An Overview of USMARC Swine Genomics Research

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1 SW2535 SW973 SW2406 SW2525 S0297 S RYR1 GPI SW2557 SW2505 S0059 SW1647 SW2419 SW2415 S S0035 SW1329 SW1353 SW1841 SW1057 SW1038,SW1108,SWR2149 SW1302 S0087 SW1376 SWR1130 SWR1634 SW492 SW193 SW1067 SW782 SW855,SW133 S0300 S0333 SW1129 S W4 SW1355,SW122 DGC SWR987,SW617,SW1053 SW446 SWR1923 SW316,SW709 SW1823 SW1473 SW2173 SW71 SW1059,SW280 S0003,SW1818,S0031 S0146 SW353 TTR SW1055 S0228,SWR1211 SW2098 SW917 S0299 SWR726,SW824 SWR1384,S0121 SW1881 SW322 SW1680 SW1069 SW1328 SW1202 SWR823,SW2466,SW2052 SW607 An Overview of USMARC Swine Genomics Research SW1841 SW1057 SW1038 SW1108 SW1302 SWR cccccggagg aggcaaaacc cggcctggat ctctgtacca ctgcgctccg cacggcgcac 61 cccggcgcct tcagtcccaa ctcatgccca ttgcagaggg tgcgccatca ccccgctcct 121 gctgcggggc cgggctccta cgggggggtt aaaagcccct tcctccccct cctgcccgcc 181 ctcccagaag cccccgggcc tggatcccgc ctctgaggat cggattcccg agagccagca 241 ggcctgtttc tccgcaggcg cttggatcac gggtcagggg ctaggctggg aggcgaagaa 301 agaagaggcg tttctgagga gcgggacgca gttctctggc gcggagggcc tggccctccc 361 gcaggacggc ctctactacc tctactgtca cgtcggctac cggggccggg cacctcctcc 421 cggcggggac cccctggacc gctcggtcac gctgctcagc cggctgtacc gggcgggggg 481 cgcctacgga ccggggactc ccgagctgct gctggagggc gcggagactg tgactccggt 541 cttggacccc agtcggaggc acgagtacgg gcccctctgg tacacgagcg tggggttcgg 601 tggcctggtg cagctccgga ggggcgagag ggtgtacgtt

2 To know where you are going, you must first know where you have been.

3 A Brief History of Swine Genetics Research at USMARC

4 Swine Genetics Research at USMARC Dr. Gordon Dickerson Bio-economic modeling of life-cycle efficiency of pork production Conducted crossbreeding experiments to estimate heterosis retention 4 breed maternal cross composite population 4 breed terminal sire cross composite population

5 Swine Genetics Research at USMARC Dr. Dewey Harris Part of the collaboration that developed STAGES Conducted research to develop computerized decision support algorithms for swine production systems.

6 Swine Genetics Research at USMARC Dr. Larry Young Studied the genetic components controlling litter size Led the ARS component to characterizing three different Chinese breeds of swine. Fengjing, Meishan and Minzhu

7 Swine Genetics Research at USMARC Dr. Larry Young Studied the genetic components controlling litter size Led the ARS component to characterizing three different Chinese breeds of swine. Fengjing, Meishan and Minzhu

8 Swine Genetics Research at USMARC Dr. Dale Van Vleck Developed MT-DFREML

9 Swine Genomics Research at USMARC The beginning of the Genomics Era Microsatellite-based linkage maps Libraries BAC map consortium member Swine Genome Sequencing consortium member International Porcine SNP Consortium member

10 Genome Scans at USMARC First scans utilized a multi-generational Meishan x White Composite population Reproductive characters Uterine capacity Ovulation rate Testes size Carcass composition Fat accretion Feed intake and efficiency on a limited number of pigs

11 Genome Scans at USMARC Second generation of scans has utilized a ½ Landrace, ¼ Duroc, ¼ Yorkshire population Reproductive characters Carcass composition and pork quality Behavior Most studies utilized the Illumina SNP60 BeadChip Population has 10 generations of animals

12 Current Approach Validate and identify causative genetic variation for economically important traits Redesign/redefine research population(s) Utilize latest technological advances in genomics

13 Swine Focus Group Report USMARC swine population needs to be more similar to commercial production Upgrade genetics Modify production schedule Continuous farrowing Retain sows for more parities

14 Swine Population at USMARC Beginning in the 2011, all pigs are produced via artificial insemination with semen purchased from commercial boar studs. Alternate annually between Landrace and Yorkshire/Large White boars. 19 litters are farrowed each week and sows are retained through their 4 th parity unless culled for structural or reproductive problems.

15 Additional Swine Populations National Pork Board funded Sow Lifetime Productivity project, phase 1 and phase 2, will provide a large number of commercial animals with detailed reproductive information (Dr. Vallet). Meat Safety and Quality Research Unit samples commercial abattoirs to study pork quality parameters (Drs. Shackelford and King).

16 Phenotypes Studied - Reproduction Objective is to increase the number of full-value piglets produced by a sow in her lifetime. Litter size Longevity Piglet viability

17 Phenotypes Studied Pork Quality Objective is to increase the quality and consistency of lean pork. Tenderness Intra-muscular fat Color Stability Consistency

18 Phenotypes Studied - Behavior Objective is to understand how pigs interact with their environment. Other pigs Caretakers Temperature

19 Phenotypes Studied Nutrient Utilization Objective is to understand how nutrient utilization in a growing pig will reflect how a sow utilize nutrient during her lifetime.

20 BX Population Duroc X YL sows X Landrace NGS 2001 DU-YL X LA-YL 2002 F1 BX line pigs 2005 BX line pigs NGS Phenotypes 2008 BX line pigs 2010 Final BX line pigs SNP60 Microsatellites GGP

21 Future Genotyping Strategies In the new swine population, NeoGen s GGP HD chip is ran on all AI sires. Founding dams were from the BX population and have similar genotypic data as the AI sires. Dams in this population get genotyped with NeoGen s GGP LD chip. Target SNP genotypes collected with Sequenom. Envision NGS data or genotyping-by-sequencing will dramatically change this approach.

22 Improving the Swine Genome Sequencing a boar from the animals used to study the stress defect using the PacBio Sequencer. Goals: Increase contig length Improve contig orientation Lead to improved gene prediction Enhanced assembly of the Y chromosome

23 Conclusions A tremendous amount of phenotypic data have been collected within our population(s). Detailed genotypic information collected throughout the population along with sequence variants discovered in the BX population will provide useful candidate SNP to test. Finding QTN will provide valuable information towards understanding quantitative genetic variation and basic biology.

24 Acknowledgements Kreg Leymaster Gary Bennett John Keele Dan Nonneman Ralph Wiedmann Tim Smith Tami Brown-Brandl Brad Freking Andy King Clay Lents Jeremy Miles Lea Rempel Steven Shackelford Jeff Vallet

25 MARC

26 Questions Thank you for your attention.