Student Award

Student Paper Award Competition ­
Section on Statistics in Genomics and Genetics
American Statistical Association

The Section on Statistics in Genomics and Genetics (SSGG) of the American Statistical Association is pleased to announce the 2024 Distinguished Student Paper Award Competition. Papers considered in this competition should contain methodological innovations and/or novel applications of statistical and computational methods to problems arising in genetics and genomics. Three to six awards will be given. 

Applicants for the SGG Student Paper Award must meet all of the following criteria at the time of submission:

  • Be a current undergraduate or graduate student at any level, or have received their degree in statistics, biostatistics, or related quantitative field in 2023.
  • Be a current member of SSGG.  The applicant can join SSGG at the time of submission.  Instructions on how to join are provided below.  Note that ASA membership does not automatically confer SSGG membership; ASA members must join individual sections in addition to generic membership.
  • Be first author of the paper and scheduled to present the same paper submitted for the award at the 2024 Joint Statistical Meeting (current scheduled to be held in Oregon, Portland) as either a talk, SPEED, or poster.
  • Have submitted the paper to no more than one other ASA section 2024 student or early-stage investigator competition. (Note that in the event a paper wins two awards, the author may only accept one of the two awards)
  • Have not previously won an SSGG student paper award.

Applications should include:

  1. A cover letter including name, current affiliation and status including actual or intended date of graduation, and contact information (address, telephone, e-mail) of the applicant;
    The paper submitted for the competition which should be up to 25 pages (double-spaced, 1-inch margins) including an abstract and references, but not including figures and tables. Figures and tables should be placed at the end of the manuscript. No supplemental materials and appendices beyond the 25-page limit will be accepted.  Papers do not need to be anonymized.
    A letter from the advisor who should certify student status (or completion of degree within the past year), and in the case of joint first-authorship, should indicate the fraction of the applicant's contribution to the paper.
  2. All materials must be received by the Section by 11:59 PM (Pacific Time) December 15, 2023. Winners will be notified by January 15, 2024. Applications must be submitted by email (as separate PDF files). For further information or to apply, please contact Ni Zhao, Chair of the SGG Distinguished Student Paper Award Committee nzhao10@jhu.edu with “SSGG Distinguished Student Paper Award” in the subject line.
  3. For section members who are faculty or mentors, we would like to encourage you to become a section member, and please bring this to the attention of your students and encourage them to apply. Section members and friends are welcome to contribute funds towards the endowment for future student awards. Please contact Nancy Zhang at nzh@wharton.upenn.edu, for directions.


To become a SGG section member, please first become an ASA member by signing up at http://www.amstat.org/membership/becomeamember.cfm. If you are already an ASA member, there are two ways you can become an SGG section member: (1) call the ASA Headquarters at (703) 684-1221 and request the SGG section be added to your membership or (2) renew your ASA membership online via the ASA member only website https://www.amstat.org/membersonly/index.cfm and add the “Section on Statistics in Genomics and Genetics” when you are asked to "verify your Publications, Chapters, and Sections, making any necessary additions or removals.” 

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Past Awardees

2024

  • Xiaochen Yang (Purdue University): "Multi-organ imaging-derived polygenic indexes for brain and body health"
  • Li Hui (Harvard University): "Efficient heritability enrichment analysis with graphREML"
  • Nathaniel Osher (University of Michigan): "Dual Random Effect and Main Effect Spatial Selection for Pathology Imaging and Genomics Data"
  • Yuzheng Dun (Johns Hopkins University): "Flexible, Robust, and Scalable Approach to Building Polygenic Risk Scores via Bayesian Bridge"

2023

  • Kwangmoon Park (University of Wisconsin-Madison): "Joint tensor modeling of single cell 3D genome and epigenetic data with Muscle"
  • Qinmengge Li (University of Michigan): "Bregman Divergence-Based Data Integration with Application to Polygenic Risk Score (PRS) Heterogeneity Adjustment"
  • Peng Yang (Rice University): "A novel Bayesian model for assessing intratumor heterogeneity of tumor infiltrating leukocytes with multi-region gene expression sequencing"
  • Soham Ghosh (University of Wisconsin-Madison): "A flexible generative model based knockoff filter for selecting microbial biomarkers with FDR control"
  • Wenjing Ma (Emory University): "Cellcano: supervised cell type identification for single cell ATAC-seq data"

2022

  • Nam Nguyen (Rice University): "Bayesian Estimation of a Joint Semiparametric Recurrent Event Model of Multiple Cancer Types with Applications to the Li-Fraumeni Syndrome"
  • Luxiao Chen (Emory University): "Incorporating cell type hierarchy improves cell type specific differential analyses in bulk omics data"
  • Jiacheng Miao (University of Wisconsin-Madison): "A quantile integral linear model to quantify genetic effects on phenotypic variability"
  • Kyle Coleman (University of Pennsylvania): "SpaDecon: cell-type deconvolution in spatial transcriptomics with transfer learning"
  • Xihao Li (Harvard University): "Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whol"e-genome sequencing studies"

2021

  • Zhenxing Guo (Emory University): "Detecting m6A methylation regions from Methylated RNA Immunoprecipitation Sequencing"
  • Amanda Brucker (North Carolina State University): "Association Test Using Copy Number Profile Curves (CONCUR) Enhances Power in Rare Copy Number Variant Analysis"
  • Jian Hu (University of Pennsylvania): "Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network"
  • Yujie Jiang (The University of Texas M.D. Anderson Cancer Center): "CliP: fast subclonal architecture reconstruction for cancer cells from genomic DNA sequencing data"

2020

  • Xiyu Peng (Iowa State University): “AmpliCI: A High-resolution Model-Based Approach for Denoising Illumina Amplicon Data”
  • Lan Luo (University of Wisconsin - Madison): "Multi-trait Analysis of Rare-variant Association Summary Statistics using MTAR"
  • Fan Chen (University of Wisconsin - Madison): "SURF: Integrative Analysis of a Compendium of RNA-seq and CLIP-seq Datasets Highlights Complex Governing of Alternative Transcriptional Regulation by RNA-binding Proteins"
  • Yuan Gao (The Ohio State University): "A Phylogenetic Approach to Inferring the Order in Which Mutations Arise during Cancer Progression"

2019

  • Fangda Song (The Chinese University of Hong Kong): "Flexible experimental designs for valid single-cell RNA-sequencing experiments allowing batch effects correction"
  • Zachary McCaw (Harvard University): "Cross-tissue eQTL calling via surrogate expression analysis"
  • Haoyu Zhang (Johns Hopkins University): "A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics"
  • Peng Liu (University of Pittsburgh): "MethylSeqDesign: A framework for Methyl-Seq genome-wide power calculation and study design issues"

2018

  • Alex Kin Yau Wong (University of North Carolina – Chapel Hill): “Robust score tests with missing data in genomics studies”
  • Rachel Ruixuan Zhou (University of Illinois – Urbana-Champaign): “Estimation and inference for the indirect effect in high-dimensional linear mediation analysis models”
  • Ye Zheng (University of Wisconsin – Madison): “Statistical methods for profiling 3-dimensional chromatin interactions from repetitive regions of genomes”
  • Guanghao Qi (Johns Hopkins University): “Heritability informed power optimization (HIPO) leads to enhanced detection of genetic associations across multiple traits”

2017

  • Chong Jin (University of North Carolina – Chapel Hill): "Inferring Intra-Tumor Heterogeneity by Jointly Modeling Copy Number Aberrations and Somatic Point Mutations"
  • Zilin Li (Harvard University): “Detection of Signal Regions in Whole Genome Association Studies”
  • Xiang Zhu (University of Chicago): "A large-scale genome-wide enrichment analysis identifies new trait-associated genes, pathways and tissues across 31 human phenotypes"

2016

  • Zhicheng (Jason) Ji (Johns Hopkins University): “TSCAN: Pseudo-time Reconstruction and Evaluation in Single-cell RNAseq Analysis”
  • Jean Morrison (University of Washington): “Simultaneous detection and estimation of trait associations with genomic phenotypes”
  • Chong Wu (University of Minnesota): “An Adaptive Association Test for Microbiome Data”