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Phylogenomic perspectives on reproductive Phylogenomic perspectives - - PowerPoint PPT Presentation

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Phylogenomic perspectives on reproductive Phylogenomic perspectives on reproductive isolation and introgression isolation and introgression Botany Conference 2019, Tucson Deren Eaton, Columbia University Deren Eaton, Columbia University 1

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Phylogenomic perspectives on reproductive Phylogenomic perspectives on reproductive isolation and introgression isolation and introgression

Botany Conference 2019, Tucson

Deren Eaton, Columbia University Deren Eaton, Columbia University

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The goal of phylogenomics The goal of phylogenomics

Characterize evoluonary relaonships from a subset of sampled genomes.

few genes across many taxa many genes across few taxa

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WGS vs. RAD-seq genomic sampling WGS vs. RAD-seq genomic sampling

Characterize whole genomes from a subset of sequenced markers.

Full genome Shotgun reads Assembly Full genome RADseq reads Assembly

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Coalescent variaon Coalescent variaon

Different genomic regions have different genealocial histories.

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Can sparse SNP sampling reconstruct Can sparse SNP sampling reconstruct genome-wide paerns? genome-wide paerns?

Filtering and formang to deal with missing data...

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Viburnum Viburnum Phylogeny Phylogeny

Species-level phylogenetic sampling Published: 65 species; Eaton et al. (2015) Current: 127 species; In Prep. Assembled in ipyrad (Eaton 2014; Eaton & Overcast) 290K RAD loci (75% missing) 3.1M SNPs across 127 species Species tree inferred with tetrad (Eaton et al. 2015) Uses all SNP information for each quartet (average ~30K SNPs per quartet)

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Viburn Viburn'ers 'ers

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Viburnum Viburnum global RAD sampling global RAD sampling

From global to populaon-level variaon.

  • V. microcarpum
  • V. caudatum
  • V. stenocalyx
  • V. loeseneri
  • V. acutifolium
  • V. sulcatum
  • V. lautum
  • V. jucundum
  • V. disjunctum
  • V. stellato-tom.
  • V. hartwegii
  • V. costaricanum
  • V. tinoides
  • V. jamesonii
  • V. triphyllum
  • V. reticulatum

*

  • V. sulcatum
  • V. sulcatum
  • V. sulcatum

hybrid

  • V. acutifolium
  • V. acutifolium
  • V. acutifolium

hybrid hybrid

global sub-clade populations

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Viburnum Viburnum Orienonus rapid radiaon Orienonus rapid radiaon

~35 species from Mexico to Bolivia over ~10Ma

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Viburnum Viburnum Orienonus rapid radiaon Orienonus rapid radiaon

~35 species from Mexico to Bolivia over ~10Ma

A B C D E F G A B C D E F G E E F F G G A A B B C C D D

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Viburnum Viburnum Orienonus rapid radiaon Orienonus rapid radiaon

~35 species from Mexico to Bolivia over ~10Ma

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stellato-tomentosum lautum jucundum blandum disjuctum acutifolium sulcatum fuscum sulcatum-tuton microphyllum hartwegii-tuton ciliatum microcarpum caudatum tiliafolium

  • btusatum

hartwegii-chi costaricanum pastasanum jamesonii hallii pichinchense stenocalyx loesneri seemenii ayavacense triphyllum divaricatum reticulatum triphyllum hallii tinoides hallii tinoides anabaptista undulatum

69 97 79 93 97 97 59 86 86 11

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Outline: RAD-seq phylogenomics in Outline: RAD-seq phylogenomics in ipyrad ipyrad

  • 1. ipyrad-analysis toolkit.
  • 2. Gene tree extracon: concatenaon.
  • 3. Gene tree distribuons: sliding window consensus.
  • 4. Scking with SNPs: genome-wide inference.

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ipyrad-analysis toolkit (and toytree) and jupyter ipyrad-analysis toolkit (and toytree) and jupyter

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ipyrad-analysis toolkit ipyrad-analysis toolkit

Filter or impute missing data; easily distribute massively parallel jobs.

import ipyrad.analysis as ipa # initiate an analysis tool with arguments tool = ipa.pca(data=data, ...) # run job (distribute in parallel) tool.run() # examine results ...

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PCA: very sensive to missing data PCA: very sensive to missing data

10 20 30

PC0 (14.8%)

  • 4
  • 2

2 4

PC1 (13.0%)

10 20 30

PC0 (14.8%)

  • 5

5

PC2 (5.4%)

  • 4
  • 2

2 4

PC1 (13.0%)

  • 5

5

PC2 (5.4%)

No imputation (3% missing; 1250 SNPs)

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PCA: missing data imputed PCA: missing data imputed

  • 4
  • 2

2 4

PC0 (27.4%)

  • 2

2

PC1 (8.6%)

  • 4
  • 2

2 4

PC0 (27.4%)

  • 2

2

PC2 (5.2%)

  • 2

2

PC1 (8.6%)

  • 2

2

PC2 (5.2%)

Pop 'Sampled' imputation (3.5% missing; 1207 SNPs)

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PCA: missing data imputed PCA: missing data imputed

  • 10

10

PC0 (8.3%)

  • 10

10

PC1 (4.8%)

  • 10

10

PC0 (8.3%)

  • 10

10

PC2 (3.5%)

  • 10

10

PC1 (4.8%)

  • 10

10

PC2 (3.5%)

Pop 'Sampled' imputation (22% missing; 10K SNPs)

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PCA + T-SNE: missing data imputed PCA + T-SNE: missing data imputed

TSNE manifold projecon method (sckit-learn)

6 8 8 10

TSNE component 1

10 10 12 12

TSNE component 2

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PCA + T-SNE: missing data imputed PCA + T-SNE: missing data imputed

TSNE manifold projecon method (sckit-learn)

6 8 8 10

TSNE component 1

10 10 12 12

TSNE component 2

Bolivia Colombia Jamaica Chiapas Oaxaca Veracruz

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Outline: RAD-seq phylogenomics in Outline: RAD-seq phylogenomics in ipyrad ipyrad

  • 1. ipyrad-analysis toolkit.
  • 2. Gene tree extracon: concatenaon.
  • 3. Gene tree distribuons: sliding window consensus.
  • 4. Scking with SNPs: genome-wide inference.

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Missing data in phylogenecs Missing data in phylogenecs

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5

complete species-level sampling

... ... ... ... ...

  • 1. concatenation
  • 2. two-step inference
  • 3. quartets joining (SNPs+SVD)

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Missing data in phylogenecs Missing data in phylogenecs

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5

complete species-level sampling

... ... ... ... ...

  • 1. concatenation
  • 2. two-step inference
  • 3. quartets joining (SNPs+SVD)

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Missing data in phylogenecs Missing data in phylogenecs

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5

complete species-level sampling

... ... ... ... ...

  • 1. concatenation
  • 2. two-step inference
  • 3. quartets joining (SNPs+SVD)

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Window_extracter: extract, filter, format. Window_extracter: extract, filter, format.

Reference mapped RAD loci can be "spatially binned" to form larger loci.

import ipyrad.analysis as ipa # initiate an analysis tool with arguments tool = ipa.window_extacter( data=data, scaffold_idx=0, start=0, end=1000000, ) # writes a phylip file tool.run()

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Window_extracter: extract, filter, format. Window_extracter: extract, filter, format.

Reference mapped RAD loci can be "spatially binned" to form larger loci.

100-300 15500-15700 30500-30700 42500-42700 51000-51200 62000-62200 74500-74700 89000-89300 95000-95300 Position

... ... ... ... ...

concatenation (mincov=4)

... ... ... ... ... ... ... ... ... ... ...

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Window_extracter: extract, filter, format. Window_extracter: extract, filter, format.

Reference mapped RAD loci can be "spatially binned" to form larger loci.

100-300 15500-15700 30500-30700 42500-42700 51000-51200 62000-62200 74500-74700 89000-89300 95000-95300 Position

... ... ... ... ...

concatenation (mincov=8)

... ... ... ... ... ... ... ... ... ... ...

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Herbicide resistance among Herbicide resistance among Amaranthus Amaranthus species. species.

retroflexus retroflexus retroflexus wrightii wrightii powellii powellii powellii acutilobus acutilobus cruentus cruentus cruentus cruentus cruentus hybridus hybridus hybridus hypochondriacus hypochondriacus hypochondriacus hypochondriacus hypochondriacus hypochondriacus reference hybridus hybridus caudatus quitensis quitensis caudatus caudatus caudatus caudatus quitensis palmeri palmeri palmeri palmeri palmeri watsonii palmeri spinosus spinosus spinosus spinosus spinosus dubius dubius dubius tricolor tricolor tricolor tricolor asplundii graecizans graecizans graecizans blitum blitum blitum viridis viridis deflexus deflexus deflexus muricatus standleyanus albus albus albus californicus blitoides blitoides crassipes crassipes tuberculatus tuberculatus tuberculatus arenicola tuberculatus tuberculatus floridanus greggii greggii arenicola acanthochiton cannabinus cannabinus australis australis fimbriatus fimbriatus quinoa reference hypochondriacus hybridus hypochondriacus hypochondriacus hypochondriacus hypochondriacus hypochondriacus cruentus cruentus cruentus cruentus cruentus hybridus hybridus hybridus hybridus quitensis quitensis caudatus quitensis caudatus caudatus caudatus caudatus spinosus spinosus spinosus spinosus spinosus dubius dubius dubius acutilobus acutilobus retroflexus retroflexus retroflexus wrightii wrightii powellii powellii powellii fimbriatus fimbriatus greggii greggii arenicola acanthochiton tuberculatus tuberculatus floridanus tuberculatus tuberculatus arenicola tuberculatus palmeri palmeri palmeri palmeri palmeri watsonii palmeri cannabinus cannabinus australis australis albus albus albus californicus blitoides blitoides crassipes crassipes tricolor tricolor tricolor tricolor asplundii graecizans graecizans graecizans blitum blitum blitum standleyanus deflexus deflexus muricatus viridis deflexus viridis quinoa

1Mb window at known herbicide resistance gene Chromosome 1 concatenation tree

Introgression among the two most notorious weeds:

  • A. palmeri (pigweed)
  • A. tuberculatus (waterhemp)

Sandra Hoffberg Eaton Lab Postdoc

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Missing data in phylogenecs Missing data in phylogenecs

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5

complete species-level sampling

... ... ... ... ...

  • 1. concatenation
  • 2. two-step inference
  • 3. quartets joining (SNPs+SVD)

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Missing data in phylogenecs Missing data in phylogenecs

Goal: A distribuon of gene trees represenng every species.

few genes across many taxa many genes across few taxa

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Missing data: Consensus sampling Missing data: Consensus sampling

Represent species by the consensus genotype across sampled individuals

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5

Missing data in individual- level sampling complete species-level sampling

consensus sampling

... ... ... ... ... ... ... ... ... ...

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treeslider: extract windows across chromosomes. treeslider: extract windows across chromosomes.

Runs raxml on windows and parses results into a "tree_table"

# define population groups imap = { "sp1": ["a0", "a1", "a2", "a3"], "sp2": ["b0", "b1", "b2", "b3"], "sp3": ["c0", "c1", "c2", "c3"], "sp4": ["d0", "d1", "d2", "d3"], } # initiate an analysis tool with arguments tool = ipa.treeslider( data=data, window_size=1e6, slide_size=1e6, imap=imap, ) # distributes raxml jobs across all 1M windows in data set tool.run()

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treeslider: extract windows across chromosomes. treeslider: extract windows across chromosomes.

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Consensus sampling Consensus sampling

Recovers 5,500 informave gene trees (>50 SNPs) with no missing data across 25 taxa. ASTRAL species tree and cloud tree of RAxML gene trees

COL-tinoides COL-triphyllum COL-lasiophyllum COL-anabaptista COL-subsessile ECU-hallii ECU-pichinchense ECU-jamesonii ECU-reticulatum ECU-triphyllum BOL-seemenii MEX-hartwegii MEX-obtusatum JAM-villosum MEX-lautum MEX-jucundum MEX-sulcatum MEX-acutifolium MEX-fuscum MEX-microphyllum MEX-tiliaefolium MEX-caudatum MEX-microcarpum MEX-ciliatum

  • utg-dentatum

74 95 89 99 51 98 50 86 54 100 100 100 53 89 100 100 100 100 100 97 100 100 100 100

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Another data set: Another data set: Quercus Quercus secon Virentes secon Virentes

Consensus sampling yields 3X as many fully sampled loci (>30K)

  • Q. fusiformis

(F)

  • Q. brandegeei

(B)

  • Q. sagraeana

(S)

  • Q. oleoides

(O)

  • Q. virginiana

(V)

  • Q. geminata

(G)

  • Q. minima

(M)

K=3 K=5 K=7

  • Q. minima
  • Q. geminata
  • Q. virginiana
  • Q. oleoides
  • Q. sagraeana
  • Q. brandegeei
  • Q. fusiformis

94 54 96 95 35 56 98

  • utgroups

VFL VLA

OMX VLA FTX S

VFL VLA

VLA OMX FMX S

B Ftx V S O B Fmx V S O

Hipp et al. (2014); Eaton et al. (2015); Cavender-Bares et al. (2015)

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Sliding windows Sliding windows

How well do concatenated RAD windows represent gene tree variaon?

711 trees 1Mb windows mean 10K sites mean 96 SNPs 139 trees 5Mb windows mean 50K sites mean 480 SNPs 352 trees 2Mb windows mean 20K sites mean 193 SNPs

minima geminata virginiana

  • leoides

sagraeana fusiformis-S brandegeei fusiformis-N

  • utg

1 tree chromosome 2 ~1.1M sites 10,139 SNPs

RAxML gene trees.

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Sliding windows Sliding windows

How well do concatenated RAD windows represent gene tree variaon?

711 trees 1Mb windows mean 10K sites mean 96 SNPs 139 trees 5Mb windows mean 50K sites mean 480 SNPs 352 trees 2Mb windows mean 20K sites mean 193 SNPs

minima geminata virginiana

  • leoides

sagraeana fusiformis-S brandegeei fusiformis-N

  • utg

1 tree chromosome 2 ~1.1M sites 10,139 SNPs

geminata minima virginiana

  • leoides

sagraeana fusiformis-S brandegeei fusiformis-N

  • utg

100 100 100 100 100 100 100

minima geminata virginiana sagraeana

  • leoides

brandegeei fusiformis-S fusiformis-N

  • utg

100 100 100 100 100 100 100

minima geminata virginiana

  • leoides

sagraeana brandegeei fusiformis-S fusiformis-N

  • utg

100 100 100 100 100 100 100

Astral species trees inferred from gene trees.

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Clade weights ( Clade weights (sensu sensu Marn et al. 2017) Marn et al. 2017)

Chrom 1 weighted support for a (Cuba, Florida) vs (Cuba, Mexico)

400 800 1200

Chromosome 1 window (200Kb)

0.0 0.3 0.6 0.9

Clade weight

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Missing data in phylogenecs Missing data in phylogenecs

Locus 1 Locus 2 Locus 3 Locus 4 Locus 5

complete species-level sampling

... ... ... ... ...

  • 1. concatenation
  • 2. two-step inference
  • 3. quartets joining (SNPs+SVD)

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SNP-based species trees SNP-based species trees

SNAPP: joint inference of gene trees and species trees.

(Bryant et al. 2012)

SVDquartets: infers quartet trees from SNPs and joins these into a species tree.

(Chifman and Kubatko 2014)

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Advantages to SVDquartets-based methods Advantages to SVDquartets-based methods

Each quartet is inferred independently: missing data has almost no effect. Huge sptrees: 204 Viburnum taxa; 2.4M SNPs; 176K SNPs/quartet; >70M quartets. It is fast! tetrad: 40 cores, one bootstrap ~24 hours.

Col-anabaptista-Antioquia-PWS_2165 Col-anabaptista-Antioquia-PWS_2164 Col-tinoides-Antioquia-PWS_2170F Col-anabaptista-Antioquia-PWS_2173 Col-tinoides-Antioquia-PWS_2170D Col-anabaptista-Antioquia-PWS_2160 Col-tinoides-Antioquia-PWS_2170E Col-anabaptista-Antioquia-PWS_2162 Col-subsessile-Antioquia-PWS_2170A Col-tinoides-Antioquia-PWS_2170C Col-subsessile-Antioquia-PWS_2171 Col-tinoides-Antioquia-PWS_2170B Col-subsessile-Antioquia-PWS_2172 Col-subsessile-Antioquia-PWS_2169 Col-tinoides-Antioquia-PWS_2170G Col-tinoides-Caldas-PWS_2158 Col-tinoides-Caldas-PWS_2157 Col-tinoides-Cundinamarca-PWS_2151 Col-tinoides-Antioquia-PWS_2168 Col-anabaptista-Caldas-PWS_2156 Col-tinoides-Tolima-PWS_2155 Col-triphyllum-Boyaca-WC_287C Col-triphyllum-Boyaca-WC_288C Col-triphyllum-Boyaca-WC_287D Col-triphyllum-Boyaca-WC_288E Col-triphyllum-Boyaca-WC_286 Col-triphyllum-Boyaca-WC_285 Col-triphyllum-Boyaca-WC_288b Col-triphyllum-Boyaca-WC_287B Col-triphyllum-Cundinamarca-PWS_2179 Col-tinoides-Cundinamarca-PWS_2178 Col-lasiophyllum-Cundinamarca-PWS_2175 Col-lasiophyllum-Granada-PWS_2175 Col-lasiophyllum-Cundinamarca-PWS_2174 Col-tinoides-Cundinamarca-PWS_2152 Col-tinoides-Cundinamarca-PWS_2177 Col-tinoides-Cundinamarca-PWS_2176 Col-hallii-Cundinamarca-PWS_2180 Col-hallii-Santander-WC_293 Col-triphyllum-Boyaca-WC_291 Bol-seemenii-Franz_Tomayo-Maldonado_3040 Bol-ayavacense-Nor_Yungas-PWS_3883 Bol-seemenii-Franz_Tomayo-Fuentes_4724 Bol-ayavacense-Santa_Cruz-PWS3903 Bol-seemenii_f_minus-Santa_Cruz-PWS3897 Bol-seemenii-Franz_Tomayo-Fuentes_8750 Ecu-pastasanum-Tungurahua-PWS_1799 Ecu-hallii-Imbabura-PWS_1830 Ecu-hallii-Imbabura-PWS_1634 Ecu-pichinchense-Pichincha-PWS_1665 Ecu-pichinchense-Pichincha-PWS_1669 Ecu-pichinchense-Imbabura-PWS_1621 Ecu-jamesonii-Carchi-PWS_1636 Ecu-jamesonii-Carchi-PWS_1656 Ecu-reticulatum-Loja-PWS_1719 Ecu-reticulatum-Loja-PWS_1702 Ecu-reticulatum-Zamora-Chinchipe-PWS_1706 Ecu-triphyllum-Loja-PWS_1757 Ecu-triphyllum-Loja-PWS_1769 Ecu-divaricatum-El_Oro-PWS_1773 Ecu-reticulatum-Loja-PWS_1737 Ecu-reticulatum-Loja-PWS_1735 Ecu-triphyllum-Loja-PWS_1783 Ecu-triphyllum-Loja-PWS_1682 Per-triphyllum-nan-Edwards_2014_04 Mex-hartwegii-Chiapas-PWS_3195 Mex-hartwegii-Chiapas-PWS_3194 Mex-hartwegii-Chiapas-Ocosingo-PWS_3186 Mex-hartwegii-Chiapas-Tenehapa-PWS_3193 Mex-hartwegii-Chiapas-Pueblo_nuevo-PWS_3108 Mex-hartwegii-nan-MJD_81 Mex-hartwegii-Chiapas-Amatenango-PWS_3190 Mex-obtusatum-Chiapas-Tzontehuiz-Tzont Mex-obtusatum-Chiapas-Tzontehuiz-PWS_3100 Mex-jucundum-Chiapas-Huitepec-MKM_16 Mex-lautum-Chiapas-Moxviqil-MKM_22 Mex-lautum-Chiapas-Teopisca-MKM_1 Mex-jucundum-Chiapas-Huitepec-MKM_17 Mex-jucundum-Chiapas-Huitepec-MKM_15 Mex-jucundum-Chiapas-Huitepec-MKM_13 Mex-jucundum-Chiapas-Huitepec-MKM_18 Mex-jucundum-Chiapas-Huitepec-MKM_14 Mex-jucundum-Chiapas-Huitepec-HUL_H_31.1 Mex-jucundum-Chiapas-Pueblo_nuevo-PWS_3107 Mex-jucundum-Chiapas-Huitepec-MKM_12 Mex-jucundum-Chiapas-Ocosingo-PWS_3188 Mex-lautum-Chiapas-Moxviqil-MKM_23 Mex-jucundum-nan-MJD_69 Mex-lautum-Chiapas-Moxviqil-MKM_24 Mex-lautum-Chiapas-Teopisca-PWS_3105 Mex-lautum-Chiapas-Teopisca-MKM_9 Mex-lautum-Chiapas-Teopisca-PWS_3106 Mex-lautum-Chiapas-Teopisca-MKM_3 Mex-jucundum-Chiapas-Yaletenay-MKM_11 Mex-jucundum-Chiapas-Huitepec-MKM_20 reference Mex-lautum-Chiapas-Yashtinin-Isabels-PWS_3189 Mex-lautum-Chiapas-MJD_73 Mex-lautum-Chiapas-Moxviqil-MKM_26 Mex-lautum-Chiapas-Teopisca-MKM_2 Mex-lautum-Chiapas-Moxviqil-MKM_25 Mex-lautum-Chiapas-Moxviquil-PWS_3191 Gua-discolor-Totonicapan-Veliz_35_99 Mex-blandum-Chiapas-Tzontehuiz-PWS_3088 Mex-disjunctum-nan-MJD_66 Mex-disjunctum-nan-MJD_66_2 Jam-alpinum-Clarendon-PWS_3919 Jam-villosum-St_Andrew-PWS_3935 Jam-villosum-St_Andrew-PWS_3932 Jam-villosum-St_Andrew-PWS_3929 Jam-villosum-St_Andrew-PWS_3928 Jam-villosum-St_Andrew-PWS_3936 Jam-villosum-St_Thomas-PWS_3931 Jam-villosum-St_Andrew-PWS_3937 Jam-arboretum-Trelawny-PWS_3923 Jam-villosum-St_Andrew-PWS_3930 Jam-villosum-St_Ann-PWS_3917 Jam-arboretum-Trelawny-PWS_3920 Jam-alpinum-Clarendon-PWS_3924 Jam-villosum-St_Andrew-PWS_3927 Jam-villosum-St_Ann-PWS_3918 Jam-villosum-St_Ann-PWS_3916 Jam-alpinum-St_Thomas-PWS_3934 Gua-stellato-tomentosum-nan-MJD_83 Mex-sulcatum-Oaxaca-MEX_003 Mex-sulcatum-Oaxaca-MEX_004 Mex-sulcatum-nan-MJD_79 Mex-acutifolium-Oaxaca-Tutontepec-MJD_012_acutifoliu Mex-acutifolium-Oaxaca-Tutontepec-MJD_011_acutifolium Mex-acutifolium-Oaxaca-MJD_60 Mex-acutifolium-Oaxaca-Mirador-PWS_3050 Mex-acutifolium-Oaxaca-Ixtlan-PWS_3059 Mex-acutifolium-Oaxaca-MEX_005 Mex-acutifolium-Oaxaca-?-DRY3_MEX_006 Mex-fuscum-Oaxaca-Mirador-EJE_608 Mex-fuscum-Oaxaca-Mirador-EJE_609 Mex-fuscum-Oaxaca-Cerro_pelon-PWS_3054 Mex-fuscum-Oaxaca-Cerro_pelon-PWS_3058 Mex-sulcatum-Oaxaca-MJD_007_sulcatum_oaxaca_1118 Mex-shiny-Oaxaca-Totontepec-EJE_602 Mex-sulcatum-Oaxaca-Tutontepec-MJD_014_sulcatum_t Mex-sulcatum-Oaxaca-Tutontepec-MJD_013_sulcatum_t Mex-stenocalyx-Oaxaca-Mirador-EJE_607 Mex-stenocalyx-Oaxaca-Mirador-EJE_606 Mex-microphyllum-Morelia-DE_002 Mex-microphyllum-Morelia-DE_001 Mex-microphyllum-Morelia-DE_003 Mex-fuscum-Oaxaca-Tutontepec-MJD_009_hartwegii_fus Mex-shiny-Oaxaca-Totontepec-EJE_604 Mex-shiny-Oaxaca-Totontepec-EJE_603 Mex-fuscum-Oaxaca-Tutontepec-MJD_010_hartwegii_fus Mex-microcarpum-Puebla-Honey-Transect-Zamia-ZA2 Mex-microcarpum-Puebla-Honey-Transect-Viper-V1 Mex-microcarpum-Puebla-Honey-Transect-Zamia-ZA4 Mex-microcarpum-Puebla-Honey-MJD_76 Mex-microcarpum-Puebla-Honey-Transect-Tenango-PWS Mex-microcarpum-Puebla-Honey-Transect-Tenango-PWS Mex-microcarpum-Veracruz-Jalapa-PWS_3202 Mex-microcarpum-Veracruz-Mazatepec-PWS_3210 Mex-microcarpum-Veracruz-LaJoya-PWS_3204 Mex-ciliatum-Puebla-Honey-PWS_3220 Mex-ciliatum-Puebla-Honey-Transect-C1 Mex-ciliatum-Puebla-Honey-Transect-SH2 Mex-ciliatum-Puebla-Honey-PWS_3225 Mex-stenocalyx-Veracruz-Saucal-PWS_3208 Mex-stenocalyx-Veracruz-Saucal-PWS_3206 Mex-tiliafolium-Puebla-Honey-Transect-C4 Mex-tiliafolium-Puebla-Honey-Transect-C5 Mex-tiliafolium-Veracruz-Tenango-PWS_3230 Mex-caudatum-Puebla-Honey-Transect-sh-hill-SH3 Mex-caudatum-Puebla-Honey-PWS_3221 Mex-caudatum-Puebla-Honey-Transect-Cumbre-C2 Mex-caudatum-Puebla-Honey-Transect-SH4 Mex-caudatum-Puebla-Honey-Transect-abandoned-mine Mex-caudatum-Puebla-Honey-Transect-water-spigot-PW Mex-caudatum-Puebla-Honey-Transect-PWS_3211 Mex-caudatum-Puebla-Honey-Transect-M2 Mex-caudatum_x_microcarpum-Puebla-Honey-Transect- Mex-caudatum-Puebla-Honey-MJD_64 Mex-caudatum-Puebla-Honey-Transect-PWS_3223_M1 Mex-caudatum-Puebla-Honey-Transect-PWS_3215 Mex-caudatum-Puebla-Honey-Transect-Tenango-T1 Mex-jucundum-Oaxaca-jucundum_6_oaxaca_2017 Mex-sulcatum-Oaxaca-MJD_006_sulcatum_hybrid_11171 Mex-tiliafolium-Puebla-Honey-Transect-Cumbre-PWS_32 Mex-tiliafolium-Puebla-Honey-Transect-Honey-PWS_322 Mex-tiliafolium-Puebla-Honey-Transect-Tenango-T5 Mex-tiliafolium-Oaxaca-jucundum_2_oaxaca_2017 Mex-tiliafolium-Veracruz-Zapotel-PWS_3209 Mex-tiliafolium-Veracruz-Zapotel-PWS_3205 Mex-loesnerii-DF-MJD_75 Mex-stenocalyx-DF-Dynamos-PWS_3234 Mex-sulcatum-Oaxaca-MJD_001_sulcatum_111716 Mex-sulcatum-Oaxaca-MJD_002_sulcatum_111716 Mex-sulcatum-Oaxaca-MJD_005_sulcatum_111716 USA-dentatum-nan-ELS_082 USA-dentatum-nan-ELS_072 USA-dentatum-nan-ELS_052 Pan-venustum-nan-21064A USA-dentatum-nan-ELS_027 USA-dentatum-nan-ELS_015 USA-dentatum-nan-ELS_004 Mex-elatum-Chiapas-PWS_3196 Mex-elatum-nan-MJD_30

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SLIDE 42

Conclusions Conclusions

  • 1. With ipyrad-analysis it is easy to run dozens of analyses opmized for RAD

missing-ness with a few lines of code.

  • 2. Concatenang RAD loci in scaffold windows, and consensus or

imputaon sampling, dramacally improve the ulity of RAD.

  • 3. SNP based methods are in their infancy, but work well with RAD data.

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SLIDE 43

Announcement Announcement

RADcamp wetlab AND bioinformacs workshop in New York City Oct. 2019 Bring your DNA samples. Library preparaon and sequencing will be free. (sponsored by SSB, SSE, Columbia, CCNY). hps:/ /radcamp.github.io/NYC2019/

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SLIDE 44

Acknowledgements Acknowledgements

Viburn'ers: Donoghue-lab, Edwards-lab, M. Olson, I. Cacho. ipyrad development: Isaac Overcast Eaton lab members Funding: NSF DEB 1557059; Columbia University

Quesons? Quesons?

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