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The Journal of Clinical Endocrinology and Metabolism logoLink to The Journal of Clinical Endocrinology and Metabolism
. 2010 May 13;95(8):3777–3782. doi: 10.1210/jc.2009-1715

Runs of Homozygosity Identify a Recessive Locus 12q21.31 for Human Adult Height

Tie-Lin Yang 1, Yan Guo 1, Li-Shu Zhang 1, Qing Tian 1, Han Yan 1, Christopher J Papasian 1, Robert R Recker 1, Hong-Wen Deng 1
PMCID: PMC2913044  PMID: 20466785

Abstract

Background: Runs of homozygosity (ROHs) have recently been proposed to have potential recessive significance for complex traits. Human adult height is a classic complex trait with heritability estimated up to 90%, and recessive loci that contribute to adult height variation have been identified.

Methods: Using the Affymetrix 500K array set, we performed a genome-wide ROHs analysis to identify genetic loci for adult height in a discovery sample including 998 unrelated Caucasian subjects from the midwest United States. For the significant ROHs identified, we replicated these findings in a family-based sample of 8385 Caucasian subjects from the Framingham Heart Study (FHS).

Results: Our results revealed one ROH, located in 12q21.31, that had a strong association with adult height variation both in the discovery (P = 6.69 × 10−6) and replication samples (P = 5.40 × 10−5). We further validated the presence of this ROH using the HapMap sample.

Conclusion: Our findings open a new avenue for identifying loci with recessive contributions to adult height variation. Further molecular and functional studies are needed to explore and clarify the potential mechanism.

A locus in 12q21.31 is shown to make recessive contributions to adult height variation in Caucasians by genome-wide runs of homozygosity analyses.

Region of extended homozygosity is a locus with two identical alleles, ranging in size from 200 kb to more than 15 Mb, which has been commonly found in the human genome (1,2). Previous studies have shown that these regions of extended homozygosity appear to contribute to the development of complex diseases or traits involving recessive variants, such as heart disease (3), hypertension, and elevated total/low-density lipoprotein cholesterol levels (4). Recently, a new approach toward identifying regions of homozygosity, termed runs of homozygosity (ROHs), has been developed. This method uses high-density single nucleotide polymorphism (SNP) genome-scan data to identify regions of extended homozygosity across variable numbers of consecutive SNPs (2,5). Some ROHs have been revealed to have potential functional significance (5,6). For example, Lencz et al. (5) found nine risk ROHs that have significant associations with schizophrenia. And Nalls et al. (6) detected a ROH on chromosome 8 significantly associated with Alzheimer’s disease.

As a classic complex trait, adult height is an ideal phenotype for genetic studies of quantitative traits in humans, with up to 90% of variation in adult height explained by genetic variation (7,8,9,10,11). Final adult height is the result of growth and development processes, and variation in height has been associated with disease-specific mortalities and risk of common diseases such as cardiovascular disease, type II diabetes, and osteoporosis in different populations (12,13,14,15). Therefore, the identification of genes that influence adult height might provide insights into mechanisms that control growth and development and also reveal specific pathogenic mechanisms contributing to the development of other related diseases.

Recent genome-wide association studies (16,17,18,19,20,21) have successfully discovered several genes or SNPs contributing to the variation of adult height. Collectively, however, all of these implicated genes or SNPs account for less than approximately 5% of height variation, so additional studies using innovative methodologies are required to identify other genetic factors that influence adult height. Because adult height could be affected by recessive loci (22,23,24), we adopted the newly described method, ROHs, to perform a genome-wide association study using our current high-density SNP genome-scan data from 998 Caucasian subjects to identify novel variants for adult height. We further replicated the significant results in another sample containing 8385 Caucasian subjects.

Subjects and Methods

Subjects

The study was approved by the required Institutional Review Board or Research Administration of the involved institutions. Signed informed consent documents were obtained from all study participants before entering the study. The study was initially performed with genome-wide ROH analyses in a discovery sample. Significant results identified were subsequently replicated in an independent sample. The basic characteristics of the study sample sets are summarized in Table 1, with additional descriptions below.

Table 1.

Basic characteristics of the study subjects

Trait Discovery sample Replication sample
n 998 8385
Age (yr) 50.23 (18.24) 45.47 (11.45)
Weight (kg) 80.16 (17.79) 76.50 (17.54)
Height (cm) 170.83 (9.74) 168.65 (9.51)
Males/females 498/500 3833/4552
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Data are shown as mean (sd). 

Discovery sample

A random sample of 998 unrelated healthy Caucasian subjects (500 women and 498 men) was identified from our established and expanding database containing more than 10,000 subjects. All of the identified subjects were U.S. Caucasians of northern European origin, living in Omaha, Nebraska, and its surrounding regions in the midwestern United States. These subjects were normal, healthy subjects defined by a comprehensive suite of exclusion criteria (25). Briefly, subjects with chronic diseases and conditions involving vital organs (heart, lung, liver, kidney, and brain) and severe endocrine, metabolic diseases, or other skeletal diseases (Paget’s disease, osteogenesis imperfecta, rheumatoid arthritis, etc.) were excluded from this study. Nurses measured the height of each subject without shoes using a standard wall-mounted stadiometer in the clinic.

Replication sample

The replication sample came from the Framingham Heart Study (FHS), which is a longitudinal study of 14,277 phenotyped subjects to identify the risk factors for cardiovascular disease. Details and descriptions about the FHS have been previously reported (26,27). Subjects eligible for the current investigation were drawn from the FHS SNP Health Association Resource (SHARe) project (28), for which genotyping was conducted in over 9300 subjects from three generations representing over 900 families. Adult height measurements were available for 8385 phenotyped Caucasian subjects: 1307 from the original cohort (521 men and 786 women), 3189 from the second-generation cohort (1491 men and 1698 women), and 3889 from the third-generation cohort (1821 men and 2068 women). The original cohort participants had height measured at exam 1, the second-generation cohort participants were measured at exams 5 or 6, and the third-generation cohort participants were measured at exam 1.

Genotyping

Genomic DNA was extracted from peripheral blood leukocytes using standard protocols. For the discovery sample, SNP genotyping was performed using the Affymetrix Human Mapping 500K array set (Affymetrix, Santa Clara, CA) with strict adherence to the Affymetrix protocol, as described in our previous publications (29,30). The final average Bayesian Robust Linear Model with Mahalanobis Distance Classifier genotyping call rate (31) of this sample was 99.14%. SNPs with a call rate less than 90% or failing Hardy-Weinberg equilibrium (HWE) at a threshold of 0.0001 were discarded from subsequent analyses. For the FHS replication sample, genotyping was performed using approximately 550,000 SNPs (Affymetrix 500K mapping array plus Affymetrix 50K supplemental array). Quality control was the same as that adopted for the discovery sample by excluding SNPs with a call rate less than 90% and deviating from Hardy-Weinberg equilibrium (P < 0.0001). The final average genotyping call rate of this sample was 98.00%.

Identification of ROHs

ROHs were identified using the Runs of Homozygosity program implemented in PLINK 1.01 (32). To avoid segments that were homozygous by chance, we defined an ROH as segments that contained a minimum of 100 consecutive SNPs (5). Because strong linkage disequilibrium within 100 kb is common throughout the human genome, we set the minimum length for an ROH at 500 kb to exclude very common ROHs that occur in all individuals in all populations. In addition, the required minimum density in an ROH was set at 50 kb per SNP, and the maximum gap between two consecutive homozygous SNPs was set at 100 kb (2). PLINK uses a sliding window of 5 Mb (minimum 50 SNPs) across the genome to scan the defined ROHs, and it allows five missing SNPs and one heterozygous site per window (32). All SNPs located in the X chromosome were excluded from the analyses because male subjects have only one copy of the X chromosome.

Association analyses between ROHs and adult height

We used the following protocol to examine associations between ROHs and adult height. First, individual ROHs were divided into different ROH groups by using the homozyg-group command in the Runs of Homozygosity program. An ROH group contains individual ROHs with overlapping regions, i.e. all the individual ROHs in a group include a consensus core region. SNPs in the consensus core region must be allelically matched. Allelic matching was confirmed when at least 95% of jointly nonmissing and homozygous SNPs were identical.

Next, for each ROH group containing more than 50 subjects in the discovery sample, we used the Student’s t test to compare the adult height of subjects with this ROH group to the height of subjects without this ROH group. In addition, because copy number changes can influence the accuracy of SNP genotyping calls, we excluded ROH groups from the analyses if they overlapped with regions containing copy number polymorphisms. Copy number polymorphisms were defined as copy number variations (CNVs) with frequencies higher than 1%. A total of 243 copy number polymorphisms were detected in our discovery sample, with the detailed information shown in our previous study (33). The conservative significance threshold for a single test was assessed at a type I error rate of 0.05/N (N was the total number of the tested ROH groups). For significant ROHs identified in the discovery sample, we further examined their associations in the FHS replication sample using the family-based association tests (FBAT) program (34). FBAT, which tests for differences in probability of transmission of a genotype from parents to offspring based on phenotype, is a powerful approach for handling family samples. P < 0.05 was considered significant for the replication analyses. Adult height was adjusted by age and sex, and height residuals were used for association analyses, both in the discovery and replication samples.

Validation of the presence of ROHs using high-density markers in the HapMap sample

To validate the presence of the ROH that was significantly associated with height variation in the discovery and replication samples, we used the HapMap sample with high-density markers to refine it. First, based on the publicly available Affymetrix 500K data for 90 HapMap Caucasians, we identified subjects with the ROH of interest using the same approach described above (see Identification of ROHs). Then, we used high-density SNP markers from the HapMap Phase 1 and 2 B36 data set to determine whether a consistent ROH could be detected in the same region. The SNP exclusion criteria were the same as that described in the Genotyping section.

Results

Discovery study

As described in our previously published genome-wide association studies, the 998 Caucasian subjects in the discovery sample were from a relatively homogeneous midwest U.S. population, and there was no significant population stratification in the discovery sample (21,30). After quality-control procedures, 465,469 autosomal SNPs were available for subsequent ROHs analyses. The mean homozygous rate for these SNPs was 72.3%. We identified a total of 113,910 individual ROHs across all 998 subjects. The average length of these individual ROHs was 883.1 kb. For each subject, on average, 113 ROHs were detected, covering 3.3% (99.4 Mb) of the human genome.

For the association analyses between human adult height and ROHs, 3322 ROH groups containing more than 50 individual ROHs with allele-matched overlapping regions were identified. Among these ROH groups, 80 groups overlapped with copy number polymorphisms and were excluded from the subsequent association analyses. The remaining 3242 ROH groups were distributed in 189 chromosomal regions (Supplemental Table 1, published on The Endocrine Society’s Journals Online web site at http://jcem.endojournals.org). We detected one ROH group, named ROH 12q21.31, that was significantly associated with adult height (P = 6.69 × 10−6), even after Bonferroni correction (significance threshold, 0.05/3242 = 1.54 × 10−5). The height of subjects with ROH 12q21.31 (n = 76; 7.6% of the sample) was significantly greater (increased by 3.5 cm; Table 2) than that of subjects without this ROH group (n = 922). ROH group 12q21.31 was defined by homozygosity across the consensus region of 84,922,501 bp to 84,972,309 bp. This region contains four SNPs (rs7313373, rs7975457, rs7979846, and rs9669353) that are shown in Supplemental Table 2. Individual ROHs within ROH group 12q21.31 extended beyond this consensus sequence in either direction, resulting in 76 distinct, but overlapping ROHs in the 76 subjects containing ROH group 12q21.31. We further checked the results in a CNV map, which was generated by McCarroll et al. (35) using Affymetrix SNP 6.0 array. And we did not find any CNV located in 12q21.31. In addition, we detected the association between SNPs in the consensus region and human height in the recessive model. However, no significant result was found, which suggested that this ROH cannot be represented by any individual SNP.

Table 2.

Association of adult height with ROH 12q21.31 in the two sample sets

Sample Overlapping region Frequencya Height [means (sd) in cm] by ROH 12q21.31
P value R2
Start End Subjects with this ROH Subjects without this ROH
Discovery sample 84922501 84972309 7.6% 174.1 (9.89) 170.6 (9.69) 6.69 × 10−6 1.8%
Replication sample 84829113 84970692 4.3% 169.5 (9.37) 168.6 (9.53) 5.40 × 10−5 0.54%
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a

Frequency means the proportion of subjects in the entire sample containing an ROH that overlapped with ROH 12q21.31. 

Replication study

For the 8,385 Caucasian subjects from the FHS study, a total of 488,587 autosomal SNPs passed our quality control for ROH analyses. We were also able to identify an ROH group, located in 12q21.31 (84,829,113 bp to 84,972,309 bp), in this population. This region contains 17 SNPs, including all the SNPs in the consensus region in the discovery sample (Supplemental Table 2). No CNV was found in this region. As with the discovery sample, individual ROHs within ROH group 12q21.31 extended beyond this consensus sequence in either direction, resulting in 361 distinct, but overlapping ROHs in the 361 subjects (4.3%) containing ROH group 12q21.31. SNPs in this region were allele-matched between the discovery and replication samples and were found to be the same. A significant association with adult height (P = 5.40 × 10−5) was successfully replicated for this ROH group by FBAT analysis. In addition, to assess the effect direction of this ROH, we compared the phenotype difference of height between those with and without this ROH. We found that the height of subjects with ROH group 12q21.31 (n = 361; 4.3% of the total sample) was significantly greater than that of subjects without this ROH group (n = 8024). The contribution of this ROH to height variation was estimated to be approximately 0.54%. In addition, rs9669353 was detected to be nominally associated with height (P = 0.031) in the recessive model.

Validation of the presence of ROH 12q21.31 using high-density markers in the HapMap sample

Using the public Affymetrix 500K data for 90 HapMap Caucasian samples, we identified three subjects (NA7022, NA7029, and NA12155) who had this ROH 12q21.31, with the consensus region ranging from 84,694,390 bp to 85,784,137 bp. Then we downloaded 1229 SNPs within this region from the HapMap phase 1 and 2 B36 data set. A total of 1166 SNPs within this region passed our quality control, with an average density of 935 bp per SNP. Using these high-density SNPs, a consistent ROH was detected, which spanned from 84,806,302 bp to 85,784,137 bp, and included 1,038 SNPs. This result strongly supports our finding of an ROH in 12q21.31.

Discussion

Using genome-wide microarray SNP data, we found that ROHs were common in Caucasian populations, a finding that is consistent with recently published studies (2,5) and of importance because ROHs could contribute recessive effects to human complex diseases or traits (5). Human adult height is a classic complex trait, and previous studies have identified recessive loci that were associated with adult height variation (22,23,24). In this study, we detected an ROH, 12q21.31, that was strongly associated with adult height variation both in the discovery and replication samples. We further validated the presence of this ROH using the HapMap sample. Our results lend strong support to the previous finding that there are recessive genetic effects on height variation.

A gene encoding mannosyl (α-1,3-)-glycoprotein β-1,4-N- acetylglucosaminyltransferase, isozyme C (MGAT4C), is located within 12q21.31 (36). The MGAT4C gene is expressed in many human tissues, including bone marrow, kidney, and muscle (37,38). Until now, there has been no direct evidence suggesting that the function of this gene is associated with adult height. However, the MGAT4C protein contributes to the carbohydrate metabolic process, which could potentially impact human growth. One previous study revealed that infants with carbohydrate malabsorption had a 20% reduction in growth (39). Additionally, Schmidt et al. (40) found that using a modified carbohydrate diet to treat patients with Prader-Willi syndrome can cause shorter stature. Thus, the link between carbohydrate metabolism and adult height and a potential mechanistic role for MGAT4C are intriguing.

It is noteworthy that high-density genome-wide SNP arrays are a powerful tool for identifying ROHs in the human genome (2,5). However, there is a limitation of this approach that exact boundaries of an ROH cannot be given. Furthermore, this could influence the accuracy of SNP genotyping. For example, a SNP in the region with heterozygosity deletion might be considered as homozygosity. Therefore, to obtain precise results for ROHs, we excluded ROHs that overlapped with copy number polymorphisms from the analyses. A previous study showed that ROHs less than 1 Mb were common in healthy individuals, and some of them were risk ROHs (5). In this study, to avoid missing potential risk ROH with length less than 1 Mb, we defined the length of ROH as more than 500 kb. However, a short length of ROH might increase the probability of finding by chance. We thus used an independent sample to replicate the ROHs that were significantly associated with height.

In our association analyses for adult height, we adjusted the phenotype data by the two most commonly used covariates for height: age and sex. Other factors, such as socioeconomic status, may also have potential effect on adult height. However, due to the lack of data in those aspects, this study represents the best we can do under present conditions.

In conclusion, using data from 9385 individuals, our study revealed an ROH located in 12q21.31 that was significantly associated with adult height variation. This finding opens a new avenue for identifying loci with recessive contributions to adult height variation. Further molecular and functional studies will be needed for confirmation and clarification of this potential mechanism.

Footnotes

Investigators of this work were partially supported by National Institutes of Health Grants R01 AR050496, R21 AG027110, R01 AG026564, P50 AR055081, and R21 AA015973. The study also benefited from grants from National Science Foundation of China, the Fundamental Research Funds for the Central Universities, Xi’an Jiaotong University, and the Ministry of Education of China. The Framingham Heart Study and the Framingham SHARe project are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University. The Framingham SHARe data used for the analyses described in this manuscript were obtained through dbGaP (phs000007.v3.p2, phs000008.v3.p2). This manuscript was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or the NHLBI.

Disclosure Summary: The authors have nothing to disclose.

First Published Online May 13, 2010

Abbreviations: CNV, Copy number variation; FBAT, family-based association tests; ROH, run of homozygosity; SNP, single nucleotide polymorphism.

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