Reference Panel Research Articles

O-103 A method for mapping sex-specific meiotic crossovers from PGT-A data elucidates the role of aberrant recombination in the origins of aneuploidy

Abstract Study question How do the number and location of meiotic crossovers contribute to the formation of aneuploidies observed in preimplantation human embryos? Summary answer Normalized across chromosomes, trisomies possess 35% fewer crossovers on average compared to disomies, while the genomic distribution of crossovers is also substantially altered. What is known already Meiotic recombination is a crucial source of genetic diversity and is also critical for ensuring the accuracy of chromosome segregation. Understanding the landscape of meiotic recombination, its variation across individuals, and the processes by which it goes awry are long-standing goals in human genetics. Current approaches for inferring the landscape of recombination either rely on population genetic patterns of linkage disequilibrium—capturing a time-averaged view—or direct detection of crossovers in gametes or multi-generation pedigrees, limiting the scale and availability of relevant datasets. Moreover, most of these methods are designed for discovering recombination using data from normal, disomic chromosomes. Study design, size, duration We present a method for mapping sex-specific recombination landscapes from low-coverage (<0.1×) data from preimplantation genetic testing for aneuploidy (PGT-A) of embryos with arbitrary ploidy configurations. To overcome the sparsity of these data, our method exploits its inherent relatedness structure, knowledge of haplotypes from external population reference panels, as well as the frequent occurrence of chromosome loss in embryos, whereby the remaining chromosome is phased by default. Participants/materials, setting, methods We benchmarked our method by simulating crossovers between known haplotypes. Encouraged by the performance on simulated data we extended our study to retrospective analysis utilizing de-identified PGT-A data obtained between April 2021 and August 2022 at the CReATe Fertility Centre (Toronto, Canada). The data include 20,160 embryos (2,559 IVF patients) with an average depth of coverage of ∼0.05×, facilitating the mapping of crossovers at an average resolution of ∼150 kbp. Main results and the role of chance Our benchmarking results demonstrate high sensitivity and specificity across all ancestries at a coverage of 0.05x per homolog (AUC = 0.989), with AUC declining by 0.014 and 0.053 for when coverage is reduced to 0.025x and 0.013x, respectively. Extending our analysis to real PGT-A data, we observed that our inferred sex-specific landscapes of meiotic crossovers on disomic chromosomes were strongly correlated with published genetic maps from studies based on high-coverage sequencing of parent-offspring trios (r = 0.86 for female map; r = 0.53 for male map), broadly supporting the accuracy of our method. Notably, the total length of the female genetic map was reduced by 35% for trisomies compared to disomies, consistent with the hypothesized role of reduced crossovers and exchangeless chromosomes in the origins of female meiotic aneuploidy. In addition, the genomic distribution of crossovers is also altered in a chromosome-specific manner. Examples include a reduction in crossovers near the centromere of trisomies versus disomies of chromosome 16, as well as an enrichment of crossovers on the q-arm of trisomies versus disomies of chromosome 22. Together, our results provide a detailed corroboration of the hypothesis that aberrant meiotic recombination contributes to the origins of aneuploidies. Limitations, reasons for caution The accuracy of our method is influenced by genomic heterogeneity in depth of coverage, rates of heterozygosity, and mismatches between the ancestry of the reference panel and the tested sequence. Moreover, technical errors such as spurious alignment and genotyping could hinder analysis in repetitive genomic regions. Wider implications of the findings Together, our study helps clarify the dual function of meiotic recombination in generating genetic diversity while ensuring meiotic fidelity. Our method for patient-specific mapping of meiotic recombination phenotypes may offer clues about how dysregulation of this process contributes to infertility. Trial registration number NIH r R35GM13374

74-OR: Improving Performance of Type 2 Diabetes Polygenic Scores in African and Hispanic Ancestries

Most polygenic scores (PS) are developed based on data from persons of European (EUR) ancestry and perform poorly in non-EUR populations. To improve the performance of type 2 diabetes (T2D) PS in the under-studied African (AFR) and Hispanic (HIS) ancestries, we created more comprehensive reference panels using the “Tagging Iterative of single nucleotide variant in multiple populations” (TagIt) method and aggregated genome-wide association study (GWAS) meta-analyses from 5 major continental ancestries (360K cases, 1.8M controls, 32% of non-EUR ancestry). We used PRS-CS and PRS-CSx to develop ancestry-specific and multi-ancestry PSs with HapMap3 (HM3) and TagIt panels and evaluated their performance based on the variance explained by the liability R2 (lR2). Trained models were then tested in independent datasets. Compared to HM3 panels, TagIt panels for AFR and HIS ancestries had 4X improved coverage for variants with minor allele frequency 1-5% and showed significant improvement of ancestry-specific PSs (p of difference<0.01). Multi-ancestry PSs based on linkage disequilibrium (LD) and GWAS data from 5 ancestries outperformed the ancestry-specific PSs (lR2 AFR: 0.024 AFR-based PS vs. 0.038 multi-ancestry PS, p of difference = 1.3×10-3; lR2 HIS: 0.017 HIS-based PS vs. 0.061 multi-ancestry PS, p of difference = 4×10-8). It also outperformed the PS based on the EUR GWAS and LD data (lR2 AFR: 0.024 EUR-based PS, p of difference = 0.003; lR2 HIS: 0.045 EUR-based PS, p of difference = 0.003). TagIt reference panels did not show significant improved performance when applying multi-ancestry PS. By leveraging existing GWAS data from multiple ancestries and building more comprehensive reference panels we can improve the performance of the PS for diverse ancestries. However, increasing available GWAS data from diverse ancestries is still needed to improve performance in non-EUR populations and reduce health disparities. Disclosure A.Huerta: None. M.Vujkovic: None. B.F.Voight: None. X.Sim: None. R.J.Loos: None. J.E.Below: None. T.Ge: None. Va million veteran program: n/a. J.C.Florez: Consultant; AstraZeneca, Novo Nordisk, Other Relationship; AstraZeneca, Merck & Co., Inc. A.Manning: None. M.Ng: None. J.Kim: None. J.M.Mercader: None. I.Diabetes polygenic risk scores in multiple ancestr: n/a. R.Mandla: None. Y.Lu: None. L.E.Petty: None. K.Suzuki: None. S.S.Lee: None. H.Ng: None. M.Loh: None. Funding American Diabetes Association (11-22-ICTSPM-16 to J.M.M.); National Human Genome Research Institute (U01HG011723)

1466-P: Genome-Wide Association Studies in the GRADE Study Identify Genetic Variation Influencing the Response to Pharmacotherapy for Type 2 Diabetes

Treatment selection for type 2 diabetes (T2D) is largely driven by guidelines based on population-level clinical data and does not incorporate genetics into decision making. To address this gap, we conducted genome-wide association studies in GRADE, a randomized trial comparing the metabolic effects of glargine, glimepiride, liraglutide, or sitagliptin, when added to metformin over a mean follow-up of 5 years. 4,689 participants with T2D from diverse ancestries underwent genome-wide genotyping with the Illumina Infinium GSA 3.0 array, and high-quality imputation was performed with the TOPMed reference panel. We defined cases as meeting the two outcome criteria (primary: HbA1c ≥7%; secondary: HbA1c ≥7.5%) after 1 or 4 years of treatment. Whole-genome logistic regression using an additive model tested for case-control associations for each treatment. Results were filtered to minor allele frequency >0.005 and imputation R2 ≥0.80. We identified four loci associated with glargine response and one with sitagliptin response at standard genome-wide significance (p<5.0×10−8). For glargine, rs116991960 proximal to the RPSAP52 pseudogene region was associated with increased risk of the primary outcome at year 1 (p=2.6×10−8). Interestingly, RPSAP52 interacts with the RNA binding protein IGF2BP2, and genetic variation in IGF2BP2 has been associated with increased T2D risk. Moreover, rs142895019, located in ITPK1, was associated with the secondary outcome for sitagliptin at year 1 (p=9.4×10−9). In a separate Cox proportional hazards survival analysis, C-allele carriers of rs142895019 had an increased risk of treatment failure on sitagliptin (HR 3.6, p=1.0×10−7). In summary, we identified new genetic loci that may be associated with glycemic response in a multi-ancestry clinical trial of common glucose-lowering medications for T2D. These findings require independent validation but have potential utility in guiding therapy selection. Disclosure J.H. Li: None. L. Szczerbinski: None. H. Liu: None. M. Tripputi: None. A. Kretowski: None. S.E. Kahn: Advisory Panel; Anji Pharmaceuticals, Bayer Inc., Boehringer Ingelheim Inc., Eli Lilly and Company, Merck & Co., Inc. Other Relationship; Novo Nordisk. J.M. Lachin: None. N. Younes: None. J.M. Mercader: None. J.C. Florez: Consultant; AstraZeneca. Other Relationship; AstraZeneca, Merck & Co., Inc. Consultant; Novo Nordisk. Funding National Institute of Diabetes and Digestive and Kidney Diseases (U01DK098246, U34DK088043, R01DK123019)

138-OR: Characterization and Prediction of Genetic Risk of T1D in Individuals without HLA-DR3 or HLA-DR4

The Major Histocompatibility (MHC) class II haplotypes HLA-DR3 and HLA-DR4 confer substantial genetic risk for type 1 diabetes (T1D), where 90% of individuals with T1D carry at least one copy of DR3 and/or DR4. We sought to understand whether the remaining 10% of T1D individuals without DR3 or DR4 haplotypes (non-DR3/DR4) have differences in genetic risk and whether current risk scores are effective for these individuals. We tested for T1D association using 12,716 non-DR3/DR4 samples from five European ancestry cohorts using genotypes imputed into the Michigan HLA and TOPMed v2 reference panels and identified independent signals using stepwise conditional analyses. There were 21 signals with significant effects on T1D, including 13 at the MHC locus and 8 genome-wide, of which 5 were novel. Across all T1D signals, 14 lead variants exhibited significant heterogeneity in effects on T1D in the non-DR3/DR4 subset compared to the full T1D population, including HLA-B*39 and the PTPN22 locus. Nearly 50% of non-DR3/DR4 T1D would be misclassified by a current T1D genetic risk score (T1D GRS1), which is weighted heavily by DR3/DR4 tag variants. We developed a new GRS using the 21 independent signals and determined whether this GRS could better discriminate T1D in individuals with non-DR3/DR4 haplotypes. The non-DR3/DR4 T1D GRS effectively differentiated T1D case and control samples (AUC=0.87) including in samples not in the initial discovery set (AUC=0.86), and improved prediction compared to GRS1 (AUC=0.52 using all controls, AUC=0.79 using only non-DR3/DR4 controls). These findings reveal a subset of T1D cases with heterogeneity in genetic risk and which are not as well represented by current risk scores, and argue that risk scores developed within heterogeneous subsets may enable even more accurate prediction of T1D. Disclosure C.Mcgrail: None. K.J.Gaulton: Consultant; Genentech, Inc., Stock/Shareholder; Neurocrine Biosciences, Inc., Vertex Pharmaceuticals Incorporated. Funding National Institutes of Health (DK120429, DK122607)

137-OR: Metabolite Ratios and Development of Type 1 Diabetes

Ratios between metabolites more directly reflect flux through chemical reactions than individual metabolites. Variation in metabolites and their ratios are determined, in part, by genetic factors. Thus, genetic loci associated with variation in metabolite ratios may help disentangle genetic from non-genetic processes captured by the metabolome. We integrated metabolite ratios with genetics to predict changes preceding type 1 diabetes (T1D) in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Quantitative trait loci (QTLs) were identified for 132 metabolite ratios previously associated with progression from islet autoimmunity (IA) to T1D. Ratios were calculated from untargeted metabolomics data available at the time of seroconversion to IA for 338 cases and 921 matched controls in TEDDY. Genotyping was performed using a genome-wide array with custom content with imputation to the TOPMed reference panel. Ratio-SNP associations were estimated using Matrix eQTL software, adjusting for case-control status and genetic relatedness. Genome-wide significant association (P < 4.9×10-9) was observed at 202 independent loci, involving 49 metabolite ratios. Twenty-six loci (12.9%) involved ratios between polyunsaturated fatty-acids (PUFAs) and fatty acid esters of hydroxy fatty acids (FAHFAs), a novel class of bioactive lipids with antidiabetic and anti-inflammatory effects. Variation in the metabolite ratio of eicosadienoic acid to FAHFA (18:1/O-15:1) was significantly determined by a site on chromosome 3 (ITPR1); ITPR1 encodes a receptor integral to calcium signaling and glucose-induced insulin secretion. Other genetic loci contributing to variation of PUFA:FAHFA ratios reside in genes controlling inflammatory and innate immune response (e.g., TSPO, TTLL12, NCAM1) and apoptosis (PPM1L). These novel metabolite ratio QTLs, some with known association with T1D risk, expand potential biomarkers of T1D risk, with PUFA:FAHFA ratios representing candidate FAHFA biosynthesis genes. Disclosure R.K. Johnson: None. A. Romero: None. E. Willems: Employee; Amgen Inc. S.S. Rich: None. M. Rewers: Research Support; Provention Bio, Juvenile Diabetes Research Foundation (JDRF), Hemsley Charitable Trust, Dexcom, Inc., Janssen Research & Development, LLC. O. Fiehn: None. J.M. Norris: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R03DK127472); National Institute of Allergy and Infectious Diseases; National Institute of Child Health and Human Development; National Institute of Environmental Health Sciences; JDRF; Centers for Disease Control and Prevention

Minimal positional substring cover is a haplotype threading alternative to Li and Stephens model.

The Li and Stephens (LS) hidden Markov model (HMM) models the process of reconstructing a haplotype as a mosaic copy of haplotypes in a reference panel. For small panels, the probabilistic parameterization of LS enables modeling the uncertainties of such mosaics. However, LS becomes inefficient when sample size is large, because of its linear time complexity. Recently the PBWT, an efficient data structure capturing the local haplotype matching among haplotypes, was proposed to offer a fast method for giving some optimal solution (Viterbi) to the LS HMM. Previously, we introduced the minimal positional substring cover (MPSC) problem as an alternative formulation of LS whose objective is to cover a query haplotype by a minimum number of segments from haplotypes in a reference panel. The MPSC formulation allows the generation of a haplotype threading in time constant to sample size (O(N)). This allows haplotype threading on very large biobank-scale panels on which the LS model is infeasible. Here, we present new results on the solution space of the MPSC. In addition, we derived a number of optimal algorithms for MPSC, including solution enumerations, the length maximal MPSC, and h-MPSC solutions. In doing so, our algorithms reveal the solution space of LS for large panels. We show that our method is informative in terms of revealing the characteristics of biobank-scale data sets and can improve genotype imputation.

A Parallel Computing Approach to Gene Expression and Phenotype Correlation for Identifying Retinitis Pigmentosa Modifiers in Drosophila

As a genetic eye disorder, retinitis pigmentosa (RP) has been a focus of researchers to find a diagnosis through either genome-wide association (GWA) or RNAseq analysis. In fact, GWA and RNAseq are considered two complementary approaches to gaining a more comprehensive understanding of the genetics of different diseases. However, RNAseq analysis can provide information about the specific mechanisms underlying the disease and the potential targets for therapy. This research proposes a new approach to differential gene expression (DGE) analysis, which is the heart of the core-analysis phase in any RNAseq study. Based on the Drosophila Genetic Reference Panel (DGRP), the gene expression dataset is computationally analyzed in light of eye-size phenotypes. We utilized the foreach and the doParallel R packages to run the code on a multicore machine to reduce the running time of the original algorithm, which exhibited an exponential time complexity. Experimental results showed an outstanding performance, reducing the running time by 95% while using 32 processes. In addition, more candidate modifier genes for RP were identified by increasing the scope of the analysis and considering more datasets that represent different phenotype models.

Multi-ethnic Imputation System (MI-System): A genotype imputation server for high-dimensional data

ObjectiveGenotype imputation is a commonly used technique that infers un-typed variants into a study’s genotype data, allowing better identification of causal variants in disease studies. However, due to overrepresentation of Caucasian studies, there’s a lack of understanding of genetic basis of health-outcomes in other ethnic populations. Therefore, facilitating imputation of missing key-predictor-variants that can potentially improve a risk health-outcome prediction model, specifically for Asian ancestry, is of utmost relevance. MethodsWe aimed to construct an imputation and analysis web-platform, that primarily facilitates, but is not limited to genotype imputation on East-Asians. The goal is to provide a collaborative imputation platform for researchers in the public domain towards rapidly and efficiently conducting accurate genotype imputation. ResultsWe present an online genotype imputation platform, Multi-ethnic Imputation System (MI-System) (https://misystem.cgm.ntu.edu.tw/), that offers users 3 established pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1 for conducting imputation analyses. In addition to 1000 Genomes and Hapmap3, a new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. MI-System further offers functions to create customized reference panels to be used for imputation, conduct quality control, split whole genome data into chromosomes, and convert genome builds. ConclusionUsers can upload their genotype data and perform imputation with minimum effort and resources. The utility functions further can be utilized to preprocess user uploaded data with easy clicks. MI-System potentially contributes to Asian-population genetics research, while eliminating the requirement for high performing computational resources and bioinformatics expertise. It will enable an increased pace of research and provide a knowledge-base for genetic carriers of complex diseases, therefore greatly enhancing patient-driven research. Statement of significanceMulti-ethnic Imputation System (MI-System), primarily facilitates, but is not limited to, imputation on East-Asians, through 3 established prephasing-imputation pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1, where users can upload their genotype data and perform imputation and other utility functions with minimum effort and resources. A new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. Utility functions include (a) create customized reference panels, (b) conduct quality control, (c) split whole genome data into chromosomes, and (d) convert genome builds. Users can also combine 2 reference panels using the system and use combined panels as reference to conduct imputation using MI-System.

South Asian medical cohorts reveal strong founder effects and high rates of homozygosity

The benefits of large-scale genetic studies for healthcare of the populations studied are well documented, but these genetic studies have traditionally ignored people from some parts of the world, such as South Asia. Here we describe whole genome sequence (WGS) data from 4806 individuals recruited from the healthcare delivery systems of Pakistan, India and Bangladesh, combined with WGS from 927 individuals from isolated South Asian populations. We characterize population structure in South Asia and describe a genotyping array (SARGAM) and imputation reference panel that are optimized for South Asian genomes. We find evidence for high rates of reproductive isolation, endogamy and consanguinity that vary across the subcontinent and that lead to levels of rare homozygotes that reach 100 times that seen in outbred populations. Founder effects increase the power to associate functional variants with disease processes and make South Asia a uniquely powerful place for population-scale genetic studies.

Comparison of multiple imputation and other methods for the analysis of imputed genotypes

BackgroundAnalysis of imputed genotypes is an important and routine component of genome-wide association studies and the increasing size of imputation reference panels has facilitated the ability to impute and test low-frequency variants for associations. In the context of genotype imputation, the true genotype is unknown and genotypes are inferred with uncertainty using statistical models. Here, we present a novel method for integrating imputation uncertainty into statistical association tests using a fully conditional multiple imputation (MI) approach which is implemented using the Substantive Model Compatible Fully Conditional Specification (SMCFCS). We compared the performance of this method to an unconditional MI and two additional approaches that have been shown to demonstrate excellent performance: regression with dosages and a mixture of regression models (MRM).ResultsOur simulations considered a range of allele frequencies and imputation qualities based on data from the UK Biobank. We found that the unconditional MI was computationally costly and overly conservative across a wide range of settings. Analyzing data with Dosage, MRM, or MI SMCFCS resulted in greater power, including for low frequency variants, compared to unconditional MI while effectively controlling type I error rates. MRM andl MI SMCFCS are both more computationally intensive then using Dosage.ConclusionsThe unconditional MI approach for association testing is overly conservative and we do not recommend its use in the context of imputed genotypes. Given its performance, speed, and ease of implementation, we recommend using Dosage for imputed genotypes with MAF ge 0.001 and Rsq ge 0.3.

Modeling the longitudinal changes of ancestry diversity in the Million Veteran Program

BackgroundThe Million Veteran Program (MVP) participants represent 100 years of US history, including significant social and demographic changes over time. Our study assessed two aspects of the MVP: (i) longitudinal changes in population diversity and (ii) how these changes can be accounted for in genome-wide association studies (GWAS). To investigate these aspects, we divided MVP participants into five birth cohorts (N-range = 123,888 [born from 1943 to 1947] to 136,699 [born from 1948 to 1953]).ResultsAncestry groups were defined by (i) HARE (harmonized ancestry and race/ethnicity) and (ii) a random-forest clustering approach using the 1000 Genomes Project and the Human Genome Diversity Project (1kGP + HGDP) reference panels (77 world populations representing six continental groups). In these groups, we performed GWASs of height, a trait potentially affected by population stratification. Birth cohorts demonstrate important trends in ancestry diversity over time. More recent HARE-assigned Europeans, Africans, and Hispanics had lower European ancestry proportions than older birth cohorts (0.010 < Cohen’s d < 0.259, p < 7.80 × 10−4). Conversely, HARE-assigned East Asians showed an increase in European ancestry proportion over time. In GWAS of height using HARE assignments, genomic inflation due to population stratification was prevalent across all birth cohorts (linkage disequilibrium score regression intercept = 1.08 ± 0.042). The 1kGP + HGDP-based ancestry assignment significantly reduced the population stratification (mean intercept reduction = 0.045 ± 0.007, p < 0.05) confounding in the GWAS statistics.ConclusionsThis study provides a characterization of ancestry diversity of the MVP cohort over time and compares two strategies to infer genetically defined ancestry groups by assessing differences in controlling population stratification in genome-wide association studies.

Identifying the Optimal Radiometric Calibration Method for UAV-Based Multispectral Imaging

The development of UAVs and multispectral cameras has led to remote sensing applications with unprecedented spatial resolution. However, uncertainty remains on the radiometric calibration process for converting raw images to surface reflectance. Several calibration methods exist, but the advantages and disadvantages of each are not well understood. We performed an empirical analysis of five different methods for calibrating a 10-band multispectral camera, the MicaSense RedEdge MX Dual Camera System, by comparing multispectral images with spectrometer measurements taken in the field on the same day. Two datasets were collected, one in clear-sky and one in overcast conditions on the same field. We found that the empirical line method (ELM), using multiple radiometric reference targets imaged at mission altitude performed best in terms of bias and RMSE. However, two user-friendly commercial solutions relying on one single grey reference panel were only slightly less accurate and resulted in sufficiently accurate reflectance maps for most applications, particularly in clear-sky conditions. In overcast conditions, the increase in accuracy of more elaborate methods was higher. Incorporating measurements of an integrated downwelling light sensor (DLS2) did not improve the bias nor RMSE, even in overcast conditions. Ultimately, the choice of the calibration method depends on required accuracy, time constraints and flight conditions. When the more accurate ELM is not possible, commercial, user-friendly solutions like the ones offered by Agisoft Metashape and Pix4D can be good enough.

Passive cooling of photovoltaic panel by aluminum heat sinks and numerical simulation

One of the biggest problems of generating electricity by photovoltaic panels is that about 80% of the incoming solar energy is transformed into heat. The heat causes the rise of operating temperature of the panel, thereby reducing its efficiency and performance characteristics. In this research, photovoltaic panel was passively cooled by means of aluminum heat sinks with different geometries in order to determine the enhancement of output characteristics. Decrease in temperature by an average of 7.5 °C by means of heat sinks lead to increase in open-circuit voltage of 0.27 V, compared to the reference panel. Based on the experimentally obtained results, software was used to simulate the temperature and velocity fields for a string of three heat sinks, thus obtaining a better insight in heat transfer process of heat sinks. A polyhedral mesh and laminar fluid flow were applied for the simulation, leading to a good agreement between the experimentally obtained and simulated temperatures of the heat sinks, with average difference of about 1 °C. In order to determine the most efficient geometry of the heat sink for passive PV cooling applications, the data obtained by the experiment and simulation were numerically analyzed. The second heat sink, with its overall design (contact area with the panel, fin length, fin spacing), more effectively dissipates accumulated thermal energy to the surrounding air compared to the other two heat sinks. A model was developed to simulate the characteristics of a heat sink under various conditions using the laminar fluid regime and air temperature and the base temperature as input parameters. The results of this study can be used to optimize the design of heat sinks and improve their efficiency in cooling PV panels.

Unique molecular underpinnings by genetic ancestry in localized clear cell renal cell carcinoma.

4536 Background: Clinical studies evaluating the impact of race on outcomes in renal cell carcinoma (RCC) are inconsistent and conceivably influenced by both biological and socioeconomic factors. Self-reported African Americans (AA) are also largely underrepresented in clinical trials and molecular studies of clear cell (cc) RCC compared to their White (W) counterparts. Herein, we sought to comprehensively characterize molecular differences by genetic ancestry in localized ccRCC. Methods: We performed whole exome (WES) and transcriptome (RNAseq) sequencing on a case-matched cohort of AA and W patients (pts) who underwent nephrectomy for localized ccRCC at our institution. Germline single nucleotide polymorphisms were combined with reference panels from the 1000 Genomes Project, and ancestry was estimated using ADMIXTURE. Molecular subtypes were defined according to the IMmotion151 (IMm151) subtypes, and a random forest model was trained to predict subgroups. Results: From an initial selected cohort of 84 pts, WES and RNAseq data of adequate quality were available for 61 pts including 28 of African (AFR) and 33 of European (EUR) descent. We augmented our institutional cohort with a 3:1 EUR:AFR matched subset of the TCGA KIRC by stage, nuclear grade (NG), and gender. The final analytic cohort contained 75 pts of AFR and 177 pts of EUR descent. There were no significant differences in stage, NG, or gender between AFR and EUR groups. We observed that VHL [OR 0.22, CI [0.11-0.43], p &lt;0.001) and PBRM1 (OR 0.4, CI [0.19-0.79], p = 0.005) mutations are enriched in pts with EUR ancestry and identified a unique association of KMT2C somatic mutations (OR 3.7, CI [1.1-13.5], p = 0.027) with AFR ancestry. Consistent with a lower frequency of VHL mutations, gene-set variation analysis revealed lower enrichment for angiogenesis (-0.23 vs 0.045; p &lt; 0.001) in AFR vs EUR ancestry, respectively. AFR ancestry was associated with an increased proportion of the “Proliferative” IMm151 subtype (14.9% [n = 11/74] vs 3.4% [n = 6/177], p = 0.002). There was no association between overall survival (OS) and ancestry; however, IMm151 subtypes stratified OS with longest survival in the “Angiogenic/Stromal” group (median OS NR) and shortest OS in the T-Effector/Proliferative subtype (5.5yrs CI [2.3 - NR], p = 0.01). Conclusions: Our findings indicate that genetic ancestry is associated with distinct molecular subtypes of ccRCC. Biological drivers of ccRCC pathogenesis and therapeutic response may vary by ancestry, underscoring the need for racially diverse representation in future studies.

Trans-ethnic polygenic risk scores for body mass index: An international hundred K+ cohorts consortium study.

While polygenic risk scores hold significant promise in estimating an individual's risk of developing a complex trait such as obesity, their application in the clinic has, to date, been limited by a lack of data from non-European populations. As a collaboration model of the International Hundred K+ Cohorts Consortium (IHCC), we endeavored to develop a globally applicable trans-ethnic PRS for body mass index (BMI) through this relatively new international effort. The polygenic risk score (PRS) model was developed, trained and tested at the Center for Applied Genomics (CAG) of The Children's Hospital of Philadelphia (CHOP) based on a BMI meta-analysis from the GIANT consortium. The validated PRS models were subsequently disseminated to the participating sites. Scores were generated by each site locally on their cohorts and summary statistics returned to CAG for final analysis. We show that in the absence of a well powered trans-ethnic GWAS from which to derive marker SNPs and effect estimates for PRS, trans-ethnic scores can be generated from European ancestry GWAS using Bayesian approaches such as LDpred, by adjusting the summary statistics using trans-ethnic linkage disequilibrium reference panels. The ported trans-ethnic scores outperform population specific-PRS across all non-European ancestry populations investigated including East Asians and three-way admixed Brazilian cohort. Here we show that for a truly polygenic trait such as BMI adjusting the summary statistics of a well powered European ancestry study using trans-ethnic LD reference results in a score that is predictive across a range of ancestries including East Asians and three-way admixed Brazilians.

Validation of a breast cancer polygenic risk score in 15,490 Brazilians using exome sequencing.

10631 Background: Brazil has a highly admixed population with significant Southern European, African and Native American genetic ancestries. Approximately 75% of the population has two or more continental ancestries. Therefore, breast cancer polygenic risk scores (PRS) developed in predominantly European populations requires validation in the Brazilian population. Compared with genotyping arrays and whole genome sequencing, affordable and scalable whole exome sequencing (WES) allows genotyping of both rare and common variants in the targeted regions and imputation of common variants not directly captured. In this study we validate a previously developed 3820-variant PRS using WES in 15,490 Brazilians. Methods: The cohort consisted of 6,362 women with a history of breast cancer (mean age 49.6, stddev 11.6) and 9,128 unphenotyped adults as controls (mean age 41.6, stddev 13.3). WES were performed on germline DNA and variants were called and curated. Imputation was done using the 1000 Genomes Phase 3 reference panel for variants falling outside the WES captured regions. After excluding subjects with known pathogenic or likely pathogenic variants in BRCA1, BRCA2, PALB2, PTEN, or TP53, and first or second-degree relatives of the probands, 14,577 individuals remained in the final analysis (5,730 cases and 8,847 controls). We restricted PRS calculation to 2,893 of the original 3,820 variants with an estimated global imputation accuracy of 96%. Odds ratios (OR) were adjusted using the top 10 principal components. Results: The breast cancer PRS showed a highly significant correlation with cancer risk (p-value = 7.1e-81, OR = 1.43, 95% confidence interval (CI) = 1.38-1.48 per PRS standard deviation). Compared with women in the middle deciles, those in the highest 10% had an OR of 1.93, while those in the lowest 10% had an OR of 0.52 (Table). Conclusions: In this study we show that WES can capture 76% of the variants included in a previously developed and validated breast cancer PRS, and the calculated PRS is correlated with breast cancer risk in the admixed Brazilian population. This opens the door for clinical studies into the application of PRS in Brazil, particularly in women without mutations in breast cancer high-penetrance genes. In addition, our WES-based PRS strategy can be quickly, affordably, and seamlessly adopted by labs that already offer Hereditary Breast Cancer panels that use WES as a backbone. [Table: see text]

Novel HLA allele associations with susceptibility, staging, symptomatic state, autoimmune hepatitis and hepatocellular carcinoma events for primary biliary cholangitis in the Japanese population.

Primary biliary cholangitis (PBC) is a rare autoimmune disease with a clear predisposition for human leukocyte antigen (HLA)-DR/DQ-associated loss of immune tolerance for the E2 component of the pyruvate dehydrogenase complex. Three-field-resolution HLA imputation of 1,670 Japanese PBC patients and 2,328 healthy controls was conducted using Japanese population-specific HLA reference panels. Eighteen previously reported Japanese PBC-associated HLA alleles were confirmed and extended to 3-field-resolution, including HLA-DRB1*08:03 to HLA-DRB1*08:03:02, HLA-DQB1*03:01 to HLA-DQB1*03:01:01, HLA-DQB1*04:01 to HLA-DQB1*04:01:01 and HLA-DQB1*06:04 to HLA-DQB1*06:04:01. In addition, additional significant novel HLA alleles were identified, including 3 novel susceptible HLA-DQA1 alleles: HLA-DQA1*03:03:01, HLA-DQA1*04:01:01, HLA-DQA1*01:04:01 and 1 novel protective HLA-DQA1 allele, HLA-DQA1*05:05:01. In addition, PBC patients carrying HLA-DRB1*15:01:01 and HLA-DQA1*03:03:01 would have a higher predisposition toward developing concomitant autoimmune hepatitis (AIH). Further, late-stage and symptomatic PBC shared the same susceptible HLA alleles of HLA-A*26:01:01, HLA-DRB1*09:01:02 and HLA-DQB1*03:03:02. Lastly, HLA-DPB1*05:01:01 was identified as a potential risk HLA allele for development of hepatocellular carcinoma (HCC) in PBC patients. In conclusion, we have extended the current knowledge of HLA allele associations to 3-field resolution and identified novel HLA allele associations with predisposition risk, staging, symptomatic state, and AIH and HCC events for Japanese PBC patients.

Discordant calls across genotype discovery approaches elucidate variants with systematic errors.

Large-scale high-throughput sequencing data sets have been transformative for informing clinical variant interpretation and for use as reference panels for statistical and population genetic efforts. Although such resources are often treated as ground truth, we find that in widely used reference data sets such as the Genome Aggregation Database (gnomAD), some variants pass gold-standard filters, yet are systematically different in their genotype calls across genotype discovery approaches. The inclusion of such discordant sites in study designs involving multiple genotype discovery strategies could bias results and lead to false-positive hits in association studies owing to technological artifacts rather than a true relationship to the phenotype. Here, we describe this phenomenon of discordant genotype calls across genotype discovery approaches, characterize the error mode of wrong calls, provide a list of discordant sites identified in gnomAD that should be treated with caution in analyses, and present a metric and machine learning classifier trained on gnomAD data to identify likely discordant variants in other data sets. We find that different genotype discovery approaches have different sets of variants at which this problem occurs, but there are characteristic variant features that can be used to predict discordant behavior. Discordant sites are largely shared across ancestry groups, although different populations are powered for the discovery of different variants. We find that the most common error mode is that of a variant being heterozygous for one approach and homozygous for the other, with heterozygous in the genomes and homozygous reference in the exomes making up the majority of miscalls.

The impact of variable illumination on vegetation indices and evaluation of illumination correction methods on chlorophyll content estimation using UAV imagery

BackgroundThe advancements in unmanned aerial vehicle (UAV) technology have recently emerged as an effective, cost-efficient, and versatile solution for monitoring crop growth with high spatial and temporal precision. This monitoring is usually achieved through the computation of vegetation indices (VIs) from agricultural lands. The VIs are based on the incoming radiance to the camera, which is affected when there is a change in the scene illumination. Such a change will cause a change in the VIs and subsequent measures, e.g., the VI-based chlorophyll-content estimation. In an ideal situation, the results from VIs should be free from the impact of scene illumination and should reflect the true state of the crop’s condition. In this paper, we evaluate the performance of various VIs computed on images taken under sunny, overcast and partially cloudy days. To improve the invariance to the scene illumination, we furthermore evaluated the use of the empirical line method (ELM), which calibrates the drone images using reference panels, and the multi-scale Retinex algorithm, which performs an online calibration based on color constancy. For the assessment, we used the VIs to predict leaf chlorophyll content, which we then compared to field measurements.ResultsThe results show that the ELM worked well when the imaging conditions during the flight were stable but its performance degraded under variable illumination on a partially cloudy day. For leaf chlorophyll content estimation, The r^2 of the multivariant linear model built by VIs were 0.6 and 0.56 for sunny and overcast illumination conditions, respectively. The performance of the ELM-corrected model maintained stability and increased repeatability compared to non-corrected data. The Retinex algorithm effectively dealt with the variable illumination, outperforming the other methods in the estimation of chlorophyll content. The r^2 of the multivariable linear model based on illumination-corrected consistent VIs was 0.61 under the variable illumination condition.ConclusionsOur work indicated the significance of illumination correction in improving the performance of VIs and VI-based estimation of chlorophyll content, particularly in the presence of fluctuating illumination conditions.

Genome-wide association analysis identifies ancestry-specific genetic variation associated with acute response to metformin and glipizide in SUGAR-MGH.

Characterisation of genetic variation that influences the response to glucose-lowering medications is instrumental to precision medicine for treatment of type 2 diabetes. The Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH) examined the acute response to metformin and glipizide in order to identify new pharmacogenetic associations for the response to common glucose-lowering medications in individuals at risk of type 2 diabetes. One thousand participants at risk for type 2 diabetes from diverse ancestries underwent sequential glipizide and metformin challenges. A genome-wide association study was performed using the Illumina Multi-Ethnic Genotyping Array. Imputation was performed with the TOPMed reference panel. Multiple linear regression using an additive model tested for association between genetic variants and primary endpoints of drug response. In a more focused analysis, we evaluated the influence of 804 unique type 2 diabetes- and glycaemic trait-associated variants on SUGAR-MGH outcomes and performed colocalisation analyses to identify shared genetic signals. Five genome-wide significant variants were associated with metformin or glipizide response. The strongest association was between an African ancestry-specific variant (minor allele frequency [MAFAfr]=0.0283) at rs149403252 and lower fasting glucose at Visit 2 following metformin (p=1.9×10-9); carriers were found to have a 0.94 mmol/l larger decrease in fasting glucose. rs111770298, another African ancestry-specific variant (MAFAfr=0.0536), was associated with a reduced response to metformin (p=2.4×10-8), where carriers had a 0.29 mmol/l increase in fasting glucose compared with non-carriers, who experienced a 0.15 mmol/l decrease. This finding was validated in the Diabetes Prevention Program, where rs111770298 was associated with a worse glycaemic response to metformin: heterozygous carriers had an increase in HbA1c of 0.08% and non-carriers had an HbA1c increase of 0.01% after 1 year of treatment (p=3.3×10-3). We also identified associations between type 2 diabetes-associated variants and glycaemic response, including the type 2 diabetes-protective C allele of rs703972 near ZMIZ1 and increased levels of active glucagon-like peptide 1 (GLP-1) (p=1.6×10-5), supporting the role of alterations in incretin levels in type 2 diabetes pathophysiology. We present a well-phenotyped, densely genotyped, multi-ancestry resource to study gene-drug interactions, uncover novel variation associated with response to common glucose-lowering medications and provide insight into mechanisms of action of type 2 diabetes-related variation. The complete summary statistics from this study are available at the Common Metabolic Diseases Knowledge Portal ( https://hugeamp.org ) and the GWAS Catalog ( www.ebi.ac.uk/gwas/ , accession IDs: GCST90269867 to GCST90269899).