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Aging Dis. 2012 October; 3(5): 385–403.
Published online 2012 September 10.
PMCID: PMC3501394

Developmental Origins of Genotype-Phenotype Correlations in Chronic Diseases of Old Age

Abstract

In recent years, genome wide association studies have revolutionized the understanding of the genetic architecture of complex disease, particularly in the context of disorders that present in old age, such as type 2 diabetes and cardiovascular disease. This new era is made all the more compelling by the fact that, through extensive validation efforts, there is now very strong consensus among human geneticists on what the key loci are that contribute to the pathogenesis of these traits. However, as these variants have been almost exclusively uncovered in an adult setting, there is the question of when these genetic variants start exerting their effects; indeed many may start setting up an individual’s predisposition to a disease of old age very early on in life. To this end, we review what breakthroughs have been made in elucidating which of these genetic factors are operating in childhood and conversely what discoveries have actually been made in the pediatric setting that have then been found subsequently to increase one’s risk of a late-onset disease. After all, it well known that complex traits like obesity, type 2 diabetes and inflammatory bowel disease are strongly determined by genetic factors, but the isolation of genes in these complex phenotypes in adults has been impeded by interaction with strong environmental factors. Distillation of the genetic component in these complex traits, which will at least partially have origins in childhood, should be easier to determine in a pediatric setting, where the relatively short period of a child’s lifetime limits the impact of environmental exposure.

Keywords: Disease, late-onset, childhood, genetic, association

Large-scale genome-wide association studies (GWAS) have enabled investigators in the last five years to scan the genome in a hypothesis-generating manner to uncover genetic variants robustly associated with diseases of old age, such as type 2 diabetes and cardiovascular disease, but much remains to be learned about how they exert their effects [1]. Increasingly, there is evidence that events occurring as early as the intrauterine stage may modify the effects of genetic and environmental characteristics on both current phenotype and disease risk in later life. Evidence suggests that many of the common chronic illnesses of adulthood have developmental origins, and the antecedents of these conditions may be apparent in childhood or even in utero [2, 3] (Figure 1).

Figure 1:
The link between genotype and phenotype in early life and how it impacts related chronic diseases in old age

The objective of this review is to illustrate the importance of a using a developmental approach to understand the impact of genetic variants on risk for chronic disease. Specifically, we will consider type 2 diabetes, obesity, osteoporosis, neurological disease and inflammatory bowel disease, as instructive examples. We will demonstrate how risk-conferring genetic variants associated with adult disease can manifest differently or in developmentally specific ways in infancy and childhood, and how this could illuminate our thinking about the pathophysiology of these conditions across the human lifespan.

Type 2 Diabetes

Genetic variants that confer increased risk of type 2 diabetes in adulthood can manifest differently in childhood, and careful investigation of pediatric phenotypes can yield insights into the pathogenesis of disease and identify options for its prevention. The development of obesity in infancy or childhood, the duration of obesity, insulin-related linear growth acceleration and/or adverse BMI trajectory, including early attainment of adiposity rebound, could all contribute to the capacity of obesity-related genotypes to affect risk for type 2 diabetes.

Furthermore, low birth weight is a factor related to the development of metabolic disease in adulthood [4]. Decreased weight at birth may or may not independently contribute to adult-onset disease, but could reflect an important combination of interacting genetic, epigenetic, intrauterine, nutritional, developmental, hormonal and environmental factors; likely the etiology is multifactorial and complex.

Glucokinase

A frequently cited example illustrates how the role of genetic variation in modulating disease risk factors is best understood in a physiologic context. Variants in the glucokinase (GCK) gene, which encodes a protein important for pancreatic glucose-sensing, have different effects on birth weight whether they are carried by the mother or by the fetus [5]. When carried by the mother alone, these defects result in persistent mild hyperglycemia, and the fetal response (increased insulin production), yields increased birth weight (by around 601g), as can occur in pregnancies complicated by gestational diabetes mellitus resulting in macrosomia. In contrast, when carried by the fetus alone, relatively reduced insulin production in a normoglycemic environment results in a lower birth weight (by around 533 g). These effects were also found to be additive, such that if both mother and fetus carried the mutation, no difference in birth weight was noted. If the effect of this mutation had been studied exclusively in either mothers or infants, its impact would have been estimated incorrectly. The importance of endogenous fetal insulin-secreting capacity in determining antenatal growth has been termed the “fetal insulin hypothesis” [6].

This seminal investigation was carried out in individual heterozygous for mutations in the GCK gene, and subsequently the effect of common variants at this locus on birth weight was also studied. By one estimate, 30% of a European population carries a variant in the GCK beta-cell specific promoter that is associated with an increase in fasting plasma glucose in both non-diabetic adults and women pregnant at 28 weeks’ gestation. The presence of a risk-conferring maternal allele was associated with a 64-g increase in birth weight; no effect of fetal genotype was identified [7]. Similar results have been obtained in follow-up studies [8]. No apparent long-term risk for diabetes appears to be conferred to the fetus by antenatal exposure mild hyperglycemia related to maternal GCK genotype.

TCF7L2

The effects on birth weight by other genotypes associated with type 2 diabetes have been investigated using similar approaches. Variation within intron 3 of the transcription factor 7-like gene 2 (TCF7L2) has been reproducibly associated with an increased risk of type 2 diabetes, with effect sizes corresponding to a population-attributable risk of around 21% [9]. The TCF7L2 gene encodes a transcription factor that acts via the Wnt signaling pathway, and could exert its effects on glucose homeostasis by influencing the expression of the proglucagon gene (encoding both glucagon and the incretin hormone glucagon-like peptide 1) in the gut [10].

One study of 15,709 individuals and 8,344 mothers concluded that each maternal risk-conferring allele at rs7903146 increased birth weight by around 31 g; the combined effect of three or four maternal risk-conferring alleles of TCF7L2 and GCK amounted to an increase in birth weight of 119 g [11]. Some subsequent studies have not identified an effect of fetal TCF7L2 genotype on fetal and postnatal growth [12], or an interaction between TCF7L2 genotype and birth weight that affects disease risk [13]. It could be that these studies were underpowered and/or did not account for the effects of important factors including maternal genotype. TCF7L2 genotype has been shown to influence beta-cell function and subsequent progression to diabetes in adults [14]. Smaller studies in pediatric populations have shown some possible interactions of TCF7L2 genotype with lifestyle modification (in obese children) [15] or GH therapy (in lean children born small for gestational age) [16], but larger, longitudinal studies and/or more detailed investigations are required to follow up on these interesting findings.

Other type 2 diabetes risk-conferring loci

The role of other type 2 diabetes risk-conferring variants on birth weight has been studied in several large series, and has confirmed that other variants that are related to increased risk of type 2 diabetes are also associated with lower birth weight. In a study of 19,200 infants and 7,986 mothers from four cohorts of European ancestry, CDKAL1 variants demonstrated effects similar to GK variants (i.e., fetal risk allele decreased and maternal risk allele increased birth weight); in contrast, fetal HHEX-IDE genotype also had a negative effect on birth weight, but maternal genotype did not have a demonstrable effect [17]. CDKAL1 (CDK5 regulatory subunit associated protein 1-like 1) is a member of the methylthiotransferase family whose function has not been well-characterized, although could be related to reduced insulin secretion [18]. The capacity of the CDKAL1 locus to affect birth weight was independently replicated [19]. Hematopoetically expressed homeobox (HHEX) gene is a transcription factor in the homeobox family that influences a number of developmental processes. In the Auckland Birthweight Collaborative (which focused on fetal genotype), obesity- and diabetes-related loci were found to be associated with increased incidence of being born small for gestational age (KCNJ11, BDNF, PFKP, PTER and SEC16B) [20].

A meta-analysis of six GWAS of birth weight in Europeans identified additional variants (near LEKR1-CCNL1 and near ADCY5) associated with lower birth weight. ADCY5 (adenylate cyclase 5) had previously been identified as related to both fasting blood glucose and subsequent development of type 2 diabetes. The presence C-allele of rs900400 near LEKR1 and CCNL1 is also related to increased insulinogenic index and disposition index during OGTT in non-diabetic adults [21]. The recent publication of two additional large GWAS associated with type 2 diabetes and associated metabolic traits has identified novel loci whose developmental effects remain to be investigated [22, 23].

It remains to be determined to what extent the increased risk of type 2 diabetes could actually be caused by the manifestations of these genotypes in early life. Longitudinal studies of sufficient duration are particularly useful for addressing this important question. In the Helsinki Birth Cohort study of 2,003 individuals, ages 56 – 70, 311 of whom had developed type 2 diabetes, risk-conferring alleles in HHEX, CDKN2A/B and JAZF1 interacted with birth weight in influencing risk of type 2 diabetes [13]. In the Dutch Famine Cohort Study, the presence of a risk-conferring allele in IGFBP2 (associated with development of diabetes), reduced the glucose area under the curve in individuals who had been exposed to famine in utero [24]. The full significance of these associations deserves additional investigation.

Obesity

Approximately 70% of obese adolescents grow up to become obese adults [2527] and thus such children are at much higher risk of the chronic co-morbidities in later life, such as type 2 diabetes and cardiovascular disease. As such, understanding the genetics of childhood obesity has major implications for disease of old age. Indeed, carrying out genome wide studies of this disease have detected loci that were below the limit of detection in the adult setting[28] due to environmental confounders, suggesting the pediatric setting to be key study environment for obesity researchers.

FTO

The fat mass and obesity related locus (FTO) demonstrates a consistent and robust association with BMI in both adulthood and childhood [29], and is considered the most strongly associated locus with obesity to date [30]. The gene encodes a 2-oxoglutarate-dependent nucleic acid demethylase [31], which is expressed in areas of the brain that influence appetite [32] and thus could explain its association with increased energy intake [33]. Subsequent work has shown that the increase in BMI associated with the minor allele of the associated single nucleotide polymorphism (SNP), rs9939609, is related almost entirely to greater fat mass and more recently, it has also been shown to affect fat distribution [34]. Cross-sectional studies have demonstrated an apparent age-dependent effect of high-risk FTO variants on BMI. Initial observations suggested no effect on fetal growth or birth weight, and an effect on BMI that became most apparent by 7 years of age [29]; interestingly, few of the risk alleles associated with adult BMI appear to exhibit direct associations with birth weight[35], in contrast to risk alleles related to type 2 diabetes, which do seem related to birth weight, as reviewed later and in [36]. Later, a separate hospital-based study showed that positive associations between FTO genotype and weight, ponderal index total fat mass and fat distribution are already apparent in babies at two weeks of life; despite this, no association with birth weight was detected in these infants either [37]. A subsequent study suggested a negative association between FTO and BMI before age 2 years, and a positive association thereafter that peaked at around age 20 years and then declined [38].

Longitudinal cohort studies have clarified these findings. Such investigations have used statistical modeling techniques that characterize the important milestones in pediatric BMI trajectories. In children with typical growth patterns, BMI rises steeply after birth until attaining what has been called the “infancy peak” [39], which in one recent study occurred at a median of around 7 months of [40]. After reaching this maximum, BMI declines gradually, and then reaches a nadir called the “adiposity rebound” [41] usually before the onset of puberty, and then rises again until adulthood. Adiposity rebound can occur anywhere from 2 until 7 years of age, but usually happens around 4 – 5 years of age [40]. The age-dependent relationship between BMI and FTO variation is best understood when considered in terms of these milestones. Individuals with high-risk FTO variants do not simply have a higher BMI throughout their lives, but instead have the kind of developmental trajectory that has been associated with higher risk of future obesity [42] and metabolic disease [43]. In a meta-analysis of 8 cohorts of European ancestry (the Early Growth Genetics Consortium), risk-conferring alleles of FTO were associated with lower BMI at adiposity peak, higher BMI at adiposity rebound, and importantly, an earlier age at adiposity rebound [44]. The surprising apparent negative association between BMI and risk-conferring FTO alleles in infants less than two years of age, previously identified in cross-sectional studies, makes sense when we appreciate that it probably reflects the steeper downward inflection of the BMI trajectory after infancy peak towards an earlier adiposity rebound in these individuals [44]. This important insight illustrates the benefits of using a developmental approach when considering the impact of genotype on phenotype, particularly in pediatrics.

The presence of this developmental phenotype (lower BMI at adiposity peak, earlier age and higher BMI at adiposity rebound) could be a more significant determinant of future metabolic health and disease than absolute BMI per se [42]. Indeed, although one large (N=17,037) cross-sectional study concluded that the effect of FTO genotype on metabolic traits (fasting insulin, glucose, triglycerides and HDL) was entirely accounted for by its effect on BMI [45], a meta-analysis of 41,504 adult subjects [46] employing both cross-sectional and longitudinal approaches found that an FTO variant impacted risk of incident type 2 diabetes independent of its effect on absolute BMI. It is important to note that in the latter study, the 0.28 kg/m2 effect on BMI conferred by each risk allele was detectable at the time of the first study measurement, and remained stable across the adult life span. It is interesting to conjecture that the increased risk of metabolic disease related to FTO genotype is related not only to the sequelae of persistently greater adiposity, but also to the adverse long-term effects of the earlier adiposity rebound during childhood. Adverse or ectopic fat deposition patterns and earlier puberty are both potential consequences of this developmental phenotype. Another recent longitudinal study noted that although FTO genotype could not be directly correlated with birth weight directly, risk alleles at this locus could be more likely to amplify the adverse effects of low birth weight on future BMI [47]. Large-scale, longitudinal investigations that make detailed measurements in infancy and childhood are warranted to address these hypotheses.

Factors related to maternal FTO status, which is correlated with infant FTO genotype, could influence the intrauterine environment and therefore contribute to the observed effects of infant FTO genotype on BMI trajectory, and so also deserve additional study. Indeed, although to our knowledge no direct relationship between FTO genotype and birth weight has been reported, FTO status may modify the relationship between birth weight (BW) and both childhood and adult BMI. In the Bogalusa Heart Study, having a lower birth weight amplified the apparent capacity of FTO status to increase later BMI [47]. These interactions also merit further investigation, especially given the well-established association between low birth weight (< 2.5 kg) and subsequent development of type 2 diabetes in many populations [4].

Hypothalamic leptin-melaonocortin pathway: MC4R, POMC, PCSK1, BDNF and SH2B1

The developmental phenotype associated with FTO genotype can be contrasted to what has been observed in individuals with risk-conferring alleles near the melanocortin 4 receptor (MC4R), another gene strongly associated with BMI by both monogenic and GWAS analyses [48]. MC4R is an important component in the hypothalamic leptin-melanocortin pathway that modulates energy balance. Individuals heterozygous or homozygous for mutations in MC4R exhibit significant obesity, increased lean mass, increased linear growth, hyperphagia and severe hyperinsulinemia, and there are gene dosage and receptor function effects on the magnitude of these abnormalities [49]. Decreased energy expenditure could also contribute to adiposity in affected patients [50].

Like individuals with risk-conferring alleles in FTO, those with risk alleles for variation near MC4R weigh more, as expressed in standard deviations (SDS) above or below the mean for age and sex, than those without risk-conferring genotypes. However, in contrast to FTO, common variation at the MC4R locus is more closely associated with weight SDS than with BMI SDS. This was demonstrated in a longitudinal study of 2479 individuals followed from ages 2 through 53 years as part of the British Medical Research Council Survey of National Health and Development [38]. This could be due to individuals with high-risk alleles near MC4R being also taller than average by age 7 years in this study. As a result, BMI SDS, reflecting height-adjusted weight, was less affected by genotype than weight SDS. Individuals with risk-conferring alleles near MC4R retained this height advantage such that they achieved greater final adult height SDS. Overall, overweight pre-pubertal children have also been reported to be tall for age [51]. However, even those with a higher known cumulative genetic risk for obesity are not taller than average as adults [52], and FTO genotype is not associated with final adult height [38].

In contrast, variation at the MC4R locus has been reported to exhibit a different effect on growth trajectory. Physiologic studies in individuals heterozygous for loss-of-function mutations in MC4R suggest that at least two mechanisms could underlie this observation. MC4R-deficient children have normal birth weight and length, but then demonstrate increased linear growth that is apparent by the first year of life and accompanied by advanced bone age (reflecting more rapid epiphyseal maturation), higher fasting insulin levels, and greater final adult height [53]. These effects are more pronounced than those observed in equally obese control subjects. Elevated insulin in obese children could lead to more rapid linear growth velocity because of insulin’s role as a growth factor. In the same study, adults with MC4R deficiency maintained growth hormone (GH) pulsatility, in contrast to obese controls, suggesting that both elevated fasting insulin levels and preserved GH activity could be responsible for taller stature in these individuals.

Less is known about the significance of the height and BMI trajectories associated with variants in MC4R for the development of eventual metabolic disease. Characterization of infancy peak and adiposity rebound in individuals with high-risk alleles near MC4R would be informative. Epidemiologic studies have suggested that while tall individuals are at greater risk for some malignancies [54], they could also have lower rates of insulin resistance and other cardiovascular disease; this could be true in particular when increased height is related to longer leg length, which may itself reflect, in part, childhood nutritional status [55].

A large cross-sectional study in 29,568 individuals of Danish descent concluded that some variants near MC4R were associated with the incidence of type 2 diabetes in later life, but that this appeared to be entirely mediated by their effect on BMI [56]. A longitudinal study in Pima Indians, including 2.4% with loss-of-function mutations in MC4R, noted both increased gain in BMI and increased rate of diagnosis of type 2 diabetes in childhood, but not adulthood. Specifically, BMI-adjusted hazard ratio of developing type 2 diabetes before age 20 years was increased in those with one MC4R mutation (3.3 [1.1 – 9.2], P=0.03), but was not significantly increased for diagnosis between ages 20 – 45 years. It could be that rapid childhood weight gain, in concert with preserved GH pulsatility noted in other studies, precipitates type 2 diabetes; GH is also thought to be related to the insulin resistance of puberty [57]. They also noted that menarche was delayed by around 6 months in women carrying one mutation in MC4R even after adjusting for clinical factors, including BMI, which is unusual, given that increased adiposity is usually associated with earlier menarche, and many SNPs associated with higher BMI are also related to early menarche [58]. Systolic blood pressure also displayed an apparently paradoxical relationship, with those with one MCR4 mutation demonstrating lower systolic blood pressures; although disordered autonomic signaling is posited as one possible mechanism. Detailed developmental and metabolic phenotyping of those subjects with MCR4 variants will likely yield additional important insights into the function of this pathway.

Ancillary evidence exists in support of a unique developmental trajectory in children with disturbances of the hypothalamic leptin-melanocortin pathway. The proopiomelanocortin gene encodes a precursor protein of the same name (POMC) that is cleaved to form melanocortin peptides, including adrenocorticotrophin (ACTH), beta-endorphin and the melanocyte-stimulating hormones [59], including anorexigenic □-MSH, the latter of which signals MCR4. Agouti-related peptide, an orexigenic signal, also binds to MCR4. Some genetic defects in the POMC gene cause severe obesity, early onset adrenal insufficiency, and red hair pigmentation [60]. Variants in POMC have also been associated with obesity traits in Hispanic Americans [61] and Europeans [62]. Case reports of individuals with POMC deficiency note accelerated growth in these children [63]. POMC is also in the vicinity of one SNP associated with human height [64]; the effects of variation in this gene on height have not been fully elucidated.

The PCSK1 gene encodes a neuroendocrine-specific prohormone convertase 1/3 (PC1/3) that influences the processing of POMC and also of proinsulin, the inactive precursor of insulin and C-peptide. Individuals with deficiencies in prohormone convertase 1/3 (PC1/3) develop childhood obesity, hyperphagia, diarrhea, pituitary hypofunction and disordered glucose homeostasis [65, 66]. These complications appear to be direct consequences of impaired catalytic activity or localization of the enzyme. GWAS in both children and adults of European descent have confirmed the role of variants in PCSK1 in obesity [67] and rare functional mutations in PCSK1 also contribute to extreme obesity phenotypes [68]. There are relatively fewer studies that address the potential developmental specificity of PCSK1 genotype. There could be an age-specific effect of PCSK1 genotype on obesity risk; in the EPIC-Norfolk study, rs6232 (N221D) was shown to exert an effect in individuals younger than 59 years [69]. In one case report of PC 1/3 deficiency [66], birth weight was reported to be normal, as was linear growth, although at age 6 years he was in the 98th percentile. In contrast, mice deficient PC1/3 are normal at birth, but then achieve only about 60% of typical size by 10 weeks of age, which is likely related to deficiencies in growth hormone releasing hormone (GHRH), GH and insulin-like growth factor 1 (IGF-1) [70]. They do not develop obesity. Additional studies of the effect of PCSK1 on growth would be informative.

The presence of the risk-conferring allele of rs6232 for obesity at PCSK1 was also associated with elevated fasting proinsulin (adjusted for insulin) in 27,079 non-diabetic adults of European ancestry [71]. An association with lower fasting blood glucose was also detected. PCSK1 is expressed at lower rates (35–45%) in human islets isolated from subjects with type 2 diabetes, as compared to controls. Prohormone processing defects in enterendocrine cells are thought to accompany malabsorption, and could additionally modulate blood glucose levels [72] in individuals with PC1/3 deficiency. Levels of GLP1, GLP2 and glucagon during a mixed meal tolerance test in adults could be affected by PCSK1 variation [73]. The unique pleiotropic effects of PCSK1 variants on hypothalamic, pituitary, and pancreatic function could affect its contribution to subsequent development of metabolic disease.

Downstream targets of MC4R have been implicated in obesity and metabolic disease as well. Brain-derived neurotrophic factor (BDNF) is a nerve growth factor affecting appetite and energy balance whose expression in the ventromedial hypothalamus appears to be regulated by MC4R [74]. Functional loss of one copy of the BDNF gene leads to hyperphagia, obesity, impaired cognitive function and hyperactivity [75], and a de novo mutation in the gene encoding its downstream tyrosine kinase receptor, TrkB, has been observed in association with severe obesity and developmental delay [76]. A subgroup of patients with the WAGR syndrome (Wilms’ tumor, aniridia, genitourinary anomalies and mental retardation) display childhood-onset obesity that is associated with both BDNF haploinsufficiency and lower circulating levels BDNF [77]. In animal studies, BDNF mutants also exhibit similarities to the POMC and MC4R mutants described above. They are obese, with hyperinsulinemia and more rapid linear growth [78]. They also display increased locomotor activity. Central infusion of BDNF rescued this phenotype, resulting in weight loss, mostly accounted for by a decrease in fat stores; energy expenditure may also change. This is consistent with the role of BDNF as an effector of in the same complex hypothalamic leptin-melanocortin pathway.

Using GWAS, an association between variation in BDNF and adult BMI was identified [79] that has been also implicated in the development of pediatric obesity [80]. Risk alleles near BDNF could affect the risk of adult metabolic disease by contributing to the development of adiposity as early as childhood. However, genetic variation near BDNF is also associated with being born small for gestational age, even after adjustment for clinical covariates [20], and being born small for gestational age is an established risk factor for subsequent diabetes [4]. Finally, BDNF may also be directly involved in the central control of glucose homeostasis. Adults with type 2 diabetes had lower levels of circulating of BDNF that are inversely related to blood glucose, and apparently independent of either insulin levels or obesity [81]. In a cross-sectional study of 18,014 Danish adults, there was a surprisingly protective effect of obesity risk variation at BDNF against the subsequent development of type 2 diabetes after accounting for its effect on BMI [82]. The authors pointed out that another obesity risk allele near BDNF is associated with increased fasting glucose and CRP, though not independently of current BMI. They posit that tissue-specific regulation of BDNF expression by a nearby gene encoding an antisense transcript [83] may explain the apparently opposing effects on glycemia of alleles at two SNPs that both relate to obesity. It may be that variation near BDNF affects risk for diabetes via multiple mechanisms, including lower birth weight, adverse growth trajectory, higher absolute BMI and altered glucose homeostasis. Future developmental and physiologic studies should focus on these intriguing possibilities.

Other genes affecting neurodevelopment have also been associated with BMI. Deletions of a region containing SH2B1 (16p11.2) have been associated with BMI greater than the 95th percentile and developmental delay [84]. Variation at both SH2B1 [82] and TMEM18 [85] have been associated with increased risk for type 2 diabetes even after accounting for BMI. Additional studies have suggested a role for risk-conferring alleles in this pathway and dietary preferences, which may also mediate their long-term effects [86].

Other obesity susceptibility loci, cumulative genetic risk

Other genes related to pediatric obesity have been identified using either GWAS [80] and/or candidate gene studies. The beta-2 adrenergic receptor (ADRB2) mediates catecholamine-induced lipolysis and variation at the gene encoding it has been suggested to confer increased risk for obesity and alter metabolic phenotype, including energy expenditure and lipid profile [87]. A large population-based study of Danish subjects identified an increased risk of obesity in those carrying a rare variant in ADRB2, but did not find a BMI-independent effect of this genotype on diabetes risk [85]. In the Bogalusa Heart study, a sex-specific effect of ADRB2 genotype on adiposity and body composition (reflected in skin-fold thickness) was found; in males, the association of genotype with BMI was stronger over time. Overall, attempts to associate ADRB2 genotypes with either essential or obesity-related hypertension have not demonstrated a conclusive link, perhaps because of the potentially important effect of ethnicity [88].

A number of studies have used cumulative genetic risk scores for obesity and assessed growth trajectories and subsequent disease risk. This strategy avoids the problem of multiple comparisons (incurred when evaluating individual loci one at a time) and could improve the power of the study, if, as has been posited, the net impact of multiple risk-conferring variants can be expressed as a linear combination of individual effect sizes. However, recent insights suggest that other techniques, for example, limiting pathway modeling, may more accurately reflect the function of biological systems [89]. Despite this possible limitation, a number of studies have demonstrated important physiologic consequences of cumulative genetic susceptibility to obesity. A 38 year prospective study of 1,037 individuals showed that a 32 SNP obesity risk score was related to more rapid early childhood growth, early attainment of adiposity rebound, and at a higher BMI, and accounted for around half the genetic risk of adult obesity [90]. Importantly, this score provided information independent of family history. In the EPIC Norfolk cohort, BMI-increasing alleles across 12 loci were related to menarche prior to age 12 years [91]. The cumulative genetic risk (17 risk-conferring alleles for obesity) was associated with increased BMI, skinfold thickness and waist circumference in 2,042 children and adolescents participating in the European Youth Heart Study [92]. This study also noted that the effect on BMI of several variants (near SEC16B, TMEM18 and KCTD15) appeared more pronounced in the pediatric population than in adults, whereas the effect of BDNF variation appeared less pronounced. In the Bogalusa Heart Study, variants in FAIM2, involved in apoptosis [93] and MAP2K5, a signaling kinase [94], both associated with adult BMI [79], appeared to exhibit a stronger association with adiposity in childhood [47]. The previous examples of FTO and MC4R suggest that these age-dependent relationships may make more sense when the developmental trajectories related to variants in these individual genes and/or the function of associated biological pathways are explored. As noted previously, even a cumulative obesity risk score does not exhibit strong associations with birth weight [95]. This could be related to the confounding effects of maternal genotype, the unique determinants of antenatal growth and/or that individual risk alleles may have opposing effects on birth weight, as did MTCH2 and FTO in this study.

Osteoporosis

We have used a developmental approach to show how risk-conferring alleles for obesity and type 2 diabetes act in childhood to influence the adult manifestations of metabolic disease. Next, we will consider how genotypic variation that is associated with osteoporosis in older adults exerts its effect as early as early childhood, such that pediatric bone health has clear implications for the state of the skeleton in old age.

Consensus definitions of osteoporosis refer to a disease process characterized by loss of bone mass and worsening microarchitectural features that leads to the clinically important outcome of fragility fractures [96]. Low areal bone mineral density (bone mineral content per unit area of bone), as assessed most often measured using dual energy x-ray absorptiometry (DXA), is related to future fracture risk [97]; as a result, osteoporosis has been defined in adults using BMD criteria [96]. Tools that incorporate other risk factors along with BMD to predict fracture risk (e.g., FRAX, Fracture Risk Assessment Tool) have been developed and are used both clinically and in research settings [98].

Of note, BMD captures only one dimension of bone strength that affects future fracture risk; the importance of bone remodeling and turnover, geometry and microarchitecture is increasing appreciated [99]. Likely related to the widespread availability and clinical use of DXA, BMD remains the most commonly measured parameter related to osteoporosis besides fracture. In the most commonly held view, peak BMD is attained in early adulthood, after which it is maintained and then declines; the height of the peak BMD and the timing and pace of BMD loss with age are important determinants of future risk for osteoporosis [100]. Clinical factors, including sex, size, race/ethnicity and exposures (tobacco, glucocorticoids, hormonal replacement therapy, alcohol, chronic inflammation) interact in complex ways to affect bone health at all ages [101]. Estimates of the heritability of BMD range from 50–80% as reviewed in [102], and the familial tendency for increased or decreased BMD is apparent even before the pubertal growth spurt [103]. Interestingly, estimates for the heritability of osteoporotic fractures are lower than for BMD, from around 25–48% [102], and decline with increasing age [104].

At least two potential explanations exist for this discrepancy. First, the tendency to fracture is related to a multitude of risk factors that have environmental and socioeconomic (i.e., non-genetic) components, including the frequency of falls [105]. Their cumulative effect over time may outweigh the effect of inherited tendencies towards bone fragility or strength on fracture risk. Second, there is an alternative framing of the relationship between BMD in childhood and adulthood [106]. In this view, there is an individual, genetically determined “set point” for bone mass that is defended by homeostatic mechanisms, such that BMD in childhood predicts BMD in adulthood because the individual “set point” is similar, not because adult BMD reflects what remains of the peak BMD attained after puberty. Fracture risk might reflect the interaction of recent clinical and environmental conditions with BMD. A better appreciation of how genetic factors modify BMD across the lifespan would inform our understanding of these complex relationships.

First, we will briefly review the normal process of acquisition of mineralized bone; for a comprehensive review, readers are referred to [107]. In fetal life, there are two main ways in which bone forms, intramembranous (skull and facial bones) and endochondral (the remainder of the skeleton) ossification. Both types of bone begin with mesenchymal cells that become vascularized, however, in endochondral ossification, a cartilaginous “model” forms an initial scaffolding for osteoblasts (cells that form bone) that produce matrix which is eventually mineralized. The fetus acquires the majority of its calcium, transferred from the mother, in the third trimester of pregnancy [108]. Growth in early infancy, reflected in weight by one year of age, has been found to predict adult bone mass [109],[110]. (The developmental origins of bone health have been reviewed [111].) In adulthood, bone formation (by osteoblasts) and bone resorption (by osteoclasts) is tightly coupled; during childhood, these processes are uncoupled to achieve net bone formation so that bones grow both longitudinally (at the growth plate) and in cross-section (at the periosteum). Accrual of BMD occurs most rapidly during puberty; growth of the appendicular skeleton (limbs) precedes the axial skeleton (vertebrae) [112]. Peak height velocity precedes peak acquisition of bone mineral content by around one year [113]. Considerable inter-individual variation exists in the pace of gain in bone mineral content relative to increase in height during pubertal growth [114]. The effect of regional and temporal variation in bone growth during puberty could lead to a skeleton that is bigger, but only 10–30% more dense, despite the marked increase in bone mineral content of 50–150% [112]. The interval between height gain and bone gain could define a “window” of vulnerability to fracture in active pubertal children [115]. Bone geometry and micro-architecture may also be uniquely modifiable during rapid growth [116]. In a separate study in pre- or early-pubertal girls, bone trait Z-scores remained very similar over two years of growth, suggesting that children retained their relative bone strength or fragility during this time [103]. Parenthetically, it is important to note for all studies of bone health in children, that interpretation of pediatric DXA or other bone density measurements requires specific standards [117]; consideration of height and pubertal stage are also critical [118].

Osterix

The genetic determinants of pediatric bone health have been investigated previously. Candidate genes [119, 120], selected either by virtue of their established role in bone homeostasis and/or the results of adult GWAS [121125] have been studied in children as well. To date, few GWAS have included pediatric measurements of BMD. The first such study in a Northern European cohort [126], identified an association between variants near Osterix (SP7), an osteoblast-specific transcription factor [127, 128] and BMD at age 9 (as well as in adulthood); indeed, this locus was first detected in the GWAS analyses of adult BMD described above and a frameshift mutation in Oxsterix has also been subsequently reported in a patient with recessive osteogenesis imperfecta [129]. The four common variants in the Osterix gene were associated with taller stature in childhood, but not greater final adult height. The relationship between genotype and BMD was attenuated after accounting for height. Taken together, these two observations suggest that Osterix influences the contribution of osteoblast activity to the timing of skeletal maturation as well as the accrual of mineralized bone. Of note, the same variation near Osterix that increases BMD is also associated with higher BMI in girls [130], which further implicates body size and skeletal loading in its potential mechanism of action. The authors rightly note the limitations of DXA in assessing BMD, and refer to ongoing pQCT assessments that will add to their initial observations.

COL1A1

Another gene that has been investigated in both adults and children is COL1A1, which encodes the α1 chain of collagen 1; collagen 1 is made of a triple helix of α1 and α 2 chains and is the main component of bone extracellular matrix. COL1A2 encodes the α2 chain. Mutations in COL1A1 and COL1A2 can cause osteogenesis imperfecta [131]. Polymorphisms in the intron of the COL1A1 gene affect binding of the transcription factor Sp1, which is reflected in relative amounts of COL1A1 mRNA and the resulting ratio of α1 to α2 chains in bone [132, 133]. Not only is this associated with reduced BMD, but bone strength is also adversely affected, such that adults with one or two risk-conferring “s” alleles are more likely to fracture than those homozygous for the “S” allele [133, 134]. In a case-control study of 394 children and adolescents aged 4 to 16 years who presented to an emergency room status-post trauma, 205 had sustained a fracture [135]. Prepubertal children who had one “s” allele had triple the rate of trauma-associated fracture, but there was no association with BMD; bone fragility in these patients might be better captured by geometric or microarchitectural parameters. In addition, children who were pre-, early- or mid-pubertal had a much reduced fracture rate if they had the COL1A2 “PP” genotype; they also had increased areal BMD at the lumbar spine. Other studies have also examined the association between polymorphisms in COL1A1 and COL1A2 with BMD and fracture risk, with varying results suggesting differences in the sample populations and/or assessment techniques [136138]. Additional longitudinal studies would be informative. Taken together, these findings demonstrate the potential utility of careful phenotyping in pediatric subjects for investigating the pathophysiologic significance of genetic variation. As tools for characterizing bone density, geometry and microarchitecture improve, the potential “window” of fracture risk during rapid pubertal growth could provide additional opportunities for investigation.

VDR

The vitamin D receptor is a nuclear hormone receptor that mediates the important effects of circulating 1,25-dihydroxyvitamin D on the skeleton and calcium/phosphorus homeostasis. Mutations in the vitamin D receptor (VDR) gene have been reported in association with a vitamin D resistant form of rickets additionally characterized by alopecia and high circulating levels of 1,25-dihydroxyvitamin D [139]. Overall, the evidence for the role of variants in VDR and BMD or fracture has remained debatable [120]. Studies of mother-infant pairs have suggested that VDR genotype modulate the relationship between birth weight, growth in infancy and adult BMD [140, 141]; the latter reference investigates these relationships for COL1A1 variants as well. A separate study in elderly adults suggested that VDR genotype affected the response of BMD to calcium intake [142]. The genotype-specific roles of intrauterine environment and/or nutritional status on bone health also deserves additional study.

Additional loci

Another effector of calcium and phosphorus homeostasis is the parathyroid hormone receptor (PTHR); indeed there is evidence that variation in the PTHR1 gene is related to osteoporosis in adults [143], and a separate study suggests an age-dependent relationship with BMD, such that peak BMD is most affected [144]. Variants in the RANK/RANKL/OPG pathway (a system that regulates, among other processes, osteoclast formation and activation) have all been identified by GWAS as associated with BMD. Cortical BMD, as assessed by peripheral quantitative computed tomography (pQCT), but not bone size, was associated with RANK and OPG variants in a cohort of 15 year-olds [145].

Low-density lipoprotein receptor-related protein 5 is encoded by LRP5, and is a co-receptor that participates in the Wnt signaling pathway that affects development of many tissue types, including bone. Mutations in LRP5 have been associated with changes in bone mineral density, including some patients in whom BMD is actually increased [146]. Common variants in LRP5 are related to both BMD and fracture with a modest effect size [147]; these effects on volumetric BMD are apparent in early childhood and strengthened after accounting for pubertal status, suggesting that, unlike, for example, variants in Osterix, they may not exert their effects by affecting linear growth [148]. However, a different study does report an association between LRP5 genotype and height that may not have been detected in the previous population of mostly pre-pubertal children [149], although this effect was specific to males.

The role of sex steroids in bone health has long been recognized clinically. A large meta-analysis of 18,917 individuals of European descent confirmed that a variant in intron 1 was associated with fracture risk in a manner independent of BMD [150]. Studying pre-pubertal children, in whom circulating concentrations of estradiol are low, offers unique insights into the role of variants in this gene; interestingly, in one study, the relationship between haplotype and BMD was stronger in prepubertal than pubertal subjects; no relationship with timing of menarche was detected [151]. A later study found that bone accrual in late puberty was associated with ESR1 variation in girls but not boys, perhaps suggesting an estradiol-mediated effect [152]. Sex steroids and related activity of the growth hormone axis, reviewed in [153], are developmental characteristics that may affect the impact of ESR1 variation.

Finally, a recent GWAS of 2,660 children of different ethnicities, associations with SNPs near Wnt16 (associated with fracture risk in adults [154]) and C7orf58 were identified that affect both total and skull BMD. In replication cohorts comprised of both adults and children, children contributed significantly to the skull BMD signal [155]. Skull BMD increases across the lifespan, and reflects aspects of bone mineral density independent of skeletal loading. The authors posit that femoral neck and lumbar BMD have allelic architectures that are more polygenic than forearm and total body BMD. The physiologic significance of C7orf58 is not well-characterized.

Even taking into account the genes not included in the above discussion, much of the inherited variation in BMD and fracture risk remains to be explained. Creating a cohort of fracture cases [156], particularly those occurring in younger patients, may be a strategy to identify additional variants. Chronic illness, medications, physical activity, and other environmental factors can act in childhood [157] or later [106] and modify or obscure the potentially important effects of risk-conferring alleles. Including patients of all ages, and conducting longitudinal studies could further illuminate the genetic contributions to the pathogenesis of osteoporosis.

Neurological Disease - Infant Head Circumference

Head circumference is used as an index of brain weight in the fetus and newborn. Brain weight has been found to be correlated with head circumference in early infancy and directly correlated with head circumference during the first six months of life [158, 159]. Several studies of child development have demonstrated a significant relationship between head circumference and IQ. These studies suggest that children with a smaller head circumference (i.e., below the mean) could be more likely to have defects in neurological development, while, in general, larger head circumference in infancy seems to be associated with higher IQ scores in childhood [160, 161]. As such, understanding the genetics of head circumference has implications for improved mental health and ability in later life.

A meta-analysis of seven genome-wide association studies from 10,768 individuals of European and six replication studies from 19,089 individuals identified three genetic variants associated with head circumference in infancy, including on 17q21. Interestingly, these two loci were first identified by genome-wide association studies for adult height [162]. In addition, Interestingly, common variants at 17q21 have also been associated with intracranial volume Parkinson’s disease, supranuclear palsy, corticobasal degeneration and other neurodegenerative disorders [163166], suggesting that common genetic variation on chromosome 17q21 indeed genetically connects infant head circumference with neurological disorders in adults [167].

Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is classified into two major types, Crohn’s disease (CD) and ulcerative colitis (UC), according to the location and nature of the inflammatory changes. CD can influence the entire gastrointestinal tract, from mouth to anus, while UC affects only the colon and the rectum. Both CD and UC present with the following symptoms, including but not limited to: abdominal pain, vomiting, diarrhea, rectal bleeding, severe internal cramps/muscle spasms in the region of the pelvis and weight loss. The peak age of onset for IBD is 15 to 30 years [168]. In spite of the lack of “gold standard” criteria for diagnosis, in North America incidence rates for IBD have been estimated at around 2.2 to 14.3 cases per 100,000 persons per year for UC, and from 3.1 to 14.6 cases per 100,000 persons per year for CD, and prevalence rates have been estimated of around 26 to 199 cases per 100,000 persons for CD and from 37 to 246 cases per 100,000 persons for UC colitis [169].

The first genetic risk-conferring genetic variants identified for CD were at the NOD2/CARD15 locus, which encodes a member of the Apaf-1/Ced-4 superfamily of apoptosis regulators. Subsequently, variation at the ATG16L1 gene, encoding autophagy related 16-like protein 1 which is involved in intracellular bacteria processing, was strongly implicated through a survey of 19,779 non-synonymous SNPs in 735 CD cases and 368 controls [170]. In contrast, and as a result of the first full GWAS of IBD, a variant of the IL23R gene, encoding the interleukin-23 receptor, has been robustly shown associated with both CD and UC; indeed, this variant, Arg381Gln, has been shown to confer a protective effect against CD compared with controls [171].

Despite a number of a large number of loci now been uncovered for adult IBD, these genetic variants only account for a small proportion of the genetic risk for the disease. Interestingly, stratification of IBD by age of onset has helped in identifying additional genes associated with the disease. A GWAS in a cohort of 1,011 individuals with pediatric-onset IBD and 4,250 controls identified three loci that are significantly associated with pediatric IBD [172], while another GWAS in a larger early-onset IBD setting, involving 3,426 cases and 11,963 controls, identified a further five loci[173]. But most interestingly, this latter pediatric study also replicated associations at 8 of 17 loci detected in adult-onset UC and at 23 of 32 loci previously detected in adult-onset CD[173] [172] suggesting that early-onset IBD and adult-onset disease share a similar pathogenic mechanism and that the seeds of predisposition to IBD are sown early on in life.

Summary and Conclusions

In conclusion, we have demonstrated, through examples relevant for type 2 diabetes, obesity, osteoporosis, neurologic disease and IBD, that a developmental approach to the investigation of the role of genetic variation in complex diseases yields important insights into the pathophysiology of these conditions.

Indeed, leveraging a pediatric model can clearly shed light on the role of many variants shown to be associated with a trait in adults. As we describe, some of the loci found for type 2 diabetes exert their effect as early as influencing birth weight, many BMI genes found in adults in fact influence risk for childhood obesity and genes influencing infant head circumference have implications for neurological development in later life. In addition, it is clear that many IBD associated loci set the ground work for the risk of developing this disease early on in life; indeed using the pediatric model for IBD and potentially other diseases could facilitate the discovery of more genes than in the adult setting due to a much lower exposure time.

It is clear that more research that considers age- and developmental stage-specific effects of genetic variation on phenotype is needed, particularly in the form of longitudinal studies. Illnesses whose impact is felt most profoundly in the aged likely have antecedents that are apparent in early life. Recognizing this can inform efforts to detect, treat and ultimately, prevent these chronic diseases.

References

[1] Visscher PM, Brown MA, McCarthy MI, Yang J. Five years of GWAS discovery. American journal of human genetics. 2012;90:7–24. [PubMed]
[2] Barker DJ. Sir Richard Doll Lecture. Developmental origins of chronic disease. Public health. 2012;126:185–189. [PubMed]
[3] Gluckman PD, Hanson MA, Cooper C, Thornburg KL. Effect of in utero and early-life conditions on adult health and disease. The New England journal of medicine. 2008;359:61–73. [PubMed]
[4] Whincup PH, Kaye SJ, Owen CG, Huxley R, Cook DG, Anazawa S, Barrett-Connor E, Bhargava SK, Birgisdottir BE, et al. Birth weight and risk of type 2 diabetes: a systematic review. JAMA. 2008;300:2886–2897. [PubMed]
[5] Hattersley AT, Beards F, Ballantyne E, Appleton M, Harvey R, Ellard S. Mutations in the glucokinase gene of the fetus result in reduced birth weight. Nature genetics. 1998;19:268–270. [PubMed]
[6] Hattersley AT, Tooke JE. The fetal insulin hypothesis: an alternative explanation of the association of low birthweight with diabetes and vascular disease. Lancet. 1999;353:1789–1792. [PubMed]
[7] Weedon MN, Frayling TM, Shields B, Knight B, Turner T, Metcalf BS, et al. Genetic regulation of birth weight and fasting glucose by a common polymorphism in the islet cell promoter of the glucokinase gene. Diabetes. 2005;54:576–581. [PubMed]
[8] Weedon MN, Clark VJ, Qian Y, Ben-Shlomo Y, Timpson N, Ebrahim S, et al. A common haplotype of the glucokinase gene alters fasting glucose and birth weight: association in six studies and population-genetics analyses. American journal of human genetics. 2006;79:991–1001. [PubMed]
[9] Grant SF, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu A, Sainz J, et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nature genetics. 2006;38:320–323. [PubMed]
[10] Yi F, Brubaker PL, Jin T. TCF-4 mediates cell type-specific regulation of proglucagon gene expression by beta-catenin and glycogen synthase kinase-3beta. The Journal of biological chemistry. 2005;280:1457–1464. [PubMed]
[11] Freathy RM, Weedon MN, Bennett A, Hypponen E, Relton CL, Knight B, et al. Type 2 diabetes TCF7L2 risk genotypes alter birth weight: a study of 24,053 individuals. American journal of human genetics. 2007;80:1150–1161. [PubMed]
[12] Mook-Kanamori DO, de Kort SW, van Duijn CM, Uitterlinden AG, Hofman A, Moll HA, Steegers EA, Hokken-Koelega AC, Jaddoe VW. Type 2 diabetes gene TCF7L2 polymorphism is not associated with fetal and postnatal growth in two birth cohort studies. BMC medical genetics. 2009;10:67. [PMC free article] [PubMed]
[13] Pulizzi N, Lyssenko V, Jonsson A, Osmond C, Laakso M, Kajantie E, Barker DJ, Groop LC, Eriksson JG. Interaction between prenatal growth and high-risk genotypes in the development of type 2 diabetes. Diabetologia. 2009;52:825–829. [PubMed]
[14] Florez JC, Jablonski KA, Bayley N, Pollin TI, de Bakker PI, Shuldiner AR, Knowler WC, Nathan DM, Altshuler D. TCF7L2 polymorphisms and progression to diabetes in the Diabetes Prevention Program. The New England journal of medicine. 2006;355:241–250. [PMC free article] [PubMed]
[15] de Kort SW, Mook-Kanamori DO, Jaddoe VW, Hokken-Koelega AC. Interactions between TCF7L2 genotype and growth hormone-induced changes in glucose homeostasis in small for gestational age children. Clinical endocrinology. 2010;72:47–52. [PubMed]
[16] Reinehr T, Friedel S, Mueller TD, Toschke AM, Hebebrand J, Hinney A. Evidence for an influence of TCF7L2 polymorphism rs7903146 on insulin resistance and sensitivity indices in overweight children and adolescents during a lifestyle intervention. Int J Obes (Lond) 2008;32:1521–1524. [PubMed]
[17] Freathy RM, Bennett AJ, Ring SM, Shields B, Groves CJ, Timpson NJ, Weedon MN, Zeggini E, Lindgren CM, Lango H, Perry JR, Pouta A, Ruokonen A, Hypponen E, Power C, Elliott P, Strachan DP, Jarvelin MR, Smith GD, McCarthy MI, Frayling TM, Hattersley AT. Type 2 diabetes risk alleles are associated with reduced size at birth. Diabetes. 2009;58:1428–1433. [PMC free article] [PubMed]
[18] Steinthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R, Jonsdottir T, Walters GB, et al. A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nature genetics. 2007;39:770–775. [PubMed]
[19] Zhao J, Li M, Bradfield JP, Wang K, Zhang H, Sleiman P, Kim CE, Annaiah K, Glaberson W, Glessner JT, Otieno FG, Thomas KA, Garris M, Hou C, Frackelton EC, Chiavacci RM, Berkowitz RI, Hakonarson H, Grant SF. Examination of type 2 diabetes loci implicates CDKAL1 as a birth weight gene. Diabetes. 2009;58:2414–2418. [PMC free article] [PubMed]
[20] Morgan AR, Thompson JM, Murphy R, Black PN, Lam WJ, Ferguson LR, Mitchell EA. Obesity and diabetes genes are associated with being born small for gestational age: results from the Auckland Birthweight Collaborative study. BMC medical genetics. 2010;11:125. [PMC free article] [PubMed]
[21] Andersson EA, Harder MN, Pilgaard K, Pisinger C, Stancakova A, Kuusisto J, Grarup N, Faerch K, Poulsen P, Witte DR, Jorgensen T, Vaag A, Laakso M, Pedersen O, Hansen T. The birth weight lowering C-allele of rs900400 near LEKR1 and CCNL1 associates with elevated insulin release following an oral glucose challenge. PloS one. 2011;6:e27096. [PMC free article] [PubMed]
[22] Morris AP, Voight BF, Teslovich TM, Ferreira T, Segre AV, Steinthorsdottir V, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nature genetics 2012 [PMC free article] [PubMed]
[23] Manning AK, Hivert MF, Scott RA, Grimsby JL, Bouatia-Naji N, Chen H, et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nature genetics. 2012;44:659–669. [PubMed]
[24] van Hoek M, Langendonk JG, de Rooij SR, Sijbrands EJ, Roseboom TJ. Genetic variant in the IGF2BP2 gene may interact with fetal malnutrition to affect glucose metabolism. Diabetes. 2009;58:1440–1444. [PMC free article] [PubMed]
[25] Nicklas TA, Baranowski T, Cullen KW, Berenson G. Eating patterns, dietary quality and obesity. J Am Coll Nutr. 2001;20:599–608. [PubMed]
[26] Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH. Predicting obesity in young adulthood from childhood and parental obesity. The New England journal of medicine. 1997;337:869–873. [PubMed]
[27] Parsons TJ, Power C, Logan S, Summerbell CD. Childhood predictors of adult obesity: a systematic review. Int J Obes Relat Metab Disord. 1999;23(Suppl 8):S1–107. [PubMed]
[28] Bradfield JP, Taal HR, Timpson NJ, Scherag A, Lecoeur C, Warrington NM, et al. A genome-wide association meta-analysis identifies new childhood obesity loci. Nature genetics. 2012;44:526–531. [PMC free article] [PubMed]
[29] Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316:889–894. [PMC free article] [PubMed]
[30] Dina C, Meyre D, Gallina S, Durand E, Korner A, Jacobson P, et al. Variation in FTO contributes to childhood obesity and severe adult obesity. Nature genetics. 2007;39:724–726. [PubMed]
[31] Gerken T, Girard CA, Tung YC, Webby CJ, Saudek V, Hewitson KS, Yeo GS, McDonough MA, Cunliffe S, McNeill LA, Galvanovskis J, Rorsman P, Robins P, Prieur X, Coll AP, Ma M, Jovanovic Z, Farooqi IS, Sedgwick B, Barroso I, Lindahl T, Ponting CP, Ashcroft FM, O’Rahilly S, Schofield CJ. The obesity-associated FTO gene encodes a 2-oxoglutarate-dependent nucleic acid demethylase. Science. 2007;318:1469–1472. [PMC free article] [PubMed]
[32] Lein ES, Hawrylycz MJ, Ao N, Ayres M, Bensinger A, Bernard A, et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature. 2007;445:168–176. [PubMed]
[33] Cecil JE, Tavendale R, Watt P, Hetherington MM, Palmer CN. An obesity-associated FTO gene variant and increased energy intake in children. The New England journal of medicine. 2008;359:2558–2566. [PubMed]
[34] Fox CS, Liu Y, White CC, Feitosa M, Smith AV, Heard-Costa N, et al. Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women. PLoS genetics. 2012;8:e1002695. [PMC free article] [PubMed]
[35] Andersson EA, Pilgaard K, Pisinger C, Harder MN, Grarup N, Faerch K, Sandholt C, Poulsen P, Witte DR, Jorgensen T, Vaag A, Pedersen O, Hansen T. Do gene variants influencing adult adiposity affect birth weight? A population-based study of 24 loci in 4,744 Danish individuals. PloS one. 2010;5:e14190. [PMC free article] [PubMed]
[36] Manco M, Dallapiccola B. Genetics of pediatric obesity. Pediatrics. 2012;130:123–133. [PubMed]
[37] Lopez-Bermejo A, Petry CJ, Diaz M, Sebastiani G, de Zegher F, Dunger DB, Ibanez L. The association between the FTO gene and fat mass in humans develops by the postnatal age of two weeks. The Journal of clinical endocrinology and metabolism. 2008;93:1501–1505. [PubMed]
[38] Hardy R, Wills AK, Wong A, Elks CE, Wareham NJ, Loos RJ, Kuh D, Ong KK. Life course variations in the associations between FTO and MC4R gene variants and body size. Human molecular genetics. 2010;19:545–552. [PMC free article] [PubMed]
[39] Silverwood RJ, De Stavola BL, Cole TJ, Leon DA. BMI peak in infancy as a predictor for later BMI in the Uppsala Family Study. Int J Obes (Lond) 2009;33:929–937. [PubMed]
[40] Wen X, Kleinman K, Gillman MW, Rifas-Shiman SL, Taveras EM. Childhood body mass index trajectories: modeling, characterizing, pairwise correlations and socio-demographic predictors of trajectory characteristics. BMC medical research methodology. 2012;12:38. [PMC free article] [PubMed]
[41] Rolland-Cachera MF, Deheeger M, Bellisle F, Sempe M, Guilloud-Bataille M, Patois E. Adiposity rebound in children: a simple indicator for predicting obesity. The American journal of clinical nutrition. 1984;39:129–135. [PubMed]
[42] Rolland-Cachera MF, Deheeger M, Maillot M, Bellisle F. Early adiposity rebound: causes and consequences for obesity in children and adults. Int J Obes (Lond) 2006;30(Suppl 4):S11–17. [PubMed]
[43] Barker DJ, Osmond C, Forsen TJ, Kajantie E, Eriksson JG. Trajectories of growth among children who have coronary events as adults. The New England journal of medicine. 2005;353:1802–1809. [PubMed]
[44] Sovio U, Mook-Kanamori DO, Warrington NM, Lawrence R, Briollais L, Palmer CN, Cecil J, Sandling JK, Syvanen AC, Kaakinen M, Beilin LJ, Millwood IY, Bennett AJ, Laitinen J, Pouta A, Molitor J, Davey Smith G, Ben-Shlomo Y, Jaddoe VW, Palmer LJ, Pennell CE, Cole TJ, McCarthy MI, Jarvelin MR, Timpson NJ. Association between common variation at the FTO locus and changes in body mass index from infancy to late childhood: the complex nature of genetic association through growth and development. PLoS genetics. 2011;7:e1001307. [PMC free article] [PubMed]
[45] Freathy RM, Timpson NJ, Lawlor DA, Pouta A, Ben-Shlomo Y, Ruokonen A, Ebrahim S, Shields B, Zeggini E, Weedon MN, Lindgren CM, Lango H, Melzer D, Ferrucci L, Paolisso G, Neville MJ, Karpe F, Palmer CN, Morris AD, Elliott P, Jarvelin MR, Smith GD, McCarthy MI, Hattersley AT, Frayling TM. Common variation in the FTO gene alters diabetes-related metabolic traits to the extent expected given its effect on BMI. Diabetes. 2008;57:1419–1426. [PMC free article] [PubMed]
[46] Hertel JK, Johansson S, Sonestedt E, Jonsson A, Lie RT, Platou CG, Nilsson PM, Rukh G, Midthjell K, Hveem K, Melander O, Groop L, Lyssenko V, Molven A, Orho-Melander M, Njolstad PR. FTO, type 2 diabetes, and weight gain throughout adult life: a meta-analysis of 41,504 subjects from the Scandinavian HUNT, MDC, and MPP studies. Diabetes. 2011;60:1637–1644. [PMC free article] [PubMed]
[47] Mei H, Chen W, Jiang F, He J, Srinivasan S, Smith EN, Schork N, Murray S, Berenson GS. Longitudinal replication studies of GWAS risk SNPs influencing body mass index over the course of childhood and adulthood. PloS one. 2012;7:e31470. [PMC free article] [PubMed]
[48] Loos RJ, Lindgren CM, Li S, Wheeler E, Zhao JH, Prokopenko I, et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nature genetics. 2008;40:768–775. [PMC free article] [PubMed]
[49] Farooqi IS, Keogh JM, Yeo GS, Lank EJ, Cheetham T, O’Rahilly S. Clinical spectrum of obesity and mutations in the melanocortin 4 receptor gene. The New England journal of medicine. 2003;348:1085–1095. [PubMed]
[50] Krakoff J, Ma L, Kobes S, Knowler WC, Hanson RL, Bogardus C, Baier LJ. Lower metabolic rate in individuals heterozygous for either a frameshift or a functional missense MC4R variant. Diabetes. 2008;57:3267–3272. [PMC free article] [PubMed]
[51] Metcalf BS, Hosking J, Fremeaux AE, Jeffery AN, Voss LD, Wilkin TJ. BMI was right all along: taller children really are fatter (implications of making childhood BMI independent of height) EarlyBird 48. Int J Obes (Lond) 2011;35:541–547. [PubMed]
[52] Elks CE, Loos RJ, Hardy R, Wills AK, Wong A, Wareham NJ, Kuh D, Ong KK. Adult obesity susceptibility variants are associated with greater childhood weight gain and a faster tempo of growth: the 1946 British Birth Cohort Study. The American journal of clinical nutrition. 2012;95:1150–1156. [PMC free article] [PubMed]
[53] Martinelli CE, Keogh JM, Greenfield JR, Henning E, van der Klaauw AA, Blackwood A, O’Rahilly S, Roelfsema F, Camacho-Hubner C, Pijl H, Farooqi IS. Obesity due to melanocortin 4 receptor (MC4R) deficiency is associated with increased linear growth and final height, fasting hyperinsulinemia, and incompletely suppressed growth hormone secretion. The Journal of clinical endocrinology and metabolism. 2011;96:E181–188. [PubMed]
[54] Gunnell D, Okasha M, Smith GD, Oliver SE, Sandhu J, Holly JM. Height, leg length, and cancer risk: a systematic review. Epidemiologic reviews. 2001;23:313–342. [PubMed]
[55] Lawlor DA, Ebrahim S, Davey Smith G. The association between components of adult height and Type II diabetes and insulin resistance: British Women’s Heart and Health Study. Diabetologia. 2002;45:1097–1106. [PubMed]
[56] Zobel DP, Andreasen CH, Grarup N, Eiberg H, Sorensen TI, Sandbaek A, Lauritzen T, Borch-Johnsen K, Jorgensen T, Pedersen O, Hansen T. Variants near MC4R are associated with obesity and influence obesity-related quantitative traits in a population of middle-aged people: studies of 14,940 Danes. Diabetes. 2009;58:757–764. [PMC free article] [PubMed]
[57] Moran A, Jacobs DR, Jr., Steinberger J, Cohen P, Hong CP, Prineas R, Sinaiko AR. Association between the insulin resistance of puberty and the insulin-like growth factor-I/growth hormone axis. The Journal of clinical endocrinology and metabolism. 2002;87:4817–4820. [PubMed]
[58] Elks CE, Perry JR, Sulem P, Chasman DI, Franceschini N, He C, et al. Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies. Nature genetics. 2010;42:1077–1085. [PMC free article] [PubMed]
[59] Seidah NG, Benjannet S, Hamelin J, Mamarbachi AM, Basak A, Marcinkiewicz J, Mbikay M, Chretien M, Marcinkiewicz M. The subtilisin/kexin family of precursor convertases. Emphasis on PC1, PC2/7B2, POMC and the novel enzyme SKI-1. Annals of the New York Academy of Sciences. 1999;885:57–74. [PubMed]
[60] Krude H, Biebermann H, Luck W, Horn R, Brabant G, Gruters A. Severe early-onset obesity, adrenal insufficiency and red hair pigmentation caused by POMC mutations in humans. Nature genetics. 1998;19:155–157. [PubMed]
[61] Sutton BS, Langefeld CD, Williams AH, Norris JM, Saad MF, Haffner SM, Bowden DW. Association of proopiomelanocortin gene polymorphisms with obesity in the IRAS family study. Obesity research. 2005;13:1491–1498. [PubMed]
[62] Chen Y, Snieder H, Wang X, Kaviya B, McCaffrey C, Spector TD, Carter ND, O’Dell SD. Proopiomelanocortin gene variants are associated with serum leptin and body fat in a normal female population. European journal of human genetics: EJHG. 2005;13:772–780. [PubMed]
[63] Krude H, Biebermann H, Schnabel D, Tansek MZ, Theunissen P, Mullis PE, Gruters A. Obesity due to proopiomelanocortin deficiency: three new cases and treatment trials with thyroid hormone and ACTH4-10. The Journal of clinical endocrinology and metabolism. 2003;88:4633–4640. [PubMed]
[64] Gudbjartsson DF, Walters GB, Thorleifsson G, Stefansson H, Halldorsson BV, Zusmanovich P, et al. Many sequence variants affecting diversity of adult human height. Nature genetics. 2008;40:609–615. [PubMed]
[65] Jackson RS, Creemers JW, Ohagi S, Raffin-Sanson ML, Sanders L, Montague CT, Hutton JC, O’Rahilly S. Obesity and impaired prohormone processing associated with mutations in the human prohormone convertase 1 gene. Nature genetics. 1997;16:303–306. [PubMed]
[66] Farooqi IS, Volders K, Stanhope R, Heuschkel R, White A, Lank E, Keogh J, O’Rahilly S, Creemers JW. Hyperphagia and early-onset obesity due to a novel homozygous missense mutation in prohormone convertase 1/3. The Journal of clinical endocrinology and metabolism. 2007;92:3369–3373. [PubMed]
[67] Benzinou M, Creemers JW, Choquet H, Lobbens S, Dina C, Durand E, et al. Common nonsynonymous variants in PCSK1 confer risk of obesity. Nature genetics. 2008;40:943–945. [PubMed]
[68] Creemers JW, Choquet H, Stijnen P, Vatin V, Pigeyre M, Beckers S, Meulemans S, Than ME, Yengo L, Tauber M, Balkau B, Elliott P, Jarvelin MR, Van Hul W, Van Gaal L, Horber F, Pattou F, Froguel P, Meyre D. Heterozygous mutations causing partial prohormone convertase 1 deficiency contribute to human obesity. Diabetes. 2012;61:383–390. [PMC free article] [PubMed]
[69] Kilpelainen TO, Bingham SA, Khaw KT, Wareham NJ, Loos RJ. Association of variants in the PCSK1 gene with obesity in the EPIC-Norfolk study. Human molecular genetics. 2009;18:3496–3501. [PMC free article] [PubMed]
[70] Zhu X, Zhou A, Dey A, Norrbom C, Carroll R, Zhang C, Laurent V, Lindberg I, Ugleholdt R, Holst JJ, Steiner DF. Disruption of PC1/3 expression in mice causes dwarfism and multiple neuroendocrine peptide processing defects. Proceedings of the National Academy of Sciences of the United States of America. 2002;99:10293–10298. [PubMed]
[71] Strawbridge RJ, Dupuis J, Prokopenko I, Barker A, Ahlqvist E, Rybin D, et al. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes. 2011;60:2624–2634. [PMC free article] [PubMed]
[72] Jackson RS, Creemers JW, Farooqi IS, Raffin-Sanson ML, Varro A, Dockray GJ, Holst JJ, Brubaker PL, Corvol P, Polonsky KS, Ostrega D, Becker KL, Bertagna X, Hutton JC, White A, Dattani MT, Hussain K, Middleton SJ, Nicole TM, Milla PJ, Lindley KJ, O’Rahilly S. Small-intestinal dysfunction accompanies the complex endocrinopathy of human proprotein convertase 1 deficiency. The Journal of clinical investigation. 2003;112:1550–1560. [PMC free article] [PubMed]
[73] Gjesing AP, Vestmar MA, Jorgensen T, Heni M, Holst JJ, Witte DR, Hansen T, Pedersen O. The effect of PCSK1 variants on waist, waist-hip ratio and glucose metabolism is modified by sex and glucose tolerance status. PloS one. 2011;6:e23907. [PMC free article] [PubMed]
[74] Xu B, Goulding EH, Zang K, Cepoi D, Cone RD, Jones KR, Tecott LH, Reichardt LF. Brain-derived neurotrophic factor regulates energy balance downstream of melanocortin-4 receptor. Nature neuroscience. 2003;6:736–742. [PMC free article] [PubMed]
[75] Gray J, Yeo GS, Cox JJ, Morton J, Adlam AL, Keogh JM, Yanovski JA, El Gharbawy A, Han JC, Tung YC, Hodges JR, Raymond FL, O’Rahilly S, Farooqi IS. Hyperphagia, severe obesity, impaired cognitive function, and hyperactivity associated with functional loss of one copy of the brain-derived neurotrophic factor (BDNF) gene. Diabetes. 2006;55:3366–3371. [PMC free article] [PubMed]
[76] Yeo GS, Connie Hung CC, Rochford J, Keogh J, Gray J, Sivaramakrishnan S, O’Rahilly S, Farooqi IS. A de novo mutation affecting human TrkB associated with severe obesity and developmental delay. Nature neuroscience. 2004;7:1187–1189. [PubMed]
[77] Han JC, Liu QR, Jones M, Levinn RL, Menzie CM, Jefferson-George KS, Adler-Wailes DC, Sanford EL, Lacbawan FL, Uhl GR, Rennert OM, Yanovski JA. Brain-derived neurotrophic factor and obesity in the WAGR syndrome. The New England journal of medicine. 2008;359:918–927. [PMC free article] [PubMed]
[78] Rios M, Fan G, Fekete C, Kelly J, Bates B, Kuehn R, Lechan RM, Jaenisch R. Conditional deletion of brain-derived neurotrophic factor in the postnatal brain leads to obesity and hyperactivity. Mol Endocrinol. 2001;15:1748–1757. [PubMed]
[79] Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nature genetics. 2010;42:937–948. [PMC free article] [PubMed]
[80] Zhao J, Bradfield JP, Zhang H, Sleiman PM, Kim CE, Glessner JT, Deliard S, Thomas KA, Frackelton EC, Li M, Chiavacci RM, Berkowitz RI, Hakonarson H, Grant SF. Role of BMI-associated loci identified in GWAS meta-analyses in the context of common childhood obesity in European Americans. Obesity (Silver Spring) 2011;19:2436–2439. [PubMed]
[81] Krabbe KS, Nielsen AR, Krogh-Madsen R, Plomgaard P, Rasmussen P, Erikstrup C, Fischer CP, Lindegaard B, Petersen AM, Taudorf S, Secher NH, Pilegaard H, Bruunsgaard H, Pedersen BK. Brain-derived neurotrophic factor (BDNF) and type 2 diabetes. Diabetologia. 2007;50:431–438. [PubMed]
[82] Sandholt CH, Vestmar MA, Bille DS, Borglykke A, Almind K, Hansen L, Sandbaek A, Lauritzen T, Witte D, Jorgensen T, Pedersen O, Hansen T. Studies of metabolic phenotypic correlates of 15 obesity associated gene variants. PloS one. 2011;6:e23531. [PMC free article] [PubMed]
[83] Pruunsild P, Kazantseva A, Aid T, Palm K, Timmusk T. Dissecting the human BDNF locus: bidirectional transcription, complex splicing, and multiple promoters. Genomics. 2007;90:397–406. [PMC free article] [PubMed]
[84] Bachmann-Gagescu R, Mefford HC, Cowan C, Glew GM, Hing AV, Wallace S, Bader PI, Hamati A, Reitnauer PJ, Smith R, Stockton DW, Muhle H, Helbig I, Eichler EE, Ballif BC, Rosenfeld J, Tsuchiya KD. Recurrent 200-kb deletions of 16p11.2 that include the SH2B1 gene are associated with developmental delay and obesity. Genetics in medicine: official journal of the American College of Medical Genetics. 2010;12:641–647. [PubMed]
[85] Thomsen M, Dahl M, Tybjaerg-Hansen A, Nordestgaard BG. beta2-adrenergic receptor Thr164Ile polymorphism, obesity, and diabetes: comparison with FTO, MC4R, and TMEM18 polymorphisms in more than 64,000 individuals. The Journal of clinical endocrinology and metabolism. 2012;97:E1074–1079. [PubMed]
[86] McCaffery JM, Papandonatos GD, Peter I, Huggins GS, Raynor HA, Delahanty LM, Cheskin LJ, Balasubramanyam A, Wagenknecht LE, Wing RR. Obesity susceptibility loci and dietary intake in the Look AHEAD Trial. The American journal of clinical nutrition. 2012;95:1477–1486. [PubMed]
[87] Large V, Hellstrom L, Reynisdottir S, Lonnqvist F, Eriksson P, Lannfelt L, Arner P. Human beta-2 adrenoceptor gene polymorphisms are highly frequent in obesity and associate with altered adipocyte beta-2 adrenoceptor function. The Journal of clinical investigation. 1997;100:3005–3013. [PMC free article] [PubMed]
[88] Lou Y, Liu J, Huang Y, Liu J, Wang Z, Liu Y, Li Z, Li Y, Xie Y, Wen S. A46G and C79G polymorphisms in the beta2-adrenergic receptor gene (ADRB2) and essential hypertension risk: a meta-analysis. Hypertension research : official journal of the Japanese Society of Hypertension. 2010;33:1114–1123. [PubMed]
[89] Zuk O, Hechter E, Sunyaev SR, Lander ES. The mystery of missing heritability: Genetic interactions create phantom heritability. Proceedings of the National Academy of Sciences of the United States of America. 2012;109:1193–1198. [PubMed]
[90] Belsky DW, Moffitt TE, Houts R, Bennett GG, Biddle AK, Blumenthal JA, Evans JP, Harrington H, Sugden K, Williams B, Poulton R, Caspi A. Polygenic risk, rapid childhood growth, and the development of obesity: evidence from a 4-decade longitudinal study. Archives of pediatrics & adolescent medicine. 2012;166:515–521. [PMC free article] [PubMed]
[91] Mumby HS, Elks CE, Li S, Sharp SJ, Khaw KT, Luben RN, Wareham NJ, Loos RJ, Ong KK. Mendelian Randomisation Study of Childhood BMI and Early Menarche. Journal of obesity. 2011;2011:180729. [PMC free article] [PubMed]
[92] den Hoed M, Ekelund U, Brage S, Grontved A, Zhao JH, Sharp SJ, Ong KK, Wareham NJ, Loos RJ. Genetic susceptibility to obesity and related traits in childhood and adolescence: influence of loci identified by genome-wide association studies. Diabetes. 2010;59:2980–2988. [PMC free article] [PubMed]
[93] Somia NV, Schmitt MJ, Vetter DE, Van Antwerp D, Heinemann SF, Verma IM. LFG: an anti-apoptotic gene that provides protection from Fas-mediated cell death. Proceedings of the National Academy of Sciences of the United States of America. 1999;96:12667–12672. [PubMed]
[94] Zhou G, Bao ZQ, Dixon JE. Components of a new human protein kinase signal transduction pathway. The Journal of biological chemistry. 1995;270:12665–12669. [PubMed]
[95] Kilpelainen TO, den Hoed M, Ong KK, Grontved A, Brage S, Jameson K, Cooper C, Khaw KT, Ekelund U, Wareham NJ, Loos RJ. Obesity-susceptibility loci have a limited influence on birth weight: a meta-analysis of up to 28,219 individuals. The American journal of clinical nutrition. 2011;93:851–860. [PubMed]
[96] Kanis JA, Melton LJ, 3rd, Christiansen C, Johnston CC, Khaltaev N. The diagnosis of osteoporosis. Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research. 1994;9:1137–1141. [PubMed]
[97] Cummings SR, Marcus R, Palermo L, Ensrud KE, Genant HK. Does estimating volumetric bone density of the femoral neck improve the prediction of hip fracture? A prospective study Study of Osteoporotic Fractures Research Group. Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research. 1994;9:1429–1432. [PubMed]
[98] Kanis JA, Johnell O, Oden A, Johansson H, McCloskey E. FRAX and the assessment of fracture probability in men and women from the UK. Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 2008;19:385–397. [PMC free article] [PubMed]
[99] Seeman E, Delmas PD. Bone quality--the material and structural basis of bone strength and fragility. The New England journal of medicine. 2006;354:2250–2261. [PubMed]
[100] Heaney RP, Abrams S, Dawson-Hughes B, Looker A, Marcus R, Matkovic V, Weaver C. Peak bone mass. Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 2000;11:985–1009. [PubMed]
[101] Rosen CJ. Pathogenesis of osteoporosis. Bailliere’s best practice & research Clinical endocrinology & metabolism. 2000;14:181–193. [PubMed]
[102] Ferrari S. Human genetics of osteoporosis. Best practice & research. Clinical endocrinology & metabolism. 2008;22:723–735. [PubMed]
[103] Ferrari S, Rizzoli R, Slosman D, Bonjour JP. Familial resemblance for bone mineral mass is expressed before puberty. The Journal of clinical endocrinology and metabolism. 1998;83:358–361. [PubMed]
[104] Michaelsson K, Melhus H, Ferm H, Ahlbom A, Pedersen NL. Genetic liability to fractures in the elderly. Archives of internal medicine. 2005;165:1825–1830. [PubMed]
[105] Rosen CJ. Clinical practice. Postmenopausal osteoporosis The New England journal of medicine. 2005;353:595–603. [PubMed]
[106] Gafni RI, Baron J. Childhood bone mass acquisition and peak bone mass may not be important determinants of bone mass in late adulthood. Pediatrics. 2007;119(Suppl 2):S131–136. [PubMed]
[107] Javaid MK, Cooper C. Prenatal and childhood influences on osteoporosis. Best practice & research. Clinical endocrinology & metabolism. 2002;16:349–367. [PubMed]
[108] Schauberger CW, Pitkin RM. Maternal-perinatal calcium relationships. Obstetrics and gynecology. 1979;53:74–76. [PubMed]
[109] Cooper C, Cawley M, Bhalla A, Egger P, Ring F, Morton L, Barker D. Childhood growth, physical activity, and peak bone mass in women. Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research. 1995;10:940–947. [PubMed]
[110] Cooper C, Fall C, Egger P, Hobbs R, Eastell R, Barker D. Growth in infancy and bone mass in later life. Annals of the rheumatic diseases. 1997;56:17–21. [PMC free article] [PubMed]
[111] Cooper C, Westlake S, Harvey N, Javaid K, Dennison E, Hanson M. Review: developmental origins of osteoporotic fracture. Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 2006;17:337–347. [PubMed]
[112] Bass S, Delmas PD, Pearce G, Hendrich E, Tabensky A, Seeman E. The differing tempo of growth in bone size, mass, and density in girls is region-specific. The Journal of clinical investigation. 1999;104:795–804. [PMC free article] [PubMed]
[113] Bailey DA, McKay HA, Mirwald RL, Crocker PR, Faulkner RA. A six-year longitudinal study of the relationship of physical activity to bone mineral accrual in growing children: the university of Saskatchewan bone mineral accrual study. Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research. 1999;14:1672–1679. [PubMed]
[114] Theintz G, Buchs B, Rizzoli R, Slosman D, Clavien H, Sizonenko PC, Bonjour JP. Longitudinal monitoring of bone mass accumulation in healthy adolescents: evidence for a marked reduction after 16 years of age at the levels of lumbar spine and femoral neck in female subjects. The Journal of clinical endocrinology and metabolism. 1992;75:1060–1065. [PubMed]
[115] Bonjour JP, Theintz G, Law F, Slosman D, Rizzoli R. Peak bone mass. Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 1994;4(Suppl 1):7–13. [PubMed]
[116] Wang Q, Seeman E. Skeletal growth and peak bone strength. Best practice & research. Clinical endocrinology & metabolism. 2008;22:687–700. [PubMed]
[117] Gilsanz V, Wren T. Assessment of bone acquisition in childhood and adolescence. Pediatrics. 2007;119(Suppl 2):S145–149. [PubMed]
[118] Gordon CM, Bachrach LK, Carpenter TO, Crabtree N, El-Hajj Fuleihan G, Kutilek S, Lorenc RS, Tosi LL, Ward KA, Ward LM, Kalkwarf HJ. Dual energy X-ray absorptiometry interpretation and reporting in children and adolescents: the 2007 ISCD Pediatric Official Positions. Journal of clinical densitometry : the official journal of the International Society for Clinical Densitometry. 2008;11:43–58. [PubMed]
[119] Richards JB, Kavvoura FK, Rivadeneira F, Styrkarsdottir U, Estrada K, Halldorsson BV, et al. Collaborative meta-analysis: associations of 150 candidate genes with osteoporosis and osteoporotic fracture. Annals of internal medicine. 2009;151:528–537. [PMC free article] [PubMed]
[120] Uitterlinden AG, Ralston SH, Brandi ML, Carey AH, Grinberg D, Langdahl BL, et al. The association between common vitamin D receptor gene variations and osteoporosis: a participant-level meta-analysis. Annals of internal medicine. 2006;145:255–264. [PubMed]
[121] Styrkarsdottir U, Halldorsson BV, Gretarsdottir S, Gudbjartsson DF, Walters GB, Ingvarsson T, Jonsdottir T, Saemundsdottir J, Snorradottir S, Center JR, Nguyen TV, Alexandersen P, Gulcher JR, Eisman JA, Christiansen C, Sigurdsson G, Kong A, Thorsteinsdottir U, Stefansson K. New sequence variants associated with bone mineral density. Nature genetics. 2009;41:15–17. [PubMed]
[122] Styrkarsdottir U, Halldorsson BV, Gretarsdottir S, Gudbjartsson DF, Walters GB, Ingvarsson T, Jonsdottir T, Saemundsdottir J, Center JR, Nguyen TV, Bagger Y, Gulcher JR, Eisman JA, Christiansen C, Sigurdsson G, Kong A, Thorsteinsdottir U, Stefansson K. Multiple genetic loci for bone mineral density and fractures. The New England journal of medicine. 2008;358:2355–2365. [PubMed]
[123] Richards JB, Rivadeneira F, Inouye M, Pastinen TM, Soranzo N, Wilson SG, Andrew T, Falchi M, Gwilliam R, Ahmadi KR, Valdes AM, Arp P, Whittaker P, Verlaan DJ, Jhamai M, Kumanduri V, Moorhouse M, van Meurs JB, Hofman A, Pols HA, Hart D, Zhai G, Kato BS, Mullin BH, Zhang F, Deloukas P, Uitterlinden AG, Spector TD. Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet. 2008;371:1505–1512. [PMC free article] [PubMed]
[124] Estrada K, Styrkarsdottir U, Evangelou E, Hsu YH, Duncan EL, Ntzani EE, Oei L, Albagha OM, et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nature genetics. 2012;44:491–501. [PMC free article] [PubMed]
[125] Richards JB, Zheng HF, Spector TD. Genetics of osteoporosis from genome-wide association studies: advances and challenges. Nature reviews Genetics. 2012;13:576–588. [PubMed]
[126] Timpson NJ, Tobias JH, Richards JB, Soranzo N, Duncan EL, Sims AM, Whittaker P, Kumanduri V, Zhai G, Glaser B, Eisman J, Jones G, Nicholson G, Prince R, Seeman E, Spector TD, Brown MA, Peltonen L, Smith GD, Deloukas P, Evans DM. Common variants in the region around Osterix are associated with bone mineral density and growth in childhood. Human molecular genetics. 2009;18:1510–1517. [PMC free article] [PubMed]
[127] Gao Y, Jheon A, Nourkeyhani H, Kobayashi H, Ganss B. Molecular cloning, structure, expression, and chromosomal localization of the human Osterix (SP7) gene. Gene. 2004;341:101–110. [PubMed]
[128] Nakashima K, Zhou X, Kunkel G, Zhang Z, Deng JM, Behringer RR, de Crombrugghe B. The novel zinc finger-containing transcription factor osterix is required for osteoblast differentiation and bone formation. Cell. 2002;108:17–29. [PubMed]
[129] Lapunzina P, Aglan M, Temtamy S, Caparros-Martin JA, Valencia M, Leton R, Martinez-Glez V, Elhossini R, Amr K, Vilaboa N, Ruiz-Perez VL. Identification of a frameshift mutation in Osterix in a patient with recessive osteogenesis imperfecta. American journal of human genetics. 2010;87:110–114. [PubMed]
[130] Zhao J, Bradfield JP, Li M, Zhang H, Mentch FD, Wang K, Sleiman PM, Kim CE, Glessner JT, Frackelton EC, Chiavacci RM, Berkowitz RI, Zemel BS, Hakonarson H, Grant SF. BMD-associated variation at the Osterix locus is correlated with childhood obesity in females. Obesity (Silver Spring) 2011;19:1311–1314. [PubMed]
[131] Rauch F, Lalic L, Roughley P, Glorieux FH. Genotype-phenotype correlations in nonlethal osteogenesis imperfecta caused by mutations in the helical domain of collagen type I. European journal of human genetics : EJHG. 2010;18:642–647. [PMC free article] [PubMed]
[132] Grant SF, Reid DM, Blake G, Herd R, Fogelman I, Ralston SH. Reduced bone density and osteoporosis associated with a polymorphic Sp1 binding site in the collagen type I alpha 1 gene. Nature genetics. 1996;14:203–205. [PubMed]
[133] Mann V, Hobson EE, Li B, Stewart TL, Grant SF, Robins SP, Aspden RM, Ralston SH. A COL1A1 Sp1 binding site polymorphism predisposes to osteoporotic fracture by affecting bone density and quality. The Journal of clinical investigation. 2001;107:899–907. [PMC free article] [PubMed]
[134] Uitterlinden AG, Burger H, Huang Q, Yue F, McGuigan FE, Grant SF, Hofman A, van Leeuwen JP, Pols HA, Ralston SH. Relation of alleles of the collagen type Ialpha1 gene to bone density and the risk of osteoporotic fractures in postmenopausal women. The New England journal of medicine. 1998;338:1016–1021. [PubMed]
[135] Blades HZ, Arundel P, Carlino WA, Dalton A, Crook JS, Freeman JV, Bishop NJ. Collagen gene polymorphisms influence fracture risk and bone mass acquisition during childhood and adolescent growth. Bone. 2010;47:989–994. [PubMed]
[136] Willing MC, Torner JC, Burns TL, Janz KF, Marshall T, Gilmore J, Deschenes SP, Warren JJ, Levy SM. Gene polymorphisms, bone mineral density and bone mineral content in young children: the Iowa Bone Development Study. Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 2003;14:650–658. [PubMed]
[137] Suuriniemi M, Mahonen A, Kovanen V, Alen M, Cheng S. Relation of PvuII site polymorphism in the COL1A2 gene to the risk of fractures in prepubertal Finnish girls. Physiological genomics. 2003;14:217–224. [PubMed]
[138] Tao C, Garnett S, Petrauskas V, Cowell CT. No association was found between collagen alphaI type 1 gene and bone density in prepubertal children. The Journal of clinical endocrinology and metabolism. 1999;84:4293–4294. [PubMed]
[139] Kristjansson K, Rut AR, Hewison M, O’Riordan JL, Hughes MR. Two mutations in the hormone binding domain of the vitamin D receptor cause tissue resistance to 1,25 dihydroxyvitamin D3. The Journal of clinical investigation. 1993;92:12–16. [PMC free article] [PubMed]
[140] Keen RW, Egger P, Fall C, Major PJ, Lanchbury JS, Spector TD, Cooper C. Polymorphisms of the vitamin D receptor, infant growth, and adult bone mass. Calcified tissue international. 1997;60:233–235. [PubMed]
[141] Dennison EM, Arden NK, Keen RW, Syddall H, Day IN, Spector TD, Cooper C. Birthweight, vitamin D receptor genotype and the programming of osteoporosis. Paediatric and perinatal epidemiology. 2001;15:211–219. [PubMed]
[142] Ferrari S, Rizzoli R, Chevalley T, Slosman D, Eisman JA, Bonjour JP. Vitamin-D-receptor-gene polymorphisms and change in lumbar-spine bone mineral density. Lancet. 1995;345:423–424. [PubMed]
[143] Duncan EL, Brown MA, Sinsheimer J, Bell J, Carr AJ, Wordsworth BP, Wass JA. Suggestive linkage of the parathyroid receptor type 1 to osteoporosis. Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research. 1999;14:1993–1999. [PubMed]
[144] Vilarino-Guell C, Miles LJ, Duncan EL, Ralston SH, Compston JE, Cooper C, Langdahl BL, Maclelland A, Pols HA, Reid DM, Uitterlinden AG, Steer CD, Tobias JH, Wass JA, Brown MA. PTHR1 polymorphisms influence BMD variation through effects on the growing skeleton. Calcified tissue international. 2007;81:270–278. [PubMed]
[145] Paternoster L, Ohlsson C, Sayers A, Vandenput L, Lorentzon M, Evans DM, Tobias JH. OPG and RANK polymorphisms are both associated with cortical bone mineral density: findings from a metaanalysis of the Avon longitudinal study of parents and children and gothenburg osteoporosis and obesity determinants cohorts. The Journal of clinical endocrinology and metabolism. 2010;95:3940–3948. [PMC free article] [PubMed]
[146] Van Wesenbeeck L, Cleiren E, Gram J, Beals RK, Benichou O, Scopelliti D, Key L, Renton T, Bartels C, Gong Y, Warman ML, De Vernejoul MC, Bollerslev J, Van Hul W. Six novel missense mutations in the LDL receptor-related protein 5 (LRP5) gene in different conditions with an increased bone density. American journal of human genetics. 2003;72:763–771. [PubMed]
[147] van Meurs JB, Trikalinos TA, Ralston SH, Balcells S, Brandi ML, Brixen K, et al. Large-scale analysis of association between LRP5 and LRP6 variants and osteoporosis. JAMA. 2008;299:1277–1290. [PMC free article] [PubMed]
[148] Koay MA, Tobias JH, Leary SD, Steer CD, Vilarino-Guell C, Brown MA. The effect of LRP5 polymorphisms on bone mineral density is apparent in childhood. Calcified tissue international. 2007;81:1–9. [PMC free article] [PubMed]
[149] Ferrari SL, Deutsch S, Choudhury U, Chevalley T, Bonjour JP, Dermitzakis ET, Rizzoli R, Antonarakis SE. Polymorphisms in the low-density lipoprotein receptor-related protein 5 (LRP5) gene are associated with variation in vertebral bone mass, vertebral bone size, and stature in whites. American journal of human genetics. 2004;74:866–875. [PubMed]
[150] Ioannidis JP, Ralston SH, Bennett ST, Brandi ML, Grinberg D, Karassa FB, Langdahl B, van Meurs JB, Mosekilde L, Scollen S, Albagha OM, Bustamante M, Carey AH, Dunning AM, Enjuanes A, van Leeuwen JP, Mavilia C, Masi L, McGuigan FE, Nogues X, Pols HA, Reid DM, Schuit SC, Sherlock RE, Uitterlinden AG. Differential genetic effects of ESR1 gene polymorphisms on osteoporosis outcomes. JAMA : the journal of the American Medical Association. 2004;292:2105–2114. [PubMed]
[151] Boot AM, van der Sluis IM, de Muinck Keizer-Schrama SM, van Meurs JB, Krenning EP, Pols HA, Uitterlinden AG. Estrogen receptor alpha gene polymorphisms and bone mineral density in healthy children and young adults. Calcified tissue international. 2004;74:495–500. [PubMed]
[152] Tobias JH, Steer CD, Vilarino-Guell C, Brown MA. Estrogen receptor alpha regulates area-adjusted bone mineral content in late pubertal girls. The Journal of clinical endocrinology and metabolism. 2007;92:641–647. [PubMed]
[153] Niu T, Rosen CJ. The insulin-like growth factor-I gene and osteoporosis: a critical appraisal. Gene. 2005;361:38–56. [PubMed]
[154] Zheng HF, Tobias JH, Duncan E, Evans DM, Eriksson J, Paternoster L, et al. WNT16 Influences Bone Mineral Density, Cortical Bone Thickness, Bone Strength, and Osteoporotic Fracture Risk. PLoS genetics. 2012;8:e1002745. [PMC free article] [PubMed]
[155] Medina-Gomez C, Kemp JP, Estrada K, Eriksson J, Liu J, Reppe S, Evans DM, et al. Meta-Analysis of Genome-Wide Scans for Total Body BMD in Children and Adults Reveals Allelic Heterogeneity and Age-Specific Effects at the WNT16 Locus. PLoS genetics. 2012;8:e1002718. [PMC free article] [PubMed]
[156] Ladouceur M, Leslie WD, Dastani Z, Goltzman D, Richards JB. An efficient paradigm for genetic epidemiology cohort creation. PloS one. 2010;5:e14045. [PMC free article] [PubMed]
[157] DiVasta AD, Gordon CM. Bone health in adolescents. Adolescent medicine clinics. 2006;17:639–652. abstract xi. [PubMed]
[158] Cooke RW, Lucas A, Yudkin PL, Pryse-Davies J. Head circumference as an index of brain weight in the fetus and newborn. Early human development. 1977;1:145–149. [PubMed]
[159] Winick M, Rosso P. Head circumference and cellular growth of the brain in normal and marasmic children. The Journal of pediatrics. 1969;74:774–778. [PubMed]
[160] Pryor HB, Thelander H. Abnormally small head size and intellect in children. The Journal of pediatrics. 1968;73:593–598. [PubMed]
[161] Desch LW, Anderson SK, Snow JH. Relationship of head circumference to measures of school performance. Clinical pediatrics. 1990;29:389–392. [PubMed]
[162] Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F, Willer CJ, Jackson AU, et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature. 2010;467:832–838. [PMC free article] [PubMed]
[163] Ikram MA, Fornage M, Smith AV, Seshadri S, Schmidt R, Debette S, Vrooman HA, et al. Common variants at 6q22 and 17q21 are associated with intracranial volume. Nat Genet. 2012;44:539–544. [PubMed]
[164] Simon-Sanchez J, Schulte C, Bras JM, Sharma M, Gibbs JR, Berg D, et al. Genome-wide association study reveals genetic risk underlying Parkinson’s disease. Nat Genet. 2009;41:1308–1312. [PMC free article] [PubMed]
[165] Nalls MA, Plagnol V, Hernandez DG, Sharma M, Sheerin UM, Saad M, Simon-Sanchez J, Schulte C, Lesage S, Sveinbjornsdottir S, Stefansson K, Martinez M, Hardy J, Heutink P, Brice A, Gasser T, Singleton AB, Wood NW. Imputation of sequence variants for identification of genetic risks for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet. 2011;377:641–649. [PubMed]
[166] Webb A, Miller B, Bonasera S, Boxer A, Karydas A, Wilhelmsen KC. Role of the tau gene region chromosome inversion in progressive supranuclear palsy, corticobasal degeneration, and related disorders. Arch Neurol. 2008;65:1473–1478. [PMC free article] [PubMed]
[167] Taal HR, St Pourcain B, Thiering E, Das S, Mook-Kanamori DO, Warrington NM, et al. Common variants at 12q15 and 12q24 are associated with infant head circumference. Nat Genet. 2012;44:532–538. [PubMed]
[168] Loftus EV., Jr Clinical epidemiology of inflammatory bowel disease: Incidence, prevalence, and environmental influences. Gastroenterology. 2004;126:1504–1517. [PubMed]
[169] Lakatos PL. Recent trends in the epidemiology of inflammatory bowel diseases: up or down? World journal of gastroenterology : WJG. 2006;12:6102–6108. [PubMed]
[170] Hampe J, Franke A, Rosenstiel P, Till A, Teuber M, Huse K, Albrecht M, Mayr G, De La Vega FM, Briggs J, Gunther S, Prescott NJ, Onnie CM, Hasler R, Sipos B, Folsch UR, Lengauer T, Platzer M, Mathew CG, Krawczak M, Schreiber S. A genome-wide association scan of nonsynonymous SNPs identifies a susceptibility variant for Crohn disease in ATG16L1. Nature genetics. 2007;39:207–211. [PubMed]
[171] Duerr RH, Taylor KD, Brant SR, Rioux JD, Silverberg MS, Daly MJ, Steinhart AH, Abraham C, Regueiro M, Griffiths A, Dassopoulos T, Bitton A, Yang H, Targan S, Datta LW, Kistner EO, Schumm LP, Lee AT, Gregersen PK, Barmada MM, Rotter JI, Nicolae DL, Cho JH. A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science. 2006;314:1461–1463. [PubMed]
[172] Kugathasan S, Baldassano RN, Bradfield JP, Sleiman PM, Imielinski M, Guthery SL, et al. Loci on 20q13 and 21q22 are associated with pediatric-onset inflammatory bowel disease. Nature genetics. 2008;40:1211–1215. [PMC free article] [PubMed]
[173] Imielinski M, Baldassano RN, Griffiths A, Russell RK, Annese V, Dubinsky M, et al. Common variants at five new loci associated with early-onset inflammatory bowel disease. Nature genetics. 2009;41:1335–1340. [PMC free article] [PubMed]

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