Ordered subset linkage analysis supports a susceptibility locus for age-related macular degeneration on chromosome 16p12

Abstract

Background: Age-related macular degeneration (AMD) is a complex disorder that is responsible for the majority of central vision loss in older adults living in developed countries. Phenotypic and genetic heterogeneity complicate the analysis of genome-wide scans for AMD susceptibility loci. The ordered subset analysis (OSA) method is an approach for reducing heterogeneity, increasing statistical power for detecting linkage, and helping to define the most informative data set for follow-up analysis. OSA assesses the linkage evidence in subsets of potentially more homogeneous families by rank-ordering family-specific lod scores with respect to trait-associated covariates or phenotypic features. Here, we present results of incorporating five continuous covariates into our genome-wide linkage analysis of 389 microsatellite markers in 62 multiplex families: Body mass index (BMI), systolic (SBP) and diastolic (DBP) blood pressure, intraocular pressure (IOP), and pack-years of cigarette smoking. Chromosome-wide significance of increases in nonparametric multipoint lod scores in covariate-defined subsets relative to the overall sample was assessed by permutation.

Genetic and Functional Dissection of ARMS2 in Age-Related Macular Degeneration and Polypoidal Choroidal Vasculopathy

“…A concordance for AMD phenotypes in twins, and a higher risk of siblings of individuals with AMD have been reported [13], [14], [15], [16], [17], [18]. These early studies lead to genome-wide linkage analyses using microsatellite markers to search for chromosomal regions associated with affected individuals [19], [20], [21], [22], [23], [24], [25], [26], [27]. Several candidate regions including 1q32 and 10q26 were confirmed by a metaanalysis [28].…”

Genetics of Age-Related Macular Degeneration: Current Concepts, Future Directions

“…For example, sibling pairs concordant for advanced AMD from the Family Age-related Maculopathy Study (FARMS) showed significant linkage to 5q33.3, 14q32.33, 16p13.13, 19q13.31, 21q21.2, 40 while other investigators who also studied populations characterized by advanced AMD did not report significant linkage to these same regions. 34,37,104,105 Similarly, an association between advanced AMD and the 16p12 region has been observed to be dependent on factors such as body mass index (BMI) and hypertension in some studies 38 , but not others. 37 In summary, genome wide scans employing both linkage and genome wide association methods have helped to identify candidate genes for AMD and candidate regions in need of further investigation.…”

“…Unlike ABCA4, ELOVL4 is located in a region (6q24–q14) previously identified from genome-wide scans to harbor AMD susceptibility genes. 35,38 A five base pair deletion in this gene has been found to be associated with an autosomal dominant Stargardt-like macular dystrophy, which has phenotypic similarity to the dry or atrophic form of AMD. The M299V (rs3812153) variant of ELOVL4 has been demonstrated to increase risk of neovascular AMD, 90 although an earlier study did not find this association for either the early or more advanced stages of AMD.…”

“…Chromosomal regions found by more than one genome wide scan to be significantly linked to all subtypes of AMD include 1q23.3– q31.1, 21,33,34,37,93,104,105 2p25.3– p12, 36,37 6q14, 35,38 9q31, 36,37 10q26, 34,37,39,93,104,106 12q23– q24.31, 35,37 15q13– q15, 35,37 16p12– p12.1, 35,37,38 17q25, 104,105 and 22q12.1. 36,93 Pooled analysis of several of these genome wide linkage studies and examining all types of AMD confirmed two of the most consistently reported chromosomal regions (1q23.3– q31.1 and 10q26) while identifying others that were not initially significant in the separate studies alone.…”

Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach

“…The most common approach to heterogeneity is to try to remove its confounding effect by data stratification. This has been done using strategies such as ordered subset analysis,23 latent class analysis,24 25 tree-based recursive partitioning,26 and clustering 2728 These methods preprocess the dataset based on genetic risk factors, demographic data, phenotypic data, or endophenotypes in order to form more homogeneous subsets of subjects.…”