opulation genetics undamentals for SNP datasets

Transcription

1 opulation genetics undamentals for SNP datasets with crocodiles) Sam Banks Charles Darwin University

2 I ve got a SNP genotype dataset, now what? Do my data meet the requirements of the analyses I want to do? Identifying babies and bathwater Effects of biological processes and data quality on population genetic patterns Alleles and genotypes Hardy-Weinberg Linkage disequilibrium Do I really have to think about population genetics fundamentals if I have fancier analyses to do?

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4 Legend Source: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community

5 DArTSeq genotypes * * * * * * * DCroc DCroc DCroc16-253DCroc DCroc DCroc16-253DCroc DCroc * * * * * * * 9.09E E E E E E E E+11 * * * * * * * * * * * * * * A F G B H A H B * * * * * * * * * * * * * * none [Originanone [Originanone [Originanone [Originanone [Originanone [Originanone [Originanone [Origina eid AlleleSequenChrom_CrocoChromPos_CrAlnCnt_CrocoAlnEvalue_CrSNP SnpPosition CP0001 CP0005 CP0006 CP0007 CP0009 CP0010 CP0019 CP FTGCAGACCTA :T>C FTGCAGCTCCCKN _ ld E-28 51:G>A F 0-21:T>A-21:T>A TGCAGCTCCTGGGTCTGGTTATGAGTGTGGGTTGTGGGGCACCATGGAAGGATGTGAGGCAGGCCAGGA KN _scaffold E-28 21:T>A F 0-65:G>A-65:G>A TGCAGGCGCGGCGTATCGTGGGATGGAGTGAGGCACCGCAGCGCGGGGCATGAGCGGTTCAGCAGGAAT :G>A F 0-11:G>A-11:G>A TGCAGAGGAGCGAGGGAGTGGCTTGGGCCGTGTGTTGGAGAATGAGATGCATGAGCGGTTCAGCAGGAA :G>A F 0-20:C>G-20:C>G TGCAGCAATGGTGATTCTGGCCAGCAGCCACAGTGTGGCTCTGGGCAGCCTGGCTGTGCTTTTGAGTGG JRXG _scaffold-6215_ E-28 20:C>G F 0-66:T>C-66:T>C TGCAGCAGCCCTAGGAATTAACTCCAGATCCCCAAATCCCTTTCCAGGACCTTAAATCACACGCCTTGC KN _scaffold E-27 66:T>C F 0-46:T>C-46:T>C TGCAGCATACAGCCATGGCACAGGGTGTGTCCCCCTGCCCGCCTCCTATGTGGCGCTCTGTGGCTGCAT :T>C F 0-51:C>T-51:C>T TGCAGGGAGAGGAGCGAGGCTGTCCTCGCCCCGGGGAGATGCGCGCCTTGCCCCCCCTTGCCCCGTCAC KN _scaffold E-28 51:C>T F 0-11:G>C-11:G>C TGCAGAAAAAAGGACCTGGGGGTTACAGTAGACAATAAGCTGAATTTAAGCTAACAGCATGAGCGGTTC KN _scaffold E-24 11:G>C F 0-12:C>T-12:C>T TGCAGAAAAAGACCAGTGGGTCACAGTGGGCAATAAGCTGGATATGAGTCAACAGTGCGCCATTGTTGT KN _scaffold E-28 12:C>T F 0-54:C>A-54:C>A TGCAGAAAAATCTACCCAGTGTTCAGATCTTCTTCCTCAACCATTTTTTCTGGGCCAGAGATTCATTTC KN _scaffold E-27 54:C>A F 0-14:G>A-14:G>A TGCAGAAAAGGACCGGGGGGGGTTACAGTGAACAATAAGCAGCATGAGCGGTTCAGCAGGAATGCCGAG :G>A F 0-41:A>T-41:A>T TGCAGAAAAGGACCTGGGAGTTACCGTGGAGAATAAGCTGGATATGAGTCAGCAGCATGAGCGGTTCAG KN _scaffold E-23 41:A>T F 0-52:C>A-52:C>A TGCAGAAAAGGAGCTGGGGGTTACAGTGGACAATAACCTGAATATGAGCCAGCAGTGTACCCTTTTTGC KN _scaffold E-28 52:C>A F 0-9:T>C-9:T>C TGCAGAAAATTACTACCTTGTATATATAGAAGAACTCTTGCTGTGAAAGTGGACAGAAGATAGAACCAT KN _scaffold E-28 9:T>C F 0-51:G>A-51:G>A TGCAGAAACAAGGAGATATTTTTTTCTTCTTTTTGCCTTTCAAGCAAGGTAGATACCTTATTCTGGGGC KN _scaffold E-28 51:G>A F 0-34:G>C-34:G>C TGCAGAAACAGGCGGCAGATGCTGGGTTTCCATAGAGCTCTAAAGGAATCAGCATAAAAAGAGAAAATA KN _scaffold E-28 34:G>C F 0-63:G>T-63:G>T TGCAGAAACCAGTTGGGGTGGAAGGGAAGGCGCAGTACCCTTTAGCATTGCTTGGGTCAGCTTGTGTGA KN _scaffold E-28 63:G>T F 0-31:A>G-31:A>G TGCAGAAACTCTCACATTAACTTGTGCACCAAGCAGAAAAGCATGAGCGGTTCAGCAGGAATGCCGAGA :A>G F 0-58:T>C-58:T>C TGCAGAAACTCTTATGAAGAAAACTAGCTGATTTTTGCCCATCAAGAATGACTGGGGGTGGGGACAGTG KN _scaffold E-27 58:T>C F 0-44:G>A-44:G>A TGCAGAAAGACAGACATTGCCAGGGTGAAATGCAGCACTCCCGCGGCAGCATGAGCGGTTCAGCAGGAA :G>A F 0-25:C>T-25:C>T TGCAGAAAGCCAGATGACTTTGCCACGGGGTCACTGCCTCCTCTGTAACCCTCCCTGCCCCTGGTGCAC KN _scaffold E-28 25:C>T F 0-18:G>T-18:G>T TGCAGAAAGGGCAAACATGGAGCTTGTGGTGTGGCTGAAAGGAAGTAAAGAGGTGATCACTCTGGGGGT KN _scaffold E-28 18:G>T F 0-24:C>T-24:C>T TGCAGAAATTCACAGGGAGCAAAACGCTAAAAACAGACTTGGCTCCTTTGCTACTTATTCTCAGTTACT KN _scaffold E-28 24:C>T x 700 samples x SNPs

6 Are our data fit for purpose? What do we want from our dataset? (Population genetics bias) Homologous, informative loci Accurate genotypes Independent loci for pop genetics studies

7 How do we find out if that s what we ve got in our dataset?

8 Some population genetics fundamentals We re usually starting with a with a bunch of called genotypes AA, AB, BB 0,1,2 We can measure genetic diversity at the level of alleles: Freq of allele A (p) Freq of allele B (q) Typically measured from observed genotypes

9 Hardy-Weinberg expected genotype proportions can be useful for data exploration Hardy-Weinberg expected genotype proportions F(AA) = p 2 F(AB) = 2pq F(BB) = q 2

10 Deviations from H-W genotype proportions can indicate: Genotyping errors You re not actually looking at homologous, Mendelian etc loci As well as a bunch of interesting biological processes: Random mating Infinite population (no sampling error) No migration No mutation No selection Plus some others like non-overlapping generations

11 Deviations from H-W genotype proportions can indicate: Boring stuff for filtering Genotyping errors You re not actually looking at homologous, Mendelian etc loci As well as a bunch of interesting biological processes: Random mating Infinite population (no sampling error) No migration No mutation No selection Plus some others like non-overlapping generations Cool stuff you want to study

12 Deviations from H-W genotype proportions can indicate: Genotyping errors You re not actually looking at homologous, Mendelian etc loci As well as a bunch of interesting biological processes: Random mating Infinite population (no sampling error) No migration No mutation No selection Plus some others like non-overlapping generations

13 What would HWE look like in crocs? Define sub-population units that might approximate random mating assumption

14 Simulated croc genotypes (under HWE assumptions and observed allele freqs WITHIN sub-populations) Ho f(a) Ho He Hs FIS F IS = (H S H O )/H S

15 HO Real data f(a) HO Mean HS Mean HS FIS

16 Getting your expectations correct wrt population structure Given mean Ho = Sub-populations Mean H S (loci and sub-populations) = Total population Mean H T = F ST = (H T mean H S ) / H T = 0.08 (represents structure among sub-pops) Mean F IS = Mean F IT = 0.178

17 What if I filtered on HWE on the wrong scale? (Incorrect expectations) F ST = 0.08 F ST = 0.03

18 Sometimes you have no idea of expected population genetic patterns Given mean Ho = 0.157

19 So, where to start with filtering? I think HWE criteria are lousy filtering metrics unless you don t want to discover anything unexpected in your data But you can use H-W metrics to explore data quality effects on biological signals And you can explore your data in other ways

20 Reference genomes provide some useful context

21 Frequency Frequency Croc genome assembly is pretty basic But still enables filtering on e-value and alignment count criteria SNPs mapped to crocodile genome. BUT Mapped to 6610 separate scaffolds Alignment Count Alignment Count E value E value

22 Ho Reference genomes are handy for filtering Filtering to mapped reads plus e-value and alignment count criteria Allele frequency Ho vs He Ho He mean FISHs

23 Frequency Frequency Mean HO Density Mean FIS What about basic SNP quality metrics? Call Rate Basic SNP info and pop gen stats post filter ing on mapping criteria RepAvg Depth Mean FIS vs Mean HS Depth (density plot) Ref/SNP depth ratio Depth MAF Ratio of seq depth of alleles Mean HO Mean vs HS Mean HS Mean HS Mean MAFHO Mean FIS

24 Metrics associated with under-calling heterozygotes? FIS FIS by Sequence Depth Depth FIS Call Rate

25 Frequency Frequency Mean HO Density Mean FIS Allelic dropout due to cut site polymorphism? Call Rate Basic SNP info and pop gen stats post filter ing on mapping criteria RepAvg Depth Mean FIS vs Mean HS Depth (density plot) Ref/SNP depth ratio Depth MAF Ratio of seq depth of alleles Mean HO Mean vs HS Mean HS Mean HS Mean MAFHO Mean FIS

26 Filtering on basic quality metrics Depth (incl. ratio among alleles), Call Rate, repeatability, MAF) Down to ~3,000 SNPs Ho Allele frequency Ho vs He Ho mean Hs He FIS

27 (I left out anything about linkage filtering )

28 F Some biological patterns in the pop gen metrics too F M Body Length (m) Big crocodiles tend to be more inbred. Legacy of past hunting impacts?

29 r Strong spatial structure, but over a large scale 0.14 Spatial autocorrelation of multilocus genotypes r U L Interval (Km)

30 Admixture analysis (LEA)

31 Take home messages Don t skip the undergrad-level stuff Graphical visualisations are REALLY useful Explore associations between data quality indices and pop gen metrics Try to avoid forcing your data to look nice