milt thomas, department of animal and range sciences identification of molecular markers to improve...

Milt Thomas, Department of Animal and Range Sciences

Identification of Molecular Markers to Improve Fertility of Beef Cattle

(USDA-NRI 200835205-18751)


OutlineI. Molecular markers and research - background. II.Fertility traits in beef cattle –background. III.Evolution of a fertility project. IV.Fertility project specifics.

A. Team of scientists.B. Hypothesis and objectives (I and II).C. Populations.D. Additional efforts.

V. Questions.


Quantitative Trait Loci (QTL): chromosomal-locus associated with a quantitative trait.

Genetic Marker: unique DNA sequence passed from parent to progeny (molecular alleles).

Marker Assisted Selection (MAS): using genetic marker(s) in selection.

Marker Assisted Management (MAM): using genetic markers for management decisions.

bovineSNP50: a genotyping tool containing ~54,000 SNP across the bovine genome.

gEPD: genetic marker information used in EPD estimation.

Whole Genome Selection (WGS): using genetic markers across the genome in selection

Linkage disequilibrium: non-random association of alleles at 2 or more loci.

Molecular Markers – Background

Important Definitions


Research approach: QTL Detection


Trait = pregnancy rate

http://www.animalgenome.org/QTLdb/cattle.html

http://www.animalgenome.org/cgi-bin/QTLdb/BT/qdetails?QTL_ID=3574

http://www.animalgenome.org/cgi-bin/QTLdb/BT/draw_chromap?chromos=29



































































http://www.animalgenome.org/cgi-bin/QTLdb/BT/draw_chromap?chromos=X

http://www.animalgenome.org/cgi-bin/QTLdb/BT/draw_chromap?chromos=X


SOCS2 (15.4 Mb)

STAT6 (32.5 Mb)

IGF1 (38 Mb)PMCH (37.7 Mb)

IGFBP6 (20.5 Mb)

STAT2 (33.3 Mb)

LCTN (16.2 Mb)

DeAtley, K. L., G. Rincon, C. R. Farber, J. F. Medrano, R. M. Enns, G. A. Silver, and M. G. Thomas. 2008. Association of microsatellite ETH10 genotypes with growth and carcass trait levels in Brangus cattle. Proc. West. Sect. Am. Soc. Anim. Sci. 59:69-71.

Research approach: Fine mapping/resequencing of genes under a QTL


Eth10

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Research approach: identify important/functional markers


2006047

GG

2004087

GG

2004102

AG

1993002

GG

2002026

GG

2002001

AG

2000128

AA

ANGUS ANGUS

ANGUS

BRAHMAN1/2 BRAH:1/2 ANGUS

3/4 BRAH:1/4 ANGUS

3/8 BRAH:5/8 ANGUS

Growth and carcass QTL on Chromosome 20.

Growth hormone receptor gene underlies the QTL

Resequenced ~1,000 bp flanking GT-SSR

Discovered A/G tag SNP (ss86273136) segregating in Brangus

Garrett, A.J., G. Rincon, J.F. Medrano, M.A. Elzo, G.A. Silver, and M.G. Thomas. 2008. Promoter region of the bovine growth hormone receptor gene: single nucleotide polymorphisms discovery in cattle and association with performance in Brangus bull. J. Anim. Sci. published ahead of print: doi:10.2527/jas.2008-0990.


Research approach: association of genotype:phenotype


ItemGenotypes Pooled

SE P > FAA AG GG

n 87 283 180

205-d weight, kg 271.85 267.44 273.39 4.37 0.2114

365-d weight, kg 510.34 500.00 499.14 6.07 0.1855

Test ADG, kg/d 1.57 1.52 1.51 0.04 0.1558

Scrotal circumference, cm 35.42 35.14 35.08 0.35 0.3525

Intramuscular fat, % 3.56 3.55 3.64 0.06 0.1202

LM area/BW, cm2/kg 0.17 0.17 0.17 0.002 0.8178

LM area, cm2 81.81 81.27 82.01 1.18 0.6062

Rib fat, cm 0.62 0.62 0.66 0.015 0.0204

Table 4. Least squares means among tag SNP ss86273136 genotypes in the GHR gene in Brangus bulls.

+


Validation:



Age at puberty: number of Julian days from birth until puberty is achieved (h2 ~0.4).

Age at first calving: number of Julian days from birth until first parturition (~24 mo).

Pregnant as a yearling: pregnant after yearling breeding season (h2 ~0.2; yes or no).

Calving interval: number of days between successive calvings.

Stayability: probability a cow will remain in the herd until six years of age.

Whole herd reporting: data inventory/recording system for each cow each year.

Fertility traits – Background

Important Definitions:


Reproduction has been described as the most economically relevant component in beef production systems (Willham, 1973 and 1991; Melton, 1995).

National survey results suggest opportunities exist to improve calf survivability and reproductive efficiency in many beef herds (Dargatz et al., 2004).

Genetic improvement programs for reproductive traits have been much slower to develop than for growth and carcass traits due to the difficulty in developing whole-herd data collection systems and the time required to ample data (personal experience with IBBA).

Genetic marker association studies involving fertility are limited as are the number of SNP within dbSNP that are within functional regions of genes (current research program).

Fertility traits – Background

Important Information:

Konni Shirley, Department of Animal and Range Sciences

Trait CC CT TT χ2

P-

value

First observed estrus 61.8 36.4 1.8 30.0 0.01

Estrus at synchronization 66.3 33.7 0 9.1 0.01

Pregnant during 1st breeding season 57.1 41.9 1.0 53.2 0.01

AI pregnancy 66.7 33.3 0 3.3 0.07

Calved by 2 yr of age 59.2 39.8 1.0 51.6 0.01

Percentages of IGF-I genotypes for categorical reproductive trait

Evolution of a Fertility ProjectNBCEC Large Herd Managers Symposium, KC -2005

– Spring born Brangus (n = 190) heifers

– Born 1997 to 2002

– Progeny of 14 Brangus sires


Evolution of a Fertility ProjectNBCEC Large Herd Managers Symposium, KC -2005

Initial Results from Rex Ranch Project Relative NMSU Angus Heifers

HerdPregnancy

StatusNumber

of Heifers

Genotypic Frequency, %

CC CT TT

1 non-pregnant 160 15 49.4 35.6

1 pregnant 166 19.2 41.6 39.2

3 non-pregnant 75 20 44 36

3 pregnant 85 20 40 40

NMSUPregnancy rate > 90% 110 10 51.8 38.2


Discussions at the Large Herd Managers helped form a team of scientist with many industry and ag-experiment cooperators.

Identification of Molecular Markers to Improve Fertility of Beef Cattle; USDA-NRI 200835205-18751.

Milt Thomas, PD, New Mexico State University, Reproductive PhysiologyJim Reecy, Co-PD, Iowa State University, Molecular Genetics and BioinformaticsRohan Fernando, Co-PD, Iowa State University, Quantitative Genetics (QTL)Bob Weaber, Supporting Scientist, University of Missouri, Quantitative Genetics John Pollak, Supporting Scientist, Cornell University, Quantitative Genetics

Sunday Peters, Ph.D. student, New Mexico State University, Molecular Biology(Associate Professor, Department of Animal Breeding and Genetics, University of Agriculture, Abeokuta, Nigeria)


QTL Detection

Validation of MAS tools in commercial

herds across environments and

production systems.

Technology Transfer

Fine Mapping

QTL Detection

Validation of MAS tools in commercial

herds across environments and

production systems.

Technology Transfer

Fine Mapping

Long-term goal: understand in molecular detail the genetic pathways regulating reproductive performance in beef cattle, with the intent of using this information to develop genetic improvement programs for fertility.


Objective 1: Conduct a SNP-based whole-genome scan to identify QTL associated with heifer pregnancy rate.

Objective 2: Develop data and DNA resources from large commercial beef operations to serve as test populations for validation and technology transfer created by achieving Objective 1 and other future candidate gene association efforts.

Objectives

Identification of Molecular Markers to Improve Fertility of Beef Cattle (USDA-NRI 200835205-18751)


Objective 1: Conduct a SNP-based whole-genome scan to identify QTL associated with heifer pregnancy rate.

Use Brangus cattle as admixed populationsof livestock have proven useful in detecting QTL.

Relationship fostered from efforts of IBBA –BIC.


Population for Objective 1: Registered Brangus heifers from Camp Cooley Ranch1.Brangus heifer pregnancy records collected since 1993. 2.DNA and phenotypes on >800 heifers from 54 sires born 2005, 2006, 2007.3.Brangus generation, 4.5 ± 0.044.Trait, pregnant as a yearling, yes (80.6%) or no (19.4%).5.Pregnancy success appears similar among heifers born in the fall vs. spring6.Good success rate with AI.7.Simple statistics for growth and carcass traits:

Birth weight 77.2 ± 0.4 lbs205-day weight 532.2 ± 2.2 lbs365-day weight 799.0 ± 3.3 lbsRibeye area 9.8 ± 0.05 in2

Fat thickness 0.24 ± 0.002 inintramuscular fat % 4.24 ± 0.032 %

0

20

40

60

80

100

Not Pregnant Pregnant

0

10

20

30

40

50

60

70

80

Spring Fall

Number

of Sires

Heifer Pregnancy Percent

0

20

40

60

80

100

Not Pregnant Pregnant

0

10

20

30

40

50

60

70

80

Spring Fall

Number

of Sires

Heifer Pregnancy Percent


Lab work and Statistical Analyses for Objective 1.

1. DNA extraction of WBC-buffy coat at NMSU.2. bovineSNP50 genotyping (54,000 SNP across the genome).3. QTL detection at ISU using a Bayesian approach (Meuwissen et al.,

2001 and XU, 2003). 4. QTL visualization: QTLdb

Trait = pregnancy rate

http://www.animalgenome.org/QTLdb/cattle.html


Objective 2: Develop data and DNA resources from large commercial beef operations to serve as test populations for validation and technology transfer created by achieving Objective 1 and other future candidate gene association efforts.

Note: failure of the 1st-calf heifer to rebreed has been considered to be one of the largest economic drains on the beef industry (Clark et al., 2005).

1. Varied production systems and environments which represent the beef industry in the U.S.

2. Obtain resources that could grow into additional trait evaluations (1st calf heifer rebreeding, stayability, etc.)


Locations of Cooperators (numbers 1-19) and Research Scientists (letters A-D) Working to Study:Identification of Molecular Markers to Improve Fertility in Beef Cattle

1

23

45

6

78

9

10

11

12

1314

15

16

17

18

19

A

B

C

D


Additional Efforts1. Whole genome breeding value estimates (collaboration with Dorian Garrick, ISU).2. Transcriptome sequencing of important tissues (i.e., hypothalamus-NCGR)

and evaluate bovineSNP50 QTL-SNP relative gene expression loci.


Identification of Molecular Markers to Improve Fertility of Beef Cattle (USDA-NRI 200835205-18751)

1.Conclusions2.Acknowledgements3.Questions/Discussion

milt thomas, department of animal and range sciences identification of molecular markers to improve...

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