Application of Linkage to SLE

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There are several different study designs with variable ascertainment approaches that have been used for genomewide scan to identify novel susceptibility loci for SLE. Some of the study designs involve (a) sibling pairs who may or may not have parents available or (b) small and large pedigrees with several generations. Numerous genome scans have been made by the four major scientific groups (located in California, Oklahoma, and Minnesota in the United States and in Sweden), revealing many loci spread across the genome (reviewed in refs. 33 and 34). There are seven major cytogenetic locations, which show significant evidence of linkage to SLE based on the recommended criteria

Table 2

Different Studies for Whole-Genome Scan for Finding SLE Susceptibility Loci

Table 2

Different Studies for Whole-Genome Scan for Finding SLE Susceptibility Loci

Study

Study

Number

Major ethnicity

Major linkage Reference

center

design

of families

(%)

findings

OMRF 1

Extended pedigrees

94

Caucasian (58), African American (33)

1q23, 1q25, 13q32, 20q13

64

OMRF 2

Extended pedigrees

126

Caucasian (63), African American (27)

4p16-16, 1q22-24

65

UMN 1

Sibpairs

105

Caucasian (80), Hispanic (8), African American (5)

6p11-21, 16q13, 14q21, 20p12

66

UMN 2

Sibpairs

82

Caucasian (78), African American (15), Hispanic (6)

7p22, 7q21, 10p13, 7q36

67

UMN 1 + 2

Sibpairs

187

Caucasian (79), African American (10), Hispanic (7)

6p11-12, 16q13, 2p15

67

USC

Extended pedigrees

80

Caucasian (46), Hispanic (54)

1q43

68

Uppsala

Extended

pedigrees

17

Caucasian (100)

2q37, 4p15-13, 19p13, 19q13

13

OMRF, Oklahoma Medical Research Foundation; USC, University of Southern California; UMN, University of Minnesota; Uppsaella, Uppsala University, Sweden.

OMRF, Oklahoma Medical Research Foundation; USC, University of Southern California; UMN, University of Minnesota; Uppsaella, Uppsala University, Sweden.

for genome scan. These key regions, along with several suggested regions identified by at least two independent groups, are summarized in Table 2 and Fig. 1.

However, for the reasons discussed, linkages to many loci have not been replicated across different population groups and studies, although replication in SLE may be better than that in many other genetically complex human diseases (see ref. 34 for complete details of these data). Thus far, genomewide scanning has led to the identification of only one susceptibility gene for SLE, the programmed cell death 1 gene (PDCD1, also called PD-1) (14).

2.4.1. Pedigree Stratification Strategy

As discussed, SLE is a complex autoimmune disease with a definite genetic predisposition. However, the exploration of SLE genetics is in its infancy. SLE is an extremely complicated clinical illness with a wide range of manifestations. Clinical manifestations of SLE can be very diverse, with glomerulonephritis, dermatitis, thrombosis, vasculitis, seizures, arthritis, hemolytic anemia, and thrombocytopenia counted among the disease's manifestations. Consequently, the variation between patients is incredible. Indeed, it is possible to have two patients afflicted with SLE who satisfy the classification criteria three different ways with no features in common. This degree of clinical heterogeneity may be because of the involvement of multiple major and modifier genes. Thus far, the genome scans have been performed using a general SLE phenotype.

As an alternative, a set of "etiologic classes" could be considered, reflecting different genes or interactive combinations resulting in SLE for particular subsets of individuals or families (the "pedigree stratification approach"). Detection of a main effect will depend on the relative proportion of individuals carrying a particular genetic variant (or interactive combination that includes that gene) among the individuals studied. As this proportion is likely to fluctuate between data sets, it is unlikely that a particular linkage finding could be replicated in many other data sets, even if the same underlying model were at play. This is because the combination of families from different "classes" in the same linkage or association study will reduce the ability to detect the effects of any particular gene. The well-known example is the BCR1 gene, found only when early-onset breast cancer was considered among families that also had ovarian cancer.

The strategy of using pedigree stratification as a way to discover linkage effects has only been pursued by our Oklahoma group. We have taken the advantage of a pedigree stratification strategy from our huge collection of pedigrees with relevant clinical and medical information available for each individual, especially for the patients with SLE. Therefore, the extraordinary clinical heterogeneity in lupus is consistent with this phenotype as a treasure trove of genetic linkages based on stratifying pedigrees by clinical or demographic features. The rationale behind the subgrouping of the SLE families with a common clinical feature is to make the SLE families more genetically homogeneous. It was anticipated that, regardless of the actual number of genes involved in SLE, decreasing sample heterogeneity by subgrouping families based on race and common associated traits would increase the likelihood of identifying genes for SLE. Although this approach has many advantages, the major disadvantage is the reduced sample size after phenotype stratification, which urgently requires independent verification of the findings.

Thrombocytopenia And Genetic
  1. 1. Several groups have performed independent genomewide linkage studies. Results established and confirmed, or with suggestive conformation, are shown by chromosomal location. Oklahoma, Oklahoma Medical Research Foundation; Uppsala, Uppsala University, Sweden; California, University of Southern California; Minnesota, University of Minnesota.
  2. 1. Several groups have performed independent genomewide linkage studies. Results established and confirmed, or with suggestive conformation, are shown by chromosomal location. Oklahoma, Oklahoma Medical Research Foundation; Uppsala, Uppsala University, Sweden; California, University of Southern California; Minnesota, University of Minnesota.
  3. 4.2. Effect of Stratification

Because we are using a pedigree stratification strategy and dealing with a very low number of families, there is always a chance for false-positive results. Here, we have used an ad hoc criterion to adjust the critical value for declaring the significance of a linkage. For example, we performed the linkage analyses on a subset from our total pedigree collection, in which pedigrees were ascertained by the presence or absence of a certain phenotype (e.g., rheumatoid arthritis positive or negative, although study phenotype for linkage was maintained as SLE), and six different parametric models (based on different penetrances because the true model is unknown) along with a nonparametric model were tested in initial genome screen so that several genome scans were performed. This is a case of a multiple testing problem in which 14 [2 x (6 + 1)] tests were performed.

To assess the significance of any linkage findings, we may use two types of experimental solutions. First, we took a simulation-based approach. We randomly selected n (number of pedigrees used for subset analysis) pedigrees from the total available pedigrees 10,000 times (sampling with replacement) and calculated the logarithmic odds (LOD) score at the peak marker for each resampled set of n to determine an empirical distribution of LOD scores. Therefore, this gives an empirical p value for our original n selected pedigrees from the observed LOD score distribution.

Second, we have used an ad hoc correction designed to maintain the overall genomewide significance level (5% level) to detect the significant linkage by raising the LOD score limit to 4.4. This is calculated as LOD(new) = LOD(conv) + log10(#test) (35,36), where LOD(conv) is the conventional LOD score to be significant at 3.3. To date, we have identified many genomic regions with high statistical significance that may harbor genes predisposing to SLE. Some of the most convincing linkage results (Table 3) have been found by subgrouping the SLE families based on a specific autoimmune feature, for example, thrombocytopenia, hemolytic anemia, or diagnosis of rheumatoid arthritis.

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