GlymaID navigation

Ex: 01g000900 searches Chr 01 for ID 000900
Select one or more chromosomes. The first table will show the number of SNP sites for each glymaID on the chromosome.
Select one or more chromosomes to see the distribution of SNPs.

Manual navigation

Choose chromosome and position
For a selected glymaID, the second table shows each SNP location, and the number of varieties with SNPs at that position.

Use the table below to see what GlymaIDs have SNPs on a specific chromosome.
Use the table below to see SNPs at specific locations within a GlymaID.

Matching Glyma IDs

Matching Glyma IDs

Matching SNPs


Data is available for cultivars shown in black.
SNP Documentation

Data Collection

The data displayed in this applet is derived from next generation sequencing of 120 soybean lines. Seventy-nine new lines were sequenced as part of this project and represent plant introductions and milestone cultivars. The remaining sequences represent the 41 parents used for developing the soybean NAM population. NAM parent sequences were provided by Dr. Perry Cregan (USDA-ARS) and the Soybean NAM project. For the new sequences generated by this project, twenty seeds from each line were acquired from the USDA Soybean Germplasm Collection. Seeds were planted in the USDA greenhouse at Iowa State University. Once plants reached the trifoliolate stage, leaves from up to 10 plants were pooled and genomic DNA was extracted. DNA was sent to Hudson Alpha Institute for Biotechnology for next-generation sequencing. In addition, replicated field trials were conducted on a subset of lines (30 of the 79 lines, plus ancestral varieties that were not sequenced) to measure protein, oil, yield, and other characteristics under standard growth conditions, to dissociate the effect of on-farm improvements from genetic gain [1],[2].

Processing Data and SNP Identification

  1. Raw reads were aligned to the reference soybean genome (version 2) using GSNAP (version 2013-8-31) [3].
  2. Reads that mapped uniquely were converted from SAM format to BAM format using samtools [4].
  3. Read groups were added for each soybean line and duplicate reads were removed using AddOrReplaceReadGroups and MarkDuplicates functions in picard tools.
  4. The resulting alignment BAM files were realigned using IndelRealigner function in GATK [5]. The ReduceReads function was used to compress the alignment files by removing non-informative and redundant reads (default parameters except for downsample_coverage=1).
  5. SNPs and InDels were called on all reduced BAM files together using the HaplotypeCaller function in GATK (version 2.7-2-g6bda569).

SNP Sampling


SNPS displayed in this applet are reliable SNPs from 120 lines. 86944 unique SNPs were identified for further analysis.

Need to delineate "reliable" criterion.

The lines included in this analysis are:
180501 CL0J173-6-8 Hood LG92-1255 PI574
4J105-3-4 Clark HS6-3976 LG94-1128 Pickett
506-13640-2 Clark (NAM) Hutcheson LG94-1906 Prohio
5601T Clemson IA3023 LG97-7012 Raleigh
5M20-2-5-2 CNS Illini LG98-1605 Ralsoy
88788 Cook Jackson Lincoln Ransom
A3127 Corsoy Kanro Magellan Richland
Adams Cumberland Kent Mandarin Roanoke
A.K. Davis Lawrence Maverick S06-13640
Amcor Dillon LD00-3309 Merit S-100
Amsoy Dorman LD01-5907 Mukden Shelby
Anderson Douglas LD02-4485 NCRoy Skylla
Beeson Dunfield LD02-9050 NE3001 TN05-3027
Blackhawk Essex Lee Oakland Tokyo
Bonus Ford LG00-3372 Ogden Tracy
Bragg Forrest LG03-2979 Pella U03-100612
Braxton Gasoy17 LG03-3191 Perry Volstate
Brim Haberlandt LG04-4717 PI398 Wayne
Calland Hagood LG04-6000 PI404 Williams
Capital Harcor LG05-4292 PI427 Williams82
Centennial Harosoy LG05-4317 PI437 Woodworth
Century Hawkeye LG05-4464 PI507 York
Chippewa Hill LG05-4832 PI518 Young
CL0J095-4-6 Holladay LG90-2550 PI561 Zane

Kinship Analysis

Sampling Methodology

Kinship matrices were generated with TASSEL [6] using a subset of the SNP data, where one random SNP was taken from every 10,000 base interval in the genome or the next closest SNP (Supplementary Script 1).

Clustering and Plot of Kinship Distance

These matrices were then clustered using Ward's method, using the distance (2- similarity). Clusters were used to lay out the rows and columns of the heatmap.

library(ggplot2) # need to use fork of ggplot2: install_github("tdhock/ggplot2") for animint compatibility

# Symmetric kinship matrix except for the first column, which contains the variety names
tmp <- read.csv("kinshipMatrix.txt", sep="\t", skip=1, head=F, stringsAsFactors=F)
names(tmp) <- c("Variety", tmp[,1])

# Fix names to correspond with actual usage
rownames <- tmp$Variety
rownames <- gsub("506-13640-2", "S06-13640-2", rownames)
rownames <- gsub("901-G0\\d{1}_RS_", "", rownames)
rownames <- gsub("_NuGEN", "", rownames)
rownames <- gsub("Gasoy17", "Gasoy 17", rownames)
rownames <- gsub("RS_", "", rownames)
rownames <- gsub("Williams82", "Williams 82", rownames)
rownames <- gsub("AK_004", "A.K. (Harrow)", rownames)
rownames <- gsub("NCR", "R", rownames)

# Enforce row and column names
tmp$Variety <- rownames
names(tmp) <- c("Variety", rownames)

# Create numerical matrix for use in clustering algorithms
meanMatrix <- data.matrix(tmp[,-1])
# Ensure dimnames are accurate
dimnames(meanMatrix)[[1]] <- rownames
dimnames(meanMatrix)[[2]] <- rownames

# Convert kinship (similarity) to distance matrix by subtracting relatedness from 2 (maximum relatedness)
dd.col <- rev(as.dendrogram(hclust(as.dist(2-meanMatrix), method="ward.D")))
dd.row <- as.dendrogram(hclust(as.dist(2-meanMatrix), method="ward.D"))
col.ord <- labels(dd.col)
row.ord <- labels(dd.row)
# Get x and y coordinates
ddata_x <- dendro_data(dd.row)
ddata_y <- dendro_data(dd.col)

The clusters were then formatted for plotting and plotted using ggplot2.

# Melt kinship matrix into long form
matrixLong <- melt(tmp, id.vars = 1)
names(matrixLong) <- c("variety1", "variety2", "value")
matrixLong$value <- as.numeric(matrixLong$value)
write.csv(matrixLong, "LongKinshipMatrix.csv")

# Make long matrix factor names match clustered ordering (so tree matches x and y coordinates)
matrixLong$variety1 <- factor(matrixLong$variety1, levels=col.ord)
matrixLong$variety2 <- factor(matrixLong$variety2, levels=row.ord)

# Multiplier to ensure the tree has reasonable range compared to the heatmap
dendro.multiplier <- 1

# Static Plot
qplot(x=as.numeric(variety1), y=as.numeric(variety2), geom="tile", fill=value, data=matrixLong) + 
  theme_bw() + 
  theme(axis.text.x=element_text(angle=90, hjust=1, vjust=.5)) +  
  xlab("Variety 1") + 
  ylab("Variety 2") + 
  scale_x_continuous(expand=c(0, 1), breaks=1:79, labels=col.ord) + 
  scale_y_continuous(expand=c(0, 1), breaks=1:79, labels=row.ord) + 
  scale_fill_gradient("Relatedness",low="#ffffff", high="#374f6b") + 
  ggtitle("Kinship Matrix-based Relatedness") + 
  geom_segment(data=segment(ddata_y), aes(x=x, y=y*dendro.multiplier+80, xend=xend, yend=yend*dendro.multiplier+80), inherit.aes=F) + 
  geom_segment(data=segment(ddata_x), aes(x=y*dendro.multiplier+80, y=x, xend=yend*dendro.multiplier+80, yend=xend), inherit.aes=F)

SNP Imputation and QTL Analysis

  1. SNPs were imputed using BEAGLE [7]
  2. TASSEL [6] was used to identify QTLs using a general linear model to control for population structure.

Need more detail here from Andrew. Also, need QTL results?

Plot Documentation

Using the combined, phased and imputed VCF file generated as a result of the SNP sampling, the following steps are performed.

Convert the VCF file into a data frame

We used the vcf2tsv command from vcflib to convert the vcf file into an easily plotable format.
vcf2tsv combined.REDUCED.1.realign.123013.uniq.sorted.SNPsOnly.MeanPlus0p150.nospace.vcf > MeanPlus0p150SD.tsv
Other options for performing the same function include the VariantAnnotation bioconductor package [8] and the ggbio bioconduuctor package [9].

# Read in the data from the TSV
vcfTable <- read.table("MeanPlus0p150SDimputed.tsv", sep="\t", stringsAsFactors=FALSE, header=TRUE)

# Correct column names
col.names <- c("Chromosome", "Position", "id", "Reference", "Alternate", 
               "qual", "filter", "Allele.Freq", "AlleleRSquared", "DosageRSquared",
               "Variety", "Alt_Allele_Freq", "Genotype_Probability", "Gene_State")

names(vcfTable) <- col.names

Rearranging Data for Plotting

Plots in this applet were generated using ggplot2 [10], and are rendered interactively using Shiny [11].


vcfTable$Alt.Allele.Count <- round(vcfTable$Alt.Allele.Freq)

varieties <- as.character(unique(vcfTable$Variety))
seqnames <- unique(vcfTable$Chromosome[grepl("Chr", vcfTable$Chromosome)])

# remove scaffolds for now to make display easier
vcfTable <- filter(vcfTable, grepl("Chr", vcfTable$Chromosome))

# Change names to correspond to actual usage (instead of lab names) according to Jim's suggestions.
vcfTable$Variety <- str_replace(vcfTable$Variety, fixed("901-G04_RS_5601T"), "5601T")
vcfTable$Variety <- str_replace(vcfTable$Variety, fixed("901-G06_RS_Bonus"), "Bonus")
vcfTable$Variety <- str_replace(vcfTable$Variety, fixed("RS_"), "")
vcfTable$Variety <- str_replace(vcfTable$Variety, fixed("AK_004"), "A.K.")
vcfTable$Variety <- str_replace(vcfTable$Variety, fixed("Clark_NuGEN"), "Clark")
vcfTable$Variety <- str_replace(vcfTable$Variety, fixed("NCRaleigh"), "Raleigh")

SNP Browser Plot

The goal here is to calculate the proportion of SNPs of each base pair (including those varieties that did not differ from the reference genome at the base pair in question).
n <- length(unique(varieties))
snp <- snpList %>% ungroup %>% group_by(Chromosome, Position, Reference) %>% 
            total=sum(Alt_Allele_Count)) %>%
  mutate(A = A + (Reference=="A")*(2*n-total), 
         G = G + (Reference=="G")*(2*n-total),
         C = C + (Reference=="C")*(2*n-total),
         T = T + (Reference=="T")*(2*n-total)) 
snp <- snp[,1:7]

snp.counts <- snp %>% gather(Nucleotide, Count, 4:7)
snp.counts <- filter(snp.counts, Count>0)
save(snp.counts, file="SNPCounts.RData")

SNP Density Plots

This code calculates the density of SNPs along each chromosome. For each group of data, the SNP density is calculated at 8192 points along the chromosome, with a bandwidth 10% of the default kernel bandwidth. SNPs are weighted according to the count of alternate alleles.
snp.density <- group_by(snpList, Chromosome, Variety) %>%
  do($Position, n=2048*4, adjust=0.1, from=1, to=max(.$Position), weights=(.$Alt_Allele_Count)/sum(.$Alt_Allele_Count))[1:2]))
save(snp.density, file="SNPDensity.RData")


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