Fine Mapping and Positional Candidate Studies Identify HLA-G as an Asthma Susceptibility Gene on Chromosome 6p21

Abstract

Asthma affects nearly 14 million people worldwide and has been steadily increasing in frequency for the past 50 years. Although environmental factors clearly influence the onset, progression, and severity of this disease, family and twin studies indicate that genetic variation also influences susceptibility. Linkage of asthma and related phenotypes to chromosome 6p21 has been reported in seven genome screens, making it the most replicated region of the genome. However, because many genes with individually small effects are likely to contribute to risk, identification of asthma susceptibility loci has been challenging. In this study, we present evidence from four independent samples in support of HLA-G as a novel asthma and bronchial hyperresponsiveness susceptibility gene in the human leukocyte antigen region on chromosome 6p21, and we speculate that this gene might contribute to risk for other inflammatory diseases that show linkage to this region.

We conducted a genomewide screen of families who participated in the Collaborative Study on the Genetics of Asthma (CSGA) (Collaborative Study on the Genetics of Asthma ¹⁹⁹⁷; Xu et al. ²⁰⁰¹). The strongest linkage signal in 129 white families was on chromosome 6p21 at marker D6S1281 (LOD=1.91; P=.003), which is 2.5 cM telomeric to the human leukocyte antigen (HLA) complex. All the evidence of linkage to asthma was found in the 35 white families ascertained in Chicago (LOD=3.6) (fig. 1A). We focused our subsequent studies on these families, as well as on 46 white child-parent trios with asthma (MIM 600807) also ascertained in Chicago and two other populations that had previously shown evidence of linkage of asthma-associated phenotypes to markers in this region (table 1) (Ober et al. 2000; Koppelman et al. ²⁰⁰²). Details of the statistical methods are described in appendix A (online only).

Figure 1.

Linkage to 6p21 in the Chicago families. A, The dashed line shows the results of the initial genome screen with framework markers. The solid line shows the results of five additional STRPs between framework markers D6S1281 and D6S1019. B, 1-Mb region from D6S258 to D6S265 with positional candidate genes. (All known genes [blue]—but not all pseudogenes [brown], STRPs [green], and intragenic SNPs [purple]—are included.) Circles = SNPs; triangles = in/dels; squares = STRPs; rectangle = HLA-A genotype, which is comprised of multiple SNPs. See appendix A (online only) for allele frequencies and results of association studies.

Table 1.

Clinical Characteristics of Patient Samples^[Note]

Characteristic	Chicago Familiesa,b(n=138)	Chicago Triosa,c(n=46)	Hutterite Familiesd(n=156)	Dutch Familiese(n=200)	Descendents ofDutch Familiese(n=327)
Sex (% male)	59.4	60.6	53.2	62.0	42.3
Asthma (%)	100	100	48.0	100	40.7
BHR (%)	100	100	100	100	100
Mean % predicted FEV1 (SD)	88.64 (17.12)	80.13 (16.83)	95.58 (15.71)	69.62 (24.68)	89.49 (12.10)
Mean FEV1/FVC (SD)	.82 (.09)	.77 (.10)	.79 (.10)	.597f (.149)	.80f (.086)
Mean log IgE (SD) IU	2.11 (.73)	2.19 (.71)	1.60 (.77)	1.95 (.69)	1.95 (.76)
Atopyg (%)	67.2	79.8	51.0	81.9	59.1

Note.— These studies were approved by the institutional review boards at each institution. Not all individuals were genotyped at all marker loci.

^aCSGA families were ascertained through two siblings with asthma and were extended to include other affected relatives, never skipping more than one unaffected relative (Lester et al. 2001). Asthma was diagnosed as follows: (1) either a fall in baseline FEV1 by ⩾20% at ⩽25 mg/ml methacholine (BHR) or an increase of ⩾15% in baseline FEV1 after bronchodilator use; (2) two of the following symptoms: cough, wheeze, or shortness of breath (dyspnea); and (3) current medication use or doctor’s diagnosis of asthma. All participating relatives of the siblings were studied.

^bFamily size ranged from 4 to 18 members (mean family size, 7 members).

^cTrios were individuals meeting the same criteria as the CSGA patients with asthma and their parents, who were not evaluated (n=46).

^dHutterites (n=693), who are related to each other in a 13-generation, 1,623-member pedigree, were evaluated using a modified CSGA protocol (Ober et al. 2000). BHR in this group was defined as a fall in baseline FEV1 by ⩾20% at ⩽25 mg/ml methacholine.

^eThe 200 Dutch families were recruited ∼30 years ago through a proband who was diagnosed as having asthma; they were reevaluated in the 1990s, along with their spouses, children, and grandchildren (Panhuysen et al. 1995). The families included 1,183 individuals. BHR in this group was defined as a fall in baseline FEV1 by ⩾20% at ⩽32 mg/ml histamine (30′ protocol).

^fFEV1/VC.

^gDefined as a positive skin-prick test to airborne allergens.

To further narrow the linked region, we genotyped the Chicago families for five additional STRPs—two that reside within the HLA region (DQ.CAR and TNFa) and three flanking markers (D6S258, MOGc, and D6S1680) (fig. 1A). The LOD score increased to 3.8, peaking at MOGc. The information content (Nicolae and Kong 2004) in this region was 95%, indicating that we could not increase the LOD score or improve the resolution by adding more markers. Furthermore, the MOGc 136-bp allele was overtransmitted to asthmatic children in the families (42 transmissions [TR]:23 nontransmissions [NT]; P [corrected for relatedness and number of alleles] = .06). In the trios, the MOGc 134-bp allele was overtransmitted to asthmatic children (17 TR:6 NT). Because of the extensive linkage disequilibrium (LD) in the HLA region (Begovich et al. 1992), the disease locus could have been located at a far distance, despite the evidence of association between MOGc alleles and asthma in two independent samples. To determine whether the haplotype shared among affected individuals extended into the HLA region, we genotyped the families at the HLA-A gene and at six STRPs spanning the HLA class I region between TNFa and HLA-A. There was no evidence of association with HLA-A or with markers proximal to it (data not shown).

Chromosome 6p21 is one of the best characterized regions in the human genome, and the nucleotide sequence between D6S258 and D6S265 is known (Mungall et al. 2003; Stewart et al. ²⁰⁰⁴). This region is gene rich, with 20 known or predicted genes and at least 30 pseudogenes in the 1-Mb region from HLA-A to OR2B3 (fig. 1B). To localize the susceptibility-associated variation, we genotyped the Chicago families and trios for an additional 59 polymorphisms in 19 genes, two pseudogenes, and the intergenic regions flanking the HLA-A and HLA-G loci (fig. 1B; tables A1 and A4 [online only]). Our strategy was to genotype one common SNP every 10–20 kb across each gene between D6S258 and D6S265, as well as all nonsynonomous SNPs, when possible. SNPs were selected from dbSNP or were discovered in our laboratory. We previously extensively characterized the haplotype structure of the HLA-G gene (MIM 142871) in the Hutterites (Ober et al. 1996, 2003). In this gene, we selected SNPs that identify clusters of variants either that are in perfect or near-perfect LD or that uniquely define all of the common HLA-G alleles, as in our previous study (Ober et al. 2003). SNPs in intergenic regions were selected to characterize the LD pattern in the proximal end of the region.

To investigate whether any of these variants explained some or all of the original evidence of linkage in the families, we conditioned on the genotype at each polymorphic site and reexamined the evidence of linkage. Multipoint analysis conditional on the genotypes of one variant in the HLA-G gene (1489C/T, His93His) yielded a LOD score of 0.9, which is substantially lower than the maximum LOD score (3.8) in this region. Analyses conditional on the other variants led to LOD scores >0.9. Thus, variation in the HLA-G gene accounted for most of the linkage in the families; the remaining evidence of linkage could be due to chance sharing among affected family members (Sun et al. 2002) or to the presence of a second susceptibility locus in the linked region. Nonetheless, it is notable that conditioning on a single SNP could result in a reduction in the LOD score of this magnitude (Sun et al. 2002).

We next examined the pattern of LD across this region in unrelated individuals from the Chicago families and trios and identified five LD blocks (Zhang et al. ^2002a) (fig. 2A). To determine which LD block contains variation that contributes to asthma susceptibility, we examined pairwise combinations of SNPs within each block by the transmission/disequilibrium test (TDT), conditional on the evidence of linkage at each position. Only pairwise combinations of SNPs in block 2 showed significant nonrandom transmission of haplotypes (P<.001) in both the families and the trios (fig. 2B; table A2 [online only]).

Figure 2.

LD block structure in the extended class I region. A, Graph of LD map, showing LDUs on the Y-axis and distance on the X-axis (Zhang et al. ^2002a). Shaded boxes show the five blocks in this region. B, Pairwise TDT of variants within each block. Results for Chicago families are shown in the lower half, and results for Chicago trios are in the upper half. P values were derived by simulations that were conditioned on the evidence of linkage but were not corrected for multiple comparisons.

Analyses of the individual variants revealed that polymorphisms in HLA-G (block 2) were associated with asthma in both the families and the trios; SNPs in two genes in block 4 (OR12D2 and OR10C1) and one gene each in block 3 (GABBR1) and block 5 (OR5V) were associated with asthma in the families only (table A3 [online only]). Thus, the HLA-G gene in block 2 was the only gene that showed evidence of association with asthma in both the families and the trios. However, in the families, the association was with a haplotype carrying the −964G allele (43 TR:25 NT), whereas the association in the trios was with a haplotype carrying the −964A allele (23 TR:12 NT).

To further localize and characterize the susceptibility locus, we genotyped selected markers in two populations that previously showed linkage of asthma-related phenotypes to 6p21: (1) the Hutterites, a founder population of European descent, and (2) Dutch families (Ober et al. 2000; Koppelman et al. ²⁰⁰²). Because of the different ascertainment schemes, we used different approaches in each sample. Ascertainment in the Hutterites was population based; individuals in this single, large pedigree were not selected on the basis of any particular phenotype. As a result, we used a test of association that was designed for large, multigenerational pedigrees (Bourgain et al. 2003). Here, we defined “cases” as individuals with bronchial hyperresponsiveness (BHR [MIM 600807]) (n=156) and “controls” as individuals with a negative history of asthma symptoms and without BHR (n=434). We note that all of the affected individuals in the Chicago families and trios also had BHR (table 1). Only 3 SNPs in HLA-G (of 21 SNPs genotyped) showed evidence of association with BHR in the Hutterites (P<.05) in whom the −964G allele was associated, similar to the results in the Chicago families.

The 200 Dutch families were ascertained through a parent in whom asthma was diagnosed ∼30 years ago (Panhuysen et al. 1995). Therefore, we first examined the prevalence of BHR and atopy (MIM 147050) in the descendents of each proband. The prevalence of atopy differed significantly by genotype at HLA-G −964 (60% of AA children, 49% of AG children, and 41% of GG children were atopic; P=.006); there was no association with BHR (table 2). However, because of the method of ascertainment used in the Dutch families, because HLA-G is an important immunomodulatory molecule during pregnancy (Hunt et al. 2000; Le Bouteiller et al. ²⁰⁰³), and because maternal asthma is a significant risk factor for asthma (Martinez 1997), we next analyzed these data stratified by mothers’ and fathers’ affection status (BHR+ or BHR−). The −964A allele was overtransmitted to children with BHR if the mother was unaffected (57 TR:27 NT; P=.004), whereas the −964G allele was overtransmitted to children with BHR if the mother was affected (61 TR:45 NT; P=.15). The differences in transmission patterns of alleles to children with BHR from mothers with and without BHR was highly significant (P=.0008). Similar analyses stratified by father’s asthma status did not show as significant a trend. Furthermore, the prevalences of BHR are not different if maternal status is ignored, but the prevalence of BHR among children with the GG genotype is significantly influenced by maternal status (table 2). Among children whose mothers have BHR, 56% of GG children have BHR; among children of mothers without BHR, 26% of GG children have BHR (P=.001). No such relationship was observed for AA or AG children. Thus, in the Dutch families, GG children are less likely to be atopic but are more likely to have BHR if their mother also has BHR. None of the other five markers typed in the Dutch families showed associations with either BHR or atopy (table A3 [online only]).

Table 2.

Number of Dutch Children with BHR and Atopy, by Child’s HLA-G −964 Genotype and Mother’s Affection Status^[Note]

	No. of Childrenwith Affected Status of
Child’s Genotypeand Mother’sAffection Status	BHR+	BHR−	Atopy+	Atopy−
AA:
BHR+	22	28	33	18
BHR−	27	30	32	25
AG:
BHR+	57	63	62	59
BHR−	56	68	60	67
GG:
BHR+	57	45	44	61
BHR−	18	50	28	44

Note.— The prevalence of BHR in children is influenced by both the child’s genotype and the mother’s affection status. Among GG children, the prevalence of BHR is 56% if the mother has BHR and 26% if the mother does not have BHR (P=.001). The prevalence of atopy in children is influenced by the child’s genotype but not the mother’s affection status (P=.006 for differences between genotypes).

To further examine interactions between mother’s affection status and child’s genotype, we reexamined the Chicago families, stratifying the sample by maternal BHR status (table 3). Genotype distributions in the children with asthma differed by mother’s BHR status for the nine polymorphisms in the HLA-G gene (data not shown), with three being significant at P<.01. Similar to the Dutch families, the −964GG genotype was associated with asthma among children of mothers with a positive BHR affection status, whereas the −964AA genotype was associated with asthma among children of mothers with a negative affection status. Other variants in HLA-G that were not genotyped in the Dutch families showed more striking differences, suggesting that variation in the gene in addition to the promoter region may contribute to risk. A similar trend was observed in the Hutterites: the frequency of the −964G allele among children with BHR whose mothers also had BHR was higher (0.69) than among children with BHR whose mothers did not have BHR (0.60), although this difference was not significant (P=.16).

Table 3.

Number of Children with Asthma in the Chicago Families, by Child’s Genotype and Mother’s Affection Status^[Note]

	No. of Childrenwith Genotype of
	HLA-G −964			HLA-G 1489
Mother’sAffection Status	GG	AG	AA	CC	CT	TT
BHR+	12	17	4	18	15	0
BHR−	9	19	18	9	30	12

Note.— The genotype distribution in the children differs by mother’s affection status. The −964GG genotype is more common among children with asthma whose mothers have BHR, whereas the −964AA genotype is more common among children with asthma whose mothers do not have BHR, although these differences do not reach statistical significance. Genotype differences are more striking for HLA-G 1489 (difference between children with asthma of BHR+ and BHR− mothers, P=.009), the same SNP that explained the evidence of linkage.

Thus, in the Chicago families and trios and in the Hutterite and Dutch families, variants in the HLA-G gene were associated with asthma or BHR. Although no other variation that we examined in this region showed associations in all four populations, we cannot exclude the possibility that unidentified variation in HLA-G or in other genes in block 2 also contributes to susceptibility. Furthermore, because we observed different alleles and haplotypes associated in the different populations and also when stratified by maternal affection status, susceptibility at this locus is complex, influenced by maternal factors, and associated with multiple related phenotypes.

HLA-G is a novel HLA gene that has limited polymorphisms in the coding region and a restricted tissue distribution (Ober and Aldrich 1997). This gene is the most highly expressed HLA gene in placental cells at the maternal-fetal interface, where it plays important immunoregulatory roles, including the inhibition of maternal NK and T cells and the promotion maternal tolerance of the allogeneic fetus (Hunt et al. 2000; Le Bouteiller et al. ²⁰⁰³). Recently, it has been demonstrated that HLA-G is expressed in adult macrophages, dendritic cells, and myoblasts in response to inflammation (Yang et al. 1996; Khosrotehrani et al. ²⁰⁰¹; Wiendl et al. ²⁰⁰³), in intestinal biopsies in patients with Crohn disease (Torres et al. 2004), and in malignant and nonmalignant lung diseases (Pangault et al. 2002). Furthermore, in biopsied myocardial cells from transplanted hearts, expression of HLA-G is correlated with prolonged graft survival and transplantation success (Rouas-Freiss et al. ²⁰⁰³). In this context, HLA-G is thought to inhibit Th1-mediated inflammation, perhaps in a concentration-dependent manner (Kapasi et al. 2000). The association of HLA-G variants with asthma is particularly intriguing, because asthma, like pregnancy, is characterized by a predominance of Th2 cytokines (Lin et al. 1993). Furthermore, the interaction between maternal phenotype and the HLA-G genotype in children in the Dutch and Chicago families is notable, given the important role that this gene plays in pregnancy and the fact that maternal asthma is a well-established risk factor for asthma (Martinez 1997).

To evaluate whether the HLA-G gene could contribute toward the immunologic milieu in the asthmatic lung, we studied the expression pattern of HLA-G in lung tissues from two individuals with asthma, one individual without asthma, and one individual without asthma but with a history of cigarette smoking. We demonstrated by immunohistochemistry the expression of HLA-G in bronchial epithelial cells (fig. 3). Expression in the lung was limited to the soluble isoform, HLA-G5 (also called “soluble G1”) (Fujii et al. 1994). Neither the transmembrane G1 and G2 isoforms nor the soluble G6 isoform were identified in these tissues. Thus, expression of HLA-G in the lung might contribute toward the aberrant immunologic response to inhaled allergens in genetically susceptible individuals.

Figure 3.

A, Control using an irrelevant IgG antibody. B–F, Sections labeled with the anti-HLA-G5 antibody. Epithelial cell labeling may be increased in basal epithelial cells immediately above the basement membrane (arrows in panels B, C, and E) but also may label columnar cells (arrowheads in panel B). In areas of damage and focal denudation (D), labeling may be found in remaining epithelial cells. Labeling is also seen in mucosal and submucosal gland cells (F) in the airway, which are of epithelial origin. Panels A and E are from individuals without asthma; panel C is from an individual without asthma but with a 30-year smoking history; panels B, D, and F are from individuals with asthma. mAB 1-2C3 detects soluble HLA-G5 in human bronchial epithelial cells. Human donor lungs that could not be used for transplantation were obtained under an institutional review board–approved protocol from the Regional Organ Bank of Illinois. Diagnoses were extracted from medical records. Bronchi were dissected and frozen in OCT. Five-micron sections were stained for antibodies against HLA-G1, HLA-G2, HLA-G5, and HLA-G6 isoforms, as described elsewhere (Morales et al. 2003). Original magnification was ×400 for all images. Antibodies specific for HLA-G1, HLA-G2, and HLA-G6 were negative in lung sections from patients with asthma as well as those without asthma (not shown).

Although the LD pattern in this region makes it impossible to rule out the possibility that variation in other genes in block 2 contributes to susceptibility, several lines of evidence suggest that HLA-G is an asthma susceptibility gene. First, a SNP in HLA-G accounted for nearly all the linkage in this region. For other variation to be causal, it would have to be in nearly perfect LD with and at a similar frequency to the HLA-G 1489C/T SNP (Sun et al. 2002) and not in LD with the other SNPs tested. Moreover, if the effect size of our initial linkage signal was biased upwards because of chance sharing in the families (Sun et al. 2002), then HLA-G may be the sole asthma susceptibility gene in this region. Even if the signal was not biased, additional variation in HLA-G that we did not sample could also contribute to risk. Second, among the variants that were surveyed across the linked region, only SNPs in HLA-G were associated with asthma or BHR in four different populations, which were ascertained using different strategies. Third, we demonstrated by immunohistochemistry that the soluble HLA-G isoform, G5, was highly expressed in bronchial epithelial cells in asthmatic lungs. The presence of soluble HLA-G protein in bronchial epithelial cells indicates that it could participate in a local inflammatory response to airborne allergens or other agents. On the basis of these combined data, we propose that HLA-G is an asthma susceptibility gene on chromosome 6p21. The differential association of alleles (or haplotypes) with childhood disease on the basis of maternal affection status is intriguing. Although the contribution of prenatal events to metabolic diseases such as diabetes and obesity is well established (Osmond and Barker 2000), the fetal origins of immune-mediated diseases have received less attention (Jones et al. 2000; Adams and Nelson ²⁰⁰⁴). Nonetheless, the programming of the fetal immune system likely begins in utero, and our data suggest that the maternal immunologic milieu (captured in this study by the mother's disease status) may influence the child’s subsequent response to inhaled allergens in a genotype-specific manner. One potential mechanism is through differential methylation, which has been previously implicated in regulating the expression levels of HLA-G (Onno et al. 1997; Moreau et al. ²⁰⁰³; Ober et al. ²⁰⁰³), a hypothesis that we are currently investigating.

Finally, many other inflammatory diseases—such as multiple sclerosis (GAMES and Transatlantic Multiple Sclerosis Genetics Cooperative ²⁰⁰³), psoriasis (Zhang et al. ^2002b), atopic dermatitis (Soderhall et al. 2001), inflammatory bowel disease (Mathew and Lewis 2004), and schizophrenia (Wright et al. 2001)—have been linked to the HLA region. To date, all of the variation underlying these linkages has not been identified. It is tempting to speculate that variation in the HLA-G gene may contribute to susceptibility to a wide range of inflammatory diseases, including asthma. Thus, this gene that likely evolved to promote tolerance in pregnancy may contribute to risk for many common diseases, suggesting that novel therapeutic strategies could have broad relevance to these immune-mediated diseases.

Appendix A: Statistical Analyses

Linkage analyses were performed with the program ALLEGRO (Gudbjartsson et al. 2000) with the use of affecteds-only allele-sharing methods. We used the exponential allele-sharing model (Kong and Cox 1997) on the scoring function Spairs. The identity-by-descent (IBD) status was determined using multipoint calculations; P values were calculated on the basis of large-sample approximations. To search for variants that explain the evidence of linkage, we calculated conditional LOD scores as described elsewhere (Sun et al. 2002). Given a marker, the distribution of the IBD sharing, conditional on the marker genotypes, was calculated and was used in deriving the conditional multipoint LOD scores for the markers in the region. The maximum conditional LOD score was reported. This approach assumes no recombination between the markers in the tested region.

The TDT was performed on the affected individuals in the families from whom both parents were genotyped (Spielman et al. 1993). We used the χ² statistic—and, in markers with more than two alleles, the sum of the individual χ² functions—as the test statistic. For the markers with more than two alleles and in the data sets in which there were more than two affected individuals per family, the significance of the test was evaluated using simulations. The simulations were performed conditional on the IBD process at that location. The missing genotype patterns were identical in the simulated and observed data sets.

Two-marker TDT was performed for selected pairs of markers within each of five blocks (Zhang et al. ^2002a), which were defined so that adjacent blocks were separated by an increase in LD units (LDU) of ∼0.30 or greater and so that markers in each block had small estimated LD measures. This procedure leads to a smaller number of tests and, therefore, to a less stringent threshold of significance. Only trios without missing genotypes for the pair of markers were used. The transmission counts were estimated on the basis of the two-marker haplotype frequencies that were calculated from unrelated individuals. The test statistic for one pair of markers is the maximum over all the haplotypes of , where t is the estimated number of transmissions and n is the estimated number of nontransmissions of the haplotype from heterozygous parents. The statistical significance was evaluated by simulation. Founder haplotypes were simulated within each block of markers, and Mendelian transmissions, under the assumption of no recombination, were simulated conditional on the IBD process. The missing genotype patterns were identical in the simulated and observed data sets. Two-marker analyses, identical to those described above, were performed in each simulated data set. The P values were calculated as the number of simulated data sets in which the test statistic was larger than the observed statistic. The difference in prevalences was calculated using χ² statistics. P values were obtained by simulation, conditional on the pedigree structure and the IBD process.

The difference in the prevalence of atopy and BHR in the Dutch children was tested using χ² statistics. The significance was assessed using simulations. For each simulation, the family structure and phenotypes were the same as in the original data. Genotypes were simulated in the founders of the families, by use of the observed genotype frequencies, and in the nonfounders, conditional on the pedigree structure and the IBD process. The difference in genotype frequencies in the Chicago children with asthma whose mothers had positive BHR status compared with those whose mothers had negative BHR status was also tested using χ² statistics on 2×3 tables. The significance was assessed using simulated data sets obtained by permuting the mother’s BHR status.

Table A1.

Allele Frequencies in Study Samples^[Note]

			Allele Frequency in
Locus	Distance from pter(in Mb)	Allele	Patients with Asthma in Chicago Families(n=124)	Patients with Asthma in Chicago Trios(n=46)	Hutterite Patients with BHR(n=156)	Dutch Subjects with BHR(n=598)	Controlsa(n=123)
RFP*2	28.98	T	.679	.725
RFP*1	29.03	A	.564	.537
HS6M1-4P*1	29.17	A	.566	.575
OR2J3*1	29.25	T	.828	.890			.880
OR2J2*1	29.25	A	.568	.534
OR5V1*1	29.43	T	.851	.792	.823		.915
OR5V1*2	29.43	C	.881	.902			.694
OR12D3*3	29.44	T	.639	.655			.509
OR12D3*2	29.44	C	.595	.525
OR12D3*1	29.46	A	.592	.526
OR12D2*3	29.47	G (47V)	.598	.625	.401
OR12D2*2	29.47	T (56P)	.578	.557	.448		.537
OR12D2*1	29.47	G (159I)	.571	.554	.410	.611
OR11A1*1	29.50	G	.686	.652
OR10C1*1	29.52	C	.835	.878	.775		.817
OR10C1*2	29.52	C	.572	.488	.500		.477
OR2H1*1	29.54	C	.768	.767			.752
OR2H1*2	29.54	C	.605	.511			.524
MRG*1	29.56	A	.768	.795
UBD*1	29.63	G	.538	.585
UBD*2	29.64	C	.516	.598
OR2H3*1	29.66	T (587L)	.758		.729
OR2H3*2	29.66	C (642A)	.581		.559
GABBR1*8	29.68	T	.942	.893
GABBR1*7	29.68	T	.966	.952
GABBR1*1	29.68	G	.930	.900
GABBR1*19	29.68	G	.914	.988
GABBR1*6	29.68	C	.925	.881
GABBR1*5	29.68	T/C	.986	.988
GABBR1*2	29.68	T (658F)	.835	.875	.860	.870
GABBR1*21	29.69	T	.844	.867
GABBR1*22	29.69	T	.918	.900
GABBR1*4	29.69	ΔA	.956
GABBR1*3	29.70	G	.858	.814
GABBR1*24	29.70	C	.917	.977			.938
GABBR1*23	29.71	C	.997	.989			.980
MOG*3	29.74	G	.906	.859
MOG*2	29.74	G	.749	.756	.665	.745
MOG*4	29.74	T	.762	.854	.842
LOC222567*1	29.75	G	.780	.782
HLA-F*2	29.80	G	.521	.633	.470		.624
HLA-F*1	29.80	A	.880	.897
HCGIV.9*1	29.87	C	.553	.560
HLA-G*-1306	29.90	A	.510	.529	.367
HLA-G*-964	29.90	G	.500	.423	.633	.432
HLA-G*-725b	29.90	C	.886	.837	.800	.850	.844
HLA-G*-689	29.90	A	.524	.443	.671		.508
HLA-G*-666	29.90	A	.520	.556	.329
HLA-G*-201	29.90	G	.511	.434	.633
HLA-G*1489	29.90	C (93H)	.611	.618	.747
HLA-G*1538	29.90	C (110L)	.940	.936	.926
HLA-G*3741ins	29.90	14-bp deletion	.549	.511	.647	.590	.561
IG.6-1	29.93	A	.563	.500
IG.6-2b,c	29.96	C	.324	.211
HCGIV.9*2b,c	29.99	A	.422	.276
HCGIV.9*1b,c	30.00	A	.435	0.324
IG.6-3b	30.00	C	.615	.538
IG.6-4	30.05	G	.568	.550

Note.— Genotypes at all loci were in Hardy-Weinberg proportions in all samples (nominal P>.01), except for three markers (IG.6-2, IG.6-3, and IG.6-4) that showed deviations from Hardy-Weinberg equilibrium (nominal P<.01) in the Chicago trios only. These were genotyped at the same time as the Chicago families and by the same method, suggesting that the deviations are not due to genotyping errors. The Chicago trios, Hutterite families, and Dutch families were genotyped for a subset of the markers typed in the Chicago families.

^aControls were white adults (⩾18 years old), who were recruited in Chicago and who reported a negative personal and family history (among first-degree relatives) of asthma.

^bTriallelic markers.

^cMarkers residing within a polymorphic deletion (Geraghty et al. 1992); the deletion was scored as a third allele.

Table A2.

P Values for Pairwise TDTs in Figure 2B^[Note]

Locus			P Value for
1	2	Block	Families	Trios
RFP*2	RFP*1	5	.7120	.8550
RFP*2	HS6M1-4P*1	5	.8330	.8050
RFP*2	OR2J3*1	5	.4790	.3250
RFP*2	OR2J2*1	5	.7620	.7750
RFP*2	OR5V1*1	5	.1390	.5000
RFP*2	OR5V1*2	5	.4170	.7550
RFP*2	OR12D3*3	5	.2150	.8550
RFP*2	OR12D3*2	5	.0470	.9300
RFP*2	OR12D3*1	5	.4500	.4600
RFP*1	HS6M1-4P*1	5	.7490	.3500
RFP*1	OR2J3*1	5	.5720	.6200
RFP*1	OR2J2*1	5	.7680	.9250
RFP*1	OR5V1*1	5	.1000	.3750
RFP*1	OR5V1*2	5	.1770	.4300
RFP*1	OR12D3*3	5	.6030	.5400
RFP*1	OR12D3*2	5	.4310	.8600
RFP*1	OR12D3*1	5	.4250	.4800
HS6M1-4P*1	OR2J3*1	5	.4890	.5800
HS6M1-4P*1	OR2J2*1	5	.7520	.5250
HS6M1-4P*1	OR5V1*1	5	.1550	.4500
HS6M1-4P*1	OR5V1*2	5	.3490	.3650
HS6M1-4P*1	OR12D3*3	5	.3470	.2950
HS6M1-4P*1	OR12D3*2	5	.0900	.6750
HS6M1-4P*1	OR12D3*1	5	.2280	.2900
OR2J3*1	OR2J2*1	5	.6690	.3150
OR2J3*1	OR5V1*1	5	.1690	.8150
OR2J3*1	OR5V1*2	5	.3500	.4300
OR2J3*1	OR12D3*3	5	.3460	.4100
OR2J3*1	OR12D3*2	5	.1780	.4950
OR2J3*1	OR12D3*1	5	.3050	.5000
OR2J2*1	OR5V1*1	5	.1450	.6250
OR2J2*1	OR5V1*2	5	.0620	.3450
OR2J2*1	OR12D3*3	5	.5010	.3950
OR2J2*1	OR12D3*2	5	.2710	.8550
OR2J2*1	OR12D3*1	5	.5140	.6300
OR5V1*1	OR5V1*2	5	.2220	.2950
OR5V1*1	OR12D3*3	5	.0290	.5800
OR5V1*1	OR12D3*2	5	.2120	.8500
OR5V1*1	OR12D3*1	5	.2190	.4550
OR5V1*2	OR12D3*3	5	.0930	.3400
OR5V1*2	OR12D3*2	5	.1460	.3950
OR5V1*2	OR12D3*1	5	.0480	.3450
OR12D3*3	OR12D3*2	5	.4470	.5550
OR12D3*3	OR12D3*1	5	.5370	.3750
OR12D3*2	OR12D3*1	5	.0730	.4900
OR12D2*3	OR12D2*2	4	.0090	.9450
OR12D2*3	OR12D2*1	4	.0020	.8000
OR12D2*3	OR11A1*1	4	.0110	.7750
OR12D2*3	OR10C1*1	4	.0160	.8400
OR12D2*3	OR10C1*2	4	.2030	.3500
OR12D2*3	OR2H1*1	4	.1000	.6800
OR12D2*3	OR2H1*2	4	.0190	.4400
OR12D2*3	MRG*1	4	.0660	.2200
OR12D2*3	UBD*1	4	.2300	.7950
OR12D2*3	UBD*2	4	.4160	.5600
OR12D2*2	OR12D2*1	4	.1290	.6850
OR12D2*2	OR11A1*1	4	.4260	.8000
OR12D2*2	OR10C1*1	4	.5020	.7750
OR12D2*2	OR10C1*2	4	.9190	.8350
OR12D2*2	OR2H1*1	4	.4530	.5600
OR12D2*2	OR2H1*2	4	.2280	.5500
OR12D2*2	MRG*1	4	.3510	.7200
OR12D2*2	UBD*1	4	.6850	.7050
OR12D2*2	UBD*2	4	.5030	.8350
OR12D2*1	OR11A1*1	4	.0820	.8300
OR12D2*1	OR10C1*1	4	.4060	.6500
OR12D2*1	OR10C1*2	4	.6620	.8050
OR12D2*1	OR2H1*1	4	.3120	.8100
OR12D2*1	OR2H1*2	4	.1100	.5500
OR12D2*1	MRG*1	4	.0980	.6300
OR12D2*1	UBD*1	4	.1450	.5200
OR12D2*1	UBD*2	4	.1860	.9950
OR11A1*1	OR10C1*1	4	.3050	.7100
OR11A1*1	OR10C1*2	4	.8130	.5050
OR11A1*1	OR2H1*1	4	.2610	.7050
OR11A1*1	OR2H1*2	4	.7410	.2050
OR11A1*1	MRG*1	4	.1560	.8100
OR11A1*1	UBD*1	4	.8140	.7800
OR11A1*1	UBD*2	4	.7600	.9950
OR10C1*1	OR10C1*2	4	.2230	.4700
OR10C1*1	OR2H1*1	4	.4740	.7650
OR10C1*1	OR2H1*2	4	.3370	.5900
OR10C1*1	MRG*1	4	.5220	.6000
OR10C1*1	UBD*1	4	.0730	.3500
OR10C1*1	UBD*2	4	.0770	.6100
OR10C1*2	OR2H1*1	4	.2560	.3700
OR10C1*2	OR2H1*2	4	.7550	.1050
OR10C1*2	MRG*1	4	.1970	.6400
OR10C1*2	UBD*1	4	.7020	.2750
OR10C1*2	UBD*2	4	.9520	.8050
OR2H1*1	OR2H1*2	4	.5660	.5750
OR2H1*1	MRG*1	4	.0710	.8100
OR2H1*1	UBD*1	4	.1050	.7700
OR2H1*1	UBD*2	4	.3350	1.0000
OR2H1*2	MRG*1	4	.3610	.7150
OR2H1*2	UBD*1	4	.8740	.2700
OR2H1*2	UBD*2	4	.5420	.7650
MRG*1	UBD*1	4	.1080	.7900
MRG*1	UBD*2	4	.1480	.8850
UBD*1	UBD*2	4	.8720	.8400
OR2H3*1	OR2H3*2	3	.6910	NA
OR2H3*1	GABBR1*8	3	.1080	NA
OR2H3*1	GABBR1*7	3	.4670	NA
OR2H3*1	GABBR1*1	3	.5680	NA
OR2H3*1	GABBR1*19	3	.5570	NA
OR2H3*1	GABBR1*6	3	.2860	NA
OR2H3*1	GABBR1*5	3	.3630	NA
OR2H3*1	GABBR1*2	3	.3430	NA
OR2H3*1	GABBR1*21	3	.0430	NA
OR2H3*1	GABBR1*22	3	.2570	NA
OR2H3*1	GABBR1*4	3	.1320	NA
OR2H3*1	GABBR1*3	3	.4040	NA
OR2H3*1	GABBR1*24	3	.6010	NA
OR2H3*1	GABBR1*23	3	.2050	NA
OR2H3*2	GABBR1*8	3	.3770	NA
OR2H3*2	GABBR1*7	3	.8130	NA
OR2H3*2	GABBR1*1	3	.3620	NA
OR2H3*2	GABBR1*19	3	.7830	NA
OR2H3*2	GABBR1*6	3	.6720	NA
OR2H3*2	GABBR1*5	3	.7630	NA
OR2H3*2	GABBR1*2	3	.1630	NA
OR2H3*2	GABBR1*21	3	.1550	NA
OR2H3*2	GABBR1*22	3	.2600	NA
OR2H3*2	GABBR1*4	3	.3790	NA
OR2H3*2	GABBR1*3	3	.4640	NA
OR2H3*2	GABBR1*24	3	.3050	NA
OR2H3*2	GABBR1*23	3	.1970	NA
GABBR1*8	GABBR1*7	3	.0140	.6450
GABBR1*8	GABBR1*1	3	.0040	.2650
GABBR1*8	GABBR1*19	3	.0002	.4950
GABBR1*8	GABBR1*6	3	.0400	.7200
GABBR1*8	GABBR1*5	3	.0015	.2550
GABBR1*8	GABBR1*2	3	.0035	.6450
GABBR1*8	GABBR1*21	3	.0040	.6750
GABBR1*8	GABBR1*22	3	.0070	.1400
GABBR1*8	GABBR1*3	3	.3930	.5250
GABBR1*8	GABBR1*24	3	.0110	.7000
GABBR1*8	GABBR1*23	3	.0080	.2400
GABBR1*7	GABBR1*1	3	.0005	.6950
GABBR1*7	GABBR1*19	3	.0230	.3450
GABBR1*7	GABBR1*6	3	.0280	.6550
GABBR1*7	GABBR1*5	3	.0360	.3850
GABBR1*7	GABBR1*2	3	.0130	.6950
GABBR1*7	GABBR1*21	3	.0090	.7550
GABBR1*7	GABBR1*22	3	.0023	.3650
GABBR1*7	GABBR1*3	3	.7590	.6800
GABBR1*7	GABBR1*24	3	.1610	.3500
GABBR1*7	GABBR1*23	3	.1480	.3600
GABBR1*1	GABBR1*19	3	.0140	.4750
GABBR1*1	GABBR1*6	3	.0030	.4750
GABBR1*1	GABBR1*5	3	.0030	.2750
GABBR1*1	GABBR1*2	3	.0100	.4050
GABBR1*1	GABBR1*21	3	.0230	.5350
GABBR1*1	GABBR1*22	3	.0660	.1250
GABBR1*1	GABBR1*3	3	.5930	.5550
GABBR1*1	GABBR1*24	3	.0360	.5000
GABBR1*1	GABBR1*23	3	.0420	.3750
GABBR1*19	GABBR1*6	3	.0130	.4550
GABBR1*19	GABBR1*5	3	.1170	.0200
GABBR1*19	GABBR1*2	3	.0070	.5400
GABBR1*19	GABBR1*21	3	.0110	.6450
GABBR1*19	GABBR1*22	3	.0010	.3250
GABBR1*19	GABBR1*3	3	.8800	.1050
GABBR1*19	GABBR1*24	3	.3580	.0100
GABBR1*19	GABBR1*23	3	.2130	.0300
GABBR1*6	GABBR1*5	3	.0090	.3900
GABBR1*6	GABBR1*2	3	.0063	.4200
GABBR1*6	GABBR1*21	3	.0090	.6200
GABBR1*6	GABBR1*22	3	.0050	.2300
GABBR1*6	GABBR1*3	3	.3560	.7500
GABBR1*6	GABBR1*24	3	.0280	.4750
GABBR1*6	GABBR1*23	3	.0230	.5250
GABBR1*5	GABBR1*2	3	.0050	.2600
GABBR1*5	GABBR1*21	3	.0130	.4400
GABBR1*5	GABBR1*22	3	.0040	.1700
GABBR1*5	GABBR1*3	3	.8890	.4850
GABBR1*5	GABBR1*24	3	.1780	.0350
GABBR1*5	GABBR1*23	3	1.0000	1.0000
GABBR1*2	GABBR1*21	3	.0060	.2300
GABBR1*2	GABBR1*22	3	.0030	.3200
GABBR1*2	GABBR1*3	3	.1150	.6300
GABBR1*2	GABBR1*24	3	.0170	.2950
GABBR1*2	GABBR1*23	3	.0110	.1450
GABBR1*21	GABBR1*22	3	.0400	.1750
GABBR1*21	GABBR1*3	3	.1900	.5700
GABBR1*21	GABBR1*24	3	.0140	.3000
GABBR1*21	GABBR1*23	3	.0110	.4400
GABBR1*22	GABBR1*3	3	.5530	.4600
GABBR1*22	GABBR1*24	3	.0220	.2750
GABBR1*22	GABBR1*23	3	.0640	.1150
GABBR1*3	GABBR1*24	3	.8150	.2250
GABBR1*3	GABBR1*23	3	.7500	.8350
GABBR1*24	GABBR1*23	3	.1250	.0250
MOG*3	MOG*2	2	.0760	.6100
MOG*3	MOG*4	2	.5470	.1800
MOG*3	LOC222567*1	2	.4830	.0300
MOG*3	HLA-F*2	2	.8780	.5600
MOG*3	HLA-F*1	2	.5100	.6700
MOG*3	HCGIV.9*1	2	.3930	.0750
MOG*3	HLA-G*-1306	2	.1100	.1000
MOG*3	HLA-G*-964	2	.3370	.0300
MOG*3	HLA-G*-725	2	.6260	.1550
MOG*3	HLA-G*-689	2	.3680	.0900
MOG*3	HLA-G*-666	2	.2470	.0600
MOG*3	HLA-G*-201	2	.1790	.0500
MOG*3	HLA-G*1489	2	.3050	.0800
MOG*3	HLA-G*1538	2	.2720	.1000
MOG*3	HLA-G*3741ins	2	.1620	.6550
MOG*3	IG.6-1	2	.1080	.2850
MOG*2	MOG*4	2	.4390	.0950
MOG*2	LOC222567*1	2	.0490	.0002
MOG*2	HLA-F*2	2	.0560	.0600
MOG*2	HLA-F*1	2	.0910	.0900
MOG*2	HCGIV.9*1	2	.0800	.1950
MOG*2	HLA-G*-1306	2	.0030	.8200
MOG*2	HLA-G*-964	2	.0190	.0040
MOG*2	HLA-G*-725	2	.0090	.0090
MOG*2	HLA-G*-689	2	.0750	.5450
MOG*2	HLA-G*-666	2	.0090	.1250
MOG*2	HLA-G*-201	2	.0210	.0250
MOG*2	HLA-G*1489	2	.0260	.0060
MOG*2	HLA-G*1538	2	.0800	.7550
MOG*2	HLA-G*3741ins	2	.0320	.3750
MOG*2	IG.6-1	2	.0530	.0150
MOG*4	LOC222567*1	2	.0140	.0500
MOG*4	HLA-F*2	2	.0830	.6950
MOG*4	HLA-F*1	2	.0550	.1550
MOG*4	HCGIV.9*1	2	.1850	.0050
MOG*4	HLA-G*-1306	2	.0350	.2150
MOG*4	HLA-G*-964	2	.0040	.2250
MOG*4	HLA-G*-725	2	.0033	.3850
MOG*4	HLA-G*-689	2	.0001	.0650
MOG*4	HLA-G*-666	2	.2920	.0070
MOG*4	HLA-G*-201	2	.0060	.0800
MOG*4	HLA-G*1489	2	.0700	.0260
MOG*4	HLA-G*1538	2	.0050	.0460
MOG*4	HLA-G*3741ins	2	.0160	.4300
MOG*4	IG.6-1	2	.0160	.5750
LOC222567*1	HLA-F*2	2	.3690	.0100
LOC222567*1	HLA-F*1	2	.0007	.1750
LOC222567*1	HCGIV.9*1	2	.0730	.3800
LOC222567*1	HLA-G*-1306	2	.6890	.0050
LOC222567*1	HLA-G*-964	2	.1180	.2050
LOC222567*1	HLA-G*-725	2	.1160	.0150
LOC222567*1	HLA-G*-689	2	.0500	.5550
LOC222567*1	HLA-G*-666	2	.0220	.0350
LOC222567*1	HLA-G*-201	2	.3960	.0110
LOC222567*1	HLA-G*1489	2	.0480	.8050
LOC222567*1	HLA-G*1538	2	.0730	.1400
LOC222567*1	HLA-G*3741ins	2	.0520	.0200
LOC222567*1	IG.6-1	2	.0210	.9450
HLA-F*2	HLA-F*1	2	.0910	.0800
HLA-F*2	HCGIV.9*1	2	.5390	.0300
HLA-F*2	HLA-G*-1306	2	.0050	.4450
HLA-F*2	HLA-G*-964	2	.1770	.1000
HLA-F*2	HLA-G*-725	2	.2160	.0850
HLA-F*2	HLA-G*-689	2	.2110	.1650
HLA-F*2	HLA-G*-666	2	.0830	.3350
HLA-F*2	HLA-G*-201	2	.1750	.1650
HLA-F*2	HLA-G*1489	2	.1900	.1000
HLA-F*2	HLA-G*1538	2	.1620	.3350
HLA-F*2	HLA-G*3741ins	2	.2160	.1650
HLA-F*2	IG.6-1	2	.0710	.1250
HLA-F*1	HCGIV.9*1	2	.1070	.1250
HLA-F*1	HLA-G*-1306	2	.3670	.3500
HLA-F*1	HLA-G*-964	2	.4130	.1000
HLA-F*1	HLA-G*-725	2	.1680	.6500
HLA-F*1	HLA-G*-689	2	.4470	.0250
HLA-F*1	HLA-G*-666	2	.0510	.5600
HLA-F*1	HLA-G*-201	2	.1140	.2400
HLA-F*1	HLA-G*1489	2	.1610	.1750
HLA-F*1	HLA-G*1538	2	.1880	.0200
HLA-F*1	HLA-G*3741ins	2	.0680	.1300
HLA-F*1	IG.6-1	2	.0560	.7500
HCGIV.9*1	HLA-G*-1306	2	.2250	.3650
HCGIV.9*1	HLA-G*-964	2	.4330	.0700
HCGIV.9*1	HLA-G*-725	2	.0190	.9100
HCGIV.9*1	HLA-G*-689	2	.1170	.9100
HCGIV.9*1	HLA-G*-666	2	.0030	.9600
HCGIV.9*1	HLA-G*-201	2	.3850	.5450
HCGIV.9*1	HLA-G*1489	2	.0220	.8800
HCGIV.9*1	HLA-G*1538	2	.0400	.4700
HCGIV.9*1	HLA-G*3741ins	2	.2490	.0030
HCGIV.9*1	IG.6-1	2	.0370	.5350
HLA-G*-1306	HLA-G*-964	2	.0470	.7450
HLA-G*-1306	HLA-G*-725	2	.0680	.7400
HLA-G*-1306	HLA-G*-689	2	.0360	.4300
HLA-G*-1306	HLA-G*-666	2	.0440	.8350
HLA-G*-1306	HLA-G*-201	2	.1180	.8850
HLA-G*-1306	HLA-G*1489	2	.0070	.9650
HLA-G*-1306	HLA-G*1538	2	.2930	.9100
HLA-G*-1306	HLA-G*3741ins	2	.0180	.0450
HLA-G*-1306	IG.6-1	2	.0250	.3900
HLA-G*-964	HLA-G*-725	2	.1380	.2800
HLA-G*-964	HLA-G*-689	2	.0060	.0250
HLA-G*-964	HLA-G*-666	2	.0100	.0450
HLA-G*-964	HLA-G*-201	2	.0170	.0150
HLA-G*-964	HLA-G*1489	2	.0230	.4950
HLA-G*-964	HLA-G*1538	2	.0190	.8850
HLA-G*-964	HLA-G*3741ins	2	.0310	.2500
HLA-G*-964	IG.6-1	2	.0330	.1700
HLA-G*-725	HLA-G*-689	2	.0540	.4900
HLA-G*-725	HLA-G*-666	2	.2720	.0850
HLA-G*-725	HLA-G*-201	2	.0290	.0140
HLA-G*-725	HLA-G*1489	2	.0180	.0250
HLA-G*-725	HLA-G*1538	2	.0220	.0150
HLA-G*-725	HLA-G*3741ins	2	.0040	.7550
HLA-G*-725	IG.6-1	2	.2170	.5450
HLA-G*-689	HLA-G*-666	2	.0080	.6250
HLA-G*-689	HLA-G*-201	2	.0190	.1500
HLA-G*-689	HLA-G*1489	2	.0700	.7700
HLA-G*-689	HLA-G*1538	2	.0920	.1250
HLA-G*-689	HLA-G*3741ins	2	.1280	.3100
HLA-G*-689	IG.6-1	2	.2620	.7350
HLA-G*-666	HLA-G*-201	2	.1180	.0300
HLA-G*-666	HLA-G*1489	2	.3110	.0120
HLA-G*-666	HLA-G*1538	2	.1970	.0550
HLA-G*-666	HLA-G*3741ins	2	.1000	.1300
HLA-G*-666	IG.6-1	2	.0030	.6550
HLA-G*-201	HLA-G*1489	2	.2460	.0300
HLA-G*-201	HLA-G*1538	2	.0400	.0300
HLA-G*-201	HLA-G*3741ins	2	.0140	.0100
HLA-G*-201	IG.6-1	2	.1060	.5500
HLA-G*1489	HLA-G*1538	2	.0190	.8000
HLA-G*1489	HLA-G*3741ins	2	.0028	.7850
HLA-G*1489	IG.6-1	2	.0030	.7950
HLA-G*1538	HLA-G*3741ins	2	.0550	.2800
HLA-G*1538	IG.6-1	2	.0840	.4200
HLA-G*3741ins	IG.6-1	2	.0130	.5800
IG.6-2	HCGIV.9*2	1	.1440	.2800
IG.6-2	HCGIV.9*1	1	.1780	.5550
IG.6-2	IG.6-3	1	.1580	.2150
IG.6-2	IG.6-4	1	.1030	.0150
HCGIV.9*2	HCGIV.9*1	1	.1170	.3150
HCGIV.9*2	IG.6-3	1	.1520	.1850
HCGIV.9*2	IG.6-4	1	.6240	.2250
HCGIV.9*1	IG.6-3	1	.1600	.0950
HCGIV.9*1	IG.6-4	1	.5980	.0650
IG.6-3	IG.6-4	1	.6390	.2950

Note.— P values < .05 are shown in bold italics. NA = not applicable; SNP not typed.

Table A3.

Associations with Asthma in the Chicago Families and Trios and with BHR in the Hutterite and Dutch Families^[Note]

				P Value for
							Dutch Families with
Locus	Distance from pter (in Mb)	Polymorphism	dbSNP rs#	Chicago Families	Chicago Trios	Hutterites	BHR− Mother	BHR+ Mother
RFP*2	28.98	T/C	209131	.778	.715	…	…	…
RFP*1	29.03	A/G	1237485	.641	.862	…	…	…
HS6M1-4P*1	29.17	A/G	3131088	.732	.398	…	…	…
OR2J3*1	29.25	T/C	3129157	.580	.796	…	…	…
OR2J2*1	29.25	A/G	3130743	.948	.411	…	…	…
OR5V1*1	29.43	T/G	6930033	.075	.414	.482	…	…
OR5V1*2	29.43	C/T	4713210	.0013	.763	…	…	…
OR12D3*3	29.44	T/C	4713211	.873	.369	…	…	…
OR12D3*2	29.44	C/T	2354502	.185	.612	…	…	…
OR12D3*1	29.46	A/G	238881	.273	.180	…	…	…
OR12D2*3	29.47	G/T (V47F)	9257834	.0090	.670	.054	…	…
OR12D2*2	29.47	C/T (L56P)	4987411	.183	.835	.108	…	…
OR12D2*1	29.47	A/G (V159I)	2073151	.0620	.117	.056	.80	.530
OR11A1*1	29.50	G/T	7770592	.781	.384	…	…	…
OR10C1*1	29.52	C/T	2074469	.0180	.564	.896	…	…
OR10C1*2	29.52	C/T	2074464	.771	.336	.182	…	…
OR2H1*1	29.54	C/T	2021729	.290	.827	…	…	…
OR2H1*2	29.54	C/T	3128854	.913	.433	…	…	…
MRG*1	29.56	A/G	1233492	.132	.683	…	…	…
UBD*1	29.63	G/C	444013	.943	.493	…	…	…
UBD*2	29.64	C/T	362513	.706	1.00	…	…	…
OR2H3*1	29.66	T/C (L587F)	3129034	.424	…	.250	…	…
OR2H3*2	29.66	C/T (A642V)	1233387	.536	…	.332	…	…
GABBR1*8	29.68	T/C		.042	.480	…	…	…
GABBR1*7	29.68	T/C		.271	.655	…	…	…
GABBR1*1	29.68	G/A	881284	.130	.527	…	…	…
GABBR1*19	29.68	A/G		.270	.317	…	…	…
GABBR1*6	29.68	T/C	29261	.067	.705	…	…	…
GABBR1*5	29.68	C/T		.842	.317	…	…	…
GABBR1*2	29.68	T/C (F658F)	29230	.018	.617	.352	.30	.470
GABBR1*21	29.69	T/C		.037	1	…	…	…
GABBR1*22	29.69	T/C	29257	.168	.206	…	…	…
GABBR1*4	29.69	ΔA		.462	…	…	…	…
GABBR1*3	29.70	G/C	29221	.611	.670	…	…	…
GABBR1*24	29.70	C/T	29242	.296	.257	…	…	…
GABBR1*23	29.71	C/T		.019	1	…	…	…
MOG*3	29.74	G/A	3130250	.466	.655	…	…	…
MOG*2	29.74	G/A	1318631	.083	.18	.159	.027	.527
MOG*4	29.74	T/G	2535246	.154	.028	.833	…	…
LOC222567*1	29.75	G/T	2535238	.263	.071	…	…	…
HLA-F*2	29.80	G/A	136126	.450	.086	.145	…	…
HLA-F*1	29.80	A/C	176925	.635	.617	…	…	…
HCGIV.9*1	29.87	C/T	1610718	.176	.827	…	…	…
HLA-G*-1306	29.90	A/G	1736936	.065	.144	.046	…	…
HLA-G*-964	29.90	G/A	1632947	.051	.060	.046	.0040	.151
HLA-G*-725	29.90	C/G/T	12233334	.668	.514	.60	.62	.060
HLA-G*-689	29.90	A/G	2735022	.025	.105	.076	…	…
HLA-G*-666	29.90	T/G	1632945	.121	.170	.076	…	…
HLA-G*-201	29.90	G/A	1631950	.105	.034	.046	…	…
HLA-G*1489	29.90	C/T (H93H)	1624278	.182	1.0	.207	…	…
HLA-G*1538	29.90	C/T (L110I)		.495	1.0	.201	…	…
HLA-G*3741ins	29.90	14 bp in/del	16375	.013	.647	.16	.150	.720
IG.6-1	29.93	G/A	2844826	.166	.516	…	…	…
IG.6-2	29.96	G/C/Δ	1054175	.069	.523	…	…	…
HCGIV.9*2	29.99	A/G/Δ	2517782	.614	.027	…	…	…
HCGIV.9*1	30.00	A/G/Δ	1056242	.462	.444	…	…	…
IG.6-3	30.00	C/T/Δ	2517753	.310	.258	…	…	…
IG.6-4	30.05	A/G	2394255	.787	1.00	…	…	…

Note.— P values < .05 are shown in bold italics. TDT was performed for trios with asthma from CSGA families and trios and for Dutch families; a case-control test (Bourgain et al. 2003) was performed for Hutterites.

Table A4.

Methods Used for Genotyping^[Note]

Locus	dbSNP rs#	Genotyping Method
RFP*2	209131	SBE-FP
RFP*1	1237485	SBE-FP
HS6M1-4P*1	3131088	SBE-FP
OR2J3*1	3129157	DNAPrint
OR2J2*1	3130743	SBE-FP
OR5V1*1	6930033	SBE-FP
OR5V1*2	4713210	DNAPrint
OR12D3*3	4713211	DNAPrint
OR12D3*2	2354502	DNAPrint
OR12D3*1	238881	SBE-FP
OR12D2*3		SBE-FP
OR12D2*2	4987411	DNAPrint
OR12D2*1	2073151	DNAPrint
OR11A1*1	7770592	DNAPrint
OR10C1*1	2074469	DNAPrint
OR10C1*2	2074464	DNAPrint
OR2H1*1	2021729	DNAPrint
OR2H1*2	3128854	DNAPrint
MRG*1	1233492	SBE-FP
UBD*1	444013	SBE-FP
UBD*2	362513	DNAPrint
OR2H3*1	3129034	SBE-FP
OR2H3*2	1233387	SBE-FP
GABBR1*8		Seq
GABBR1*7		Seq
GABBR1*1	881284	SBE-FP
GABBR1*19		Seq
GABBR1*6	29261	Seq
GABBR1*5		Seq
GABBR1*2	29230	SBE-FP
GABBR1*21		SBE-FP
GABBR1*22	29257	SBE-FP
GABBR1*4		In/del
GABBR1*3	29221	SBE-FP
GABBR1*24	29242	DNAPrint
GABBR1*23		DNAPrint
MOG*3	3130250	SBE-FP
MOG*2	1318631	SBE-FP
MOG*4	2535246	TM
LOC222567*1	2535238	TM
HLA-F*2	136126	DNAPrint
HLA-F*1	176925	SBE-FP
HCGIV.9*1	1610718	SBE-FP
HLA-G*-1306	1736936	Dot blot
HLA-G*-964	1632947	Dot blot
HLA-G*-725	12233334	SBE-FP
HLA-G*-689	2735022	Dot blot
HLA-G*-666	1632945	Dot blot
HLA-G*-201	1631950	Dot blot
HLA-G*1489	1624278	Dot blot
HLA-G*1538		Dot blot
HLA-G*3741ins	16375	In/del
IG.6-1	2844826	TM
IG.6-2	1054175	TM/RT
HCGIV.9*2	2517782	TM/RT
HCGIV.9*1	1056242	TM/RT
IG.6-3	2517753	TM/RT
HLA-A	2394255	LAS
IG.6-4	2394255	TM

Note.— SBE-FP, single-base extension with fluorescent polarization (Chen et al. 1999); DNAPrint, DNAPrint Genomics (see DNAPrint Genomics Web site); Seq, direct sequencing on ABI 3100 (Applied Biosystems); in/del, insertion/deletion polymorphism detected by size separation; TM, Taqman Assays-on-Demand or Assays-by-Design (Applied Biosystems); dot blot, hybridization with allele-specific probes (Aldrich et al. 2001); RT, RT-PCR to determine presence or absence of deletion (third allele) (Geraghty et al. 1992); and LAS, immobilized probe linear array system (Roche Molecular Systems) (Mirel et al. 2002).