Systems And Methods For Predicting a Livestock Marketing Method
The present invention is directed to methods and systems for improving the efficiency of livestock production using genetic information obtained from the animal. The methods of the invention comprise obtaining a genetic sample from an animal or embryo, determining the genotype of the animal or embryo with respect to specific quality traits, grouping animals with like genotypes, and optionally, further sub-grouping animals based on like phenotypes. The invention is further directed to a method of predicting the carcass quality of an animal by correlating the rate of change in carcass traits with the genotype of the animal.
1 - 16 . (canceled)
17 . A method for identifying a bovine animal having a higher Placement Weight (PW), a higher Ultrasound Backfat at placement (BF), a higher Percent Choice (PC), a higher Marbling Score (MBS), a higher Calculated Yield Grade (CYG), a higher Plant Backfat (PB), and a higher Live Weight at Slaughter (LWS); and a lower Frame Score at placement (FS), a lower number of Days on Feed (DF), and a lower Dressing Percentage (DP), as compared to a general population of bovine animals, said method comprising:
(a) obtaining a biological sample from said bovine animal, said biological sample comprising nucleic acids encoding the bovine leptin gene from said bovine;
(b) detecting in said nucleic acids the presence of at least one the following combinations of an EXON2-FB (E)/UASMS2 (U) genotype: E=TT and U=CT, E=TT and U=TT, and E=CT and U=TT; and
(c) correlating the presence of the nucleic acid content of (b) with a higher PW, BF, PS, MBS, CYG, PB, and LWS; and a lower FS, DF, and DP; thereby identifying the bovine animal.
18 . A method for identifying a bovine animal having a lower Placement Weight (PW), a lower Ultrasound Backfat at placement (BF), a lower Percent Choice (PC), a lower Marbling Score (MBS), a lower Calculated Yield Grade (CYG), a lower Plant Backfat (PB), and a lower Live Weight at Slaughter (LWS); and a higher Frame Score at placement (FS), a higher number of Days on Feed (DF), and a higher Dressing Percentage (DP), as compared to a general population of bovine animals, said method comprising:
(a) obtaining a biological sample from said bovine animal, said biological sample comprising nucleic acids encoding the bovine leptin gene from said bovine;
(b) detecting in said nucleic acids the presence of at least one the following combinations of an EXON2-FB (E)/UASMS2 (U) genotype: E=CT and U=CT or E=TT and U=CC; and
(c) correlating the presence of the nucleic acid content of (b) with a lower PW, BF, PS, MBS, CYG, PB, and LWS; and a higher FS, DF, and DP; thereby identifying the bovine animal.
19 . The method of claim 1 or 2 further comprising the step of determining whether the bovine animal should be marketed via the “Dressed Weight” or “Live Weight” marketing method in order to obtain greater revenue per bovine animal.
20 . The method of claim 19 wherein bovine animals having the E=CC/U=CC, E=CC/U=CT, or E=CT/U=CC genotype are determined to be marketed via the “Dressed Weight” method, and bovine animals having all other E/U genotypes are determined to be marketed via the “Live Weight” method, in order to obtain greater revenue per bovine animal.
21 . The method of claim 20 wherein bovine animals having the E=CC/U=CC genotype generate higher revenue, as compared to a general population of bovine animals, and wherein bovine animals having the E=CC/U=TT generate a lower revenue, as compared to a general population of bovine animals.
22 . The method of claim 20 wherein greater revenue is generated per bovine animal based upon the following rank order of genotypes: E=CC/U=CC>E=CT/U=CC>E=CC/U=CT>E=TT/U=CC>E=CT/U=CT>E=CC/U=TT.
23 . The method of claim 1 , 2 , or 20 which further comprises inputting into a computer bovine genotype data, optionally including whether the bovine should be marketed via the “Dressed Weight” or “Live Weight” method, to assist an owner of cattle in obtaining the greatest revenue for the bovine animals.