Candidate Gene Studies of Human Pain Mechanisms

Abstract

THE elucidation of the human genome and proteome offers the clinical researcher the opportunity to test thousands of hypotheses in a single study. 1Clinical researchers in established areas such as oncology, diabetes, and cardiovascular disease have seized this opportunity. For example, PubMed searches of the disease name with the terms polymorphism and human yield more than 10,000 citations for cancer and 2,000 for diabetes or hypertension. Although there are many studies examining the genetic risk factors for variations in macroscopic structural disease that may trigger pain—e.g. , coronary stenosis, rheumatoid arthritis, or lumbar disc herniation—clinical pain researchers have made little use of these genomic riches to study variations in pain processing, given a uniform initiating injury. The plausibility of clinical pain genetic studies is supported by the recent findings of major differences between inbred mouse strains in the behavioral response to more than 20 different acute and chronic pain conditions, including thermal and chemical stimulation of the skin and viscera and nerve injury. 2–4These results suggest that genetic variants affecting pain processing are common and conserved in mammalian populations.Perhaps pain researchers have neglected clinical genetics because, apart from a few rarities, 5,6they have not noticed obvious familial inheritance of pain syndromes. However, familial inheritance only becomes obvious for alleles conferring a relative risk (RR) of 50 or more. Association studies, in which the frequencies of common allelic variants are compared in cases and controls, may detect relatively small increases in RR. Such studies have detected alterations in RR in Alzheimer disease, Crohn disease, venous thrombosis, diabetes, schizophrenia, osteoporotic fractures, and other common medical disorders. 7Allele-based association studies differ from locus-based family genetic studies in several ways. In family studies, in which the subjects share whole chromosomes or large portions thereof, several hundred genetic markers through the chromosome are sufficient to search the entire genome for a susceptibility locus. Several markers will be on the same preserved chromosome fragment as the disease susceptibility gene. Association studies are generally performed in unrelated individuals in whom only short segments of DNA are shared, so the density of markers studied over any length of DNA must be up to 1,000 times greater than in family studies. Conversely, two advantages of association studies are that they have greater power than family linkage studies to detect genetic effects of slight to modest size, 8and one has broad latitude in selecting unrelated subjects in a way to optimize the clinical phenotype and to standardize environmental exposures and measurement methods.Genetic association studies lend themselves to the study of the most perplexing problem in pain research, that after apparently identical structural injuries to a variety of tissues, pain resolves rapidly in most patients and persists in others. Well-studied examples include shingles, diabetic neuropathy, spinal degeneration, limb amputation, mastectomy, thoracotomy, or whiplash injury. Only a small part of the variance in pain persistence has been explained by age, severity of the injury, personality traits, social support, or economic status. 9,10Experience with analgesic clinical trials 11,12suggests that one can best detect pharmacologic effects of moderate size in conditions where the measurable effects of the injury overwhelm the other environmental sources of variance. In contrast, clinical trials in many idiopathic pain conditions without characteristic structural lesions (e.g. , fibromyalgia, chronic tension-type headache, irritable bowel disease, nonspecific low back pain) often yield inconsistent results even with large sample sizes. This poor signal-to-noise ratio may be due to the diverse environmental factors that prompt a small proportion of affected subjects to seek medical attention and might lower the sensitivity of genetic studies.One could theoretically maximize one’s chances for success in the candidate gene “lottery” by testing every gene. Technological advances over the next 5–10 yr will probably make it feasible to correlate a trait with multiple markers in every human gene, 1,13but at current genotyping costs of 20 cents an assay, it would cost $60,000 per patient to complete a 300,000–single nucleotide polymorphism panel. Moreover, sample sizes in the thousands would be required to overcome the statistical correction for this many multiple comparisons.With current technology, association studies must restrict their focus to a limited set of candidate genes. The purpose of this article is to propose a systematic approach to improving the odds of success in examining any clinical phenotype in its early stages of genetic analysis. In particular, we will suggest a method to prioritize the choice of candidate polymorphisms and describe the relation between the sample size and the number of candidate loci one can examine simultaneously with adequate power to detect a given RR. Association studies of pain candidate genes have already shown promise 14and may help to prioritize the hundreds of potential molecular targets for analgesic development, lead to diagnostic tests for risk of chronic pain, or identify novel pain mediators. The strategies outlined below will probably have to be modified in several years based on the actual results of the initial group of human pain candidate gene studies and technical advances in genotyping and bioinformatics.We devised a method for prioritizing candidate genes and polymorphisms for chronic pain studies by rating each polymorphism in a candidate gene according to three criteria: (1) strength of evidence supporting involvement of the gene in pain processing, (2) frequency of the specific variant, and (3) likelihood that the polymorphism alters function. We assigned each polymorphism zero to three points in each of these categories, with a maximum score of 9.1. Involvement in Pain Processing: We searched recent textbook chapters and reviews 15–17and the Society of Neuroscience abstracts from 2000 and 2001 to compile a list of approximately 200 molecules (appendix 1) that basic scientists have described to be involved in pain processing. We assigned one point for a single laboratory reporting involvement, two points for reports from multiple groups, and three points if there were multiple reports specifically describing involvement in animal models of neuropathic pain, the focus of our human genetic studies. Molecules without reported involvement in pain processing were excluded from our final priority list, even if they had maximum scores for the two other criteria.2. Frequency: Two authors (I. B., M. B. M.) performed a PubMed search for each of the 200 molecules using the search query [molecule name] AND human AND polymorphism and read pertinent abstracts and articles. We assigned zero points if the population frequency (proportion of all chromosomes) of the variant was less than 3%, one point for 3–10%, two points for 10–30%, and three points for 30–50%.3. Function: We examined articles resulting from the PubMed search for evidence of functional consequences of polymorphisms of the 200 candidate genes. We assigned one point if the variant changed an amino acid; two points for a single report that the variant changed the amount of message or protein expression or function, or was associated with a different clinical outcome from the common allele for a clinical phenotype; and three points for independent replication of any of these types of evidence.Most published association studies focus on individual polymorphisms, but the current approach of many laboratories is to type many regularly spaced markers on the candidate gene to determine haplotype blocks, which are combinations of common alleles that occur together over 10- to 100-kilobase lengths of DNA. Over each of these DNA segments, approximately 90% of individuals have one of the two to five most common haplotypes. When loci are present in haplotype blocks, their information can be combined and haplotype can be used as genotype. If approximately six loci are tested per block, there is little loss of power to detect the effect of a moderately abundant but unknown functional locus between the tested markers for that block, compared with testing that locus specifically. 18In the discussion that follows, we will usually refer to individual polymorphisms, but the same considerations and methods can be applied using the haplotype block as the unit.We assume the investigator is studying a cohort of patients exposed to the same injury or disease and genotyping all of the patients, regardless of whether they develop persistent pain. (If patients are plentiful and inexpensive to screen, genotyping only those with clinical outcomes at either extreme of the range may be more statistically informative and cost efficient. 19)Table 1shows the range of experimental variables we included in the sample size calculations. For ease of calculation, we assume that the outcome is dichotomous—e.g. , at a certain time after a uniform injury or disease, patients have either pain or no pain. Somewhat more information would be preserved if pain were analyzed as a lengthier ordinal scale or a continuous measure, yielding slightly greater power.We assumed an autosomal dominant model of inheritance, i.e. , one copy of the minor allele confers the maximal difference in phenotype from the homozygote for the major allele. However, depending on the relation between the phenotype and the amount and function of the protein coded by the gene, the appropriate model may be recessive (two copies of the minor allele change the phenotype) or codominant (two copies of the minor allele change the phenotype more than one copy). A dominant model will give more optimistic sample size estimates than a recessive model, but one can readily interconvert the two estimates by a method that will be illustrated. A codominant model 14may offer greater power than the dominant model presented, by providing richer information from the range of zero, one, or two copies of the polymorphic allele.Sample sizes are separately estimated for 10, 20, or 40% incidences of pain or other outcome of interest. RR is the incidence rate of pain in the group “exposed” to one or two variant alleles (P1), divided by the incidence rate in the “unexposed” group (P2) homozygous for the common allele. The association between the candidate genes and pain was assessed by the formulation of the hypothesis H0: P1− P2=0 versus HA: P1− P2>0, where P1− P2is the mean of the observed proportions p1− p2in the exposed and nonexposed groups. The test is based on the two-sample binomial test.We assume a biallelic model; the candidate genes are in Hardy-Weinberg equilibrium with susceptibility (minor) allele A and normal allele (major) a, and allele A has frequency of p in the population. Thus, in a recruited population, the expected sample size ratio of non-exposed group (aa) to exposed group (AA + Aa) is r = (1 − p) 2/(p2+ 2p (1 − p)). We assume a set of k candidate loci (k = 1 to 5,000) will be investigated. Candidate loci are assumed to be independent of each other. Multiple testing adjustment is performed using the usual Bonferroni error (α*=α/k). Therefore, the sample size could be overestimated, if there is linkage between the candidate genes. Sample sizes were determined assuming RRs of 1.5, 2.0, or 2.5 and minor allele or haplotype frequency ranging from 5 to 30%.The exposed and unexposed groups generally have unequal sample sizes caused by the disparate frequencies of the major and minor allele for the candidate genes. The sample size for the exposed group (nE) is estimated by (1) and (2) in appendix 2. 20The sample size for nonexposed group (nC) is then nC= r · nE. The total sample size needed is given as N = (r + 1) · nE.Table 2shows the highest ranked candidate polymorphisms for chronic neuropathic pain studies. 21–40Even at this early stage of genome research, many candidates ranked high by all our criteria based on replicated peer-reviewed articles. The largest single group code for cytokines that have been implicated in peripheral and central nervous system mechanisms in many studies of neuropathic and inflammatory pain: interleukin (IL)-6, tumor necrosis factor (TNF)-α, IL-1β, IL-10, and IL-13. Polymorphisms of other inflammatory mediators are also represented, such as neuronal and inducible nitric oxide synthase and the B1 and B2 bradykinin receptors. Other polymorphisms affect genes for neurotransmitters thought to transmit or inhibit pain, their receptors, transporters, and metabolic enzymes: the serotonin transporter, prodynorphin, μ-opioid receptor, α2A-adrenergic receptor, kainate-3 receptor, catechol-O-methyltransferase, and tyrosine hydroxylase. Another group consists of nerve growth factors and their receptors, such as glial-derived nerve growth factor, its receptor RET, and brain-derived nerve growth factor.Figures 1–3show total sample size plotted against the number of independent candidate polymorphisms tested if pain incidence is 10, 20, or 40% in the group without a pain-causing minor allele. (The calculations also apply to searches for pain-preventing alleles.) Within each of the three figures, the four panels represent the cases where the minor alleles have population frequency of 5, 10, 20, or 30%. Within each panel, the three curves represent an RR conferred by the minor allele of 1.5, 2.0, or 2.5. Figure 4shows similar sample size curves for a case in which one tests up to 5,000 independent polymorphisms.Figures 1–4show that the RR is the main factor driving sample size. Although N increases considerably as one increases the number of candidate genes from 1 to 10 (figs. 1–3), only modest increases in N are needed to test hundreds or thousands of loci one increases the incidence of the less common phenotype or population frequency of the minor allele panels each one can N common minor one can the required sample size from the curves in assume a dominant For example, a recessive model for a study of candidate genes with minor allele frequency of 30%. of individuals will be so sample sizes will be slightly greater than those for the dominant model curves in the panels of minor allele frequency of in which case one would of individuals to have at one copy of the allele. For less common minor one may the proportion of and the sample size from the curves in the of the N = from (The number will be slightly lower with minor the large number of unexposed patients a small in the number of those “exposed” to the homozygous recessive For a codominant model, one has a study and one would to make the RR the sample search of the published pain and human genetics many candidate polymorphisms, several of which have had in the published not that our system is the one or that our priority list all of the best candidate genes for neuropathic pain. We to one might approach the pain and clinical genetics to one’s of cytokines and other inflammatory mediators on the list may the of gene to diverse to of the polymorphisms in minor alleles with population frequencies greater than the power to detect or recessive or with common polymorphisms at other groups differ on which specific in many candidate genes is for protein expression and disease , there are of multiple such cases if not in multiple regularly spaced markers the gene be have this with a search of the published which was adequate to identify 20 common polymorphisms that have been to evidence for function. 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The chances of such a will be greater if many or all genes can be studied power for genetic which can be with a few more patients, one large increases in sample size to detect increases in RR (figs. 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The correction is given by the the 1 − 1 − and 1