Genomics in Livestock Breeding Flashcards

1
Q

Genomic information – the basic tools

A
  • Microsatellites/Restriction Fragment Length Polymorphisms • No longer used as marker of choice
  • Single Nucleotide Polymorphisms (SNPs) • Whole genome sequencing
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2
Q

What are Single Nucleotide Polymorphisms (SNPs)?

A
  • Single base pair positions in DNA at which different sequence alternatives (alleles) exist in a population
  • Only considered to be SNPs if least abundant allele has a frequency of 1% or more (minor allele frequency – MAF)
  • Frequency estimated to be 1/1000 base pairs
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3
Q

Why are SNPs useful?

A

• Distributed throughout the genome – coding and non-coding DNA
• Potential of high density of markers
• Automated genotyping of SNP arrays – more cost
effective
BUT
• Only two alleles at any SNP (less variable that micro-satellites)
• SNP arrays only available for well characterised species (e.g. livestock)

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4
Q

SNP arrays used in Livestock

A

• Low density (5K – 20K)
• 5-20 000 SNPs
• Low cost option
• Insufficient detail for many applications • Can be used with medium density arrays
• Medium density (40-50K) • 40-50 000 SNPs
• Used widely
• High density (700-800K) • Expensive
• Used for sires and research
Each SNP array is specific to one company

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5
Q

Imputation of genotypes

A

Genotype imputation is a process of estimating missing genotypes from the haplotype or genotype reference panel. It can effectively boost the power of detecting single nucleotide polymorphisms (SNPs)

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6
Q

Whole genome sequencing

A
  • Original method - Sanger Sequencing
  • Series of labelled single strands of DNA
  • Separated by electrophoresis
  • Used to construct a base by base copy of fragments of up to 1Kb
  • Genome Assembly - overlap of fragments used to construct a whole genome sequence
  • Usually only the non-repetitive DNA that is sequenced – but gaps are being filled in
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7
Q

Genotyping by sequencing - GBS

A
  • Efficient – high throughput sequencing
  • Restriction enzymes digest DNA • PCR used to replicate fragments • 100 bp fragments sequenced
  • Detailed genotyping of whole genome
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8
Q

Parentage assignment and verification

A

• Maintain/verify accuracy of pedigree records
• Extensive systems – avoids handling at birth e.g
Deer and Sheep
• Animals to small to tag at birth e.g. Fish
• Multiple sire mating
• Parentage panels - SNPs are chosen because they are variable across many breeds/populations

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9
Q

Single locus traits – e.g inherited disorders

A

Increasing number of genetic tests available:

Use individual SNPs known to be associated with locus

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10
Q

Polygenic traits

A

Polygenic traits QUANTITATIVE

• Information from SNP arrays used to increase accuracy of Estimated Breeding Values

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11
Q

Clues to an animal’s breeding value

A
• Own performance
• Repeated measures of
performance
• Performance of relatives
• Performance in related traits
•Genomic information
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12
Q

Estimated Breeding Values

A

Best Linear Unbiased Prediction

Own performance
Repeated measures of
Performance
Performance in
related traits
Performance of relatives
Genomic information
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13
Q

Genomic Breeding Values

A

• Uses Single Nucleotide Polymorphism (SNP) genotypes at thousands of sites across the genome
• 5K SNP chip – 5000 genotypes
• 700K SNP chip – 700 000 genotypes
• Increased accuracy of EBVs based on:
• Prediction equations that relate genotypic
variation to performance
• Better measure of genetic relationship between animals

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14
Q

Reference population

A

• Increased accuracy based on prediction equations that relate genotypic variation to performance

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15
Q

Genomic breeding values – increasing accuracy

A
Increased accuracy based on prediction equations that relate genotypic variation to performance
• Once prediction equations established 
• Genotypes from DNA sample can be
used to predict breeding value
DNA can be sampled from birth
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16
Q

Genomic breeding values – accuracy

A

Accuracy will depend on:
• Heritability
• The size of the reference population
Accuracy may decrease over time - recombination

17
Q

Genomic breeding values – relationships

A

• Increased accuracy based on:
• Prediction equations that relate genotypic
variation to performance
• Better measure of genetic relationship between animals
Example:
ON average full sibs share half their genes
It actually varies from ~ 40 – 60% of their genes

More precise information on shared DNA
sequences gives more precise EBVs

18
Q

gEBVS

A

– useful for hard to measure traits
Can increase accuracy of EBVs for post slaughter traits
Example: Using phenotypic information from progeny with sire notified on the passport

19
Q

gEBVS – making use of commercial data

A

Can increase accuracy of EBVs in pure lines for improved performance of crossbred production stock

20
Q

What influences the rate of improvement?

A
  • Heritability of the traits
  • Selection pressure actually achieved
  • Accuracy of selection
  • Generation interval
  • Genetic correlations with other traits
21
Q

Genomic information – the future?

A
  • Better management of inbreeding based on shared genotypes
  • Better mate selection
  • Selection of crossing lines to achieve better heterosis?