Volume 4 Supplement 1

Genetic Analysis Workshop 13: Analysis of Longitudinal Family Data for Complex Diseases and Related Risk Factors

Open Access

Genome-wide linkage scan on estimated breeding values for a quantitative trait

BMC Genetics20034(Suppl 1):S61

https://doi.org/10.1186/1471-2156-4-S1-S61

Published: 31 December 2003

Abstract

Background

A genome-wide linkage scan was performed on Replicate 1 of the simulated data for fasting triglyceride levels. The aim of this study was to implement mixed-model methodology to estimate breeding values for each individual for this trait and to assess the merit of these breeding values in linkage analysis. These breeding values utilize all the pedigree information, and the genetic and phenotypic correlations with other measured traits across the two cohorts. A genome-wide linkage scan was run on both the new breeding value traits and the original traits.

Results

Using breeding values, a maximum LOD of 7.78 was found on chromosome 5 at a position very close to a gene underlying the triglyceride levels. This effect was not detected using the original trait.

Conclusion

The results imply that estimating breeding values may be a suitable method of deriving traits for use in genome-wide scans.

Background

The best linear unbiased prediction (BLUP) of the breeding value of an individual for a quantitative trait can be calculated by taking into account the genetic and environmental covariances among all related individuals and across all correlated traits [1, 2]. Data from fixed effects such as sex or population can also be incorporated. This methodology has been the basis of many national and international animal breeding programs, where in its most complete implementation it is referred to as the animal model [3].

Here we explore the merit of using BLUP to generate breeding values for input into a genome scan. Our motivation is that frequently in human genetic analysis there is a primary trait of interest together with a number of covariates that may also be heritable. To improve the precision of measurement of the primary trait, we wish to remove the effect of any environmental covariation with the other traits, but include the effect of any genetic covariation. This is in essence what the animal model achieves.

For purposes of illustration, we have used fasting triglyceride level as our primary trait.

Methods

We chose to analyze Replicate 1 of the complete simulated data sets without any knowledge of the underlying simulation model or the location of the trait loci.

Pedigrees

The original number of pedigrees from Replicate 1 (after combining the original cohort and the offspring cohort) was 330 families. These comprised 4690 individuals with an average family size of 14, ranging between 7 and 84 individuals. After creating nuclear families (for the genome scan), 1444 pedigrees comprising 5808 individuals were formed, with a family average size of 4, ranging between 3 and 12 members.

Estimating breeding values and (co)variances

The following account is taken from Mrode [4]. Similar descriptions are to be found in many outlines of animal breeding methods, for example Lynch and Walsh [5].

For a mixed model where all genetic variance is additive, the model is

y i = X i b i + Z i a i + e i ,     (1)

where y i is a vector of observations on individuals, b i is a vector of fixed effects (sex and cohort in this case), a i is a vector of random additive genetic effects (breeding values), and e i is a vector of random residual effects. X i and Z i are incidence matrices relating the observations to the respective fixed and random effects, in all cases, subscript i relates to the ith trait.

a and b are usually estimated simultaneously by solving Henderson's [6] mixed model equations (MME) for model (1):

where X and Z are as defined in equation (1). G is the additive genetic variance and covariance matrix for individual effects, R is the variance covariance matrix for residual effects, and A is the numerator relationship matrix that indicates the additive genetic relationship between each possible pair of individuals, for example in the absence of inbreeding, 1/2 for full sibs, 1/4 for grandparent-grandchild and 1.0 for an individual with him/herself (the diagonal elements of the matrix).

A model for a multivariate analysis for two traits could be written as:

The extension of model (3) for more than two traits follows the same pattern. Multivariate models have a corresponding increase in complexity for the MME, for more details see [4].

If G and R are unknown, these are also estimated from the MME. Here, all parameters were estimated utilizing the analytical gradient method of REML (restricted maximum likelihood) implemented in VCE [7]. The method can be extended to include common environmental effects and nonadditive genetic models, but this has not been attempted here.

Estimation of breeding values in this manner takes into account the presence and absence of data for all traits on all related individuals within a population. As a result, all individuals in the population have an estimated breeding value for all traits. In the extreme case of an individual with no observations and no relatives, the breeding value for a trait on that individual is the estimate of the population mean for that trait. With complete data on all individuals in a pedigree, estimated breeding values still differ from observed phenotype values: genetic and environmental correlations are used to include data on other traits measured on the same individual to improve precision, and genetic correlations similarly allow the inclusion of data from relatives. In the present context, aside from handling problems of sporadic missing observations, this means that breeding values for traits only measured in Cohort 1 can be estimated for individuals in Cohort 2, and vice versa.

Traits

To fit limitations of time and software, principal component analyses (PCA) were used to construct new traits from the longitudinal data for each cohort using Genstat [8]. The new traits were created from the first principal component of the correlation matrix: the linear combination of the standardized original measurements that has maximum sample variance. For all new traits, the first principal component accounted for at least 90% of the variation, and loadings for each component trait were very similar so that the first principal component is almost equivalent to the mean of the measurements.

All longitudinal measurements were used from the Cohort 2 data for estimating these new PCA traits. Only a selection of such measurements were used from Cohort 1 to keep missing values in the two cohorts comparable: the implementation of PCA does not permit missing values in the component traits, and Cohort 1 had more missing values because measurements were taken over a much longer period of time. For Cohort 1, the original first measurement for fasting triglycerides was used instead of a PCA trait, as subsequent measurements have many missing values.

Although this procedure was applied to all traits with multiple measurements, due to software limitations only seven were taken forward for estimation of breeding values. These seven, three from Cohort 1 and four from Cohort 2, were selected based on the genetic and phenotypic correlations between the traits within each cohort. The aim was to select traits that contributed most in estimating genetic and phenotypic variance components for triglyceride, the primary trait of interest in this analysis. A summary of the chosen traits is in Table 1. Estimates of heritabilities, genetic, and residual (environmental) correlations, are in Table 2. Using these seven traits, two new breeding-value traits were therefore derived for all individuals: a triglyceride-Cohort 1 estimated breeding value (EBV) and a triglyceride-Cohort 2 EBV.
Table 1

Description of traits used in the REMLA (co)variance component analysis, and estimation of BLUPs

Trait

No. MeasurementsB

% Missing Observations

1st PCA % Variation

Cohort 1

   

   Alcohol (g/day)

2

7.42

100.00

   Fasting glucose (mg/dl)

5

5.14

95.98

   Fasting triglycerides (mg/dl)

1

15.17

NA

Cohort 2

   

   Alcohol (g/day)

5

6.73

100.00

   Total cholesterol (mg/dl)

5

6.73

91.37

   Fasting glucose (mg/dl)

5

6.73

94.20

   Fasting triglyceride (mg/dl)

5

6.73

95.22

ASee text for explanation. BNo. of measurements: number of consecutive measurements used for PCA. In the case of TG in Cohort 1, only one measurement was taken to keep the proportion of missing values roughly comparable across all traits from the same cohort.

Table 2

Estimates of heritabilities, genetic and residual correlations between traits from Cohorts 1 and 2

 

Cohort 2

 

Cohort 1

 

Alcohol

Cholesterol

Glucose

Triglyceride

Alcohol

Glucose

Triglyceride

Cohort 2

       

   AlcoholA

0.05 (0.02) B

0.26 (0.08)

-0.20 (0.10)

0.25 (0.08)

0.10 (0.08)

-0.06 (0.25)

0.01 (0.09)

   Cholesterol

-0.10 (0.04)

0.71 (0.04)

0.06 (0.02)

0.15 (0.02)

-0.19 (0.13)

0.07 (0.03)

0.07 (0.03)

   Glucose

0.00 (0.03)

-0.04 (0.04)

0.62 (0.03)

0.48 (0.04)

0.12 (0.16)

0.98 (0.02)

0.52 (0.04)

   Triglyceride

0.55 (0.02)

0.03 (0.03)

0.26 (0.04)

0.40 (0.03)

-0.48 (0.22)

0.58 (0.04)

0.94 (0.04)

Cohort 1

       

   Alcohol

0C

0

0

0

0.04 (0.02)

0.01 (0.18)

-0.65 (0.16)

   Glucose

0

0

0

0

0.04 (0.05)

0.65 (0.02)

0.62 (0.04)

   Triglyceride

0

0

0

0

0.56 (0.03)

-0.12 (0.07)

0.64 (0.02)

AThese are the 1st PCA of the traits described in Table 1, except for TG in Cohort 1, which is the original measurement; see text for details. BHeritabilities (in bold) are on the diagonal, genetic correlations are above and residual correlations are below the diagonal. All parameters have their standard errors in parentheses. The multivariate mixed model implemented for the parameter estimates used traits across cohorts. CThe residual correlation between the two cohorts is equal to 0 by definition with the model fitted here.

Genome-wide linkage analysis

Five traits were analyzed separately in the genome-wide scan: triglyceride EBVs from Cohorts 1 and 2 (TG1_EBV and TG2_EBV, respectively), a simple average triglyceride trait from both cohorts in which every individual with a TG measurement on any occasion had a value (TG12_pooled), first PCA for Cohort 2 (TG2_PCA), and the original first TG measurement for Cohort 1 (TG1_original). These last three traits were included to compare with the EBV traits. Mega2 [9, 10] was used to create nuclear families from the existing pedigrees and so reduce analysis time. Because many of the larger pedigrees were only connected by marriage, we presumed that any power loss would be minor.

Merlin-regress [11] was used for the genome-wide linkage analysis for the five traits. This is a new method based on the regression of the estimated IBD (identity by descent) sharing between relative pairs on the squared sums and squared differences of trait values of these pairs [11]. The variance components option in Merlin [12] was also used to confirm results for the breeding value traits because they were normally distributed (Figure 1).
Figure 1

Distributions of EBV traits for Cohorts 1 and 2 for fasting triglyceride traits. A, Distribution of fasting triglycerides in Cohort 1 (first measurement) EBV trait. B, Distribution of fasting triglycerides in Cohort 2 (first PCA) EBV trait

Results

Summaries of the maximum LOD scores from the regression method (using Merlin-regress) for the five genome-scans are in Table 3.
Table 3

Maximum LOD scores for the four analyzed traits with their position on the chromosome

TraitA

% Missing ValuesB

Chromosome

h2C

Max LOD

Position

TG1_original

60.6

18

0.60

1.83

62.65

TG2_PCA

71.6

7

0.39

1.39

177.73

TG12_pooled

32.2

18

0.29

1.36

62.65

TG1_EBV

0.0D

5

0.85

7.78

8.22

TG2_EBV

0.0

5

0.85

6.86

8.22

ATG, triglyceride trait, number suffixing the trait indicates cohort, where TG12_pooled is a pooled trait from both cohorts; see text for details. EBV: estimated breeding value trait. BMissing values: number of missing observations/total number of individuals (after forming nuclear families (5808 individuals)). Ch2 = value input in the Merlin-regress program. For EBV traits, instead of using a h2 of 1.0, a conservative value of 0.85 was assumed. DThe use of BLUP means that no EBV is missing.

The pattern of results from the two cohorts was very similar for the triglyceride traits, therefore, only figures from Cohort 1 (original and EBV trait) are presented here.

Figure 2 shows plots of the LOD scores over the whole genome for TG1_original, TG1_EBV, and TG12_pooled, respectively.
Figure 2

Genome scan LOD plots of fasting triglycerides in Cohort 1 (first measurement) (A), Cohort 1 (first measurement EBV) (B), and Cohorts 1 and 2 (pooled) (C)

Discussion

The most notable results are the peak LODs of 7.78 and 6.86 on chromosome 5 at 8.22 cM, for TG1_EBV and TG2_EBV, respectively. These two peaks correspond very closely to the location of the gene s3 at 8.46 cM in this data set. It is noticeable that none of the three remaining traits provided evidence of linkage at this location: the maximum LODs on chromosome 5 for these being 0.85, 0.28, and 0.42 for TG1_original, TG2_PCA, and TG12_pooled, respectively.

Variance component analysis of the breeding values gave LODs of 5.12 and 4.50 for TG1_EBV and TG2_EBV, consistent with the results using Merlin-regress.

The derivation of an EBV for a single trait occurs without reference to marker data, and is designed only to improve the precision with which the additive genetic value of that trait is estimated. (Note however, in animal breeding EBVs are generally derived for multiple traits, or for indices across traits.) We speculate that the improved power we see here is primarily the result of the improved precision with which this additive value is estimated. Other multivariate methods used in linkage analysis can also include genetic and environmental correlations among traits, for example [13]. These methods generally attempt to improve power to detect QTL by searching for loci with pleiotropic effects. In human genetic studies however, there is often a single trait of primary interest. Other traits, although correlated to varying degrees both genetically and environmentally, are of less interest in their own right. In such circumstances, we believe the use of EBVs has much to offer and may be advantageous over an explicit search for pleiotropic QTL.

As can be seen in Table 3, the use of predicted breeding values also has the advantage of providing a trait for analysis for every individual. However, these breeding values will be correlated. Since the estimation of breeding value is independent of the marker data, we are hopeful that the consequence of this non-independence for type I error rate in the genome scan will be minimal, although this requires further study. The absence of large LODs at other locations in the genome scan lends some support to the type I error rate not being grossly increased.

To date, we have only applied this method to a single replicate. The analyses of many more replicates and traits are required before we can use this method with confidence.

Conclusion

The estimation of breeding values using BLUP may be a suitable method of deriving traits for use in genome-wide scans. In particular, the method makes effective use of correlated traits and provides a simple framework for coping with missing data.

Authors’ Affiliations

(1)
Oxagen Limited

References

  1. Falconer DS, Mackay TFC: Introduction to Quantitative Genetics. Essex, Longman. 1996Google Scholar
  2. Henderson CR: Sire evaluation and genetic trends. In Proceedings of Animal Breeding and Genetics Symposium in Honour of Dr. J. L. Lush: 1973; Champaign, Illinois. American Society of Animal Science, and American Dairy Science Association. 1973, 10-41.Google Scholar
  3. Henderson CR: Applications of Linear Models in Animal Breeding. Ontario, University of Guelph. 1986Google Scholar
  4. Mrode RA: Linear Models for the Prediction of Animal Breeding Values. Oxfordshire, CABI Publishing. 2000Google Scholar
  5. Lynch M, Walsh B: Estimation of Breeding Values. In Genetics and Analysis of Quantitative Traits. Sunderland, MA, Sinauer Associates, Inc. 1998, 745-778.Google Scholar
  6. Henderson CR: Estimation of genetic parameters. Ann Math Stat. 1950, 21: 309-310.Google Scholar
  7. Neumaier A, Groeneveld E: Restricted maximum likelihood estimation of covariances in sparse linear models. Genet Sel Evol. 1998, 30: 3-26.PubMed CentralView ArticleGoogle Scholar
  8. Genstat statistical package release 6.1. Lawes Agricultural Trust, Rothamsted Experimental Station. 2002Google Scholar
  9. Mukhopadhyay N, Almasy L, Schroeder M, Mulvihill WP, Weeks DE: Mega2, a data handling programme for facilitating genetic linkage and association analyses [abstract]. Am J Hum Genet. 1999, 65: A436-Google Scholar
  10. Mukhopadhyay N, Almasy L, Schroeder M, Mulvihill WP, Weeks DE: Mega2 version 2.3. 2001, [http://watson.hgen.pitt.edu]Google Scholar
  11. Sham PC, Purcell S, Cherny SS, Abecasis GR: Powerful regression-based quantitative-trait linkage analysis of general pedigrees. Am J Hum Genet. 2002, 71: 238-253. 10.1086/341560.PubMed CentralView ArticlePubMedGoogle Scholar
  12. Abecasis GR, Cherny SS, Cookson WO, Cardon LR: Merlin-rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet. 2002, 30: 97-101. 10.1038/ng786.View ArticlePubMedGoogle Scholar
  13. Marlow AJ, Fisher SE, Francks C, MacPhie IL, Cherny SS, Richardson AJ, Talcott JB, Stein JF, Monaco AP, Cardon LR: Use of multivariate linkage analysis for dissection of a complex cognitive trait. Am J Hum Genet. 2003, 72: 561-570. 10.1086/368201.PubMed CentralView ArticlePubMedGoogle Scholar

Copyright

© Zabaneh and Mackay; licensee BioMed Central Ltd 2003

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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