Animal Breeding and GeneticsBig Data and Ag Tech

John J. CrowleyCanadian Beef Breeds CouncilLivestock Gentec at University of Alberta

ABIC 2017, Winnipeg, MB

@Gentec_john

Canadian Beef Breeds Council

To provide a unified voice in support of

the purebred genetics provided within

the Canadian beef cattle industry

To ensure the continuity, growth and

prosperity of the Canadian purebred

cattle sector as an integral component

of the Canadian beef cattle industry

SUPPORT PROMOTE REPRESENT

Livestock Gentec- University of Alberta

• An Alberta Innovates Bio Solutions

Center

• Commercial benefits to the Canadian

livestock industry

• Dept. of Agriculture, Food & Nutritional

Science at University of Alberta

Genetics creates potential, management delivers

End Product

• Measures of genetic merit (breeding values/EPDs)

• Indexed measures

• Mate allocation, breed composition

Herd/Farm Specific

National

International

Interface

Data collection and collation

Analysis

• Mixed Model Equations

• Relationship matrix generated through pedigree

• Animal and maternal random (genetic) effects

• Solutions of genetic merit for a suite of traits

• Indexing based on economic relevance. Unit = $

Analysis with the Advent of Genomics

Animal Chr1- 1 Chr1-2 Chr1-3 . . . . . . . . Chr15-100 Chr15-101 . . . . . . . .

1 AA AA AB . . . . . . . . AB AB . . . . . . . .

2 AB AA AB . . . . . . . . AB AA . . . . . . . .

3 AA AB AA . . . . . . . . BB AA . . . . . . . .

4 AA AA AA . . . . . . . . BB AA . . . . . . . .

Data sets get bigger

- New variable (DNA), increase computational demand multi-fold

- Increased efforts for phenotypes

DNA information in the form of base pair; ~50k loci spread across the genome

Stats Methods, Software and Hardware

Methods

• Advances in matrix algebra, Bayesian statistics

Processing

• Large RAM, Multiple Cores, GPU

Storage

Movement

Whole Genome Sequencing

Whole Genome Sequencing

Meyer and Tier, CSIRO, 2015

Genotype

Sequence

Content Format Size Computation Time

Sequence reads FASTQ ~30 GB Two hours for transfer from seq provider

QC reports HTML ~300 KB Less than one hour using one core

Mapped sequence reads BAM ~30 GB One day using 12 cores

Mapping reports Text ~1 KB A few seconds using one core

Variant calls VCF ~10 GB for 30 animals One and a half days using 30 cores

Genotype reports Text ~80 MB One hour using one core

*P. Stothard, UofA

Dairy increase in gain with genomics

www.cdn.ca/document.php?id=470

Utilizing GenomicsUse Seedstock Commercial Feedlot Packer

DNA Assisted Selection X X

Parentage X X

Recessive Allele Testing X X

Control of Inbreeding X X

Mate Selection X X

DNA-based Management X X X

DNA-based Purchasing X X

Product Differentiation X

Traceability XSource: Van Eenennaam, 2012

End Goal

Where;

ΔG is genetic gain

i is selection intensity

r is selection accuracy

L is generation interval

σa is genetic SD

• Profitability/Sustainability

• Product Quality

• Greenhouse gases

• Consumer Confidence (Social Licence)

• Anti-Microbial Resistance

Selection Goals

Challenges

• Difficult/expensive to measure traits

• Improving ease of data capture

• Speed of analysis

• Affordability

• Infrastructure for application in beef

Thank you!@Gentec_john