Bonnie A. Fijal

Dissection of Genomewide-Scan Data in Extended Families Reveals a Major Locus and Oligogenic Susceptibility for Age-Related Macular Degeneration

To examine the genetic basis of age-related macular degeneration (ARMD), a degenerative disease of the retinal pigment epithelium and neurosensory retina, we conducted a genomewide scan in 34 extended families (297 individuals, 349 sib pairs) ascertained through index cases with neovascular disease or geographic atrophy. Family and medical history was obtained from index cases and family members. Fundus photographs were taken of all participating family members, and these were graded for severity by use of a quantitative scale. Model-free linkage analysis was performed, and tests of heterogeneity and epistasis were conducted. We have evidence of a major locus on chromosome 15q (GATA50C03 multipoint P=1.98x10-7; empirical P< or =1.0x10-5; single-point P=3.6x10-7). This locus was present as a weak linkage signal in our previous genome scan for ARMD, in the Beaver Dam Eye Study sample (D15S659, multipoint P=.047), but is otherwise novel. In this genome scan, we observed a total of 13 regions on 11 chromosomes (1q31, 2p21, 4p16, 5q34, 9p24, 9q31, 10q26, 12q13, 12q23, 15q21, 16p12, 18p11, and 20q13), with a nominal multipoint significance level of P< or =.01 or LOD > or =1.18. Family-by-family analysis of the data, performed using model-free linkage methods, suggests that there is evidence of heterogeneity in these families. For example, a single family (family 460) individually shows linkage evidence at 8 loci, at the level of P<.0001. We conducted tests for heterogeneity, which suggest that ARMD susceptibility loci on chromosomes 9p24, 10q26, and 15q21 are not present in all families. We tested for mutations in linked families and examined SNPs in two candidate genes, hemicentin-1 and EFEMP1, in subsamples (145 and 189 sib pairs, respectively) of the data. Mutations were not observed in any of the 11 exons of EFEMP1 nor in exon 104 of hemicentin-1. The SNP analysis for hemicentin-1 on 1q31 suggests that variants within or in very close proximity to this gene cause ARMD pathogenesis. In summary, we have evidence for a major ARMD locus on 15q21, which, coupled with numerous other loci segregating in these families, suggests complex oligogenic patterns of inheritance for ARMD.

A Whole-Genome Screen of a Quantitative Trait of Age-Related Maculopathy in Sibships from the Beaver Dam Eye Study

Age-related maculopathy (ARM) is a leading cause of visual impairment among the elderly in Western populations. To identify ARM-susceptibility loci, we genotyped a subset of subjects from the Beaver Dam (WI) Eye Study and performed a model-free genomewide linkage analysis for markers linked to a quantitative measure of ARM. We initially genotyped 345 autosomal markers in 325 individuals ( N =263 sib pairs) from 102 pedigrees. Ten regions suggestive of linkage with ARM were observed on chromosomes 3, 5, 6, 12, 15, and 16. Prior to fine mapping, the most significant regions were an 18-cM region on chromosome 12, near D12S1300 ( P =.0159); a region on chromosome 3, near D3S1763, with a P value of .0062; and a 6-cM region on chromosome 16, near D16S769, with a P value of .0086. After expanding our analysis to include 25 additional fine-mapping markers, we found that a 14-cM region on chromosome 12, near D12S346 (located at 106.89 cM), showed the strongest indication of linkage, with a P value of .004. Three other regions, on chromosomes 5, 6, and 15, that were nominally significant at P ≤.01 are also appropriate for fine mapping.

Clinical Trials in the Genomic Era: Effects of Protective Genotypes on Sample Size and Duration of Trial

It is well known that individuals can vary widely in their disease susceptibilities. One potential source of this variation is the genetic makeup of individuals, which can confer either protection or susceptibility to disease. Here we examine the effects of protective genotypes on the sample sizes and time required to detect differences between clinical trial arms. We show that including individuals with protective genotypes in a clinical trial can increase required sample sizes and trial duration. One can deal with this issue by pregenotyping subjects and selectively enrolling them based on their genotype. Thus we also calculate the number of individuals that must be recruited and pregenotyped to fulfill sample size requirements. The benefits of genotypically screening study subjects will depend on numerous factors, including ease of patient recruitment, cost of genotyping, long-term costs of study (or long-term cost per subject), and the strength of the protective effect. We present several examples that show the potential value of incorporating information about protective genotypes into a clinical trial.

Hierarchical modeling of the relation between sequence variants and a quantitative trait: addressing multiple comparison and population stratification issues.

When analyzing the relation between genetic sequence information and disease traits, false‐positive associations can arise due to multiple comparisons and population stratification. In an attempt to address these issues, we incorporate into a conventional analytic model higher‐level—or “prior”—models that use additional information to improve estimates while allowing for differing population structures. We apply this hierarchical model to simulated data from the Genetic Analysis Workshop 12. We focus on the effects of common candidate gene sequence variants on quantitative risk factor 5 (Q5) levels. In particular, we compare the regression coefficients (and 95% confidence intervals) obtained from conventional (one‐stage) analyses versus the corresponding results from the hierarchical analyses. When examining either the marry‐ins or all subjects in the general and isolate populations, the conventional model detected numerous sites in candidate genes 1–5 and 7 that had statistically significant regression coefficients (alpha level = 0.05). In contrast, our hierarchical model primarily only detected associations for variants in candidate gene 2, which is the casual gene for Q5.

The Genetic Epidemiology of Age-Related Maculopathy

Abstract Age-related maculopathy is a leading cause of blindness in the elderly. It is a major public health issue of increasing importance as populations become older. Currently considered untreatable, it is a complex disease associated with both genetic and environment factors. As currently reviewed, the importance of genetics in the etiology of age-related macular degeneration has been demonstrated by family studies, twin studies and segregation analysis. Ongoing research at the molecular level is endeavoring to isolate genes involved in the pathogenesis of this complex disease with the goal of identifying those individuals who are susceptible to impairment of visual function prior to overt manifestation of disease. The ultimate aim of this research is to identify molecular targets for appropriate and early therapeutic intervention.

Predicting quantitative trait levels by modeling SNP interaction.

Predicting phenotype from genotype is difficult when the phenotype is affected by a gene with numerous weakly penetrant alleles that differ only in the pattern of their single nucleotide polymorphisms (SNPs). While it is probable that SNP interactions affect phenotype, to our knowledge no one has determined the most effective way of evaluating whether SNPs interact and of modeling the interaction. Therefore, to explore this issue, we investigate here three methods of modeling SNP interaction using data from Genetic Analysis Workshop 12. Since major gene 5 (MG5) has sequence information and explains 37% of the variation in quantitative trait 5 (Q5), we focus on using SNPs within MG5 to predict Q5 among individuals who married into the pedigree. As a preliminary screening step, we reduced the number of SNPs from 269 to 34 based on their association with Q5. In our first models we assumed that SNPs affected Q5 in a simple additive manner. These models explained 34% and 15% of the variation in Q5 in women and men, respectively. Our second model was a linear model, which used individual SNPs and simple interaction terms as predictors. These models explained 36% and 16% of the variation in Q5 levels for women and men, respectively. Our last model was a “hit”‐based model which was motivated by the hypothesis that disequilibrium between SNPs may reflect the fact that SNPs affect phenotype by acting in concert with other SNPs within their “disequilibrium set.” Thus, the number of hits within the disequilibrium sets were used as predictors. These models explained 35% and 19% of the variation in Q5 for women and men, respectively. Our results suggest that phenotype can be predicted from complex patterns of weakly penetrant SNPs using relatively simple models. We concluded that SNP interaction either was not included in the simulation model, or had only a weak impact on Q5 levels.

EVALUATING THE PERFORMANCE OF A GENETIC TEST USING INFORMATION FROM PREVIOUS STUDIES IN PLACE OF A GOLD STANDARD

OBJECTIVES To evaluate the performance of a diagnostic test, a researcher usually must classify study subjects with respect to (1) whether the test result was positive or negative and (2) whether the test result should have been positive or negative. To classify the subjects in the second manner, the researcher needs to have access to a gold standard (ie, a test that classifies the subjects with 100% accuracy). The authors show here how to evaluate the performance of a diagnostic test that allows researchers to determine whether a disease that is occurring within a family is attributable to one of two newly discovered genes without the use of a gold standard. METHODS By taking advantage of well-known genetic phenomena and their statistical implications, the behavior of the diagnostic test is mathematically modeled, and its performance with respect to various criteria is shown to be functions of genetic parameters. RESULTS The performance of the test over a wide range of values of the genetic parameters was evaluated, and cutoff points that would allow the test to perform very well or well with respect to all criteria were found for almost all of the situations examined. CONCLUSIONS This test can be used effectively under a wide range of conditions. In addition, because the genetic parameters have been estimated in previous studies, the effectiveness of the test for the specific conditions the researcher may need to run the study under can be evaluated before the study is performed.