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Newly cost-effective next-generation sequencing has led to an explosion of discoveries of novel genetic mutations that reveal the rampant “promiscuity” of genotype–phenotype relationships. Such discoveries should ultimately revolutionize clinical care. Ever since Mendel observed the varied phenotypes of peas — green or yellow, smooth or wrinkled — phenotypes have been used to systematically identify the genetic causes of disease. Similarly, genotype–phenotype relationships in humans could be dissected only if there were clearly recognizable, and relatively homogeneous, phenotypes. Since broad searches of genetic information were not technically feasible or cost-effective before the advent of next-generation sequencing (NGS), scientists studied well-characterized families to narrow the list of plausible genetic causes. However, being restricted to this set of “solvable” genetic problems led to ascertainment biases that favored highly penetrant mutations with straightforward functional . . .

Background Early identification of outbreaks remains a key component in continuing to reduce the burden of infectious disease in the United States. Previous studies have applied statistical methods to detect unexpected cases of disease in space or time. The objectives of our study were to assess the ability and timeliness of three spatio-temporal methods to detect known outbreaks of tuberculosis. Methods We used routinely available molecular and surveillance data to retrospectively assess the effectiveness of three statistical methods in detecting tuberculosis outbreaks: county-based log-likelihood ratio, cumulative sums, and a spatial scan statistic. Results Our methods identified 8 of the 9 outbreaks, and 6 outbreaks would have been identified 1–52 months (median = 10 months) before local public health authorities identified them. Assuming no delays in data availability, 46 (59.7%) of the 77 patients in the 9 outbreaks were identified after our statistical methods would have detected the outbreak but before local public health authorities became aware of the problem. Conclusions Statistical methods, when applied retrospectively to routinely collected tuberculosis data, can successfully detect known outbreaks, potentially months before local public health authorities become aware of the problem. The three methods showed similar results; no single method was clearly superior to the other two. Further study to elucidate the performance of these methods in detecting tuberculosis outbreaks will be done in a prospective analysis.

Interindividual variability in drug efficacy and toxicity is a major challenge in clinical practice. Variations in drug pharmacokinetics (PKs) and pharmacodynamics (PDs) can be, in part, explained by polymorphic variants in genes encoding drug metabolizing enzymes and transporters (absorption, distribution, metabolism, and excretion) or in genes encoding drug receptors. Pharmacogenomics (PGx) has allowed the identification of predictive biomarkers of drug PKs and PDs and the current knowledge of genome‐disease and genome‐drug interactions offers the opportunity to optimize tailored drug therapy. High‐throughput PGx genotyping, from targeted to more comprehensive strategies, allows the identification of PK/PD genotypes to be developed as clinical predictive biomarkers. However, a biomarker needs a robust process of validation followed by clinical‐grade assay development and must comply to stringent regulatory guidelines. We here discuss the methodological challenges and the emerging technological tools in PGx biomarker discovery and validation, at the crossroad among molecular genetics, bioinformatics, and clinical medicine.

Global genetic diversity of HIV-1 is a major challenge to the development of HIV vaccines. We aimed to estimate the regional and global distribution of HIV-1 subtypes and recombinants during 1990–2015. We searched PubMed, EMBASE (Ovid), CINAHL (Ebscohost), and Global Health (Ovid) for HIV-1 subtyping studies published between Jan 1, 1990, and Dec 31, 2015. We collected additional unpublished HIV-1 subtyping data through a global survey. We included prevalence studies with HIV-1 subtyping data collected during 1990–2015. We grouped countries into 14 regions and analysed data for four time periods (1990–99, 2000–04, 2005–09, and 2010–15). The distribution of HIV-1 subtypes, circulating recombinant forms (CRFs), and unique recombinant forms (URFs) in individual countries was weighted according to the UNAIDS estimates of the number of people living with HIV (PLHIV) in each country to generate regional and global estimates of HIV-1 diversity in each time period. The primary outcome was the number of samples designated as HIV-1 subtypes A, B, C, D, F, G, H, J, K, CRFs, and URFs. The systematic review is registered with PROSPERO, number CRD42017067164. This systematic review and global survey yielded 2203 datasets with 383 519 samples from 116 countries in 1990–2015. Globally, subtype C accounted for 46·6% (16 280 897/34 921 639 of PLHIV) of all HIV-1 infections in 2010–15. Subtype B was responsible for 12·1% (4 235 299/34 921 639) of infections, followed by subtype A (10·3%; 3 587 003/34 921 639), CRF02_AG (7·7%; 2 705 110/34 921 639), CRF01_AE (5·3%; 1 840 982/34 921 639), subtype G (4·6%; 1 591 276/34 921 639), and subtype D (2·7%; 926 255/34 921 639). Subtypes F, H, J, and K combined accounted for 0·9% (311 332/34 921 639) of infections. Other CRFs accounted for 3·7% (1 309 082/34 921 639), bringing the proportion of all CRFs to 16·7% (5 844 113/34 921 639). URFs constituted 6·1% (2 134 405/34 921 639), resulting in recombinants accounting for 22·8% (7 978 517/34 921 639) of all global HIV-1 infections. The distribution of HIV-1 subtypes and recombinants changed over time in countries, regions, and globally. At a global level during 2005–15, subtype B increased, subtypes A and D were stable, and subtypes C and G and CRF02_AG decreased. CRF01_AE, other CRFs, and URFs increased, leading to a consistent increase in the global proportion of recombinants over time. Global and regional HIV diversity is complex and evolving, and is a major challenge to HIV vaccine development. Surveillance of the global molecular epidemiology of HIV-1 remains crucial for the design, testing, and implementation of HIV vaccines. None.

Interpretation of polygenic risk scores will someday become an accepted part of clinical practice — but not just yet. It is, however, time to evaluate these scores in studies that assess clinical utility, benefits and costs, and scalable risk-communication methods.

ObjectivesSeveral common and rare risk variants have been reported for systemic sclerosis (SSc), but the effector cell(s) mediating the function of these genetic variants remains to be elucidated. While innate immune cells have been proposed as the critical targets to interfere with the disease process underlying SSc, no studies have comprehensively established their effector role. Here we investigated the contribution of monocyte-derived macrophages (MDMs) in mediating genetic susceptibility to SSc.MethodsWe carried out RNA sequencing and genome-wide genotyping in MDMs from 57 patients with SSc and 15 controls. Our differential expression and expression quantitative trait locus (eQTL) analysis in SSc was further integrated with epigenetic, expression and eQTL data from skin, monocytes, neutrophils and lymphocytes.ResultsWe identified 602 genes upregulated and downregulated in SSc macrophages that were significantly enriched for genes previously implicated in SSc susceptibility (P=5×10−4), and 270 cis-regulated genes in MDMs. Among these, GSDMA was reported to carry an SSc risk variant (rs3894194) regulating expression of neighbouring genes in blood. We show that GSDMA is upregulated in SSc MDMs (P=8.4×10−4) but not in the skin, and is a significant eQTL in SSc macrophages and lipopolysaccharide/interferon gamma (IFNγ)-stimulated monocytes. Furthermore, we identify an SSc macrophage transcriptome signature characterised by upregulation of glycolysis, hypoxia and mTOR signalling and a downregulation of IFNγ response pathways.ConclusionsOur data further establish the link between macrophages and SSc, and suggest that the contribution of the rs3894194 risk variant to SSc susceptibility can be mediated by GSDMA expression in macrophages.

Genotyping-by-sequencing (GBS) provides high SNP coverage and has recently emerged as a popular technology for genetic and breeding applications in bread wheat (Triticum aestivum L.) and many other plant species. Although GBS can discover millions of SNPs, a high rate of missing data is a major concern for many applications. Accurate imputation of those missing data can significantly improve the utility of GBS data. This study compared imputation accuracies among four genome references including three wheat references (Chinese Spring survey sequence, W7984, and IWGSC RefSeq v1.0) and one barley reference genome by comparing imputed data derived from low-depth sequencing to actual data from high-depth sequencing. After imputation, the average number of imputed data points was the highest in the B genome (~48.99%). The D genome had the lowest imputed data points (~15.02%) but the highest imputation accuracy. Among the four reference genomes, IWGSC RefSeq v1.0 reference provided the most imputed data points, but the lowest imputation accuracy for the SNPs with < 10% minor allele frequency (MAF). The W7984 reference, however, provided the highest imputation accuracy for the SNPs with < 10% MAF.

As technology advances, whole genome sequencing (WGS) is likely to supersede other genotyping technologies. The rate of this change depends on its relative cost and utility. Variants identified uniquely through WGS may reveal novel biological pathways underlying complex disorders and provide high-resolution insight into when, where, and in which cell type these pathways are affected. Alternatively, cheaper and less computationally intensive approaches may yield equivalent insights. Understanding the role of rare variants in the noncoding gene-regulating genome through pilot WGS projects will be critical to determining which of these two extremes best represents reality. With large cohorts, well-defined risk loci, and a compelling need to understand the underlying biology, psychiatric disorders have a role to play in this preliminary WGS assessment. The Whole Genome Sequencing for Psychiatric Disorders Consortium will integrate data for 18,000 individuals with psychiatric disorders, beginning with autism spectrum disorder, schizophrenia, bipolar disorder, and major depressive disorder, along with over 150,000 controls.

Deleterious alleles have long been proposed to play an important role in patterning phenotypic variation and are central to commonly held ideas explaining the hybrid vigor observed in the offspring of a cross between two inbred parents. We test these ideas using evolutionary measures of sequence conservation to ask whether incorporating information about putatively deleterious alleles can inform genomic selection (GS) models and improve phenotypic prediction. We measured a number of agronomic traits in both the inbred parents and hybrids of an elite maize partial diallel population and re-sequenced the parents of the population. Inbred elite maize lines vary for more than 350,000 putatively deleterious sites, but show a lower burden of such sites than a comparable set of traditional landraces. Our modeling reveals widespread evidence for incomplete dominance at these loci, and supports theoretical models that more damaging variants are usually more recessive. We identify haplotype blocks using an identity-by-decent (IBD) analysis and perform genomic prediction analyses in which we weigh blocks on the basis of complementation for segregating putatively deleterious variants. Cross-validation results show that incorporating sequence conservation in genomic selection improves prediction accuracy for grain yield and other fitness-related traits as well as heterosis for those traits. Our results provide empirical support for an important role for incomplete dominance of deleterious alleles in explaining heterosis and demonstrate the utility of incorporating functional annotation in phenotypic prediction and plant breeding.

High-resolution melting of DNA is a simple solution for genotyping, mutation scanning and sequence matching. The melting profile of a PCR product depends on its GC content, length, sequence and heterozygosity and is best monitored with saturating dyes that fluoresce in the presence of double-stranded DNA. Genotyping of most variants is possible by the melting temperature of the PCR products, while all variants can be genotyped with unlabeled probes. Mutation scanning and sequence matching depend on sequence differences that result in heteroduplexes that change the shape of the melting curve. High-resolution DNA melting has several advantages over other genotyping and scanning methods, including an inexpensive closed tube format that is homogenous, accurate and rapid. Owing to its simplicity and speed, the method is a good fit for personalized medicine as a rapid, inexpensive method to predict therapeutic response.