DOI: 10.5061/dryad.rbnzs7hjz
title: Genetic architecture of heritable leaf microbes

description: Background Host-associated microbiomes are shaped by both their
 environment and host genetics, and often impact host performance. The
 scale of host genetic variation important to microbes is largely unknown,
 yet fundamental to the community assembly of host-associated microbiomes,
 and with implications for the eco-evolutionary dynamics of microbes and
 hosts. Using Ipomoea hederacea, Ivy-leaved morning glory, we generated
 matrilines differing in quantitative genetic variation and leaf shape,
 which is controlled by a single Mendelian locus. We then investigated the
 relative roles of Mendelian and quantitative genetic variation in
 structuring the leaf microbiome, and how these two sources of genetic
 variation contributed to microbe heritability. Results We found that
 despite large effects of the environment, both Mendelian and quantitative
 genetic host variation were important in contributing to microbe
 heritability, and that the cumulative small effect genomic differences due
 to matriline explained as much or more microbial variation than a single
 large effect locus. Furthermore, our results are the first to suggest that
 leaf shape itself contributes to variation in the abundances of some
 microbes in the leaf microbiome. Conclusions The genetic architecture of
 plant-associated microbiomes depends on both quantitative genetic
 variation and Mendelian traits, with similar contributions to microbe
 heritability. Our results demonstrate the roles of different scales of
 host genetic variation in the assembly of a natural microbiome. The
 genetic basis and heritability of a host’s microbial phenotype is
 important for host evolution and ecology because microbes can affect host
 fitness, and because it can influence reciprocal selection between hosts
 and microbiomes. Additionally, when host-associated microbiomes have
 heritability, then this suggests they have the capacity to evolve as other
 host traits might, with potentially adaptive functions.
description: Overall goal: Using <em>Ipomoea hederacea</em>,
 Ivy-leaved morning glory, we generated matrilines differing in
 quantitative genetic variation and leaf shape, which is controlled by a
 single Mendelian locus. We then investigated the relative roles of
 Mendelian and quantitative genetic variation in structuring the leaf
 microbiome in a common garden, and how these two sources of genetic
 variation contributed to microbe heritability. Breeding
 design: We used seeds derived from a cross by Campitelli and Stinchcombe
 (2013), where individuals from the two alternate homozygous leaf shape
 phenotypes (i.e., fully lobed or whole) were collected from North
 Carolina, USA, selfed for seven generations to generate homozygous parents
 (P1), and then crossed with each other (Figure 1). A single F1 was allowed
 to self-fertilize, producing F2 progeny. We scored F2 plants for leaf
 shape, and allowed them to self-fertilize; we refer to all the selfed
 progeny of an individual F2 plant as a “matriline.”  We used F3 seeds, set
 by F2 plants we had identified as heterozygous for leaf shape, as our
 experimental seeds. Data collection: In 2021, we
 planted a total of 240 <em>I. hederacea</em> seeds from 82
 matrilines (2-3 seeds/matriline) in a common garden at Koffler Scientific
 Reserve (www.ksr.utoronto.ca) in Ontario, Canada. We scarified seeds then planted them in a greenhouse in peat pots containing soil from the field. We planted the first cohort on June 4th and a second cohort on July 2nd, because of poor germination in the first cohort. After the majority of plants in a given cohort germinated (approximately one week), we transplanted the pots into the field. The 218 surviving plants consisted of 83 individuals from the first cohort, and 135 individuals from the second cohort. On September 1st, we collected similarly-sized leaves within one foot from the ground by cutting them with sterilized scissors into a sterile plastic bag, after which they were stored in a -80°C freezer until DNA extraction. Sequencing and analysis: We performed extractions using the whole leaves with QIAGEN DNeasy® PowerSoil® Pro Kits; both epiphytic and endophytic microbes were extracted. We sent samples to Génome Québec (Montréal, Canada) for Illumina MiSeq PE 250bp 16S rRNA gene amplicon sequencing on the conserved hypervariable V4 region (primer pair 515F-806R). Raw microbiome sequence data is available at NCBI’s Sequence Read Archive under the BioProject accession number PRJNA1107536. We used Quantitative Insights Into Microbial Ecology 2 (QIIME2) v.2022.2 to trim the sequences for quality and we denoised the sequences with DADA2. Using QIIME2, we removed amplicon sequence variants (ASVs) that had fewer than 10 reads across all samples, and assigned taxonomy using the ‘sklearn’ feature classifier and Greengenes 16S rRNA gene V4 region reference, then filtered out reads assigned as cyanobacteria and mitochondria to remove plant DNA. Finally, we constructed a phylogeny using QIIME2’s MAFFT and FastTree 2 to obtain a rooted tree.
description: # Genetic architecture of heritable leaf microbes This repository includes
 code and data for analyzing *Ipomoea hederacea*'s leaf microbiome and
 identifying heritable microbes. Access this dataset on
 Dryad [https://doi.org/10.5061/dryad.rbnzs7hjz](https://doi.org/10.5061/dryad.rbnzs7hjz) ## 1. Background **Overall goal:** Using *Ipomoea hederacea*, Ivy-leaved morning glory, we generated matrilines differing in quantitative genetic variation and leaf shape, which is controlled by a single Mendelian locus. We then investigated the relative roles of Mendelian and quantitative genetic variation in structuring the leaf microbiome in a common garden, and how these two sources of genetic variation contributed to microbe heritability. **Breeding design:** We used seeds derived from a cross by Campitelli and Stinchcombe (2013), where individuals from the two alternate homozygous leaf shape phenotypes (i.e., fully lobed or whole) were collected from North Carolina, USA, selfed for seven generations to generate homozygous parents (P1), and then crossed with each other (Figure 1). A single F1 was allowed to self-fertilize, producing F2 progeny. We scored F2 plants for leaf shape, and allowed them to self-fertilize; we refer to all the selfed progeny of an individual F2 plant as a “matriline.”  We used F3 seeds, set by F2 plants we had identified as heterozygous for leaf shape, as our experimental seeds. **Data collection:** In 2021, we planted a total of 240 *I. hederacea* seeds from 82 matrilines (2-3 seeds/matriline) in a common garden at Koffler Scientific Reserve ([www.ksr.utoronto.ca](http://www.ksr.utoronto.ca)) in Ontario, Canada. We scarified seeds then planted them in a greenhouse in peat pots containing soil from the field. We planted the first cohort on June 4th and a second cohort on July 2nd, because of poor germination in the first cohort. After the majority of plants in a given cohort germinated (approximately one week), we transplanted the pots into the field. The 218 surviving plants consisted of 83 individuals from the first cohort, and 135 individuals from the second cohort. On September 1st, we collected similarly-sized leaves within one foot from the ground by cutting them with sterilized scissors into a sterile plastic bag, after which they were stored in a -80°C freezer until DNA extraction. **Sequencing and analysis:** We performed extractions using the whole leaves with QIAGEN DNeasy® PowerSoil® Pro Kits; both epiphytic and endophytic microbes were extracted. We sent samples to Génome Québec (Montréal, Canada) for Illumina MiSeq PE 250bp 16S rRNA gene amplicon sequencing on the conserved hypervariable V4 region (primer pair 515F-806R). Raw microbiome sequence data is available at NCBI’s Sequence Read Archive under the BioProject accession number PRJNA1107536. We used Quantitative Insights Into Microbial Ecology 2 (QIIME2) v.2022.2 to trim the sequences for quality and we denoised the sequences with DADA2. Using QIIME2, we removed amplicon sequence variants (ASVs) that had fewer than 10 reads across all samples, and assigned taxonomy using the ‘sklearn’ feature classifier and Greengenes 16S rRNA gene V4 region reference, then filtered out reads assigned as cyanobacteria and mitochondria to remove plant DNA. Finally, we constructed a phylogeny using QIIME2’s MAFFT and FastTree 2 to obtain a rooted tree. ## 2. Description of the data and file structure **Code** The file "**Leaf shape microbiome.rmd**" contains all R code necessary to reproduce the analysis, commented throughout. Open the R markdown file using R and  R Studio. **Metadata** The file "**ipomoea-taxa-metadata-allnumbers.csv**" contains the full metadata of the experiment, including all plants that survived and died during the experiment. More information on the column meanings and interpretations are included in section 3.  The file "**ipomoea_metadata.tsv**" contains the metadata of only plants that survived, and are included in the analysis. More information on the column meanings and interpretations are included in section 3.  **Data** The file "**table-try3-10readsmin-filtered-cyano-mito-glory.qza**" is an output from QIIME2 and contains the feature table/ASV table. The table has been filtered to 10 reads minimum per ASV. Cyanobacteria and mitochondria have been filtered out to remove plant and chloroplast DNA. Columns represent a single sampled leaf, with the number corresponding to the unique plant ID. Rows are the amplicon sequence variants and their unique name assigned by QIIME2. Amplicon sequence read abundance is contained in the table. The file "**table-try3-10readsmin-filtered-cyano-mito.csv**" is a csv file version of the QIIME2 feature table, transposed. The table has been filtered to 10 reads minimum per ASV. Cyanobacteria and mitochondria have been filtered out to remove plant and chloroplast DNA. Rows represent a single sampled leaf, with the number corresponding to the unique plant ID. Columns are the amplicon sequence variants and their unique name assigned by QIIME2. Amplicon sequence read abundance is contained in the table. The file "**rooted-tree-try3-glory.qza**" is an output from QIIME2 and contains the rooted tree of taxa in the sequencing data. Tip labels correspond to amplicon sequence variant unique identifiers. The tree includes branch lengths and node labels indicating confidence in the node. The file "**taxonomy-try3-glory.qza**" is an output from QIIME2 and contains the assigned Greengenes taxonomy linking ASV to classification. Here, rows represent amplicon sequence variants labelled with their unique identifier. Columns include the assigned taxonomy at different resolutions including Kingdom, Phylum, Class, Order, Family, Genus, and Species. NAs mean that amplicon sequence variant was unable to be assigned a confident taxonomy at that depth. The file "**taxonomy_try3.csv**" is a csv file version of the taxonomy classification. Here, each row is an amplicon sequence variant, with the unique identifier name indicated in the Feature.ID column. The Taxon column contains the assigned taxonomy as a single string. Here k_\_ indicates Kingdom, p_\_ indicates Phylum, c_\_ indicates Class, o_\_ indicates Order, f_\_ indicates Family, g_\_ indicates Genus, and s_\_ indicates species. NAs mean that amplicon sequence variant was unable to be assigned a confident taxonomy at that depth. The last column is named Confidence and is the confidence with which taxonomy was assigned by Greengenes; in other words, how well the DNA sequence matches the assigned taxon. The file "**phygloryCLRGenfeaturetable.csv**" was created in R (code to create it is in the code .rmd file. It contains the abundances of genera found in at least 30% of samples, and this is the manually-transposed version with samples as rows. Each row represents a single sampled leaf, with the number corresponding to the unique plant ID. Each column name is a genus. The table is filled with read abundance counts. If you are following the analysis, this file is next read back into R, then (following the code) centered log ratio transformed for downstream analysis. ## 3. Metadata explained in more detail **Rows** Rows and row names indicate the unique sample ID of the plant. One leaf was sampled from each unique plant. **Columns** Barcode: PCR wellplate information from the sequencing center, can be ignored for analysis. Multiplex_key: Details from the sequencing centre on how the samples were multiplexed. Can be ignored for analysis. Number_of_reads: Total number of sequencing reads in that sample. Block: The location of the plant in the common garden experiment, the experiment had three spatial blocks. Line: Matriline of the plant from crossing design. There are 82 lines, with unique number identifiers. Germination_cohort: There were two cohorts of seeds planted. The first cohort was planted June 4th and the second cohort was planted July 2nd. This column indicates which cohort that plant was planted in. Shape: Leaf shape genotype of the plant, determined visually once plant was matured. L=Homozygous lobed genotype, M= "Middle" Heterozygous genotype, H=Homozygous "Heart" whole genotype. FloweringDOY: Approximate day of the year (2021) when the plant started flowering. For example, January 1st would equal 1. Days_to_flowering: Number of days between planting to flowering for each plant Flowered_by_collection: Whether the plant was flowering when leaves were collected on September 1st, 2021. Y=Yes, N=No. Highly correlated with germination cohort. Leaf_area_cm2: Area of collected leaf in centimeters squared, determined with ImageJ. Photos of the leaves included a 1x1 centimeter grid, used to calibrate ImageJ distances. More information can be found at [https://imagej.net/ij/docs/menus/analyze.html](https://imagej.net/ij/docs/menus/analyze.html) Perimeter_cm: Perimeter of collected leaf in centimeters, determined with ImageJ. Photos of the leaves included a 1x1 centimeter grid, used to calibrate ImageJ distances. More information can be found at [https://imagej.net/ij/docs/menus/analyze.html](https://imagej.net/ij/docs/menus/analyze.html) Circularity: Circularity of collected leaf, calculated in ImageJ by outlining the main leaf and using using ImageJ formula of 4π(Area/Perimeter^2). Considers smoothness of the object. Value is between 0 and 1. Photos of the leaves included a 1x1 centimeter grid, used to calibrate ImageJ distances. More information can be found at [https://imagej.net/ij/docs/menus/analyze.html](https://imagej.net/ij/docs/menus/analyze.html) AR: Aspect ratio of collected leaf, (length of major axis/length of minor axis) calculated in ImageJ. Photos of the leaves included a 1x1 centimeter grid, used to calibrate ImageJ distances. More information can be found at [https://imagej.net/ij/docs/menus/analyze.html](https://imagej.net/ij/docs/menus/analyze.html) Round: Roundness of collected leaf, calculated in ImageJ simultaneously with circularity. Similar to aspect ratio, it does not consider the smoothness of perimeter as much as circularity. Calculated by 4*area/(π*major_axis^2), or the inverse of the aspect ratio. Photos of the leaves included a 1x1 centimeter grid, used to calibrate ImageJ distances. More information can be found at [https://imagej.net/ij/docs/menus/analyze.html](https://imagej.net/ij/docs/menus/analyze.html) Solidity: Solidity of collected leaf, calculated in ImageJ simultaneously with circularity. Calculated by the area/convex hull area. Photos of the leaves included a 1x1 centimeter grid, used to calibrate ImageJ distances. More information can be found at [https://imagej.net/ij/docs/menus/analyze.html](https://imagej.net/ij/docs/menus/analyze.html) ## 4. How to use this data, code/software Download all files, and use R and R Studio to run the Rmarkdown (.Rmd) file. Some files with the file type .qza can be used or examined in QIIME2, often accessed by command line or with [https://view.qiime2.org/](https://view.qiime2.org/). R session info: ``` R version 4.2.0 (2022-04-22 ucrt) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 10 x64 (build 22631) Matrix products: default locale: [1] LC_COLLATE=English_Canada.utf8 LC_CTYPE=English_Canada.utf8 [3] LC_MONETARY=English_Canada.utf8 LC_NUMERIC=C [5] LC_TIME=English_Canada.utf8 attached base packages: [1] parallel stats graphics grDevices utils datasets methods base other attached packages: [1] mdthemes_0.1.0 picante_1.8.2 nlme_3.1-157 [4] viridis_0.6.4 viridisLite_0.4.2 patchwork_1.1.3 [7] speedyseq_0.5.3.9018 microshades_1.10 CoDaSeq_0.99.6 [10] ALDEx2_1.28.1 phyloseqCompanion_1.1 rlang_1.1.1 [13] magrittr_2.0.3 GUniFrac_1.8 doParallel_1.0.17 [16] iterators_1.0.14 foreach_1.5.2 data.table_1.14.2 [19] zCompositions_1.4.1 truncnorm_1.0-9 NADA_1.6-1.1 [22] survival_3.3-1 MASS_7.3-56 compositions_2.0-6 [25] insight_0.19.6 cowplot_1.1.1 randomcoloR_1.1.0.1 [28] RColorBrewer_1.1-3 vegan_2.6-4 lattice_0.20-45 [31] permute_0.9-7 car_3.1-2 carData_3.0-5 [34] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 [37] purrr_1.0.1 readr_2.1.4 tidyr_1.3.0 [40] tibble_3.2.1 tidyverse_2.0.0 plyr_1.8.7 [43] ANCOMBC_1.6.0 qiime2R_0.99.6 microbiomeutilities_1.00.16 [46] dplyr_1.1.2 microbiome_1.18.0 phyloseq_1.40.0 [49] MicrobeR_0.3.2 philr_1.22.0 plotly_4.10.3 [52] ggplot2_3.5.1 adespatial_0.3-23 ranacapa_0.1.0 [55] microbiomeSeq_0.1 ape_5.7-1 pander_0.6.5 [58] lmerTest_3.1-3 lme4_1.1-35.1 Matrix_1.5-1 loaded via a namespace (and not attached): [1] Hmisc_5.1-1 class_7.3-20 ps_1.7.5 [4] glmnet_4.1-8 crayon_1.5.2 V8_4.4.0 [7] rbibutils_2.2.16 rhdf5filters_1.7.0 backports_1.4.1 [10] fBasics_4032.96 huge_1.3.5 XVector_0.35.0 [13] performance_0.10.8 nloptr_2.0.3 callr_3.7.3 [16] phylobase_0.8.10 BiocParallel_1.29.21 bit64_4.0.5 [19] glue_1.6.2 loo_2.6.0 sjPlot_2.8.15 [22] pheatmap_1.0.12 rstan_2.32.3 processx_3.8.2 [25] BiocGenerics_0.42.0 classInt_0.4-10 VGAM_1.1-9 [28] SummarizedExperiment_1.26.1 tidyselect_1.2.0 XML_3.99-0.15 [31] sf_1.0-14 ggpubr_0.6.0 sjmisc_2.8.9 [34] xtable_1.8-4 spData_2.3.0 evaluate_0.23 [37] Rdpack_2.6 cli_3.6.1 zlibbioc_1.41.0 [40] pulsar_0.3.11 rstudioapi_0.15.0 sp_2.1-1 [43] rpart_4.1.16 fastmatch_1.1-4 sjlabelled_1.2.0 [46] GLMMadaptive_0.9-1 treeio_1.20.0 shiny_1.7.5.1 [49] xfun_0.39 clue_0.3-65 inline_0.3.19 [52] pkgbuild_1.4.2 multtest_2.51.0 cluster_2.1.3 [55] stable_1.1.6 biomformat_1.24.0 ggrepel_0.9.4 [58] stabledist_0.7-1 Biostrings_2.63.3 png_0.1-8 [61] withr_2.5.2 bitops_1.0-7 RcppZiggurat_0.1.6 [64] e1071_1.7-13 coda_0.19-4 pillar_1.9.0 [67] RcppParallel_5.1.7 cachem_1.0.8 fs_1.6.3 [70] rmutil_1.1.10 vctrs_0.6.3 ellipsis_0.3.2 [73] generics_0.1.3 tools_4.2.0 foreign_0.8-82 [76] rncl_0.8.7 munsell_0.5.0 DelayedArray_0.21.2 [79] emmeans_1.8.9 proxy_0.4-27 fastmap_1.1.1 [82] compiler_4.2.0 abind_1.4-5 httpuv_1.6.12 [85] GenomeInfoDbData_1.2.8 gridExtra_2.3 ggnewscale_0.4.9 [88] QuickJSR_1.0.7 deldir_1.0-9 utf8_1.2.3 [91] later_1.3.1 wk_0.9.0 jsonlite_1.8.0 [94] scales_1.3.0 statip_0.2.3 tidytree_0.4.5 [97] estimability_1.4.1 lazyeval_0.2.2 promises_1.2.1 [100] latticeExtra_0.6-30 checkmate_2.3.0 rmarkdown_2.25 [103] statmod_1.5.0 Rtsne_0.16 Biobase_2.55.2 [106] igraph_1.5.0 numDeriv_2016.8-1.1 yaml_2.3.7 [109] htmltools_0.5.5 memoise_2.0.1 IRanges_2.29.1 [112] quadprog_1.5-8 digest_0.6.32 mime_0.12 [115] bayestestR_0.13.1 units_0.8-4 RSQLite_2.3.3 [118] Rfast_2.1.0 yulab.utils_0.1.0 microViz_0.12.1 [121] blob_1.2.4 S4Vectors_0.33.17 RNeXML_2.4.11 [124] modeest_2.4.0 splines_4.2.0 Formula_1.2-5 [127] DECIPHER_2.24.0 Rhdf5lib_1.17.3 RCurl_1.98-1.6 [130] broom_1.0.5 hms_1.1.3 modelr_0.1.11 [133] rhdf5_2.39.6 colorspace_2.0-3 base64enc_0.1-3 [136] GenomicRanges_1.47.6 shape_1.4.6 aplot_0.2.2 [139] nnet_7.3-17 Rcpp_1.0.11 mvtnorm_1.2-3 [142] fansi_1.0.4 tzdb_0.4.0 R6_2.5.1 [145] grid_4.2.0 lifecycle_1.0.4 StanHeaders_2.26.28 [148] adegraphics_1.0-21 curl_5.1.0 ggsignif_0.6.4.9000 [151] adegenet_2.1.10 minqa_1.2.6 phangorn_2.11.1 [154] robustbase_0.99-0 htmlwidgets_1.6.2 timechange_0.2.0 [157] gridGraphics_0.5-1 SpiecEasi_1.1.2 timeSeries_4031.107 [160] mgcv_1.8-40 htmlTable_2.4.2 gghalves_0.1.4 [163] tensorA_0.36.2 codetools_0.2-18 matrixStats_1.1.0 [166] rtk_0.2.6.1 prettyunits_1.2.0 GenomeInfoDb_1.32.1 [169] gtable_0.3.4 DBI_1.1.3 bayesm_3.1-6 [172] stats4_4.2.0 adephylo_1.1-16 ggfun_0.1.3 [175] httr_1.4.7 KernSmooth_2.23-20 stringi_1.7.6 [178] progress_1.2.2 reshape2_1.4.4 uuid_1.1-1 [181] spatial_7.3-15 spdep_1.2-8 timeDate_4022.108 [184] ggtree_3.4.4 DT_0.30 seqinr_4.2-30 [187] xml2_1.3.5 boot_1.3-28 ggeffects_1.3.2 [190] s2_1.1.4 interp_1.1-4 ade4_1.7-19 [193] ggplotify_0.1.2 DEoptimR_1.1-3 bit_4.0.5 [196] MatrixGenerics_1.8.0 sjstats_0.18.2 ggside_0.3.1 [199] jpeg_0.1-10 pkgconfig_2.0.3 rstatix_0.7.2 [202] knitr_1.45 ``` ## 5. Sharing/access information Code and data is also publically available on GitHub ([https://github.com/JuliaBoyle/ipomoea_microbiome](https://github.com/JuliaBoyle/ipomoea_microbiome) The raw microbiome sequence data is available at NCBI’s Sequence Read Archive under the BioProject accession number PRJNA1107536.