Genetic approaches to stature, pubertal timing, and other complex traits

Molecular Genetics and Metabolism 80 (2003) 1–10 www.elsevier.com/locate/ymgme

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Genetic approaches to stature, pubertal timing, and other complex traits Mark R. Palmerta,b and Joel N. Hirschhornc,d,e,* a

Division of Pediatric Endocrinology and Metabolism, Rainbow Babies and ChildrenÕs Hospital, University Hospitals of Cleveland, USA b Department of Pediatrics, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA c Divisions of Genetics and Endocrinology, ChildrenÕs Hospital, 300 Longwood Avenue, Boston, MA 02115, USA d Department of Genetics, Harvard Medical School, Boston, MA 02115, USA e Whitehead/MIT Center for Genome Research, Cambridge, MA 02139, USA Received 27 March 2003; received in revised form 16 May 2003; accepted 16 May 2003

Abstract The factors that regulate the timing of puberty remain largely elusive, as do the factors that modulate childhood growth and adult height. However, it is clear that these developmental processes are highly heritable—much of the natural variation in growth and timing of puberty is due to genetic variation within the population. In this review, we discuss how recent genetic and genomic advances can be exploited to help understand the genetic regulation of these processes. In particular, we describe how genomewide linkage scans and association studies, in conjunction with haplotype-based approaches, are potentially useful tools to increase our understanding of these two complex traits. Discovery of the genetic variants that regulate these two traits would expand our understanding of human neuroendocrinology, postnatal development, and the general architecture of complex genetic traits. Ó 2003 Elsevier Science (USA). All rights reserved.

Introduction Variation in stature and/or pubertal timing is a common reason for referral to pediatric endocrinology clinics. A small fraction of patients referred for short stature have pathologic causes of short stature, such as growth hormone deficiency or hypothyroidism, but most do not have a readily identifiable cause of short stature [1]. Similarly, some patients have early pubertal development secondary to a central nervous system lesion or delayed puberty due to GnRH deficiency, an undiagnosed chronic condition such as inflammatory bowel disease, or pituitary pathology. However, most patients receive a diagnosis of idiopathic precocity or constitutionally delayed pubertal development [2]. The etiology of these idiopathic conditions remains unknown, and the factors that regulate adult stature and *

Corresponding author. Fax: 1-617-277-0496. E-mail address: [email protected] (J.N. Hirschhorn).

timing of puberty within the general population remain elusive. Environmental and metabolic factors are important regulators of the neuroendocrine axes that effect growth and pubertal development, but these influences are superimposed upon substantial genetic control. Analyses of adult height in family and twin studies indicate that heritability for this trait (the fraction of population variation explained by genetic rather than environmental factors) ranges from 76 to 90%, with most estimates being above 80% [3–6]. Indeed, even under conditions of widespread malnutrition, heritability remains high [7]. Similarly, data demonstrating correlation between the ages at which a mother and her children attain pubertal milestones [8] and between the timing of puberty within racial groups [9,10] are suggestive of genetic modulation of the reproductive endocrine axis. Skeletal maturation during childhood, age of growth spurt, age of menarche, and Tanner staging during puberty all display greater concordance between monozygotic than dizygotic twins [11–16]. These and other data indicate that up to

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50–80% of the variation in pubertal onset may be genetically controlled. Adult stature and the timing of pubertal onset are both normally distributed within the general population, suggesting that these traits are unlikely to follow classic Mendelian inheritance in which trait variation is attributable to variation at a single locus. Rather, the genetic modulation is likely to be the result of additive effects of variation in multiple genes, as has been suggested for other complex traits (see for example [17–19]). In this review, we will discuss how recent advances in genetics provide new opportunities to identify the genes that modulate these two complex traits and, consequently, to expand our understanding of human neuroendocrinology.

Clues from single gene (monogenic) disorders Although the genetic modulation of complex traits within the general population likely derives from the relatively modest, potentially additive effects of multiple genes, important insights are sure to result from the identification of single gene defects that lead to pathologic abnormalities of growth and/or pubertal timing. The literature is already replete with such examples (reviewed in [1,2,20–27]). For example, mutations in the gene encoding the GnRH receptor (GNRHR) cause hypogonadotropic hypogonadism (HH), consistent with the pivotal role of GnRH in initiating and sustaining activation of the pituitary–gonadal axis [28]. Mutations in the KAL gene, encoding anosmin, lead to the X-linked Kallman syndrome, which is characterized by HH and anosmia [29,30]. Ansomin is a cellular matrix protein that is required for migration of olfactory processes and GnRH neurons from their origin in the olfactory placode, and discovery of this protein has led to insights into the regulation of neuronal migration as well as pubertal development. More recently, mutations in the fibroblast growth growth factor receptor FGFR1 were shown to cause an autosomal dominant form of Kallman syndrome, suggesting that anosmin and fibroblast growth factor are functionally related [31]. HH can also result from abnormalities of the dosagesensitive sex reversal-adrenal hypoplasia congenital (AHC)-associated gene on the X chromosome [32,33]. This gene is referred to as DAX1, and encodes a transcription factor that appears to play important developmental roles in the hypothalamus, pituitary, gonad, and adrenal cortex. A defect in prohormone convertase (PC1) has been shown to impair production of GnRH from its precursor and to result in HH along with obesity and impaired processing of insulin and POMC [34]. The interplay between reproduction and nutritional or metabolic homeostasis has been underscored by the demonstration that HH is a component of the human

phenotype caused by defects in leptin or its receptor [24,35–37]. The discovery of the genes that underlie these and other disorders identifies high likelihood candidate genes and/or biologic pathways that may underlie the genetic modulation of the timing of puberty in the general population. Numerous single gene defects can also affect growth or adult height. A query of OMIM (http://www. ncbi.nlm.nih.gov/omim/) reveals hundreds of syndromes that affect stature, and for some of these, mutations in a single gene have been identified. Examples include syndromes caused by mutations in the genes encoding growth hormone or its receptor, GH and GHR [38–40], transcription factors required for pituitary development (POU1F1, PITX1, PROP1, HESX1, etc.; see [25] for review), or mutations in genes that affect bone formation (COL1A1, FGFR3, and numerous other genes that can cause skeletal dysplasias; see [26] for review). Recently, haploinsufficiency (loss of function of one copy) of the NSD1 gene was shown to be responsible for a significant fraction of cases of Sotos syndrome [41], which is characterized by overgrowth in the prenatal and early childhood periods, variable developmental delay, and an increased rate of malignancy. The identification of this gene, whose product contains several domains that may interact with nuclear receptors and play a role in chromatin regulation, should provide valuable clues into the regulation of fetal and early postnatal growth. The PTPN11 gene, encoding a tyrosine phosphatase, is mutated in Noonan syndrome [42], which includes short stature, and shares features with Turner syndrome as well as specific cardiovascular developmental defects. The short stature seen in Turner syndrome is likely due to haploinsufficiency of the X-linked SHOX gene, which encodes a homeobox transcription factor [43]. Mutations in the SHOX gene have also been shown to cause short stature in individuals without Turner syndrome [44]. Identification of upstream regulators and downstream targets of the SHOX protein and the PTPN11 signalling pathway will likely implicate other proteins as playing important roles in the regulation of growth. It remains to be determined whether less severe variants in the genes that to lead to HH or severe growth disorders will help explain the variation in adult stature and pubertal timing within the general population. Some probands with complete HH have relatives with delayed puberty, suggesting that a subset of the population with delayed puberty might be heterozygous carriers of the rare, severe mutations that cause HH [45]. To our knowledge, it has not been determined whether the same situation applies to growth; that is, whether relatives of individuals with severe syndromes of growth disturbance show less dramatic alterations in height. Thus, the variants affecting height and pubertal timing might be found in genes already identified from studies

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of monogenic disorders. Alternatively, the causal variants might be in related genes, or even genes in other as yet undiscovered pathways.

Rare variation, common variation, and finding genes for complex traits Three major approaches have been used to find genes underlying complex traits: resequencing of candidate genes, genome-wide linkage analyses, and association studies. The most effective approach to identifying causal genetic variants depends on whether the variants are common or rare, and whether they are penetrant variants with strong genetic effects (Fig. 1). One hypothesis is that rare variants explain complex genetic traits, just as they explain most single gene disorders [46,47]. This hypothesis has a few imperfect precedents. For example, carriers of BRCA1 or BRCA2 mutations are strongly predisposed to breast cancer [48–52], although mutation carriers are usually diagnosed at an earlier age than typical cases, in whom BRCA1 or BRCA2 mutations are rarely identified [49]. Severe MC4R mutations are a dominant cause of severe obesity, and some carriers are only moderately obese [53]. However, the prevalence of MC4R mutations is probably quite low in the less severely obese population [54]. In addition, female carriers of severe mutations in CYP21A (encoding 21-hydroxylase) show elevated androgen levels that may lead to clinically significant hyperandrogenism [55,56]. Thus, rare variants may have an impact on traits that are common in the population. This

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hypothesis is especially plausible for traits that have been deleterious from an evolutionary standpoint [47]. In traits where rare variation is suspected to predominate, the resequencing of candidate genes to find rare variants and linkage-based approaches are most likely to be effective (Fig. 1). Conversely, traits that are on balance evolutionarily neutral or favorable are likely to be explained in part by common variation [17,47,57], since most of the neutral variation in the genome is explained by genetic variants that are found at high frequency in the population [58–60]. Indeed, there are already many precedents for common variants with modest effects on complex traits [61–63]. For such variants, association studies are more powerful than linkage analysis [64], and deep resequencing of candidate genes is not required since the variants are common. Given the normal distribution of stature and pubertal timing over the entire population, it is hard to imagine how all of the variation in stature or pubertal timing can have been evolutionarily disadvantageous. Thus, at least some of the variants affecting these traits will likely be common. Nevertheless, until the variants that regulate these traits are discovered, contributions from rare and common variants are plausible, and a mix of approaches may be indicated (Fig. 1).

Pedigree and segregation analysis The performance of either pedigree analysis or its more formal counterpart, segregation analysis, can

Fig. 1. Schematic of strategies for identifying genes that modulate complex traits. h2 Refers to the fraction of trait variation that is explained by (additive) genetic effects; high heritabilities (50% or greater) are consistent with a major genetic component to the trait. See text for details of the depicted schematic.

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provide important insights for linkage and association studies. These analyses permit determination of the general pattern(s) of inheritance, estimation of the degree of familiality, and sometimes even a rough estimate of the degree of genetic complexity underlying the trait [65]. As an example, we recently analyzed pedigrees for constitutional delay of growth and maturation (CD), which is the most common cause of delayed puberty within the general population [2,66–70], which is known to aggregate in families [71–73], and which may simply represent the extreme end of the normal spectrum of pubertal development. For this study [74], we examined the family histories of 53 probands (40 boys, 13 girls) with CD and compared these pedigrees with 25 control pedigrees. Mean age of menarche was 14.3  1.4 years for mothers of CD probands vs. 12.7  1.4 years for mothers of controls (p < 0:0001), indicative of histories of late pubertal development among CD mothers. CD fathers were also more likely than the control fathers to have a history of pubertal delay. For all first degree relatives, the estimated relative risk (k1 ) of having a history of pubertal delay in CD vs. control pedigrees was 5; k2 for second degree relatives was 3–4. Inheritance patterns varied, but many families showed an apparent autosomal dominant pattern, with or without incomplete penetrance. Segregation analysis provides a somewhat more rigorous comparison of the likelihood of different genetic models. Such an analysis has been performed for adult height, and suggests that recessive allele(s) have major effects, and are modified by multiple other genes [75]. We are in the process of performing segregation analysis for delayed pubertal timing, but the initial data from our pedigree study demonstrating that several different inheritance patterns are present suggests that multiple genes (possibly acting through different genetic models) likely modulate the timing of puberty in humans. This is consistent with the hypothesis that multiple genes, perhaps with additive effects, underlie the genetic regulation of complex traits [17,18]. Although multiple patterns of inheritance were observed, we also found that a high proportion of families showed apparent autosomal dominant inheritance. This unexpected result indicates that some of the genetic variation in the timing of puberty may stem from variation in a few genes with major effects. If so, the apparently heterogeneous inheritance patterns could also reflect the action of modifiers of the major genes (either other genes or environmental factors). The possibility that such major genes underlie some cases of CD suggests that the genetic basis of CD may be tractable not only to association studies but also to other methods of genetic analysis, such as genome-wide linkage studies. Below, we discuss in more detail these two main approaches to finding variants for complex traits, linkage and association studies (Fig. 1). We also describe how

recent advances in genomics and genetics should facilitate both approaches.

Linkage studies Linkage studies require multiplex families; that is, multiple related individuals affected with disease or, in the case of quantitative traits, multiple relatives with phenotypic data. By typing a genome-wide set of informative markers in these families, the genome is queried for regions that are coinherited more often than expected by relatives with similar phenotypes. Linkage analysis has the distinct advantage that it is an unbiased genome-wide search for causal genes—one need not have a priori knowledge about which genes or pathways are likely to affect the trait being studied, and common as well as rare variants can give rise to linkage signals. This approach has been very successful in identifying the genes that underlie single-gene disorders [76]. The success in single-gene disorders stems from the fact that in these cases, mutation of the causal gene is generally both necessary and sufficient for disease. Consequently, the mutations cosegregate nearly perfectly with disease in families. This strong correlation allows the location of the gene to be mapped to a small region (often 1 cM or less) using a reasonably sized set of genetic markers and often only a few multiplex families. Once the approximate location is known, the causal gene must still be identified, but the fact that many single gene disorders are caused by multiple, obviously recognizable, distinctive mutations (e.g., deletions, frameshifts, nonsense mutations, or nonconservative missense mutations) usually facilitates this process. Several features of complex traits limit the power of linkage studies. Because complex traits result from the interaction of multiple genetic factors, no genetic variant will be either necessary or sufficient to determine an individualÕs phenotype. In other words, the effect of any individual genetic variant will likely be modest. Linkage studies lack sufficient power in this scenario, especially if the allele conferring risk is common. Indeed, accumulating the required number of families with multiple affected relatives is not feasible for many reasonable genetic models [64]. For example, the effect of the insulin VNTR on type 1 diabetes is difficult to demonstrate by linkage [77]; the effect of PPARG Pro12Ala on type 2 diabetes would be impossible to detect [54]. In addition, complex traits are usually affected by environmental factors, which can further limit the correlation between the trait and any particular genetic marker. The contribution of environmental factors (or possibly random, stochastic factors) is reflected in the lower heritability of many common diseases and complex traits. Since the power of linkage analysis is roughly proportional to the square of heritability [78,79], sig-

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nificant environmental influences can severely limit the power of linkage studies. Finally, inaccurate phenotyping can also limit power, since this will yet further weaken the correlation between genetic variation and the measured phenotype, lowering heritability. As an example, the heritability of blood pressure is increased when serial, longitudinal measurements are obtained [80]. Perhaps because of these several obstacles, it has been difficult to obtain convincing evidence of linkage for most complex traits [81]. Even if convincing evidence of linkage can be obtained, it is difficult to progress from linkage to causal genes and variants. The region implicated by linkage is often much larger for complex traits than for monogenic disorders. The less efficient localization stems again from the fact that disease (or trait phenotype) does not segregate perfectly in families with any given variant or chromosomal region. Thus, individual ‘‘critical recombinants’’ (single meioses in which flanking markers do not segregate with the trait) cannot be used to narrow down the region of interest. Rather, many such recombinants must be observed before the weight of statistical evidence can narrow a region within the typical 20 cM identified in linkage studies of complex traits. Importantly, even if the right gene is selected in such a region, proof that the causal variant(s) have been found is also more difficult, since the variants are not necessarily the easily recognizable nonsense, frameshift, or severe missense mutations that underlie monogenic disorders. Thus, proof must rest on a strong statistical association between the variant and disease that is ideally consistent with the linkage evidence. Despite all of these difficulties, there are an increasing number of successful cases of cloning of complex trait genes starting with linkage. For example, variants at two loci have been shown to be associated with inflammatory bowel disease (CARD15 [82,83] and a haplotype on 5q31 [84]). The CTLA4 gene lies in a region of linkage to type 1 diabetes [85] and there is now fairly convincing evidence that variants in this gene affect type 1 diabetes risk [45,85a]. Other recent published reports of positional cloning include CAPN10 and type 2 diabetes [86], and ADAM33 and allergic asthma [87]. These successes have been and will continue to be spurred on by advances in technology, including large numbers of genetic markers (mostly single nucleotide polymorphisms, or SNPs [60]), genetic maps with increasing resolution [88–91], and high throughput genotyping technologies [92]. Furthermore, efforts are underway for many diseases to collect large numbers of families which will likely be required for such studies to be successful (e.g., the type 1 Diabetes Genetics Consortium, http://www.t1dgc.org/). Adult height is an example of a complex trait that may be particularly amenable to linkage analyses [93]. Heritability can be quite high (80–90%); phenotypic

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uncertainty is minimized by easy, reliable, and accurate measurement techniques; and large populations can be readily phenotyped. As the first example, Hirschhorn et al. analyzed genome-wide linkage data from four different populations. Using a variance components method [94], we identified several regions in the human genome with significant evidence of linkage to stature [93]. Three regions, 7q31.3-36, 12p11.2-q14, and 13q3233, all had genome-wide p values of <0.05. An additional region, 6q24-25, nearly reached genome-wide significance. Importantly, the region on chromosome 7q was independently confirmed by another group [95] studying body mass index and stature among Finnish kindreds. More recently, three other studies have been performed. One study [96] identified only suggestive regions of linkage that did not overlap with any of the previously identified regions, but another study [97] identified a new region with significant linkage (chr. 3p26), and the third study [75] confirmed two of the regions identified by Hirschhorn et al. (6q and 12q, [93]) and one of the regions with suggestive evidence in Perola et al. (9q, [95]). The causative genes within these regions of linkage still need to be identified. Nevertheless, these encouraging results demonstrate that highly heritable, easily phenotyped complex traits such as stature may be genetically tractable using linkage analysis. If so, there is hope that several variants affecting this trait could be be identified, allowing studies of the gene–gene interactions between multiple alleles affecting a single complex trait. Studies of stature and similar traits may therefore provide insight into the overall genetic architecture of complex traits. Similar analyses could be envisioned for the timing of puberty, and specifically age of menarche, because many family studies of reproductive cancers and bone density in women collect this information, potentially allowing linkage analysis to be performed.

Association studies Despite the relative success of linkage analysis in identifying regions of interest for stature and a few other traits, most studies of complex traits have not yet yielded regions of confirmed linkage [81]. Association studies are an complementary methodology that provide better power for finding common variants with modest genetic effects [64] (Fig. 1). Association studies are straightforward in principle: the frequency of a genetic variant is determined in affected individuals and in controls; if the frequency significantly differs between the two groups, an association is said to be present. (For quantitative traits, individuals with extreme trait values can be used, or the mean trait values of individuals with different genotypes can be compared.) Despite this apparent simplicity, association studies have several potential limitations.

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The difficulties with association studies have been discussed previously, and include inadequate power, the possibility of population stratification, multiple hypothesis testing, and testing a marker that is correlated imperfectly with the causal variant [62,98,99]. As a result, most association studies have not been consistently replicated. A review of association studies [62] revealed that of 166 associations between common diseases and common variants, only six were highly consistently reproducible (meaning that only these six associations achieved statistical significance in 75% or more of all published reports). In that review, however, over half of the associations were replicated at least once, suggesting that there was at least a trend toward replication. Indeed, a metaanalysis of 25 inconsistently replicated associations [63] reached two major conclusions: although the majority of published associations are not replicated (false positives), a fraction of associations are likely correct, but modest genetic effects combined with low power lead to inconsistent replication (false negatives). Thus, added care and more stringent criteria for interpretation are needed to avoid false positives, but large sample sizes are also required to avoid false negative studies. One particularly controversial potential confounder of association studies is population stratification or ethnic admixture [100,101]. Admixture occurs when the ‘‘cases’’ and ‘‘controls’’ are drawn from populations that are not perfectly matched for ethnicity, and can potentially increase the chance that one will find spurious associations [100]. In this scenario, chromosomes from one or more ethnicities will be overrepresented in the cases and underrepresented in the controls. Alleles that are more common in these ethnic groups will also be more common in the cases than in the controls, and will thus show a positive association. In a study of pubertal onset, the issue of ethnic admixture is of great theoretical concern since African-Americans are known to enter puberty earlier than whites and, therefore, the simple study of early vs. late maturing children could be confounded by the different ethnic make-up of the early and late groups. Similar difficulties could arise in the study of adult stature, which is also known to vary among different ethnic groups. However, some have argued that matching cases and controls by self-reported ethnicity should be sufficient to avoid false positive associations from stratification [101,102]. Different methods have been proposed to minimize the effects of ethnic admixture, should they exist [103–105], but a frequently used technique is using familybased methods such as the transmission disequilibrium test (TDT). The TDT overcomes the difficulties posed by admixture by using data from trios (an affected individual and his/her parents) and relying on internal rather than unrelated controls (effectively, the two chromosomes that are not transmitted to the affected offspring serve as a perfectly matched control) [106].

Other considerations that affect study design and interpretation must also be considered. First, as is evident from the low rate of replication of association studies, more stringent criteria for interpretation are required. Although there is not yet an equivalent of the formal criteria available for interpreting genome-wide linkage studies [107], one could envision analogous criteria for significance that are appropriate for genome-wide association studies. Such criteria would likely be quite stringent, with quite low p values (in the neighborhood of 10 6 ) required to establish genome-wide significance. To avoid discarding true associations that do not achieve such rigorous levels of proof, a ‘‘suggestive’’ category (again analogous to that proposed by Lander and Kruglyak [107]) may be beneficial. However, the details of such criteria remain to be determined, and may well depend not only on p values but also on additional criteria such as sample size, estimated genetic effect, and biologic plausibility. Until criteria for interpretation are agreed on, association studies must be interpreted with some caution, particularly associations that have not been replicated in multiple studies. Despite these issues, advances in genomics are making association studies increasingly attractive. Association studies rely upon candidate genes, and consequently the gene(s) (candidates) involved in regulating pubertal timing or adult stature must be tentatively identified before the tests can be performed. The sequencing of the human genome and the identification of its composite genes is making the identification of putative candidates easier. The logistical pressure to select only a small number of candidates is also diminishing as highthroughput genotyping technologies facilitate the querying of large numbers of genes [92]. The recognition of patterns of human variation (see below) will also facilitate such studies. Given the many advantages of association studies, we believe that this approach is a reasonable one to identify the genetic variants that underlie the spectrum of pubertal timing and adult stature within the general population. Indeed, emerging data from association studies have already identified one gene (CYP17) that may harbor allelic variants that modulate the timing of menarche [108,109].

Haplotypes and patterns of genetic variation Recent insights into the patterns of human genetic variation have significant implications for association studies and for identifying causal genes within regions of linkage. Recent studies have shown that most of the genome can be parsed into regions (‘‘blocks’’) in which genetic variants are correlated with each other: the alleles from neighboring variants fall into one of a few common patterns called haplotypes [110–112]. On average, these blocks span from 11 to 22 kb, depending on

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the population, and contain on average 4–5 common haplotypes [111]. Because of the correlation between variants within blocks (linkage disequilibrium), a few well-chosen variants (usually SNPs) can serve as proxies for the remaining common variants in the block. Thus, by typing a few variants that tag the common haplotypes, (htSNPs [113]), the vast majority of common variation can be efficiently assayed [111]. To take advantage of this approach, the haplotype patterns of the gene or region being studied must be empirically determined so that htSNPs can be selected. Fortunately, a large-scale international effort is being initiated to determine the haplotype patterns on a genome-wide scale [114]. After this project is completed, it should be possible to type htSNPs in appropriately large samples to comprehensively assay common variation in a candidate gene or across a region of linkage.

Conclusions It is likely that the ongoing identification of single gene defects that underlie rare disorders of the hypothalamic–pituitary–gonadal axis (e.g., hypogonadotropic hypogonadism) and growth failure will continue to advance the understanding of these biologic axes. However, genetic research is also likely to identify factors that regulate complex human traits, such as pubertal timing and adult stature, within the general population. Understanding how genetic factors contribute to the normal variation of these traits will undoubtedly increase our understanding of the complex modulation of neuroendocrine systems and of the general architecture of complex genetic traits.

Acknowledgments This work was supported by NIH Grant K23 RR15544 (M.R.P.) and a Burroughs Wellcome Career Award in Biomedical Sciences (J.N.H.).

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