Heat tolerance, real-life genomics and GxE issues

Ignacy MisztalUniversity of Georgia

ILRI – LiveGene Seminar, Addis Ababa, 3 February 2016

Research in Misztal’s lab at UGA

• 8-15 people (Postdocs + grad students+ visitors)

• Focus on practical results

• Only provider of genetic (genomic) evaluation software in US– Holsteins Assoc– Angus Assoc +_– Major pig companies – Cobb (broiler chicken)

• High scientific output: 10 papers/year over 10 years

Current Projects

• Genomic selection (single step methodology)

• Genetics of:– heat stress– mortality– competition (social interaction)

• Issues in genetic evaluation in dairy, beef, pigs and chicken (also sheep and fish)

Genetics of Heat Tolerance in Holsteins

Selection and environment

Almost all dairy bulls selected in mild or cold environments

Assume genetic relationship between mild and hot performance antagonistic

Is selection indirectly against heat tolerance?

Can one select for heat tolerance?

How to proceed with genetic studies in heat tolerance?

What data to record on heat tolerance? Recording of rectal temperatures or respiration rate

expensive

What models to use?

Production and heat indexPr

oduc

tion

Heat index

AssumptionsPr

oduc

tion

f(heat index)

cow 2

cow 3

cow 1

Breeding value: BV = a + f(THI)*v

a – regular breeding value v – heat-tolerance breeding valuef(THI) – function of temperature humidity index

Studies

Ravagnolo et al., 2000ab and 2002ab

Milk test days from Georgia or FL

NR45-90 from FL

THI from weather stations

Effect of THI on daily milk production

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59

55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85

slope= -0.46

lb

THI

0.52

0.54

0.56

0.58

0.6

0.62

0.64

0.66

0.68

0.7

0.72

50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84

THI

NR

45Effect of THI on Non-return rate at 45 days

Genetic component for heat stress present Genetic correlations between regular and heat

stress effects -0.40

National genetic evaluation of Holsteins for heat tolerance

Can one identify heat-tolerant sires in Holsteins?What are they?

Bohmanova et al. (2005 and 2006)

National US data

Differences between most 100 and least 100 heat tolerant sires

Milk -1100kgFat% +0.2%Prot% +0.1%

Dairy Form -1.4Udder +0.7

Longevity +0.90Fertility +1.6

Total Index +36

Comments Currently selection against heat tolerance in fluid markets

Heat tolerant cows may also be stress tolerant in general Welfare perspective (comfort, mortality, susceptibility to diseases)

Does heat stress vary by parity?

Ignacio AGUILAR*1,2, Ignacy MISZTAL1 and Shogo TSURUTA1

1 Animal and Dairy Science Department, University of Georgia2 Instituto Nacional de Investigación Agropecuaria, Las Brujas, Uruguay

Genetic trends of daily milk yield for 3 parities – regular effect

First Second Third

Genetic trends for heat stress effect at 5.5o C over the threshold

First Second Third

ssGBLUP for Heat Stress in Holsteins (Aguilar, 2011)

• Multiple-Trait Test-Day model, heat stress as random regression

• ~ 90 millions records, ~ 9 millions pedigrees• ~ 3,800 genotyped bulls

• Computing time• Complete evaluation ~ 16 hRegular effect -first parity Heat stress effect – first parity

Heat stress in Days Open (Oseni et al, 2003)

Seasonal trends for DO in Georgia

Days open in Thai crosses

Boonkum et al., 2011

Conception rate in Iranian Holsteins

Jan

FebMarc

hApr

May Jun Ju

lAug Sep Oct

Nov Dec35

40

45

50

55

Mokhtar et al, 2013

Heat stress-summary

• Can evaluate Holsteins for heat stress– Negative correlations with production

• Largest effect in later parities– Poor survival in hot climates

• AI companies not much interested in heat tolerance – small market

• Hot areas - revolution with sexed semen

Genetics of growth in pigs under different heat loads (Zumbach et al., 2007)

• Pigs in NC or TX exposed to heat stress

• Heat stress affect growth • How to model heat stress for

growth?

pigs with wings - 27

Heat stress and reproductive capacity of sows in Spain (Bloemhof et al.)

Two lines: Adapted (I) and Performance (D)

10.510.710.911.111.311.511.711.912.112.312.5

1 2 3 4 5 6 7 8 9 10 11 12

Month of insemination

Tota

l num

ber p

igle

ts b

orn

ID

Focus on research in animal breeding

• Quantitative genetics and BLUP (up to 1990)

• QTL and marker assisted selection (up to 2007)

• Genomic selection (now)

SNP chips and genetic evaluation

• Meuwissen et al. paper

• Early claims of prediction for life with 1000 genotypes

• Initial Experiences– Encouraging results if > 2k genotypes and 50k chips– No long-range prediction under selection

• Multistep methods to use phenotypes of ungenotyped animals

Multistep genomic evaluation in dairy (VanRaden, 2008)

BLUPDD or deregressed proofs

BayesX or GBLUP &

Index hard to calculate

Index &

y

GEBV

DGV*w1

PA *w2

PI *w3

Deregression good if only if high accuracy animals

Complex

National Swine Improvement Federation Symposium, Dec. 2008 (31) Paul VanRaden2008

Value of Genotyping More Bulls

Bulls R2 for Net Merit

Predictor Predicted PA Genomic Gain

1151 251 8 12 4

2130 261 8 17 9

2609 510 8 21 13

3576 1759 11 28 17

Typical result of assuming different SNP distributions

Verbyla et al, 2009

59586060595959

Similar results with genomic relationships and SNP effects

Single-step evaluation (Misztal et al., 2009)

Merge pedigree relationship matrix (A) and genomic relationships (G) into a joint matrix (H) and use in BLUP

H=A+”modifications due to genomic information”

Single-step GBLUP = ssGBLUP

Inverse of matrix that combines pedigree and genomic relationships

-1 -1-1 -1

22

0 0H = A +

0 G - AAguilar et al., 2010

Christensen and Lund, 2010Boemcke et al., 2010

Single-Step / Unified Method

• Simple and fast• Any model• Usually more accurate than other methods • Now industry standard

• Lots of research at UGA– Extensive quality control– Approximation of accuracies– Origin of convergence problems (except in broilers)– GWAS– …

• Large research interest in GWAS• Limitations of classical and Bayesian methods

Can ssGBLUP be used for GWAS?

ssGBLUP for GWAS

05/01/2023 PAG 2012 Meeting

Correlations between QTLs and clusters of SNP effects - simulation

1 2 4 8 16 3245

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SNP cluster size

BayesBSingle SNPssGWAS/1

Wang et al., 2012

Comparison of Three Methods (broilers)

ssGBLUPIterations on SNP (it3)

Classical GWAS

BayesB

0.8%

2.5%

23%

Wang et al., 2012

Plots and accuracies in Zhang et al. (2010)

simulation

BayesBAcc 0.83

Weighted RRGBLUPAcc 0.75

GWAS findings• Better estimates of QTL effects with a cluster of adjacent SNP – why

and what size?

• BayesB gives inflating readings but misses a lot– Multiple SNP solutions for same GEBV? Singularity problem?– Only informal reporting. Why?

• Little or no improvement in accuracy of GEBV with large number of genotypes– Are many large SNP readings artifacts?– Impacts on papers in high-impact journals

05/01/2023 PAG 2012 Meeting

Result with GWA

• Best correlations with QTL with cluster of SNP• Manhattan plots: Few or no common peaks between

breeds or lines• Peaks smaller with larger number of genotypes• Weird behavior of BayesB

– “con-artist method”

Size problem

• Number of genotyped animals– About 1 million Holsteins – Close to 200,000 Angus

• Big computations – what to do

• APY methodology – based on large haplotype sizes in farm populations

APY with Holsteins (Fragomeni et al., 2015)

G needed G-1

APY inverse

Regular inverse

Correlations of GEBV with regular inverse

23k bullsas proven

17k cows as proven

> 0.99

> 0.99

20k random animals as proven

> 0.99

Costs with 570k genotyped animals(Masuda et al., 2016)

• 10 M US Holsteins for type• Computing time for APY inverse 2h

– Would be one month with direct inverse

• Genomic single-step sGBLUP evaluation ~ 2 times more expensive than BLUP

Why APY works?

• Limited dimensionality of genomic information– Limited number of independent SNP clusters– Limited number of independent chromosomal segments (Me)

• Dimensionality ≈10000 for cattle, 4000 for swine, ≈3000 for broilers (Pocrnic et al., 2016)

Impact of reduced dimensionality• Lmited resolution of GWAS ≈1/(2Ne) Morgans

– About 0.5 to 5 Mbases farm animals– About 2-5 Kbases in humans (Li et al., 2012)

• Seems impossible to find causative SNP with GWAS in farm animals (e.g., Veerkamp et al., 2015)

• Can use APY with sequence data if causative SNP identified and qualified by other means– (Brøndum et al., 2014)– Do causative SNP with large effect exist?

Genetic selection in chicken

• 60+ generations of selection (Eitan and Soller, 2002 & 2012)• Great progress for production traits• Problems with reproduction and disease resistance• Problems solved by management (environmental changes), • not genetics

• No reduction of genetic variance• Important genes become fixed and selection follows with new genes

Selection as optimization• Some traits improve, some traits deteriorate

• Hard to detect deterioration if low heritability and small data sets

• Need improved management to compensate

• Why efficiency differs by commercial species? • Pigs and chicken - environment strongly controlled • Dairy - environment controlled• Beef – environment little controlled

Can large QTL exist despite selection?

• Genetics and genomics of mortality in US Holsteins

• (Tokuhisa et al, 2014; Tsuruta et al., 2014)

• 6M records, SNP50k genotypes of 35k bulls

Milk – first parity

Mortality – first parity

African connections at UGAGraduate students

Saudi Oseni – Dept head in NigeriaEmannuel Lutaaya –UgandaNamibia

Dr. Boly – Burkina FasoILRI (Dempfle, McClintock, Gibson)South Africa – universities

UNDP-FAO

RenumberingRENUMF90

BLUP in memoryBLUPF90

Variance component estimationREMLF90 AIREMLF90GIBBS2F90 THRGIBBS2F90

BLUP – iteration on dataBLUP90IODFCBLUP90IOD

Approximate accuraciesACCF90

Sample analysisPOSTGIBBSF90

Computing of extra matricesPreGSF90

GEBV to SNP conversionsGWASPostGSF90Predictions via SNP

PredGSF90

Summary• Long time from first idea to practical results

• Commercial twists

• Genomic selection now mature• Lots of exaggeration and misinformation• Need large data sets across species

• Selection as optimization - winners known but what are the losers

• UGA has tools and focuses on deliverables

Acknowledgements

Yutaka Masuda APY +YAMS

Shogo Tsuruta ALL

Daniela Laurenco Studies+papers+social

Breno FragomeniAPY+pigs

Ignacio AguilarProgramming+advice

Andres LegarraSound theories + papers

Ivan Pocrnic

APY

• Grants from Holsteins Assoc., Angus Assoc., Cobb-Vantress, Zoetis, Smithfield, PIC,…

• AFRI grant 2015-67015-22936 from USDA NIFA• Collaborators

Different interest of different players• Universities and research institutes

• Grants• Papers• Awards

• Genomic companies• Selling services• Selling itself

• Breeding companies• Long term profit• Keeping job

1. PROP12. ESRRG3. PPARG

Modeling issues in genomic selection

• Many quantitative issues– Nucleus - commercial performance– Mortality– Heat stress– New problems (e.g., survival of low-weight piglets)

• Need to have:– Good selection index– Good model– Troubleshooting skills