Flashcards genomics vs genetics + variants

impact of genomics on genetic change

1. increase productivity

2. longer lasting animals

3. lower cost (higher feed : gain)

Genomic breeding values

can give info on hard-to-measure traits (carcass weight)

applications of genomics

1. detect genetic disorder conditions

2. improve accuracy of selection

3. percentage testing (breed or ethnic %)

4. genomic selection

5. bio

6. custom marker panel

7. traceability + parentage verify

8. heterosis/hybrid vigor

9. QTP mapping, GWAS, candidate gene

10. biomarker development

11. ID haplotype

12. genetic diversity

genomics

study all genes in org. structure, function, compare. genotype (DNA) + phenotype = GBV

goal of genomics

use genome to predict performance

genetics

pedigree + phenotype = EBV (estimated breeding value)

Omics

study all bio molecules in org.

metabolome

list of all metabolites in organism

transcriptome

all RNA transcripts in 1 cell/tissue.

proteone

list of all proteins expressed

metagenome

all genetic material

phenome

get high-dimentional phenotypic data, different levels of activity (behavior

monogenetic trait

trait is decided by one gene (coat color)

polygenetic trait

trait is decided by multiple genes (milk production)

qualitative trait

categorical, able to be divided into categories (calving ease)

quantitative trait

bell curve, not in categories (height)

William Thomson

to measure is to know, cannot improve if cannot measure

Robert Bakewell

Old father of animal breeding. intro systemic recording performance

multidisciplinary genomics

bio, genetics, comp sci

genomics long term benefit

enhance breeding

genetic variants (markers)

1. SNPs : (short) single nucleotide variant (SNV)

2. Indels : (short) insertion or deletion

3. copy number variants : (structure) and short tandem repeats

genomics short term benefit

find haplotypes (lethal) + genetic disorders, parentage testing, sire assignment, genetic diversity- genomic relationship/inbreeding and hybrid vigor

1. structural genomics

physical nature of genomes- based on DNA (not tissue specific) gen seq and QTL mapping, GWAS, detect SNP, Indels, CNV

inbreeding

the probability that two alleles at any locus in an individual are identical by decent from a common ancestor

hybrid vigor

the offspring two genetically diverse parents has enhanced traits compared to its parents

QTL

genomic regions that hold genes or genetic variants. look for location + which genes or variants

QTL mapping

Genome wide association analysis. use full sequence to detect genes that control variation between traits. Look for causative genes.

GWAS

genome wide association study- genetic diff linked to diseases or traits (X-linked)

copy number variants

segment of DNA where copy number differences are- too many copies of one gene. happens during meiosis, mechanisms: Non Allelic homologous recombination (NAHR) + Fork stalling and Template Switching(FoSTeS)

NAHR

Nonallelic homologous recombination - mech for CNV. DNA strands switch places bc they look similar but are not matching pairs *during meiosis*

FoSTes

Fork Stalling and Template Switching. mech for CNV

DNA replication on strand 1, stops, puts new DNA from strand 1 onto strand 2. continues replicating on strand 1 *during meiosis*

KIT

ex. CNV, coat color in pigs controlled by stem cell. CNV duplications of regulatory elements up+downstream of KIT gene locus

genotype

combination of alleles at a locus

Indels

insertion or deletion

Most common genetic Variant

SNPs

Linkage disequilibrium (LD)

2 alleles are inherited more or less than expected by random chance. There IS a NON-random association between alleles at different genomic locations. alleles distanced but inherited together

2. Functional Genomics

study expression and function of whole genome. Transcriptome, meta-transcriptome, epigenomics, eQTL + gene enrichment, CHIP-seq

Meta-transcriptome

study mRNA expressed by microbes

epigenomics

study tags on DNA that control gene expression

eQTL + gene enrichment

are eQTLs clustered by chance or linkage. effects how much RNA a gene makes- expression.

ChIP-seq

ID where proteins bind to DNA - Chromatin Immunoprecipitation seq

comparative genomics

comparing genomes of diff org. detect evol changes

knowledge of gene structure + function in one species can be applicable in another (myostatin)

3 reasons for DNA testing

1. health benefit

2. traits

3. ancestry

DNA testing health benefit

1. ID haplotypes

2. recessive traits

3. GBV- select higher disease resistance

haplotype

alleles of different genes inherited together (albino + deaf)

DNA testing for traits

GBV for traits of offspring

DNA testing for ancestry

see genetic diversity of individual

genetic eval database

needs reference pop with data form all breeds being tested to be accurate- if no angus in reference pop, angus genetics will not be flagged

what is genetic eval

genetic relationship between candidate and the animals in reference pop

larger reference pop

more accurate prediction of GBV

larger # of breeds in reference

increase # SNPs identified in panel

SNP

substitution 1 nucleotide at specific position. minor allele freq less than 1%

Indel

insertion (dna gained) or deletion (dna lost). shift codon reading frame- changes amino acids connected. change peptide protein seq + gene function

frameshift mutation

insertion 1 nucleotide pushes all other nucleotides down line + incorrect amino acid sequence after insertion or deletion

indel deletion ex. MSTN

deletion of 11 base pair nucleotides in the third exon -> frame shift -> lose 102 amino acids

no Myostatin, muscles grow 40%, incomplete autosomal dominance

incomplete autosomal dominance

one allele on non-sexed chromosome does not fully mask effects of another- both phenotypes of alleles are seen

DGAT1

Diacylglycerol O-Acyltransferase. enzyme catalyzes final step of mammary triglyceride synthesis (milk fat)

SNP in DGAT1

in cattle, A (Alanine) instead of K (lysine) so lower milk fat. K (lysine) version -- more milk + higher fat

SNP version is w A (Alanine)

factors that shaped genomics

1. Seq tech + reduce cost of seq

2. HPC (high performing Computing facilities)

3. ML (machine learning) data analysis approach

ML data anal

find pattern, predict without coding

sequencing tech

high-res image of all nucleic acids

illumina seq

seq by synthesis, short read. 2nd gen seq. - copy DNA strands

Sanger DNA seq

capillary electrophoresis, shorter strand dna travels farther in gel

nanopore

long read seq- attach to cell membrane + process dna strands. real time seq

PacBio

HiFi: high fidelity

SMRT: small mol. real time seq

99% accurate long read, complete genomes, detect base mods. isolate DNA + use adapters to make circular

most challenging part of genomics

getting big data computation + analyses. HPC and cloud computing providers

Cloud computing

use when no HPC

1. AWS- amazon wed service

2. google cloud

3. digital ocean

4. microsoft azure

why cost of genome seq decrease?

because of next generation sequencing (NGS)

process of genotyping my sequencing

1. a FASTQ: store nucleotide seq + their quality scores

2. 2 reading filtering: remove low quality data, unneeded DNA

3. c alignment: compare individuals dna to other org, finds similarities

4. 4 BAM file (Binary alignment map): store genetic data aligned to a reference genome

5. e variant calling: find difference of individual + reference pop

6. 6 VCF, variant call format: stores genetic seq variations + differences

GBS

genotyping by sequencing. analyze genetic variations (SNP, indel, CNV). testing fixed set of markers on genome- not analyzing whole raw genome

FASTQ

raw data file

breadth of coverage

% of bases from reference pop that are captured at a specific depth. How much of the genome was mapped

deep sequencing

genome scanned + analyzed 15-30x , fewer errors

low pass sequencing

genome scanned + analyzed 0.5-1 x, lots of errors

results from sequencing

1. Data analysis for genomic + transcriptomic seq files

2. detect genetic variants (SNP, Indel, CNV)

3. Genotypes

COSMIC database

Catalog of somatic mutations in cancer. info abt genetic variants in caner, does SNP cause cancer or not

High quality assembled genome (HQ MAG)

precise, continuous (DNA sequence connected instead of fragments), contains whole genome. reconstruction of DNA sequence

Low density SNP panel

3-30K snps

medium density SNP panel

50-150K snps

HD - high density panel

700,000 snps. based on full/deep sequencing

most common genetic variance

SNP

1990s genotyping

use 1 marker on genome to look for QTL

2001 genotyping

add more markers onto genome, genome wide selection. easier to find QTL bc more markers

4 ways to see genetic + genomic evaluation

1. SNP

2. QTL mapping

3. genome prediction (GBV)

4. sequence genome

Dairy industry genome prediction

uses to get genomic predicted transmitting ability (GPTA = 1/2 GBV)

Beef industry genome prediction

uses to get genomic expected progeny difference (GEPD = 1/2 GBV)