ans3384marasst - Marker-Assisted/Genomic Selection ANS 3384...

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Unformatted text preview: Marker-Assisted/Genomic Selection ANS 3384 1 Source of genomic evaluations • DNA extracted from blood, hair, or semen • ~40,000 genetic markers (SNPs) evaluated • For each SNP, difference in PTA estimated between animals with 1 allele compared to the other allele • Genomic data contribute ~11 daughter equivalents to reliability Genomic Goals • Predict young bulls and cows more accurately • Compare actual DNA inherited • Use exact relationship matrix G instead of expected values in A • Trace chromosome segments • Locate genes with large effects What is Marker-Assisted Selection? • Marker-assisted/genomic selection is the use of information from the DNA of an animal to help us select the best animals • This DNA information might be through evaluation of actual genes, as in the case of BLAD or CVM or simply multiple DNA markers located close to genes that affect productivity traits 4 Marker-Assisted/ Genomic Selection further increases the accuracy of breeding value estimation and is particularly useful for traits that are expensive and/or difficult to measure, such as meat quality and animal health. 5 The Use of DNA Testing to Assist Selection isn’t really all that new • Selection against deleterious genes – BLAD – CVM – Weaver – Mulefoot • Selection for and against the gene for red color, e • Determination of parentage 6 Uses of DNA Testing in Livestock • PERMANENT ANIMAL IDENTIFICATION • PARENTAGE VERIFICATION – AI AND ET • CERTIFY BREED PURITY • SIRE IDENTIFICATION – NATURAL SERVICE FOLLOWING AI – MULTIPLE SIRE HERDS • DETERMINE SIRE OF SUPERIOR CARCASSES – FEMALES INTENTIONALLY MATED TO TWO MALES Holstein Cattle/ AKC Breeds of Dogs • SEX DETERMINATION IN PARROTS Kinds of DNA information • Microsatellites – Microsatellite are short, tandem, repeated DNA segments in the genome • SNPs – Single nucleotide polymorphism markers • SNPs are replacing microsatellite markers in the future as they are easier and cheaper to evaluate 8 DNA FACTS • Only about 3 to 5% of an individual’s DNA actually “codes for” the production of proteins (enzymes, etc.) • It is within the 95 to 97% of the DNA that is “non-coding” but close to “functional genes” that we find SNPs and Microsatellites 9 Direct DNA Testing • These tests are for genes which have been proven to have a clear effect. • These markers are usually SNP’s located in the region that codes for the gene itself. • These direct tests will work for any breed or cross and can identify animals which are homozygous favorable, heterozygous, or homozygous unfavorable 10 Genomic Markers • Indirect Tests • Markers – sequences located near the gene of interest – 50K and more! • Statistically associated with a certain phenotype • In some cases this system can be used to select for a gene even before it is actually identified and specifically located Moser, 2004 11 QTL • QTL is the acronym for “Quantitative Trait Loci” • Quantitative Trait Loci are genes that influence traits like tenderness, growth, marbling, milk production, reproduction, disease resistance, etc. 12 Single Nucleotide Polymorphisms • A DNA sequence variation occuring when a single nucleotide (A, T, C, or G) differs between animals of the same breed/species • AAGCCTA to AAGCTTA • SNPs that are close to genes of biological importance (tenderness or marbling, for example) are useful as predictors of the gene 13 Single Nucleotide Polymorphisms • Some SNPs can be within the functional gene and may be responsible for changes in the animal • Example, if a SNP occurs in the gene for Calpastatin, it could indicate an allele that is responsible for increased tenderness 14 Tender Markers Tender Tough Tough Markers Scan Chromosome with Phenotypic Data from Progeny GroupsCompare Marker Variation within Progeny Groups to Identify 15 Marker Association with Genes Gene Markers Genes 16 Marker Assisted Selection (MAS) • Markers and genes that are close together on the same chromosome and tend to stay together.This is called genetic linkage. • This linkage helps to predict if an animal will have a desired gene. 17 Crossover/Recombination Event P P M M P M M P 18 Advantages • Reduces Generation Interval! – You could know if the trait is present as soon as the embryo stage. • Improve gain in low heritability traits. 19 Advantages • Allows for trait selection prior to expression. – i.e., litter size in pigs thru the estrogen receptor gene. • Opportunity to select in environments where traits are not expressed. 20 Advantages • Combining MAS and phenotypic selection could maximize improvement per generation. – Different studies from 1% to 60%. • Allows for simultaneous selection of multiple traits. 21 Limitations of MAS • The key limitation is a lack of information as to which markers are associated with the desirable alleles. • Without the appropriate breed, line or family information, the selection could be ineffective. • Certain markers are useful only within given families and not others 22 Genomic Selection • Testing today for beef and dairy traits involves us of “chip” that simultaneously tests for 50,000 SNPs! • Effects of individual SNPs are usually small but all are totaled to get the entire effect of the genome on a trait 23 Genomic Selection • New technology revolutionized dairy cattle breeding: Genomic selection • 2 developments • Discovery of thousands of SNP • Increase in accuracy of predicted breeding value • Genomic breeding value (GEBV) • Sum of the effects of SNP • Estimate the QTL effects in reference population with phenotypic information • In subsequent generations, only marker information is required to calculate GEBV GEBV • Prediction equation based on the SNP • Estimating the effects of small segments of genome • Based on markers genotype, sum of the estimated effects of the segments • Can chieve accuracies of predicted breeding values from markers alone of 0.85 Advantages of GEBV • Same accuracy with progeny test EBV • Can be obtained at birth of cattle • Can double the rate of genetic gain • Save the cost for progeny test Limits of GEBV • SNP effects need to be reestimated periodically • Estimates differ between populations – cannot use the same set of 50K testers for Jersey as Holstein Reliabilities for young bulls 1500 Traditional Ped Ave Bulls (no.) 1250 1000 750 GPTA 500 250 0 0 10 20 30 40 50 60 70 80 90 100 Protein reliability (%) 29 30 Information for selection Genes Molec. Phenotypic data Phen. EPD genetics Identified QTL Molecular data Marker EPD 31 Advantages of Molecular Data Unknown genes Phenotypic data Phen. EPD Identified Molecular data Marker EPD genes Heritability of genotype = 1 Available on both sexes, all animals Available at early age But: Markers not available for ALL genes Accuracy marker estimates < 1 32 Selection Strategies Unknown genes Phenotypic data Phen. EPD Identified Molecular data Marker EPD genes Use both marker and phenotypic information Two-stage selection 1o Select on Marker 2o Select on EPD Index selection EPD = Marker EPD + Phen. EPD 33 Pre-selection at young age X ET Test ?? 34 Pre-selection at young age X ET QQ Qq Qq qq QQ Test 35 Evaluation of a large number of SNP with associations with QTL could eliminate aspects of Progeny Testing!! • The accuracy of the BV predicted from a large number of SNPs can be equal to that of “First Proof” bulls with 30-some daughters • This will result in big changes in breeding programs! • Fewer bulls will be tested, further increasing intensity of selection! 36 Use of New Technology in Dairy Selection • Use DNA content to determine with reasonable accuracy the genetic ability of an animal for many traits • Add this info to pedigree information and we can have a lot of confidence in the transmitting ability of an animal even BEFORE PROGENY TESTING! 37 38 Genotype Pedigree 121101011110 111211120200 101121101111 122221121111 101101111102 011111012011 121120011010 0 = homozygous for first allele (alphabetically) 1 = heterozygous 2 = homozygous for second allele (alphabetically) Genotype Data for Elevation Chromosome 1 1000111220020012111011112111101111001121100020122002220111 1202101200211122110021112001111001011011010220011002201101 1200201101020222121122102010011100011220221222112021120120 2010020220200002110001120201122111211102201111000021220200 0221012020002211220111012100111211102112110020102100022000 2201000201100002202211022112101121110122220012112122200200 0200202020122211002222222002212111121002111120011011101120 0202220001112011010211121211102022100211201211001111102111 2110211122000101101110202200221110102011121111011202102102 1211011022122001211011211012022011002220021002110001110021 1021101110002220020221212110002220102002222121221121112002 0110202001222222112212021211210110012110110200220002001002 0001111011001211021212111201010121202210101011111021102112 2111111212111210110120011111021111011111220121012121101022 202021211222120222002121210121210201100111222121101 Genotype Data from Inbred Bull Chromosome 24 of Megastar 1021222101021021011102110112112211211002202000222020002020220 0000220020222202202000020020222222000020222200000220200002002 2002000000222200022220000000000020222022002000222020222220002 2022222222200002002202022202000200022000000002202220000002200 2020002222002020020020202220222222220222020002022022022220202 2202020202200022002220220022200000220200002002002000200222220 0022220202002220022202000020200000022222020200002002002222000 2022022220022000222202200222202020002202202222002220022000200 2202000002200220222000022000022000222202002222000220020020202 2020002220002220022202202200000220220020020020220002000222202 2002220020220200222202220000020220002020020202000220022000002 2022200202220200022002000200022002002000200220222220022022000 2000020002000020220020220200200002220000222002000200222000022 0220020022002202202020202020200022202000220200202202220220000 2020200002020200022222200222200020022022220000020220020200202 022022020200002000200220220002200 Look at the genotype of an Inbred Bull. This is chromosome 24 from Megastar, a double grandson of Aerostar, who has Elevation as a paternal grandsire, and is also on his maternal side. Vast portions are homozygous; due to Elevation. From whom did the bad allele come? Round Oak Rag Apple Elevation (7HO00058) Net Merit by Chromosome Freddie (1HO08784) - highest Net Merit bull Net Merit by Chromosome O Man (7HO06417) – Sire of Freddie Net Merit by Chromosome Die-Hard (29HO08538) - maternal grandsire How good a cow can we make in theory? A “supercow” constructed from the best haplotypes in the Holstein population would have an EBV(NM$) of $7,515 49 50 Questions about Use of this New Technology • What is our degree of confidence in using this data alone to make decisions? • Will we still need to progeny test bulls? • Do we need to sample fewer bulls or more? • Will this change increase in inbreeding of cattle populations? 52 • Should we be testing females? BEEF GENOMICS • Demonstrating the Value of HD 50K MVPs: • Pfizer Animal Genetics has conducted testing on 10 influential Angus sires with High-Density 50K, the first and only Molecular • Value Predictions (MVPs™) from the HighDensity (HD) panel, where more than 50,000 markers are genotyped for each animal. BREAKING NEWS!!!! 54 Demonstrating the Value of HD 50K MVPs • The following tables display the EPDs for each sire along with the HD 50K MVPs and % ranking for each. • HD 50K results reinforce the power of this technology, as the MVPs closely reflect each sire’s high-accuracy EPDs. • HD 50K MVPs can help to more accurately predict genetic merit in young, unproven animals as early as four months of age, as compared to moderate or high-accuracy EPDs that require years of data. Optimizing breeding program design with genomic selection • Reduce the generation internal by at least half • Increase the selection intensity • Benefits by implementing genomic selection on the maternal side • A more appropriate balance in the direction of genetic gain (e.g. increase genetic gain in fertility) • Inbreeding: must be careful when using genomic selection ...
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This note was uploaded on 07/16/2011 for the course ANS 3384 taught by Professor Olson during the Spring '09 term at University of Florida.

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