Immunoglobulin germline gene polymorphisms influence the function of SARS-CoV-2 neutralizing antibodies.

The human immunoglobulin heavy-chain (IGH) locus is exceptionally polymorphic, with high levels of allelic and structural variation. Thus, germline IGH genotypes are personal, which may influence responses to infection and vaccination. For an improved understanding of inter-individual differences in antibody responses, we isolated SARS-CoV-2 spike-specific monoclonal antibodies from convalescent health care workers, focusing on the IGHV1-69 gene, which has the highest level of allelic variation of all IGHV genes. The IGHV1-69∗20-using CAB-I47 antibody and two similar antibodies isolated from an independent donor were critically dependent on allele usage. Neutralization was retained when reverting the V region to the germline IGHV1-69∗20 allele but lost when reverting to other IGHV1-69 alleles. Structural data confirmed that two germline-encoded polymorphisms, R50 and F55, in the IGHV1-69 gene were required for high-affinity receptor-binding domain interaction. These results demonstrate that polymorphisms in IGH genes can influence the function of SARS-CoV-2 neutralizing antibodies.

Common variants increase risk for congenital diaphragmatic hernia within the context of de novo variants.

Congenital diaphragmatic hernia (CDH) is a severe congenital anomaly often accompanied by other structural anomalies and/or neurobehavioral manifestations. Rare de novo protein-coding variants and copy-number variations contribute to CDH in the population. However, most individuals with CDH remain genetically undiagnosed. Here, we perform integrated de novo and common-variant analyses using 1,469 CDH individuals, including 1,064 child-parent trios and 6,133 ancestry-matched, unaffected controls for the genome-wide association study. We identify candidate CDH variants in 15 genes, including eight novel genes, through deleterious de novo variants. We further identify two genomic loci contributing to CDH risk through common variants with similar effect sizes among Europeans and Latinx. Both loci are in putative transcriptional regulatory regions of developmental patterning genes. Estimated heritability in common variants is ∼19%. Strikingly, there is no significant difference in estimated polygenic risk scores between isolated and complex CDH or between individuals harboring deleterious de novo variants and individuals without these variants. The data support a polygenic model as part of the CDH genetic architecture.

Cross-population enhancement of PrediXcan predictions with a gnomAD-based east Asian reference framework.

Over the past decade, genome-wide association studies have identified thousands of variants significantly associated with complex traits. For each locus, gene expression levels are needed to further explore its biological functions. To address this, the PrediXcan algorithm leverages large-scale reference data to impute the gene expression level from single nucleotide polymorphisms, and thus the gene-trait associations can be tested to identify the candidate causal genes. However, a challenge arises due to the fact that most reference data are from subjects of European ancestry, and the accuracy and robustness of predicted gene expression in subjects of East Asian (EAS) ancestry remains unclear. Here, we first simulated a variety of scenarios to explore the impact of the level of population diversity on gene expression. Population differentiated variants were estimated by using the allele frequency information from The Genome Aggregation Database. We found that the weights of a variants was the main factor that affected the gene expression predictions, and that ~70% of variants were significantly population differentiated based on proportion tests. To provide insights into this population effect on gene expression levels, we utilized the allele frequency information to develop a gene expression reference panel, Predict Asian-Population (PredictAP), for EAS ancestry. PredictAP can be viewed as an auxiliary tool for PrediXcan when using genotype data from EAS subjects.

Inference of Host-Pathogen Interaction Matrices from Genome-Wide Polymorphism Data.

Host-pathogen coevolution is defined as the reciprocal evolutionary changes in both species due to genotype × genotype (G×G) interactions at the genetic level determining the outcome and severity of infection. While co-analyses of hosts and pathogen genomes (co-genome-wide association studies) allow us to pinpoint the interacting genes, these do not reveal which host genotype(s) is/are resistant to which pathogen genotype(s). The knowledge of this so-called infection matrix is important for agriculture and medicine. Building on established theories of host-pathogen interactions, we here derive four novel indices capturing the characteristics of the infection matrix. These indices can be computed from full genome polymorphism data of randomly sampled uninfected hosts, as well as infected hosts and their pathogen strains. We use these indices in an approximate Bayesian computation method to pinpoint loci with relevant G×G interactions and to infer their underlying interaction matrix. In a combined single nucleotide polymorphism dataset of 451 European humans and their infecting hepatitis C virus (HCV) strains and 503 uninfected individuals, we reveal a new human candidate gene for resistance to HCV and new virus mutations matching human genes. For two groups of significant human-HCV (G×G) associations, we infer a gene-for-gene infection matrix, which is commonly assumed to be typical of plant-pathogen interactions. Our model-based inference framework bridges theoretical models of G×G interactions with host and pathogen genomic data. It, therefore, paves the way for understanding the evolution of key G×G interactions underpinning HCV adaptation to the European human population after a recent expansion.

PAPipe: A Pipeline for Comprehensive Population Genetic Analysis.

Advancements in next-generation sequencing (NGS) technologies have led to a substantial increase in the availability of population genetic variant data, thus prompting the development of various population analysis tools to enhance our understanding of population structure and evolution. The tools that are currently used to analyze population genetic variant data generally require different environments, parameters, and formats of the input data, which can act as a barrier preventing the wide-spread usage of such tools by general researchers who may not be familiar with bioinformatics. To address this problem, we have developed an automated and comprehensive pipeline called PAPipe to perform nine widely used population genetic analyses using population NGS data. PAPipe seamlessly interconnects and serializes multiple steps, such as read trimming and mapping, genetic variant calling, data filtering, and format converting, along with nine population genetic analyses such as principal component analysis, phylogenetic analysis, population tree analysis, population structure analysis, linkage disequilibrium decay analysis, selective sweep analysis, population admixture analysis, sequentially Markovian coalescent analysis, and fixation index analysis. PAPipe also provides an easy-to-use web interface that allows for the parameters to be set and the analysis results to be browsed in intuitive manner. PAPipe can be used to generate extensive results that provide insights that can help enhance user convenience and data usability. PAPipe is freely available at https://github.com/jkimlab/PAPipe.

Mendelian randomization analyses for PCOS: evidence, opportunities, and challenges.

Identifying etiological risk factors is significant for preventing and treating patients with polycystic ovary syndrome (PCOS). Through genetic variation, Mendelian randomization (MR) assesses causal associations between PCOS risk and related exposure factors. This emerging technology has provided evidence of causal associations of anti-Müllerian hormone (AMH) levels, sex hormone-binding globulin (SHBG) levels, menopause age, adiposity, insulin resistance (IR), depression, breast cancer, ovarian cancer, obsessive-compulsive disorder (OCD), and forced vital capacity (FVC) with PCOS, while lacking associations of type 2 diabetes mellitus (T2DM), coronary heart disease (CHD), stroke, anxiety disorder (AD), schizophrenia (SCZ), bipolar disorder (BIP), and offspring birth weight with PCOS. In this review, we briefly introduce the concept and methodology of MR in terms of the opportunities and challenges in this field based on recent results obtained from MR analyses involving PCOS.

Human Genetic Determinants of Viral Diseases.

Much progress has been made in the identification of specific human gene variants that contribute to enhanced susceptibility or resistance to viral diseases. Herein we review multiple discoveries made with genome-wide or candidate gene approaches that have revealed significant insights into virus-host interactions. Genetic factors that have been identified include genes encoding virus receptors, receptor-modifying enzymes, and a wide variety of innate and adaptive immunity-related proteins. We discuss a range of pathogenic viruses, including influenza virus, respiratory syncytial virus, human immunodeficiency virus, human T cell leukemia virus, human papilloma virus, hepatitis B and C viruses, herpes simplex virus, norovirus, rotavirus, parvovirus, and Epstein-Barr virus. Understanding the genetic underpinnings that affect infectious disease outcomes should allow tailored treatment and prevention approaches in the future.

PredictONCO: a web tool supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning.

PredictONCO 1.0 is a unique web server that analyzes effects of mutations on proteins frequently altered in various cancer types. The server can assess the impact of mutations on the protein sequential and structural properties and apply a virtual screening to identify potential inhibitors that could be used as a highly individualized therapeutic approach, possibly based on the drug repurposing. PredictONCO integrates predictive algorithms and state-of-the-art computational tools combined with information from established databases. The user interface was carefully designed for the target specialists in precision oncology, molecular pathology, clinical genetics and clinical sciences. The tool summarizes the effect of the mutation on protein stability and function and currently covers 44 common oncological targets. The binding affinities of Food and Drug Administration/ European Medicines Agency -approved drugs with the wild-type and mutant proteins are calculated to facilitate treatment decisions. The reliability of predictions was confirmed against 108 clinically validated mutations. The server provides a fast and compact output, ideal for the often time-sensitive decision-making process in oncology. Three use cases of missense mutations, (i) K22A in cyclin-dependent kinase 4 identified in melanoma, (ii) E1197K mutation in anaplastic lymphoma kinase 4 identified in lung carcinoma and (iii) V765A mutation in epidermal growth factor receptor in a patient with congenital mismatch repair deficiency highlight how the tool can increase levels of confidence regarding the pathogenicity of the variants and identify the most effective inhibitors. The server is available at https://loschmidt.chemi.muni.cz/predictonco.

Polygenic subtype identified in ACCORD trial displays a favorable type 2 diabetes phenotype in the UKBiobank population.

INTRODUCTION: We previously identified a genetic subtype (C4) of type 2 diabetes (T2D), benefitting from intensive glycemia treatment in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Here, we characterized the population of patients that met the C4 criteria in the UKBiobank cohort. RESEARCH DESIGN AND METHODS: Using our polygenic score (PS), we identified C4 individuals in the UKBiobank and tested C4 status with risk of developing T2D, cardiovascular disease (CVD) outcomes, and differences in T2D medications. RESULTS: C4 individuals were less likely to develop T2D, were slightly older at T2D diagnosis, had lower HbA1c values, and were less likely to be prescribed T2D medications (P < .05). Genetic variants in MAS1 and IGF2R, major components of the C4 PS, were associated with fewer overall T2D prescriptions. CONCLUSION: We have confirmed C4 individuals are a lower risk subpopulation of patients with T2D.

Polymorphisms in transcription factor binding sites and enhancer regions and pancreatic ductal adenocarcinoma risk.

Genome-wide association studies (GWAS) are a powerful tool for detecting variants associated with complex traits and can help risk stratification and prevention strategies against pancreatic ductal adenocarcinoma (PDAC). However, the strict significance threshold commonly used makes it likely that many true risk loci are missed. Functional annotation of GWAS polymorphisms is a proven strategy to identify additional risk loci. We aimed to investigate single-nucleotide polymorphisms (SNP) in regulatory regions [transcription factor binding sites (TFBSs) and enhancers] that could change the expression profile of multiple genes they act upon and thereby modify PDAC risk. We analyzed a total of 12,636 PDAC cases and 43,443 controls from PanScan/PanC4 and the East Asian GWAS (discovery populations), and the PANDoRA consortium (replication population). We identified four associations that reached study-wide statistical significance in the overall meta-analysis: rs2472632(A) (enhancer variant, OR 1.10, 95%CI 1.06,1.13, p = 5.5 × 10-8), rs17358295(G) (enhancer variant, OR 1.16, 95%CI 1.10,1.22, p = 6.1 × 10-7), rs2232079(T) (TFBS variant, OR 0.88, 95%CI 0.83,0.93, p = 6.4 × 10-6) and rs10025845(A) (TFBS variant, OR 1.88, 95%CI 1.50,1.12, p = 1.32 × 10-5). The SNP with the most significant association, rs2472632, is located in an enhancer predicted to target the coiled-coil domain containing 34 oncogene. Our results provide new insights into genetic risk factors for PDAC by a focused analysis of polymorphisms in regulatory regions and demonstrating the usefulness of functional prioritization to identify loci associated with PDAC risk.

Prenatal diagnosis and genetic analysis of small supernumerary marker chromosomes in the eastern chinese han population: A retrospective study of 36 cases.

BACKGROUND: Small supernumerary marker chromosomes (sSMCs) are additional chromosomes with unclear structures and origins, and their correlations with clinical fetal phenotypes remain incompletely understood, which reduces the accuracy of genetic counseling. METHODS: We conducted a retrospective analysis of a cohort of 36 cases of sSMCs diagnosed in our center. We performed G-banding and chromosomal microarray analysis (CMA). The resulting karyotypes were compared with case reports in the literature and various databases including OMIM, DECIPHER, ClinVar, ClinGen, ISCA, DGV, and PubMed. RESULTS: Karyotype analysis data revealed that 19 out of 36 fetuses were mosaic. Copy number variants (CNVs) analysis results showed that 27 out of 36 fetuses harbored pathogenic/likely pathogenic variants. Among these 27 cases, 11 fetuses carried sex chromosome-related CNVs, including 4 female cases exhibiting Turner syndrome phenotypes and 7 cases showing Y chromosome deletions. In the remaining 16 fetuses with autosomal CNVs, 9 fetuses carried variants associated with Cat eye syndrome, Emanuel syndrome, Tetrasomy 18p, and 15q11-q13 duplication syndrome. Among these, 22 fetuses were terminated, and the remaining 5 fetuses were delivered and developed normally. Additionally, we identified a few variants with unclear pathogenicity. CONCLUSION: Cytogenetic analysis is essential for identifying the pathogenicity of sSMCs and increasing the accuracy of genetic counseling.

Hereditary and cortical morphological biomarker of sensitivity to reward in short-term withdrawal methamphetamine abusers.

Higher sensitivity to reward (SR) and weaker sensitivity to punishment (SP) construct the fundamental craving characteristics of methamphetamine abuse. However, few studies have appraised relationships between SR/SP (SR or SP) and cortical morphological alterations in methamphetamine abusers and whether hereditary factors take effects on SR/SP is unclear. Based on surface-based morphometric analysis, cortical discrepancy was investigated between 38 methamphetamine abusers and 37 healthy controls. Within methamphetamine abusers, correlation profiling was performed to discover associations among aberrant neuroimaging substrates, SR, SP, and craving. According to nine single nucleotide polymorphism sites of dopamine-related genes, we conducted univariate general linear model to find different effects of genotypes on cortical alterations and SR/SP/craving (SR, SP, or craving). Ultimately, mediation analyses were conducted among single nucleotide polymorphism sites, SR/SP/craving, and cortical morphological alterations to discover their association pathways. Compared to healthy controls, thinner cortices in inferior temporal gyrus, lateral orbitofrontal cortex, medial orbitofrontal cortex, inferior parietal lobule, and lateral occipital cortex in the left hemisphere were found in methamphetamine abusers (P < 0.05, family-wise error corrected). Cortical thickness in the inferior temporal gyrus was negatively correlated with SR scores. We found that rs1800497 A-containing genotypes had lower cortical thickness in the left inferior parietal lobule than the GG genotype. The rs5751876 had effects on SR scores. This study would provide convincing biomarkers for SR in methamphetamine abusers and offer potential genetic targets for personalizing relapse prevention.

Contiguously hydrophobic sequences are functionally significant throughout the human exome.

Hydrophobic interactions have long been established as essential for stabilizing struc-tured proteins as well as drivers of aggregation, but the impact of hydrophobicity on thefunctional significance of sequence variants has rarely been considered in a genome-wide context. Here we test the role of hydrophobicity on functional impact across70,000 disease- and non­disease-associated single-nucleotide polymorphisms (SNPs),using enrichment of disease association as an indicator of functionality. We find thatfunctional impact is uncorrelated with hydrophobicity of the SNP itself and only weaklycorrelated with the average local hydrophobicity, but is strongly correlated with boththe size and minimum hydrophobicity of the contiguously hydrophobic sequence (or"blob") that contains the SNP. Disease association is found to vary by more than sixfoldas a function of contiguous hydrophobicity parameters, suggesting utility as a prior foridentifying causal variation. We further find signatures of differential selective constrainton hydrophobic blobs and that SNPs splitting a long hydrophobic blob or joiningtwo short hydrophobic blobs are particularly likely to be disease associated. Trends arepreserved for both aggregating and nonaggregating proteins, indicating that the role ofcontiguous hydrophobicity extends well beyond aggregation risk.

Polymorphisms of the PD-L1 gene 3'-untranslated region are associated with the expression of PD-L1 in non-small cell lung cancer.

Recent results show that polymorphisms of programmed death ligand 1 (PD-L1, also known as CD274 or B7-H1) might be used as a possible marker for effectiveness of chemotherapy and cancer risk. However, the effect of PD-L1 gene variations on PD-L1 expression remain unclear. Given the post-transcriptional machinery in tumor PD-L1 expression, we investigated single nucleotide polymorphisms (SNPs) in the 3'-untranslated region (3'-UTR) of the PD-L1 gene, rs4143815 and rs4742098, using formalin-fixed paraffin-embedded sections of 154 patients with non-small cell lung cancers (NSCLCs). In rs4143815, the GG genotype showed significant association with PD-L1 expression (P = 0.032). In rs4742098, the AA genotype was significantly associated with histology and PD-L1 expression (P = 0.022 and P = 0.008, respectively). In multivariate logistic regression analysis, the AA genotype in rs4742098 was correlated with PD-L1 expression (odds ratio 0.408, P = 0.048). Interestingly, approximately 10% of the NSCLC cases showed somatic mutation when we compared genotypes of these SNPs between NSCLC tissues and non-tumor tissues from the same patients. In addition, cases with somatic mutation showed higher levels of PD-L1 expression than cases with germline mutation in rs4143815 GG. In conclusion, we demonstrated that the rs4143815 and rs4742098 SNPs in the 3'-UTR of PD-L1 were associated with tumor PD-L1 expression in NSCLCs.

Functional variants of the pentraxin 3 gene are associated with the metastasis and progression of prostate cancer.

Age, ethnic background and genetic components have been identified as the established risks for prostate cancer (PCa). Pentraxin 3 (PTX3), originally identified as a pattern-recognition molecule for defence against infectious agents, has multiple functions in tissue repair and in the regulation of cancer-associated inflammation. In this study, we sought to investigate the impact of PTX3 gene variants on the development of PCa. Genotypes of four common single-nucleotide polymorphisms (SNPs) of PTX3 gene, including rs1840680, rs2305619, rs3816527 and rs2120243, were profiled among 705 PCa patients and 705 ethnicity-matched controls. In this study, we found that patients who carry at least one minor allele (C) of rs3816527 (AC and CC) tended to develop advanced forms of diseases (clinical large T stage, OR, 1.593, p = 0.032; pathologically-confirmed nodal spread, OR, 1.987, p = 0.011; metastatic tumour, OR, 3.896, p = 0.032) as compared with those homologous for the major allele (AA). Further stratification analysis showed that such association of rs3816527 with lymphatic and distal metastasis of PCa was accentuated in the younger age group (≤65 at diagnosis) but not seen in the older age group (>65 at diagnosis), suggesting an age-specific effect of PTX3 variants. Prediction of PTX3 protein structure implied that polymorphism may alter the quaternary organization and oligomerization of PTX3 protein. Moreover, our gene silencing experiments and survey of public datasets revealed that elevation of PTX3 levels in PCa was required for cell migration and associated with tumour metastasis. Our results highlight an association of PTX3 rs3816527 with the progression of PCa.

MAOB expression correlates with a favourable prognosis in prostate cancer, and its genetic variants are associated with the metastasis of the disease.

Monoamine oxidase B (MAOB), a neurotransmitter-degrading enzyme, was reported to reveal conflicting roles in various cancers. However, the functional role of MAOB and impacts of its genetic variants on prostate cancer (PCa) is unknown. Herein, we genotyped four loci of MAOB single-nucleotide polymorphisms (SNPs), including rs1799836 (A/G), rs3027452 (G/A), rs6651806 (A/C) and rs6324 (G/A) in 702 PCa Taiwanese patients. We discovered that PCa patients carrying the MAOB rs6324 A-allele exhibited an increased risk of having a high initial prostate-specific antigen (iPSA) level (>10 ng/mL). Additionally, patients with the rs3027452 A-allele had a higher risk of developing distal metastasis, particularly in the subpopulation with high iPSA levels. In a subpopulation without postoperative biochemical recurrence, patients carrying the rs1799836 G-allele had a higher risk of developing lymph node metastasis and recurrence compared to those carrying the A-allele. Furthermore, genotype screening in PCa cell lines revealed that cells carrying the rs1799836 G-allele expressed lower MAOB levels than those carrying the A-allele. Functionally, overexpression and knockdown of MAOB in PCa cells respectively suppressed and enhanced cell motility and proliferation. In clinical observations, correlations of lower MAOB expression levels with higher Gleason scores, advanced clinical T stages, tumour metastasis, and poorer prognosis in PCa patients were noted. Our findings suggest that MAOB may act as a suppressor of PCa progression, and the rs3027452 and rs1799836 genetic variants of MAOB are linked to PCa metastasis within the Taiwanese population.

Prioritizing cardiovascular disease-associated variants altering NKX2-5 and TBX5 binding through an integrative computational approach.

Cardiovascular diseases (CVDs) are the leading cause of death worldwide and are heavily influenced by genetic factors. Genome-wide association studies have mapped >90% of CVD-associated variants within the noncoding genome, which can alter the function of regulatory proteins, such as transcription factors (TFs). However, due to the overwhelming number of single-nucleotide polymorphisms (SNPs) (>500,000) in genome-wide association studies, prioritizing variants for in vitro analysis remains challenging. In this work, we implemented a computational approach that considers support vector machine (SVM)-based TF binding site classification and cardiac expression quantitative trait loci (eQTL) analysis to identify and prioritize potential CVD-causing SNPs. We identified 1535 CVD-associated SNPs within TF footprints and putative cardiac enhancers plus 14,218 variants in linkage disequilibrium with genotype-dependent gene expression in cardiac tissues. Using ChIP-seq data from two cardiac TFs (NKX2-5 and TBX5) in human-induced pluripotent stem cell-derived cardiomyocytes, we trained a large-scale gapped k-mer SVM model to identify CVD-associated SNPs that altered NKX2-5 and TBX5 binding. The model was tested by scoring human heart TF genomic footprints within putative enhancers and measuring in vitro binding through electrophoretic mobility shift assay. Five variants predicted to alter NKX2-5 (rs59310144, rs6715570, and rs61872084) and TBX5 (rs7612445 and rs7790964) binding were prioritized for in vitro validation based on the magnitude of the predicted change in binding and are in cardiac tissue eQTLs. All five variants altered NKX2-5 and TBX5 DNA binding. We present a bioinformatic approach that considers tissue-specific eQTL analysis and SVM-based TF binding site classification to prioritize CVD-associated variants for in vitro analysis.

Synthetic receptor platform to identify loss-of-function single nucleotide variants and designed mutants in the death receptor Fas/CD95.

Synthetic biology has emerged as a useful technology for studying cytokine signal transduction. Recently, we described fully synthetic cytokine receptors to phenocopy trimeric receptors such as the death receptor Fas/CD95. Using a nanobody as an extracellular-binding domain for mCherry fused to the natural receptor's transmembrane and intracellular domain, trimeric mCherry ligands were able to induce cell death. Among the 17,889 single nucleotide variants in the SNP database for Fas, 337 represent missense mutations that functionally remained largely uncharacterized. Here, we developed a workflow for the Fas synthetic cytokine receptor system to functionally characterize missense SNPs within the transmembrane and intracellular domain of Fas. To validate our system, we selected five functionally assigned loss-of-function (LOF) polymorphisms and included 15 additional unassigned SNPs. Moreover, based on structural data, 15 gain-of-function or LOF candidate mutations were additionally selected. All 35 nucleotide variants were functionally investigated through cellular proliferation, apoptosis and caspases 3 and 7 cleavage assays. Collectively, our results showed that 30 variants resulted in partial or complete LOF, while five lead to a gain-of-function. In conclusion, we demonstrated that synthetic cytokine receptors are a suitable tool for functional SNPs/mutations characterization in a structured workflow.

Rice Pangenome Genotyping Array: an efficient genotyping solution for pangenome-based accelerated genetic improvement in rice.

The advent of the pangenome era has unraveled previously unknown genetic variation existing within diverse crop plants, including rice. This untapped genetic variation is believed to account for a major portion of phenotypic variation existing in crop plants. However, the use of conventional single reference-guided genotyping often fails to capture a large portion of this genetic variation leading to a reference bias. This makes it difficult to identify and utilize novel population/cultivar-specific genes for crop improvement. Thus, we developed a Rice Pangenome Genotyping Array (RPGA) harboring probes assaying 80K single-nucleotide polymorphisms (SNPs) and presence-absence variants spanning the entire 3K rice pangenome. This array provides a simple, user-friendly and cost-effective (60-80 USD per sample) solution for rapid pangenome-based genotyping in rice. The genome-wide association study (GWAS) conducted using RPGA-SNP genotyping data of a rice diversity panel detected a total of 42 loci, including previously known as well as novel genomic loci regulating grain size/weight traits in rice. Eight of these identified trait-associated loci (dispensable loci) could not be detected with conventional single reference genome-based GWAS. A WD repeat-containing PROTEIN 12 gene underlying one of such dispensable locus on chromosome 7 (qLWR7) along with other non-dispensable loci were subsequently detected using high-resolution quantitative trait loci mapping confirming authenticity of RPGA-led GWAS. This demonstrates the potential of RPGA-based genotyping to overcome reference bias. The application of RPGA-based genotyping for population structure analysis, hybridity testing, ultra-high-density genetic map construction and chromosome-level genome assembly, and marker-assisted selection was also demonstrated. A web application (http://www.rpgaweb.com) was further developed to provide an easy to use platform for the imputation of RPGA-based genotyping data using 3K rice reference panel and subsequent GWAS.

Analysis of >3400 worldwide eggplant accessions reveals two independent domestication events and multiple migration-diversification routes.

Eggplant (Solanum melongena) is an important Solanaceous crop, widely cultivated and consumed in Asia, the Mediterranean basin, and Southeast Europe. Its domestication centers and migration and diversification routes are still a matter of debate. We report the largest georeferenced and genotyped collection to this date for eggplant and its wild relatives, consisting of 3499 accessions from seven worldwide genebanks, originating from 105 countries in five continents. The combination of genotypic and passport data points to the existence of at least two main centers of domestication, in Southeast Asia and the Indian subcontinent, with limited genetic exchange between them. The wild and weedy eggplant ancestor S. insanum shows admixture with domesticated S. melongena, similar to what was described for other fruit-bearing Solanaceous crops such as tomato and pepper and their wild ancestors. After domestication, migration and admixture of eggplant populations from different regions have been less conspicuous with respect to tomato and pepper, thus better preserving 'local' phenotypic characteristics. The data allowed the identification of misclassified and putatively duplicated accessions, facilitating genebank management. All the genetic, phenotypic, and passport data have been deposited in the Open Access G2P-SOL database, and constitute an invaluable resource for understanding the domestication, migration and diversification of this cosmopolitan vegetable.