Whole genome sequencing of 4,787 individuals identifies gene-based rare variants in age-related macular degeneration.

Genome-wide association studies have contributed extensively to the discovery of disease-associated common variants. However, the genetic contribution to complex traits is still largely difficult to interpret. We report a genome-wide association study of 2394 cases and 2393 controls for age-related macular degeneration (AMD) via whole-genome sequencing, with 46.9 million genetic variants. Our study reveals significant single-variant association signals at four loci and independent gene-based signals in CFH, C2, C3, and NRTN. Using data from the Exome Aggregation Consortium (ExAC) for a gene-based test, we demonstrate an enrichment of predicted rare loss-of-function variants in CFH, CFI, and an as-yet unreported gene in AMD, ORMDL2. Our method of using a large variant list without individual-level genotypes as an external reference provides a flexible and convenient approach to leverage the publicly available variant datasets to augment the search for rare variant associations, which can explain additional disease risk in AMD.

Inferring genes that escape X-Chromosome inactivation reveals important contribution of variable escape genes to sex-biased diseases.

The X Chromosome plays an important role in human development and disease. However, functional genomic and disease association studies of X genes greatly lag behind autosomal gene studies, in part owing to the unique biology of X-Chromosome inactivation (XCI). Because of XCI, most genes are only expressed from one allele. Yet, ∼30% of X genes "escape" XCI and are transcribed from both alleles, many only in a proportion of the population. Such interindividual differences are likely to be disease relevant, particularly for sex-biased disorders. To understand the functional biology for X-linked genes, we developed X-Chromosome inactivation for RNA-seq (XCIR), a novel approach to identify escape genes using bulk RNA-seq data. Our method, available as an R package, is more powerful than alternative approaches and is computationally efficient to handle large population-scale data sets. Using annotated XCI states, we examined the contribution of X-linked genes to the disease heritability in the United Kingdom Biobank data set. We show that escape and variable escape genes explain the largest proportion of X heritability, which is in large part attributable to X genes with Y homology. Finally, we investigated the role of each XCI state in sex-biased diseases and found that although XY homologous gene pairs have a larger overall effect size, enrichment for variable escape genes is significantly increased in female-biased diseases. Our results, for the first time, quantitate the importance of variable escape genes for the etiology of sex-biased disease, and our pipeline allows analysis of larger data sets for a broad range of phenotypes.

End Group Engineering for Constructing A-D-A Fused-Ring Photosensitizers with Balanced Phototheranostics Performance.

Phototheranostics continues to flourish in cancer treatment. Due to the competitive relationships between these photophysical processes of fluorescence emission, photothermal conversion, and photodynamic action, it is critical to balance them through subtle photosensitizer designs. Herein, it is provided a useful guideline for constructing A-D-A photosensitizers with superior phototheranostics performance. Various cyanoacetate group-modified end groups containing ester side chains of different length are designed to construct a series of A-D-A photosensitizers (F8CA1 ∼ F8CA4) to study the structure-property relationships. It is surprising to find that the photophysical properties of A-D-A photosensitizers can be precisely regulated by these tiny structural changes. The results reveal that the increase in the steric hindrance of ester side chains has positive impacts on their photothermal conversion capabilities, but adverse impacts on the fluorescence emission and photodynamic activities. Notably, these tiny structural changes lead to their different aggregation behavior. The molecule mechanisms are detailedly explained by theoretical calculations. Finally, F8CA2 nanoparticles with more balanced photophysical properties perform well in fluorescence imaging-guided photothermal and type I&II photodynamic synergistic cancer therapy, even under hypoxic conditions. Therefore, this work provides a novel practicable construction strategy for desired A-D-A photosensitizers.

Enhanced Pathology Image Quality with Restore-Generative Adversarial Network.

Whole slide imaging is becoming a routine procedure in clinical diagnosis. Advanced image analysis techniques have been developed to assist pathologists in disease diagnosis, staging, subtype classification, and risk stratification. Recently, deep learning algorithms have achieved state-of-the-art performances in various imaging analysis tasks, including tumor region segmentation, nuclei detection, and disease classification. However, widespread clinical use of these algorithms is hampered by their performances often degrading due to image quality issues commonly seen in real-world pathology imaging data such as low resolution, blurring regions, and staining variation. Restore-Generative Adversarial Network (GAN), a deep learning model, was developed to improve the imaging qualities by restoring blurred regions, enhancing low resolution, and normalizing staining colors. The results demonstrate that Restore-GAN can significantly improve image quality, which leads to improved model robustness and performance for existing deep learning algorithms in pathology image analysis. Restore-GAN has the potential to be used to facilitate the applications of deep learning models in digital pathology analyses.

End Group Nonplanarization Enhances Phototherapy Efficacy of A-D-A Fused-Ring Photosensitizer for Tumor Phototherapy.

Enhancing the phototherapy efficacy of organic photosensitizers through molecular design is a fascinating but challenging task. Herein, we propose a simple design strategy to first realize the generation of superoxide anion radical (O2•-) by A-D-A fused-ring photosensitizers. Through replacing one cyano group of traditional end group with an ester group, we designed a novel nonplanar end group (A unit) to synthesize a novel A-D-A photosensitizer F8CA. In a comparison with its counterpart F8CN with the traditional end group, F8CA displays more loose packing and larger spin-orbit coupling constants. The F8CA nanoparticles showed higher photodynamic activities with the generation capability of singlet oxygen (1O2), hydroxyl radical (•OH), and O2•-, while F8CN nanoparticles could only generate 1O2 and •OH. In addition, F8CA nanoparticles still remain high photothermal conversion efficiency (61%). As a result, F8CA nanoparticles perform well in hypoxia-tolerant tumor phototherapy. This study brings an effective design thought for A-D-A photosensitizers.

Triplets with a Twist: Ultrafast Intersystem Crossing in a Series of Electron Acceptor Materials Driven by Conformational Disorder.

Control over the populations of singlet and triplet excitons is key to organic semiconductor technologies. In different contexts, triplets can represent an energy loss pathway that must be managed (i.e., solar cells, light-emitting diodes, and lasers) or provide avenues to improve energy conversion (i.e., photon upconversion and multiplication systems). A key consideration in the interplay of singlet and triplet exciton populations in these systems is the rate of intersystem crossing (ISC). In this work, we design, measure, and model a series of new electron acceptor molecules and analyze them using a combination of ultrafast transient absorption and ultrafast broadband photoluminescence spectroscopies. We demonstrate that intramolecular triplet formation occurs within several hundred picoseconds in solution and is accelerated considerably in the solid state. Importantly, ISC occurs with sufficient rapidity to compete with charge formation in modern organic solar cells, implicating triplets in intrinsic exciton loss channels in addition to charge recombination. Density functional theory calculations reveal that ISC occurs in triplet excited states characterized by local deviations from orbital π-symmetry associated with rotationally flexible thiophene rings. In disordered films, structural distortions, therefore, result in significant increases in spin-orbit coupling, enabling rapid ISC. We demonstrate the generality of this proposal in an oligothiophene model system where ISC is symmetry-forbidden and show that conformational disorder introduced by the formation of a solvent glass accelerates ISC, outweighing the lower temperature and increased viscosity. This proposal sheds light on the factors responsible for facile ISC and provides a simple framework for molecular control over spin states.

A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization.

Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research. However, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedures and produces massive data that capture tumor histologic details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists. Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nucleus segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nucleus segmentation and TME quantification within image patches. However, existing algorithms are computationally intensive and time consuming for WSI analysis. This study presents Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nucleus segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing WSI analysis methods in nucleus detection, classification accuracy, and computation time. We validated the advantages of the system on 3 different tissue types: lung cancer, liver cancer, and breast cancer. For breast cancer, nucleus features by HD-Yolo were more prognostically significant than both the estrogen receptor status by immunohistochemistry and the progesterone receptor status by immunohistochemistry. The WSI analysis pipeline and a real-time nucleus segmentation viewer are available at https://github.com/impromptuRong/hd_wsi.

Spatial molecular profiling: platforms, applications and analysis tools.

Molecular profiling technologies, such as genome sequencing and proteomics, have transformed biomedical research, but most such technologies require tissue dissociation, which leads to loss of tissue morphology and spatial information. Recent developments in spatial molecular profiling technologies have enabled the comprehensive molecular characterization of cells while keeping their spatial and morphological contexts intact. Molecular profiling data generate deep characterizations of the genetic, transcriptional and proteomic events of cells, while tissue images capture the spatial locations, organizations and interactions of the cells together with their morphology features. These data, together with cell and tissue imaging data, provide unprecedented opportunities to study tissue heterogeneity and cell spatial organization. This review aims to provide an overview of these recent developments in spatial molecular profiling technologies and the corresponding computational methods developed for analyzing such data.

Analysis of KIR gene variants in The Cancer Genome Atlas and UK Biobank using KIRCLE.

BACKGROUND: Natural killer (NK) cells represent a critical component of the innate immune system's response against cancer and viral infections, among other diseases. To distinguish healthy host cells from infected or tumor cells, killer immunoglobulin receptors (KIR) on NK cells bind and recognize Human Leukocyte Antigen (HLA) complexes on their target cells. However, NK cells exhibit great diversity in their mechanism of activation, and the outcomes of their activation are not yet understood fully. Just like the HLAs they bind, KIR receptors exhibit high allelic diversity in the human population. Here we provide a method to identify KIR allele variants from whole exome sequencing data and uncover novel associations between these variants and various molecular and clinical correlates. RESULTS: In order to better understand KIRs, we have developed KIRCLE, a novel method for genotyping individual KIR genes from whole exome sequencing data, and used it to analyze approximately sixty-thousand patient samples in The Cancer Genome Atlas (TCGA) and UK Biobank. We were able to assess population frequencies for different KIR alleles and demonstrate that, similar to HLA alleles, individuals' KIR alleles correlate strongly with their ethnicities. In addition, we observed associations between different KIR alleles and HLA alleles, including HLA-B*53 with KIR3DL2*013 (Fisher's exact FDR = 7.64e-51). Finally, we showcased statistically significant associations between KIR alleles and various clinical correlates, including peptic ulcer disease (Fisher's exact FDR = 0.0429) and age of onset of atopy (Mann-Whitney U FDR = 0.0751). CONCLUSIONS: We show that KIRCLE is able to infer KIR variants accurately and consistently, and we demonstrate its utility using data from approximately sixty-thousand individuals from TCGA and UK Biobank to discover novel molecular and clinical correlations with KIR germline variants. Peptic ulcer disease and atopy are just two diseases in which NK cells may play a role beyond their "classical" realm of anti-tumor and anti-viral responses. This tool may be used both as a benchmark for future KIR-variant-inference algorithms, and to better understand the immunogenomics of and disease processes involving KIRs.

Reducing Energy Disorder in Perovskite Solar Cells by Chelation.

In inverted perovskite solar cells (PSCs), the fullerene derivative [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) is a widely used electron transport material. However, a high degree of energy disorder and inadequate passivation of PCBM limit the efficiency of devices, and severe self-aggregation and unstable morphology limit the lifespan of devices. Here, we design a series of fullerene dyads FP-Cn (n = 4, 8, 12) to replace PCBM as an electron transport layer, where [60]fullerene is linked with a terpyridine chelating group via a flexible alkyl chain of different lengths as a spacer. Among three fullerene dyads, FP-C8 shows the most enhanced molecule ordering and adhesion with the perovskite surface due to the balanced decoupling between the chelation effect from terpyridine and the self-assembly of fullerene, leading to lower energy disorder and higher morphological stability relative to PCBM. The FP-C8/C60-based devices using Cs0.05FA0.90MA0.05PbI2.85Br0.15 as a light absorber show a power conversion efficiency of 21.69%, higher than that of PCBM/C60 (20.09%), benefiting from improved electron extraction and transport as well as reduced charge recombination loss. When employing FAPbI3 as a light absorber, the FP-C8/C60-based devices exhibit an efficiency of 23.08%, which is the champion value of inverted PSCs with solution-processed fullerene derivatives. Moreover, the FP-C8/C60-based devices show better moisture and thermal stability than PCBM/C60-based devices and maintain 96% of their original efficiency after 1200 h of operation, while their counterpart PCBM/C60 maintains 60% after 670 h.

A Bayesian zero-inflated negative binomial regression model for the integrative analysis of microbiome data.

Microbiome omics approaches can reveal intriguing relationships between the human microbiome and certain disease states. Along with identification of specific bacteria taxa associated with diseases, recent scientific advancements provide mounting evidence that metabolism, genetics, and environmental factors can all modulate these microbial effects. However, the current methods for integrating microbiome data and other covariates are severely lacking. Hence, we present an integrative Bayesian zero-inflated negative binomial regression model that can both distinguish differentially abundant taxa with distinct phenotypes and quantify covariate-taxa effects. Our model demonstrates good performance using simulated data. Furthermore, we successfully integrated microbiome taxonomies and metabolomics in two real microbiome datasets to provide biologically interpretable findings. In all, we proposed a novel integrative Bayesian regression model that features bacterial differential abundance analysis and microbiome-covariate effects quantifications, which makes it suitable for general microbiome studies.

Clinically undetected polyclonal heteroresistance among Pseudomonas aeruginosa isolated from cystic fibrosis respiratory specimens.

BACKGROUND: Pseudomonas aeruginosa infection is the leading cause of death among patients with cystic fibrosis (CF) and a common cause of difficult-to-treat hospital-acquired infections. P. aeruginosa uses several mechanisms to resist different antibiotic classes and an individual CF patient can harbour multiple resistance phenotypes. OBJECTIVES: To determine the rates and distribution of polyclonal heteroresistance (PHR) in P. aeruginosa by random, prospective evaluation of respiratory cultures from CF patients at a large referral centre over a 1 year period. METHODS: We obtained 28 unique sputum samples from 19 CF patients and took multiple isolates from each, even when morphologically similar, yielding 280 unique isolates. We performed antimicrobial susceptibility testing (AST) on all isolates and calculated PHR on the basis of variability in AST in a given sample. We then performed whole-genome sequencing on 134 isolates and used a machine-learning association model to interrogate phenotypic PHR from genomic data. RESULTS: PHR was identified in most sampled patients (n = 15/19; 79%). Importantly, resistant phenotypes were not detected by routine AST in 26% of patients (n = 5/19). The machine-learning model, using the extended sampling, identified at least one genetic variant associated with phenotypic resistance in 94.3% of isolates (n = 1392/1476). CONCLUSION: PHR is common among P. aeruginosa in the CF lung. While traditional microbiological methods often fail to detect resistant subpopulations, extended sampling of isolates and conventional AST identified PHR in most patients. A machine-learning tool successfully identified at least one resistance variant in almost all resistant isolates by leveraging this extended sampling and conventional AST.

Fused-Ring Electron Acceptors for Photovoltaics and Beyond.

ConspectusEmerging solar cells that convert clean and renewable solar energy to electricity, such as organic solar cells (OSCs) and perovskite solar cells (PSCs), have attracted increasing attention owing to some merits such as facile fabrication, low cost, flexibility, and short energy payback time. The power conversion efficiencies (PCEs) of OSCs and PSCs have exceeded 18% and 25%, respectively.Fullerene derivatives have high electron affinity and mobility with an isotropic transport feature. Fullerene-based OSCs yielded superior PCEs to other acceptors and have dominated electron acceptor materials from 1995 to 2015. However, some drawbacks of fullerenes, such as weak visible absorption, limited tunability of electronic properties, laborious purification, and morphological instability, restrict further development of OSCs toward higher PCEs and practical applications. The theoretical PCE of fullerene-based OSCs is limited to ∼13% due to the relatively large energy losses. Many efforts have been dedicated to developing new acceptor systems beyond fullerenes, and some successful systems such as rylene diimides have achieved PCEs up to ca. 11%.In 2015, our group pioneered a new class of electron acceptors, fused-ring electron acceptor (FREA), as represented by the star molecule ITIC. The chemical features of FREAs include: (1) a modular structure, consisting of an electron-donating core, electron-withdrawing end groups, π-bridges, and side chains, which benefits molecular tailoring; (2) facile synthesis, purification, and scalability. The physical features of FREAs include: (1) a broad modulation range of absorption and energy levels; (2) strong absorption, especially in the 700-1000 nm region; (3) high electron mobility. The device features of FREAs include: (1) low voltage loss; (2) high efficiency; (3) good stability. The FREAs boosted PCEs of the OSCs up to 18% and initiated the transformation from the fullerene to nonfullerene era of this field. FREAs can also be used in PSCs as interfacial layers, electron transport layers, or active layers, improving both efficiency and stability of the devices. Beyond photovoltaic applications, FREAs can also be used in photodetectors, field-effect transistors, two-photon absorption, photothermal therapy, solar water splitting, etc.In this Account, we review the development of the FREAs and their applications in OSCs, PSCs, and other related fields. Molecular design, device engineering, photophysics, and applications of FREAs are discussed in detail. Future research directions toward performance optimization and commercialization of FREAs are also proposed.

Integrating germline and somatic genetics to identify genes associated with lung cancer.

Genome-wide association studies (GWAS) have successfully identified many genetic variants associated with complex traits. However, GWAS experience power issues, resulting in the failure to detect certain associated variants. Additionally, GWAS are often unable to parse the biological mechanisms of driving associations. An existing gene-based association test framework, Transcriptome-Wide Association Studies (TWAS), leverages expression quantitative trait loci data to increase the power of association tests and illuminate the biological mechanisms by which genetic variants modulate complex traits. We extend the TWAS methodology to incorporate somatic information from tumors. By integrating germline and somatic data we are able to leverage information from the nuanced somatic landscape of tumors. Thus we can augment the power of TWAS-type tests to detect germline genetic variants associated with cancer phenotypes. We use somatic and germline data on lung adenocarcinomas from The Cancer Genome Atlas in conjunction with a meta-analyzed lung cancer GWAS to identify novel genes associated with lung cancer.

Fast Response Organic Tandem Photodetector for Visible and Near-Infrared Digital Optical Communications.

Organic photodetectors (OPDs), which usually work as photodiodes, photoconductors, or phototransistors, have emerged as candidates for next-generation light sensing. However, low response speed caused by low carrier mobility and resistance-capacitance (RC) time constant, severely hinders the commercialization of OPDs. Herein, the authors demonstrate a state-of-the-art OPD with a record response speed of 146.8 ns by employing tandem structure to simultaneously reduce both the carrier transit time and RC time constant of the device, which is faster than that of previously reported OPDs as far as they know. Moreover, benefitting from the multi-level barrier enhancement and voltage division engendered by tandem structure, an ultralow noise current of 7.82 × 10-14 A Hz-1/2 is obtained, as well as a wide detection range in 300-1000 nm. In addition, the tandem OPDs are successfully integrated into the optical communication system as signal receivers, demonstrating the precise digital signal communication from visible to near-infrared light. It is believed that tandem OPDs have promising application potential in the wireless transmission system.

Seqminer2: an efficient tool to query and retrieve genotypes for statistical genetics analyses from biobank scale sequence dataset.

SUMMARY: Here, we present a highly efficient R-package seqminer2 for querying and retrieving sequence variants from biobank scale datasets of millions of individuals and hundreds of millions of genetic variants. Seqminer2 implements a novel variant-based index for querying VCF/BCF files. It improves the speed of query and retrieval by several magnitudes compared to the state-of-the-art tools based upon tabix. It also reimplements support for BGEN and PLINK format, which improves speed over alternative implementations. The improved efficiency and comprehensive support for popular file formats will facilitate method development, software prototyping and data analysis of biobank scale sequence datasets in R. AVAILABILITY AND IMPLEMENTATION: The seqminer2 R package is available from https://github.com/zhanxw/seqminer. Scripts used for the benchmarks are available in https://github.com/yang-lina/seqminer/blob/master/seqminer2%20benchmark%20script.txt. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dominant atopy risk mutations identified by mouse forward genetic analysis.

BACKGROUND: Atopy, the overall tendency to become sensitized to an allergen, is heritable but seldom ascribed to mutations within specific genes. Atopic individuals develop abnormally elevated IgE responses to immunization with potential allergens. To gain insight into the genetic causes of atopy, we carried out a forward genetic screen for atopy in mice. METHODS: We screened mice carrying homozygous and heterozygous N-ethyl-N-nitrosourea (ENU)-induced germline mutations for aberrant antigen-specific IgE and IgG1 production in response to immunization with the model allergen papain. Candidate genes were validated by independent gene mutation. RESULTS: Of 31 candidate genes selected for investigation, the effects of mutations in 23 genes on papain-specific IgE or IgG1 were verified. Among the 20 verified genes influencing the IgE response, eight were necessary for the response, while 12 repressed IgE. Nine genes were not previously implicated in the IgE response. Fifteen genes encoded proteins contributing to IgE class switch recombination or B-cell receptor signaling. The precise roles of the five remaining genes (Flcn, Map1lc3b, Me2, Prkd2, and Scarb2) remain to be determined. Loss-of-function mutations in nine of the 12 genes limiting the IgE response were dominant or semi-dominant for the IgE phenotype but did not cause immunodeficiency in the heterozygous state. Using damaging allele frequencies for the corresponding human genes and in silico simulations (Monte Carlo) of undiscovered atopy mutations, we estimated the percentage of humans with heterozygous atopy risk mutations. CONCLUSIONS: Up to 37% of individuals may be heterozygous carriers for at least one dominant atopy risk mutation.

VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning.

Antimicrobial resistance (AMR) is an increasing threat to public health. Current methods of determining AMR rely on inefficient phenotypic approaches, and there remains incomplete understanding of AMR mechanisms for many pathogen-antimicrobial combinations. Given the rapid, ongoing increase in availability of high-density genomic data for a diverse array of bacteria, development of algorithms that could utilize genomic information to predict phenotype could both be useful clinically and assist with discovery of heretofore unrecognized AMR pathways. To facilitate understanding of the connections between DNA variation and phenotypic AMR, we developed a new bioinformatics tool, variant mapping and prediction of antibiotic resistance (VAMPr), to (1) derive gene ortholog-based sequence features for protein variants; (2) interrogate these explainable gene-level variants for their known or novel associations with AMR; and (3) build accurate models to predict AMR based on whole genome sequencing data. We curated the publicly available sequencing data for 3,393 bacterial isolates from 9 species that contained AMR phenotypes for 29 antibiotics. We detected 14,615 variant genotypes and built 93 association and prediction models. The association models confirmed known genetic antibiotic resistance mechanisms, such as blaKPC and carbapenem resistance consistent with the accurate nature of our approach. The prediction models achieved high accuracies (mean accuracy of 91.1% for all antibiotic-pathogen combinations) internally through nested cross validation and were also validated using external clinical datasets. The VAMPr variant detection method, association and prediction models will be valuable tools for AMR research for basic scientists with potential for clinical applicability.

Proper conditional analysis in the presence of missing data: Application to large scale meta-analysis of tobacco use phenotypes.

Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values, i.e. the summary association statistics are measured for all variants in contributing studies. In practice, genotype imputation is not always effective. This may be the case when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Existing methods for imputing missing summary association statistics and using imputed values in meta-analysis, approximate conditional analysis, or simple strategies such as complete case analysis all have theoretical limitations. Applying these approaches can bias genetic effect estimates and lead to seriously inflated type-I or type-II errors in conditional analysis, which is a critical tool for identifying independently associated variants. To address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amounts of missing values. Based on this estimator, we proposed a score statistic called PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to one of the largest meta-analyses to date for the cigarettes-per-day phenotype. Using the new method, we identified multiple novel independently associated variants at known loci for tobacco use, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants was 1.1%, improving that of previously reported associations by 71%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants.

Butterfly Effects Arising from Starting Materials in Fused-Ring Electron Acceptors.

We designed and synthesized a series of fused-ring electron acceptors (FREAs) based on naphthalene-fused octacyclic cores end-capped by 3-(1,1-dicyanomethylene)-5,6-difluoro-1- indanone (NOICs) using a bottom-up approach. The NOIC series shares the same end groups and side chains, as well as similar fused-ring cores. The butterfly effects, arising from different methoxy positions in the starting materials, impact the design of the final FREAs, as well as their molecular packing, optical and electronic properties, charge transport, film morphology, and performance of organic solar cells. The binary-blend devices based on this NOIC series show power conversion efficiencies varying from 7.15% to 14.1%, due to the different intrinsic properties of the NOIC series, morphologies of blend films, and voltage losses of devices.