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1. Mining High-Level Imaging Genetic Associations via Clustering AD Candidate Variants with Similar Brain Association Patterns

   https://doi.org/10.3390/genes13091520  

   Wu, Ruiming; Bao, Jingxuan; Kim, Mansu; Saykin, Andrew J.; Moore, Jason H.; Shen, Li (September 2022, Genes)

   Brain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer’s disease (AD). The individual level SNP-QT signals are high dimensional and typically have small effect sizes, making them hard to be detected and replicated. To overcome this limitation, this work proposes a new approach that identifies high-level imaging genetic associations through applying multigraph clustering to the SNP-QT association maps. Given an SNP set and a brain QT set, the association between each SNP and each QT is evaluated using a linear regression model. Based on the resulting SNP-QT association map, five SNP–SNP similarity networks (or graphs) are created using five different scoring functions, respectively. Multigraph clustering is applied to these networks to identify SNP clusters with similar association patterns with all the brain QTs. After that, functional annotation is performed for each identified SNP cluster and its corresponding brain association pattern. We applied this pipeline to an AD imaging genetic study, which yielded promising results. For example, in an association study between 54 AD SNPs and 116 amyloid QTs, we identified two SNP clusters with one responsible for amyloid beta clearances and the other regulating amyloid beta formation. These high-level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts. 

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2. Prioritizing Amyloid Imaging Biomarkers in Alzheimer’s Disease via Learning to Rank

   https://doi.org/10.1007/978-3-030-33226-6_16  

   Peng, B; Ren, Z; Yao, X; Liu, K; Saykin, AJ; Shen, L (October 2019, MBIA 2019: International Workshop on Multimodal Brain Image Analysis)

   We propose an innovative machine learning paradigm enabling precision medicine for AD biomarker discovery. The paradigm tailors the imaging biomarker discovery process to individual characteristics of a given patient. We implement this paradigm using a newly developed learning-to-rank method 𝙿𝙻𝚃𝚁 . The 𝙿𝙻𝚃𝚁 model seamlessly integrates two objectives for joint optimization: pushing up relevant biomarkers and ranking among relevant biomarkers. The empirical study of 𝙿𝙻𝚃𝚁 conducted on the ADNI data yields promising results to identify and prioritize individual-specific amyloid imaging biomarkers based on the individual’s structural MRI data. The resulting top ranked imaging biomarker has the potential to aid personalized diagnosis and disease subtyping. 

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3. STAGdb: a 30K SNP genotyping array and Science Gateway for Acropora corals and their dinoflagellate symbionts

   https://doi.org/10.1038/s41598-020-69101-z  

   Kitchen, S. A.; Von Kuster, G.; Kuntz, K. L. Vasquez; Reich, H. G.; Miller, W.; Griffin, S.; Fogarty, Nicole D.; Baums, I. B. (July 2020, Scientific Reports)

   Abstract Standardized identification of genotypes is necessary in animals that reproduce asexually and form large clonal populations such as coral. We developed a high-resolution hybridization-based genotype array coupled with an analysis workflow and database for the most speciose genus of coral,Acropora, and their symbionts. We designed the array to co-analyze host and symbionts based on bi-allelic single nucleotide polymorphisms (SNP) markers identified from genomic data of the two CaribbeanAcroporaspecies as well as their dominant dinoflagellate symbiont,Symbiodinium ‘fitti’.SNPs were selected to resolve multi-locus genotypes of host (called genets) and symbionts (called strains), distinguish host populations and determine ancestry of coral hybrids between Caribbean acroporids. Pacific acroporids can also be genotyped using a subset of the SNP loci and additional markers enable the detection of symbionts belonging to the generaBreviolum, Cladocopium, andDurusdinium. Analytic tools to produce multi-locus genotypes of hosts based on these SNP markers were combined in a workflow called theStandardTools forAcroporidGenotyping (STAG). The STAG workflow and database are contained within a customized Galaxy environment (https://coralsnp.science.psu.edu/galaxy/), which allows for consistent identification of host genet and symbiont strains and serves as a template for the development of arrays for additional coral genera. STAG data can be used to track temporal and spatial changes of sampled genets necessary for restoration planning and can be applied to downstream genomic analyses. Using STAG, we uncover bi-directional hybridization between and population structure within Caribbean acroporids and detect a cryptic Acroporid species in the Pacific. 

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4. Tests for segregation distortion in tetraploid F1 populations

   https://doi.org/10.1007/s00122-025-04816-z  

   Gerard, David; Thakkar, Mira; Ferrão, Luis_Felipe_V (January 2025, Theoretical and Applied Genetics)

   Abstract Key message:In tetraploid F1 populations, traditional segregation distortion tests often inaccurately flag SNPs due to ignoring polyploid meiosis processes and genotype uncertainty. We develop tests that account for these factors. Abstract:Genotype data from tetraploid F1 populations are often collected in breeding programs for mapping and genomic selection purposes. A common quality control procedure in these groups is to compare empirical genotype frequencies against those predicted by Mendelian segregation, where SNPs detected to havesegregation distortionare discarded. However, current tests for segregation distortion are insufficient in that they do not account for double reduction and preferential pairing, two meiotic processes in polyploids that naturally change gamete frequencies, leading these tests to detect segregation distortion too often. Current tests also do not account for genotype uncertainty, again leading these tests to detect segregation distortion too often. Here, we incorporate double reduction, preferential pairing, and genotype uncertainty in likelihood ratio and Bayesian tests for segregation distortion. Our methods are implemented in a user-friendly R package, . We demonstrate the superiority of our methods to those currently used in the literature on both simulations and real data. 

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5. TimeFlies: an snRNA-seq aging clock for the fruit fly head sheds light on sex-biased aging

   https://doi.org/10.1101/2024.11.25.625273  

   Tennant, Nikolai; Pavuluri, Ananya; O’Connor-Giles, Kate; Singh, Gunjan; Larschan, Erica; Singh, Ritambhara (November 2024, bioRxiv)

   Abstract Although multiple high-performing epigenetic aging clocks exist, few are based directly on gene expression. Such transcriptomic aging clocks allow us to extract age-associated genes directly. However, most existing transcriptomic clocks model a subset of genes and are limited in their ability to predict novel biomarkers. With the growing popularity of single-cell sequencing, there is a need for robust single-cell transcriptomic aging clocks. Moreover, clocks have yet to be applied to investigate the elusive phenomenon of sex differences in aging. We introduce TimeFlies, a pan-cell-type scRNA-seq aging clock for theDrosophila melanogasterhead. TimeFlies uses deep learning to classify the donor age of cells based on genome-wide gene expression profiles. Using explainability methods, we identified key marker genes contributing to the classification, with lncRNAs showing up as highly enriched among predicted biomarkers. The top biomarker gene across cell types is lncRNA:roX1, a regulator of X chromosome dosage compensation, a pathway previously identified as a top biomarker of aging in the mouse brain. We validated this finding experimentally, showing a decrease in survival probability in the absence of roX1in vivo. Furthermore, we trained sex-specific TimeFlies clocks and noted significant differences in model predictions and explanations between male and female clocks, suggesting that different pathways drive aging in males and females. Graphical Abstract 

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