About

Stephen Dellaporta, Ph.D., is a Professor of Molecular, Cellular and Developmental Biology at Yale University and serves as the Director of Undergraduate Studies. His academic background includes studies at the University of Rhode Island, Iowa State University, and Worcester Polytechnic Institute, followed by postdoctoral work in plant molecular genetics at Cold Spring Harbor Laboratory. He has held various positions at Cold Spring Harbor Laboratory, progressing from Staff Scientist to Full Professor since 1996. His research program focuses on the molecular genetics of maize, rice, and grasses, with significant contributions to understanding transposable elements, functional genomics, and plant development. Dr. Dellaporta has co-authored numerous publications in prestigious scientific journals such as Cell, Nature, Science, Proceedings of the National Academy of Sciences, and Genetics. He has also served on scientific advisory panels for the National Institute of Health, the National Science Foundation, and the United States Department of Agriculture. Currently, he is a member of the Board of Control of the Connecticut Agricultural Experiment Station.

Research topics

• Genetics
• Biology
• Evolutionary biology
• History
• Genealogy
• Botany
• Computational biology
• Geography

Selected publications

• Prioritized candidate causal haplotype blocks in plant genome-wide association studies

  PLoS Genetics · 2022 · 14 citations

  Senior authorCorresponding
  • Biology
  • Genetics
  • Computational biology

  Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects in many GWAS. In plant, the relatively small population size in GWAS and the high genetic diversity found in many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to prioritize the candidate causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, GMMAT, and BLINK in both simulated and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in high polygenicity simulation setting. Moreover, it resulted in smaller mapping intervals, especially in regions of high LD, achieved by prioritizing small candidate causal blocks in the larger haplotype blocks. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA's results, and the average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved on mapping resolution to facilitate crop improvement.

  PublisherDOI

• Causal Haplotype Block Identification in Plant Genome-Wide Association Studies

  bioRxiv (Cold Spring Harbor Laboratory) · 2021-10-29

  preprintOpen accessSenior authorCorresponding

  Abstract Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) used in many GWAS that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects. In plants, the relatively small population size in GWAS and the high genetic diversity found many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to infer the causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, and GMMAT in both simulation and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in simulations with high polygenicity. Moreover, it resulted in higher mapping resolution, especially in regions of high LD, by identifying small causal blocks in the larger haplotype block. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA’s results, and its average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved mapping resolution to facilitate crop improvement.

  PublisherOA PDFDOI

• Corrigendum to: “Social network analysis of the genealogy of strawberry: retracing the wild roots of heirloom and modern cultivars”

  G3 Genes Genomes Genetics · 2021-08-27 · 4 citations

  erratumOpen access
  PublisherOA PDFDOI

• Social network analysis of the genealogy of strawberry: retracing the wild roots of heirloom and modern cultivars

  G3 Genes Genomes Genetics · 2021 · 42 citations

  • Biology
  • Genealogy
  • Evolutionary biology

  The widely recounted story of the origin of cultivated strawberry (Fragaria × ananassa) oversimplifies the complex interspecific hybrid ancestry of the highly admixed populations from which heirloom and modern cultivars have emerged. To develop deeper insights into the three-century-long domestication history of strawberry, we reconstructed the genealogy as deeply as possible-pedigree records were assembled for 8,851 individuals, including 2,656 cultivars developed since 1775. The parents of individuals with unverified or missing pedigree records were accurately identified by applying an exclusion analysis to array-genotyped single-nucleotide polymorphisms. We identified 187 wild octoploid and 1,171 F. × ananassa founders in the genealogy, from the earliest hybrids to modern cultivars. The pedigree networks for cultivated strawberry are exceedingly complex labyrinths of ancestral interconnections formed by diverse hybrid ancestry, directional selection, migration, admixture, bottlenecks, overlapping generations, and recurrent hybridization with common ancestors that have unequally contributed allelic diversity to heirloom and modern cultivars. Fifteen to 333 ancestors were predicted to have transmitted 90% of the alleles found in country-, region-, and continent-specific populations. Using parent-offspring edges in the global pedigree network, we found that selection cycle lengths over the past 200 years of breeding have been extraordinarily long (16.0-16.9 years/generation), but decreased to a present-day range of 6.0-10.0 years/generation. Our analyses uncovered conspicuous differences in the ancestry and structure of North American and European populations, and shed light on forces that have shaped phenotypic diversity in F. × ananassa.

  PublisherOA PDFDOI

• Social Network Analysis of the Genealogy of Strawberry: Retracing the Wild Roots of Heirloom and Modern Cultivars

  bioRxiv (Cold Spring Harbor Laboratory) · 2020 · 3 citations

  • Biology
  • Genealogy
  • Evolutionary biology

  ABSTRACT The widely recounted story of the origin of cultivated strawberry ( Fragaria × ananassa ) oversimplifies the complex interspecific hybrid ancestry of the highly admixed populations from which heirloom and modern cultivars have emerged. To develop deeper insights into the three century long domestication history of strawberry, we reconstructed the genealogy as deeply as possible—pedigree records were assembled for 8,851 individuals, including 2,656 cultivars developed since 1775. The parents of individuals with unverified or missing pedigree records were accurately identified by applying exclusion analysis to array-genotyped single nucleotide polymorphisms. We identified 187 wild octoploid and 1,171 F. × ananassa founders in the genealogy, from the earliest hybrids to modern cultivars. The pedigree networks for cultivated strawberry are exceedingly complex labyrinths of ancestral interconnections formed by diverse hybrid ancestry, directional selection, migration, admixture, bottlenecks, overlapping generations, and recurrent hybridization with common ancestors that have unequally contributed allelic diversity to heirloom and modern cultivars. Fifteen to 333 ancestors were predicted to have transmitted 90% of the alleles found in country-, region-, and continent-specific populations. Using parent-offspring edges in the global pedigree network, we found that selection cycle lengths over the last 200 years of breeding have been extraordinarily long (16.0-16.9 years/generation) but decreased to a present-day range of 6.0-10.0 years/generation. Our analyses uncovered conspicuous differences in the ancestry and structure of North American and European populations and shed light on forces that have shaped phenotypic diversity in F. × ananassa .

  PublisherOA PDFDOI

• Additional file 4 of Benchmarking variant identification tools for plant diversity discovery

  Figshare · 2019-01-01

  datasetOpen accessSenior author

  Table S4. Functional annotation summary of variants identified by different variant calling programs (XLSX 9 kb)

  PublisherDOI

• Additional file 3: of Benchmarking variant identification tools for plant diversity discovery

  Figshare · 2019-01-01

  datasetOpen accessSenior author

  Table S3. Summary of synteny analysis (XLSX 11 kb)

  PublisherDOI

• Additional file 2: of Benchmarking variant identification tools for plant diversity discovery

  Figshare · 2019-01-01

  datasetOpen accessSenior author

  Table S2. Summary of alignment time (XLSX 39 kb)

  PublisherDOI

• Additional file 2: of Translocation of a parthenogenesis gene candidate to an alternate carrier chromosome in apomictic Brachiaria humidicola

  Figshare · 2019-01-01

  datasetOpen access

  Table S2. Detailed results of embryo sac analysis. (XLSX 14 kb)

  PublisherDOI

• Benchmarking Variant Identification Tools for Plant Diversity Discovery

  Research Square · 2019-05-18 · 1 citations

  preprintOpen accessSenior authorCorresponding

  <title>Abstract</title> Background The ability to accurately and comprehensively identify genomic variations is critical for plant studies utilizing high-throughput sequencing. Most bioinformatics tools for processing next-generation sequencing data were originally developed and tested in human studies, raising questions as to their efficacy for plant research. A detailed evaluation of the entire variant calling pipeline, including alignment, variant calling, variant filtering, and imputation was performed on different programs using both simulated and real plant genomic datasets. Results A comparison of SOAP2, Bowtie2, and BWA-MEM found that BWA-MEM was consistently able to align the most reads with high accuracy, whereas Bowtie2 had the highest overall accuracy. Comparative results of GATK HaplotypCaller versus SAMtools mpileup indicated that the choice of variant caller affected precision and recall differentially depending on the levels of diversity, sequence coverage and genome complexity. A cross-reference experiment of S. lycopersicum and S. pennellii reference genomes revealed the inadequacy of single reference genome for variant discovery that includes distantly-related plant individuals. Machine-learning-based variant filtering strategy outperformed the traditional hard-cutoff strategy resulting in higher number of true positive variants and fewer false positive variants. The 2-step imputation which utilized a set of high-confidence SNPs as the reference panel showed up to 60% higher accuracy than direct LD-based imputation method. Conclusions Programs in the variant discovery pipeline have different performance on plant genomic dataset. Choice of the programs is subjected to the goal of the study and available resources. This study serves as an important guiding information for plant biologists utilizing next-generation sequencing data for diversity characterization and crop improvement.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01GM038148

  NIH · $4.8M · 2008

• BREAD: Hybrid Technologies for Heterosis in Rice and Related Cereals

  NSF · $2.7M · 2010–2017

• NIH Grant R56AI079103

  NIH · $417k · 2011

Frequent coauthors

• Maria A. Moreno

  Universidad Católica Andrés Bello

  51 shared

• John P. Mottinger

  University of Rhode Island

  30 shared

• Christopher Heffelfinger

  Yale University

  27 shared

• Hélène Laparra

  Philip Morris International (Switzerland)

  26 shared

• Sandra P. Romero

  Yale University

  26 shared

• Iván F. Acosta

  Max Planck Institute of Molecular Plant Physiology

  26 shared

• Mats Hámberg

  Karolinska Institutet

  25 shared

• Eric A. Schmelz

  University of California, San Diego

  25 shared

Labs

• Jacob LabPI