QTL Course Toulouse

8/6/2019 QTL Course Toulouse

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XI QTLMAS

2007

Papers and abstracts from the Workshop on

QTL

and

Marker Assisted Selection

22-23 March 2007, Toulouse, France

Edited by Andres Legarra

INRA

Station dAmelioration Genetique des Animaux

BP52627

31326 Castanet Tolosan CedexFrance

2007

INRA


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Contents

1 Program 5

2 Advances in QTL detection theory 1 9

3 QTL in practice 25

4 A bit on plants 64

5 Genomic selection 65

6 Advances in QTL detection theory 2 82


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3 QTL in practice


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,")","'$"1LJ0+/,#EX?0.0012). As these two families were

sampled in Austria with the different sampling logistic we presumed an error in the sampling

process and omitted these families in the further analyses. Totally 3701 tests had a correctvariance between the two replicates. Out of 3701 tests, 531 was significant with a probability

value of P0.01. The significant results were

distributed over all 29 autosomes. The results of the single marker tests for the QTL linkage

on the given chromosome were combined together by approximate interval mapping (AIM)

analysis, performed as described by DOLEZAL et al. (2005). Finally, we detected 31 QTL

regions distributed across 26 chromosomes.

GDD20: First granddaughter design was comprised of twenty families (GDD20) with totally

1332 sons. Number of sons varied from 39 to 145, with an average of 67 sons per family.

Some GDD families were strongly related since some family sons were also grandsires in the

design. At the same time eleven DD18 family sires were presented as sons in a granddaughter

design, thereby making a connection between these two designs and assuring an independent

sample for a confirmation of mapping results.

Initial interval mapping was performed in GDD20 with a QTLexpress program

(SEATON et al., 2002). We used model for half-sib design with corrected breeding values

(cBV) as a phenotype, weighted by respective reliabilities. Interval mapping was performed

using one-QTL model on every cM along the chromosome. Chromosome-wise significance

threshold was calculated based on 10,000 permutations. Confidence interval was calculated

by 10,000 iterations of the bootstrapping option. Analysis for one chromosome was done first

with all families together. For significant or indicative results we also performed family-wise

analyses in order to determine QTL status for each family (similar to SCHNABEL

et al.

, 2005).

FV-ROOT: For seventeen DD18 and seventeen GDD20 families, respectively, we were able

to sample the sire of the sire and all available male ancestors up to important founders. This

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way we built up a complex five generation pedigree (FV-ROOT) comprised of 75 animals

(Fig.1). FV-ROOT was genome-wide genotyped for 381 markers.

FIGURE 1.- Complex pedigree (FV-ROOT) showing the 18 daughter design sires (DD18, arrows), 20

granddaughter design sires (GDD20; gray circles) of the initial GDD and 11 granddaughter design

sires (GDD11; black circles) of the GDD for intensive study, with all available ancestors back to

important founders (F1, F2). Only one granddaughter design sire is coming from an independent

family that connects neither to both F1 and F2 nor to remaining family sires (*). In the pedigree

squares are representing male and circles female animals. Symbols for non-genotyped animals are

crossed with a diagonal line. In order to reduce the complexity of the picture founder F1 is shown

twice.

STAGE TWOHaplotyping analysis and identity by descent (IBD) mapping: Haplotyping analysis was

performed by SimWALK2 program for all 29 autosomes in FV-ROOT and was able to assign

origin of a chromosome region to the Fleckvieh or to the Red Holstein (RH) ancestor.

Obtained haplotypes were graphically presented for all chromosomes. In the graphics, the

haplotype flow from a founder to actual generation was much easier to track and

recombination sites were easier to observe. As family sires are part of FV-ROOT these were

genome-wide haplotyped in context of complex pedigree and scanned for QTL by selective

DNA pooling. Following the principles of IBD mapping, all markers for QTL affecting PP

and/or MY in ABFV families were checked for their origin, determined by haplotyping

analysis. If the haplotyping analysis was pointing to the RH origin we investigated these

regions closely. There were totally eight QTL regions, which were closely inspected forpossible RH introgression: BTA01 proximal, BTA05 distal, BTA09 distal, BTA10 proximal,

BTA19 central, BTA23 central to distal, BTA25 central and BTA28 distal. As the QTL

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mapping by selective DNA pooling was performed in different stages and there was a good

concordance between single marker test and AIM results on the one hand and haplotype

analysis for two ABFV families, segregating for the QTL affecting PP, on the other hand,

BTA19 was selected as the most promising candidate for further intensive study.

On BTA19 we used nine markers in the genome wide scan. AIM results showed the

presence of a highly significant QTL affecting PP. The most possible position of the PP-QTL

was estimated between 20 and 70 cM, with the highest peak on the marker URB44 at 39.01cM (Fig. 2). The evaluations of family wise AIM-statistic curves showed that two ABFV

families are contributing to the highest effect on marker URB44 with the negative effect onthe PP (-0.017). Family-wise AIM also indicates a possibility of one more segregating family

of purely FV origin at the adjacent marker (BM17132).

According to single marker test and AIM results all the ABFV sires were grouped into

two groups. In the first group were two sires showing the significant effect on the PP and in

the second group four non-significant ABFV sires. Sires haplotypes were compared for these

two groups in order to possibly localize the PP-QTL better (similar to RIQUETet al.; 1999, Fig

2). The two significant families shared the same Red Holstein haplotype in the vicinity of

URB44. Four non-significant sires, sorted on the size of the received RH haplotype, got:

The proximal and distal part of RH haplotype block but not the central part (URB44and BM17132) Only the distal part, including BM17132 Short chromosomal fragment in the vicinity of marker URB44, possibly also the next

proximal marker

Didnt receive the RH haplotype at allIf one sire received the Red Holstein haplotype on marker BM17132 and the other one on

URB44 but both of them are not significant for the QTL affecting PP while the significant

sires got Red Holstein haplotype on both markers then it could mean that the QTL lies

between these two markers, in a region of approximately 20 cM. Of course, this inference is

correct only under assumption that all performed analyses are accurate and there are neither

false positive nor false negative. Both PP-QTL segregating families were selected for the

intensive study.

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FIGURE 2.- Identity by descent (IBD) mapping for chromosome 19 (BTA19). A) Results from

approximate interval mapping (AIM) for protein percentage (PP) and B) for milk yield (MY) are

shown. C) The haplotyping analysis for BTA19 is shown only for advanced backcross population

(ABFV). The important founder F2 is marked red and eight family sires are marked A-H. F2s

haplotype coming from Red Holstein is marked red, F2s haplotype coming from Fleckvieh is marked

blued and non-F2 haplotypes are marked grey. Each square in the haplotype presents one marker usedin the analysis. Paternal haplotypes are placed left and maternal right. In the pedigree squares are

presenting male and circles female animals. Symbols for non-genotyped animals have a diagonal line

through them. Three ABFV families, segregating for the quantitative trait locus (QTL) affecting PP

and QTL affecting MY, are marked with a yellow arrow. Families excluded from analysis are marked

with an asterisk (*).

STAGE THREE

Initial interval mapping: Initial interval mapping on BTA19 was conducted by QTLexpress

in GDD20 with the data coming from previous mapping studies in the Fleckvieh population.

Totally 16 markers were used for the analysis. Because all marker genotypes were not coming

from one project but were collected from different projects, not all families were genotypedfor all the markers (Fig. 3). The interval mapping procedure by QTLexpress and similar linear

regression based programs are able to combine different families genotyped for different

marker sets into one across-family analysis. Initial interval mapping gave us indications that

there might be two QTL on BTA19 (both with F-statistic score between 2 and 3)- one QTL

affecting PP at approximately 55 cM and second one affecting MY and PY at approximately

102 cM (Fig. 3). Family-wise analyses were conducted in order to determine QTL status for

each family (similar to SCHNABELet al., 2005). Altogether six families were heterozygous for

QTL affecting PP (PP-QTL), out of three families are ABFV families and three sires are pure

FV sires. Surprisingly, two out of three ABFV sires didnt get the Red Holstein haplotype at

all. Results from initial interval mapping are indicating, together with family-wise AIM

results, the possibility that this PP-QTL is already present in FV. To test this possibilityfurther all six families segregating for PP-QTL were included in set for the intensive study.

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FIGURE 3.- Initial interval mapping in

granddaughter design (GDD20). Interval

mapping results for milk yield (MY), fat yield

(FY), protein yield (PY), fat percent (FP) and

protein percent (PP) are shown. Positions of

markers used in analysis are denoted on the X-axis and the number of families, genotyped for

each marker, is denoted. F-statistic values are

presented on Y-axis and chromosome length in

n centiMorgans (cM) on X-axis.

STAGE FOURGDD11: Totally eleven granddaughter design families (GDD11) were chosen for the

intensive study: Two families were chosen according to the results of selective DNA

pooling (DD18) and six families, according to results from initial interval mapping

(GDD20). Additional three families, two closely related and one unrelated, which were non-

significant by the selective DNA pooling but are very important for the genetic active

Fleckvieh population, were chosen for the set to contribute the fine mapping by maternal

haplotypes. GDD11 consisted of totally 681 animals. Number of sons varied from 22 to 96,

with an average of 63 sons per family.

Markers were selected mostly in the region of the possible location of the PP-QTL

(Fig. 4). Out of 22 markers in intensive study three were excluded from the further analysisdue to the inconsistent results (UW33 and DIK4688) and null allele (DIK5098). On the other

hand, two markers form the GWS, ILSTS73 and RM388, were included in the final analyses

because they were genotyped for some of the chosen families in the previous projects. Thus,

final analyses were performed with total of 21 markers (Fig.4).

All produced genotypes were submitted to two systems of quality control. First system

comprises of repeated genotyping of already genotyped animals. Second system includes

paternity check. Additional controls included mistyping analysis by SimWALK2 program

(SOBEL and LANGE, 1996), which was performed in FV-ROOT pedigree and the chrompic

option of the CRI-MAP program (LANDER and GREEN, 1987), which was performed in

GDD11 pedigree.

In order to confirm the marker order, given by the published linkage map (USDAlinkage map; ITOHet al., 2005; IHARAet al., 2004) and to separate the markers that were at

the same position on that map for BTA19 we used build option of the CRI-MAP program.

The fixed orders were ascertain by comparison of results from published linkage map, high-

resolution radiation hybrid map (ITOHet al., 2005; EVERTS-VAN DER WINDet al., 2005) and

the whole genome shotgun sequence results for a corresponding chromosome. The best order,

which integrates all available information on BTA19, was chosen (Fig.4).

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FIGURE 4. Markers used for analyses on

chromosome 19 in genome wide scan (GWS)

and intensive study (set 1 and set 2). Markers

positions, in centiMorgans, are taken from the

publicly available linkage map (USDA map)

except for the underlined markers, whose

position is result of own linkage analysis.Markers in parentheses were left out of the

final analyses. Two GWS markers (*) were

included into final analyses because some of

GDD11 families were genotyped for them in

other projects (number of genotyped families

given in parentheses).

STAGE FIVEFinal interval mapping: Results of interval mapping across the families confirmed the

presence of the QTL affecting PP at the approximate position of 55 cM with P=0.025 (Fig. 5).

Even though we used closely spaced marker map the PP-QTL position couldnt be refined by

the interval mapping. The reason for that lies in the fact that there are only few informativerecombinations between closely spaced markers (OLSEN et al., 2004). The 95% confidence

interval of the QTL position was placed in a broad range from 0 to 95 cM, with the best

results between 54 and 62 cM. However, we observed that bootstrapping procedure

implemented in QTLexpress program often produces an extra peak on the beginning and/or

the end of the chromosome, resulting in very broad range of confidence interval.

FIGURE 5.- Final interval mapping in

granddaughter design (GDD11). Interval

mapping results for milk yield (MY), fat yield

(FY), protein yield (PY), fat percent (FP),

protein percent (PP) and the results ofbootstrapping procedure for all families are

shown. The number of bootstrap samples has

been rescaled. Positions of markers used in

analysis are denoted on the X-axis. F-statistic

values are presented on Y-axis and

chromosome length in centiMorgans (cM) on

X-axis.

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STAGE SIXCombined linkage disequilibrium and linkage analyses: Combined LDL mapping based

on variance component approach was performed as described by LEE and VAN DER WERF

(2004; 2005; 2006). For the analyses the mutation age and past effective population size were

held 100. Initial homozygosity on each locus was 0.25. Two programs were used:

combined linkage disequilibrium (LD) and linkage (L) analysis with the random walkapproach (ra) and the meiosis Gibbs sampling (ms) - LDL_rams, which makes use ofunordered genotypes and

combined linkage disequilibrium (LD) and linkage (L) analysis - LDL which makesuse of reconstructed haplotypes.

For the final haplotyping analysis (by SimWALK2) and the combined LDL analyses GDD11

animals were, together with their sires and dams, connected through ancestors to the FV-

ROOT, building a complex pedigree based on GDD11. This pedigree was then filtered on

those animals genotyped for 12 to 21 markers. The applied filter left 593 genotyped animals,

i.e. totally 1460 animals in the pedigree. The threshold of 12 markers for rating the success of

genotyping process (>50%) was established empirically.

LDL_rams was started with 1100 iterations and initial burn-in of 100. Parameter

estimates were collected every 10th

round. The LDL_rams analysis locates the PP-QTL atposition 53.69 cM and the log-likelihood ratio test (LRT=-2(log(L0)-log(Lp)) value from 8.46

(Fig. 6). According to OLSEN et al. (2004) the significance level of the LRT value is

chisquared distributed with 1 degree of freedom. Assuming this probability distribution PP-

QTL is highly significant (P=0.0036). To calculate the confidence interval of the QTL

position we use 1-LOD drop-off criteria (LANDER AND BOTSTEIN, 1989; Fig. 6).

Reconstructed haplotypes, made by SimWALK2, were used for LDL analysis. The

same pedigree, consisting 1460 animals, was used. The LDL analysis located the PP-QTL at

the same position like the LDL_rams analysis (53.69 cM). The LRT value is 11.63 and PP-

QTL is highly significant at this position (P=0.0006). The second peak at 55.89 cM, which is

also coming by LDL_rams analysis, is now more prominent and significant as well.

Calculated LOD-score is 2.52 at position 53.69 cM and 2.25 at 55.89 cM. Because of thesecond peak the 1-LOD drop-off confidence interval lies now between approximately 50 and

59 cM.

FIGURE 6.- Results of combined linkage disequilibrium and linkage analysis (LDL) by LDL_rams

(left) and LDL (right) program for quantitative trait locus affecting the protein percent (PP-QTL).

Positions of markers used in analysis are denoted on the X-axis. The 1-LOD drop-off confidence

interval for PP-QTL is marked grey. The log-likelihood ratio test values (LRT) are presented on Y-

axis and the chromosome length in cM on the X-axis.

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The peak at the approximately 67 cM, which shows with LDL_rams analysis, is gone by

using the most probable haplotypes. This makes us conclude that the peak arose most

probably due to the small number of samplings used (1100 samplings).

Checking for possible associated effects: To estimate possible associated effects of the here

mapped QTL we analyzed our data on all available traits. Analysis was performed for thefollowing traits: milk yield (MY), fat yield (FY), protein (PY), fat percent (FP), milk somatic

cell count (SCC), milkability (MA), persistency (PE), productive life (PL), maternal non-return rate (mNR), paternal non-return rate (pNR), maternal calving ease (mCE), paternal

calving ease (pCE), maternal stillbirth (mSB) and paternal stillbirth (pSB). Out of all analysed

traits we become significant results for QTL affecting three traits (Fig. 7):

QTL for the milkability at 59.21 cM with the LTR value of 14.51 (P=0.0001) QTL for the productive life with two distinct peaks at 53.69 cM and the LRT value of

9.72 and at 57.86 cM and the LTR value of 8.38 (P=0.002 and P=0.004, respectively)

QTL for the milk somatic cell count at 25.94 cM with the LTR value of 6.73(P=0.009).

The QTL with effect on milkability (MA-QTL) shows very high LTR value and it is highlysignificant. In order to test whether the PP-QTL and MA-QTL are one QTL with effect on

both traits or they are two separated QTL we analysed the data with QTLexpress program.

The interval mapping results across families for both traits showed that the estimated sire

effects are going in the same direction. F-test statistic curve in family wide analysis showed

that there are:

Three families significant for both QTL, One family with significant results for MA-QTL and indicative results for PP-QTL, One family indicative for both QTL and One family with significant results for MA-QTL, but without any effect for PP-QTL.

According to these results we could conclude that it is most possibly one QTL with effectboth on PP and MA with stronger effect on MA. However, more formal test of one pleiotropic

mutation versus two distinct QTL can be performed by application of multi-trait multi-QTL

model (MEUWISSEN AND GODDARD, 2004).

FIGURE 7.- Results of combined linkage

disequilibrium and linkage analysis (LDL) by

LDL program for the quantitative trait locus

affecting milkability (MA), productive life

(PL) and somatic cell count (SCC). Positions

of markers used in analysis are denoted on the

X-axis. The log-likelihood ratio test values

(LRT) are presented on Y-axis and thechromosome length in cM on the X-axis.

The productive life is one complex trait dependent on many productivity, fertility and

conformation traits and is also difficult to estimate. Because it is based on the productivity

traits it is to expect to map it together with some production trait as well. There was also an

indication of the somatic cell count proximally on the BTA19 (BENNEWITZ et al., 2003),

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which is in good concordance with the QTL for SCS detected here. The marker map used for

the analyses here was not dense enough in the proximal part of the chromosome so the QTL

position can not be better resolved.

LD map: Combined LDL method can use only LD amount existing in mapping population. If

there is no substantial LD between used markers the mapping result will be based mostly on

linkage information. To test out this possibility we made a linkage disequilibrium (LD) mapfor the studied region. LD map was constructed by first calculating the amount of LD

(measured as D; HEDRICK 1987) for all possible marker pairs and then, for each marker,finding the average D with that marker and all markers residing within 5 cM in each

direction from that marker (OLSENet al., 2005; Fig. 8). D for the markers with distance to

nearest marker exciding 5 cM was set to 0. The figure 8 shows that the D values for the

genotyped marker on the BTA19 usually do not exceed 0.15. Only four markers have D

values higher than 0.15 (IDVGA46, ILSTS014, DIK4051 and URB32). Markers IDVGA46

and ILSTS014 show very high D values compared to the other markers. The explanation is

that both markers are poorly informative. Both have three alleles, out of which one allele has

frequency over 95%.

FIGURE 8.- Linkage disequilibrium map for the 15 markers on the chromosome 19 (BTA19). The

average D values (Y-axis) between the named markers (X-axis) and the markers within a distance of

5 cM.

DISSCUSION

The IBD QTL mapping method was successfully applied in the in humans (DE VRIES

et al., 1996; FALLIN

et al., 2001), cattle (RIQUET

et al., 1999; LI

et al., 2004) and pigs (NEZER

et al., 2003). However, it was not always applied with the same success in cattle. The fine

mapping of QTL with effect on milk production on BTA14 (RIQUET et al., 1999) was

hampered by selected mapping population consisting of Dutch Holstein-Friesian population

and New Zealand Holstein-Friesian population. As later proved, haplotype of one of the New

Zealand sires was coincidentally identical by state with haplotypes of Dutch sires, leading to

the erroneous QTL localization (FARNIR et al., 2002). On the other hand, LI et al. (2004)

reported successful application of the IBD method in mapping the QTL for backfat on

chromosome 2, 5, 6, 19, 21, and 23 in a commercial cattle population. This population was

developed from an Angus base and is expected to derive from one or limited number of

founders. It was also under selection for over 30 years, which should be an extra factor

contributing positively to the IBD mapping (LI et al., 2004). Here presented IBD mappingdiffers from the one proposed by RIQUET et al. (1999) in the fact that we compared the

haplotype of highly related sires so we were able to include the haplotypes of the non-

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11

segregating sires into comparison in order to refine the QTLR as much as possible. Also the

chosen mapping population an advanced backcross population Fleckvieh x Red Holstein

(ABFV) is meant to represent the unique opportunity for IBD mapping, as the influence of

the founder in one such population is substantial. Also, the ABFV is under selection for here

examined milk production traits.

The feasibility to use IBD mapping method depends on the extent of the linkage

disequilibrium (LIet al.

, 2004). Analysis in Dutch Holstein-Friesian population revealedsurprisinglyhigh levels of LD extended over several tens of centiMorgan (FARNIR et al.,

2000). These findings were confirmed in North American Holstein population (VALLEJOet

al., 2003), two Japanese beef breeds (ODANIet al., 2006), U.K. Holstein population (TENESA

et al., 2003) and Australian Holstein population (KHATKAR et al., 2006). Our results on

BTA19 do not agree with prediction from FARNIR et al. (2000) who expects that situation

similar to the one described in Dutch Holstein-Friesian population will be encountered in

most other dairy cattle populations. Even though they found substantial LD on BTA19 the

results in our population contribute to the conclusion that there is a difference in degree of LD

between different populations. The lower LD can arise either from lower marker density or

higher effective population size. Bavarian and Austrian Fleckvieh breeders use large numbers

of tested parents to produce and select along successive cattle generations. Around 400 bullsin Bavaria and 140 bulls in Austria, coming from a broad population of dames, are tested

every year. The large number of used parents leads to high effective size (Ne>250; PIRCHNER,

2002), in contrast to the estimated effective population size of 50 in Dutch black-and-white

population (BIOCHARD, 1996), and consequently low LD degree in the population. In order to

use LD we should have far denser marker map in Fleckvieh. Supposing denser marker map,

the lower LD will allow for finer QTL mapping in Fleckvieh. However, our observations

demonstrate importance of previously check of the LD degree in the mapping population in

order to decide about mapping methods and the required marker density.

The method that combines linkage disequilibrium with linkage analysis was chosen

for refinement of the QTL position. This method in comparison with method using linkage

only was able to refine the QTL position sustainable. As it was already mentioned, the LDdegree in our mapping population is low so the most information was, once more, extracted

from the data by linkage analysis. Included pedigree information, as discussed by by LEE AND

VAN DER WERF (2004) has a big impact on the final mapping result when only linkage is used

but is not critical when the LD information is used. Our results suggest that pedigree

information should be used whenever available, especially in the case when the LD quantity

and LD distribution over the genome in the mapping population is not known.

Some important questions should be resolved in future:

- QTL origin

- Refinement of the QTL position

- Existence one or two QTL affecting PP and MA

The PP-QTL is most probably introduced in ABFV through their RH founder but also

exists in the FV population affecting PP and MA in both populations.

Here presented mapping population offer different opportunities for fine QTL mapping by (a)

combining dense marker map with low LD in effectively large Fleckvieh population, (b)

combining different breed origin of most possibly the same causal mutation (c) combining

effect of single causal mutation on two different traits. These opportunities could be exploited

only in consideration of more sophisticated mapping models.

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12

LITERATURE CITED

BENNEWITZet al. (2003) Genet Sel Evol. 35(3):319-38.

BIOCHARD (1996) Prod. Anim., 9: 323-335.

DARVASI AND SOLLER(1994) Genetics 138(4): 1365-73.

DE VRIESet al. (1996) Hum Genet. 98(3):304-9.

DOLEZALet al. (2005) EAAP-Book of Abstracts No.11 p118.

EVERTS-VAN DER WINDet al. (2005) Proc Natl Acad Sci U S A 102(51): 18526-31.FALLINet al. (2001) Genome Res. 11(1):143-51.

FARNIR et al. (2000) Genome Res 10(2): 220-7.

FARNIR et al. (2002) Genetics 161(1): 275-87.

HEDRICK (1987) Genetics 117(2):331-41.

IHARA et al. (2004) Genome Res 14(10A): 1987-98.

ITOH et al. (2005) Genomics 85(4): 413-24.

KHATKAR et al. (2006) Genet Sel Evol 38:463-477.

LANDER AND BOTSTEIN (1989) Genetics 121(1):185-99.

LANDER AND GREEN (1987) Proc Natl Acad Sci U S A 84(8):2363-7.

LEE AND VAN DER WERF (2004) Genet Sel Evol 36(2): 145-61.

LEE AND VAN DER WERF (2005) Genetics 169(1): 455-66.

LEE AND VAN DER WERF (2006) Genet Sel Evol 38(1):25-43.

LI et al. (2004) J Anim Sci 82(4): 967-72.

LIPKIN et al. (1998) Genetics 149(3): 1557-67.

MEUWISSEN AND GODDARD (2004) Genet Sel Evol., 36(3):261-79.

MOSIGet al. (2001) Genetics 157(4): 1683-98.

NEZERet al. (2003) Genetics 165(1):277-85.

ODANI et al. (2006) Anim Genet. 37(2):139-44.

OLSENet al. (2004) J Dairy Sci. 87(3):690-8.

OLSEN et al. (2005) Genetics 169(1): 275-83.

PIRCHNER (2002) Arch. Tierz., Dummerstorf 45, 4:331-339.

RIQUET et al. (1999) Proc Natl Acad Sci U S A 96(16): 9252-7.

SCHNABELet al.

(2005) Proc OLSEN Acad Sci U S A 102(19): 6896-901.SEATON et al. (2002) Bioinformatics 18: 339-340.

SOBEL AND LANGE (1996) Am J Hum Genet 58(6):1323-37.

TANKSLEY AND NELSON (1996) Theor Appl Genet 92: 191203.

VALLEJOet al. (2003) J Dairy Sci. 86(12):4137-47.

TENESAet al. (2003) J Anim Sci. 81(3):617-23.

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4 A bit on plants


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5 Genomic selection


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Does genomic selection work in a mice population?

A Legarra, C Robert-Granie, E Manfredi, JM Elsen

22 March 2007, QTLMAS

A Legarra, C Robert-Granie, E Manfredi, JM Elsen ()Does genomic selection work in a mice population?22 March 2007, QTLMAS 1 / 18

Accuracy of genomic selection

In the genomic selection a la Meuwissen et al. we have a SNP model:

yi = +n

j=1

wijaj + ei

and Gi =n

j=1 wijaj = wia where

1 aj effect of the j-th SNP

2 wij indicator variable depending on the SNP carried by the i-thindividual


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Testing genomic selection

Let G be an estimator of the true breeding value, with an accuracy.Genomic selection works better than classical BLUP if accuracy ofGi = wia (SNP model) is better than accuracy of Gi = ui (classicalinfinitesimal model).How to validate a model?


How to compare accuracies?

We use a cross-validation criteria with genetic and statisticalinterpretations.

1 Split the data into two at random : y = [y1, y2]

2 Estimate

(G|y1, genealogy) BLUP or(G|y1,SNPs) wiaconditional on y1 only.

3 For every individual in y2, compute Gi .

4 And compute r(y2, Ggenomic), and r(y2, Ginfinitesimal).(y2 is corrected by fixed effects).


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How to compare accuracies?

Note that r(y2, G) = r(y2, y2). What is that?

1 r(y2, y2) is proportional to the expected genetic gain in y2 selectingby G estimated from y1. Mimicks a selection process.

2 r(y2, G) is a measure of model fitting from a genetic point of view.

3 r(y2, y2) is a robust, general measure of model fitting by

cross-validation.


The data

Nature Genetics - 38, 879 - 887 (2006) Genome-wide genetic associationof complex traits in heterogeneous stock mice William Valdar, Leah CSolberg, Dominique Gauguier, Stephanie Burnett, Paul Klenerman,William O Cookson, Martin S Taylor, J Nicholas P Rawlins, Richard Mott

& Jonathan FlintOur data set, freely available at http://gscan.well.ox.ac.uk . . . Weobtained genotypes for 13,459 SNPs on 1,904 fullyphenotyped mice and 298 parents . . .


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The pedigree

In fact the pedigree is composed of many nuclear families...

and no parent is phenotyped.


The data

id weight sex genotype

1 A048005080 20.3 1 12121212221222121212221222121222121211221212121

2 A048006063 26.7 2 12122212211221221212221221112221121212212212121

3 A048006555 19.5 2 22112222222222222211221122112222112211222222222

4 A048007096 22.2 2 12121212211221221212221221112221121212212212121

5 A048010273 17.3 1 22112222222222222211221122112222112211222222222

6 A048010371 18.1 2 12121212221222121212221222121222121211221212121

8 A048011287 25.6 2 121222122112212212122212211122211212122122121219 A048011567 20.6 2 12122212211221221212221221112221121212212212121

10 A048013559 17.3 1 2211222222222222221122112211222211221122222222

11 A048015047 16.3 2 2211222222222222221122112211222211221122222222

12 A048017615 21.8 2 1212121222122212121222122212122212121122121212

13 A048019267 18.5 2 2211222222222222221122112211222211221122222222

14 A048021023 22.3 2 1212121222122212121222122212122212121122121212

15 A048022858 17.4 1 1212121222122212121222122212122212121122121212

16 A048023355 18.7 1 1212121222122212121222122212122212121122121212

17 A048023581 21.2 2 1212221221122122121222122111222112121221221212

18 A048028854 20.6 1 221122222222222222112211221122221122112222222219 A048028871 19.7 1 1122111122112112112221122112122112111122121112


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Assumptions and estimation

We use a mixed model.

y = X + Zu+ e

Works very well, h2 = 0.96 0.03. All variation is additive.

y = X + Wa+ e

This model, although not optimal, gave good results in the simulations byMeuwissen et al. (accuracy 0.71).

y = X + Wa+ Zu+ e

a N(0, I2a), e N(0, I2e),u N(0,A

2g)

We use MCMC to estimate variance components once in y1. Otherwisewe use BLUP (full MCMC gave similar results, not shown).


Cross-validation

The question is: How to split y in [y1, y2] ? Options:

By families: Most DL is only at the populational level, less powerful.BLUP does not give information in this case (without any relative in

y1, yi = 0, yi y2). Splitting families in two. High DL because there is a family structure

and we use full-brothers to predict full-brothers. Comparable to atwo-generations design.


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Sampling families

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Splitting the data

For the two ways of sampling (animals or families), in 100 differentpartitions y = [y1, y2] , we have computed:

1 r(y2, Ggenomic) ,Gi =

aj

2 r(y2, Ggenomic&BLUP), Gi = ui +

aj

3 r(y2, GBLUP), Gi = ui.


Results: sampling families at random; 100 replicates

Table: Correlations r(y2, G)

Method Mean 95% quantilesr(y2, Ggenomic) 0.21 0.14-0.29

r(y2, Ggenomic&BLUP) 0.19 0.12-0.27

r(y2, GBLUP) 0 NA


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Results: splitting families in half at random; 100 replicates

Table: Correlations r(y2, G)

Method Mean 95% quantiles

r(y2, Ggenomic) 0.48 0.45-0.51

r(y2, Ggenomic&BLUP) 0.60 0.58-0.62

r(y2, GBLUP) 0.59 0.56-0.61


The end

Conclusions:

1 The genomic model performs better than classical BLUP when thereis no information from relatives. Apparently, it recovers either familyinformation or population LD.

2 The genomic model alone performs worse than classical BLUP whenthere is information from close relatives. It is able nevertheless torecover a good part of the family information.

3 The genomic model together with BLUP performs slightly betterthanclassical BLUP.


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The end

This might indicate either a truly wrong model or overfitting (too manyvariables).However the simulations of Meuwissen et al. 2001 did not show problemsin overfitting. The BLUP approach weve used here worked pretty well,with accuracies of 0.73 for h2 = 0.5. Was their model or their simulationstoo unrealistic?Questions:

1 Can we recover population LD + family LD and do better thanBLUP?

2 How to choose the SNPs?


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6 Advances in QTL detection theory 2


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Haplotype sampling in crossbred populations

Albart Coster, [email protected]

March 6, 2007

Abstract

Current methods for haplotype inference without pedigree information assume random-

mating populations. Crossbred populations, however, are non-random-mating. Haplotype

frequencies in both parental populations provide information for haplotype frequencies in

the crossbred population. We introduce a new method for haplotype inference without pedi-

gree that does not need rely on the assumption of random-mating and that can use genotype

data of the parental lines and of the crossbred line in the inference. The method uses a

Bayesian layout with a Dirichlet Process as prior for the haplotypes in the population. The

basic idea is that only a subset of the whole set of possible haplotypes is present in the pop-

ulation. Both haplotypes corresponding to an individual are sampled jointly. Running the

algorithm on simulated data showed that it compares to other methods in random-mating

populations and that it over performs them in non-random-mating populations. The method

is robust against incomplete data, i.e. only a sample of parental genotypes is needed in the

inference of crossbred haplotype frequencies.

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