A family-based study to identify genetic biomarkers of fibromyalgia: consideration of patients' subgroups

OBJECTIVES

Evidence from genome-wide and candidate gene association studies, familial aggregation and linkage analyses demonstrate the genetic contribution to fibromyalgia (FM) disease. This study aimed to identify genetic biomarkers of FM and its related comorbid disorders, by exploring 41 polymorphisms potentially involved in FM pathogenesis in families with at least one patient with FM.

METHODS

Core symptoms were assessed, and blood samples collected from 556 patients with FM and 395 healthy relatives. For the genetic study, a final sample of 401 FM patients and 232 healthy controls was selected, discarding patients with concomitant pathologies and controls with chronic pain. A family-based approach using DFAM test (Plink) and SNPs (single nucleotide polymorphisms) combination analyses to compare FM patients vs. controls were first applied. Second, the genotypic distribution of subgroups of FM patients, stratified by severe vs. mild symptoms of pain, depression and sleep impairment, was considered.

RESULTS

No evidence of associations with FM per se were detected, using either a family-based approach or SNPs combination analyses. However, considering the subgroups of FM patients, the SNP rs6454674 (CNR1, cannabinoid receptor 1 gene) was found as a potential genetic marker of FM correlated with depression (p<.001).

CONCLUSIONS

No significant associations using either the family-based analysis or the SNPs combination tests dissociated FM patients and their healthy relatives. FM patients with and without depression showed a significant difference in the genotypic distribution related to the SNP rs6454674 in the cannabinoid receptor 1 gene (CNR1) indicating that FM is not a homogenous disorder.

Combinations of genetic variants associated with bipolar disorder

The main objective of the study was to find genetic variants that in combination are significantly associated with bipolar disorder. In previous studies of bipolar disorder, combinations of three and four single nucleotide polymorphisms (SNP) genotypes taken from 803 SNPs were analyzed, and five clusters of combinations were found to be significantly associated with bipolar disorder. In the present study, combinations of ten SNP genotypes taken from the same 803 SNPs were analyzed, and one cluster of combinations was found to be significantly associated with bipolar disorder. Combinations from the new cluster and from the five previous clusters were identified in the genomes of 266 or 44% of the 607 patients in the study whereas none of the 1355 control participants had any of these combinations in their genome.The SNP genotypes in the smaller combinations were the normal homozygote, heterozygote or variant homozygote. In the combinations containing 10 SNP genotypes almost all the genotypes were the normal homozygote. Such a finding may indicate that accumulation in the genome of combinations containing few SNP genotypes may be a risk factor for bipolar disorder when those combinations contain relatively many rare SNP genotypes, whereas combinations need to contain many SNP genotypes to be a risk factor when most of the SNP genotypes are the normal homozygote.

Combinations of genetic data in a study of oral cancer

In the single locus strategy a number of genetic variants are analyzed, in order to find variants that are distributed significantly different between controls and patients. A supplementary strategy is to analyze combinations of genetic variants. A combination that is the genetic basis for a polygenic disorder will not occur in in control persons genetically unrelated to patients, so the strategy is to analyze combinations of genetic variants present exclusively in patients. In a previous study of oral cancer and leukoplakia 325 SNPs were analyzed. This study has been supplemented with an analysis of combinations of two SNP genotypes from among the 325 SNPs. Two clusters of combinations containing 95 patient specific combinations were significantly associated with oral cancer or leukoplakia. Of 373 patients with oral cancer 205 patients had a number of these 95 combinations in their genome, whereas none of 535 control persons had any of these combinations in their genome.

Combinations of Genetic Data Present in Bipolar Patients, but Absent in Control Persons

The main objective of the study was to find combinations of genetic variants significantly associated with bipolar disorder. In a previous study of bipolar disorder, combinations of three single nucleotide polymorphism (SNP) genotypes taken from 803 SNPs were analyzed, and four clusters of combinations were found to be significantly associated with bipolar disorder. In the present study, combinations of four SNP genotypes taken from the same 803 SNPs were analyzed, and one cluster of combinations was found to be significantly associated with bipolar disorder. Combinations from the new cluster and from the four previous clusters were identified in the genomes of 209 of the 607 patients in the study whereas none of the 1355 control participants had any of these combinations in their genome.

Operations planning with VERT

The Venture Evaluation and Review Technique (VERT) is a computerized, mathematically oriented network-based simulation technique designed to analyze risk existing in three parameters of most concern to managers in new projects or ventures--time, cost, and performance. As such, the VERT technique is more powerful than techniques such as GERT, which are basically time and cost oriented. VERT has been successfully utilized to assess the risks involved in new ventures and projects, in the estimation of future capital requirements, in control monitoring, and in the overall evaluation of ongoing projects, programs, and systems. It has been helpful to management in cases where there is a requirement to make decisions with incomplete or inadequate information about the alternatives. An example describing the application of VERT to an operational planning problem--the evaluation of electric power generating methods--is illustrated.