Asymmetric linkage disequilibrium: Tools for assessing multiallelic LD.

Standard measures of linkage disequilibrium (LD) provide an incomplete description of the correlation between two loci. Recently, Thomson and Single (2014) described a new asymmetric pair of LD measures (ALD) that give a more complete description of LD. The ALD measures are symmetric and equivalent to the correlation coefficient r when both loci are bi-allelic. When the numbers of alleles at the two loci differ, the ALD measures capture this asymmetry and provide additional detail about the LD structure. In disease association studies the ALD measures are useful for identifying additional disease genes in a genetic region, by conditioning on known effects. In evolutionary genetic studies ALD measures provide insight into selection acting on individual amino acids of specific genes, or other loci in high LD (see Thomson and Single (2014) for these examples). Here we describe new software for computing and visualizing ALD. We demonstrate the utility of this software using haplotype frequency data from the National Marrow Donor Program (NMDP). This enhances our understanding of LD patterns in the NMDP data by quantifying the degree to which LD is asymmetric and also quantifies this effect for individual alleles.

Conditional asymmetric linkage disequilibrium (ALD): extending the biallelic r2 measure.

For multiallelic loci, standard measures of linkage disequilibrium provide an incomplete description of the correlation of variation at two loci, especially when there are different numbers of alleles at the two loci. We have developed a complementary pair of conditional asymmetric linkage disequilibrium (ALD) measures. Since these measures do not assume symmetry, they more accurately describe the correlation between two loci and can identify heterogeneity in genetic variation not captured by other symmetric measures. For biallelic loci the ALD are symmetric and equivalent to the correlation coefficient r. The ALD measures are particularly relevant for disease-association studies to identify cases in which an analysis can be stratified by one of more loci. A stratified analysis can aid in detecting primary disease-predisposing genes and additional disease genes in a genetic region. The ALD measures are also informative for detecting selection acting independently on loci in high linkage disequilibrium or on specific amino acids within genes. For SNP data, the ALD statistics provide a measure of linkage disequilibrium on the same scale for comparisons among SNPs, among SNPs and more polymorphic loci, among haplotype blocks of SNPs, and for fine mapping of disease genes. The ALD measures, combined with haplotype-specific homozygosity, will be increasingly useful as next-generation sequencing methods identify additional allelic variation throughout the genome.

Association of Parkinson disease with structural and regulatory variants in the HLA region.

Historically, association of disease with the major histocompatibility complex (HLA) genes has been tested with HLA alleles that encode antigen-binding affinity. The association with Parkinson disease (PD), however, was discovered with noncoding SNPs in a genome-wide association study (GWAS). We show here that several HLA-region SNPs that have since been associated with PD form two blocks tagged by rs3129882 (p = 9 × 10(-11)) and by rs9268515 and/or rs2395163 (p = 3 × 10(-11)). We investigated whether these SNP-associations were driven by HLA-alleles at adjacent loci. We imputed class I and class II HLA-alleles for 2000 PD cases and 1986 controls from the NeuroGenetics Research Consortium GWAS and sequenced a subset of 194 cases and 204 controls. We were therefore able to assess accuracy of two imputation algorithms against next-generation-sequencing while taking advantage of the larger imputed data sets for disease study. Additionally, we imputed HLA alleles for 843 cases and 856 controls from another GWAS for replication. PD risk was positively associated with the B(∗)07:02_C(∗)07:02_DRB5(∗)01_DRB1(∗)15:01_DQA1(∗)01:02_DQB1(∗)06:02 haplotype and negatively associated with the C(∗)03:04, DRB1(∗)04:04 and DQA1(∗)03:01 alleles. The risk haplotype and DQA1(∗)03:01 lost significance when conditioned on the SNPs, but C(∗)03:04 (OR = 0.72, p = 8 × 10(-6)) and DRB1(∗)04:04 (OR = 0.65, p = 4 × 10(-5)) remained significant. Similarly, rs3129882 and the closely linked rs9268515 and rs2395163 remained significant irrespective of HLA alleles. rs3129882 and rs2395163 are expression quantitative trait loci (eQTLs) for HLA-DR and HLA-DQ (9 × 10(-5) ≥ PeQTL ≥ 2 × 10(-79)), suggesting that HLA gene expression might influence PD. Our data suggest that PD is associated with both structural and regulatory elements in HLA. Furthermore, our study demonstrates that noncoding SNPs in the HLA region can be associated with disease irrespective of HLA alleles, and that observed associations with HLA alleles can sometimes be secondary to a noncoding variant.

Analytical methods for immunogenetic population data.

In this chapter, we describe analyses commonly applied to immunogenetic population data, along with software tools that are currently available to perform those analyses. Where possible, we focus on tools that have been developed specifically for the analysis of highly polymorphic immunogenetic data. These analytical methods serve both as a means to examine the appropriateness of a dataset for testing a specific hypothesis, as well as a means of testing hypotheses. Rather than treat this chapter as a protocol for analyzing any population dataset, each researcher and analyst should first consider their data, the possible analyses, and any available tools in light of the hypothesis being tested. The extent to which the data and analyses are appropriate to each other should be determined before any analyses are performed.

Analytical methods for disease association studies with immunogenetic data.

Disease association studies involving highly polymorphic immunogenetic data may involve analyses at one or many units of analysis, including amino acid, allele, genotype and haplotype levels, as well as consideration of gene-gene or gene-environment interactions. The selection of the appropriate statistical tests is critical and will be dependent on the nature of the dataset (e.g., case-control vs. family data) as well as the specific research hypotheses being tested. This paper describes the various study and analysis categories used for such analyses, including the advantages and limitations of such techniques.

Tracking human migrations by the analysis of the distribution of HLA alleles, lineages and haplotypes in closed and open populations.

The human leucocyte antigen (HLA) system shows extensive variation in the number and function of loci and the number of alleles present at any one locus. Allele distribution has been analysed in many populations through the course of several decades, and the implementation of molecular typing has significantly increased the level of diversity revealing that many serotypes have multiple functional variants. While the degree of diversity in many populations is equivalent and may result from functional polymorphism(s) in peptide presentation, homogeneous and heterogeneous populations present contrasting numbers of alleles and lineages at the loci with high-density expression products. In spite of these differences, the homozygosity levels are comparable in almost all of them. The balanced distribution of HLA alleles is consistent with overdominant selection. The genetic distances between outbred populations correlate with their geographical locations; the formal genetic distance measurements are larger than expected between inbred populations in the same region. The latter present many unique alleles grouped in a few lineages consistent with limited founder polymorphism in which any novel allele may have been positively selected to enlarge the communal peptide-binding repertoire of a given population. On the other hand, it has been observed that some alleles are found in multiple populations with distinctive haplotypic associations suggesting that convergent evolution events may have taken place as well. It appears that the HLA system has been under strong selection, probably owing to its fundamental role in varying immune responses. Therefore, allelic diversity in HLA should be analysed in conjunction with other genetic markers to accurately track the migrations of modern humans.

Evidence for more than one Parkinson's disease-associated variant within the HLA region.

Parkinson's disease (PD) was recently found to be associated with HLA in a genome-wide association study (GWAS). Follow-up GWAS's replicated the PD-HLA association but their top hits differ. Do the different hits tag the same locus or is there more than one PD-associated variant within HLA? We show that the top GWAS hits are not correlated with each other (0.00≤r(2)≤0.15). Using our GWAS (2000 cases, 1986 controls) we conducted step-wise conditional analysis on 107 SNPs with P<10(-3) for PD-association; 103 dropped-out, four remained significant. Each SNP, when conditioned on the other three, yielded P(SNP1) = 5×10(-4), P(SNP2) = 5×10(-4), P(SNP3) = 4×10(-3) and P(SNP4) = 0.025. The four SNPs were not correlated (0.01≤r(2)≤0.20). Haplotype analysis (excluding rare SNP2) revealed increasing PD risk with increasing risk alleles from OR = 1.27, P = 5×10(-3) for one risk allele to OR = 1.65, P = 4×10(-8) for three. Using additional 843 cases and 856 controls we replicated the independent effects of SNP1 (P(conditioned-on-SNP4) = 0.04) and SNP4 (P(conditioned-on-SNP1) = 0.04); SNP2 and SNP3 could not be replicated. In pooled GWAS and replication, SNP1 had OR(conditioned-on-SNP4) = 1.23, P(conditioned-on-SNP4) = 6×10(-7); SNP4 had OR(conditioned-on-SNP1) = 1.18, P(conditioned-on-SNP1) = 3×10(-3); and the haplotype with both risk alleles had OR = 1.48, P = 2×10(-12). Genotypic OR increased with the number of risk alleles an individual possessed up to OR = 1.94, P = 2×10(-11) for individuals who were homozygous for the risk allele at both SNP1 and SNP4. SNP1 is a variant in HLA-DRA and is associated with HLA-DRA, DRB5 and DQA2 gene expression. SNP4 is correlated (r(2) = 0.95) with variants that are associated with HLA-DQA2 expression, and with the top HLA SNP from the IPDGC GWAS (r(2) = 0.60). Our findings suggest more than one PD-HLA association; either different alleles of the same gene, or separate loci.

Juvenile idiopathic arthritis and HLA class I and class II interactions and age-at-onset effects.

OBJECTIVE: The aim of this study was to quantitate risk and to examine heterogeneity for HLA at high resolution in patients with the most common subtypes of juvenile idiopathic arthritis (JIA), IgM rheumatoid factor-negative polyarticular JIA and oligoarticular JIA. Use of 4-digit comprehensive HLA typing enabled great precision, and a large cohort allowed for consideration of both age at disease onset and disease subtype. METHODS: Polymerase chain reaction-based high-resolution HLA typing for class I and class II loci was accomplished for 820 patients with JIA and 273 control subjects. Specific HLA epitopes, potential interactions of alleles at specific loci and between loci (accounting for linkage disequilibrium and haplotypic associations), and an assessment of the current International League of Associations for Rheumatology classification criteria were considered. RESULTS: An HLA-DRB1/DQB1 effect was shown to be exclusively attributable to DRB1 and was similar between patients with oligoarticular JIA and a younger subgroup of patients with polyarticular JIA. Furthermore, patients with polyarticular JIA showed age-specific related effects, with disease susceptibility in the group older than age 6 years limited to an effect of the HLA-DRB1*08 haplotype, which is markedly different from the additional susceptibility haplotypes, HLA-DRB1*1103/1104, found in the group with oligoarticular JIA and the group of younger patients with polyarticular JIA. Also in contrast to findings for oligoarticular JIA, patients with polyarticular arthritis had no evidence of an HLA class I effect. Markers associated with a reduced risk of disease included DRB1*1501, DRB1*0401, and DRB1*0701. DRB1*1501 was shown to reduce risk across the whole cohort, whereas DRB1*0401 and DRB1*0701 were protective for selected JIA subtypes. Surprisingly, the disease predisposition mediated by DPB1*0201 in individuals without any disease-predisposing DRB1 alleles was great enough to overcome even the very strong protective effect observed for DRB1*1501. CONCLUSION: Inherited HLA factors in JIA show similarities overall as well as differences between JIA subtypes.

Analysis of maternal-offspring HLA compatibility, parent-of-origin effects, and noninherited maternal antigen effects for HLA-DRB1 in systemic lupus erythematosus.

OBJECTIVE: Genetic susceptibility to systemic lupus erythematosus (SLE) is well established, with the HLA class II DRB1 and DQB1 loci demonstrating the strongest association. However, HLA may also influence SLE through novel biologic mechanisms in addition to genetic transmission of risk alleles. Evidence for increased maternal-offspring HLA class II compatibility in SLE and differences in maternal versus paternal transmission rates (parent-of-origin effects) and nontransmission rates (noninherited maternal antigen [NIMA] effects) in other autoimmune diseases have been reported. Thus, we investigated maternal-offspring HLA compatibility, parent-of-origin effects, and NIMA effects at DRB1 in SLE. METHODS: The cohort comprised 707 SLE families and 188 independent healthy maternal-offspring pairs (total of 2,497 individuals). Family-based association tests were conducted to compare transmitted versus nontransmitted alleles (transmission disequilibrium test) and both maternally versus paternally transmitted (parent-of-origin) and nontransmitted alleles (using the chi-square test of heterogeneity). Analyses were stratified according to the sex of the offspring. Maternally affected offspring DRB1 compatibility in SLE families was compared with paternally affected offspring compatibility and with independent control maternal-offspring pairs (using Fisher's test) and was restricted to male and nulligravid female offspring with SLE. RESULTS: As expected, DRB1 was associated with SLE (P < 1 x 10(-4)). However, mothers of children with SLE had similar transmission and nontransmission frequencies for DRB1 alleles when compared with fathers, including those for the known SLE risk alleles HLA-DRB1*0301, *1501, and *0801. No association between maternal-offspring compatibility and SLE was observed. CONCLUSION: Maternal-offspring HLA compatibility, parent-of-origin effects, and NIMA effects at DRB1 are unlikely to play a role in SLE.

Sequence feature variant type (SFVT) analysis of the HLA genetic association in juvenile idiopathic arthritis.

The immune response HLA class II DRB1 gene provides the major genetic contribution to Juvenile Idiopathic Arthritis (JIA), with a hierarchy of predisposing through intermediate to protective effects. With JIA, and the many other HLA associated diseases, it is difficult to identify the combinations of biologically relevant amino acid (AA) residues directly involved in disease due to the high level of HLA polymorphism, the pattern of AA variability, including varying degrees of linkage disequilibrium (LD), and the fact that most HLA variation occurs at functionally important sites. In a subset of JIA patients with the clinical phenotype oligoarticular-persistent (OP), we have applied a recently developed novel approach to genetic association analyses with genes/proteins sub-divided into biologically relevant smaller sequence features (SFs), and their "alleles" which are called variant types (VTs). With SFVT analysis, association tests are performed on variation at biologically relevant SFs based on structural (e.g., beta-strand 1) and functional (e.g., peptide binding site) features of the protein. We have extended the SFVT analysis pipeline to additionally include pairwise comparisons of DRB1 alleles within serogroup classes, our extension of the Salamon Unique Combinations algorithm, and LD patterns of AA variability to evaluate the SFVT results; all of which contributed additional complementary information. With JIA-OP, we identified a set of single AA SFs, and SFs in which they occur, particularly pockets of the peptide binding site, that account for the major disease risk attributable to HLA DRB1. These are (in numeric order): AAs 13 (pockets 4 and 6), 37 and 57 (both pocket 9), 67 (pocket 7), 74 (pocket 4), and 86 (pocket 1), and to a lesser extent 30 (pockets 6 and 7) and 71 (pockets 4, 5, and 7).

Novel sequence feature variant type analysis of the HLA genetic association in systemic sclerosis.

We describe a novel approach to genetic association analyses with proteins sub-divided into biologically relevant smaller sequence features (SFs), and their variant types (VTs). SFVT analyses are particularly informative for study of highly polymorphic proteins such as the human leukocyte antigen (HLA), given the nature of its genetic variation: the high level of polymorphism, the pattern of amino acid variability, and that most HLA variation occurs at functionally important sites, as well as its known role in organ transplant rejection, autoimmune disease development and response to infection. Further, combinations of variable amino acid sites shared by several HLA alleles (shared epitopes) are most likely better descriptors of the actual causative genetic variants. In a cohort of systemic sclerosis patients/controls, SFVT analysis shows that a combination of SFs implicating specific amino acid residues in peptide binding pockets 4 and 7 of HLA-DRB1 explains much of the molecular determinant of risk.

High-density SNP screening of the major histocompatibility complex in systemic lupus erythematosus demonstrates strong evidence for independent susceptibility regions.

A substantial genetic contribution to systemic lupus erythematosus (SLE) risk is conferred by major histocompatibility complex (MHC) gene(s) on chromosome 6p21. Previous studies in SLE have lacked statistical power and genetic resolution to fully define MHC influences. We characterized 1,610 Caucasian SLE cases and 1,470 parents for 1,974 MHC SNPs, the highly polymorphic HLA-DRB1 locus, and a panel of ancestry informative markers. Single-marker analyses revealed strong signals for SNPs within several MHC regions, as well as with HLA-DRB1 (global p = 9.99 x 10(-16)). The most strongly associated DRB1 alleles were: *0301 (odds ratio, OR = 2.21, p = 2.53 x 10(-12)), *1401 (OR = 0.50, p = 0.0002), and *1501 (OR = 1.39, p = 0.0032). The MHC region SNP demonstrating the strongest evidence of association with SLE was rs3117103, with OR = 2.44 and p = 2.80 x 10(-13). Conditional haplotype and stepwise logistic regression analyses identified strong evidence for association between SLE and the extended class I, class I, class III, class II, and the extended class II MHC regions. Sequential removal of SLE-associated DRB1 haplotypes revealed independent effects due to variation within OR2H2 (extended class I, rs362521, p = 0.006), CREBL1 (class III, rs8283, p = 0.01), and DQB2 (class II, rs7769979, p = 0.003, and rs10947345, p = 0.0004). Further, conditional haplotype analyses demonstrated that variation within MICB (class I, rs3828903, p = 0.006) also contributes to SLE risk independent of HLA-DRB1*0301. Our results for the first time delineate with high resolution several MHC regions with independent contributions to SLE risk. We provide a list of candidate variants based on biologic and functional considerations that may be causally related to SLE risk and warrant further investigation.

Balancing selection and heterogeneity across the classical human leukocyte antigen loci: a meta-analytic review of 497 population studies.

This paper presents a meta-analysis of high-resolution human leukocyte antigen (HLA) allele frequency data describing 497 population samples. Most of the datasets were compiled from studies published in eight journals from 1990 to 2007; additional datasets came from the International Histocompatibility Workshops and from the AlleleFrequencies.net database. In all, these data represent approximately 66,800 individuals from throughout the world, providing an opportunity to observe trends that may not have been evident at the time the data were originally analyzed, especially with regard to the relative importance of balancing selection among the HLA loci. Population genetic measures of allele frequency distributions were summarized across populations by locus and geographic region. A role for balancing selection maintaining much of HLA variation was confirmed. Further, the breadth of this meta-analysis allowed the ranking of the HLA loci, with DQA1 and HLA-C showing the strongest balancing selection and DPB1 being compatible with neutrality. Comparisons of the allelic spectra reported by studies since 1990 indicate that most of the HLA alleles identified since 2000 are very-low-frequency alleles. The literature-based allele-count data, as well as maps summarizing the geographic distributions for each allele, are available online.

Searching for additional disease loci in a genomic region.

Our aim is to review methods to optimize detection of all disease genes in a genetic region. As a starting point, we assume there is sufficient evidence from linkage and/or association studies, based on significance levels or replication studies, for the involvement in disease risk of the genetic region under study. For closely linked markers, there will often be multiple associations with disease, and linkage analyses identify a region rather than the specific disease-predisposing gene. Hence, the first task is to identify the primary (major) disease-predisposing gene or genes in a genetic region, and single nucleotide polymorphisms thereof, that is, how to distinguish true associations from those that are just due to linkage disequilibrium with the actual disease-predisposing variants. Then, how do we detect additional disease genes in this genetic region? These two issues are of course very closely interrelated. No existing programs, either individually or in aggregate, can handle the magnitude and complexity of the analyses needed using currently available methods. Further, even with modern computers, one cannot study every possible combination of genetic markers and their haplotypes across the genome, or even within a genetic region. Although we must rely heavily on computers, in the final analysis of multiple effects in a genetic region and/or interaction or independent effects between unlinked genes, manipulation of the data by the individual investigator will play a crucial role. We recommend a multistrategy approach using a variety of complementary methods described below.

Effect of linkage disequilibrium between markers in linkage and association analyses.

Contributions to Group 17 of the Genetic Analysis Workshop 15 considered dense markers in linkage disequilibrium (LD) in the context of either linkage or association analysis. Three contributions reported on methods for modeling LD or selecting a subset of markers in linkage equilibrium to perform linkage analysis. When all markers were used without modeling LD, inflated evidence for linkage was observed when parental genotypes were missing. All methods for handling LD led to some decreased linkage evidence. Two groups performed a genome-wide association scan using either mixed models to account for known or unknown relatedness between individuals, trend tests or combination statistics. All methods failed to detect four of the eight simulated loci because of low LD in some regions. Three groups performed association analysis using simulated dense markers on chromosome 6, where a simulated HLA-DRB1 locus played a major role in disease susceptibility along with two additional loci of smaller effect. The overall conditional genotype method correctly identified both additional loci while a novel transmission disequilibrium test-statistic to combine studies with non-overlapping markers identified one HLA locus after stratifying on the parental HLA-DRB1 genotypes; LD mapping using the Malécot model mapped two loci in this region, even when using greatly reduced marker density. While LD between markers appears to be a nuisance that may cause spurious linkage results with missing parental genotypes in linkage analysis, association analysis thrives on LD, and disease genes fail to be detected in regions of low LD.

Conditional genotype analysis: detecting secondary disease loci in linkage disequilibrium with a primary disease locus.

A number of autoimmune and other diseases have well established HLA associations; in many cases there is strong evidence for the direct involvement of the HLA class II peptide-presenting antigens, e.g., HLA DR-DQ for type 1 diabetes (T1D) and HLA-DR for rheumatoid arthritis (RA). The involvement of additional HLA region genes in the disease process is implicated in these diseases. We have developed a model-free approach to detect these additional disease genes using genotype data; the conditional genotype method (CGM) and overall conditional genotype method (OCGM) use all patient and control data and do not require haplotype estimation. Genotypes at marker genes in the HLA region are stratified and their expected values are determined in a way that removes the effects of linkage disequilibrium (LD) with the peptide-presenting HLA genes directly involved in the disease. A statistic has been developed under the null hypothesis of no additional disease genes in the HLA region for the OCGM method and was applied to the Genetic Analysis Workshop 15 simulated data set of Problem 3, which mimics RA (answers were known). In addition to the primary effect of the HLA DR locus, the effects of the other two HLA region simulated genes involved in disease were detected (gene C, 0 cM from DR, increases RA risk only in women; and gene D, 5.12 cM from DR, rare allele increases RA risk five-fold). No false negatives were found. Power calculations were performed.

Heterogeneity at the HLA-DRB1 locus and risk for multiple sclerosis.

Variation in major histocompatibility complex genes on chromosome 6p21.3, specifically the human leukocyte antigen HLA-DR2 or DRB1*1501-DQB1*0602 extended haplotype, confers risk for multiple sclerosis (MS). Previous studies of DRB1 variation and both MS susceptibility and phenotypic expression have lacked statistical power to detect modest genotypic influences, and have demonstrated conflicting results. Results derived from analyses of 1339 MS families indicate DRB1 variation influences MS susceptibility in a complex manner. DRB1*15 was strongly associated in families (P=7.8x10(-31)), and a dominant DRB1*15 dose effect was confirmed (OR=7.5, 95% CI=4.4-13.0, P<0.0001). A modest dose effect was also detected for DRB1*03; however, in contrast to DRB1*15, this risk was recessive (OR=1.8, 95% CI=1.1-2.9, P=0.03). Strong evidence for under-transmission of DRB1*14 (P=5.7x10(-6)) even after accounting for DRB1*15 (P=0.03) was present, confirming a protective effect. In addition, a high risk DRB1*15 genotype bearing DRB1*08 was identified (OR=7.7, 95% CI=4.1-14.4, P<0.0001), providing additional evidence for trans DRB1 allelic interactions in MS. Further, a significant DRB1*15 association observed in primary progressive MS families (P=0.0004), similar to relapsing-remitting MS families, suggests that DRB1-related mechanisms are contributing to both phenotypes. In contrast, results obtained from 2201 MS cases argue convincingly that DRB1*15 genotypes do not modulate age of onset, or significantly influence disease severity measured using expanded disease disability score and disease duration. These results contribute substantially to our understanding of the DRB1 locus and MS, and underscore the importance of using large sample sizes to detect modest genetic effects, particularly in studies of genotype-phenotype relationships.

Signatures of demographic history and natural selection in the human major histocompatibility complex Loci.

Many lines of evidence show that several HLA loci have experienced balancing selection. However, distinguishing among demographic and selective explanations for patterns of variation observed with HLA genes remains a challenge. In this study we address this issue using data from a diverse set of human populations at six classical HLA loci and, employing a comparative genomics approach, contrast results for HLA loci to those for non-HLA markers. Using a variety of analytic methods, we confirm and extend evidence for selection acting on several HLA loci. We find that allele frequency distributions for four of the six HLA loci deviate from neutral expectations and show that this is unlikely to be explained solely by demographic factors. Other features of HLA variation are explained in part by demographic history, including decreased heterozygosity and increased LD for populations at greater distances from Africa and a similar apportionment of genetic variation for HLA loci compared to putatively neutral non-HLA loci. On the basis of contrasts among different HLA loci and between HLA and non-HLA loci, we conclude that HLA loci bear detectable signatures of both natural selection and demographic history.

PTPN22 genetic variation: evidence for multiple variants associated with rheumatoid arthritis.

The minor allele of the R620W missense single-nucleotide polymorphism (SNP) (rs2476601) in the hematopoietic-specific protein tyrosine phosphatase gene, PTPN22, has been associated with multiple autoimmune diseases, including rheumatoid arthritis (RA). These genetic data, combined with biochemical evidence that this SNP affects PTPN22 function, suggest that this phosphatase is a key regulator of autoimmunity. To determine whether other genetic variants in PTPN22 contribute to the development of RA, we sequenced the coding regions of this gene in 48 white North American patients with RA and identified 15 previously unreported SNPs, including 2 coding SNPs in the catalytic domain. We then genotyped 37 SNPs in or near PTPN22 in 475 patients with RA and 475 individually matched controls (sample set 1) and selected a subset of markers for replication in an additional 661 patients with RA and 1,322 individually matched controls (sample set 2). Analyses of these results predict 10 common (frequency >1%) PTPN22 haplotypes in white North Americans. The sole haplotype found to carry the previously identified W620 risk allele was strongly associated with disease in both sample sets, whereas another haplotype, identical at all other SNPs but carrying the R620 allele, showed no association. R620W, however, does not fully explain the association between PTPN22 and RA, since significant differences between cases and controls persisted in both sample sets after the haplotype data were stratified by R620W. Additional analyses identified two SNPs on a single common haplotype that are associated with RA independent of R620W, suggesting that R620W and at least one additional variant in the PTPN22 gene region influence RA susceptibility.

Hardy-Weinberg testing of a single homozygous genotype.

No proper statistical test is available for the evaluation of deviation of a single homozygous genotype from Hardy-Weinberg equilibrium (HWE) proportion. We propose a 1-d.f. chi2-test. The power of the proposed test is favorable compared to existing HWE testing procedures. The applications of this test are discussed.