary Fig. 13), DNAm sites that overlap TF-binding sites (TFBSs) were relatively hypomethylated (weighted mean DNAm levels = 21% versus 52%, P < 2.2 × 10 −16 ; Supplementary Fig. 24).Next, we hypothesized that, if a trans-mQTL is driven by TF activity 8,10 , then particular TF-TF pairs may exhibit preferential enrichment 32 . An mQTL has a pair of TFBS annotations 31 , one for the SNP and one for the DNAm site. We evaluated whether the annotation pairs among 18,584 interchromosomal trans-mQTLs were associated with TF binding in a nonrandom pattern (Supplementary Note and Extended Data Fig. 6a,b). We found that 6.1% (22,962 of 378,225) of possible pairwise combinations of SNP-DNAm site annotations were more over-or underrepresented than expected by chance after strict multiple testing correction (Supplementary Note, Supplementary Table 9 and Extended Data Fig. 6c).After accounting for abundance and other characteristics, the strongest pairwise enrichments involved sites close to TFBSs for proteins in the cohesin complex, ...

BackgroundGenome-wide DNA methylation (DNAm) profiling has allowed for the development of molecular predictors for a multitude of traits and diseases. Such predictors may be more accurate than the self-reported phenotypes and could have clinical applications.ResultsHere, penalized regression models are used to develop DNAm predictors for ten modifiable health and lifestyle factors in a cohort of 5087 individuals. Using an independent test cohort comprising 895 individuals, the proportion of phenotypic variance explained in each trait is examined for DNAm-based and genetic predictors. Receiver operator characteristic curves are generated to investigate the predictive performance of DNAm-based predictors, using dichotomized phenotypes. The relationship between DNAm scores and all-cause mortality (n = 212 events) is assessed via Cox proportional hazards models. DNAm predictors for smoking, alcohol, education, and waist-to-hip ratio are shown to predict mortality in multivariate models. The predictors show moderate discrimination of obesity, alcohol consumption, and HDL cholesterol. There is excellent discrimination of current smoking status, poorer discrimination of college-educated individuals and those with high total cholesterol, LDL with remnant cholesterol, and total:HDL cholesterol ratios.ConclusionsDNAm predictors correlate with lifestyle factors that are associated with health and mortality. They may supplement DNAm-based predictors of age to identify the lifestyle profiles of individuals and predict disease risk.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1514-1) contains supplementary material, which is available to authorized users.

NRG1, encoding neuregulin 1, is a susceptibility gene for schizophrenia, but no functional mutation causally related to the disorder has yet been identified. Here we investigate the effects of a variant in the human NRG1 promoter region in subjects at high risk of schizophrenia. We show that this variant is associated with (i) decreased activation of frontal and temporal lobe regions, (ii) increased development of psychotic symptoms and (iii) decreased premorbid IQ.

Theories of abnormal anatomical and functional connectivity in schizophrenia and bipolar disorder are supported by evidence from functional magnetic resonance imaging (MRI), structural MRI and diffusion tensor imaging (DTI). The presence of similar abnormalities in unaffected relatives suggests such disconnectivity is genetically mediated, albeit through unspecified loci. Neuregulin 1 (NRG1) is a psychosis susceptibility gene with effects on neuronal migration, axon guidance and myelination that could potentially explain these findings. In the current study, unaffected subjects were genotyped at the NRG1 single nucleotide polymorphism (SNP) rs6994992 (SNP8NRG243177) locus, previously associated with increased risk for psychosis, and the effect of genetic variation at this locus on white matter density (T 1 -weighted MRI) and integrity (DTI) was ascertained. Subjects with the riskassociated TT genotype had reduced white matter density in the anterior limb of the internal capsule and evidence of reduced structural connectivity in the same region using DTI. We therefore provide the first imaging evidence that genetic variation in NRG1 is associated with reduced white matter density and integrity in human subjects. This finding is discussed in the context of NRG1 effects on neuronal migration, axon guidance and myelination.