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Heritability and genetic associations of triglyceride and HDL-C levels using pedigree-based and empirical kinships

  • 1Email author,
  • 1,
  • 1, 2 and
  • 1, 2
BMC Proceedings201812 (Suppl 9) :34

https://doi.org/10.1186/s12919-018-0133-x

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Abstract

The heritability of a phenotype is an estimation of the percent of variance in that phenotype that is attributable to additive genetic factors. Heritability is optimally estimated in family-based sample populations. Traditionally, this involves use of a pedigree-based kinship coefficient generated from the collected genealogical relationships between family members. An alternative, when dense genotype data are available, is to directly measure the empirical kinship between samples. This study compares the use of pedigree and empirical kinships in the GAW20 data set. Two phenotypes were assessed: triglyceride levels and high-density lipoprotein cholesterol (HDL-C) levels pre- and postintervention with the cholesterol-reducing drug fenofibrate. Using SOLAR (Sequential Oligogenic Linkage Analysis Routines), pedigree-based kinships and empirically calculated kinships (using IBDLD and LDAK) were used to calculate phenotype heritability. In addition, a genome-wide association study was conducted using each kinship model for each phenotype to identify genetic variants significantly associated with phenotypic variation. The variant rs247617 was significantly associated with HDL-C levels both pre- and post-fenofibrate intervention. Overall, the phenotype heritabilities calculated using pedigree based kinships or either of the empirical kinships generated using IBDLD or LDAK were comparable. Phenotype heritabilities estimated from empirical kinships generated using IBDLD were closest to the pedigree-based estimations. Given that there was not an appreciable amount of unknown relatedness between the pedigrees in this data set, a large increase in heritability in using empirical kinship was not expected, and our calculations support this. Importantly, these results demonstrate that when sufficient genotypic data are available, empirical kinship estimation is a practical alternative to using pedigree-based kinships.

Background

SOLAR (Sequential Oligogenic Linkage Analysis Routines) [1], software developed for the genetic analysis of pedigrees, can be used to calculate the heritability (h2) of a phenotype. This calculation requires the phenotype measurement, relevant covariates, and a kinship matrix. Traditionally, the kinship matrix is derived from a carefully curated pedigree (or pedigrees) joining together the individuals with phenotypes by their self-reported genealogical relationships. The use of self-reported genealogical relationships has one obvious drawback: incorrectly specified relationships. These pedigree errors can arise for multiple reasons, including paternity, recording errors, as well as cultural differences in the understanding of the definition of biological kinship relationships. In addition, when a cohort of pedigrees is recruited from the same geographical region, it’s possible that there may be unknown kinship connections between seemingly discrete pedigrees.

Accurate biological relationships are necessary for the calculation of phenotype heritability. Uncertainty surrounding pedigree relationships in a data set reduces the power of heritability calculations and leads to inaccurate results at best, or false results at worst.

With the availability of dense genotyping array data, a potential solution to this problem is to employ the use of empirical kinship estimates. Empirical kinship is when the kinship between each individual in a cohort is estimated using dense genotyping data from single-nucleotide polymorphism (SNP) arrays or next-generation sequencing. Empirical kinship estimates will overall closely align with the kinship calculated from pedigrees, but, importantly, are also able to clarify pedigree relationships, provide an additional quality-control measure to identify sample swaps or duplicates, identify unknown or distant relationships, and overall remove the need to rely on genealogical records. Furthermore, where individuals are unrelated in a pedigree kinship matrix, some level of empirical kinship can be calculated for all pairs in the data set.

Intuitively, the use of a matrix of empirical kinship estimates should improve heritability calculations as the observed kinship measurement is used rather than the kinship expectation based on genealogy. We examined in the GAW20 data set from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study [2] how employing empirical kinship specifically affects heritability calculations. We used SOLAR for all heritability calculations and for the calculation of the pedigree kinship matrix using the pedigrees provided in the GAW20 data set. To calculate the empirical kinship matrices we used 2 established methods: LDAK [3] and IBDLD [4]. We further extended this analysis by using measured genotype-association testing in SOLAR to identify variants that are associated with the phenotypes under examination. We hypothesize that using empirical kinships will strengthen the association results and effect sizes detected in comparison to the use of pedigree kinships.

Methods

Data set

The distributed GAW20 genotypes of 718,544 autosomal SNPs were converted to their corresponding DNA nucleotide bases and the hg18 mapping coordinates were uplifted to hg19. This resulted in 718,407 SNPs for analysis, with 135 excluded because of failing the conversion to hg19. The pedigree distributed with the GAW20 data set was converted to SOLAR format. The phenotype data distributed with the GAW20 data set was merged into a single SOLAR format phenotype file.

Prest-plus analysis within-pedigrees and across-pedigrees

Prest-Plus [5] was used to assess recorded pedigree relationships and to identify evidence of relatedness outside of the GAW20 pedigrees. Using PLINK (v1.90b3m) [6], GAW20 genotypes were linkage disequilibrium pruned (−-indep-pairwise 2000 10 0.1) and Hardy-Weinberg equilibrium pruned (nominal significance of P = 0.05 used as the threshold) resulting in 22,697 SNPs for within-pedigree and across-pedigree Prest-Plus analysis.

Empirical kinship calculation

LDAK version 4.9 [3] and IBDLD version 3.33 [4] were used to derive 2 empirical kinship matrices based on the GAW20 genotype data. For LDAK, in principle, this kernel should correspond to a genetic relationship matrix; in practice, however, we observed that LDAK estimates of self-relatedness were widely spread around their expectation of 1 (Fig. 1a). For IBDLD the estimates of self-relatedness were closer to 1 (Fig. 1b). The empirical kinship estimate matrices from LDAK and IBDLD were postprocessed to remove negative nonzero values and scaled to have a diagonal equal to 1.
Fig. 1
Fig. 1

Distribution of diagonal entries (estimates of self-relatedness) in the unscaled matrices from (a) LDAK and (b) IBDLD

SOLAR heritability analysis

The 2 phenotypes assessed were triglyceride levels and high-density lipoprotein cholesterol (HDL-C) levels pre- and post-fenofibrate intervention. For an individual, when multiple phenotype measurements were available at the 2 visits pre- or 2 visits post-fenofibrate intervention, these were averaged into single pre- and postintervention phenotype values; otherwise, the single pre- or postmeasurement was used. Phenotypes were analyzed using SOLAR (SOLAR Eclipse version 7.6.4) [1]. All phenotypes were residualized with SOLAR for the available covariates, including age, sex, their interactions (age × sex, age2, age2 × sex), study center, smoking, and principal components 1 to 4 (to control for possible population stratification, estimated only on pedigree founders using the SNP data in R and projected to the full sample set). Residualized phenotypes were inverse-normalized in SOLAR to prevent nonnormal distribution errors during analysis, ensuring that all phenotypes had a mean of 0 and SD of unity. Heritability was estimated using SOLAR’s variance components framework. These analyses were completed separately using the pedigree kinship matrix derived from SOLAR and each of the empirical kinship matrices.

Measured genotype analysis

Single-variant association testing was conducted using measured genotype analysis (MGA) in SOLAR for the 718,407 SNPs available for analysis in the GAW20 data set. This analysis takes into account the nonindependence of participants, using the kinship matrix, incorporating each SNP separately into the analysis model as a covariate measured as a genotype dosage (0, 1, 2) and evaluating the genotype-specific difference in the phenotype means. For genome-wide suggestive significance a P-value threshold of P ≤ 1.00 × 10− 5 was used, and for Bonferroni-corrected genome-wide significance a threshold of P ≤ 6.9 × 10− 8 was applied. Manhattan plots of MGA results were constructed in R using qqman [7].

Results

Within-pedigree relationship analysis and detection of distant relationships between unrelated samples

Prest-Plus identified unexpected relationships within the GAW20 data set when assessing relationships within-pedigrees (Fig. 2a), and limited evidence of distant relationships outside of the pedigree between “unrelated” individuals (Fig. 2b). The unexpected relationships based on the within-pedigree analysis suggest sample swap issues and the samples contributing to these errors were excluded from the data set (samples circled in Fig. 2a and summarized in Table 1).
Fig. 2
Fig. 2

Prest-Plus relationship analysis of the GAW20 data set from the GOLDN study. (a) Within-pedigree analysis; monozygotic twins are included in the full-sibling subset, and (b) across-pedigree relationship analysis

Table 1

Erroneous samples identified through Prest-Plus within-pedigree analysis

Family ID

Individual IDs

Expected relationship

Measured relationship

198

5604, 8117

Avuncular

Parent–offspring

375

1927, 4078

Full-sibling

Unknown

198

3621, 8117

First cousin

Full-sibling

Heritability of triglyceride and HDL-C levels pre- and post-fenofibrate intervention, using SOLAR with pedigree-based and empirical kinship

Heritability estimates using SOLAR identified that both triglyceride levels and HDL-C were significantly and highly heritable pre- and post-fenofibrate intervention (Table 2), regardless of whether IBDLD, LDAK, or pedigree kinship was used. General observations that can be made are that LDAK consistently estimated the lowest heritability of the 3 methods with pedigree-based and IBDLD-based estimates comparably similar. A decrease in sample size for triglyceride post-fenofibrate intervention, which is a factor of the samples measured and genotyped in the GOLDN data set, correspondingly decreases the magnitude of heritability estimates for the phenotype, except for estimates using LDAK.
Table 2

Heritability estimates of triglyceride and HDL-C phenotypes using pedigree-based and empirical kinships

Phenotype

Kinship

h2

p Value

h2 SE

Sample size

Triglyceride pre-fenofibrate

Pedigree

0.424

6.09E-11

0.076

817

IBDLD

0.443

4.71E-11

0.075

817

LDAK

0.335

8.79E-10

0.064

817

Triglyceride post-fenofibrate

Pedigree

0.397

1.59E-09

0.078

774

IBDLD

0.404

3.75E-09

0.078

774

LDAK

0.350

4.20E-10

0.065

774

HDL-C pre-fenofibrate

Pedigree

0.553

4.05E-20

0.068

817

IBDLD

0.545

1.17E-19

0.065

817

LDAK

0.480

2.02E-18

0.059

817

HDL-C post-fenofibrate

Pedigree

0.580

8.82E-21

0.068

817

IBDLD

0.561

6.79E-20

0.064

817

LDAK

0.472

9.38E-17

0.061

817

Measured genotype association analysis using SOLAR of triglyceride and HDL-C measurements, using both pedigree-based and empirical kinship

MGA of 718,407 SNPs across both triglyceride and HDL-C, pre- and post-fenofibrate intervention identified 1 genome-wide significant SNP, rs247617 on chromosome 16, associated with HDL-C pre- and post-fenofibrate intervention under all 3 kinship models. Figure 3 shows the Manhattan and quantile–quantile (Q-Q) plots for the MGA results of HDL-C measurements for the pre- and post-fenofibrate interventions for pedigree-based kinship (IBDLD and LDAK results not shown). Table 3 summarizes the association results for rs247617. Even though the data are not shown here, associations with suggestive significance were observed for triglyceride levels both pre- and post-fenofibrate intervention. Indeed, in the companion paper by Peralta et al. in which a genome-wide linkage analysis of the triglyceride levels from the GAW20 GOLDN data set was conducted, a linkage peak was detected on chromosome 10, covering the region of the strongest MGA association for that phenotype in this study [8].
Fig. 3
Fig. 3

Manhattan and Q-Q plots of results from MGA analysis using pedigree kinships in SOLAR for (a) HDL-C pre-fenofibrate intervention and (b) post-fenofibrate intervention

Table 3

MGA identifies SNP rs247617 associated with HDL-C levels

Phenotype

Kinship model

Chi

p.SNP

Beta SNP

Beta SNP (SE)

HDL-C pre-fenofibrate

Pedigree

31.97

1.56 × 10−8

0.314

0.056

IBDLD

32.27

1.34 × 10−8

0.315

0.055

LDAK

32.55

1.16 × 10−8

0.309

0.054

HDL-C post-fenofibrate

Pedigree

35.07

3.18 × 10−9

0.329

0.055

IBDLD

35.97

2.00 × 10−9

0.332

0.055

LDAK

35.60

2.43 × 10−9

0.324

0.054

Discussion

The analysis presented here using the GAW20 data set from the GOLDN study sought to examine whether the use of empirical kinship for the estimation of phenotype heritability and genetic associations in a data set of related individuals was an improvement over relying on pedigree-based kinship. From this analysis, we determined that empirical kinship is analogous, if not equivalent, to pedigree-based kinship. A limitation of the current data set was the minimal unknown relatedness outside of the known pedigrees. It could be expected that in a data set with greater unknown relatedness, or incorrect relatedness (eg, full-siblings reported, when empirically the pair are half-siblings) that heritability estimations from pedigree-based and empirical kinships would be more divergent, with the empirical more accurate.

Pedigree-based kinship in this data set resulted in the highest heritability estimates, with empirical kinships from LDAK generating the lowest heritability estimates. IBDLD empirical kinship resulted in heritability estimates most similar to the pedigree-based estimates. Both phenotypes used from this data set, triglyceride and HDL-C measurements, were significantly heritable pre- and post-fenofibrate intervention, indicating a strong genetic component to phenotype variation.

MGA in SOLAR, accounting for the nonindependence of related samples, identified 1 genome-wide significant SNP, rs247617, associated with HDL-C levels (see Fig. 3). rs247617 has previously shown evidence of association with HDL-C levels [9], low-density lipoprotein (LDL) levels [10] and metabolic syndrome [11]. rs247617 is located upstream of the gene CETP (cholesteryl ester transfer protein). The protein product of CETP is found in the plasma and has the role of transferring cholesterol esters from HDL-C to LDL [12]. Defects in CETP are reported to be the cause of hyperalphalipoproteinemia 1 (HALP1), a disease characterized by abnormally elevated levels of HDL-C [13, 14]. Genetic associations of suggestive genome-wide significance, not reported here, were observed in a linkage peak identified in the companion paper by Peralta et al. [8]. Furthermore, the companion paper by Porto et al. shows that genetic association studies can benefit from the use of empirical genetic values in the context of genomic predictions [15]. Using the empirical genetic values calculated for triglyceride and HDL-C may identify additional genome-wide significant associations.

To further examine the strength of using empirical kinship, the known pedigrees in this data set could be selectively broken into smaller pedigrees, to reduce the pedigree kinship matrix. We could then assess whether the triglyceride and HDL-C phenotypes remain significantly heritable, whether genetic associations detected using the full pedigree kinship matrix are replicated and whether in this context whether stronger support is provided for using empirical kinship in phenotype heritability estimation and genetic association studies.

Conclusions

The analysis presented here on the GAW20 data set from the GOLDN study has shown that empirical kinship is a practical alternative to pedigree-based kinships, when dense genotypic data are available, within the limitations of this study of a data set with little unknown kinship. Although we only examined phenotypes with moderate heritability, it is likely that the near functional equivalence of empirical and pedigree relatedness matrices holds across the spectrum of heritabilities. Analytical theory supports this as the expected power across heritabilities is determined by the eigenvalues of the relatedness kernel itself [16]. In this data set heritability estimates of triglyceride and HDL-C phenotypes obtained using empirical kinships from IBDLD more closely resembled those obtained with the pedigree based kinship estimations than those obtained using LDAK-based empirical kinships. The phenotypes assessed here were found to be highly and significantly heritable and measured genotype association testing identified a single variant, rs247617, as significantly associated with variation in HDL-C in line with the known biology of the gene closest to this variant, CETP.

Declarations

Funding

Publication of this article was supported by NIH R01 GM031575.

Availability of data and materials

The data that support the findings of this study are available from the Genetic Analysis Workshop (GAW), but restrictions apply to the availability of these data, which were used under license for the current study. Qualified researchers may request these data directly from GAW.

About this supplement

This article has been published as part of BMC Proceedings Volume 12 Supplement 9, 2018: Genetic Analysis Workshop 20: envisioning the future of statistical genetics by exploring methods for epigenetic and pharmacogenomic data. The full contents of the supplement are available online at https://bmcproc.biomedcentral.com/articles/supplements/volume-12-supplement-9.

Authors’ contributions

NBB and JB conceived the overall study. NBB, JMP, AP, and JB developed the statistical analyses. NBB performed the analyses and wrote the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Authors’ Affiliations

(1)
South Texas Diabetes and Obesity Institute, Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, One University Blvd., Modular Building #100, Brownsville, TX 78250, USA
(2)
Menzies Institute for Medical Research, University of Tasmania, Liverpool St, Hobart, TAS, 17, Australia

References

  1. Almasy L, Blangero J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet. 1998;62(5):1198–211.View ArticleGoogle Scholar
  2. Irvin MR, Zhi D, Joehanes R, Mendelson M, Aslibekyan S, Claas SA, Thibeault KS, Patel N, Day K, Jones LW, et al. Epigenome-wide association study of fasting blood lipids in the genetics of lipid-lowering drugs and diet network study. Circulation. 2014;130(7):565–72.View ArticleGoogle Scholar
  3. Speed D, Cai N, UCLEB Consortium JM, Nejentsev S, Balding D. Reevaluation of SNP heritability in complex human traits. Nat Genet. 2017;49(7):986–92.View ArticleGoogle Scholar
  4. Han L, Abney M. Identity by descent estimation with dense genome-wide genotype data. Genet Epidemiol. 2011;35(6):557–67.PubMedPubMed CentralGoogle Scholar
  5. Sun L, Dimitromanolakis A. PREST-plus identifies pedigree errors and cryptic relatedness in the GAW18 sample using genome-wide SNP data. BMC Proc. 2014;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S23.View ArticleGoogle Scholar
  6. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75.View ArticleGoogle Scholar
  7. Turner SD: qqman: an R package for visualizing GWAS results using Q-Q and Manhattan plots. bioRxiv 2014 - https://www.biorxiv.org/content/early/2014/05/14/005165.
  8. Peralta JM, Blackburn NB, Porto A, Blangero J, Charlesworth JC. Genome-wide linkage scan for loci influencing plasma triglyceride levels. BMC Proc. 2018;12(Suppl 9) https://doi.org/10.1186/s12919-018-0137-6.
  9. Coram MA, Duan Q, Hoffmann TJ, Thornton T, Knowles JW, Johnson NA, Ochs-Balcom HM, Donlon TA, Martin LW, Eaton CB, et al. Genome-wide characterization of shared and distinct genetic components that influence blood lipid levels in ethnically diverse human populations. Am J Hum Genet. 2013;92(6):904–16.View ArticleGoogle Scholar
  10. Surakka I, Horikoshi M, Mägi R, Sarin A-P, Mahajan A, Lagou V, Marullo L, Ferreira T, Miraglio B, Timonen S, et al. The impact of low-frequency and rare variants on lipid levels. Nat Genet. 2015;47(6):589–97.View ArticleGoogle Scholar
  11. Kristiansson K, Perola M, Tikkanen E, Kettunen J, Surakka I, Havulinna AS, Stancáková A, Barnes C, Widen E, Kajantie E, et al. Genome-wide screen for metabolic syndrome susceptibility loci reveals strong lipid gene contribution but no evidence for common genetic basis for clustering of metabolic syndrome traits. Circ Cardiovasc Genet. 2012;5(2):242–9.View ArticleGoogle Scholar
  12. Barter PJ. Hugh Sinclair lecture: the regulation and remodelling of HDL by plasma factors. Atheroscler Suppl. 2002;3(4):39–47.View ArticleGoogle Scholar
  13. Calabresi L, Nilsson P, Pinotti E, Gomaraschi M, Favari E, Adorni MP, Bernini F, Sirtori CR, Calandra S, Franceschini G, et al. A novel homozygous mutation in CETP gene as a cause of CETP deficiency in a Caucasian kindred. Atherosclerosis. 2009;205(2):506–11.View ArticleGoogle Scholar
  14. Cefalù AB, Noto D, Magnolo L, Pinotti E, Gomaraschi M, Martini S, Vigna GB, Calabresi L, Tarugi P, Averna MR. Novel mutations of CETP gene in Italian subjects with hyperalphalipoproteinemia. Atherosclerosis. 2009;204(1):202–7.View ArticleGoogle Scholar
  15. Porto A, Peralta JM, Blackburn NB, Blangero J. Reliability of genomic predictions of complex human phenotypes. BMC Proc. 2018;12(Suppl 9) https://doi.org/10.1186/s12919-018-0138-5.
  16. Blangero J, Diego VP, Dyer TD, Almeida M, Peralta J, Kent JW Jr, Williams JT, Almasy L, Göring HH. A kernel of truth: statistical advances in polygenic variance component models for complex human pedigrees. Adv Genet. 2013;81:1–31.PubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s). 2018

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