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Genome Wide SNPs for Admixture Analysis and Selection Signatures
- 1. Genome Wide SNPs for Admixture Analysis and Selection Signatures Dr. Sheikh Firdous Ahmad Scientist Division of Animal Genetics ICAR-Indian Veterinary Research Institute, Izatnagar, 243 122
- 2. INTRODUCTION Genes/ DNA – Genetic makeup
- 4. POLYMORPHISM
- 5. OMICS: UNIQUE APPROACHES IN BIOLOGICAL SCIENCE
- 6. Applicability on Livestock -- LIVESTOCK GENOMICS
- 7. Investigation of genetic diversity/population structure Genomic signatures of selection, Admixture studies Identification of genetic variants and QTLs related to economically important traits Genome-wide association studies (GWAS), copy number variation Genomic selection, CNV detection and association analysis GENOMICS AND LIVESTOCK 1 2 3 4 5
- 8. Elite indigenous/ exotic breed (A) Bull Non-descript breed (B) cow F1 progeny (50% A + 50% B) Backcross progeny 75% A + 25% B Bull of breed A (100%) --- after 6-8 Generations -- Vrindavani Frieswal Karan Fries Karan Swiss Phule Triveni CROSSBREEDING IN INDIA
- 9. x 1/2 1/2 x 3/4 1/4 A crossbred/ composite population Inheritance generally ranges between 12.5 and 87.5 percent of two breeds ADMIXTURE GENERATION Admixture proportion related to production, reproduction and fitness performance Studying any trait in crossbred population --- take care of admixture correction
- 10. Vrindavani composite cattle Holstein Brown-Swiss Holstein Hariana Holstein Sahiwal Frieswal ADMIXTURE GENERATION
- 11. RECENT VS ANCIENT ADMIXTURE
- 12. Genome-wide data generation is based on hybridization and imaging principles Already fixed primers (specific for SNPs) at specific locations GENERATION OF GENOME-WIDE DATA Genotyping or sequencing Whole-genome sequencing Reduced representation approaches BeadChip Genotyping
- 13. GENOMIC ERA IN LIVESTOCK SNP Beadchip Array principle Immobilized ssDNA probes on the chip Hybridization Scanning
- 14. Bovine 50K SNP chip 2008 2008 2009 2009 2010 2012 Porcine 60K SNP chip Ovine 50K SNP chip Bovine HD chip (777K) Chicken 60K SNP chip Caprine 50K SNP chip Micro-array bead chips available for cattle Buffalo 90K SNP chip 2017 Chip SNPs 3K 2,900 LD (7K) 6909 LD2(7K) 9912 50K V1 54,001 50K V2 54,609 50K V3 53,714 HD(777K) 777,962 Chip SNPs G 7K 7083 GGP 9K 8762 GP220K 19,809 GP3 27K 26,151 GP4 30K 30,112 GHD 75K 77,068 GGP 100K 100,000 GH2 140K 1,39,480 Chip SNPs Affy10K 9713 Affy15K 15,036 Affy25K 25,068 Affy700K 6,48,875 BEADCHIP AVAILABILITY FOR LIVESTOCK
- 15. Breeds used for development of these chips Angus Beefmaster Bos indicus Gyr Bos indicus Nelore Brahman Charolais Guernsey Hereford Holstein Jersey Limousin N’Dama Santa Gertrudis Sheko Red Angus Romagnola Novel SNPs derived from sequencing HapMap data Btau assembly SNPs Whole-genome shortgun reads Holstein AC sequence data Other sources Content sources GENOME-WIDE DATA – DEVELOPMENT OF SNP ARRAY Bovine 50K BeadChip
- 16. Data formats PLINK format EIGENSTRAT format HapMap format .ped and .map format Binary file format VCF format GENOME-WIDE DATA FORMATS
- 17. PED file is white-space (space or tab) delimited file With six mandatory columns Column 7 onwards Genotypes at fixed chromosomal coordinates MAP file pertains to chromosomal coordinates of SNPs in PED files Mandatory 4 columns Chromosome rs# or SNP identifier Genetic distance (morgans) Base pair position (bp units) Family ID Individual ID Paternal ID Maternal ID Sex info Phenotype X-chr -- 23 Y chr -- 24 PAR (X) -- 25 Mit. SNP -- 26 GENOME-WIDE DATA FORMATS Plink format files
- 19. Binary file format .bed file Binary PED file .fam file Pedigree/phenotype file .bim file Extended MAP file Binary file Genotype information First six columns of PED file Contains two extra columns Allele names GENOME-WIDE DATA FORMATS
- 20. Assess the admixture and ancestry levels of a particular population Global ancestry: Ancestry proportions averaged across the genome of an individual Local ancestry: Inferences made at a certain loci – Whether the admixed individual inherited both, single or no alleles from ancestral/ founder population ACTUAL ANALYSIS USING GENOME-WIDE SNP DATA
- 21. Remove sex-chromosome and Mitochondrial SNP markers Filter based on Genotype coverage for markers: Markers whose genotype is known for less than X% of individuals from at least one population will be ignored Genotype coverage of individuals: Individuals whose genotype is known for less than X% of markers will be ignored Minor allele frequency (MAF): Markers for which MAF<X within the selected dataset will be ignored Hardy-Weinberg equilibrium: Markers for which pValue<X for at least one population will be ignored GENOME-WIDE SNP DATA PRUNING
- 23. Input genome-wide SNP data Admixture analysis Likelihood based approach Based on estimating log likelihood ADMIXTURE software Model-based Bayesian approach Based on priori distribution Structure software for admixture analysis Approaches available STATISTICAL APPROACHES FOR ADMIXTURE ANALYSIS
- 24. Probability that a person named X will attend an online lecture Basic answer -- 1/2 Timing of training – office timing – more participation 1/n1 Age of participant-- 1/n2 Importance and relevance of topic-- 1/n3 Background information SNP1 SNP2 SNP3 SNP4 SNP5 SNP6 SNP7 SNP8 SNP9 SNP - n ADMIXTURE ANALYSIS – BAYESIAN STATISTICS
- 25. Maximum- likelihood analysis ADMIXTURE ANALYSIS -- LIKELIHOOD APPROACH
- 26. ADMIXTURE ANALYSIS USING STRUCTURE SOFTWARE Demonstration
- 28. India is home to 50 cattle breeds Prioritize 5 topmost breeds Based on population size and milk production status Tharparkar Gir Sahiwal Kankrej Rathi Design selective breeding programmes with open nucleus herds for their propagation Progeny testing programmes MANAGING PUREBRED INDIGENOUS POPULATION
- 30. Gene frequency = 0 Gene frequency >0=x<1 Gene frequency ~1 SELECTION SIGNATURES Interested in studying evidences of evolutionary pressures? Normal gene frequency Evolutionary forces Evolutionary forces
- 31. METHODS OF DETECTING SELECTION SIGNATURES Nature of signatures being investigated Recent versus ancient signatures Fst statistic -- --50000-75000 years back ~2000-3000 genx. Reduction in genetic diversity Tajima’s D statistic – 250,000 years back – 10000 genx. Tajima’s D statistic – Accounts for ascertainment bias 1 2 Fay and Wu’s H Most recent positive signatures Haplotype analysis Within population iHS methodology Across population XP-EHH methodology Extended haplotype homozygosity 3 Random drift Population bottleneck Population expansion Beware
- 32. SELECTION SIGNATURES Data requirements Phased haplotypes Normal SNP data SNP genotypes per individual Other conversions Tajima’s D statistic FST-Statistic iHS XP-EHH
- 33. METHODS OF DETECTING SELECTION SIGNATURES Adopted from https://doi.org/10.1016/j.livsci.2020.104257
- 34. METHODS OF DETECTING SELECTION SIGNATURES Application Use in workflow R programming software >3.0 Rehh and detectRUNS package and customization Rehh v1.11 Calculation of iHS and EHH measures vcfools Calculate FST and Tajima’s D statistic SHAPEIT Produce phased haplotype files Beagle Phasing and imputation SelScan Various measurements related to selection sweeps Variscan Calculation of Fay and Wu’s H statistic
- 35. National Mission on Bovine Productivity • Establishment of National Bovine Genomic Centre for Indigenous Breeds (NBGC-IB) • Autonomous body for undertaking all activities related to the introduction of genomic selection INITIATIVES IN INDIA W.R.T. GENOMIC SELECTION
- 36. 36 • BAIF (Bharatiya Agro Industries Foundation) (http://www.baif.org.in) • INAPH (Information Network for Animal Productivity and Health) recording system of NDDB – integration with state AHDs INITIATIVES IN INDIA W.R.T. GENOMIC SELECTION