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24-hour biological rhythms are essential to maintain physiological homeostasis. Disruption of these rhythms increases the risks of multiple diseases. Biological rhythms are known to have a genetic basis formed by core clock genes, but how individual genetic variation shapes the oscillating transcriptome and contributes to human chronophysiology and disease risk is largely unknown. Here, we mapped interactions between temporal gene expression and genotype to identify quantitative trait loci (QTLs) contributing to rhythmic gene expression. These newly identified QTLs were termed as rhythmic QTLs (rhyQTLs), which determine previously unappreciated rhythmic genes in human subpopulations with specific genotypes. Functionally, rhyQTLs and their associated rhythmic genes contribute extensively to essential chronophysiological processes, including bile acid and lipid metabolism. The identification of rhyQTLs sheds light on the genetic mechanisms of gene rhythmicity, offers mechanistic insights into variations in human disease risk, and enables precision chronotherapeutic approaches for patients. Circadian rhythms influence key physiological functions. Here, the authors defined rhythmic quantitative trait loci that reveal novel genotype-specific rhythmic genes, explaining individual variations in rhythmic gene expression and disease risk.

Mark Daly and colleagues present a statistical framework to evaluate the role of de novo mutations in human disease by calibrating a model of de novo mutation rates at the individual gene level. The mutation probabilities defined by their model and list of constrained genes can be used to help identify genetic variants that have a significant role in disease. Spontaneously arising ( de novo ) mutations have an important role in medical genetics. For diseases with extensive locus heterogeneity, such as autism spectrum disorders (ASDs), the signal from de novo mutations is distributed across many genes, making it difficult to distinguish disease-relevant mutations from background variation. Here we provide a statistical framework for the analysis of excesses in de novo mutation per gene and gene set by calibrating a model of de novo mutation. We applied this framework to de novo mutations collected from 1,078 ASD family trios, and, whereas we affirmed a significant role for loss-of-function mutations, we found no excess of de novo loss-of-function mutations in cases with IQ above 100, suggesting that the role of de novo mutations in ASDs might reside in fundamental neurodevelopmental processes. We also used our model to identify ∼1,000 genes that are significantly lacking in functional coding variation in non-ASD samples and are enriched for de novo loss-of-function mutations identified in ASD cases.

Background Characterizing large genomic variants is essential to expanding the research and clinical applications of genome sequencing. While multiple data types and methods are available to detect these structural variants (SVs), they remain less characterized than smaller variants because of SV diversity, complexity, and size. These challenges are exacerbated by the experimental and computational demands of SV analysis. Here, we characterize the SV content of a personal genome with Parliament, a publicly available consensus SV-calling infrastructure that merges multiple data types and SV detection methods. Results We demonstrate Parliament’s efficacy via integrated analyses of data from whole-genome array comparative genomic hybridization, short-read next-generation sequencing, long-read (Pacific BioSciences RSII), long-insert (Illumina Nextera), and whole-genome architecture (BioNano Irys) data from the personal genome of a single subject (HS1011). From this genome, Parliament identified 31,007 genomic loci between 100 bp and 1 Mbp that are inconsistent with the hg19 reference assembly. Of these loci, 9,777 are supported as putative SVs by hybrid local assembly, long-read PacBio data, or multi-source heuristics. These SVs span 59 Mbp of the reference genome (1.8%) and include 3,801 events identified only with long-read data. The HS1011 data and complete Parliament infrastructure, including a BAM-to-SV workflow, are available on the cloud-based service DNAnexus. Conclusions HS1011 SV analysis reveals the limits and advantages of multiple sequencing technologies, specifically the impact of long-read SV discovery. With the full Parliament infrastructure, the HS1011 data constitute a public resource for novel SV discovery, software calibration, and personal genome structural variation analysis.

Purpose Cardiovascular disease (CVD) is the leading cause of death in adults in the United States, yet the benefits of genetic testing are not universally accepted. Methods We developed the “HeartCare” panel of genes associated with CVD, evaluating high-penetrance Mendelian conditions, coronary artery disease (CAD) polygenic risk, LPA gene polymorphisms, and specific pharmacogenetic (PGx) variants. We enrolled 709 individuals from cardiology clinics at Baylor College of Medicine, and samples were analyzed in a CAP/CLIA-certified laboratory. Results were returned to the ordering physician and uploaded to the electronic medical record. Results Notably, 32% of patients had a genetic finding with clinical management implications, even after excluding PGx results, including 9% who were molecularly diagnosed with a Mendelian condition. Among surveyed physicians, 84% reported medical management changes based on these results, including specialist referrals, cardiac tests, and medication changes. LPA polymorphisms and high polygenic risk of CAD were found in 20% and 9% of patients, respectively, leading to diet, lifestyle, and other changes. Warfarin and simvastatin pharmacogenetic variants were present in roughly half of the cohort. Conclusion Our results support the use of genetic information in routine cardiovascular health management and provide a roadmap for accompanying research.

Escherichia coli is a model laboratory bacterium, a species that is widely distributed in the environment, as well as a mutualist and pathogen in its human hosts. As such, E. coli represents an attractive organism to study how environment impacts microbial genome structure and function. Uropathogenic E. coli (UPEC) must adapt to life in several microbial communities in the human body, and has a complex life cycle in the bladder when it causes acute or recurrent urinary tract infection (UTI). Several studies designed to identify virulence factors have focused on genes that are uniquely represented in UPEC strains, whereas the role of genes that are common to all E. coli has received much less attention. Here we describe the complete 5,065,741-bp genome sequence of a UPEC strain recovered from a patient with an acute bladder infection and compare it with six other finished E. coli genome sequences. We searched 3,470 ortholog sets for genes that are under positive selection only in UPEC strains. Our maximum likelihood-based analysis yielded 29 genes involved in various aspects of cell surface structure, DNA metabolism, nutrient acquisition, and UTI. These results were validated by resequencing a subset of the 29 genes in a panel of 50 urinary, periurethral, and rectal E. coli isolates from patients with UTI. These studies outline a computational approach that may be broadly applicable for studying strain-specific adaptation and pathogenesis in other bacteria.

We report on results from whole-exome sequencing (WES) of 1,039 subjects diagnosed with autism spectrum disorders (ASD) and 870 controls selected from the NIMH repository to be of similar ancestry to cases. The WES data came from two centers using different methods to produce sequence and to call variants from it. Therefore, an initial goal was to ensure the distribution of rare variation was similar for data from different centers. This proved straightforward by filtering called variants by fraction of missing data, read depth, and balance of alternative to reference reads. Results were evaluated using seven samples sequenced at both centers and by results from the association study. Next we addressed how the data and/or results from the centers should be combined. Gene-based analyses of association was an obvious choice, but should statistics for association be combined across centers (meta-analysis) or should data be combined and then analyzed (mega-analysis)? Because of the nature of many gene-based tests, we showed by theory and simulations that mega-analysis has better power than meta-analysis. Finally, before analyzing the data for association, we explored the impact of population structure on rare variant analysis in these data. Like other recent studies, we found evidence that population structure can confound case-control studies by the clustering of rare variants in ancestry space; yet, unlike some recent studies, for these data we found that principal component-based analyses were sufficient to control for ancestry and produce test statistics with appropriate distributions. After using a variety of gene-based tests and both meta- and mega-analysis, we found no new risk genes for ASD in this sample. Our results suggest that standard gene-based tests will require much larger samples of cases and controls before being effective for gene discovery, even for a disorder like ASD.

Hydroxyurea has proven efficacy in children and adults with sickle cell anemia (SCA), but with considerable inter-individual variability in the amount of fetal hemoglobin (HbF) produced. Sibling and twin studies indicate that some of that drug response variation is heritable. To test the hypothesis that genetic modifiers influence pharmacological induction of HbF, we investigated phenotype-genotype associations using whole exome sequencing of children with SCA treated prospectively with hydroxyurea to maximum tolerated dose (MTD). We analyzed 171 unrelated patients enrolled in two prospective clinical trials, all treated with dose escalation to MTD. We examined two MTD drug response phenotypes: HbF (final %HbF minus baseline %HbF), and final %HbF. Analyzing individual genetic variants, we identified multiple low frequency and common variants associated with HbF induction by hydroxyurea. A validation cohort of 130 pediatric sickle cell patients treated to MTD with hydroxyurea was genotyped for 13 non-synonymous variants with the strongest association with HbF response to hydroxyurea in the discovery cohort. A coding variant in Spalt-like transcription factor, or SALL2, was associated with higher final HbF in this second independent replication sample and SALL2 represents an outstanding novel candidate gene for further investigation. These findings may help focus future functional studies and provide new insights into the pharmacological HbF upregulation by hydroxyurea in patients with SCA.

Background Establishing a genetic diagnosis for individuals with intellectual disability (ID) benefits patients and their families as it may inform the prognosis, lead to appropriate therapy, and facilitate access to medical and supportive services. Exome sequencing has been successfully applied in a diagnostic setting, but most clinical exome referrals are pediatric patients, with many adults with ID lacking a comprehensive genetic evaluation. Methods Our unique recruitment strategy involved partnering with service and education providers for individuals with ID. We performed exome sequencing and analysis, and clinical variant interpretation for each recruited family. Results All five families enrolled in the study opted‐in for the return of genetic results. In three out of five families exome sequencing analysis identified pathogenic or likely pathogenic variants in KANSL1, TUSC3, and MED13L genes. Families discussed the results and any potential medical follow‐up in an appointment with a board certified clinical geneticist. Conclusion Our study suggests high yield of exome sequencing as a diagnostic tool in adult patients with ID who have not undergone comprehensive sequencing‐based genetic testing. Research studies including an option of return of results through a genetic clinic could help minimize the disparity in exome diagnostic testing between pediatric and adult patients with ID. Exome sequencing has been successfully applied in a diagnostic setting, but most clinical exome referrals are pediatric patients, with many adults with intellectual disability lacking a comprehensive genetic evaluation. Our study points to high yield of exome sequencing as a diagnostic tool in adult patients who have not undergone comprehensive sequencing‐based genetic testing. Research studies including an option of return of results through a genetic clinic could help minimize the disparity in exome diagnostic testing between pediatric and adult patients.

Exome sequencing of 175 autism spectrum disorder parent–child trios reveals that few de novo point mutations have a role in autism spectrum disorder and those that do are distributed across many genes and are incompletely penetrant, further supporting extreme genetic heterogeneity of this spectrum disorder. Heterogeneity in the genetics of autism Although it is well accepted that genetics makes a strong contribution to autism spectrum disorder, most of the underlying causes of the condition remain unknown. Three groups present large-scale exome-sequencing studies of individuals with sporadic autism spectrum disorder, including many parent–child trios and unaffected siblings. The overall message from the three papers is that there is extreme locus heterogeneity among autistic individuals, with hundreds of genes involved in the condition, and with no single gene contributing to more than a small fraction of cases. Sanders et al . report the association of the gene SCN2A , previously identified in epilepsy syndromes, with the risk of autism. Neale et al . find strong evidence that CHD8 and KATNAL2 are autism risk factors. O'Roak et al . observe that a large proportion of the mutated proteins have crucial roles in fundamental developmental pathways, including β-catenin and p53 signalling. Autism spectrum disorders (ASD) are believed to have genetic and environmental origins, yet in only a modest fraction of individuals can specific causes be identified 1 , 2 . To identify further genetic risk factors, here we assess the role of de novo mutations in ASD by sequencing the exomes of ASD cases and their parents ( n = 175 trios). Fewer than half of the cases (46.3%) carry a missense or nonsense de novo variant, and the overall rate of mutation is only modestly higher than the expected rate. In contrast, the proteins encoded by genes that harboured de novo missense or nonsense mutations showed a higher degree of connectivity among themselves and to previous ASD genes 3 as indexed by protein-protein interaction screens. The small increase in the rate of de novo events, when taken together with the protein interaction results, are consistent with an important but limited role for de novo point mutations in ASD, similar to that documented for de novo copy number variants. Genetic models incorporating these data indicate that most of the observed de novo events are unconnected to ASD; those that do confer risk are distributed across many genes and are incompletely penetrant (that is, not necessarily sufficient for disease). Our results support polygenic models in which spontaneous coding mutations in any of a large number of genes increases risk by 5- to 20-fold. Despite the challenge posed by such models, results from de novo events and a large parallel case–control study provide strong evidence in favour of CHD8 and KATNAL2 as genuine autism risk factors.