Figures
Abstract
Background
Human height is a classical example of a polygenic quantitative trait. Recent large-scale genome-wide association studies (GWAS) have identified more than 200 height-associated loci, though these variants explain only 2∼10% of overall variability of normal height. The objective of this study was to investigate the variance explained by these loci in a relatively isolated population of European descent with limited admixture and homogeneous genetic background from the Adriatic coast of Croatia.
Methodology/Principal Findings
In a sample of 1304 individuals from the island population of Hvar, Croatia, we performed genome-wide SNP typing and assessed the variance explained by genetic scores constructed from different panels of height-associated SNPs extracted from five published studies. The combined information of the 180 SNPs reported by Lango Allen el al. explained 7.94% of phenotypic variation in our sample. Genetic scores based on 20∼50 SNPs reported by the remaining individual GWA studies explained 3∼5% of height variance. These percentages of variance explained were within ranges comparable to the original studies and heterogeneity tests did not detect significant differences in effect size estimates between our study and the original reports, if the estimates were obtained from populations of European descent.
Conclusions/Significance
We have evaluated the portability of height-associated loci and the overall fitting of estimated effect sizes reported in large cohorts to an isolated population. We found proportions of explained height variability were comparable to multiple reference GWAS in cohorts of European descent. These results indicate similar genetic architecture and comparable effect sizes of height loci among populations of European descent.
Citation: Zhang G, Karns R, Sun G, Indugula SR, Cheng H, Havas-Augustin D, et al. (2011) Extent of Height Variability Explained by Known Height-Associated Genetic Variants in an Isolated Population of the Adriatic Coast of Croatia. PLoS ONE 6(12): e29475. https://doi.org/10.1371/journal.pone.0029475
Editor: Qingyang Huang, Central China Normal University, China
Received: August 24, 2011; Accepted: November 29, 2011; Published: December 27, 2011
Copyright: © 2011 Zhang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The study was supported by grants from the National Institutes of Health, United States of America (R01 DK069845, R01 DK069845-4S and P30 ES006096) and from the Croatian Ministry of Science, Education and Sports (196-1962766-2751, 196-1962766-2747 and 196-0342282-0291). RK was supported by a training grant fellowship from the National Institutes of Environmental Health Sciences, USA (T32 ES010957). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Heritability of normal variation in human height is estimated to be ∼80%–90% [1]–[4], much of which is attributed to inherited factors [5]. Genome-wide association studies (GWAS) and gene-centric analyses have uncovered over 200 single nucleotide polymorphisms (SNPs) associated with human height, but these variants explain only 2∼10% of the phenotypic variance [6]–[12]. Arguing that a large fraction of missing heritability could potentially stem from the large number of height-related variants with small effect sizes, a recent study analyzed all genome-wide SNPs simultaneously and reported that 45% of genetic variation in height could be explained by common variants [13]–[15] without specifying their individual contributions.
The above mentioned GWAS and meta-analysis were based on large cohorts primarily of European descent sampled from Europe and North America, although a few have included other ethnicities [6], [12]. In this study, we have assessed the variance explained by known height-associated loci in a relatively isolated island population from the Adriatic coast of Croatia with limited admixture and homogeneous genetic background. Rather than conducting a discovery study, our purpose was to evaluate whether the effects of genetic variants uncovered in larger, heterogeneous study populations are portable to smaller, homogeneous populations. Specifically, we conducted a GWAS using a panel of 500K SNPs in a sample of >1300 adults to identify height-associated variants and estimated variance explained using genetic scores that combine information from panels of known loci from published GWAS [6]–[9], [11]. Our study showed that comparable variance may be explained by previously reported height-associated GWAS variants, if the comparison population is of European decent, and that the associated loci and effect sizes of previously identified loci are comparable in our population.
Methods
Study Population
The study population was derived from a genetic study of metabolic syndrome [16], [17]. Briefly, 1445 men and women were recruited from Hvar, a middle Dalmatian island on the eastern Adriatic coast of Croatia. The present day Adriatic islanders are primarily of Slavic descent, who had emigrated from the mainland before the 18th century and remained relatively isolated since that time with attributes of relative homogeneous genetic, cultural and environmental background [18], [19]. Blood samples and anthropometric data were collected in two field seasons of May 2007 and May 2008. For this study, we used the data on 1304 participants for whom genome-wide SNP data and height measurements were available. The study was approved by the Ethics Committee of the Institute for Anthropological Research in Zagreb, Croatia and the Institutional Review Board of the University of Cincinnati. Written informed consent was obtained from all participants.
Genotyping and imputation
Genome-wide SNP genotyping was performed using the Affymetrix Human SNP Array 5.0 following the manufacture's protocol. Genotype calls were determined using the CRLMM algorithm [20], [21] among chips that passed the DM QC call rate (>0.86). Following further QC filtering of the genotype data (MAF>0.02, HWE P>0.0001, call rate>95%) using the check.marker function implemented in GenABEL [22], we obtained a cleaned data set of 344,512 SNPs in 1304 samples (565 males and 739 females). On this data set, we performed genotype imputation using MACH [23] and the reference haplotype data from the Phase II CEU HapMap [24], yielding genotype data on 2.5 million SNPs.
Genome-wide association analyses
The study sample included 533 unrelated and 771 related individuals from 238 families. Therefore, two different methods were used to account for the relatedness – the genomic control (GC) [25], [26] and the measured genotype (MG) approach [27]. In GC, all samples were treated as unrelated and the inflation factor (λ) was estimated using the median method [28]. In the MG approach, the kinship matrix was estimated from all autosomal SNPs instead of pedigree data. To assess the empirical performance of the two methods, we examined how many of the previously identified height loci (http://www.genome.gov/gwastudies/) could be recovered from the top 100 significant regions (defined as 250 kb on either side of associated SNPs) using each of these two methods. Standardized height adjusted for age and gender and their interaction term was used in all analyses. P-values and effect size estimates assuming additive allelic effect are reported throughout the manuscript. All statistical analyses were performed in R v2.12 and the GenABEL package (v.1.6) [22].
Variance explained by identified loci
To assess the overall fitting of previously identified height-associated loci in our study sample, we estimated the percentage of variance explained using genetic scores that combine information from different panels of known loci reported in previous studies. The unweighted genetic score was computed as an individual's number of height-increasing alleles, and the weighted genetic score was constructed as ;
indicates the number of reference alleles for specific SNP,
is the appropriate weight proportional to previously reported estimates of allelic effect (measured as beta-coefficient or log transformed odds ratio). The variance explained (r2) was then estimated based on a linear regression model using the scores as predictor and age-, gender-, and their interaction term adjusted height residuals as outcome.
We estimated and compared the variance explained based on different panels of height-associated SNPs and their effect size estimates extracted from published large-scale GWAS, which included 1) 180 genome-wide significant SNPs reported by Lango Allen et al. [11], 2) height-associated SNPs reported by the three GWA studies published in the 2008 May issue of Nature Genetics [6]–[8], and 3) 54 height SNPs used by Aulchenko et al [9], which were derived from the three aforementioned GWAS [6]–[8].
Results
Height was normally distributed in both males (N = 565) and females (N = 739), and males (177.7±7.5 cm) were significantly taller than females (164.4±7.1 cm). Regression analysis showed that adult height was negatively associated with age in both genders with a larger age-effect in females. Estimated height decrease per year in females was 0.239 cm (P = 9.05×10−55, r2 = 28.0%) and in males was 0.195 cm (P = 3.50×10−24, r2 = 16.6%, Figure S1).
Genetic scores based on the five published studies are shown in Table 1. The allele frequencies of the 180 genome-wide significant SNPs reported by Lango Allen et al. [11] in our samples were similar to the original report (Figure S2). The correlation of effect size estimates of these SNPs between our study and the original report (Stage 1+2) was highly significant although not perfect (Pearson ρ = 0.472, P = 2.32×10−11). The effect size estimates of 58 SNPs were in different directions (points in the 2nd and 4th quadrants in Figure 1). After removing these 58 SNPs, Pearson's ρ increased to 0.851 for the remaining 122 SNPs. However, heterogeneity test did not show significant differences in estimates of effect sizes between our study and that of Lango Allen et al. [11]. Though 12 of the 180 SNPs showed nominally significant heterogeneity (P<0.05) none survived adjustment for multiple testing (a full list of these 180 SNPs are provided in the Table S1). As expected, based on the central limit theorem, the genetic scores were normally distributed (Figure S3). The weighted genetic score and adjusted height were significantly (P<1×10−16) correlated (Figure 2). Combining the information from the 180 reference SNPs the explained variance was 7.94%, which was at the lower bound of the range of variance explained reported by Lango Allen et al. – 10.5% (range 7.9–11.2%) [11]. As reflected in Figure 2, the correlation of genetic score with adjusted height was higher in females than males with a larger fraction of variance explained (10.09% vs 5.87% in females and males respectively). The unweighted genetic scores explained a lower fraction (5.45%) of the overall variance.
The weighted genetic scores constructed using the SNPs and effect size estimates reported by the three Nature Genetics studies [6]–[8] explained 3∼4% of the height variance in our study sample. The study by Gudbjartsson et al. [6] included an African American cohort (n = 1,148) and the variance explained by the genetic scores built on the effect sizes estimated from this group was significantly smaller (P<0.001) than the scores based on the effect sizes estimated from other groups of European descent (Icelandic, Dutch or the Europeans Americans), which may reflect effect size differences among diverse populations.
In addition to testing height as a quantitative variable, Lettre et al. [7] evaluated their identified SNPs in a height ‘case-control’ sample (the USHT tall-short panel with subjects selected from tails of the height distribution) and reported allelic effects using odds ratios (ORs). Our effect size estimates correlated well with the log transformed ORs (Pearson ρ = 0.639) and the genetic scores weighted by these log transformed ORs explained even higher variance (4.62%) than the genetic scores weighted by the beta value estimates (Table 1).
In addition, we estimated the variance explained by the panel of 54 SNPs analyzed by Aulchenko et al. [9], which were identified by the three aforementioned GWAS [6]–[8]. The weighted and unweighted genetic scores based on these 54 SNPs explained slightly larger variances than the scores based on any of the individual studies (Table 1). This study also reported genotypic effect sizes, which enabled us to construct weighted genotype scores without the assumption of allelic additivity, though the variance explained by this genotypic score was similar to that obtained by weighted genetic scores assuming additive allelic effects.
The reported height-associated SNPs that overlapped across studies may capture more genuine effects than those obtained from a single study. Therefore, we tested whether the variance explained can be increased by including multiple genetic scores as multi-dimensional predictors. By including the scores with largest variance explained from each study, the explained variance was increased to 10.21%.
We also performed genome-wide association analysis in order to replicate previous findings. As indicated by QQ-plots (Figure S4), both GC and MG appropriately controlled type I error rates. The genome-wide single-locus test statistics were inflated compared to the null distribution with an estimated inflation factor λ = 1.41. As expected this estimate was the highest among all metabolic traits we studied (unpublished data) since the inflation factor is larger for traits with higher heritability and substantial inflation is likely attributable to the large number of SNPs associated with height [29].
The P-values obtained by GC and MG were highly correlated (Pearson's ρ = 0.766). Interestingly, the GC method recovered 9 previously known loci (Table 2 and Figure S5) compared to 4 recovered by the MG approach, three of which were in the 9 GC regions. Due to our limited sample size, however, none of these loci reached genome-wide significance (P<5×10−8). The 9 GC regions include several well known height loci, including EFEMP1, UQCC and HMGA2. Of particular interest is the UQCC locus (also known as the GDF5-UQCC region; Figure 3A). This region encompasses approximately 850 kb, covering more than 20 genes, and has a relatively low recombination rate. The most significant signal was observed on SNP rs6058227 (P = 8.21×10−6); the minor allele (T) was associated with an average increase of height 1.93 cm. Re-analysis of the region conditional on rs6058227 failed to completely eliminate the signals of the other SNPs, suggesting the possibility of multiple causal variants within the region (Figure 3B). Similar evidence for allelic heterogeneity was also reported by Lango Allen et al., and the presence of multiple functional variants could be a potential source of missing heritability [11].
The most significant SNP was rs17092784 after conditioning on rs6058227.
Discussion
We presented an estimation of variance explained by previously reported height-associated loci in a relatively isolated population based on a genome-wide scan of 2.5 M genotyped and imputed SNPs in 1304 individuals. Given our relatively small sample size, we did not aim to identify novel loci; instead, our purpose was to evaluate whether height-associated SNPs identified by GWA studies in large population cohorts share similar effects in an isolated and homogeneous population.
We evaluated the percentage of variance explained by genetic scores based on previously reported panels of height-associated SNPs and their effect size estimates. Under the assumption of additive effect between loci, the weighted genetic score captures the majority of genetic effects at multiple loci. The corresponding explained variance can be used as an indicator of the overall agreement of the effect size estimates between reference studies (from which the effect sizes/weights were developed) and our current study. The results indicate that a comparable fraction of variance can be explained in our samples if the genetic score was constructed from a population of European descent. In our sample the variance explained by the 180 height-associated SNPs reported by Lango Allen et al. [11] was 7.94%, which was within the range (7.9–11.2%) of the original report, though at the lower end. The higher percentage of variance explained by the weighted scores compared to the unweighted scores (5.45%) also suggested that the effect sizes estimated from meta-analysis of multiple cohorts of European descent can be applied to our relatively isolated population, which is also of European descent. At the individual SNP level, we did not detect significant differences in effect size estimates between our study and the reference study. Altogether, these observations indicate similar genetic architecture (i.e. associated loci and their effect sizes) of height in the same continental populations.
Using the SNPs reported by the three Nature Genetics studies [6]–[8], the percentages of variance explained were around 3∼4%, which were within the range of the original reports. We also found that the log transformed odds ratios estimated from extreme “case/control” samples [7] provided accurate estimates of real effect sizes, illustrated by the larger variance explained compared to scores using beta coefficients estimated from unascertained population-based cohorts with larger sample sizes. The significantly lower variance explained using the genetic scores based on effect size estimates from US African samples demonstrated substantially different effect size estimates between distantly related populations. Since our sampled individuals contained familial relatedness, we tested two methods to account for relatedness. The measured genotype (MG) approach uses a mixed model to quantify the polygenic effect and the fixed effect (association) simultaneously and is a robust and powerful method for the analysis of quantitative traits in related samples [30]. Genomic control (GC) is a simpler and computationally efficient method originally developed to control the inflated false positive rate introduced by population stratification [26], [28] and has been recently applied to account for relatedness in association tests [31]. The GC method corrects the false positive rate by adjusting test statistics using a uniform inflation factor and it ranks significant findings in the same order as conventional association tests that do not account for relatedness.
Our results indicate that the empirical performance of the GC method was better than the MG approach by recovering a larger number of previously-identified height loci among the top ranking regions. This implied that the P-values reported by GC or association tests ignoring relatedness may better reflect the real order of significance than the complex approach of MG. Recent simulation studies indicate that GC or conventional tests are appropriate methods to study genetic association in randomly ascertained, moderate-size pedigrees for both quantitative [32] and qualitative traits [33]. However, for polygenic traits with high heritability (such as height), GC may be over-conservative since all SNPs, a substantial fraction of which are truly associated with the trait, are used to estimate the inflation factor [29], [32]. The inflation factor (λ = 1.41) for height observed in current analysis was the highest among all traits we studied in the same population sample (data not shown). This high estimate is likely explained by high heritability and the large number of associated SNPs rather than by population stratification or relatedness; therefore rendering the reported association P-values (Table 2) conservative.
Of the 9 height-associated loci described in Table 2, several were among the top of the significant lists in multiple GWAS; for example, HMGA2 [34] and GDF5-UQCC [35] were the first two identified height loci and were widely replicated in multiple studies [6]–[8]. The EFEMP1 locus was first reported by Weedon et al. [8] and Gudbjartsson et al. [6] and was subsequently replicated in Australians of European descent [36] and Asian populations [37]–[39]. Four other gene regions, L3MBTL3 [6], [11], CRADD [6], [8], JAZF1 [10], [40], and ANAPC13 [8] were also reported in European GWA studies. Of note is the GRIA1 locus, which was previously reported in a GWAS in another Adriatic island population [41]. Though the IGF1 (insulin-like growth factor 1) pathway is integral to growth and mutations in IGF1 usually result in growth retardation [42], candidate gene studies testing for associations between common variants of IGF1 and height variation have been inconclusive [43]–[45]. Two recent GWAS [38], [39] have, however, confirmed IGF1 as a height-associated locus in Asian populations. However, this locus has not been identified in European GWA studies with even much larger sample sizes, suggesting weak statistical powers due to differences in allele frequencies or linkage disequilibrium patterns between populations.
As in the study by Lango Allen et al. [11], through conditional analysis we found evidence supporting allelic heterogeneity in many height-associated regions. In the GDF5-UQCC region, for example, our most significant SNP (rs6058227) was not in close linkage disequilibrium with six index SNPs reported in previous GWA studies and analysis conditional on rs6058227 could not eliminate the significances of the adjacent SNPs in that region. These all are highly suggestive of multiple effective SNPs in this region. This observation again highlighted that allelic heterogeneity might be a common feature of polygenic loci and identification of additional functional variants beyond the lead SNPs may increase the proportion of variance explained for complex traits [11], [46].
In summary, we have evaluated the portability of height-related loci and their estimated effect sizes reported in large cohorts to an isolated population with a homogeneous genetic background. We found proportions of explained height variability were comparable to multiple reference GWAS in populations of European descent. These results indicate similar genetic architecture and comparable effect sizes of height loci among populations of European descent.
Supporting Information
Figure S1.
Correlation of height with age in males and females.
https://doi.org/10.1371/journal.pone.0029475.s001
(TIF)
Figure S2.
Minor allele frequencies of the 180 height-associated SNPs were highly correlated between our study and the reference study by Lango Allen et al [11] .
https://doi.org/10.1371/journal.pone.0029475.s002
(TIF)
Figure S3.
Genetic scores follow normal distribution.
https://doi.org/10.1371/journal.pone.0029475.s003
(TIF)
Figure S4.
QQ plot of GC (A) and MG (B) association test.
https://doi.org/10.1371/journal.pone.0029475.s004
(TIF)
Figure S5.
Manhattan plot (the red bars indicated the nine replicated GWA regions).
https://doi.org/10.1371/journal.pone.0029475.s005
(TIF)
Table S1.
Frequencies and effect sizes of the 180 height-associated SNPs reported by Lango Allelen et al. [ Nature 467 (7317): 832-8].
https://doi.org/10.1371/journal.pone.0029475.s006
(PDF)
Acknowledgments
We thank T. Saric, J. Sirac, B. Cvitkusic, A. Vekic of the Institute for Anthropological Research, Zagreb, Croatia for their efforts in the field research and phenotype data collection.
Author Contributions
Conceived and designed the experiments: RD PR RC. Performed the experiments: DH-A NN DR SM ZD PR GS SRI HC. Analyzed the data: GZ. Wrote the paper: GZ RK RD RC.
References
- 1. Fisher RA (1918) The correlation between relatives on the supposition of Mendelian inheritance. Transactions of the Royal Society of Edinburgh 52: 399–433.
- 2. Silventoinen K (2003) Determinants of variation in adult body height. Journal of Biosocial Science 35: 263–285.
- 3. Perola M, Sammalisto S, Hiekkalinna T, Martin NG, Visscher PM, et al. (2007) Combined genome scans for body stature in 6,602 European twins: evidence for common Caucasian loci. PLoS Genet 3: e97.
- 4. Visscher PM (2008) Sizing up human height variation. Nat Genet 40: 489–490.
- 5. Visscher PM, Medland SE, Ferreira MA, Morley KI, Zhu G, et al. (2006) Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS Genet 2: e41.
- 6. Gudbjartsson DF, Walters GB, Thorleifsson G, Stefansson H, Halldorsson BV, et al. (2008) Many sequence variants affecting diversity of adult human height. Nat Genet 40: 609–615.
- 7. Lettre G, Jackson AU, Gieger C, Schumacher FR, Berndt SI, et al. (2008) Identification of ten loci associated with height highlights new biological pathways in human growth. Nat Genet 40: 584–591.
- 8. Weedon MN, Lango H, Lindgren CM, Wallace C, Evans DM, et al. (2008) Genome-wide association analysis identifies 20 loci that influence adult height. Nat Genet 40: 575–583.
- 9. Aulchenko YS, Struchalin MV, Belonogova NM, Axenovich TI, Weedon MN, et al. (2009) Predicting human height by Victorian and genomic methods. Eur J Hum Genet 17: 1070–1075.
- 10. Johansson A, Marroni F, Hayward C, Franklin CS, Kirichenko AV, et al. (2009) Common variants in the JAZF1 gene associated with height identified by linkage and genome-wide association analysis. Hum Mol Genet 18: 373–380.
- 11. Lango AH, Estrada K, Lettre G, Berndt SI, Weedon MN, et al. (2010) Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467: 832–838.
- 12. Lanktree MB, Guo Y, Murtaza M, Glessner JT, Bailey SD, et al. (2011) Meta-analysis of Dense Genecentric Association Studies Reveals Common and Uncommon Variants Associated with Height. Am J Hum Genet 88: 6–18.
- 13. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, et al. (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42: 565–569.
- 14. Yang J, Manolio TA, Pasquale LR, Boerwinkle E, Caporaso N, et al. (2011) Genome partitioning of genetic variation for complex traits using common SNPs. Nat Genet 43: 519–525.
- 15. Visscher PM, Yang J, Goddard ME (2010) A commentary on ‘common SNPs explain a large proportion of the heritability for human height’ by Yang et al. (2010). Twin Res Hum Genet 13: 517–524.
- 16. Zhang G, Karns R, Narancic NS, Sun G, Cheng H, et al. (2010) Common SNPs in FTO gene are associated with obesity related anthropometric traits in an island population from the eastern Adriatic coast of Croatia. PLoS One 5: e10375.
- 17. Karns R, Zhang G, Jeran N, Havas-Augustin D, Missoni S, et al. (2011) Replication of genetic variants from genome-wide association studies with metabolic traits in an island population of the Adriatic coast of Croatia. Eur J Hum Genet 19: 341–346.
- 18.
Rudan P, Sujoldzic A, Simic D, Bennett LA, Roberts DF (1992) Population structure in the eastern Adriatic: the influence of historical processes, migration patterns, isolation and ecological pressures, and their interaction. In: Roberts DF, Fujiki N, Torizuka K, editors. Isolation, Migration and Health. Cambridge England: Cambridge University Press. pp. 204–218.
- 19. Rudan P, Janicijevic B, Jovanovic V, Milicic J, Narancic NS, et al. (2004) Holistic anthropological research of Hvar Islanders, Croatia–from parish registries to DNA studies in 33 years. Coll Antropol 28: Suppl 2321–343.
- 20. Carvalho BS, Louis TA, Irizarry RA (2010) Quantifying uncertainty in genotype calls. Bioinformatics 26: 242–249.
- 21. Carvalho B, Bengtsson H, Speed TP, Irizarry RA (2007) Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data. Biostatistics 8: 485–499.
- 22. Aulchenko YS, Ripke S, Isaacs A, van Duijn CM (2007) GenABEL: an R library for genome-wide association analysis. Bioinformatics 23: 1294–1296.
- 23. Li Y, Willer C, Sanna S, Abecasis G (2009) Genotype imputation. Annu Rev Genomics Hum Genet 10: 387–406.
- 24. Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, et al. (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449: 851–861.
- 25. Bacanu SA, Devlin B, Roeder K (2002) Association studies for quantitative traits in structured populations. Genet Epidemiol 22: 78–93.
- 26. Devlin B, Roeder K, Bacanu SA (2001) Unbiased methods for population-based association studies. Genet Epidemiol 21: 273–284.
- 27. Boerwinkle E, Chakraborty R, Sing CF (1986) The use of measured genotype information in the analysis of quantitative phenotypes in man. I. Models and analytical methods. Ann Hum Genet 50: 181–194.
- 28. Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55: 997–1004.
- 29. Yang J, Weedon MN, Purcell S, Lettre G, Estrada K, et al. (2011) Genomic inflation factors under polygenic inheritance. Eur J Hum Genet 19: 807–812.
- 30. Saint PA, Vitezica Z, Martinez M (2009) A comparative study of three methods for detecting association of quantitative traits in samples of related subjects. BMC Proc 3: Suppl 7S122.
- 31. Steinthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R, Jonsdottir T, et al. (2007) A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet 39: 770–775.
- 32. Amin N, van Duijn CM, Aulchenko YS (2007) A genomic background based method for association analysis in related individuals. PLoS One 2: e1274.
- 33. Knight S, Camp NJ (2011) Validity and power of association testing in family-based sampling designs: evidence for and against the common wisdom. Genet Epidemiol 35: 174–181.
- 34. Weedon MN, Lettre G, Freathy RM, Lindgren CM, Voight BF, et al. (2007) A common variant of HMGA2 is associated with adult and childhood height in the general population. Nat Genet 39: 1245–1250.
- 35. Sanna S, Jackson AU, Nagaraja R, Willer CJ, Chen WM, et al. (2008) Common variants in the GDF5-UQCC region are associated with variation in human height. Nat Genet 40: 198–203.
- 36. Liu JZ, Medland SE, Wright MJ, Henders AK, Heath AC, et al. (2010) Genome-wide association study of height and body mass index in Australian twin families. Twin Res Hum Genet 13: 179–193.
- 37. Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, et al. (2009) A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 41: 527–534.
- 38. Kim JJ, Lee HI, Park T, Kim K, Lee JE, et al. (2010) Identification of 15 loci influencing height in a Korean population. J Hum Genet 55: 27–31.
- 39. Okada Y, Kamatani Y, Takahashi A, Matsuda K, Hosono N, et al. (2010) A genome-wide association study in 19 633 Japanese subjects identified LHX3-QSOX2 and IGF1 as adult height loci. Hum Mol Genet 19: 2303–2312.
- 40. Soranzo N, Rivadeneira F, Chinappen-Horsley U, Malkina I, Richards JB, et al. (2009) Meta-analysis of genome-wide scans for human adult stature identifies novel Loci and associations with measures of skeletal frame size. PLoS Genet 5: e1000445.
- 41. Polasek O, Marusic A, Rotim K, Hayward C, Vitart V, et al. (2009) Genome-wide association study of anthropometric traits in Korcula Island, Croatia. Croat Med J 50: 7–16.
- 42. Walenkamp MJ, Wit JM (2006) Genetic disorders in the growth hormone - insulin-like growth factor-I axis. Horm Res 66: 221–230.
- 43. Frayling TM, Hattersley AT, McCarthy A, Holly J, Mitchell SM, et al. (2002) A putative functional polymorphism in the IGF-I gene: association studies with type 2 diabetes, adult height, glucose tolerance, and fetal growth in U.K. populations. Diabetes 51: 2313–2316.
- 44. Yang TL, Xiong DH, Guo Y, Recker RR, Deng HW (2008) Comprehensive association analyses of IGF1, ESR2, and CYP17 genes with adult height in Caucasians. Eur J Hum Genet 16: 1380–1387.
- 45. Lettre G, Butler JL, Ardlie KG, Hirschhorn JN (2007) Common genetic variation in eight genes of the GH/IGF1 axis does not contribute to adult height variation. Hum Genet 122: 129–139.
- 46. Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, et al. (2010) Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466: 707–713.