Usage Cases of GBS. Jeff Glaubitz Senior Research Associate, Buckler Lab, Cornell University Panzea Project Manager
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1 Usage Cases of GBS Jeff Glaubitz Senior Research Associate, Buckler Lab, Cornell University Panzea Project Manager Cornell CBSU Workshop Sept 15 16, 2011
2 Some potential applications of GBS Data Marker discovery convert to SNP or PCR assays of indels Linkage mapping of QTL in a biparental cross Fine mapping QTL Bulked segregant analysis Genome Wide Association Studies (GWAS) NAM GWAS Genomic selection Phylogeny Improving reference genome assembly
3 Some potential applications of GBS Data Marker discovery convert to SNP or PCR assays of indels Linkage mapping of QTL in a biparental cross Fine mapping QTL Bulked segregant analysis Genome Wide Association Studies (GWAS) NAM GWAS Genomic selection Phylogeny Improving reference genome assembly
4 Marker Discovery ApeKI site (GCWGC) ( ) 64 base sequence tag B73 Loss of cut site < 450 bp Mo17 Develop SNP assays from polymorphic tags at same location Develop PCR primers from adjacent tags & hope for large indels
5 Some potential applications of GBS Data Marker discovery convert to SNP or PCR assays of indels Linkage mapping of QTL in a biparental cross Fine mapping QTL Bulked segregant analysis Genome Wide Association Studies (GWAS) NAM GWAS Genomic selection Phylogeny Improving reference genome assembly
6 Some potential applications of GBS Data Marker discovery convert to SNP or PCR assays of indels Linkage mapping of QTL in a biparental cross Fine mapping QTL Bulked segregant analysis Genome Wide Association Studies (GWAS) NAM GWAS Genomic selection Phylogeny Improving reference genome assembly
7 Some potential applications of GBS Data Marker discovery convert to SNP or PCR assays of indels Linkage mapping of QTL in a biparental cross Fine mapping QTL Bulked segregant analysis Genome Wide Association Studies (GWAS) NAM GWAS Genomic selection Phylogeny Improving reference genome assembly
8 Some potential applications of GBS Data Marker discovery convert to SNP or PCR assays of indels Linkage mapping of QTL in a biparental cross Fine mapping QTL Bulked segregant analysis Genome Wide Association Studies (GWAS) NAM GWAS Genomic selection Phylogeny Improving reference genome assembly
9 Some potential applications of GBS Data Marker discovery convert to SNP or PCR assays of indels Linkage mapping of QTL in a biparental cross Fine mapping QTL Bulked segregant analysis Genome Wide Association Studies (GWAS) NAM GWAS Genomic selection Phylogeny Improving reference genome assembly
10 Some potential applications of GBS Data Marker discovery convert to SNP or PCR assays of indels Linkage mapping of QTL in a biparental cross Fine mapping QTL Bulked segregant analysis Genome Wide Association Studies (GWAS) NAM GWAS Genomic selection Phylogeny Improving reference genome assembly
11 Phylogeny of switchgrass association panel Upland Lowland Fei Lu, Postdoc, Buckler lab, unpublished
12 Details
13 Some potential applications of GBS Data Marker discovery convert to SNP or PCR assays of indels Linkage mapping of QTL in a biparental cross Fine mapping QTL Bulked segregant analysis Genome Wide Association Studies (GWAS) NAM GWAS Genomic selection Phylogeny Improving reference genome assembly
14 The NAM population was built for NAM-GWAS 25 DL Tzi8 B97 CML103 CML228 CML247 CML277 CML322 CML333 CML52 CML69 Hp301 Il14H Ki11 Ki3 Ky21 M162W M37W Mo18W MS71 NC350 NC358 Oh43 Oh7B P39 Tx303 B73 F 1 s SSD 1 NAM 2 200
15 We are using GBS to pinpoint the location of cross overs in the NAM RILs B73 is the reference genome: complete knowledge Remaining NAM parents whole genome sequenced via Illumina at 4x coverage (paired end random sheared) 50 million high quality SNPs Precise knowledge of crossover locations in NAM RILs allows us to more accurately project sequences of parents onto RILs: B73 MS71 Z019E001 Z019E001
16 Advantages of GWAS on NAM Population structure eliminated by design Can Compare Linkage & GWAS NAM GWAS Project HapMap SNPs To 5000 RILs Accurately Wide range of incremental models can be evaluated Control for 90% Genetic Variance
17 NAM-GWAS works beautifully for simple traits Bootstrap Posterior Probablity (BPP) p Cob color Physical Position on Chr1 (Mb) Direct hit within p1 locus Tian, Bradbury, et al 2011 Nature Genetics
18 NAM-GWAS works beautifully for simple traits Bootstrap Posterior Probablity (BPP) Y Physcial Postion on chr6 (Mb) Kernel color Within 200bp of the promoter of Y1
19 liguleless1 and liguleless2 explained the two biggest leaf angle QTL Upper leaf angle Associations with positive effect Associations with negative effect Linkage QTL peak BPP log(p) 0 lg1 lg3 lg2 lg4 0 cm/mb Chromosomes Tian, Bradbury, et al 2011 Nature Genetics
20 NAM GWAS software is implemented on CBSU s High Performance Computing Cluster Accessible through the web. ( Calculation is carried out on the BioHPC computer cluster. GWAS analysis using the maize Hapmap v1 genotyping data.
21 Some potential applications of GBS Data Marker discovery convert to SNP or PCR assays of indels Linkage mapping of QTL in a biparental cross Fine mapping QTL Bulked segregant analysis Genome Wide Association Studies (GWAS) NAM GWAS Genomic selection Phylogeny Improving reference genome assembly
22 Improving the maize B73 reference genome The B73 reference genome accurate for B73 but less so for other maize lines (e.g., Mo17) Even for B73, some regions of the genome are in the wrong place Some large (multiple BAC) contigs could not be anchored assigned to chromosome 0 30 chr0 contigs in B73 RefGenV1 17 chr0 contigs in B73 RefGenV2 Some regions of the genome are missing 5% of B73 sequence is not in the B73 reference genome
23 Improving the maize B73 reference genome The B73 reference genome accurate for B73 but less so for other maize lines (e.g., Mo17) Even for B73, some regions of the genome are in the wrong place Some large (multiple BAC) contigs could not be anchored assigned to chromosome 0 30 chr0 contigs in B73 RefGenV1 17 chr0 contigs in B73 RefGenV2 Some regions of the genome are missing 5% of B73 sequence is not in the B73 reference genome
24 Most tags can be mapped as individual alleles In a biparental cross such as maize IBM (B73 x Mo17) Provided that they are polymorphic between the parents ApeKI site (GCWGC) ( ) 64 base sequence tag B73 Loss of cut site < 450 bp Mo17
25 Genetically Mapping Individual GBS Alleles SNPs (e.g., from Illumina 55K chip) RILs (e.g., from IBM) B73 Mo17 Heterozygote
26 Genetically Mapping Individual GBS Alleles SNPs (e.g., from Illumina 55K chip) RILs (e.g., from IBM) B73 Mo17 Heterozygote
27 Genetically Mapping Individual GBS Alleles SNPs (e.g., from Illumina 55K chip) RILs (e.g., from IBM) B73 Mo17 Heterozygote SNP being tested
28 Genetically Mapping Individual GBS Alleles SNPs (e.g., from Illumina 55K chip) ( ) 64 base sequence tag (GBS coverage ~0.4x) RILs (e.g., from IBM) B73 Mo17 Heterozygote SNP being tested
29 Genetically Mapping Individual GBS Alleles SNPs (e.g., from Illumina 55K chip) ( ) 64 base sequence tag (GBS coverage ~0.4x) RILs (e.g., from IBM) Binomial Test for linkage prob. success: segregation ratio of the SNP being tested (~0.5) n trials: n RILs with GBS tag (10) n successes: n co occurrences with presumed parental allele at SNP being tested (co segregation) p value: (<10 3 ) B73 Mo17 Heterozygote SNP being tested
30 Genetically Mapping Individual GBS Alleles SNPs (e.g., from Illumina 55K chip) ( ) 64 base sequence tag (GBS coverage ~0.4x) RILs (e.g., from IBM) B73 Mo17 Heterozygote These 10 SNPs all tie
31 Genetically Mapping Individual GBS Alleles in IBM
32 B73 reference genome highly accurate for B73 0.4% of B73 tags genetically map to different chromosome than they align to
33 B73 reference genome highly accurate for B73 but far less so for other maize lines 9.3% of Mo17 tags genetically map to different chromosome than they align to
34 Some chunks of the B73 reference genome are in the wrong place Physical Chr Start (Mb) End (Mb) Genetic Chr Approx. Genetic Location (Mb) # Tags
35 Improving the maize B73 reference genome The B73 reference genome accurate for B73 but less so for other maize lines (e.g., Mo17) Even for B73, some regions of the genome are in the wrong place Some large (multiple BAC) contigs could not be anchored assigned to chromosome 0 30 chr 0 contigs in B73 RefGenV1 17 chr 0 contigs in B73 RefGenV2 Some regions of the genome are missing 5% of B73 sequence is not in the B73 reference genome
36 Some contigs are on Chr0 & some chunks are missing from the B73 assembly The sequence of Chr0 contigs is known so we know which ApeKI GBS tags are there De novo contigs are being constructed from WGS* Created a pipeline to genetically map novel contigs using linkage populations Used IBM GBS data for proof of concept Genetically anchored 20% of full length cdnas (407 novel) 8 of 17 chromosome 0 contigs in B73 RefGenV2 Small proportion (>70,000) of the de novo WGS contigs of B73 & Mo17 (as most of these 5 million contigs are very small) * B73 (454), FLcDNA: Shiran Pasternak, Josh Stein, Andrew Olson, Doreen Ware (CSHL) Mo17 (Illumina GAII): Hainan Zhao, Jinsheng Lai (Chinese Agricultural Univ.)
37 Improving the reference genome ApeKI site (GCWGC) ( ) 64 base sequence tag B73 < 450 bp
38 Improving the reference genome ApeKI site (GCWGC) ( ) 64 base sequence tag B73 contig < 450 bp
39 Improving the reference genome ApeKI site (GCWGC) ( ) 64 base sequence tag B73 contig < 450 bp de novo (e.g., from 454 or Illumina sequence)
40 Improving the reference genome ApeKI site (GCWGC) ( ) 64 base sequence tag B73 contig < 450 bp de novo (e.g., from 454 or Illumina sequence) Novel? (not included in B73 RefGen_v2)
41 SNPs (e.g., from Illumina 55K chip) Improving the reference genome ( ) 64 base sequence tag (GBS coverage ~0.4x) RILs (e.g., from IBM) B73 Mo17 Heterozygote These 3 SNPs tie
42 Improving the reference genome
43 Improving the reference genome p < 10 7
44 >70% contigs genetically map to within 1 Mb of true position
45 >70% contigs genetically map to within 1 Mb of true position This is just with the IBM population Will vastly improve with NAM
46 Some regions of reference genome are missing large chunks Telomere of Chr4 is a prime target for future improvement
47 This coming year Improving the genome Add in GBS data from NAM for much higher resolution Anchor as many novel genes & contigs as possible Reorder contigs within BACs by LD (HapMapV2) & using GBS data from linkage populations (NAM & IBM) Also check order and orientation of BACs themselves
48 Some potential applications of GBS Data Marker discovery convert to SNP or PCR assays of indels Linkage mapping of QTL in a biparental cross Fine mapping QTL Bulked segregant analysis Genome Wide Association Studies (GWAS) NAM GWAS Genomic selection Phylogeny Improving reference genome assembly
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