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Potential for Genomic Selection in indigenous breeds and results of GWAS in Gir dairy cattle of gujrat
The document discusses the potential of genomic selection in indigenous cattle breeds, highlighting its methodology, advantages, limitations, and impact on animal breeding. It details the shift from traditional breeding methods to modern genomic approaches utilizing SNP markers and emphasizes the need for accurate phenotypic records. The findings suggest significant improvements in breeding efficiency, genetic gain, and selection accuracy, particularly for traits that are typically challenging or costly to measure.
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Potential for Genomic Selection in indigenous breeds and results of GWAS in Gir dairy cattle of gujrat
- 1. Potential for GenomicSelection in Indigenous Cattle Breeds POSTGRADUATE INSTITUTE OF VET. EDU. & RESEARCH, KAMDHENU UNIVERSITY, GANDHINAGAR, GUJRAT Dr. P. H. Vataliya Dr. Nilesh Nayee Dr. Rajeshkumar Thakkar
- 2. FLOW OF PRESENTATION….. Introduction Pre Genomic Era Genomic Selection SNPs Methodology of Genomic Selection Comparison of methods of Genomic Selection How does Genomic Selection alter Selection ? Factors affecting the Accuracy of Genomic Selection Impact of Genomic Selection in Animal Breeding Advantages of Genomic Selection Limitations of Genomic Selection Genomic Selection around the world Genomic Selection in INDIA Conclusions Future Prospects
- 3. The maingoal in animal breeding is to select individuals having high breeding values for traits of interest as parents to produce the next generation as quickly as possible. Most programs rely on statistical analysis of large databases with phenotypes on breeding populations by linear mixed model to estimate breeding values. Use of genetic markers to identify QTLs and their use in marker-assisted selection. The advent of high-density SNP genotyping, combined with novel statistical methods for the use of data to estimate breeding values, has resulted in application of genomic selection in dairy cattle. INTRODUCTION
- 4. Selection i. Differentialrate of reproduction ii. Choosing parents of Next Generation A form of MAS using genetic marker covering whole genome genomic selection is not an exact science but it is a powerful new tool for predicting results based on SNPs INTRODUCTION
- 5. (Intensity of Selection• Accuracy of Selection •Genetic Standard Deviation ) ∆G = --------------------------------------------------------------------------------- Generation interval ∆G depends primarily upon the ability to recognize genotypes with superior breeding values Generation interval inversely related to genetic gain Thus, assessment of breeding value of all individuals comprising a population is required INTRODUCTION
- 6. Individual’s breedingvalues assessed on the basis of Own performance Performance of collateral relatives Pedigree information Performance of progeny The progeny testing has a HIGH ACCURACY OF SELECTION, among the methods described above, but the GENERATION INTERVAL is also HIGH INTRODUCTION
- 7. Works wellif: – Heritability of traits is medium to high – Progeny’s performance data is available Less effective if: – Heritability of traits is low or sex limited traits – Animals are young (No progeny and thus no records are available) Traditional Breeding
- 8. MOLECULAR GENETICS: Studiesthe structure and function of gene at a molecular level QTL DETECTION AND MAPPING: Quantitative trait loci(QTLs) are scattered genes over the genome and they are responsible for inheritance of quantitative trait MAS STRATEGIES: Marker assisted selection (MAS) is indirect selection process where a trait of interest is selected, not based on the trait itself, but on a marker linked to it GENOMICS: Study of genomes of individuals GENOMIC ERA
- 9. MAS explainsonly a limited proportion of the total genetic variance captured by the markers An alternative to tracing a limited number of QTL with markers is to trace all the QTL This can be done by dividing the entire genome up into Chromosomal Segments. PRE GENOMIC ERA
- 10. Selection basedon the prediction of breeding values from the information of dense markers covering the whole genome GS is MAS on a genome-wide scale By using Genome Wide-dense markers : use Total Genetic Variance (Vg) Traditional MAS : ~10% of Total Genetic Variance (Vg) (Meuwissen et al. 2001) GENOMIC SELECTION
- 11. Genomic selectionexploits linkage disequilibrium The assumption is that the effects of the chromosome segments will be the same across the population because the markers are in LD with the QTL that they bracket (Hastbacka et al., 1992) Hence the marker density must be sufficiently high to ensure that all QTL are in LD with a marker or haplotype of markers Cont... GENOMIC SELECTION
- 12. 1. A largenumber of progeny tested bulls (or recorded females preferably with known pedigree) and their DNA samples 2. Genotyping these samples for a large number of Polymorphic SNPs 3. Appropriate statistical methods and models for estimating GBVs REQUIREMENT FOR GENOMIC SELECTION
- 13. There aredifferent types of markers available for MAS, like RFLP, AFLP, Microsatellite, Minisatellite and SNPs. RFLP, AFLP, Microsatellite and Minisatellite are not evenly spread over genome compare to SNPs SNPs are single nucleotide variation in DNA sequence of Genome differs between member of population. SNPs
- 14. Now adays Genomic Selection is based on DNA chip which are of SNPs. This kind of chips are called as SNP chip or Snip chip. Sequencing of livestock genomes has discovered 100s of 1000s of Single Nucleotide Polymorphism (SNP) markers Cont... SNPs
- 15. Why SNPs ? More frequent in the genome Polymorphic Low mutation rate Work well across laboratories Already available as part of genomic selection breeding programs SNPs
- 16. Marker located relativelycloser to genes of interest SNPs
- 17. (Illumina) Micro-array bead chipsfor various species released till now….. SNPs
- 18. SNPs are mostvaluable for traits that are Of low heritability Difficult or expensive to measure (feed efficiency) Unmeasurable until after selection has occurred (carcass data) Currently not selected due to lack of available phenotypic data (tenderness) Sex limited traits (milk production) (Van Eenennaam, 2011) 18 SNPs
- 19. Part ofthe population is genotyped using the dense SNP chip and phenotyped for quantitative traits. This is referred as the Reference Population The establishment of an appropriate Reference Population is one of the key aspects in Genomic Selection METHODOLOGY OF GS
- 20. For dairycattle Reference Populations, Saatchi et al, (2010) recommended to use reliable (>90%) progeny tested sires from recent generations rather than older bulls. Size of the Reference Population is inversely proportional to the heritability of the trait and directly proportional to the effective population size (Goddard, 2009). METHODOLOGY OF GS
- 21. The effectof all the SNPs is estimated in the Reference Population by statistical models; where the association between SNPs and phenotypes is calculated. For working out the prediction equations The rest of the population (other then Reference Population) is genotyped using the same SNP chip and the total genetic value (GEBV) of the animals is predicted by using the prediction equations derived from Reference Population (Meuwissen et al., 2001) Cont.. METHODOLOGY OF GS
- 22. Methods for estimationof GEBV Least Squares Analysis (LS) Genome-wide Best Linear Unbiased Prediction (GW-BLUP) Bayesian approach Least Squares: Test all the genes one by one for their statistical significance, and set the effects of the non-significant genes to zero, while estimating the effects of the significant genes simultaneously using LS. (Meuwissen et al., 2001) METHODOLOGY OF GS
- 23. Cont... Best LinearUnbiased Prediction: Fit the allelic effects as random effects instead of as fixed effects. The fitting of random effects does not require degrees of freedom, and thus all allelic effects can be estimated simultaneously. Bayesian estimation: This is similar to BLUP, except that the variance of the allelic effects is assumed different for every gene, and is estimated by using a prior distribution for this variance. METHODOLOGY OF GS
- 24. Accuracy Meuwissen etal., (2001) reported Accuracy of Genomic Selection by using various statistical models. Method Accuracy LS .36 BLUP .74 Bayes .84 COMPARISON OF METHODS
- 25. On the basisof Marker density Low – little differences between GW-BLUP and Bayesian methods, accuracy low to moderate High – saturation of efficiency of GW-BLUP, whereas Bayesian approaches increase in accuracy (Boichard, 1996) COMPARISON OF METHODS
- 26. Reliabilities Reliability GEBVsof Bayes only 1% higher than GW-BLUP Reliability of Bayes is substantially higher for traits influenced by large QTL (Van Raden et al, 2008) COMPARISON OF METHODS
- 27. Population structure/history •Size of sib families • Generation interval Availability of dense marker maps Availability of many genotyped individuals with records USEFULNESS OF GS DEPENDS ON:
- 28.
- 29. Based on markerapproach Comparing the accuracy of genomic selection with – Haplotypes (IBD / IBS approach) IBD- Identical by descent i.e. two genes have originated from replication of one single gene in a previous generation IBS- Identical by state i.e. two genes may not have originated from replication of same gene but still they have functional identity (Falconer,1996) ACCURACY OF GS
- 30. Calus et al.(2007) used simulated data Based on marker approach ACCURACY OF GS
- 31. Reliabilities ofSNP markers of various densities were reported by VanRaden (2010) as follows: SNP density Reliabilities 3k 70% - 80% 50k 83% 700k 84% Based on marker density ACCURACY OF GS
- 32. Linkage disequilibriumbetween QTL and markers = density of markers In dairy cattle populations, an average r2 of 0.2 between adjacent markers is only achieved when markers are spaced every 100kb Based on LD ACCURACY OF GS
- 33. Number ofrecords used to estimate chromosome segment effects Meuwissen et al. (2001) evaluated accuracy using LS, BLUP, Bayes using 500, 1000 or 2000 records in the reference population No. of phenotypic records 500 1000 2000 Least squares 0.124 0.204 0.318 BLUP 0.579 0.659 0.732 Bayes 0.708 0.787 0.848 Based on No of records ACCURACY OF GS
- 34. (Goddard and Hayes.2009) Based on No of records ACCURACY OF GS
- 35. How often tore-estimate GEBVs? Meuwissen et al. (2001) reported the correlation between Estimated BV(EBV) and True BV(TBV) in generations 3rd -8th Denser markers; require more generations between re-estimation of GEBV (Meuwissen et al., 2001) Generation 3rd 0.848 4th 0.804 5th 0.768 6th 0.758 7th 0.734 8th 0.718 RE-ESTIMATION OF GEBVs
- 36. Dairy Cattle 60-120%(Pryce et al., 2011) Meat sheep 21% (Van der Werf, 2011) Wool sheep 38% (Van der Werf, 2011) Layers 40% (Dekkers et al., 2009) Broilers 20% (Dekkers et al., 2009) ESTIMATIED INCREASED ∆G From GS
- 37. Selection of youngbulls (no progeny test) Saves costs of progeny test: ~ 40,000 $/bull Reduces generation interval by factor 2 Annual savings of $23 million (92%) Condition: Updated marker information are available (Schaeffer, 2006) IMPACT OF GS ON DAIRY CATTLE BREEDING
- 38. Once markereffects are estimated they can be useful for a few generations Selection possible on novel traits, which otherwise require expensive phenotyping New breeding strategies can be implemented Increased genetic gain – By increasing accuracy of selection – By reducing the generation interval due to early selection Select animals before they are of productive and/or reproductive age Eliminate the need for progeny testing ADVANTAGE OF GS
- 39. Genotyping stillcostly Marker estimates must be estimated in population that they will be used in If generation intervals are shortened substantially then annual inbreeding rates could be higher Need to genotype a sufficiently large set of animals for accurate marker estimates For traits with lower heritability - more records needed LIMITATION OF GS
- 40. ADHIS producedgenomic based breeding values for bulls in September and December 2010. 2,381 Holstein bulls were included in the September 2010 analysis (2,193 reference bulls and 188 young bulls). In this group of 188 young bulls with almost no daughter performance data, an improvement in reliability across all key traits is evident. (Hayes et al, 2009) GLOBAL SCENARIO OF GS
- 41. GEBVs obtainedby USDA in collaboration with Canada for Holstein bulls have been released in public every year since 2008 A project at Guelph with 820 bulls was carried out with the following results Traits Reliability – Traditional methods Reliability – Genomic Selection Protein yield 38 46 Fat yield 38 43 SCS 30 48 Conformation 39 47 (Pryce and Daetwyler, 2012) GLOBAL SCENARIO OF GS- CANADA
- 42. High Ranking GenomicYoung Bulls
- 43. CRV launchedInSire bulls – Designated to GS selected bulls, since 2008, for Holstein and Jersey breeds Increases in the reliability using GEBVs were also reported by CRV as follows : Traits Additional reliability Protein production 17% Overall Conformation 14% SCS 11% (Hayes et al, 2008) GLOBAL SCENARIO OF GS- NATHERLAND(CRV)
- 44. LIC hadthe foresight to store DNA from every sire that was progeny tested since 1980. This enabled LIC to genotype sires that were the best, and the worst too, of their progeny test cohort and thus evaluate markers across the genetic range. The degree of accuracy of GEBVs was measured by their correlation with Progeny test BVs and were found to be ranging from 0.45 to 0.60 for production traits in HF breed (Hayes et al, 2008) GLOBAL SCENARIO OF GS- NATHERLAND(LIC)
- 45. Nordic countries (VikingGenetics) VikingGenetics got the first genomic indexes of Holstein, Jersey and Red Breeds during the later parts of year 2007 They have genomic indexes every two months for purposes of purchase of new bulls to their breeding programme (Pryce and Daetwyler, 2012) GLOBAL SCENARIO OF GS
- 46. Policy makersneed to be convinced of the potential of effective breeding programmes Extensive validation of association between genotypes and phenotypes needed GS can be use if accurate performance records are available Only genotyping without phenotyping and efficient data analysis will be wasteful expenditure GS IN INDIA
- 47. Genome wide associationstudy of udder type traits and 305 day milk yield in Gir cattle
- 48. Genome Wide AssociationStudy of udder type traits and 305 day milk yield in Gir cattle Total 559 animals were available for genotyping Animals were genotyped using the customized Illumina Bovine HD microarray chip assay INDUSCHIP 1 The 45,024 SNPs were distributed over all 29 Bos indicus autosomes Certain chromosomes having large numbers of SNPs affecting a trait 9 udder type traits recommended by ICAR, investigated for genome wide association study A total of 838 genome-wise significant SNPs associated with 9 udder type traits (p- value < 0.0001) were found
- 49. Trait wise no.of significant SNPs Sr. No. Traits Number of significant SNPs 1 Fore udder attachment 11 2 Central ligament 06 3 Fore teat placement 03 4 Rear teat placement 07 5 Rear udder height 07 6 Rear udder width 38 7 Teat length 166 8 Udder depth 14 9 Teat thickness 89 10 305day milk yield 497 Total 838
- 50. Chromosome wise no.of SNPs per Trait CHR No. FUA CL FTPL RTPL RUH RUW TL UD TTH MILK Total 1 2 3 4 5 6 7 8 9 10 11 12 1 0 0 0 0 0 1 8 0 2 19 30 2 1 0 0 0 0 1 3 0 6 19 30 3 2 0 0 0 1 1 6 0 1 18 29 4 0 0 0 2 2 0 3 0 2 15 24 5 1 2 0 0 0 2 31 1 17 59 113 6 1 0 2 0 0 2 10 5 6 25 51 7 0 0 0 1 0 1 9 0 3 17 31 8 1 0 0 0 0 0 3 0 2 12 18 9 0 0 0 0 0 8 14 0 1 28 51 10 1 0 0 0 0 2 3 0 2 15 23 11 1 1 0 0 0 1 7 0 4 17 31 12 0 0 0 1 0 0 5 0 3 13 22 13 0 0 0 0 0 1 6 0 3 15 25 14 0 0 0 0 0 4 10 2 5 27 48 15 0 0 0 1 1 1 1 1 1 9 15 16 1 0 0 0 0 0 6 0 4 23 34 17 0 0 0 0 0 4 7 1 5 21 38 18 0 0 0 1 0 0 0 0 2 11 14 19 0 0 0 1 0 2 3 2 6 14 28 20 1 0 0 0 0 2 0 0 1 20 24 21 0 0 1 0 1 2 0 0 4 24 32 22 0 0 0 0 0 1 9 0 0 12 22 23 0 1 0 0 0 0 5 0 3 14 23 1 2 3 4 5 6 7 8 9 10 11 12 24 0 0 0 0 0 1 4 2 0 8 15 25 0 0 0 0 1 0 5 0 2 11 19 26 0 0 0 0 0 0 2 0 1 13 16 27 0 1 0 0 1 1 2 0 2 5 12 28 0 0 0 0 0 0 3 0 0 11 14 29 1 1 0 0 0 0 1 0 1 2 6 Total 11 6 3 7 7 38 166 14 89 497 838
- 51. Fore udderattachment A total 11 SNPs were found to be associated significantly (p-value <0.0001) Maximum numbers of SNPs were located on chromosome No. 3 (2 SNPs) in this region between base pair position 85.5MB to 100.8MB. Genome Wide Association Study
- 52. Central ligament A total 6 SNPs were found to be associated significantly (p-value <0.0001) Maximum no. of SNPs were on chromosome No. 5 (2 SNPs: NDDB-BOVINEHD0500024877 and NDDB-BOVINEHD0500026042) in this region between base pair position 87.7MB to 91.8MB. Genome Wide Association Study
- 53. Fore teatplacement A total 3 SNPs were found to be associated significantly (p-value <0.0001) Maximum no. of SNPs were located on chromosome No. 6 (2 SNPs: BTB-01530236 and NDDB-BOVINEHD0600006154) in this region between base pair position 22.30MB to 22.32MB Genome Wide Association Study
- 54. Rear teatplacement A total 7 SNPs were found to be associated significantly (p-value <0.0001) Maximum no. of SNPs were located on chromosome No. 4 (2 SNPs) in this region between base pair position 1.57MB to 21.8MB Genome Wide Association Study
- 55. Rear udderheight A total 7 SNPs were found to be associated significantly (p-value <0.0001) Maximum no. of SNPs were located on chromosome No. 4 (2 SNPs) in this region between base pair position 27.96MB to 109.04MB Genome Wide Association Study
- 56. Rear udderwidth A total 38 SNPs were found to be associated significantly (p-value <0.0001) Maximum no. of SNPs were located on chromosome No. 14 (4 SNPs) in this region between base pair position 3.08MB to 58.35MB Genome Wide Association Study
- 57. Teat length A total 166 SNPs were found to be associated significantly (p-value <0.0001) Maximum no. of SNPs were located on chromosome No. 5 (31 SNPs) in this region between base pair position 8.15MB to 116.9MB Genome Wide Association Study
- 58. Udder depth A total 14 SNPs were found to be associated significantly (p-value <0.0001) Maximum no. of SNPs were located on chromosome No. 6 (5 SNPs) in this region between base pair position 17.86MB to 61.52MB Genome Wide Association Study
- 59. Teat thickness A total 89 SNPs were found to be associated significantly (p-value <0.0001) Maximum no. of SNPs were located on chromosome No. 5 (17 SNPs) in this region between base pair position 14.81MB to 93.47MB Genome Wide Association Study
- 60. 305day milkyield A total 497 SNPs were found to be associated significantly (p-value <0.0001) Maximum no. of SNPs were located on chromosome No. 5 (59 SNPs) in this region between base pair position 3.49MB to 118.9MB Genome Wide Association Study
- 61. The GWASof these important traits with the SNP chip array i.e. INDUSCHIP1 can be the footstep to be embark upon the option of Genomic Selection in Gir cattle The Genomic Selection thus incorporated into the Cattle Breeding Programme would ensure rapid genetic progress of Gir, an important indigenous dairy cattle of India Genome Wide Association Study
- 62. GENOME WIDE ASSOCIATIONSTUDY FOR BODY CONFORMATION TRAITS AND 305 DAY MILK YIELD
- 63. • The 45024SNPs included in the INDUSCHIP 1 SNP chip were distributed over all 29 Bos indicus autosomes. • Majority of SNPs were (26,007 out of 45,024) spread under MAF class 0.3-0.5, while, only 3883 SNPs were observed under MAF class 0.1-0.2. • A total 2009 genome-wise significant SNPs associated with 10 conformation traits and milk yield (p-value < 0.0001) were found by PLINK software Genome Wide Association Study
- 64. 2941 4415 4112 39483946 3539 28642893 2788 2286 2594 2003 1827 17071710 1439 12 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 No.ofSNPs Chromosome No. Histogram ofchromosome wise no. of SNPs 10682 3883 4452 9890 16117 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 0-0.1 0.1-0.2 0.2-0.3 0.3-0.4 0.4-0.5 No.ofSNPs Minor Allele Frequency (MAF) Histogram of MAF wise no. of SNPs Genome Wide Association Study
- 65. Manhattans plot ofStature • A total 632 SNPs were found to be genome wise significant (p-value < 0.0001) for stature in the present study • The chromosome with the highest number of associated SNPs was chromosome 5 which had 64 significantly associated SNPs in this region between base pair position 1 Mb to 155 Mb. Genome Wide Association Study
- 66. Manhattans plot ofBody depth • A total 148 genome-wise significant SNPs associated with body depth (p-value < 0.0001) were found in the present study. • The chromosome with the highest number of associated SNPs was chromosome 5 which had 23 significantly associated SNPs in this region between base pair position 3 Mb to 130 Mb. Genome Wide Association Study
- 67. Body length • Atotal 209 SNPs were found to be genome wise significant (p-value < 0.0001) for body length in present study. • The chromosome with the highest number of associated SNP was chromosome 6 which had 22 significantly associated SNPs in this region between base pair position 1 Mb to 125 Mb. Genome Wide Association Study
- 68. Manhattans plot ofHeart girth • A total of 451 genome-wise significant SNPs associated with heart girth (p-value < 0.0001) were found in present study • The chromosome with the highest number of associated SNPs was chromosome 5 which had 46 significantly associated SNPs in this region between base pair position 1 Mb to 155 Mb. Genome Wide Association Study
- 69. Manhattans plot ofAngularity A total 11 SNPs were found to be genome wise significant (p-value < 0.0001) for angularity in present study Genome Wide Association Study
- 70. Manhattans plot ofRump angle • A total of 14 genome-wise significant SNPs associated with rump angle (p-value < 0.0001) were found in present study • The highest number of 5 significantly associated SNPs were found with chromosome 1. Genome Wide Association Study
- 71. Manhattans plot ofRump width • A total 30 SNPs were found to be genome wise significant (p-value < 0.0001) for rump width in present study • The chromosome with the highest number of associated SNPs was chromosome 9 which had 6 significantly associated SNPs in this region between base pair position 1 Mb to 117 Mb. Genome Wide Association Study
- 72. Manhattans plot ofRear leg side view • Genome-wise 7 significant SNPs associated with rear leg set were found in present study • The significant SNPs associated with rear leg were observed on chromosome 1, 3, 5 and 24. Genome Wide Association Study
- 73. Manhattans plot ofRear leg rear view • A total 5 SNPs were found to be genome wise significant (filter p-value < 0.0001) for rear leg rear view in present study • The significant SNPs associated with rear leg rear view were observed on chromosome 1, 5, 15, 21 and 25 Genome Wide Association Study
- 74. Manhattans plot ofFoot angle • Genome-wise significant SNPs associated with foot angle were 5 in the present study • The significant SNPs associated with foot angle were observed on chromosome 6, 8, 10 and 11. Genome Wide Association Study
- 75. Manhattans plot of305day Milk yield • A total of 497 genome-wise significant SNPs associated with milk yield (with filter p-value < 0.0001) were found by genome-wide association study • The chromosome with the highest number of associated SNPs was chromosome 5 which had 59 significantly associated SNPs in this region between base pair position 1Mb to 118 Mb. Genome Wide Association Study
- 76. CONCLUSIONS FROM GWAS All the conformation traits in Gir animals were intermediate except rear leg rear view. Most of the conformation traits were desirable, however, some traits showed the undesirable characteristics viz. heart girth, body depth, body length, stature and foot angle. So, such traits needed to improvement. The body depth, heart girth, body length and stature were highly positively and significantly correlated with 305day milk yield. This indicated that the deeper, longer and taller cows might produce more milk than the narrower and shorter cows. The phenotypic correlation of body depth and heart girth, body length, stature were highly correlated and positive highly significant (p<0.01)
- 77. POSSIBILITIES IN INDIA Large Non Descript animals need to be graded up with High Genetic Merit Sires of known breeds. Establishment of well organized breeding network, performance recording system and reference population Initially globally available 50k SNP chip may be tried in indigenous breeds INAPH recording system of NDDB is coming up which can be used as performance records for Genomic Selection
- 78. CONCLUSIONS Dairy industryuniquely suited for genomic selection GS in dairy cattle is need of day for obtaining faster genetic gain (High Accuracy) Several countries are implementing genomic selection Hybrid systems merging classic and genomic selection are arising Usefulness of GS depends on population structure, availability of dense marker maps and large reference population
- 79. FUTURE PROSPECTUS…. Genotypingof more SNP to get clearer ‘picture’ of genetic variation Genotyping and get records for more animals Refinement and development of new estimation methods Accurate performance recording and reliable record keeping as well as precise genotyping initiatives will be required for developing nations like India
- 80. THANK YOU genomic selectionis not an exact science but it is a powerful new tool for predicting results !
Editor's Notes
- #6 All the selection processes and the breeding programs revolve around the central theme of genetic gain per year. Genetic Gain directly proportionate to Intensity of Selection, Accuracy of Selection and Standard deviation where it inversely proportionate to Generation interval
- #12 In population genetics LD is non random association of alleles at different loci. Loci are said to be in linkage disequilibrium when the freq. of association of their different allele is higher or lower than what would be expected if the loci were independent and associated randomly.
- #14 This SNP chips are of different kind.. Based on that Markers are distributed SNPs have more value compare to these other markers. So SNPs are favourite for GS
- #30 HAPLOTYPES are A set of closely linked genetic markers present on one chromosome which tend to be inherited together (not easily separable by recombination)
- #37 In dairy cattle high h2 trait selection, beef low h2 Fecundity of animal Complex trait
- #41 Australian Dairy Herd Improvement Scheme
- #45 LIC=live Stock improvement coorporation
- #47 There is no evident literature on Genomic Selection in India.
- #50 After correction with least square constant and GWAS in PLINK these results were obtained.
- #63 Before carrying out GWAS, Remove fixed effects of lactation no. and stage of lactation with least square constants The least square means conformation traits which were significantly affected by lactation stage and lactation number were corrected before GWAS
- #78 Initial available 50k snpchip globally, 50k snp chip available may be tried in indigenous breed