Common genetic variants have been shown to explain a fraction of the inherited variation for many common diseases and quantitative traits, including height, a classic polygenic trait. The extent to which common variation determines the phenotype of highly heritable traits such as height is uncertain, as is the extent to which common variation is relevant to individuals with more extreme phenotypes. To address these questions, we studied 1,214 individuals from the top and bottom extremes of the height distribution (tallest and shortest ∼1. 5%), drawn from ∼78,000 individuals from the HUNT and FINRISK cohorts. We found that common variants still influence height at the extremes of the distribution: common variants (49/141) were nominally associated with height in the expected direction more often than is expected by chance (p<5×10−28), and the odds ratios in the extreme samples were consistent with the effects estimated previously in population-based data. To examine more closely whether the common variants have the expected effects, we calculated a weighted allele score (WAS), which is a weighted prediction of height for each individual based on the previously estimated effect sizes of the common variants in the overall population. The average WAS is consistent with expectation in the tall individuals, but was not as extreme as expected in the shortest individuals (p<0. 006), indicating that some of the short stature is explained by factors other than common genetic variation. The discrepancy was more pronounced (p<10−6) in the most extreme individuals (height<0. 25 percentile). The results at the extreme short tails are consistent with a large number of models incorporating either rare genetic non-additive or rare non-genetic factors that decrease height. We conclude that common genetic variants are associated with height at the extremes as well as across the population, but that additional factors become more prominent at the shorter extreme.
Height is a highly heritable trait, with estimates of heritability as high as 90% [1]. Recent genome-wide association studies of height have discovered over 180 common variants associated with height [2]. These variants have small effect sizes and collectively explain approximately 10% of the heritability. While these 180 common variants are robustly associated with height when studied as a quantitative trait in the general population, it is not known whether these variants have similar associations with stature in individuals at the extreme tails of the height distribution. If these common variants do not show the expected association with stature at the extremes (based on their continuous distribution effect sizes), then other factors beyond common variants must contribute to extreme stature. Although there are multiple possible scenarios, one possible explanation is the existence of rare or low frequency variants with larger effect sizes, which have been proposed to explain a portion of the heritability not accounted for by the known common variants [3]–[5] and which may provide novel biological insights into mechanisms that affects height. Understanding the role of common variants in the tails of the height distribution will also provide methodological insight into the utility of extreme tails analysis for future genetic studies of quantitative traits. In this paper, we describe our approach to determine whether common alleles known to be associated with height in the general population have the expected distribution in individuals from the extremes of the height distribution. We used DNA samples from individuals with extreme heights from two population-based cohorts of Finnish (FINRISK) and Norwegian (HUNT) ancestry and genotyped them for common variants known to be associated with height. Under a polygenic model in which there are many variants and each variant additively contributes a small effect to the phenotype, we found that for individuals within ∼2. 81 standard deviations of the mean, the common variants have the predicted associations with height, consistent with their effect sizes estimated from the previous population study [2]. However, in individuals with more extreme short stature (the shortest 0. 25% of the distribution), common variants play a less prominent role in explaining phenotype, and the data are consistent with various models in which rare variants, non-additive effects or rare non-genetic factors contribute to short stature in these individuals.
We attempted to genotype SNPs at the 180 loci previously associated with height in individuals from the short and tall extremes of the FINRISK and HUNT cohorts and then performed association analyses for each SNP with height using the Cochran-Mantel-Haenszel test and logistic regression respectively. In FINRISK, SNPs at 158 of the height loci were successfully genotyped in 181 short and 192 tall individuals from the 1% tails of the height distribution. In the HUNT study, SNPs at 160 of the height loci were successfully genotyped in 385 short and 456 tall individuals from the ∼1. 5% tails of height. Here we focus on the 279 short and 309 tall individuals from the 1% tails of the HUNT study, so as to provide consistency with the FINRISK study. In both cohorts, the majority of SNPs had effect directions consistent with the published results [2] (HUNT 137/160, p<0. 0001; FINRISK 122/155, p<0. 0001) and there was a significant enrichment in SNPs reaching nominal significance for association with height (Table S1; Table S2). We then combined the data from both cohorts in a meta-analysis of 141 overlapping loci (Table S3). Ninety-one percent of SNPs (128/141, p<0. 0001) had directions of effect consistent with previously published results [2] and 49 SNPs had p-values<0. 05, as opposed to 7 expected by chance (p<5×10−28). This result confirms that, as a group, SNPs found to be associated with height in the general population are also associated with height at the extremes of the phenotypic spectrum. We next tested whether the observed odds ratios (OR) are consistent with the expected odds ratios, based on the previously estimated effect sizes from the GIANT study [2] and study specific allele frequencies (see Materials and Methods). Overall, the number of SNPs with observed odds ratio greater than expected odds ratios was no different than expectation under the model of equal effect sizes in extremes and the general population (HUNT 79/160 SNPs, p = 0. 94; FINRISK 75/155 SNPs, p = 0. 48 and combined 75/141, p = 0. 45); (Table S1; Table S2 and Table S3). Next, for each SNP we tested for a difference between the expected and observed odds ratio in the individual studies and in the meta-analysis. Overall there were no more or fewer significant associations than would be expected under the equal effect size model (Figure S1). This result demonstrates that the individual SNPs have similar effects at the extremes as in the general population. After determining that the individual SNPs have similar effects at the extremes of the height distribution as in the general population, we then performed additional analyses on the combined set of height-associated variants. We asked whether extremely short and extremely tall individuals show overall enrichment of height-decreasing and height-increasing alleles, respectively, to the extent expected under a purely polygenic additive model. If the enrichment is less than expected, this result would suggest that the common variants are not explaining as much of the phenotypic variation in the extremes as in the general population. To test this possibility, we first calculated the weighted allele score (WAS) for each individual using the height-associated SNPs previously described. The WAS is the cumulative effect of all of the SNPs on height weighted by each SNP' s estimated effect size (β). In Figure 1, we show a plot of each individual' s WAS based on the 143 loci genotyped in both cohorts versus the individual height Z-scores. As expected, the WAS are significantly different between the tall extremes and the short extremes (p<3×10−86), with individuals in the tall extreme having higher WAS on average than individuals in the short extremes. We then tested whether the WAS in the short and tall groups are within expectations based on the population specific allele frequencies and previously estimated effect sizes of these SNPs, assuming a purely polygenic model. To generate the distribution of WAS under these expectations, we simulated populations that mimicked our ascertainment of extreme samples from the HUNT and FINRISK populations (see Materials and Methods). For each cohort, we compared the observed mean WAS with the distribution of mean WAS under the simulated model (Figure S2 and Figure S3). For the HUNT study the sample of 1224 individuals from the middle of the distribution suggest our modeling is behaving as expected (Figure S2). Finally, we analyzed the data by combining both studies using the 143 SNPs present in both data-sets (Figure 2). In each study separately and in the combined analysis, the mean observed WAS for the tall individuals was within expectation, but we observed a significant upward deviation of the mean observed WAS in the short extremes (p = 0. 006 for the combined-analysis). These results suggest that the collective effect of the common variants in the short extremes do not account for as much of the phenotypic variation in height as predicted from the effects seen in the general population. Having established that the common variants do not explain as much phenotypic variation in the short extremes, we then sought to determine if this finding was accentuated in individuals with the most extreme short stature. We stratified our analysis in several ways (Figure 2; Figure S2; Figure S3). First, we removed the most extreme individuals: those below the 0. 25 percentile and above the 99. 75 percentile. In the combined cohorts, the mean observed WAS in the short extremes was no longer significantly different than expected (p = 0. 526), indicating that the shift in WAS is driven by the most extremely short individuals. To further explore this hypothesis, we then selected more extreme individuals at two thresholds, including only the top and bottom 0. 5% or 0. 25% of the population (See Materials and Methods). For both strata, there was a more pronounced deviation of the mean observed WAS in the short extremes (p = 7. 12×10−6 and p = 9. 88×10−7 for the 0. 5% and 0. 25% extremes respectively), but again no deviation in the tall extremes. Similar observations occurred when we analyzed the cohorts separately using the same stratification procedure (Figure S2; Figure S3). We repeated the analysis using Z-scores based on inverse normal transformation, and with the three −6 SD outliers removed, and the results were essentially unchanged. The difference observed in the WAS analysis is also supported by the individual SNP analysis: when we performed the combined analysis described above for the 0. 25% extremes rather than the entire cohort, 60% (84/139) of the SNPS have an observed effect size smaller than expected (p = 0. 02) (Table S4). This analyses clearly suggest that the initial marginally significant shift of the mean observed WAS in the short extremes is primarily driven by the most extreme short individuals. Therefore, in general, as one selects individuals with more extreme short stature, in particular those with heights below the 0. 25 percentile, the common variants play a much smaller role in explaining stature, indicating that there must be other factors contributing to the phenotypic variation in these extremely short individuals. We hypothesized that lower frequency and rare genetic variants with larger effect sizes than the common variants may explain the phenotypic variation in the short extremes. To test this hypothesis, we performed population simulations with rare-variants of various allele frequencies and effect sizes, and asked if our observed data were consistent with these simulated scenarios (Figure 3; Figure S4; Figure S5). As a negative control, we first modeled an additional 180 SNPs, each with allele frequency of 0. 3 and average effect sizes of −0. 05 SD, which is similar to the allele frequency and effect size for previously discovered common variants associated with height. In this simulation, the mean WAS distribution did not change, indicating that adding additional common variants of similar effect sizes cannot explain the phenotypic variation in the short extremes. We then modeled a single rare variant of very large effect: frequency 0. 005 and effect size of −4 SD. In this model, the mean WAS distribution in the extremely short individuals shifts more than we observed in our population. This simulation essentially excludes the possibility of a 0. 5% variant of very large effect within our cohort. Such a variant would also be likely to be discovered in linkage studies of several thousand sib-pairs [6]. However, there are several rare variant models that would likely not have been detected in previous linkage analyses of height and generate a shift in the mean WAS consistent with our observed data (Figure 3; Figure S4; Figure S5). One such possibility is a single low frequency variant (allele frequency = 0. 005) with an effect size of −2 SD; another model consistent with our data includes 10 variants each with an allele frequency of 0. 005 and a moderate effect size of −1 SD. These simulations suggest that individuals with very short stature may harbor small numbers of low frequency variants of moderately large effect or a greater number of low frequency variants of moderate effects contributing to their short stature. This result stands in contrast to the remainder of the height distribution in which a polygenic effect of common and rare variants with small effects could explain the majority of the heritability of height, even though only a small percentage of height-associated common variants have been identified. To provide further support for a different genetic architecture in individuals in the extreme short tails we performed an analysis in siblings from the HUNT study. We queried the entire HUNT database (N = 106,455) and identified 21,365 siblings pairs. The correlation of age and gender adjusted height between siblings was high (r = 0. 466). We then identified 98 individuals (aged between 20–70 yrs) with a Z-score<−2. 81 (∼0. 25% tails) and 80 with a Z-score>2. 81 who also had at least one sibling in the database (the results are similar if we use inverse normal transformation). The average height Z-score for the siblings of the extreme short group was −0. 97 (95% CI: −0. 80, −1. 15); the average Z-score for the full siblings of the extreme tall group was 1. 29 (95% CI: 1. 14,1. 45) which are significantly different (t-test, p = 0. 007 after reversing signs for the short group). We then performed this same analysis for the 0. 25% to 1. 5% tails individuals and there was no significant difference in z-scores of siblings between the short (−1. 05 95% CI: −1. 13, −0. 97) and tall (1. 11 95% CI: 1. 03,1. 18) groups (t-test, p = 0. 28). So the differential regression to the mean appears to be limited to the shortest ∼0. 25% of individuals with this group regressing more quickly than the tall extreme group. This is consistent with the results we observe with the weighted allele score (WAS) approach. We do not have the twin data that would allow us to separate out the environmental and genetic effects in this group and our data is consistent with both. If the effect were due to genetics, then a model with de novo mutations and/or multiple recessive rare variants could cause an increased regression to the mean in extremely short individuals, although there are other plausible explanations.
We have assessed whether common variants robustly associated with height in the general population also associate with height at the extreme tails of the height distribution. We further tested whether this association is to the extent expected under a purely polygenic model. By genotyping ∼160 height SNPs identified from the GIANT study [2] (that explain ∼10% of the population variation in height) in individuals from the ∼1% tails of height from two large population based cohorts, we have shown that the polygenic model can explain the associations in the ∼1% tails of height. However, our data indicate that the polygenic model starts to break down in extreme short individuals near the 0. 25 percentile cut off. This conclusion is supported by our sibling analysis, which demonstrated that siblings in the 0. 25% short tail regress to the mean more than those in the 0. 25% tall group. Interestingly, the overall height distribution also shows a slight asymmetric deviation from normality, with an excess of individuals with extremely short stature but not for extremely tall stature. While in general the individuals in the ∼1% tails carry as many height increasing alleles as would be predicted based on their height, there was a clear deviation for individuals in the shortest 0. 25% tail. On average, these individuals carry significantly more “tall” alleles at the 160 SNPs than would be expected if common alleles were explaining their short stature. This suggests that the heights of these individuals are explained by factors other than common variants. Our simulations suggest that rare variants could explain this difference in the 0. 25% shortest tail. For example, 10 rare variants with modest effects on height (1SD) are consistent with our observed data, as is a single variant with a 2SD effect. The sibling analysis also suggests a role for de novo or multiple recessive variants in the extreme short individuals. While rare height-decreasing variants of large effect are a plausible explanation, there are many other genetic models consistent with our data, including a mixture of height-decreasing with a smaller number of height-increasing rare variants, or variants having non-additive effects. While non-additive genetic effects could explain the data, no evidence was found for dominance or gene-gene interaction effects for the SNPs used in this study in the original GIANT publication [2]. It is also possible that these individuals are short for non-genetic reasons. One could suggest that these individuals are short because of differences in ancestry, but we have taken steps to remove any possible ethnic outliers from our extremes (See Materials and Methods, Figure S6 and Figure S7). Measurement or recording error is another possibility, although the fact that the tall group does not show this effect (which presumably is equally likely to contain measurement error as the short group) suggests this is an unlikely explanation. Non-genetic factors could also be a possibility, for example, poor early-life nutrition, severe infection, or other chronic childhood diseases could have prevented these individuals from reaching their genetic height potential. This result also suggests that these families would be good candidates to investigate in sequencing studies, as they may be enriched for rare or de novo, higher penetrance alleles. More generally, the weighted allele score (WAS) method developed here could be used to select individuals to sequence in the search for these types of rarer variants, not only for height but also for other polygenic traits and diseases. Specifically, individuals in the extreme tails of a trait distribution who have an unexpectedly high or low weighted allele score may be particularly useful to sequence, especially if multiple relatives with these characteristics were present in the extreme tails. Our study also demonstrates empirically that selecting individuals from the extreme tails of a complex trait distribution is an efficient approach for genetic studies, as was proposed both for linkage studies [7], [8] and association studies [9], [10]. Despite a quite modest sample size (N<1000), we replicated a large fraction of the individual SNPs identified in the GIANT study in our extreme height analysis. Ninety-one percent of the SNPs had odds ratios that were directionally consistent with the direction in the published GIANT study (p<0. 0001), and 35% (49/141) of SNPS had p<0. 05 in the consistent direction. Our analyses also demonstrate that, outside of the 0. 25% tails, this level of association is entirely consistent with that expected given the extreme tail ascertainment of our samples and the individual SNP continuous distribution effect sizes. Given this result, the ascertainment of our 923 samples from the ∼1% to 0. 25% tails provides equivalent power to approximately 6000 samples randomly selected from the general population for a variant explaining approximately 0. 1% of the variation in height. Indeed, the ability to detect associations in samples ascertained for extreme phenotypes has been recently demonstrated in studies of bone mineral density [11], body mass index [12], triglyceride levels [13], and type 2 diabetes (using a liability threshold model [14]). Also, our results suggest that the statistical power of detecting these small effect variants would be reduced if we were to include the most extreme tails of the phenotypic distribution (in our case, the shortest 0. 25% of individuals), consistent with predictions made based on simulation studies of mixtures of common and rare variants [15]. Nonetheless, our findings suggest that the use of individuals with the most extreme phenotypes could be particularly valuable to detect rarer variants with larger effect sizes more efficiently. In conclusion, we have shown that common genetic variants associated with height in the general population are also associated with height at the ∼1% tails of the height distribution. Our data suggest that common variants play less of a role, and the effect of rarer larger-effect alleles and/or strong environmental factors start to predominate around the 0. 25% extreme. This finding may also have broader implications for studies of disease, in that the polygenic model may apply well to those diseases that represent the tails of an underlying normal distribution, but perhaps less well to diseases that correspond to more extreme phenotypes.
Both studies were conducted according to the principles expressed in the Declaration of Helsinki. Attendance was voluntary, and each participant signed a written informed consent including information on genetic analyses. Local institutional review boards approved study protocols.
Although there are many loci in the human genome that have been discovered to be significantly associated with height, it is unclear if these loci have similar effects in extremely tall and short individuals. Here, we examine hundreds of extremely tall and short individuals in two population-based cohorts to see if these known height determining loci are as predictive as expected in these individuals. We found that these loci are generally as predictive of height as expected in these individuals but that they begin to be less predictive in the most extremely short individuals. We showed that this result is consistent with models that not only include the common variants but also multiple low frequency genetic variants that substantially decrease height. However, this result is also consistent with non-additive genetic effects or rare non-genetic factors that substantially decrease height. This finding suggests the possibility of a major role of low frequency variants, particularly in individuals with extreme phenotypes, and has implications on whole-genome or whole-exome sequencing efforts to discover rare genetic variation associated with complex traits.
genetics
biology
human genetics
genetics and genomics
Common Variants Show Predicted Polygenic Effects on Height in the Tails of the Distribution, Except in Extremely Short Individuals