| Human Molecular Genetics | Pages |
Transmission of haplotypes of microsatellite markers rather than single marker alleles in the mapping of a putative type 1 diabetes susceptibility gene (IDDM6)
Introduction
Results
Discussion
Materials And Methods
Diabetic families
Physical mapping, microsatellite marker isolation and genotyping
Analysis of linkage disequilibrium
Acknowledgements
References
Transmission of haplotypes of microsatellite markers rather than single marker alleles in the mapping of a putative type 1 diabetes susceptibility gene (IDDM6)
Allelic association methods based on increased transmission of marker alleles will have to be employed for the mapping of complex disease susceptibility genes. However, because the extent of association of single marker alleles with disease is a function of the relative frequency of the allele on disease-associated chromosomes versus non disease-predisposing chromosomes, the most associated marker allele in a region will not necessarily be closest to the disease locus. To overcome this problem we describe a haplotype-based approach developed for mapping of the putative type 1 diabetes susceptibility gene IDDM6. Ten microsatellite markers spanning a 550 kb segment of chromosome 18q21 in the putative IDDM6 region were genotyped in 1708 type 1 diabetic Caucasian families from seven countries. The most likely ancestral diabetogenic chromosome was reconstructed in a stepwise fashion by analysing linkage disequilibrium between a previously defined haplotype of three adjacent markers and the next marker along the chromosome. A plot of transmission from heterozygous parents to affected offspring of single marker alleles present on the ancestral chromosome versus the physical distance between them, was compared with a plot of transmission of haplotypes of groups of three adjacent markers. Analysing transmission of haplotypes largely negated apparent decreases in transmission of single marker alleles. Peak support for association of the D18S487 region with IDDM6 is P = 0.0002 (corrected P = 0.01). The results also demonstrate the utility of polymorphic microsatellite markers to trace and delineate extended and presumably ancient haplotypes in the analysis of common disease and in the search for identical-by-descent chromosome regions that carry an aetiological variant.
INTRODUCTION
Since 1994 whole genome scanning of several common multifactorial diseases (1-11), including type 1 diabetes mellitus, has revealed the approximate chromosome map position of several putative susceptibility loci. In type 1 diabetes evidence for IDDM4 (11q13), IDDM5 (6q25) and IDDM8 (6q27) has been extended to a genome-wide level of significance (12,13). However, linkage analysis in common polygenic diseases has limited mapping resolution (14) owing both to the incomplete concordance between the presence of a disease-predisposing polymorphism and the occurrence of the disease and to the limited number of meioses available for study. Instead association mapping is suggested for fine mapping complex disease loci (15-17) but even the most recently developed methods apply only to rare highly penetrant mutations (18,19). Association-based methods have a higher resolution than linkage methods for fine mapping in families because they exploit recombination events that occur during the history of the population. However, the association of marker alleles with alleles of a disease locus is dependent on the relative frequencies of marker alleles on disease-predisposing chromosomes versus non-predisposing chromosomes, implying that a marker with a particularly high association is not necessarily the closest marker to the aetiological locus.
In general, the extent of linkage disequilibrium, or association, of a marker allele with another polymorphic locus (for example a disease locus) depends on the physical distance between the two loci (20). The magnitude of linkage disequilibrium of marker alleles with disease should be maximal in and immediately around the disease locus. In the vicinity of the disease locus present day chromosomes will share flanking regions identical-by-descent with the ancestral disease-predisposing chromosome. This relationship has been clearly demonstrated in the cloning of candidate genes for monogenic diseases by the drawing of plots of the magnitude of linkage disequilibrium of marker alleles with disease versus the distance between them (19,21-23). Because of the rarity of disease alleles and the virtually complete concordance in monogenic disease between the presence of the disease-predisposing chromosome and expression of the disease phenotype, measures such as [delta] (pexcess) (24) are useful for quantitating the degree of linkage disequilibrium of a marker allele with disease. However, in complex disease the incomplete disease risk conferred by any single disease-predisposing allele (which is likely to be a common, normal variant which in other circumstances may act to maintain the health of the individual) makes the mapping and identification of disease predisposing loci difficult, with pexcess being inappropriate (25). In contrast, the straightforward transmission disequilibrium test (TDT) (26) is proving useful in the initial stages of fine-mapping complex disease loci (16,25,27,28). The TDT is a valid test of association in simplex families and the TDT statistic can be modified to give a valid test of association in multiplex families (Materials and Methods; 29). The presence of a disease locus is indicated by increased transmission of a marker allele to affected offspring and normal or decreased transmission to unaffected siblings.
Here, to overcome the problem of the inability to predict the frequency of a single marker allele on disease-predisposing chromosomes versus non disease-predisposing chromosomes, we present a haplotype-based approach of measuring linkage (by the TDT) with disease across a chromosome region. We analyse by the TDT transmission from heterozygous parents to diabetic children of three-marker haplotypes across a 365 kb region of chromosome 18q21 associated with the putative type 1 diabetes susceptibility locus IDDM6 (25). This is based on the premise that a combination of three alleles on a disease-predisposing haplotype is less likely to be identical-by-state with the same three-marker haplotype on non disease-predisposing chromosomes than will single marker alleles.
RESULTS
A 650 kb contig encompassing D18S487-positive HBAC 22_d_8 (25) was assembled and microsatellite markers cloned and positioned on the contig (Materials and Methods). Markers 114,1, 30T7, 129,6, 129,12, 129,11, 49,12 and 49,22 (Materials and Methods) were typed in 1349 previously described type 1 diabetic families from the UK, USA, Sardinia, Norway and Denmark (Materials and Methods) (25). Markers IO43,56, D18S487 and A181,2 had previously been typed in these families, in which haplotype 2-4-2 was positively transmitted to type 1 diabetics (25), consistent with the presence of a type 1 diabetes susceptibility gene (IDDM6) in the vicinity. All 10 markers were also typed over an additional 51 UK, 204 Romanian and 104 Finnish simplex families (Materials and Methods; Table 1). To choose alleles on the ancestral diabetogenic chromosome marked by haplotype 2-4-2 at markers IO43,56-D18487-A181,2, four-marker haplotypes were constructed between the previously defined three-marker haplotype and the new adjacent marker. Chromosomes transmitted to affected children were used for this analysis (see Materials and Methods). The allele at the new marker in strongest linkage disequilibrium with the three-marker haplotype was selected as being present on the ancestral 2-4-2 chromosome (Fig. 1). For all markers, excepting 129,11, the decision was unambiguous. For example, starting with the 2-4-2 haplotype defined by markers IO43,56-D18S487-A181,2, four-marker haplotypes were constructed between it and 49,12. In the UK transmitted chromosomes, the most common four-marker haplotype containing 2-4-2 was 2-4-2-2 (6.2% in diabetic children) and the next most common was 2-4-2-4 (0.6%). D[prime] values (Fig. 1) (30) between the 2-4-2 haplotype and alleles 2 and 4 at 49,12 were 0.81 and -0.35, respectively. Thus allele 2 at 49,12 was selected as most likely present on the ancestral 2-4-2 diabetogenic chromosome. In only one case was there two similarly frequent four-marker haplotypes-at 129,11 where there were two equally frequent haplotypes containing 2-4-2 at markers IO43,56-D18S487-A181,2. The haplotypes were 10-2-4-2 (3.2%) and 12-2-4-2 (3.2%) with D[prime] values for alleles 10 and 12 of 0.38 and 0.36, respectively. Moving further centromerically along the chromosome, haplotypes containing allele 12 and allele 10 traced onto separate ancestral chromosomes, with choice of alleles as unambiguous as for the 49,12 example outlined above (Fig. 1). In the UK transmitted chromosomes, allele 6 of 129,6 was rarely found on the same chromosome as haplotype 1-10-2 at markers 129,12- 129,11-IO43,56 (frequency of 6-1-10-2 was 0.1%; D[prime] = -0.76 between 6 and 1-10-2), and similarly, allele 2 of 129,6 was rarely found on the same chromosome as haplotype 1-12-2 of the same three-markers (frequency of 2-1-12-2 was 0.2%; D[prime] = -0.45 between 2 and 1-12-2). The composition of the ancestral chromosomes presented in Figure 1 was supported by examination of the frequency of the 10-marker haplotypes in the UK transmitted chromosomes. Haplotype 2-6-2-1-10-2-4-2-2-1 was present at a frequency of 1.7% (the third most common 10-marker haplotype) and 3-3-6-1-12-2-4-2-2-1 was present at a frequency of 1.5% (fourth most common). The next most frequent 10-marker haplotype containing 2-4-2 at markers IO43,56-D18S487-A181,2 was 4-3-6-1-12-2-4-2-2-1 at a frequency of 0.7% (22nd most common). Because of the significant positive transmission of allele 2 of 129,6 to diabetic children (Table 1; 344T versus 288NT, %T = 54.4, P = 0.02) and the non-significant transmission of allele 6 (397T versus 380NT, %T = 51.1, P > 0.05) it was considered most likely that the extended ancestral diabetogenic haplotype was 2-6-2-1- 10-2-4-2-2-1.
Figure
Table 1. The 2-4-2 haplotype of markers IO43,56-D18S487-A181,2 is preferentially transmitted to type 1 diabetics whereas transmission of the individual alleles varied at the three-markers (25). To take account of this variation when plotting transmission of marker alleles to affected individuals versus the physical distance between the markers we compared the transmission of the individual alleles at each marker on the 2-6-2-1-10-2-4-2-2-1 ancestral chromosome to the transmission of haplotypes of groups of three alleles at adjacent markers (Table 2; Fig. 2). When transmission of the associated alleles was assessed individually only three were significantly transmitted to the diabetic children (Table 1). Per cent transmission of the 10 single alleles ranged between 50.1 and 54.8 (Table 1; Fig. 2). When transmission of three-marker haplotypes was analysed (Table 2; Fig. 2), all eight haplotype points were significant (P ranged from 0.009 to 0.0002) with per cent transmission ranging between 55.6 and 59.7. Most notably, points with a %T near 50 on the plot drawn using single microsatellite alleles as markers (for example, allele 6 of 30T7 and allele 2 of 49,12) were increased to a %T similar to that of neighbouring markers when placed on the three-marker haplotype plot (Fig. 2).
Figure Table 2. We tested by the TDT haplotype 1-12-2 at markers 129,12-129,11-IO43,56 and haplotype 3-3-6 [to which 1-12-2 traced (Fig. 1)] at markers 114,1-30T7-129,6 for increased transmission to diabetics in the total 1708 families. Data for 1-12-2 were 150T versus 107NT (%T = 58.4, P = 0.004) and for 3-3-6 were 247T versus 229NT (%T = 51.9, P > 0.05). Thus, at markers 129,12-129,11-IO43,56 both haplotypes analysed (1-10-2 and 1-12-2) were positively transmitted to type 1 diabetics at less than the 5% level of significance (%T = 56.6, P = 0.009 and %T = 58.4, P = 0.004) whereas at the centromeric markers 114,1-30T7-129,6 the only haplotype to be positively transmitted was 2-6-2 (%T = 56.4, P = 0.001), to which 1-10-2 traced (Fig. 1). This supports our choice of 2-6-2-1-10-2-4-2-2-1 as the ancestral diabetogenic chromosome. Transmission of the eight three-marker haplotypes was analysed to unaffected siblings of the diabetic children (Table 3). In all cases per cent transmission was <50.4. This is independent support for true association of the 2-6-2-1-10-2-4-2-2-1 haplotype with type 1 diabetes and confirms that segregation distortion (26) is not causing increased transmission to affected children.
DISCUSSION
Previously analysis of transmission of haplotypes of three-markers (IO43,56-D18S487-A181,2) was necessary to explain heterogeneity observed between data sets when individual microsatellite alleles were tested for linkage in the presence of association to type 1 diabetes (25). With the aim of developing a transmission-based approach for mapping the putative type 1 diabetes susceptibility locus IDDM6, we extended the contig to 650 kb and cloned seven further markers in DNA flanking HBAC 22_d_8. Transmission of single microsatellite alleles, determined to be present on the ancestral 2-4-2 IDDM6 diabetogenic chromosome, was compared with transmission of three-marker haplotypes (Fig. 2). The allele at each new marker present on the ancestral 2-4-2 chromosome was selected as the allele in strongest linkage disequilibrium with the previously defined three-marker haplotype (Fig. 1). In all cases but one this choice was unambiguous. Crucially, points on the single allele plot close to 50% (neutral) transmission, when placed on the haplotype plot, had a value very similar to that of neighbouring points. This resulted in more consistent statistical support for association along the plot [P between 0.009 and 0.0002 (Table 2)]. The peak evidence for association of the D18S487-region with a type 1 diabetes susceptibility gene (IDDM6) is P = 0.0002 (Table 2; transmission of haplotype 10-2-4 at markers 129,11- IO43,56-D18S487; corrected P = 0.01), extended from the previously reported P = 0.0005 (P corrected = 0.02) [see Materials and Methods (25)].
Allele 6 of 30T7 and allele 2 of 49,12, for example, were present at a high frequency on non disease-predisposing chromosomes. When transmission of haplotypes was analysed the %T at 30T7 was 56.4 (compared with the single allele value of 50.1) and the %T at 49,12 was 55.9 (compared with the single allele value of 50.6) (Fig. 2). Thus the regions of chromosome marked by 30T7 and 49,12 are, in fact, as transmitted as those regions at neighbouring markers (haplotype transmission at 30T7 was within 0.4% of that at 129,6 whereas single allele transmission differed by 4.3%, and haplotype transmission at 49,12 was within 0.3% of that at A181,2 with single allele transmission differing by 4.2%). Apparent decreases in transmission to a level close to 50% present in the single allele plot now do not compromise evaluation of the degree of transmission of the D18S487 chromosome region to type 1 diabetics. This is because the disease-predisposing chromosome is more precisely defined using information at flanking microsatellite markers. These results confirm that single point analyses are limited for the reason that the transmission of any particular marker is determined by the relative frequency of the ancestral allele on disease-predisposing versus non disease-predisposing chromosomes. Thus it is clear that analysis of haplotypes will be necessary for mapping IDDM6 and perhaps other complex disease susceptibility genes.
If marker 30T7 or 49,12 had initially been used [instead of D18S487 (25)] to detect linkage disequilibrium with type 1 diabetes then the region may have been overlooked as potentially containing a type 1 diabetes susceptibility gene. To exclude a region of chromosome for linkage disequilibrium with a complex disease susceptibility gene it is therefore advisable to genotype multiple appropriately polymorphic markers at as high a density as possible and analyse the transmission of haplotypes assembled from these markers. Using markers at a high density will facilitate the tracing of the ancestral disease chromosome(s) across the region. Here we analysed 10 microsatellite markers at an average density of one every 65 kb in the cloned DNA.
When choosing the allele at a new marker most likely to be present on the ancestral 2-4-2 diabetogenic chromosome linkage disequilibrium was assessed between the previously defined three-marker haplotype and allele(s) at the new marker (Fig. 1). Doing this minimises the risk of selecting the incorrect allele and also takes account of possible allelic heterogeneity at the putative disease locus-only one ancestral diabetogenic haplotype was traced. However there are potential ambiguities owing to mutation at microsatellite markers. At 129,11 it was possible to choose two alleles (10 or 12). Allele 12 was present on a 3-3-6-1-12-2-4-2-2-1 ancestral chromosome, and allele 10 was present on a 2-6-2-1-10-2-4-2-2-1 ancestral chromosome (Fig. 1). The three-marker haplotype containing allele 12 of 129,11 (1-12-2) was transmitted at a similar rate to 1-10-2 (%T of 58.4, P = 0.004 and of 56.6, P = 0.009, respectively). A possible explanation for this is that either allele 10 or 12 at 129,11 was on the ancestral 2-4-2 diabetogenic chromosome and that the observed split at 129,11 (Fig. 1) is due to microsatellite mutation (either from allele 10 to 12 or from allele 12 to 10) after the occurrence of the IDDM6 mutation marked by the 2-4-2 chromosome. This could be tested by the typing of single nucleotide polymorphisms (SNPs) within 10-20 kb of 129,11-if the split at 129,11 was due to mutation then alleles 10 and 12 should be contained on a largely similar haplotype of SNPs.
At the centromeric three-markers the haplotype to which allele 10 at 129,11 traces (2-6-2; Fig. 1) is positively transmitted to type 1 diabetics (%T = 56.4, P = 0.001) whereas that to which allele 12 at 129,11 traces (3-3-6) is not (%T = 51.1, P > 0.05). A likely explanation for this is the occurrence of an ancient recombination event between 129,11 and 129,6, with the diabetes susceptibility gene remaining in linkage disequilibrium with the 2-6-2 haplotype at the centromeric markers. The exact position of the recombination could also be revealed by the genotyping of SNPs between 129,11 and 129,6-the two chromosomes (Fig. 1) should share a haplotype on the telomeric side of the recombination, but not on the centromeric side. If this is the case then it could be hypothesised that the putative susceptibility locus is on the telomeric side of the recombination. Exploitation of such ancestral recombination events is important for fine-mapping of polygenes (31,32). Our results also prove the utility of moderately polymorphic microsatellite markers in identifying and characterising ancestral and ancient haplotypes and potential regions of identity-by-descent across extended chromosome regions. For these microsatellites, their mutation rate is not confounding.
The haplotype-based approach presented here has been useful for analysing the involvement of the D18S487 chromosome 18q21 region in disease and may be useful in the analysis of other regions in complex disease. For example, a plot of extent of linkage disequilibrium versus physical distance in the IDDM2 region (11p15) defined a maximal region of 4.1 kb, including the INS-VNTR, as containing the diabetes predisposing locus (33). In that study (33) the allele of the HUMTH01 microsatellite locus (9 kb upstream of INS) with the highest statistic for association was chosen. However, when the HUMTH01 microsatellite allele in strongest linkage disequilibrium with the susceptible class I alleles of the INS-VNTR is chosen then the region containing IDDM2 is not 4.1 kb but is at least 9.1 kb (34) and so the extent of the associated region is yet to be defined. If our haplotype-based approach had been used the correct allele at HUMTH01 would have been chosen as the most frequent allele on the ancestral predisposing chromsome.
MATERIALS AND METHODS
Diabetic families
All families used in this study were Caucasian with at least one affected sibling per family and both parents included. The UK data set consisted of 399 multiplex families, 80 simplex families from the Yorkshire region, 32 simplex families from the South-West region (all described in ref. 25) and a further 51 simplex families where all cases were diagnosed under the age of 5 years (35). 189 US multiplex families were obtained from the Human Biological Data Interchange (36), the 181 Sardinian families (comprising 175 simplex and six multiplex families) and 420 Norwegian families (comprising 380 simplex and 40 multiplex families) are described in ref. 25, and the 48 Danish multiplex families are described elsewhere (37). The Finnish data set comprised 104 simplex families and all cases were diagnosed under the age of 15 years (38) and the Romanian data set comprised 204 simplex families with all cases diagnosed under the age of 30 years. Healthy siblings were collected in all data sets, excepting those from Denmark and the South-West of the UK.
Physical mapping, microsatellite marker isolation and genotyping
Further clones flanking D18S487 positive HBAC 22_d_8 (25) were isolated by first cloning the 22_d_8 insert end sequences using vectorette PCR and designing a sequence-tagged site PCR reaction (39). Flanking HBAC and HPAC clones were then isolated from libraries obtained from Research Genetics, Inc. until a 650 kb contig was assembled. Microsatellite markers were cloned from the HBACs and HPACs using a previously described PCR-based method (25) and intermarker distances were elucidated using standard restriction enzyme mapping techniques. Centromeric to telomeric the markers are (with intermarker distance in kb bracketed) 114,1-(135)-30T7-(60)-129,6-(45)-129,12-(70)-12,11-(20)-IO43,56-(60)-D18S487-(70)-A181,2-(40)-49,12-(50)-49,22. Throughout the paper, haplotypes are given with the marker alleles in centromeric to telomeric order. Primer sequences for amplifying 114,1 are GGGCAGAACTAAATGAAACTG and CCCAATTGATAAAGCTGGCTGG; for 30T7 are ACAGCCAGCATTCTGTCCTT and TGTGCCTGGCCTAGCTTCTAC; for 129,6 are TTCATCTCAAATGAATAAATT and GGCTGATCTTTTCTACATTTC; for 129,12 are CCCTGACTCTACCAGCACTG and GAAAATAAACTGGAGGTCTG; for 129,11 are CTTGCTCCAGCTCCAGAATC and CTTTCATTTCTAATAAGTTC; for 49,12 are AATACCTTAAATGGTTACTA and CCCTTATTTCAGTGACTGAA; and for 49,22 are GTTTCCTTATTGCCTCCCAC and GGGATAGGGGTGTGTCAGGG. Primer sequences for IO43,56 and A181,2 are given in ref. 25 and sequences for D18S487 are obtainable from public databases. Genotyping PCRs using fluorescently labelled primers were performed and analysed as described previously (40).
Table 3.
| Haplotype | T | NT | %T |
| 2-6-2 | 130 | 143 | 47.6 |
| 6-2-1 | 100 | 117 | 46.1 |
| 2-1-10 | 86 | 91 | 48.6 |
| 1-10-2 | 87 | 99 | 46.8 |
| 10-2-4 | 93 | 97 | 48.9 |
| 2-4-2 | 121 | 137 | 46.9 |
| 4-2-2 | 112 | 120 | 48.3 |
| 2-2-1 | 122 | 120 | 50.4 |
Analysis of linkage disequilibrium
Transmission of single microsatellite marker alleles and three-marker haplotypes was assessed from heterozygous parents to both affected and unaffected offspring using the transmission disequilibrium test (TDT) (26,41). Extent of transmission of an allele or haplotype was quantitated by per cent transmission (%T = number of times an allele or haplotype was transmitted from heterozygous parents divided by the total of transmissions plus non-transmissions from heterozygous parents, expressed as a percentage) where %T = 50 represents the expected 50% transmission under the null hypothesis of no association between the marker allele and disease. The test statistic is a [chi]2 (1 df) statistic and thus there is an assumption of complete independence between meioses. To take account of the lack of independence (owing to linkage) between siblings in multiplex families and obtain a valid estimate of association, the statistic (Tsp) described by Martin et al. (29) was used. The Tsp ([chi]2 with 1 df) focuses on a set of transmissions from a heterozygous parent to children, rather than transmissions to individual children, which is preferable to the strategy of discarding a significant amount of available data by analysing transmission to probands only. For comparative purposes the Tsp for allele 2 of A181,2 was calculated for the families analysed in ref. 25 (P = 0.0005). Because the [lambda]s value for this region of 18q21 is close to 1.0 [[lambda]s = 1.11 (25)], as expected these Tsp values are almost identical to TDT [chi]2 values obtained by analysing transmission to both siblings.
For reconstructing the most likely ancestral diabetogenic chromosome, D[prime] values (30) were calculated using chromosomes transmitted to affected children. This was to guard against the possibility that, if the disease allele was rare, the ancestral diabetogenic haplotype might not be that contributing most strongly to intermarker linkage disequilibrium in the parental chromosomes. The D[prime] value was calculated between the three-marker haplotype and the allele of the next marker. Individual P values for transmission of haplotypes do not have to be corrected for number of haplotypes tested owing to the linkage disequilibrium between the separate points along the plot (Fig. 1).
The maximal P value for association of the D18S487 region with type 1 diabetes [that for transmission of haplotype 10-2-4 at markers 129,11-IO43,56-D18S487 (Table 2)] was Bonferroni corrected by a factor of two for the additional ancestral chromosome tested (the 3-3-6 haplotype at markers 114,1-30T7-129,6) and for the multiple tests done previously (a further factor of 36; see ref. 25), to give an experiment-wide P value.
ACKNOWLEDGEMENTS
We thank A. Barnett, J. Carr-Smith, B. Rowe, C. Smyth, E. Wadsworth and A. Wilson for their help and contribution to this work. This work was funded by the Wellcome Trust, British Diabetic Association (BDA), UK Medical Research Council, Juvenile Diabetes Foundation International (JDFI) and The Norwegian Diabetes Association. We are grateful for the assistance of the Childhood Diabetes in Finland (DiMe) Study Group in the collection of the Finnish family material. The Finnish collaboration was partially funded by grants from NIH (DK 73957) and the Novo Nordisk Foundation. The BDA, Human Biological Data Interchange and The Norwegian Study Group for Childhood Diabetes are thanked for the collection of families. T.R.M. was a Wellcome Trust-New Zealand Health Research Council Overseas Postdoctoral Fellow and is currently a JDFI Postdoctoral Fellow, I.A.E is the recipient of a Wellcome Trust Prize Studentship and J.A.T. is a Wellcome Trust Principal Research Fellow.
REFERENCES
*To whom correspondence should be addressed. Tel: +44 1865 740014; Fax: +44 1865 742193; Email: john.todd@well.ox.ac.uk
This page is run by Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, as part of the OUP Journals
Comments and feedback: www-admin{at}oup.co.uk
Last modification: 14 Feb 1998
Copyright© Oxford University Press, 1998.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  M. Martinelli, M. Di Stazio, L. Scapoli, J. Marchesini, F. Di Bari, F. Pezzetti, F. Carinci, A. Palmieri, P. Carinci, and A. Savoia Cleft lip with or without cleft palate: implication of the heavy chain of non-muscle myosin IIA J. Med. Genet., June 1, 2007; 44(6): 387 - 392. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 W.-D. Li, C. Dong, D. Li, C. Garrigan, and R. A. Price A Quantitative Trait Locus Influencing Fasting Plasma Glucose in Chromosome Region 18q22-23 Diabetes, September 1, 2004; 53(9): 2487 - 2491. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 F. Zhang, Z. Liang, N. Matsuki, L. Van Kaer, S. Joyce, E. K. Wakeland, and T. M. Aune A Murine Locus on Chromosome 18 Controls NKT Cell Homeostasis and Th Cell Differentiation J. Immunol., November 1, 2003; 171(9): 4613 - 4620. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 K. V. Chowdari, K. Mirnics, P. Semwal, J. Wood, E. Lawrence, T. Bhatia, S. N. Deshpande, T. B.K., R. E. Ferrell, F. A. Middleton, et al. Association and linkage analyses of RGS4 polymorphisms in schizophrenia Hum. Mol. Genet., June 1, 2002; 11(12): 1373 - 1380. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 V. Kumar, M. Rajadhyaksha, and J. Wortsman Celiac Disease-Associated Autoimmune Endocrinopathies Clin. Vaccine Immunol., July 1, 2001; 8(4): 678 - 685. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. Sakai, S. Shirasawa, N. Ishikawa, K. Ito, H. Tamai, K. Kuma, T. Akamizu, M. Tanimura, K. Furugaki, K. Yamamoto, et al. Identification of susceptibility loci for autoimmune thyroid disease to 5q31-q33 and Hashimoto's thyroiditis to 8q23-q24 by multipoint affected sib-pair linkage analysis in Japanese Hum. Mol. Genet., June 1, 2001; 10(13): 1379 - 1386. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. R. Merriman, H. J. Cordell, I. A. Eaves, P. A. Danoy, F. Coraddu, R. Barber, F. Cucca, S. Broadley, S. Sawcer, A. Compston, et al. Suggestive Evidence for Association of Human Chromosome 18q12-q21 and Its Orthologue on Rat and Mouse Chromosome 18 With Several Autoimmune Diseases Diabetes, January 1, 2001; 50(1): 184 - 194. [Abstract] [Full Text]
|
|
|
|
|
|
P. Zavattari, R. Lampis, A. Mulargia, M. Loddo, E. Angius, J. A. Todd, and F. Cucca Confirmation of the DRB1-DQB1 loci as the major component of IDDM1 in the isolated founder population of Sardinia Hum. Mol. Genet., December 1, 2000; 9(20): 2967 - 2972. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Herr, F. Dudbridge, P. Zavattari, F. Cucca, C. Guja, R. March, R. D. Campbell, A. H. Barnett, S. C. Bain, J. A. Todd, et al. Evaluation of fine mapping strategies for a multifactorial disease locus: systematic linkage and association analysis of IDDM1 in the HLA region on chromosome 6p21 Hum. Mol. Genet., May 22, 2000; 9(9): 1291 - 1301. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. W. Morris, L. Robinson, D. Turic, M. Duke, V. Webb, C. Milham, E. Hopkin, K. Pound, S. Fernando, M. Easton, et al. Family-based association mapping provides evidence for a gene for reading disability on chromosome 15q Hum. Mol. Genet., March 22, 2000; 9(5): 843 - 848. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Cox, N. J. Camp, C. Cannings, F. S. d. Giovine, M. Dale, J. Worthington, S. John, W. E. R. Ollier, A. J. Silman, and G. W. Duff Combined sib-TDT and TDT provide evidence for linkage of the interleukin-1 gene cluster to erosive rheumatoid arthritis Hum. Mol. Genet., September 1, 1999; 8(9): 1707 - 1713. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||