Clustering by genetic ancestry using genome-wide SNP data

<p>Abstract</p> <p>Background</p> <p>Population stratification can cause spurious associations in a genome-wide association study (GWAS), and occurs when differences in allele frequencies of single nucleotide polymorphisms (SNPs) are due to ancestral differences between cases and controls rather than the trait of interest. Principal components analysis (PCA) is the established approach to detect population substructure using genome-wide data and to adjust the genetic association for stratification by including the top principal components in the analysis. An alternative solution is genetic matching of cases and controls that requires, however, well defined population strata for appropriate selection of cases and controls.</p> <p>Results</p> <p>We developed a novel algorithm to cluster individuals into groups with similar ancestral backgrounds based on the principal components computed by PCA. We demonstrate the effectiveness of our algorithm in real and simulated data, and show that matching cases and controls using the clusters assigned by the algorithm substantially reduces population stratification bias. Through simulation we show that the power of our method is higher than adjustment for PCs in certain situations.</p> <p>Conclusions</p> <p>In addition to reducing population stratification bias and improving power, matching creates a clean dataset free of population stratification which can then be used to build prediction models without including variables to adjust for ancestry. The cluster assignments also allow for the estimation of genetic heterogeneity by examining cluster specific effects.</p

Imputation of missing genotypes: an empirical evaluation of IMPUTE

<p>Abstract</p> <p>Background</p> <p>Imputation of missing genotypes is becoming a very popular solution for synchronizing genotype data collected with different microarray platforms but the effect of ethnic background, subject ascertainment, and amount of missing data on the accuracy of imputation are not well understood.</p> <p>Results</p> <p>We evaluated the accuracy of the program IMPUTE to generate the genotype data of partially or fully untyped single nucleotide polymorphisms (SNPs). The program uses a model-based approach to imputation that reconstructs the genotype distribution given a set of referent haplotypes and the observed data, and uses this distribution to compute the marginal probability of each missing genotype for each individual subject that is used to impute the missing data. We assembled genome-wide data from five different studies and three different ethnic groups comprising Caucasians, African Americans and Asians. We randomly removed genotype data and then compared the observed genotypes with those generated by IMPUTE. Our analysis shows 97% median accuracy in Caucasian subjects when less than 10% of the SNPs are untyped and missing genotypes are accepted regardless of their posterior probability. The median accuracy increases to 99% when we require 0.95 minimum posterior probability for an imputed genotype to be acceptable. The accuracy decreases to 86% or 94% when subjects are African Americans or Asians. We propose a strategy to improve the accuracy by leveraging the level of admixture in African Americans.</p> <p>Conclusion</p> <p>Our analysis suggests that IMPUTE is very accurate in samples of Caucasians origin, it is slightly less accurate in samples of Asians background, but substantially less accurate in samples of admixed background such as African Americans. Sample size and ascertainment do not seem to affect the accuracy of imputation.</p

Antistaphylococcal activity of DNA-interactive pyrrolobenzodiazepine (PBD) dimers and PBD-biaryl conjugates

Objectives: pyrrolobenzodiazepine (PBD) dimers, tethered through inert propyldioxy or pentyldioxy linkers, possess potent bactericidal activity against a range of Gram-positive bacteria by virtue of their capacity to cross-link duplex DNA in sequence-selective fashion. Here we attempt to improve the antibacterial activity and cytotoxicity profile of PBD-containing conjugates by extension of dimer linkers and replacement of one PBD unit with phenyl-substituted or benzo-fused heterocycles that facilitate non-covalent interactions with duplex DNA.Methods: DNase I footprinting was used to identify high-affinity DNA binding sites. A staphylococcal gene microarray was used to assess epidemic methicillin-resistant Staphylococcus aureus 16 phenotypes induced by PBD conjugates. Molecular dynamics simulations were employed to investigate the accommodation of compounds within the DNA helix.Results: increasing the length of the linker in PBD dimers led to a progressive reduction in antibacterial activity, but not in their cytotoxic capacity. Complex patterns of DNA binding were noted for extended PBD dimers. Modelling of DNA strand cross-linking by PBD dimers indicated distortion of the helix. A majority (26 of 43) of PBD-biaryl conjugates possessed potent antibacterial activity with little or no helical distortion and a more favourable cytotoxicity profile. Bactericidal activity of PBD-biaryl conjugates was determined by inability to excise covalently bound drug molecules from bacterial duplex DNA.Conclusions: PBD-biaryl conjugates have a superior antibacterial profile compared with PBD dimers such as ELB-21. We have identified six PBD-biaryl conjugates as potential drug development candidate

Predicting functional responses in agro-ecosystems from animal movement data to improve management of invasive pests

Functional responses describe how changing resource availability affects con- sumer resource use, thus providing a mechanistic approach to prediction of the invasibility and potential damage of invasive alien species (IAS). However, functional responses can be context dependent, varying with resource characteristics and availability, consumer attributes, and environmental variables. Identifying context dependencies can allow invasion and damage risk to be predicted across different ecoregions. Understanding how ecological factors shape the functional response in agro-ecosystems can improve predictions of hotspots of highest impact and inform strategies to mitigate damage across locations with varying crop types and avail- ability. We linked heterogeneous movement data across different agro-ecosystems to predict ecologically driven variability in the functional responses. We applied our approach to wild pigs (Sus scrofa), one of the most successful and detrimental IAS worldwide where agricultural resource depredation is an important driver of spread and establishment. We used continental- scale movement data within agro-ecosystems to quantify the functional response of agricul- tural resources relative to availability of crops and natural forage. We hypothesized that wild pigs would selectively use crops more often when natural forage resources were low. We also examined how individual attributes such as sex, crop type, and resource stimulus such as dis- tance to crops altered the magnitude of the functional response. There was a strong agricul- tural functional response where crop use was an accelerating function of crop availability at low density (Type III) and was highly context dependent. As hypothesized, there was a reduced response of crop use with increasing crop availability when non-agricultural resources were more available, emphasizing that crop damage levels are likely to be highly heterogeneous depending on surrounding natural resources and temporal availability of crops. We found sig- nificant effects of crop type and sex, with males spending 20% more time and visiting crops 58% more often than females, and both sexes showing different functional responses depend- ing on crop type. Our application demonstrates how commonly collected animal movement data can be used to understand context dependencies in resource use to improve our under- standing of pest foraging behavior, with implications for prioritizing spatiotemporal hotspots of potential economic loss in agro-ecosystems

Probing conformational states of glutaryl-CoA dehydrogenase by fragment screening

The first crystal structure is reported of a glutaryl-CoA dehydrogenase in the apo state without flavin adenine dinucleotide cofactor bound. Additional structures with small molecules complexed in the catalytic active site were obtained by fragment-based screening

Evaluating the Immune Response in Treatment-Naive Hospitalised Patients With Influenza and COVID-19

The worldwide COVID-19 pandemic has claimed millions of lives and has had a profound effect on global life. Understanding the body’s immune response to SARS-CoV-2 infection is crucial in improving patient management and prognosis. In this study we compared influenza and SARS-CoV-2 infected patient cohorts to identify distinct blood transcript abundances and cellular composition to better understand the natural immune response associated with COVID-19, compared to another viral infection being influenza, and identify a prognostic signature of COVID-19 patient outcome. Clinical characteristics and peripheral blood were acquired upon hospital admission from two well characterised cohorts, a cohort of 88 patients infected with influenza and a cohort of 80 patients infected with SARS-CoV-2 during the first wave of the pandemic and prior to availability of COVID-19 treatments and vaccines. Gene transcript abundances, enriched pathways and cellular composition were compared between cohorts using RNA-seq. A genetic signature between COVID-19 survivors and non-survivors was assessed as a prognostic predictor of COVID-19 outcome. Contrasting immune responses were detected with an innate response elevated in influenza and an adaptive response elevated in COVID-19. Additionally ribosomal, mitochondrial oxidative stress and interferon signalling pathways differentiated the cohorts. An adaptive immune response was associated with COVID-19 survival, while an inflammatory response predicted death. A prognostic transcript signature, associated with circulating immunoglobulins, nucleosome assembly, cytokine production and T cell activation, was able to stratify COVID-19 patients likely to survive or die. This study provides a unique insight into the immune responses of treatment naïve patients with influenza or COVID-19. The comparison of immune response between COVID-19 survivors and non-survivors enables prognostication of COVID-19 patients and may suggest potential therapeutic strategies to improve survival

Social context and sex moderate the association between type D personality and cardiovascular reactivity

peer-reviewedType D personality has been consistently associated with adverse cardiovascular health with atypical cardiovascular reactions to psychological stress one plausible underlying mechanism. However, whether this varies by sex and social context has received little attention. This study examined the interaction between Type D personality, sex and social context on cardiovascular reactivity to acute stress. A sample of 76 healthy undergraduate students (47 female) completed the DS14 Type D measure, before undergoing a traditional cardiovascular reactivity protocol. The social context of the laboratory environment was manipulated to create a social and non-social context using a between-subjects design. Systolic blood pressure (SBP), diastolic blood pressure (DBP) and heart rate (HR) were monitored throughout. No associations were evident for blood pressure. However, a significant personality × sex × social context interaction on HR reactivity was found; here Type D was associated with a higher HR response to the social task amongst males but not females, while Type D females typically exhibited blunted reactions. While these atypical reactions indicate a possible psychophysiological pathway leading to adverse cardiovascular events amongst Type Ds, it appears that Type D males are particularly vulnerable to socially based stressors, exhibiting exaggerated cardiovascular reactions.peer-reviewe

Mutations in LAMA1 Cause Cerebellar Dysplasia and Cysts with and without Retinal Dystrophy

Cerebellar dysplasia with cysts (CDC) is an imaging finding typically seen in combination with cobblestone cortex and congenital muscular dystrophy in individuals with dystroglycanopathies. More recently, CDC was reported in seven children without neuromuscular involvement (Poretti-Boltshauser syndrome). Using a combination of homozygosity mapping and whole-exome sequencing, we identified biallelic mutations in LAMA1 as the cause of CDC in seven affected individuals (from five families) independent from those included in the phenotypic description of Poretti-Boltshauser syndrome. Most of these individuals also have high myopia, and some have retinal dystrophy and patchy increased T2-weighted fluid-attenuated inversion recovery (T2/FLAIR) signal in cortical white matter. In one additional family, we identified two siblings who have truncating LAMA1 mutations in combination with retinal dystrophy and mild cerebellar dysplasia without cysts, indicating that cysts are not an obligate feature associated with loss of LAMA1 function. This work expands the phenotypic spectrum associated with the lamininopathy disorders and highlights the tissue-specific roles played by different laminin-encoding genes

A hierarchical and modular approach to the discovery of robust associations in genome-wide association studies from pooled DNA samples

[Background] One of the challenges of the analysis of pooling-based genome wide association studies is to identify authentic associations among potentially thousands of false positive associations. [Results] We present a hierarchical and modular approach to the analysis of genome wide genotype data that incorporates quality control, linkage disequilibrium, physical distance and gene ontology to identify authentic associations among those found by statistical association tests. The method is developed for the allelic association analysis of pooled DNA samples, but it can be easily generalized to the analysis of individually genotyped samples. We evaluate the approach using data sets from diverse genome wide association studies including fetal hemoglobin levels in sickle cell anemia and a sample of centenarians and show that the approach is highly reproducible and allows for discovery at different levels of synthesis. [Conclusion] Results from the integration of Bayesian tests and other machine learning techniques with linkage disequilibrium data suggest that we do not need to use too stringent thresholds to reduce the number of false positive associations. This method yields increased power even with relatively small samples. In fact, our evaluation shows that the method can reach almost 70% sensitivity with samples of only 100 subjects.Supported by NHLBI grants R21 HL080463 (PS); R01 HL68970 (MHS); K-24, AG025727 (TP); K23 AG026754 (D.T.)