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Evidence for Linkage of Stature to Chromosome 3p26 in a Large U.K. Family Data Set Ascertained for Type 2 Diabetes
Abstract
We have analyzed data from 573 pedigrees from the United Kingdom for evidence for linkage to loci influencing adult stature. Our data set comprised 1,214 diabetic and 163 nondiabetic siblings for whom height data were available. We used variance-components analysis implemented in GENEHUNTER 2 and a modification of the Haseman-Elston regression method, HE-COM. We found evidence for a locus on 3p26 (LOD score 3.17) influencing height in this adult sample, with less-significant evidence for loci on chromosomes 7, 10, 15, 17, 19, and 20. Our findings extend similar recent studies in Scandinavian and Quebecois populations, adding further evidence that height is indeed under the control of multiple genes.
... Apart from the direct role in GH secretion, additional evidence linking GHRL with human stature originates from a genetic linkage study on 573 British families participating in the Diabetes UK Warren 2 study where a region on chromosome 3p26 was found to have a log of odds (LOD) score for stature of 3.17 at the genome wide level [131]. This lead our group to genotype four common SNPs which captures most of the genetic diversity of the GHRL gene. ...
... In a UK based GWAS comprising 1377 siblings with diabetes, a locus near the ghrelin gene on chromosome 3p26 was found to influence stature (LOD score 3.17) [131]. Although this finding was not subsequently replicated by multiple GWAS each with data from 7000-14000 subjects [20,129], a recently published large GWAS with over 180 000 subjects did identify a SNP (rs572169) located within the GHSR gene as a locus with the 5th highest p-value out of the reported 180 loci which possessed significant association with height. ...
... This study can be perceived as an evolutionary step in the way we study complex genetic traits. No association with obesity risk [25,39,43,47,51,55,71,84,127] Association with obesity risk [3,27,64,116,120,121,124] Association with earlier onset of obesity [25,64,121] Alters response to environmental pressures [53] No association with obesity risk [31,33,39,128] Association with obesity risk [7] Alters response to environmental pressures [81] Not available No positive study Significant association with obesity at chromosome 3q24-28 [58,79,102,126,134] Stature Presence of association with IGF-I [98,120,127] No association with stature [38,40] No association with IGF-I [31,125] No association with stature [31,38] Not available Significant association with stature at chromosome 3p26 [131], but not confirmed for ghrelin in the same population Significant association between stature and GHSR SNP [70] T2D ...
... Apart from the direct role in GH secretion, additional evidence linking GHRL with human stature originates from a genetic linkage study on 573 British families participating in the Diabetes UK Warren 2 study where a region on chromosome 3p26 was found to have a log of odds (LOD) score for stature of 3.17 at the genomewide level [131]. This lead our group to genotype five common SNPs which captures most of the genetic diversity of the GHRL gene. ...
... In a UK based GWAS comprising 1377 siblings with diabetes, a locus near the ghrelin gene on chromosome 3p26 was found to influence stature (LOD score 3.17) [131]. Although this finding was not subsequently replicated by multiple GWAS each with data from 7000 to 14,000 subjects [20,129], a recently published large GWAS with over 180,000 subjects did identify a SNP (rs572169) located within the GHSR gene as a locus with the 5th highest p-value out of the reported 180 loci which possessed significant association with height. ...
... Presence of association with IGF-I [98,120,127] No association with IGF-I [31,125] Not available Significant association with stature at chromosome 3p26 [131], but not confirmed for ghrelin in the same population Significant association between stature and GHSR SNP [70] No association with stature [38,40] No association with stature [31,38] ...
Ghrelin is a 28 amino acid peptide hormone that is produced both centrally and peripherally. Regulated by the ghrelin O-acyl transferase enzyme, ghrelin exerts its action through the growth hormone secretagogue receptor, and is implicated in a diverse range of physiological processes. These implications have placed the ghrelin signaling pathway at the center of a large number of candidate gene and genome-wide studies which aim to identify the genetic basis of human heterogeneity. In this review we summarize the available data on the genetic variability of ghrelin, its receptor and its regulatory enzyme, and their association with obesity, stature, type 2 diabetes, cardiovascular disease, eating disorders, and reward seeking behavior.
... If a sufficient number of the recruited individuals are related and additionally DNA or genotype information is available, linkage analysis for several traits can be conducted. This was done in 12 publications reporting genome scans in 28 separate samples for linkage with adult height (Deng et al. 2002; Hirschhorn et al. 2001; Perola et al. 2001; Thompson et al. 1995; Wiltshire et al. 2002; Wu et al. 2003; Xu et al. 2002; Sale et al. 2005; Sammalisto et al. 2005; Willemsen et al. 2004; Liu et al. 2004). Most of these were performed in samples ascertained for specific diseases unrelated to body height such as diabetes (Wiltshire et al. 2002) or asthma (Wu et al. 2003) while a few were performed in population samples such as the Framingham Heart Study (). ...
... This was done in 12 publications reporting genome scans in 28 separate samples for linkage with adult height (Deng et al. 2002; Hirschhorn et al. 2001; Perola et al. 2001; Thompson et al. 1995; Wiltshire et al. 2002; Wu et al. 2003; Xu et al. 2002; Sale et al. 2005; Sammalisto et al. 2005; Willemsen et al. 2004; Liu et al. 2004). Most of these were performed in samples ascertained for specific diseases unrelated to body height such as diabetes (Wiltshire et al. 2002) or asthma (Wu et al. 2003) while a few were performed in population samples such as the Framingham Heart Study (). Adult height (stature) is a highly heritable trait, with heritability estimates around 0.8 (Preece 1996; Silventoinen et al. 2000; Silventoinen 2003; Xu et al. 2002). ...
... al. (2001) for the regions on chromosomes 6p, 9, 12, 14, 18 and 22 (seetable 22). However, there was no overlap with the putative linkage regions reported by Thompson et al. (1995) and Wiltshire et al. (2002). Deng et al. (2002 reported a LOD score of about 1 on chromosome 18 at 75 cM. ...
Linkage genome scans for genetically complex diseases have low power with the sample sizes that were often used in the past, and hence meta-analysis of several scans for the same disease might be a promising approach. Appropriate data are now becoming accessible as many groups worldwide investigate common diseases. The aim of this thesis is to extend and evaluate statistical methodology for meta-analysis. In addition, two meta-analyses of linkage genome scans for the complex phenotypes asthma and adult stature are performed and discussed. In the first part of this thesis, an overview of available statistical methods and current applications is given. A new meta-analysis method is introduced which is based on a weighted combination of non-parametric linkage scores. Its relationship to traditional fixed effects meta-analysis of combining parameter estimates from different studies weighted by the inverse of their respective variances is described. Recombination and low informativity of markers lead to a reduction of the effective sample size in multipoint linkage analysis. A locus specific weighting of individual studies with this effective sample size is therefore proposed. In a simulation study, the power of different methods to combine multipoint linkage scores, namely Fisher’s p-value combination (Fisher 1932), the truncated product method (Zaykin et al. 2002, a variant of Fisher's method), the Genome Search Meta-Analysis (GSMA, Wise et al. 1999) method and the proposed weighting methods were compared. In particular, the effects of different genetic marker sets and sample sizes between genome scans were investigated. The weighting methods explicitly take those differences into account and have higher power in the simulated scenarios than the other methods. The proposed meta-analysis method was applied to four linkage genome scans for the phenotype asthma and five studies of a candidate genetic region. Multipoint nonparametric linkage analysis is performed and different weighting schemes are used to combine the score statistics of individual studies to an overall statistic. For comparison, the GSMA method is also applied to the same data sets. For meta-analysis of linkage studies, a common map of genetic markers is necessary to align results obtained in different studies with different markers. In this meta-analysis, the effects of map uncertainties were evaluated. The latest versions of available combined physical and linkage maps are very precise and the small potential map errors that are left do not have relevant impact. This meta-analysis of nine asthma linkage studies does not identify significant regions of genetic linkage to asthma. A still rather small size of the combined samples may be the reason for low power to identify susceptibility genes for the complex trait asthma. The statistical methods that can be applied for a meta-analysis of linkage studies depend crucially on the available data, especially any additional information besides the usually reported linkage statistics. For the meta-analysis of linkage genome scans for the highly heritable trait adult height, only LOD scores from variance components linkage analysis, which are measures of significance and not effect estimates, could be obtained. Thus, Fisher’s method and a weighted and unweighted variant of the inverse normal method were applied. Initially, a linkage genome scan for this quantitative trait was performed in the extended pedigrees of the Framingham Heart Study. A variance components linkage analysis in this sample unselected for height gave evidence for linkage in several regions. All markers showing a LOD score greater than 1 in this analysis correspond to previously reported linkage regions, including chromosome 6q with a maximum LOD score of 2.45 and chromosomes 9, 12, 14, 18 and 22. Following this observation, a meta-analysis of all previously published genome scans for adult stature was planned. Genome scan results of 17 separate samples reported in seven publications and comprising more than 14000 phenotyped and genotyped individuals could be obtained in sufficient detail to be included in the meta-analysis. The comparison of meta-analysis results with individual studies shows that only a formal meta-analysis can exactly quantify the combined evidence for linkage and is superior to an informal classification of results as replication or non-replication. Significant linkage of stature is observed on chromosomes 6, 7, 9 and 12 (LOD scores >4) and suggestive linkage with LOD scores >2 is obtained in six additional genetic regions. This is well compatible with the concept of height as a mostly polygenic trait for which also some major genes exist. Candidate genes in the linkage regions are discussed. Kopplungsgenomscans für genetisch komplexe Krankheiten haben mit den bislang üblichen Fallzahlen oft nur eine geringe statistische Power, daher sind Meta-Analysen von mehreren Genomscans für die gleiche Krankheit ein erfolgversprechender Ansatz. Passende Datensätze werden zunehmend verfügbar, da weltweit viele Gruppen genetische Studien zu den häufigsten Krankheiten durchführen. Ziel dieser Arbeit ist es, statistische Methoden der Meta-Analyse weiter zu entwickeln und zu evaluieren. Weiterhin werden zwei Meta-Analysen von Genomscans für komplexe Phänotypen, Asthma und Körpergröße, durchgeführt. Im ersten Teil dieser Dissertation wird ein Überblick über aktuelle Anwendungen und bisherige statistische Methoden gegeben. Eine neue Methode für Meta-Analysen von genetischen Kopplungsgenomscans, die auf einer gewichteten Kombination von nicht-parametrischen Kopplungsstatistiken basiert, wird vorgestellt. Ihr Zusammenhang mit herkömmlicher „fixed-effects“ Meta-Analyse für Parameterschätzer wird erläutert. In einer Simulationsstudie wurde die statistische Power verschiedener Meta-Analyse Methoden für multipoint Kopplungsergebnisse verglichen. Dabei wurden die Methode nach Fisher zur Kombination von p-Werten (Fisher 1932), die „truncated product method“ (Zaykin et al. 2002, eine Variante von Fishers Methode), die Genome Search Meta-Analysis Methode (GSMA, Wise et al. 1999) und die vorgeschlagenen Gewichtungsmethoden angewandt. Insbesondere wurden die Einflüsse unterschiedlicher genetischer Marker und Fallzahlen zwischen Genomscans untersucht. Die Gewichtungsmethoden berücksichtigen diese Unterschiede explizit und haben eine höhere statistische Power in den untersuchten Szenarien als die anderen Methoden. Die vorgeschlagene Meta-Analyse Methode wurde auf vier Kopplungsscans und fünf Studien einer Kandidatengenregion für den Phänotyp Asthma angewandt. Zunächst wurden nicht-parametrische multipoint Kopplungsanalysen der Einzelstudien durchgeführt und die Einzel-Teststatistiken dann mit verschiedenen Gewichtungsmethoden zu einer Gesamtstatistik zusammengefasst. Für eine Meta-Analyse von Kopplungsstudien benötigt man die relative genetische Position aller in den verschiedenen Studien verwendeten Marker zueinander. Die Bedeutung von Ungenauigkeiten der genetischen Karte wurde daher in dieser Studie untersucht. Die neuesten Versionen der zur Verfügung stehenden kombinierten physikalischen und genetischen Karten sind sehr präzise und die möglicherweise noch enthaltenen geringen Fehler haben keinen relevanten Einfluss auf eine Meta-Analyse. Die Meta-Analyse der neun Asthma-Studien ergab keine signifikanten Hinweise auf Kopplung. Die relativ geringe Gesamtstichprobengröße ist ein möglicher Grund für geringe statistische Power zur Identifikation von Suszeptibilitätsgenen für die genetisch komplexe Krankheit Asthma. Welche statistischen Methoden für eine Meta-Analyse verwendet werden können, hängt stark von den zur Verfügung stehenden Daten ab, insbesondere welche weiteren Informationen neben den üblicherweise berichteten Teststatistiken vorhanden sind. Für die Meta-Analyse von Kopplungsgenomscans des Phänotyps Körpergröße standen nur LOD scores aus Varianzkomponentenanalysen zur Verfügung, welche Signifikanzmaße, nicht aber Effektstärkenschätzer sind. Daher wurden die Methode nach Fisher und eine gewichtete sowie ungewichtete Variante der Inversen-Normalverteilungsmethode angewandt. Zunächst wurde ein Kopplungsgenomscan dieses quantitativen Merkmals in den erweiterten Stammbäumen der Framingham Heart Study durchgeführt. Eine Kopplungsanalyse mit Varianzkomponentenverfahren ergab in dieser für Körpergröße nicht speziell ausgewählten Stichprobe Kopplung zu mehreren genetischen Regionen. Alle Marker, die in dieser Auswertung einen LOD score (Kopplungsteststatistik) größer als 1 zeigen entsprechen schon früher berichteten Kopplungsregionen, darunter Chromosom 6q mit einem maximalen LOD score von 2,45 und Regionen auf den Chromsomen 9, 12, 14, 18 und 22. Auf Grund dieser Beobachtung wurde eine Meta-Analyse aller publizierten Genomscans für Körpergröße geplant. Die Ergebnisse von 17 Stichproben (aus sieben Veröffentlichungen) mit insgesamt mehr als 14000 phänotypisierten und genotypisierten Personen konnten in die Meta-Analyse einbezogen werden. Der Vergleich der Ergebnisse der Meta-Analyse mit denen der Einzelstudien zeigt, dass nur eine formale Meta-Analyse die Hinweise auf Kopplung genau quantifizieren kann und einer ungenauen Einteilung der Ergebnisse im Sinne einer Replikation oder Nicht-Replikation vorzuziehen ist. Signifikante Kopplung von Körpergröße ergibt sich zu den Chromosomen 6, 7, 9 und 12 (mit Gesamt-LOD scores >4) und Hinweise auf Kopplung mit LOD scores >2 finden sich in sechs weiteren genetischen Regionen. Schließlich werden Kandidatengene in den Kopplungsregionen diskutiert.
... Recent studies have reported the heritability for height in the range 0.69-0.98 (Hirschhorn et al. 2001;Perola et al. 2001;Deng et al. 2002;Wiltshire et al. 2002;Xu et al. 2002;Mukhopadhyay et al. 2003;Wu et al. 2003). Quantative trait loci (QTLs) for height have been reported in studies of six populations of European ancestry (Hirschhorn et al. 2001;Perola et al. 2001;Deng et al. 2002;Wiltshire et al. 2002;Xu et al. 2002;Mukhopadhyay et al. 2003), one study of Pima Indians (Thompson et al. 1995) and one multi-ethnic American study (Wu et al. 2003). ...
... (Hirschhorn et al. 2001;Perola et al. 2001;Deng et al. 2002;Wiltshire et al. 2002;Xu et al. 2002;Mukhopadhyay et al. 2003;Wu et al. 2003). Quantative trait loci (QTLs) for height have been reported in studies of six populations of European ancestry (Hirschhorn et al. 2001;Perola et al. 2001;Deng et al. 2002;Wiltshire et al. 2002;Xu et al. 2002;Mukhopadhyay et al. 2003), one study of Pima Indians (Thompson et al. 1995) and one multi-ethnic American study (Wu et al. 2003). Major loci from these eight studies have been reported on several different chromosomes. ...
... Previous studies have frequently reported heritability estimates in excess of 0.6 for height (e.g., Hirschhorn et al. 2001;Perola et al. 2001;Deng et al. 2002;Wiltshire et al. 2002;Xu et al. 2002;Mukhopadhyay et al. 2003;Wu et al. 2003). In African Americans, heritability estimates have been reported to be as high as 0.87 (Luke et al. 2001), and the multi-ethnic range of heritability reported by Wu et al. (2003) of 0.75-0.98 ...
Height and body mass index (BMI) have high heritability in most studies. High BMI and reduced height are well-recognized as important risk factors for a number of cardiovascular diseases. We investigated these phenotypes in African American families originally ascertained for studies of linkage with type 2 diabetes using self-reported height and weight. We conducted a genome wide scan in 221 families containing 580 individuals and 672 relative pairs of African American descent. Estimates of heritability and support for linkage were assessed by genetic variance component analyses using SOLAR software. The estimated heritabilities for height and BMI were 0.43 and 0.64, respectively. We have identified major loci contributing to variation in height on chromosomes 15 (LOD = 2.61 at 35 cM, p = 0.0004), 3 (LOD = 1.82 at 84 cM, p = 0.0029), 8 (LOD = 1.92 at 135 cM, p = 0.0024) and 17 (LOD = 1.70 at 110 cM, p = 0.0044). A broad region on chromosome 4 supported evidence of linkage to variation in BMI, with the highest LOD = 2.66 at 168 cM (p = 0.0005). Two height loci and two BMI loci appear to confirm the existence of quantitative trait loci previously identified by other studies, providing important replicative data to allow further resolution of linkage regions suitable for positional cloning of these cardiovascular disease risk loci.
... Human adult stature (body height) has been the target of numerous genetic quantitative trait linkage studies in the past few years [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Despite high heritability estimates for all populations, based on either twin comparison [18][19][20][21] or on actual genetic resemblance in siblings [22], the results have been disappointing and inconsistent, with reports of quantitative trait loci (QTLs) scattered across the genome and rarely replicated. ...
... To date, about 20 genome scans have been published that investigate human adult body height [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Since height and various phenotypic characters are recorded in most studies, it is expected that this field will expand even more in the future. ...
... We identified a high heritability of 0.65 by segregation analyses on a Chinese sample of 1,169 informative individuals from 385 nuclear families . In Caucasian, extensive genetic studies via candidate gene association analyses and whole genome linkage scans were performed to identify genes contributing to the variation of human stature (Deng et al. 2002;Hirschhorn et al. 2001;Liu et al. 2006a;Liu et al. 2004Liu et al. , 2006bMukhopadhyay et al. 2003;Perola et al. 2001;Wiltshire et al. 2002;Wu et al. 2003;Xu et al. 2002). Recent developing microarray technology of SNP genotyping provides powerful tool for genome-wide association scan (GWAS) studies to rapidly and systematically identify/confirm functional loci underlying human stature variation. ...
... In Caucasian, extensive genetic studies using GWLS (Deng et al. 2002;Hirschhorn et al. 2001;Liu et al. 2006aLiu et al. , b, 2004Mukhopadhyay et al. 2003;Perola et al. 2001;Phillips and Matheny 1990;Wiltshire et al. 2002;Wu et al. 2003;Xu et al. 2002) or GWAS (Gudbjartsson et al. 2008;Lettre et al. 2008;Sanna et al. 2008;Weedon et al. 2007Weedon et al. , 2008) studies identified quite a few chromosomal regions potentially harboring genes underlying variation of human stature, or some common variants associated with human stature variation. However, in Chinese, genetics studies on stature still remain a largely uncharted territory. ...
In Caucasian, several studies have identified some common variants associated with human stature variation. However, no such study was performed in Chinese, which is the largest population in the world and evidently differs from Caucasian in genetic background. To identify common or ethnic specific genes for stature in Chinese, an initial GWAS and follow-up replication study were performed. Our initial GWAS study found that a group of 13 contiguous SNPs, which span a region of approximately 150 kb containing two neighboring genes, zinc finger protein (ZNP) 510 and ZNP782, achieved strong signals for association with stature, with P values ranging from 9.71 x 10(-5) to 3.11 x 10(-6). After false discovery rate correction for multiple testing, 9 of the 13 SNPs remain significant (FDR q=0.036-0.046). The follow-up replication study in an independent 2,953 unrelated southern Chinese confirmed the association of rs10816533 with stature (P=0.029). All the 13 SNPs were in consistently strong linkage disequilibrium (D'>0.99) and formed a single perfect haplotype block. The minor allele frequencies for the 13 contiguous SNPs have evidently ethnic difference, which range from 0.21 to 0.33 in Chinese but have as low as approximately 0.017 reported in dbSNP database in Caucasian. The present results suggest that the genomic region containing the ZNP510 and ZNP782 genes is an ethnic specific locus associated with stature variation in Chinese.
... A study reported linkage between the chromosome 3p26 locus, which corresponds to the location of ghrelin gene, and stature (LOD score 3.17) in a type 2 diabetes U.K. population (Wiltshire et al., 2002), but this was not confirmed with more specific markers located closer to the ghrelin gene or with ghrelin polymorphisms (Gueorguiev et al., 2007). Wang et al., described two rare novel variants in the GHSR gene, the Phe279Leu variant in an individual with short normal stature and in one proband, and the Ala204Glu variant in an obese subject ; both rare mutations were found to be associated with loss-of-function of the ghrelin receptor (Pantel et al., 2006). ...
... In the current study we did not find any association between ghrelin polymorphisms and stature in our population. Our investigation on a reported linkage between the 3p26 locus and stature (LOD score 3.17) in the Warren 2 type 2 diabetes U.K. population (Wiltshire et al., 2002) regarding SNPs in the ghrelin gene (3p26-25) found no positive association (Gueorguiev et al., 2007). Our results are in agreement with other data that ghrelin is not a decisive factor in the hypothalamic control of growth as ghrelin-null mice do not show any alteration of body length or growth (Sun et al., 2003). ...
Growth and nutrition are interrelated and influenced by multiple genetic and environmental factors. We studied whether common variants in ghrelin and ghrelin receptor (GHSR) genes could play a role in stature variation in the general population and in families ascertained for obesity.
Selected tagging SNPs in the ghrelin and GHSR genes were genotyped in 263 Caucasian families recruited for childhood obesity (1,275 subjects), and in 287 families from a general population (1,072 subjects). We performed familial testing for associations in the entire population and in a sub-set of the samples selected for a case-control study. In the case-control study for height (cases were selected from the obese cohort with mean ZH = 3.17 ± 0.15 confidence interval (CI) versus controls with mean ZH 0.14 ± 0.09), we found an association with a 2 base-pair intronic deletion in the GHSR gene (rs10618418) (p = 0.006, odds ratio (OR) 1.86, 95% CI [1.26;2.74] under additive model), although when adjusting for BMI, the association disappeared (p = 0.06). Individuals carrying no deletion or who were heterozygous were significantly more frequent among the tall obese population (52% vs. 36% in controls, p = 0.007, OR 1.97, 95%CI [1.22;3.18]). However, the association was not maintained after correcting for multiple testing. Familial association testing of the ghrelin and GHSR genes and their interaction testing failed to show that any combination of SNPs had any significant effect. Thus, our results suggest that common variants of the ghrelin and GHSR genes are not major contributors to height variation in a French population.
... Investigation of the genetic etiology underlying susceptibility to a common disorder often depends on the use of a number of related indices of severity for genetic mapping, since no single measure fully reflects the complex phenotype. This is the case for such common traits as asthma/atopy (Cookson 2002), late-onset diabetes (Wiltshire et al. 2002), osteoporosis/bone density (Peacock et al. 2002), and cardiovascular disorders (Mitchell et al. 1996), as well as for such major childhood learning disorders as developmental dyslexia (Fisher and DeFries 2002), specific language impairment (SLI Consortium 2002), and attention-deficit/hyperactivity disorder (Fisher et al. 2002b). The question of how to appropriately treat such correlated measures in genetic analyses is an acute issue for many complex traits. ...
... The question of how to appropriately treat such correlated measures in genetic analyses is an acute issue for many complex traits. In the vast majority of previous studies involving multiple correlated measures, each measure has been analyzed independently (Cookson 2002;Fisher and De-Fries 2002;Peacock et al. 2002;SLI Consortium 2002;Wiltshire et al. 2002). However, univariate approaches have a number of major drawbacks. ...
Replication of linkage results for complex traits has been exceedingly difficult, owing in part to the inability to measure the precise underlying phenotype, small sample sizes, genetic heterogeneity, and statistical methods employed in analysis. Often, in any particular study, multiple correlated traits have been collected, yet these have been analyzed independently or, at most, in bivariate analyses. Theoretical arguments suggest that full multivariate analysis of all available traits should offer more power to detect linkage; however, this has not yet been evaluated on a genomewide scale. Here, we conduct multivariate genomewide analyses of quantitative-trait loci that influence reading- and language-related measures in families affected with developmental dyslexia. The results of these analyses are substantially clearer than those of previous univariate analyses of the same data set, helping to resolve a number of key issues. These outcomes highlight the relevance of multivariate analysis for complex disorders for dissection of linkage results in correlated traits. The approach employed here may aid positional cloning of susceptibility genes in a wide spectrum of complex traits.
... Another, although less likely, explanation is that this difference in mean body height is due to environmental factors with equal effects on the whole population, thus limiting environmental variation. A systematic review of previous twin and family studies on body height showed substantial(Wiltshire et al, 2002) Caucasian (British/Irish) 89% variation in the heritability estimates (Silventoinen, 2003). Our results suggest that this variation between previous studies is more likely to be due to poor data quality and small sample sizes of many previous twin data sets rather than actual substantial differences in the genetic architecture of body height itself. ...
... Only one of the published genome scans has linked 17q to stature (Hirschhorn et al., 2001). Of the genome scans published (Table 5), 7q has been linked to stature in four independent studies (Hirschhorn et al., 2001;Perola et al., 2001;Wiltshire et al., 2002;Xu et al., 2002), which makes this region clearly the most interesting autosomal region for candidate gene selection. Interestingly, this region has been linked to BMI as well (Feitosa et al., 2002), and contains the locus for the leptin gene. ...
A major component of variation in body height is due to genetic differences, but environmental factors have a substantial contributory effect. In this study we aimed to analyse whether the genetic architecture of body height varies between affluent western societies. We analysed twin data from eight countries comprising 30,111 complete twin pairs by using the univariate genetic model of the Mx statistical package. Body height and zygosity were self-reported in seven populations and measured directly in one population. We found that there was substantial variation in mean body height between countries; body height was least in Italy (177 cm in men and 163 cm in women) and greatest in the Netherlands (184 cm and 171 cm, respectively). In men there was no corresponding variation in heritability of body height, heritability estimates ranging from 0.87 to 0.93 in populations under an additive genes/unique environment (AE) model. Among women the heritability estimates were generally lower than among men with greater variation between countries, ranging from 0.68 to 0.84 when an additive genes/shared environment/unique environment (ACE) model was used. In four populations where an AE model fit equally well or better, heritability ranged from 0.89 to 0.93. This difference between the sexes was mainly due to the effect of the shared environmental component of variance, which appears to be more important among women than among men in our study populations. Our results indicate that, in general, there are only minor differences in the genetic architecture of height between affluent Caucasian populations, especially among men.
... Recently, some real studies used affected sibships to map QTLs (e.g. Alarcon et al. 2002, Wiltshire et al. 2002. We expect affected sibships to give more information than affected sibling pairs. ...
... This in part explains the absence of findings genes with a clear and reproducible link with for body height. Loci on all autosomal chromosomes except for 10, 16, and 19 and Y-chromosome have been suggested to be linked to body height [102][103][104][105][106][107][108][109][110][111][112][113][114][115][116][117][118][119][120]. Up until this year only findings on chromosomes 3,5,6 and 7 had been suggested in more than one study [112]. ...
... The human DEC1 gene is located at p25.3-26 on chromosome 3. Its size is about 5.7 Kb, including 5 exons and 4 introns [4]. There are many transcription factor binding sites, including CAMP and E-box response element, at its 5'-end [5]. ...
This study aims to explore the correlation between expression of differentiated embryo-chondrocyte expressed gene l (DEC1) and oral squamous cell carcinoma (OSCC), which could provide the reference for treatment and prognosis assessment of OSCC.
The expression of DEC1 in tissues from 56 primary OSCC patients and 20 normal oral mucosa samples were detected using real-time polymerase chain reaction and immunohistochemical methods, respectively.
The results showed that the positive expression rate of DEC1 in the OSCC group was significantly higher than that in the normal group (P <0.05); further, the expression of DEC1 in different OSCC groups was statistically significant (P <0.05). The expression of DEC1 in the 1-year recurrence OSCC group was significantly higher than other groups. The expression of DEC1 in the 3-years no recurrence OSCC group was the lowest.
The expression of DEC1 was associated with the incidence of OSCC and there was a negative correlation between the expression of DEC1 and the prognosis of OSCC.
... Only one of the published genome scans has linked 17q to stature (Hirschhorn et al., 2001). Of the genome scans published (Table 5), 7q has been linked to stature in four independent studies (Hirschhorn et al., 2001;Perola et al., 2001;Wiltshire et al., 2002;Xu et al., 2002), which makes this region clearly the most interesting autosomal region for candidate gene selection. Interestingly, this region has been linked to BMI as well (Feitosa et al., 2002), and contains the locus for the leptin gene. ...
... In a UK based GWAS comprising 1377 siblings with diabetes, a locus near the ghrelin gene on chromosome 3p26 was found to influence stature (LOD score 3.17) (128). We have also looked into the link between the human preproghrelin gene (GHRL) with human stature by genotyping five common SNPs which capture most of the genetic diversity of the GHRL gene (129). ...
... In a UK based GWAS comprising 1377 siblings with diabetes, a locus near the ghrelin gene on chromosome 3p26 was found to influence stature (LOD score 3.17) (128). We have also looked into the link between the human preproghrelin gene (GHRL) with human stature by genotyping five common SNPs which capture most of the genetic diversity of the GHRL gene (129). ...
Ghrelin is a 28 amino-acid brain-gut peptide that is well-known for its orexigenic and metabolic effects leading to an overall positive energy balance. It stimulates appetite and growth hormone release via the GHS-R1a receptors. GOAT has been identified as the enzyme that acylates ghrelin, which is important for its endocrine function. The ghrelin/GHS-R/GOAT system has been studied extensively in view of its association with several endocrine diseases and the potential of developing an effective treatment. These include obesity, Prader-Willi syndrome, anorexia nervosa and diabetes mellitus. Ghrelin system has also been associated with growth and stature. All these conditions can affect children and have a significant impact on the quality of health and life prognosis. In this review, we look into the association of ghrelin with appetite, growth and metabolic disorders in children.
... Examples of such studies include: 1) longitudinal studies, e.g., Framingham heart study (FHS), where each subject is measured for a single trait more than one time at distinct time points; 2) investigation of genetic etiologies underlying susceptibility to common disorders. Genetic mapping for common disorders, such as diabetes (Wiltshire et al., 2002), asthma (Cookson, 2002), cardiovascular disease (Mitchell et al., 1996), and developmental dyslexia (Fisher & Defries, 2002), often depend on the use of a number of related indices of severity and no single measure fully reflects the complex etiology of these diseases. In such cases, univariate analyses may have some drawbacks, such as, how best to adjust for the multiple testing of correlated measures in respective univariate analyses and how to interpret and integrate data from univariate analyses of different trait measures. ...
Genetic association analyses with haplotypes may be more powerful than analyses with single markers, under certain conditions. Furthermore, simultaneously considering multiple correlated traits may make use of additional information that would not be considered when analyzing individual traits. In this study, we propose a haplotype based test of association for multivariate quantitative traits in unrelated samples. Specifically, we extend a population based haplotype trend regression (HTR) approach to multivariate scenarios. We mainly focused on bivariate HTR, and the simulation results showed that the proposed method had correct pre-specified type-I error rates. The power of the proposed method was largely influenced by the size and source of correlation between variables, being greatest when correlation of a specific gene was opposite in sign to the residual correlation.
... Positive linkage to the 7pter region was also found by Hirschhorn et al. (2001). In another genomewide scan from 593 pedigrees ascer-tained for a study of type 2 diabetes in the United Kingdom, Wiltshire et al. (2002) provided evidence of height linkage at 3p26 and in several other chromosomal regions. Although differences in ethnicity, sources of subjects, and statistical fluctuations may contribute to the varied findings (Altmuller et al. 2001;Hirschhorn et al. 2001), other unknown factors have yet to be identified. ...
Segregation and linkage analyses were performed for adult height in a population of 200 Dutch families, each of which was ascertained through a proband with asthma. The best-fit model from the segregation analysis was a major recessive gene with a significant residual polygenic background. Models without a polygenic component were rejected. A genomewide scan was performed, and it confirmed previous linkage results for chromosomes 6q25 (LOD = 3.06, D6S2436), 9p1 (LOD = 2.09, D9S301), and 12q1 (LOD = 1.86, D12S375). Our results provide evidence that a combination of segregation and linkage approaches is valuable in understanding genetic determination of common complex traits.
... Substantial corroborative evidence exists also from Hirschhorn et al. [9], Xu et al. [10], and Perola et al. [11] for the regions on chromosomes 6p, 9, 12, 14, 18, and 22 (see Table 3). However, there was no overlap with the putative linkage regions reported by Thompson et al. [12] and Wiltshire et al. [13]. Deng et al. [14] reported a LOD score of about 1 on chromosome 18 at 75 cM. ...
Body mass index (BMI) and adult height are moderately and highly heritable traits, respectively. To investigate the genetic background of these quantitative phenotypes, we performed a linkage genome scan in the extended pedigrees of the Framingham Heart Study. Two variance-components approaches (SOLAR and MERLIN-VC) and one regression method (MERLIN-REGRESS) were applied to the data.
Evidence for linkage to BMI was found on chromosomes 16 and 6 with maximum LOD scores of 3.2 and 2.7, respectively. For height, all markers showing a LOD score greater than 1 in our analysis correspond to previously reported linkage regions, including chromosome 6q with a maximum LOD score of 2.45 and chromosomes 9, 12, 14, 18, and 22. Regarding the analysis, the three applied methods gave very similar results in this unselected sample with approximately normally distributed traits.
Our analysis resulted in the successful identification of linked regions. In particular, we consider the regions on chromosomes 6 and 16 for BMI and the regions on chromosomes 6, 9, and 12 for stature interesting for fine mapping and candidate gene studies.
... Other studies found evidence of linkage of height on chromosomes 6, 7, 12, and 13 [15], and on chromosomes 7 and 9 [16]. More recently, evidence for linkage was found on chromosome 3 [17]; and evidence for linkage was observed on chromosomes 6q25, 9p1, and 12q1 [18]; and on chromosomes 5q31, Xp22, and Xq25 [19]. None of these studies considered imprinting effects or used sex-specific genetic maps. ...
Current linkage analysis methods for quantitative traits do not usually incorporate imprinting effects. Here, we carried out genome-wide linkage analysis for loci influencing adult height in the Framingham Heart Study subjects using variance components while allowing for imprinting effects. We used a sex-averaged map for the 22 autosomes, while chromosomes 6, 14, 18, and 19 were also analyzed using sex-specific maps. We compared results from these four analyses: 1) non-imprinted with sex-averaged maps, 2) imprinted with sex-averaged maps, 3) non-imprinted with sex-specific maps, and 4) imprinted with sex-specific maps. We found four regions on three chromosomes (14q32, 18p11-q21, 18q21-22, and 19q13) with LOD scores above 2.0, with a maximum LOD score of 3.12, allowing for imprinting and sex-specific maps, at D18S1364 on 18q21. While we obtained significant evidence of imprinting effects in both the 18p11-q21 and 19q13 regions when using sex-averaged maps, there were no significant differences between the imprinted and non-imprinted LOD scores when we used sex-specific maps. Our results illustrate the importance of allowing for gender-specific effects in linkage analyses, whether these are in the form of gender-specific recombination frequencies, or in the form of imprinting effects.
... Studies of these syndromes identified a list of causative genes and genomic regions (Cabezas et al. 2000;Gelb et al. 1996;Hamel et al. 1996;Maheshwari et al. 1998;Ramesar et al. 1996;Raynaud et al. 1998;Shiang et al. 1994;Vitale et al. 2001), which potentially may be related to normal height variation. Large-scale whole genome linkage studies of height represent the latest effort of genetic dissection of this complex trait Hirschhorn et al. 2001;Perola et al. 2001;Wiltshire et al. 2002;Xu et al. 2002). These studies have suggested quite a few genomic regions with linkage to height, among which 6q25 (Hirschhorn et al. 2001;Xu et al. 2002) and 7q36 (Hirschhorn et al. 2001;Perola et al. 2001) are the two prominent regions replicated across studies. ...
Recently, we reported a whole genome scan on a sample of 630 Caucasian subjects from 53 human pedigrees. Several genomic regions were suggested to be linked to height. In an attempt to confirm the identified genomic regions, as well as to identify new genomic regions linked to height, we conducted a whole genome linkage study on an extended sample of 1,816 subjects from 79 pedigrees, which includes the 53 pedigrees containing the original 630 subjects from our previous whole genome study and an additional 128 new subjects, and 26 further pedigrees containing 1,058 subjects. Several regions achieved suggestive linkage signals, such as 9q22.32 [MLS (multipoint LOD score) = 2.74], 9q34.3 [MLS = 2.66], Xq24 [two-point LOD score = 2.64 at the marker DXS8067], and 7p14.2 [MLS = 2.05]. The importance of the above regions is supported either by other whole genome studies or by candidate genes within these regions relevant to linear growth or pathogenesis of short stature. In addition, this study has tentatively confirmed the Xq24 region's linkage to height, as this region was also detected in the previous whole genome study. To date, our study has achieved the largest sample size in the field of genetic linkage studies of human height. Together with the findings of other studies, the current study has further delineated the genetic basis of human stature.
... Several recent genome scans for adult height revealed significant and suggestive evidence for linkage on several chromosomes. [4][5][6][7] However, few of these regions were replicated among different studies. This may not be surprising considering that height is a complex genetic trait affected by multiple genes, each having a small effect, as well as by environmental factors. ...
A combined analysis of genome scans was performed for adult height in the NHLBI Family Blood Pressure Program. Height data were available on 6752 individuals. Linkage analysis was performed first separately for each of the eight ethnic groups in the four networks using the variance component method. To increase the power to detect the common genetic components affecting height for all the individuals, a linkage analysis was performed subsequently for the combined data set by pooling the average allele-sharing IBD () for all groups. By combining the data, we replicated evidence for a QTL influencing adult height on chromosome 7 (7q31) (LOD=2.46), which has been reported in two previous studies. Suggestive linkage (LOD>1) was found in another six regions in our combined analysis. Evidence for linkage for two of these regions (2p12, 20p11) has also been reported previously.
... Genome-wide linkage analysis have identified several genomic regions that affect growth, including 3p26, 5q31, 6q24-25, 7q31-36, 7pter, 9qter, 12p11-q14, 13q32-33, Xq25, and Xp22. [8][9][10][11] Furthermore, association studies between gene variants and short stature are complementary and have led to a number of associations. 12 Gene variations in the known growth-controlling genes may also turn out to be candidates for modulating growth and adult height in the general population (Table; available online at www. jpeds.com). ...
... Intuitively, the bone cross-sectional geometry would be expected to be correlated with other bone size measurements (e.g., 2-D areal bone size) and height/stature. However, our results are largely inconsistent with previous findings on areal bone size (59,60) and height/stature, (61)(62)(63)(64)(65)(66)(67)(68)(69)(70)(71) except for a few regions on chromosome X. (67,69) This inconsistency reflects that bone cross-sectional geometry and areal bone size (and height/stature) may represent different aspects of bone properties. ...
A genome-wide linkage scan was performed in a sample of 79 multiplex pedigrees to identify genomic regions linked to femoral neck cross-sectional geometry. Potential quantitative trait loci were detected at several genomic regions, such as 10q26, 20p12-q12, and chromosome X.
Bone geometry is an important determinant of bone strength and osteoporotic fractures. Previous studies have shown that femoral neck cross-sectional geometric variables are under genetic controls. To identify genetic loci underlying variation in femoral neck cross-sectional geometry, we conducted a whole genome linkage scan for four femoral neck cross-sectional geometric variables in 79 multiplex white pedigrees.
A total of 1816 subjects from 79 pedigrees were genotyped with 451 microsatellite markers across the human genome. We performed linkage analyses on the entire data, as well as on men and women separately.
Significant linkage evidence was identified at 10q26 for buckling ratio (LOD = 3.27) and Xp11 (LOD = 3.45) for cortical thickness. Chromosome region 20p12-q12 showed suggestive linkage with cross-sectional area (LOD = 2.33), cortical thickness (LOD = 2.09), and buckling ratio (LOD = 1.94). Sex-specific linkage analyses further supported the importance of 20p12-q12 for cortical thickness (LOD = 2.74 in females and LOD = 1.88 in males) and buckling ratio (LOD = 5.00 in females and LOD = 3.18 in males).
This study is the first genome-wide linkage scan searching for quantitative trait loci underlying femoral neck cross-sectional geometry in humans. The identification of the genes responsible for bone geometric variation will improve our knowledge of bone strength and aid in development of diagnostic approaches and interventions for osteoporotic fractures.
... Therefore, although there might be a large number of genes (polygenes) influencing height, only a limited number of them may have large effects that are detectable with feasible sample sizes, with the remaining genes contributing to the polygenic background. This is consistent with the findings of this as well as other linkage studies on height (e.g., Hirschhorn et al. 2001;Xu et al. 2002;Wiltshire et al. 2002). Typically as shown in all of the genome-wide linkage scans for height so far, among all the regions reported, only very few reached the significance threshold for linkage (LOD>3.0) ...
Human height is an important and heritable trait. Our previous two genome-wide linkage studies using 630 (WG1 study) and an extended sample of 1,816 Caucasians (WG2 study) identified 9q22 [maximum LOD score (MLS)=2.74 in the WG2 study] and preliminarily confirmed Xq24 (two-point LOD score=1.91 in the WG1 study, 2.64 in the WG2 study) linked to height. Here, with a much further extended large sample containing 3,726 Caucasians, we performed a new genome-wide linkage scan and confirmed, in high significance, the two regions' linkage to height. An MLS of 4.34 was detected on 9q22 and a two-point LOD score of 5.63 was attained for Xq24. In an independent sub-sample (i.e., the subjects not involved in the WG1 and WG2 studies), the two regions also achieved significant empirical P values (0.002 and 0.004, respectively) for "region-wise" linkage confirmation. Importantly, the two regions were replicated on a genotyping platform different from the WG1 and WG2 studies (i.e., a different set of markers and different genotyping instruments). Interestingly, 9q22 harbors the ROR2 gene, which is required for growth plate development, and Xq24 was linked to short stature. With the largest sample from a single population of the same ethnicity in the field of linkage studies for complex traits, our current study, together with two previous ones, provided overwhelming evidence substantiating 9q22 and Xq24 for height variation. In particular, our three consecutive whole genome studies are uniquely valuable as they represent the first practical (rather than simulated) example of how significant increase in sample size may improve linkage detection for human complex traits.
Despite intensive study, most genetic factors that contribute to variation in human height remain undiscovered. We conducted a family-based linkage study of height in a unique cohort of very large nuclear families from a founder (Jewish) population. This design allowed for increased power to detect linkage, compared to previous family-based studies. We identified loci that together explain an estimated 6% of the variance in height. We showed that these loci are not tagging known common variants associated with height. Rather, we suggest that the observed signals arise from variants with large effects that are rare globally but elevated in frequency in the Jewish population.
Despite intensive study, most of the specific genetic factors that contribute to variation in human height remain undiscovered. We conducted a family-based linkage study of height in a unique cohort of very large nuclear families from a founder (Jewish) population. This design allowed for increased power to detect linkage, compared to previous family-based studies. Loci we identified in discovery families could explain an estimated lower bound of 6% of the variance in height in validation families. We showed that these loci are not tagging known common variants associated with height. Rather, we suggest that the observed signals arise from variants with large effects that are rare globally but elevated in frequency in the Jewish population.
This article has no abstract.
This article has no abstract.
Human height is a complex trait determined by both genetic and environmental factors. An initial whole genome study showed several genomic regions with suggestive linkage to height in a sample of 630 subjects from 53 human pedigrees. The present study was conducted in an extended sample of 1816 subjects from 79 pedigrees in an attempt to replicate and confirm the results of the previous whole genome scan. Xq24–25 on the X chromosome was confirmed as the region suggestive of linkage to height. In the previous whole genome study, a microsatellite marker of the region DXS1001 achieved a two point LOD score of 1.91 for linkage to height. In the present study on the 79 pedigrees, another marker of the same region, DXS8067, which is only 2.7 cM away from the former marker, attained a higher two point LOD score of 2.66. Moreover, the region’s significant linkage to height was sustained, with a two point LOD score of 1.00 achieved in a subset of the current sample (1026 subjects from 26 new pedigrees), which is independent of the original 630 subjects used in the whole genome study. Our results—together with identification of several syndromes with short stature, which are in linkage to Xq24–25—strongly suggest that this region may harbour a quantitative trait locus (QTL) underlying human height variation.
Human height is a typical complex trait determined by both genetic and environmental factors. Nutritional status and diseases are the most important environmental factors controlling human linear growth.1–4 However, genetic factors play a more dominant role in height determination. This is indicated by a significant familial aggregation of the trait, translating into a heritability of well above 50%.5–9
The search for genes underlying height variation has long been an endeavour in the field of genetic studies of complex traits. …
Genes involved in human growth consist of major growth genes and minor growth genes.
Major growth genes have fundamental effects on human growth, and their mutations cause
growth failure (or overgrowth) which are recognizable as single gene disorders. Minor
growth genes exert relative minor additive effects on human growth, and their combination
is involved in the development of short (or tall) stature as a multifactorial trait. This
review summarizes the current knowledge about the major and the minor growth genes, and
refers to the recent molecular approach of identification of the growth genes.
Common diseases and complex genetic traits such as adult height and the timing of puberty are modulated by multiple genes and environmental factors. Although methods such as resequencing of candidate genes, association studies, and linkage analysis have identified some of the genes that regulate common diseases and traits, most remain unknown. Novel experimental animal models such as chromosome substitution strains provide alternative means of identifying genes underlying these traits and diseases.
The factors that regulate the timing of puberty remain largely elusive, as do the factors that modulate childhood growth and adult height. However, it is clear that these developmental processes are highly heritable--much of the natural variation in growth and timing of puberty is due to genetic variation within the population. In this review, we discuss how recent genetic and genomic advances can be exploited to help understand the genetic regulation of these processes. In particular, we describe how genome-wide linkage scans and association studies, in conjunction with haplotype-based approaches, are potentially useful tools to increase our understanding of these two complex traits. Discovery of the genetic variants that regulate these two traits would expand our understanding of human neuroendocrinology, postnatal development, and the general architecture of complex genetic traits.
Recently, a quantitative trait locus for stature was reported on chromosome 3p26 in patients with type 2 diabetes.
Given that ghrelin is a peptide involved in GH release and located on 3p26, we hypothesized that variation within its gene (GHRL) may be responsible for the quantitative trait locus on 3p26.
The evidence for linkage around GHRL was refined with the genotyping of an additional four microsatellites (D3S4545, D3S1537, D3S1597, and D3S3611), giving a total of 27 markers, followed by multipoint variance components linkage analysis. Probands from the linkage families were typed for five common single nucleotide polymorphisms (SNPs) within GHRL and tested for association with adult stature using haplotype trend regression.
The maximum multipoint evidence for linkage between adult stature and the 27 microsatellites yielded an LOD score of 2.58 (P = 0.0003) between D3S1297 and D3S1304. Five common (frequency of > or =5%) SNPs were typed in the probands [two promoter SNPs (rs27647 and rs26802), two exonic (rs696217 and rs4684677), and one intronic (rs35683)] capturing 80% of the total common variation in GHRL. No association was found between any SNP (or haplotypes thereof) and adult stature.
Common genetic variation within GHRL is not responsible for variation in adult stature in this population.
Human growth and attained height are determined by a combination of genetic and environmental effects and in modern Western societies > 80% of the observed variation in height is determined by genetic factors. Height is a fundamental human trait that is associated with many socioeconomic and psychosocial factors and health measures, however little is known of the identity of the specific genes that influence height variation in the general population. This thesis work aimed to identify the genetic variants that influence height in the general population by genome-wide linkage analysis utilizing large family samples. The study focused on analysis of three separate sets of families consisting of: 1) 1,417 individuals from 277 Finnish families (FinnHeight), 2) 8,450 individuals from 3,817 families from Australia and Europe (EUHeight) and 3) 9,306 individuals from 3,302 families from the United States (USHeight). The most significant finding in this study was found in the Finnish family sample where we a locus in the chromosomal region 1p21 was linked to adult height. Several regions showed evidence for linkage in the Australian, European and US families with 8q21 and 15q25 being the most significant. The region on 1p21 was followed up with further studies and we were able to show that the collagen 11-alpha-1 gene (COL11A1) residing at this location was associated with adult height. This association was also confirmed in an independent Finnish population cohort (Health 2000) consisting of 6,542 individuals. From this population sample, we estimated that homozygous males and females for this gene variant were 1.1 and 0.6 cm taller than the respective controls. In this thesis work we identified a gene variant in the COL11A1 gene that influences human height, although this variant alone explains only 0.1% of height variation in the Finnish population. We also demonstrated in this study that special stratification strategies such as performing sex-limited analyses, focusing on dizygous twin pairs, analyzing ethnic groups within a population separately and utilizing homogenous populations such as the Finns can improve the statistical power of finding QTL significantly. Also, we concluded from the results of this study that even though genetic effects explain a great proportion of height variance, it is likely that there are tens or even hundreds of genes with small individual effects underlying the genetic architecture of height. Ihmisen kasvu ja aikuisiän pituus ovat tyypillisiä monitekijäisiä ominaisuuksia, joihin vaikuttavat sekä geneettiset tekijät että ympäristötekijät. Geneettisten tekijöiden merkitys pituuden määräytymisessä on huomattava ja on arvioitu, että kehittyneissä maissa nämä selittävät yli 80 % ihmisten välisistä pituuseroista. Aikuisiän pituudella on havaittu olevan yhteys lukuisiin sairauksiin sekä sosioekonomisiin, psykososiaalisiin ja terveydentilaa kuvaaviin muuttujiin, mutta pituuden määräytymiseen vaikuttavat geenit ovat pitkälti tuntemattomia toistaiseksi. Tässä väitöskirjatyössä pyrittiin paikantamaan pituuteen vaikuttavia kromosomialueita perimänlaajuisen kytkentäanalyysin avulla hyödyntäen poikkeuksellisen laajoja perheaineistoja. Lisäksi kytkeytyneiltä kromosomialueilta pyrittiin tunnistamaan assosiaatioanalyysin keinoin näillä alueilla sijaitsevia geenimuotoja, jotka vaikuttavat ihmisten välisiin pituuseroihin. Väitöskirjatyö koostui kolmesta osatyöstä, joissa kussakin hyödynnettiin suurta perheaineistoa: 1) FinnHeight, joka käsitti 1417 henkilöä 277 suomalaisesta perheestä, 2) EUHeight, joka sisälsi yhteensä 8450 henkilöä 3817 australialaisesta, tanskalaisesta, suomalaisesta, ruotsalaisesta ja englantilaisesta kaksosperheestä sekä 3) USHeight joka koostui yhteensä 9371 henkilöä 3032 yhdysvaltalaisesta perheestä. Tutkimuksen merkittävin tulos havaittiin ensimmäisessä osatyössä, jossa osoitettiin tilastollisesti merkitsevä kytkentä kromosomialueelle 1p21. Toisessa ja kolmannessa osatyössä puolestaan havaittiin usean kromosomialueen kytkeytyvän pituuteen, joista merkittävimmät olivat 8q21 ja 15q25. Kromosomialueen 1p21 jatkotutkimukset osoittivat alueella sijaitsevan kollageeni 11-alfa-1 (COL11A1) geenin assosioituvan pituuteen. Tämä assosiaatio toistettiin laajassa, 6542 yksilön suomalaisessa väestöaineistossa (Terveys 2000), jossa kyseisen geenimuodon suhteen samanperintäiset miehet olivat 1,1 cm ja naiset 0,6 cm pidempiä verrokkeihin nähden. Tässä tutkimuksessa paikannettiin ja tunnistettiin pituuteen vaikuttavan geenimuoto COL11A1-geenissä, joka selittää 0,1 % pituuden kokonaisvaihtelusta suomalaisväestössä. Lisäksi tutkimuksessa osoitettiin, että erityiset aineiston valikointitavat kuten sukupuolten, kaksosparien ja etnisten ryhmien erillisanalyysit voivat lisätä kytkentäanalyysin voimaa merkittävästi. Tutkimuksen merkittävin geenilöydös COL11A1-geenissä antaa myös viitteitä siitä, että vaikka suurin osa pituuden vaihtelusta on geneettisten tekijöiden määräämää, on todennäköistä, että tällaisia tekijöitä on hyvin suuri määrä ja kunkin yksittäinen geenimuodon vaikutus yksilön pituuteen on erittäin pieni.
Human height is the prototypical polygenic quantitative trait. Recently, several genetic variants influencing adult height were identified, primarily in individuals of East Asian (Chinese Han or Korean) or European ancestry. Here, we examined 152 genetic variants representing 107 independent loci previously associated with adult height for transferability in a well-powered sample of 1,016 unrelated African Americans. When we tested just the reported variants originally identified as associated with adult height in individuals of East Asian or European ancestry, only 8.3% of these loci transferred (p-values < or =0.05 under an additive genetic model with directionally consistent effects) to our African American sample. However, when we comprehensively evaluated all HapMap variants in linkage disequilibrium (r(2) > or = 0.3) with the reported variants, the transferability rate increased to 54.1%. The transferability rate was 70.8% for associations originally reported as genome-wide significant and 38.0% for associations originally reported as suggestive. An additional 23 loci were significantly associated but failed to transfer because of directionally inconsistent effects. Six loci were associated with adult height in all three groups. Using differences in linkage disequilibrium patterns between HapMap CEU or CHB reference data and our African American sample, we fine-mapped these six loci, improving both the localization and the annotation of these transferable associations.
Despite extensive research of genetic determinants of human adult height, the genes identified up until now allow to predict only a small proportion of the trait's variance. To identify new genes we analyzed 2,486 genotyped and phenotyped individuals in a large pedigree including 23,612 members in 18 generations. The pedigree was derived from a young genetically isolated Dutch population, where genetic heterogeneity is expected to be low and linkage disequilibrium has been shown to be increased. Complex segregation analysis confirmed high heritability of adult height, and suggested mixed model of height inheritance in this population. The estimates of the model parameters obtained from complex segregation analysis were used in parametric linkage analysis, which highlighted three genome-wide significant and additionally at least four suggestive loci involved in height. Significant peaks were located at the chromosomal regions 1p32 (LOD score = 3.35), 2p16 (LOD score = 3.29) and 16q24 (LOD score = 3.94). For the latter region, a strong association signal (FDR q < 0.05) was obtained for 19 SNPs, 17 of them were located in the CDH13 (cadherin 13) gene of which one (rs1035569) explained 1.5% of the total height variance.
Human height (stature) is a strongly genetic trait, with up to 90% of the variation in height within a population determined by a combination of multiple inherited factors. Recent advances in genetics and genomics now permit comprehensive genome-wide surveys of common genetic variations in those variants that are associated with stature. The first such studies have borne fruit, identifying over 40 genetic loci that can be reproducibly shown to have an influence on adult height. These unbiased searches throughout the genome identified several loci that also harbour rare mutations responsible for more severe alterations in height or skeletal growth. Although the predictive value of the common variants thus far discovered remains low, the identification of these loci has led to new insights into the biology of human growth, and may help identify genes that underlie previously uncharacterized syndromes of abnormal skeletal growth.
To map loci influencing normal adult height in 335 families from the Framingham Heart Study.
We analyzed data consisting of 1,702 genotyped individuals who have been followed over time. The first height measurement for individuals between the ages 20-55 years was analyzed in a genome-wide scan using variance component linkage analysis. Sex, age, and cohort effects were removed before analysis.
Two regions (18pter-p11, 22q11.2) with multipoint LOD scores >1.0 (-log p values >2.0) were detected: we obtained LOD scores of 1.38 at D18S1364, and of 1.10 at D22S345. Analysis of height as a sex-limited phenotype revealed a peak in the 9p21 region near D9S319 with a maximum LOD score of 1.65 (-log p value >3.0) when only male height phenotypes were used. When only female phenotypes were used, a peak with a maximum LOD score of 1.85 (-log p value of 2.70) was observed in the 11q25-qter region near D11S2359.
Our region of interest on chromosome 9 has been implicated by two prior studies. Variance components analysis appeared to be sensitive to pedigree structures as well as the method of IBD computation used.
The estrogen receptor alpha gene (ESR1) is known to be involved in metabolic pathways influencing growth. We have performed two population-based association studies using three common polymorphisms within this candidate gene to determine whether these are associated with variation in adult stature. In 607 women, aged 55-80 yr, from the Rotterdam Study, the ESR1 PvuII-XbaI haplotype 1 (px) and the L allele of the TA repeat polymorphism (<18 TA repeats) were significantly associated with an allele dose-dependent decrease in height. The per allele copy of ESR1 PvuII-XbaI haplotype 1 height was 0.9 cm shorter (P trend = 0.02) and 1.0 cm/allele copy of the TA repeat L allele (P trend = 0.003). These results were independent of age, age at menarche and menopause, and lumbar spine bone mineral density and remained significant after participants with vertebral fractures were excluded. In 483 men from the Rotterdam Study we found no association with height. In 1500 pre- and perimenopausal women from the Eindhoven Study a similar association was observed; women were 0.5 cm shorter per allele copy of the ESR1 haplotype 1 (P for trend = 0.03). In conclusion, we demonstrate a role for genetic variations in the estrogen receptor alpha gene in determining adult stature in women.
It has become commonplace to map individual quantitative trait loci (QTL) in experimental organisms; the means (line-crosses and dense maps of markers) and motivation (the close relationship between continuous physiological traits and common, complex diseases) are self-evident. Progress in mapping human QTL has been more gradual, an inevitable consequence of genetic mapping in a natural population setting. The common objective of these studies has been to understand the molecular mechanisms underlying individual QTL. Recent theoretical and practical advances shift this focus to a more comprehensive or genomic perspective on quantitative variation. Fisher's infinitesimal model of adaptive evolution, which satisfied quantitative geneticists for over 50 years, has been modified in the light of data from QTL mapping experiments in plants and animals. The resulting exponential model provides a pleasing empirical fit to the distribution of QTL effect sizes, predicts that a large amount of quantitative variation will be explained by a limited number of genes and suggests a new mathematical framework for linkage mapping. Molecular analysis of QTL suggests that coding variants (e.g. allozymes) underlie a fraction of quantitative variation and that variants that affect gene expression (expression QTL, eQTL) have a substantial role. This is supported by genomic experiments that combine expression profiling with classical genetic mapping approaches to reveal a remarkable wealth of quantitative heritable variation in the transcriptome and that cis-and trans-acting regulatory factors are organized in networks reflecting pleiotropy. It is hoped that these advances will enhance our understanding of the genetic basis of complex inherited diseases.
The objective of this study was to analyze the influence of the polymorphisms G-6A of the angiotensinogen gene, insertion/deletion (I/D) of the angiotensin-converting enzyme, and C573T of the angiotensin II AT1 receptor gene on a healthy, middle-age population. A total of 370 (194 women) healthy normotensive Caucasian subjects, aged 25-50 yr old, were selected from the general population. A significant association was found between height and the C573T polymorphism in women (P < 0.001). After adjustment for age, this association remained significant (P < 0.002). Thus, the lowest height values were from subjects carrying TT genotype (CC, 1.627 +/- 0.008 m; CT, 1.595 +/- 0.006 m; TT, 1.586 +/- 0.010 m; P = 0.002). Likewise, the I/D polymorphism was associated with height (P = 0.002) in women. It remained significant after adjustment for age and the lowest height for the DD genotype (II, 1.629 +/- 0.011 m; ID, 1.603 +/- 0.006 m; DD, 1.591 +/- 0.007 m; P = 0.016). For both C573T and I/D polymorphisms, there was an allele dosage effect. Moreover, an additive and independent effect of the C573T polymorphism (P = 0.006) and the I/D polymorphism (P = 0.045) on height was observed. In contrast, no association with height was observed for the G-6A polymorphism. In conclusion, additive effects between polymorphisms of the renin-angiotensin system genes and height were observed in healthy women. These results should be studied by other groups in other populations and ethnic groups. Whether or not these associations need to be considered in the epidemiological studies analyzing the relationship between polymorphisms of the renin-angiotensin system genes and such height-influenced parameters as blood pressure merits further study.
Height is a highly heritable, complex trait. At present, the genes responsible for the variation in height have not yet been identified. This paper summarizes the results of previous linkage studies and presents results of an additional linkage analysis. Using data from the Netherlands Twin Register, a sib-pair-based linkage analysis for adult height was conducted. For 513 sib-pairs from 174 families complete genome scans and adult height were available. The strongest evidence for linkage was found for a region on chromosome 6, near markers D6S1053 and D6S1031 (LOD = 2.32). This replicated previous findings in other data sets. LOD scores ranging from 1.53 to 2.04 were found for regions on chromosomes 1, 5, 8, 10, and 18. The region on chromosome 18 (LOD = 1.83) also corresponded with the results of previous studies. Several chromosomal regions are now implied in the variance in height, but further study is needed to draw definite conclusions with regard to the significance of these regions for adult height.
The vitamin D receptor (VDR) gene is important to human stature, as it mediates metabolic pathways, calcium homeostasis, and phosphate homeostasis, which influence growth.
We examined the relationship between VDR and adult height in 1873 white subjects from 406 nuclear families. Four SNPs, namely -4817A/G at intron 1, FokI C/T at exon 2 start codon, BsmI A/G at intron 8, and TaqI T/C at exon 9 in VDR were tested for linkage and association with adult height variation by the program QTDT (quantitative transmission disequilibrium test). The bT haplotype of the BsmI and TaqI loci was further tested for its association with height in unrelated samples randomly chosen from the 406 nuclear families by traditional population association methods.
All four tested SNPs were linked to adult height. Within family associations with height were detected at BsmI and TaqI loci (p = 0.048 and 0.039, respectively). Analyses based on BsmI/TaqI haplotypes also revealed evidence for linkage (p = 0.05) and association (p = 0.001) with height. The bT haplotype was significantly associated with higher adult height (p = 0.033, within family association test). Such an association might be female specific and influenced by menstrual status.
Our results strongly suggest that VDR may be linked to and associated with adult height variation in white populations.
Many genome-wide scans aimed at complex traits have been statistically underpowered due to small sample size. Combining data from several genome-wide screens with comparable quantitative phenotype data should improve statistical power for the localisation of genomic regions contributing to these traits.
To perform a genome-wide screen for loci affecting adult stature by combined analysis of four previously performed genome-wide scans.
We developed a web based computer tool, Cartographer, for combining genetic marker maps which positions genetic markers accurately using the July 2003 release of the human genome sequence and the deCODE genetic map. Using Cartographer, we combined the primary genotype data from four genome-wide scans and performed variance components (VC) linkage analyses for human stature on the pooled dataset of 1417 individuals from 277 families and performed VC analyses for males and females separately.
We found significant linkage to stature on 1p21 (multipoint LOD score 4.25) and suggestive linkages on 9p24 and 18q21 (multipoint LOD scores 2.57 and 2.39, respectively) in males-only analyses. We also found suggestive linkage to 4q35 and 22q13 (multipoint LOD scores 2.18 and 2.85, respectively) when we analysed both females and males and to 13q12 (multipoint LOD score 2.66) in females-only analyses.
We strengthened the evidence for linkage to previously reported quantitative trait loci (QTL) for stature and also found significant evidence of a novel male-specific QTL on 1p21. Further investigation of several interesting candidate genes in this region will help towards characterisation of this first sex-specific locus affecting human stature.
Adult height (stature), as an important parameter of human physical development, has been studied in many populations. Recently, we reported a whole genome scan of height on a sample of 630 Caucasian subjects from 53 human pedigrees. Two chromosome regions, 6q24-25 and 7q31.3-36, achieved low linkage signals (multipoint LOD score 0.5), but gained significant results in the linkage studies of height by other groups. In addition, the region 6q24-25 harbors the ER-alpha gene, an important candidate gene for linear growth. To resolve the controversies over these two regions for height, linkage exclusion analyses were performed in an extended sample of 79 pedigrees with 1816 subjects, which include the 53 pedigrees containing 630 subjects for our previous whole genome study and additional 128 new subjects, and 26 new pedigrees containing 1058 subjects. The two regions, 6q24-25 and 7q31.3-36, were excluded at a relative effect size of 10% or greater (p value < 0.0005) and 5% or greater (p value < 0.0018), respectively. Our results suggest that the two regions may not contribute substantially to height variation in our Caucasian population.
Osteoporosis is characterized by a loss of bone strength, of which bone size (BS) is an important determinant. However, studies on the factors determining BS are relatively few. The present study evaluated the independent effects of height, age, weight, sex, and race on areal BS at the hip and spine, measured by dual-energy X-ray absorptiometry, while focusing on the differential contributions of height to BS across sex, race, and skeletal site. The subjects were aged 40 years or older, including 763 Chinese (384 males and 379 females) from Shanghai, People's Republic of China, and 424 Caucasians (188 males and 236 females) from Omaha, Nebraska. Basically, Caucasians had significantly larger BS than Chinese. After adjusting for height, age, and weight, the Chinese had similar spine BS, but significantly larger intertrochanter BS in both sexes and larger total hip BS in females compared with Caucasians. Males had significantly larger BS than females before and after adjustment in both ethnic groups. The effects of age, weight, and race varied, depending on skeletal site. As expected, height had major effects on BS variation in both sexes and races. Height tended to account for larger BS variation at the spine than at the hip (except for Chinese females), and larger BS variation in Caucasians than in Chinese of the same sex (except for the trochanter in females). We conclude that height is a major predictor for BS, and its contributions vary across sex, race, and skeletal site.
Obesity, diabetes, hypertension, and heart disease are highly heritable conditions that in aggregate are the major causes of morbidity and mortality in the developed world and are growing problems in developing countries. To map the causal genes, we conducted a population screen for these conditions on the Pacific Island of Kosrae. Family history and genetic data were used to construct a pedigree for the island. Analysis of the pedigree showed highly significant heritability for the metabolic traits under study. DNA samples from 2,188 participants were genotyped with 405 microsatellite markers with an average intermarker distance of 11 cM. A protocol using loki, a Markov chain Monte Carlo sampling method, was developed to analyze the Kosraen pedigree for height, a model quantitative trait. Robust quantitative trait loci for height were found on 10q21 and 1p31. This protocol was used to map a set of metabolic traits, including plasma leptin to chromosome region 5q35; systolic blood pressure to 20p12; total cholesterol to 19p13, 12q24, and 16qter; hip circumference to 10q25 and 4q23; body mass index to 18p11 and 20q13; apolipoprotein B to 2p24–25; weight to 18q21; and fasting blood sugar to 1q31–1q43. Several of these same chromosomal regions have been identified in previous studies validating the use of loki. These studies add information about the genetics of the metabolic syndrome and establish an analytical approach for linkage analysis of complex pedigrees. These results also lay the foundation for whole genome scans with dense sets of SNPs aimed to identifying causal genes.
• loki
• quantitative trait locus
• Syndrome X
Human height is a typical and important complex trait, which is determined by both actions and interactions of multiple genes. Although an increasing number of genes or genomic regions have been discovered for their independent effects on height variation, no study has been performed to identify genes or loci that interact to control the trait.
This study aimed to search for potential genomic regions that harbor interactive genes underlying human height.
Here with a sample containing 3726 Caucasians, the largest one ever obtained from a single population of the same ethnicity among genetic linkage studies of human complex traits, we performed variance component linkage analyses of height based on a two-locus epistatic model. We examined pairwise genetic interaction among three regions, 9q22, 6p21, and 2q21, which achieved significant or suggestive linkage signals for height in our recent whole genome scan.
Significant genetic interaction between 6p21 and 2q21 was detected, with 2q21 achieving a maximum LOD score of 3.21 (P = 0.0035) under the epistatic model, compared with a maximum LOD score of 1.63 under a two-locus additive model. Interestingly, 6p21 contains a cluster of candidate genes for skeletal growth, suggesting a mechanism whereby 2q21 regulates height through 6p21.
By providing the first evidence for genetic interaction underlying human height variation, this study further delineated the genetic architecture of human height and contributed to the genetic dissection of human complex traits in general.
The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution.
Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human
genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.
Procedures are given, using sib pairs, for estimating linkage between a knownm-allele locus and a hypothesized two-allele locus that governs a quantitative trait. Random mating and linkage equilibrium are assumed. Also given are parametric and nonparametric methods for detecting linkage when the trait in question is governed by several two-allele loci, provided there is no epistasis.
Detection of linkage to genes for quantitative traits remains a challenging task. Recently, variance components (VC) techniques have emerged as among the more powerful of available methods. As often implemented, such techniques require assumptions about the phenotypic distribution. Usually, multivariate normality is assumed. However, several factors may lead to markedly nonnormal phenotypic data, including (a) the presence of a major gene (not necessarily linked to the markers under study), (b) some types of gene x environment interaction, (c) use of a dichotomous phenotype (i.e., affected vs. unaffected), (d) nonnormality of the population within-genotype (residual) distribution, and (e) selective (extreme) sampling. Using simulation, we have investigated, for sib-pair studies, the robustness of the likelihood-ratio test for a VC quantitative-trait locus-detection procedure to violations of normality that are due to these factors. Results showed (a) that some types of nonnormality, such as leptokurtosis, produced type I error rates in excess of the nominal, or alpha, levels whereas others did not; and (b) that the degree of type I error-rate inflation appears to be directly related to the residual sibling correlation. Potential solutions to this problem are discussed. Investigators contemplating use of this VC procedure are encouraged to provide evidence that their trait data are normally distributed, to employ a procedure that allows for nonnormal data, or to consider implementation of permutation tests.
The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.
A SAS\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(^{\mathrm{btc3811x.gif\ r{\circ}}}\) \end{document}macro package for performing multipoint QTL mapping using the DeFries–Fulker multiple regression method is presented.
Availability: The package is made available at http://qms2.sourceforge.net/.
Contact: Jeff.Lessem@Colorado.EDU
* To whom correspondence should be addressed.
In recent years, many genomewide screens have been performed, to identify novel loci predisposing to various complex diseases. Often, only a portion of the collected clinical data from the study subjects is used in the actual analysis of the trait, and much of the phenotypic data is ignored. With proper consent, these data could subsequently be used in studies of common quantitative traits influencing human biology, and such a reanalysis method would be further justified by the nonbiased ascertainment of study individuals. To make our point, we report here a quantitative-trait-locus (QTL) analysis of body-mass index (BMI) and stature (i.e., height), with genotypic data from genome scans of five Finnish study groups. The combined study group was composed of 614 individuals from 247 families. Five study groups were originally ascertained in genetic studies on hypertension, obesity, osteoarthritis, migraine, and familial combined hyperlipidemia. Most of the families are from the Finnish Twin Cohort, which represents a population-wide sample. In each of the five genome scans, ∼350 evenly spaced markers were genotyped on 22 autosomes. In analyzing the genotype data by a variance-component method, we found, on chromosome 7pter (maximum multipoint LOD score of 2.91), evidence for QTLs affecting stature, and a second locus, with suggestive evidence for linkage to stature, was detected on chromosome 9q (maximum multipoint LOD score of 2.61). Encouragingly, the locus on chromosome 7 is supported by the data reported by Hirschhorn et al. (in this issue), who used a similar method. We found no evidence for QTLs affecting BMI.
Genomewide linkage analysis has been extremely successful at identification of the genetic variation underlying single-gene disorders. However, linkage analysis has been less successful for common human diseases and other complex traits in which multiple genetic and environmental factors interact to influence disease risk. We hypothesized that a highly heritable complex trait, in which the contribution of environmental factors was relatively limited, might be more amenable to linkage analysis. We therefore chose to study stature (adult height), for which heritability is ∼75%–90% (Phillips and Matheny 1990; Carmichael and McGue 1995; Preece 1996; Silventoinen et al. 2000). We reanalyzed genomewide scans from four populations for which genotype and height data were available, using a variance-components method implemented in GENEHUNTER 2.0 (Pratt et al. 2000). The populations consisted of 408 individuals in 58 families from the Botnia region of Finland, 753 individuals in 183 families from other parts of Finland, 746 individuals in 179 families from Southern Sweden, and 420 individuals in 63 families from the Saguenay-Lac-St.-Jean region of Quebec. Four regions showed evidence of linkage to stature: 6q24-25, multipoint LOD score 3.85 at marker D6S1007 in Botnia (genomewide P<.06), 7q31.3-36 (LOD 3.40 at marker D7S2195 in Sweden, P<.02), 12p11.2-q14 (LOD 3.35 at markers D12S10990-D12S398 in Finland, P<.05) and 13q32-33 (LOD 3.56 at markers D13S779-D13S797 in Finland, P<.05). In a companion article (Perola et al. 2001 [in this issue]), strong supporting evidence is obtained for linkage to the region on chromosome 7. These studies suggest that highly heritable complex traits such as stature may be genetically tractable and provide insight into the genetic architecture of complex traits.
The great increase in successful linkage studies in a number of higher eukaryotes during recent years has essentially resulted from major improvements in reference genetic linkage maps, which at present consist of short tandem repeat polymorphisms of simple sequences or microsatellites. We report here the last version of the Généthon human linkage map. This map consists of 5,264 short tandem (AC/TG)n repeat polymorphisms with a mean heterozygosity of 70%. The map spans a sex-averaged genetic distance of 3,699 cM and comprises 2,335 positions, of which 2,032 could be ordered with an odds ratio of at least 1,000:1 against alternative orders. The average interval size is 1.6 cM; 59% of the map is covered by intervals of 2 cM at most and 1% remains in intervals above 10 cM.
I thank Dr. Robert Elston for pointing out this issue and for his help in the interpretation of the results. This work was supported by research grant F05 TW05285 from the Fogarty International Center and resource grant P41 RR03655 from the National Center for Research Resources, National Institutes of Health.
Sib pair-selection strategies, designed to identify the most informative sib pairs in order to detect a quantitative-trait locus (QTL), give rise to a missing-data problem in genetic covariance-structure modeling of QTL effects. After selection, phenotypic data are available for all sibs, but marker data-and, consequently, the identity-by-descent (IBD) probabilities-are available only in selected sib pairs. One possible solution to this missing-data problem is to assign prior IBD probabilities (i.e., expected values) to the unselected sib pairs. The effect of this assignment in genetic covariance-structure modeling is investigated in the present paper. Two maximum-likelihood approaches to estimation are considered, the pi-hat approach and the IBD-mixture approach. In the simulations, sample size, selection criteria, QTL-increaser allele frequency, and gene action are manipulated. The results indicate that the assignment of prior IBD probabilities results in serious estimation bias in the pi-hat approach. Bias is also present in the IBD-mixture approach, although here the bias is generally much smaller. The null distribution of the log-likelihood ratio (i.e., in absence of any QTL effect) does not follow the expected null distribution in the pi-hat approach after selection. In the IBD-mixture approach, the null distribution does agree with expectation.
In linkage studies, independent replication of positive findings is crucial in order to distinguish between true positives and false positives. Recently, the following question has arisen in linkage studies of complex traits: at what distance do we reject the hypothesis that two location estimates in a genomic region represent the same gene? Here we attempt to address this question. Sampling distributions for location estimates were constructed by computer simulation. The conditions for simulation were chosen to reflect features of "typical" complex traits, including incomplete penetrance, phenocopies, and genetic heterogeneity. Our findings, which bear on what is considered a replication in linkage studies of complex traits, suggest that, even with relatively large numbers of multiplex families, chance variation in the location estimate is substantial. In addition, we report evidence that, for the conditions studied here, the standard error of a location estimate is a function of the magnitude of the expected LOD score.
Methods based on variance components are powerful tools for linkage analysis of quantitative traits, because they allow simultaneous consideration of all pedigree members. The central idea is to identify loci making a significant contribution to the population variance of a trait, by use of allele-sharing probabilities derived from genotyped marker loci. The technique is only as powerful as the methods used to infer these probabilities, but, to date, no implementation has made full use of the inheritance information in mapping data. Here we present a new implementation that uses an exact multipoint algorithm to extract the full probability distribution of allele sharing at every point in a mapped region. At each locus in the region, the program fits a model that partitions total phenotypic variance into components due to environmental factors, a major gene at the locus, and other unlinked genes. Numerical methods are used to derive maximum-likelihood estimates of the variance components, under the assumption of multivariate normality. A likelihood-ratio test is then applied to detect any significant effect of the hypothesized major gene. Simulations show the method to have greater power than does traditional sib-pair analysis. The method is freely available in a new release of the software package GENEHUNTER.
Standard variance-components quantitative trait loci (QTL) linkage analysis can produce an elevated rate of type 1 errors when applied to selected samples and non-normal data. Here we describe an adjustment of the log-likelihood function based on conditioning on trait values. This leads to a likelihood ratio test that is valid in selected samples and non-normal data, and equal in power to alternative methods for analyzing selected samples that require knowledge of the ascertainment procedure or the trait values of non-selected individuals.
The Haseman-Elston regression method offers a simpler alternative to variance-components (VC) models, for the linkage analysis of quantitative traits. However, even the "revisited" method, which uses the cross-product--rather than the squared difference--in sib trait values, is, in general, less powerful than VC models. In this report, we clarify the relative efficiencies of existing Haseman-Elston methods and show how a new Haseman-Elston method can be constructed to have power equivalent to that of VC models. This method uses as the dependent variable a linear combination of squared sums and squared differences, in which the weights are determined by the overall trait correlation between sibs in a population. We show how this method can be used for both the selection of maximally informative sib pairs for genotyping and the subsequent analysis of such selected samples.
Improved molecular understanding of the pathogenesis of type 2 diabetes is essential if current therapeutic and preventative options are to be extended. To identify diabetes-susceptibility genes, we have completed a primary (418-marker, 9-cM) autosomal-genome scan of 743 sib pairs (573 pedigrees) with type 2 diabetes who are from the Diabetes UK Warren 2 repository. Nonparametric linkage analysis of the entire data set identified seven regions showing evidence for linkage, with allele-sharing LOD scores > or =1.18 (P< or =.01). The strongest evidence was seen on chromosomes 8p21-22 (near D8S258 [LOD score 2.55]) and 10q23.3 (near D10S1765 [LOD score 1.99]), both coinciding with regions identified in previous scans in European subjects. This was also true of two lesser regions identified, on chromosomes 5q13 (D5S647 [LOD score 1.22] and 5q32 (D5S436 [LOD score 1.22]). Loci on 7p15.3 (LOD score 1.31) and 8q24.2 (LOD score 1.41) are novel. The final region showing evidence for linkage, on chromosome 1q24-25 (near D1S218 [LOD score 1.50]), colocalizes with evidence for linkage to diabetes found in Utah, French, and Pima families and in the GK rat. After dense-map genotyping (mean marker spacing 4.4 cM), evidence for linkage to this region increased to a LOD score of 1.98. Conditional analyses revealed nominally significant interactions between this locus and the regions on chromosomes 10q23.3 (P=.01) and 5q32 (P=.02). These data, derived from one of the largest genome scans undertaken in this condition, confirm that individual susceptibility-gene effects for type 2 diabetes are likely to be modest in size. Taken with genome scans in other populations, they provide both replication of previous evidence indicating the presence of a diabetes-susceptibility locus on chromosome 1q24-25 and support for the existence of additional loci on chromosomes 5, 8, and 10. These data should accelerate positional cloning efforts in these regions of interest.
The primary goal of a genomewide scan is to estimate the genomic locations of genes influencing a trait of interest. It is sometimes said that a secondary goal is to estimate the phenotypic effects of each identified locus. Here, it is shown that these two objectives cannot be met reliably by use of a single data set of a currently realistic size. Simulation and analytical results, based on variance-components linkage analysis as an example, demonstrate that estimates of locus-specific effect size at genomewide LOD score peaks tend to be grossly inflated and can even be virtually independent of the true effect size, even for studies on large samples when the true effect size is small. However, the bias diminishes asymptotically. The explanation for the bias is that the LOD score is a function of the locus-specific effect-size estimate, such that there is a high correlation between the observed statistical significance and the effect-size estimate. When the LOD score is maximized over the many pointwise tests being conducted throughout the genome, the locus-specific effect-size estimate is therefore effectively maximized as well. We argue that attempts at bias correction give unsatisfactory results, and that pointwise estimation in an independent data set may be the only way of obtaining reliable estimates of locus-specific effect-and then only if one does not condition on statistical significance being obtained. We further show that the same factors causing this bias are responsible for frequent failures to replicate initial claims of linkage or association for complex traits, even when the initial localization is, in fact, correct. The findings of this study have wide-ranging implications, as they apply to all statistical methods of gene localization. It is hoped that, by keeping this bias in mind, we will more realistically interpret and extrapolate from the results of genomewide scans.
Health Survey for England The Stationary Office Large upward bias in estimation of locus-specific effects from genomewide scans
- B Primatesta
B, Primatesta P (2001) Health Survey for England 1998, The Stationary Office, London Gö ring HHH, Terwilliger JD, Blangero J (2001) Large upward bias in estimation of locus-specific effects from genomewide scans. Am J Hum Genet 69:1357–1369
A comprehensive genetic map of the human genome based on 5,264 microsatellites
- C Dib
- S Fauré
- C Fizames
- D Samson
- N Druout
- A Vignal
- P Millasseau
- S Marc
- J Hazan
- E Seboun
- M Lathrop
- G Gyapay
- J Morissette
- J Weissenbach
Dib C, Fauré S, Fizames C, Samson D, Druout N, Vignal A,
Millasseau P, Marc S, Hazan J, Seboun E, Lathrop M, Gyapay G, Morissette J, Weissenbach J (1996) A comprehensive
genetic map of the human genome based on 5,264 microsatellites. Nature 380:152-154
Large upward bias in estimation of locus-specific effects from genomewide scans
- B Erens
- P Primatesta
Erens B, Primatesta P (2001) Health Survey for England 1998,
The Stationary Office, London
Gö ring HHH, Terwilliger JD, Blangero J (2001) Large upward
bias in estimation of locus-specific effects from genomewide
scans. Am J Hum Genet 69:1357-1369
Confirmation of linkage results in affected-sib-pair linkage analysis for complex genetic traits
- Hauser
Hauser ER, Boehnke M (1997) Confirmation of linkage results
in affected-sib-pair linkage analysis for complex genetic
traits. Am J Hum Genet Suppl 61:A278
A comprehensive genetic map of the human genome based on 5,264 microsatellites
- Dib