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Journal of Clinical Endocrinology & Metabolism, doi:10.1210/jc.2006-0529
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The Journal of Clinical Endocrinology & Metabolism Vol. 91, No. 10 4164-4170
Copyright © 2006 by The Endocrine Society

Identification of Distinct Quantitative Trait Loci Affecting Length or Weight Variability at Birth in Humans

Delphine Fradin, Simon Heath, Jacques Lepercq, Mark Lathrop and Pierre Bougnères

Equipe d’Accueil University Paris V and Department of Obstetrics (D.F., J.L.) and Department of Pediatric Endocrinology and U561 Institut National de la Santé et de la Recherche Médicale (P.B.), Hôpital Saint-Vincent de Paul, Hôpital Saint-Vincent de Paul, 75014 Paris, France; and Centre National de Génotypage (D.F., S.H., M.L.), 91057 Evry, France

Address all correspondence and requests for reprints to: Delphine Fradin, Institut National de la Santé et de la Recherche Médicale U561, Hôpital Saint-Vincent-de-Paul, 82 Avenue Denfert-Rochereau, 75014 Paris, France. E-mail: fradin{at}paris5.inserm.fr.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Context: The variability of human fetal growth is multifactorial. Twin and family studies demonstrate that genetic determinants influence normal fetal growth, but the responsible genetic polymorphisms are unknown.

Objective: The objective of the study was the mapping of quantitative trait loci (QTLs) for birth length and weight.

Design and Methods: To approach the genetic factors implicated in the normal variation of birth length and weight, we conducted a genome-wide approach of these two quantitative traits in 220 French Caucasian pedigrees (412 sibling pairs) using a variance components method.

Results: We observed evidence for several QTLs influencing birth length or birth weight independently. Whereas birth length and weight showed a close correlation (r = 0.76, P < 0.0001), their genetic variability appeared largely determined by distinct genomic loci. Birth length was influenced by two major QTLs located in 2p21 and 2q11 (LOD scores 2.69 and 3.57). The variability of birth weight was linked to another QTL on 7q35 (LOD score 3.1). Several other regions showed more modest evidence for linkage with LOD score values of 1–2 on chromosomes 7, 8, 10, 13, and 17 for birth length and chromosomes 1, 2, 6, 8, 10, 13, 14, 15, 17, and 20 for birth weight.

Conclusion: These preliminary QTLs provide a first step toward the identification of the genomic variants involved in the variability of human fetal growth. Our results should, however, be considered preliminary until they are replicated in other studies.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
BIRTH LENGTH, as well as birth weight, follows a continuous Gaussian distribution in a given human population, with macrosomia and smallness for gestational age at the two edges of this distribution. Fetal growth is indeed a complex, dynamic, multifactorial process controlled by maternal, placental and fetal factors including the interactions between the fetus and intrauterine environment. The main maternal factors known to determine variations in fetal size at term are maternal age, maternal size, maternal workload, caloric balance, parity, maternal glycemia, and maternal weight gain (1). Many other factors can alter birth weight, e.g. residence at high altitude (2).

Human physiology and monogenic diseases provide a few examples of genetic factors necessary to fetal growth. Genes regulating insulin secretion in mother and fetus (3) or IGF-I and IGF-II are important regulators of fetal growth (4). Also, the effects of duplications, uniparental disomies, or defects of imprinting (5) on fetal growth reveal the major importance of the parental origin of specific gene alleles in the 11p15.5 region (6). Other chromosomal disorders, including common trisomies of chromosomes 13, 18, and 21, as well as many duplications, deletions, and ring chromosomes are considered responsible for approximatively 20% of all intrauterine growth retardation cases through yet-unknown mechanisms. These observations indicate that the integrity of a vast number of yet-unknown genes is a requirement to normal fetal growth. The present work, however, is not a search for causes of genetic diseases of fetal growth but is instead an attempt to identify the genetic loci responsible for normal fetal growth variability.

Classical studies of resemblance within families demonstrate that genetic determinants influence normal fetal growth (7). According to epidemiological studies, environmental factors account for about 25% of birth weight variance and genetic factors for 38–80% (8, 9). Within one generation (offspring of twins) in Norwegian families, model-fitting approaches suggest that fetal genes are responsible for more than half of the population variance in birth weight (9). There is, however, a considerable variability in the estimates of the participation of fetal (18–69%) and parental (3–20%) genetic components to the variance of birth weight (10). The heritability of adult height varies between 70 and 80% (11, 12, 13), and birth length is somewhat correlated with paternal (r = 0.33) and maternal height (r = 0.26) (1). The heritability of birth length, however, has not been estimated to our knowledge. Knight et al. showed that quantitative trait loci (QTLs) for birth weight have been identified in other mammalian species, such as cattle (14, 15, 16), pigs, or a wild population of red deer (17). These QTLs, however, did not generate data that could be extrapolated at the gene level to the human species because of the yet-limited knowledge of these mammalian genomes. In humans, we reasoned that we did not have sufficient information about potential candidate genes regulating human growth to engage in fruitful association studies. This is why we elected a genome-wide random approach to find new loci. This method has recently allowed identification of QTLs on the human genome that were linked to birth weight in a Mexican-American cohort (18). We present here the preliminary results of a genome-wide attempt to localize the main QTLs influencing birth length and birth weight in a cohort of direct European ancestry.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
Subjects

We selected families of European origin to reduce the extent of genetic heterogeneity, given the known importance of ethnic factors in the regulation of birth size (19). Family origin was carefully assessed by the analysis of paternal names and grandparents birth places. A cohort of families living in the Ile-de-France region was recruited in the Endocrinology Department of Saint Vincent de Paul Hospital (Paris V University) in which one of their children was followed up for overweight or obesity. Although birth weight and length were normal in all studied children, it could not be formally excluded that these families may be different from those ascertained without obesity. Clinical research procedures and genetic studies were approved by our institutional review board. All families signed informed consent documents before entering the study and completed a questionnaire about pregnancies; dietary habits; demographic factors; level of education; and family history of weight, height, obesity, and diabetes mellitus. Almost all children were born in hospitals and university hospitals surrounding Paris.

Inclusion criteria were: 1) a birth weight at term exceeding 2000 g (10 patients excluded) and less than 5000 g (four patients excluded); 2) gestational age strictly between 39 and 41 wk, determined by reference to the last menstrual period; 3) healthy gestation (mothers were considered healthy if they have no known diabetes, hypertension, medical condition, or known addiction associated with impaired fetal growth); 4) access to sampling at least one sibling; and 5) parental data and DNA available. The study population included 220 families, 154 pedigrees of two siblings, 53 pedigrees of three siblings, nine pedigrees of four siblings, three pedigrees of five siblings, and one pedigree of six siblings, leading to a total of 412 sibling pairs. The studied sample included 964 children who showed the same distribution of birth weight and birth length as the French general population and can therefore be considered a representative healthy sample of populations living on the European continent. Gestational age, length, and weight measurements were obtained from the Carnet de Santé filled by maternity pediatricians at birth. French children have to be precisely measured at birth to be included in the obligatory follow-up system of Carnet de Santé de l’Enfance (Child Health Bulletin). The measurement of birth length had recently gained further precision because length at birth under 47 cm at term is now used as a threshold for the French social security system for GH treatment in short small-for-gestational age children. A log transformation was applied to birth weight and length data, followed by adjustment for the effect of sex and term. Birth weight and length were analyzed as continuous traits.

Genotyping

Forty milliliters of whole blood obtained from each family participant was frozen and stored at –20 C. Genomic DNA was extracted from the blood samples with an extraction kit (Gentra, Minneapolis, MN).

Genome scan

A genome scan with 418 markers at approximately 9-cM intervals was performed in 220 families by the Centre National de Génotypage. The 418 microsatellites were taken predominantly from Applied Biosystems Inc. (Foster City, CA). The linkage mapping set comprises 418 fluorescently labeled PCR primer pairs that define an approximately 10-cM resolution human index map. The loci were selected from the Généthon human linkage map based on chromosomal locations and heterozygosity. The map positions were generated from the CEPH genotype data used for the 1996 Généthon map. Full details on these markers and the genotyping procedures can be found elsewhere (https://products.appliedbiosystems.com/ab/en/US/adirect/ab?cmd=catNavigate2&catID=600776&tab=Literature).

Before linkage analysis, family structure informations and genomic markers were carefully analyzed to identify incorrect parentship assignment using PedCheck (20). We used the variance components model (21) implemented in Merlin for quantitative traits (22) to evaluate linkage to birth length and weight separately.

The variance component methods ignores detailed aspects of any model underlying the trait mode of inheritance and base inference on the correlation between relatives’ similarity with respect to the trait and their similarity with respect to one or more markers. Criteria for significance were based on guidelines published by Lander and Kruglyak (23) asserting that a LOD score greater than 3.6 indicates genome-wide significance, a LOD score between 2.2 and 3.6 indicates suggestive linkage, and LOD scores between 0.6 and 2.2 are nominal.

Simulations

We conducted 10,000 simulations to determine how many LOD scores would be found by simple chance over the thresholds of significance using our data and genomic markers. We simulated data using the same marker spacing, allele frequencies, missing data patterns, etc. as the real data, as described by Wiltshire et al. (24), implemented in Merlin. Each simulated data set was analyzed the same way as the real data set.


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
Birth length and birth weight were normally distributed (Fig. 1Go), as expected from the multifactorial causality of fetal growth variation. There was a significant sexual dimorphism for both parameters. Birth length averaged 49.2 ± 2.3 cm (mean ± SD) in girls and 50.2 ± 2.3 cm in boys (P < 0.0001) (Fig. 1Go). Birth weight averaged 3276 ± 19 g in girls and 3439 ± 24 g in boys (P < 0.0001) (Fig. 1Go). Birth length and birth weight were closely correlated (Fig. 1Go).


Figure 1
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  		FIG. 1. Distribution of birth size traits in our cohort. A, Birth length in girls. B, Birth length in boys. C, Birth weight in girls. D, Birth weight in boys. E, Correlation between length at birth and birth weight.

 
QTLs for birth length

Suggestive linkage was found on 10p15 and chromosome 2 (Fig. 2Go). On chromosome 2, two peaks formed by six and 11 consecutive markers, respectively, showed a suggestive evidence for linkage to birth length (Table 1Go). The first peak spans approximately 10 cM on 2p, with a LOD score maximum at D2S2259 (LOD 2.69, P = 2.10–4). The second peak spans approximately 12 cM on 2q, with a LOD score maximum at D2S2216 and D2S113 (LOD 3.57, P = 2.10–5), a value close to the genome-wide threshold of significance according to Lander and Kruglyak (23).


Figure 2
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  		FIG. 2. Results of the multipoint variance components linkage analysis for birth length. Chr, Chromosome.

 

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  		TABLE 1. Linkage results focused on the two peaks localized on chromosome 2 for length at birth

 
According to the same criteria (23), several regions showed nominal LOD score peaks (Fig. 2Go). Ten peaks with LOD scores between 1.0 and 2.2 were seen on chromosomes 7q36, 8p23-p21, 10q21, 13q11, and 17q25 (Fig. 2Go).

QTLs for birth weight

The highest LOD score of 3.1 (P = 8.10–5) was found on chromosome 7q on D7S2513 (Table 2Go). Around this marker, three other markers gave a suggestive evidence for linkage to birth weight: D7S661, D7S498, and D7S2511. Several other regions showed peaks with a nominal LOD score (Fig. 3Go), according to the criteria of Lander and Kruglyak (23). Twelve peaks with LOD scores between 1.0 and 2.2 were found on chromosomes 1p36-p35, 1q42–1q44, 2qter, 6q22-q24, 7p21-p11, 8p22, 10p12-p11, 13q31-q33, 14q11, 15q21, 17q24, and 20p13.


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  		TABLE 2. LOD scores for birth weight on the major peak of chromosome 7

 

Figure 3
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  		FIG. 3. Results of multipoint variance components linkage analysis for birth weight. Chr, Chromosome.

 
QTLs for ponderal index

Multipoint linkage analysis for ponderal index (weight/height3) showed suggestive evidence for linkage in 6p21 (LOD = 2.18, P = 0.0008) and nominal evidence for linkage on chromosomes 9, between D9S285 and D9S161 (MLS = 1.51, P = 0.004), chromosome 10 (MLS = 0.82, P = 0.03), chromosome 13 (LOD = 1.23, P = 0.009), and chromosome 16 (MLS=1.22, P = 0.009) (data not shown).

Comparison of the identified QTL with those calculated from simulation tests

Ten thousand simulations indicated that six peaks with a LOD score of 1 or greater would be expected by chance, one with a LOD score of 2 or greater, and none with a LOD score of 3 or greater.

For birth length, we found that 10 loci located in six regions had a LOD score between 1 and 2. Twelve other LOD scores from four regions were between 2 and 3, and seven LOD scores were greater than 3, all of them located on 2q. The higher LOD score for length at birth reached 3.57.

For birth weight, we identified 39 QTLs located on 17 regions on 12 chromosomes whose LOD scores were between 1 and 2. Five LOD scores between 2 and 3 were found as three peaks on chromosomes 2, 7, and 10. We found only one LOD score greater than 3 on chromosome 7 for birth weight.

The genome scan for ponderal index allowed us to find eight LOD scores greater than 1 located within three regions on chromosomes 9, 13, and 16. A peak with a LOD score greater than 2 was identified on 6p (LOD = 2.18).


   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
Subtle, quantitative differences between individuals are observed for nearly all morphometric and physiological phenotypes such as fetal growth. Fetal growth can be quantified through measures of both length and weight at birth as well as the ponderal index, which reflects the weight of a newborn in proportion of his length. These traits have important implications for neonatal medicine and could also have direct relevance for later growth and for adult diseases. The current study used genome-wide linkage analysis to localize determinants of fetal growth on the human genome. The variance components method is a powerful tool for assessment of genetic linkage for quantitative traits (25). A map distance of roughly 5–10 cM is the limit of resolution of the current genome scan study. What is detected as a QTL linked to either birth length or birth weight is therefore a segment of chromosome of this length, which may contain several genomic variants in various loci capable of affecting the trait, not necessarily in the same direction.

Our analysis suggests that a locus on chromosome 2q11 (LOD 3.57) influences birth length strongly. Examination of the human genome sequence (International Human Genome Sequencing Consortium 2001) indicates a number of genes in this region, among which several could have physiological relevance, but we have no data to implicate these genes specifically.

Because the current genome scan was the first to investigate the genetics of birth length variability, we could not compare our results with other studies. It is interesting that several regions that we identified have already been implicated in the genetic regulation of adult height. Our QTL on chromosome 2q is somewhat reminiscent of those obtained in 379 sibling pairs from 58 families (Botnia cohort), in which a LOD score of 2.23 was found in the same region (26). Linkage to adult height was also found by previous studies on chromosomes 7q (27), 8p (26, 28, 29), 10q (30), and 17q (28, 31). Adult stature, although it shows a correlation with birth length (1), has a degree of complexity and variability that far exceeds those of birth length, and therefore, the QTLs apparently shared between adult height and birth length should be considered tentative.

Genomic loci linked to birth weight were different. The highest LOD score (3.1) for birth weight was on chromosome 7, near the D7S2513 marker. This region contains several genes that could be suspected to influence birth weight.

Another interesting linkage was found between birth weight and the 7p21-p11 region, which includes the glucokinase gene (32). Rare mutations of this gene influence birth weight when present in a mother and/or her offspring (3).

To our knowledge, only two genome scan linkage studies of birth weight have been performed in humans. In 269 Pima Amerindians, birth weight was analyzed as a discrete phenotype (i.e. low birth weight) (33). A significant evidence of linkage was found on chromosome 11 [at map position 88 cM, LOD (imprinting) = 3.4]. In this region, birth weight was predominantly linked to paternally derived alleles [LOD (fathers) = 4.1, LOD (mothers) = 0.0]. In 2006 another genome-wide scan linkage analyzed birth weight variability in Mexican-Americans from the San Antonio Family study, a population that is made of a recent admixture of Central America Amerindian and Caucasian populations (18). This study identified a major locus (LOD = 3.7) for birth weight on chromosome 6 and showed potential suggestive evidence for linkage on chromosomes 1, 2, 4, and 9. Our current linkage analysis did not show any linkage in these regions; neither were the QTLs of our study observed in the San Antonio study. These discrepancies as well as those cited before are common when linkage studies are compared and are generally attributed to variations in the genetic structure of the studied populations and the limited power of microsatellite-based linkage analysis of QTLs (34). The genome scan studies can also reflect differences owing to the contribution of genetics to the variation of birth weight in various populations (4, 5) and the varying environmental factors that may affect birth weight (35).

Our data were not confirmatory of earlier studies associating birth weight with INS-VNTR (11p15) (36, 37), H19 (11p15) (38), except for HLA DRB1 (6p21) (39). This may be due to discordance, or to the limited sensitivity of linkage studies compared with association studies at a specific gene locus (40).

Despite the strong phenotypic correlation between birth length and weight (Fig. 1Go), we found that only one QTL on chromosome 8p22 appeared implicated in the variability of both birth length (LOD score 1.51) and weight (LOD score 1.81). It may be tempting to think that a specific locus within this QTL interval is a pleiotropic regulator of both skeleton length and fetal body mass, but the current data are still far from allowing us to substantiate this hypothesis. According to the same reasoning, the QTLs for ponderal index at birth reflect the genetic variability of birth weight when considered in proportion of birth length. The ponderal index may thus have a different genetic determination than birth weight per se.

For the other QTLs identified in this study, our observation that genetic loci controlling the variation in birth length and birth weight are distinct could simply reflect the fact that fetal growth recapitulates intricate but specific developmental processes, including the growth of brain (12% of birth weight) and head circumference (25% of birth length), the skeletal growth of limbs and spine, and the building of muscle and fat mass.

In any human genome scan study attempting to identify QTLs for multifactorial characters, there is a consistent risk of false positivity because most relevant LOD scores values are smaller than 3.6, the stringent threshold value for significance assigned by Lander and Kruglyak (23). The current results are no exception to this rule. To document the risk of false positivity attached to our genome scan, we performed simulation tests with our data. The comparison of these tests with our findings indicates that many found QTLs may reveal true localization for birth length, birth weight, or ponderal index. Nevertheless, it should be clearly understood that only fine mapping of suspected chromosome regions and replication in other cohorts will lead to bona fide localizations and help delineate the genomic regions implicated in human fetal growth variability.


   Acknowledgments
 
The authors thank the Serono Laboratory for supporting D.F. with the Alfred Jost Research Fellowship. We acknowledge the contribution of C. Le Stunff, who recruited most families of the current study. We thank all technicians and engineers of the Centre National de Génotypage (Evry) whose genotyping work made this study feasible.


   Footnotes
 
Disclosure statement: all authors have nothing to declare.

First Published Online July 18, 2006

Abbreviation: QTL, quantitative trait loci.

Received March 9, 2006.

Accepted July 6, 2006.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

  1. Knight B, Shields BM, Turner M, Powell RJ, Yajnik CS, Hattersley AT 2005 Evidence of genetic regulation of fetal longitudinal growth. Early Hum Dev 81:823–831[CrossRef][Medline]
  2. Moore LG 2003 Fetal growth restriction and maternal oxygen transport during high altitude pregnancy. High Alt Med Biol 4:141–156[CrossRef][Medline]
  3. Hattersley AT, Beards F, Ballantyne E, Appleton M, Harvey R, Ellard S 1998 Mutations in the glucokinase gene of the fetus result in reduced birth weight. Nat Genet 19:268–270[CrossRef][Medline]
  4. Randhawa R, Cohen P 2005 The role of the insulin-like growth factor system in prenatal growth. Mol Genet Metab 86:84–90[CrossRef][Medline]
  5. Isles AR, Holland AJ 2005 Imprinted genes and mother-offspring interactions. Early Hum Dev 81:73–77[CrossRef][Medline]
  6. Murrell A, Heeson S, Cooper WN, Douglas E, Apostolidou S, Moore GE, Maher ER, Reik W 2004 An association between variants in the IGF2 gene and Beckwith-Wiedemann syndrome: interaction between genotype and epigenotype. Hum Mol Genet 13:247–255[Abstract/Free Full Text]
  7. Morton NE 1955 The inheritance of human birth weight. Ann Hum Genet 20:125–134[Medline]
  8. Magnus P, Berg K, Bjerkedal T, Nance WE 1984 Parental determinants of birth weight. Clin Genet 26:397–405[Medline]
  9. Magnus P 1984 Causes of variation in birth weight: a study of offspring of twins. Clin Genet 25:15–24[Medline]
  10. Johnston LB, Clark AJ, Savage MO 2002 Genetic factors contributing to birth weight. Arch Dis Child Fetal Neonatal Ed 86:F2–F3
  11. Carmichael CM, McGue M 1995 A cross-sectional examination of height, weight, and body mass index in adult twins. J Gerontol A Biol Sci Med Sci 50:B237–B244
  12. Preece MA 1996 The genetic contribution to stature. Horm Res 45(Suppl 2):56–58
  13. Silventoinen K, Kaprio J, Lahelma E, Viken RJ, Rose RJ 2001 Sex differences in genetic and environmental factors contributing to body-height. Twin Res 4:25–29[CrossRef][Medline]
  14. Davis KC, Kress DD, Doornbos DE, Anderson DC 1998 Heterosis and breed additive effects for Hereford, Tarentaise, and the reciprocal crosses for calf traits. J Anim Sci 76:701–705[Abstract/Free Full Text]
  15. Grosz MD, MacNeil MD 2001 Putative quantitative trait locus affecting birth weight on bovine chromosome 2. J Anim Sci 79:68–72[Abstract/Free Full Text]
  16. Stone RT, Keele JW, Shackelford SD, Kappes SM, Koohmaraie M 1999 A primary screen of the bovine genome for quantitative trait loci affecting carcass and growth traits. J Anim Sci 77:1379–1384[Abstract/Free Full Text]
  17. Slate J, Visscher PM, MacGregor S, Stevens D, Tate ML, Pemberton JM 2002 A genome scan for quantitative trait loci in a wild population of red deer (Cervus elaphus). Genetics 162:1863–1873[Abstract/Free Full Text]
  18. Arya R, Demerath E, Jenkinson CP, Goring HH, Puppala S, Farook V, Fowler S, Schneider J, Granato R, Resendez RG, Dyer TD, Cole SA, Almasy L, Comuzzie AG, Siervogel RM, Bradshaw B, DeFronzo RA, MacCluer J, Stern MP, Towne B, Blangero J, Duggirala R 2006 A quantitative trait locus (QTL) on chromosome 6q influences birth weight in two independent family studies. Hum Mol Genet 15:1569–1579[Abstract/Free Full Text]
  19. Collins Jr JW, Butler AG 1997 Racial differences in the prevalence of small-for-dates infants among college-educated women. Epidemiology 8:315–317[CrossRef][Medline]
  20. O’Connell JR, Weeks DE 1998 PedCheck: a program for identification of genotype incompatibilities in linkage analysis. Am J Hum Genet 63:259–266[CrossRef][Medline]
  21. Amos CI 1994 Robust variance-components approach for assessing genetic linkage in pedigrees. Am J Hum Genet 54:535–543[Medline]
  22. Abecasis GR, Cherny SS, Cookson WO, Cardon LR 2002 Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet 30:97–101[CrossRef][Medline]
  23. Lander E, Kruglyak L 1995 Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet 11:241–247[CrossRef][Medline]
  24. Wiltshire S, Cardon LR, McCarthy MI 2002 Evaluating the results of genomewide linkage scans of complex traits by locus counting. Am J Hum Genet 71:1175–1182[CrossRef][Medline]
  25. Pugh EW, Jaquish CE, Sorant AJ, Doetsch JP, Bailey-Wilson JE, Wilson AF 1997 Comparison of sib-pair and variance-components methods for genomic screening. Genet Epidemiol 14:867–872[CrossRef][Medline]
  26. Hirschhorn JN, Lindgren CM, Daly MJ, Kirby A, Schaffner SF, Burtt NP, Altshuler D, Parker A, Rioux JD, Platko J, Gaudet D, Hudson TJ, Groop LC, Lander ES 2001 Genomewide linkage analysis of stature in multiple populations reveals several regions with evidence of linkage to adult height. Am J Hum Genet 69:106–116[CrossRef][Medline]
  27. Perola M, Ohman M, Hiekkalinna T, Leppavuori J, Pajukanta T, Wessman M, Koskenvuo M, Palotie A, Lange K, Kaprio J, Peltonen N 2001 Quantitative-trait-locus analysis of body-mass index and of stature, by combined analysis of genome scans of five Finnish study groups. Am J Hum Genet 69:117–123[CrossRef][Medline]
  28. Deng HW, Xu FH, Liu YZ, Shen H, Deng H, Huang QY, Liu YJ, Conway T, Li LJ, Davies KM, Recker RR 2002 A whole-genome linkage scan suggests several genomic regions potentially containing QTLs underlying the variation of stature. Am J Med Genet 113:29–39[CrossRef][Medline]
  29. Wu X, Cooper RS, Boerwinkle E, Turner ST, Hunt S, Myers R, Olshen RA, Curb D, Zhu X, Kan D, Luke A 2003 Combined analysis of genomewide scans for adult height: results from the NHLBI Family Blood Pressure Program. Eur J Hum Genet 11:271–274[CrossRef][Medline]
  30. Mukhopadhyay N, Finegold DN, Larson MG, Cupples LA, Myers RH, Weeks DE 2003 A genome-wide scan for loci affecting normal adult height in the Framingham Heart Study. Hum Hered 55:191–201[CrossRef][Medline]
  31. Wiltshire S, Frayling TM, Hattersley AT, Hitman GA, Walker M, Levy JC, O’Rahilly S, Groves CJ, Menzel S, Cardon LR, MacCarthy MI 2002 Evidence for linkage of stature to chromosome 3p26 in a large U.K. family data set ascertained for type 2 diabetes. Am J Hum Genet 70:543–546[CrossRef][Medline]
  32. Weedon MN, Frayling TM, Shields B, Knight B, Turner T, Metcalf BS, Voss L, Wilkin TJ, MacCarthy A, Ben Shlomo Y, Davey Smith G, Ring S, Jones R, Golding J, Byberg L, Mann V, Axelsson T, Syvanen AC, Leon D, Hattersley AT 2005 Genetic regulation of birth weight and fasting glucose by a common polymorphism in the islet cell promoter of the glucokinase gene. Diabetes 54:576–581[Abstract/Free Full Text]
  33. Lindsay RS, Kobes S, Knowler WC, Hanson RL 2002 Genome-wide linkage analysis assessing parent-of-origin effects in the inheritance of birth weight. Hum Genet 110:503–509[CrossRef][Medline]
  34. Szatkiewicz JP, Feingold E 2005 QTL mapping with discordant and concordant sibling pairs: new statistics and new design strategies. Genet Epidemiol 28:326–340[CrossRef][Medline]
  35. Thompson LA, Goodman DC, Chang CH, Stukel TA 2005 Regional variation in rates of low birth weight. Pediatrics 116:1114–1121[Abstract/Free Full Text]
  36. Dunger DB, Ong KK, Huxtable SJ, Sherriff A, Woods KA, Ahmed ML, Golding J, Pembrey ME, Ring S, Bennett ST, Todd JA 1998 Association of the INS VNTR with size at birth. ALSPAC Study Team. Avon Longitudinal Study of Pregnancy and Childhood. Nat Genet 19:98–100[Medline]
  37. Ong KK, Phillips DI, Fall C, Poulton J, Bennett ST, Golding J, Todd JA, Dunger DB 1999 The insulin gene VNTR, type 2 diabetes and birth weight. Nat Genet 21:262–263[CrossRef][Medline]
  38. Petry CJ, Ong KK, Barratt BJ, Wingate D, Cordell HJ, Ring SM, Pembrey ME, Reik W, Todd JA, Dunger DB 2005 Common polymorphism in H19 associated with birthweight and cord blood IGF-II levels in humans. BMC Genet 6:22
  39. Aroviita P, Partanen J, Sistonen P, Teramo K, Kekomaki R 2004 High birth weight is associated with human leukocyte antigen (HLA) DRB1*13 in full-term infants. Eur J Immunogenet 31:21–26[CrossRef][Medline]
  40. Risch N, Merikangas K 1996 The future of genetic studies of complex human diseases. Science 273:1516–1517[Abstract/Free Full Text]




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Endocrinology Endocrine Reviews J. Clin. End. & Metab.
Molecular Endocrinology Recent Prog. Horm. Res. All Endocrine Journals