Mary L. Marazita
Katherine Neiswanger
Association methods are used as an adjunct to linkage approaches for gene mapping, especially for complex traits. In association analysis, one compares the allele frequencies of a genetic marker or candidate gene between groups of affected individuals vs. controls. If allele frequencies differ significantly between the two groups, then a specific allele at the marker or candidate locus is said to be associated with the disease at the population level. Genetic linkage between a marker and a disease gene implies that alleles at the marker locus co-segregate with the disease allele within families. With linkage, different marker alleles may co-segregate with the disease allele in different families and the overall frequencies of the marker alleles, as calculated from population-based samples, need not vary between affected and control groups. For the purposes of this discussion, two loci are in linkage equilibrium when they are linked but not associated. Linkage disequilibrium occurs when two genes are both linked within families and associated in the population.
The procedures for mapping, cloning, and characterizing genes for rare, Mendelian diseases are now well established. Consequently, progress in mapping Mendelian traits has been dramatic over the past several years. In contrast, genes contributing to common, complex diseases such as oral-facial clefting have been much more difficult to isolate. Complex diseases are those for which no single dominant or recessive mode of inheritance adequately explains the observed patterns of transmission in families. In the absence of specific genetic models, the etiology of complex diseases is often conceptualized as being due to multiple factors, i.e., several genetic loci interacting with each other to produce an underlying susceptibility, which in turn interacts with additional environmental factors to produce an actual disease state. This concept does not exclude the possibility that Mendelian genes for a complex disorder segregate in atypical families or that one or a few of the underlying susceptibility genes exert relatively large effects. Indeed, for several complex disorders, including coronary artery disease (Ozturk and Killeen, 1999), breast cancer (Bennett et al., 1999), and Alzheimer's disease (St. George-Hyslop, 2000), linkage analysis has been used successfully to locate unusual genetic mutations that exert major effects in a subset of families.
However, for other complex traits, such as bipolar disorder (Berrettini, 2000), obesity (Chagnon et al., 1998), and oral-facial clefting (Murray, 1995; Carinci et al., 2000), linkage analysis has produced either negative results or a plethora of weak, positive results that are not easily replicated. Theoretical research suggests several reasons for the ambiguity of the linkage results in these cases. First, if a disease gene is neither necessary nor sufficient to cause a disease but rather is a “modifier” gene that elevates a non-zero baseline risk, conventional parametric linkage analysis may not detect it, even for close genetic linkage (Greenberg, 1993).
Second, if the relative contribution of a gene to a disease phenotype is small, i.e., the disease susceptibility allele raises the risk by a factor of <2, linkage analysis using affected sib pairs will not be powerful enough to detect the gene, given realistic sample sizes (Risch and Merikangas, 1996). Thus, linkage analysis may not be a useful strategy to detect modifier genes or genes that exert small effects, precisely those genes which might operate in oral-facial clefting and many other complex disorders. In light of these issues, attention has shifted away from linkage analysis to association analysis as an alternative means of locating disease-susceptibility genes, especially since association studies can sometimes detect weaker effects than linkage analysis (Hodge, 1994).
Two types of association analysis are commonly employed in genetic studies: population-based and family-based (Hodge, 1993). The population-based approach utilizes a standard case-control design, in which marker allele frequencies are compared between cases (affected individuals) and controls (either unaffected individuals or individuals randomly chosen from the population). When a positive association is found, several interpretations are possible: (1) the associated allele itself is the disease-predisposing allele, (2) the associated allele is in linkage disequilibrium with the actual disease-predisposing locus, (3) the association is due to population stratification, or (4) the association is a sampling, or statistical, artifact.
The first two interpretations represent the alternative hypotheses of interest in a gene-mapping context. These two alternatives are not readily distinguishable using either population-based or family-based association strategies (Hodge, 1994). In case 1, the marker itself is the disease-susceptibility locus. This outcome is the rationale behind candidate-gene studies, in which the genes being tested have some a priori expectation of being directly involved in the disease process. A classic example is the human leukocyte antigen (HLA) system, in which various HLA haplotypes are associated with a number of diseases, including insulin-dependent diabetes mellitus, rheumatoid arthritis, and ankylosing spondylitis (Thomson, 1988).
Case 2 occurs if the actual disease-susceptibility mutation is at a second, unknown locus that is so close to the marker that it is still in linkage disequilibrium with it. This interpretation may be invoked if there is no obvious functional connection between the marker and the disease, e.g., if the associated allele is not located within the promoter or coding region of a gene. To fully appreciate the nature of associations due to linkage disequilibrium, it is necessary to consider carefully how linkage disequilibrium occurs. When a new mutation first appears in the genome, it occurs on one specific chromosome and is in complete linkage disequilibrium with any polymorphic markers in the adjacent DNA. For example, if a mutation, M, occurs near the Al allele of genetic locus A (with allele frequencies of 0.6 and 0.4 for Al and A2, respectively), Al will be associated with 100% of the M mutations, while A2 is never associated with M mutations. This is a population-based association between M and Al, due to complete linkage disequilibrium between mutation M and locus A. With time, recombination will shuffle M and Al so that eventually M will be on the same chromosome as A1 60% of the time and on the same chromosome as A2 the other 40% of the time. Linkage still exists, but the population-based association due to linkage disequilibrium is gone; i.e., M and A have reached linkage equilibrium.
In the absence of selection, the degree of linkage disequilibrium depends on two factors: (1) the distance of the marker from the disease-susceptibility mutation and (2) the time elapsed since either the disease or the marker mutation occurred. A relatively recent disease-susceptibility mutation will be in linkage disequilibrium with its adjacent markers as a function of the genetic distance between disease mutation and marker. For example, disease mutations occurring up to 60,000 years ago demonstrate linkage disequilibrium with adjacent markers when the recombination rate between them is 1 in 1000 (Cavalli-Sforza and Bodmer, 1971). However, except for the most tightly linked markers, ancient disease-susceptibility mutations will show a pattern of linkage disequilibrium that does not depend solely on the distance to adjacent markers but also on the time at which the adjacent markers mutated. Recent marker mutations will be in varying degrees of linkage disequilibrium with the disease mutation, while older marker mutations will not, even when they are physically closer to it. Thus, lack of association still allows for the possibility that the marker is closely linked but in linkage equilibrium with the disease-susceptibility gene. A positive association suggests that the disease-susceptibility gene is close to the marker, probably within 1 cM and possibly within kilobases of it. However, the degree of linkage disequilibrium can vary throughout the genome or in different populations. In specific cases, such as genetically isolated populations, linkage disequilibrium can exist even if the distance between a marker and a disease locus is greater than 1 cM. However, a positive result from a population-based association test can also be artifactual, either because of population stratification (case 3 above) or sampling error (case 4). Stringent significance levels and larger sample sizes can help to reduce sampling error. Population stratification is more problematic. If the marker allele frequencies differ among ethnic groups and the case and control samples contain different proportions of these ethnic groups, an artifactual positive association due to allele frequency differences among ethnic groups may be misinterpreted as evidence for a disease-susceptibility gene. Thus, case and control groups should be matched as closely as possible for ethnicity, to avoid creating an association due to population stratification. The population-based association of alcoholism with the TaqI Al allele from the dopamine D2 receptor locus illustrates the difficulty in deciding among various possible interpretations (Neiswanger et al., 1995a,b).
To avoid the effects of unknown population stratification, alternative association analysis strategies that use family members as controls have been proposed.
Such statistical methods are collectively known as family-based association studies (Hodge, 1993). Tests of this type include genotype- and haplotype-based haplotype relative risk methods (Falk and Rubinstein, 1987; Terwilliger and Ott, 1992), affected family-based controls (Thomson, 1995), the heterozygote transmission test (Swift et al., 1990), and the transmission disequilibrium test (TDT) (Spielman et al., 1993; Ewens and Spielman, 1995). Of these methods, the TDT has been widely applied in studies of complex traits, including a oral-facial clefting. The TDT determines whether a given marker allele is transmitted from a heterozygous parent to an affected child more often than the 50% expected frequency. The TDT cannot detect either linkage or association in the absence of linkage disequilibrium. Therefore, in general populations, the TDT will not detect linkage at distances much greater than 1 cM. However, neither will it generate an artifactual association due to population stratification.
Spielman and co-workers (Spielman et al., 1993; Ewens and Spielman, 1995) introduced the TDT in 1993, with modifications by several groups to extend the test to multiple alleles (Bickeboller and Clerget-Darpoux, 1995; Rice et al.,1995; Sham and Curtis, 1995; Cleves et al., 1997; Kaplan et al., 1997a,b; Sham, 1997). Cleves et al. (1997) and Kaplan et al. (1997a) have proposed methods to obtain exact pvalues for TDTs for multiallelic markers. A sibship test has also been developed, in which unaffected siblings, rather than parents, are used as controls (Curtis, 1997; Boehnke and Langefeld, 1998; Horvath and Laird, 1998; Spielman and Ewens, 1998). Extensions of the TDT have been proposed that use nuclear family or pedigree data (Cleves et al., 1997; Martin et al., 1997, 2000), allow for incomplete data (Weinberg, 1999), incorporate covariates (Lunetta et al., 2000), and extend to quantitative traits (Allison, 1997; Rabinowitz, 1997; Fulker et al., 1999; Abecasis et al., 2000; Monks and Kaplan, 2000). As increasingly dense marker maps become available, many investigators advocate the TDT for genomewide screening. Camp (1997, 1999) reviewed the arguments, provided calculations of power under a range of assumptions for genome scans of complex traits, and demonstrated that TDT approaches have greater power than sib-pair identity-by-descent linkage methods. The TDT methods continue to develop rapidly; any such new approaches may become relevant to studies of oral-facial clefts in the future.
Gene-mapping studies of oral-facial clefts have utilized both linkage and association methods. Table 20.1 summarizes chromosomal regions that have shown positive results (p < 0.05, lod > 3.0) from at least one association or linkage study in humans, along with any additional evidence from animal models or chromosomal rearrangements [see also the reviews by Wyszynski et al. (1996) and Carinci et al. (2000)]. Regions on chromosomes 2, 4, 6, 14, 17, and 19 have had positive findings. Chapter 21 provides more detail about linkage results in oral-facial clefting, Chapter 22 details animal model studies, and Chapter 23 summarizes gene-environment studies. This chapter provides a comprehensive review of the results from published allelic association studies.
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TABLE 20.1. Chromosomal Regions that Have Shown Positive Linkage or Association Results for Oral-Facial Clefting |
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Table 20.2 summarizes all published association studies with nonsyndromic cleft lip with or without cleft palate (CL/P) and cleft palate (CP), whether utilizing population-based or family-based controls. Presented in the table are all published studies of the regions with positive association results in at least one study. There are a few additional loci and chromosomal regions that have only negative results reported in the literature, although there are no comprehensive, genomewide association results. Also, there are many studies for some loci and few studies for others; this is not a reflection of the strength of the evidence for any particular association but merely a reflection of the interest in particular loci. In Table 20.2 we present the statistics reported in the original papers. Wyszynski et al. (1996), Mitchell (1996), and Carinci et al. (2000) instead present odds ratios calculated from the raw case-control data of the original references.
The first published association studies for oral-facial clefts evaluated association with alleles at loci within the major histocompatibility system (HLA) (Bonner et al., 1978; Van Dyke et al., 1980, 1983; Watanabe et al., 1984). They examined HLA because susceptibility to cortisone-induced CP in some mouse strains is associated primarily with genotypes at the H2 locus (Bonner and Slavkin, 1975; see also Chapter 22). Although several studies have been conducted in Caucasian and Asian populations, no overall positive associations between HLA and CL/P or CP have been found.
The first positive association with oral-facial clefts was a population-based association between CL/P and a TaqI restriction site polymorphism in the transforming growth factor-a locus (TGFA) (Ardinger et al., 1989). Interestingly, this locus was studied as a candidate because of its involvement in CP in the mouse. The TGFA association with CL/P has since been replicated in several studies, but several other studies have failed to confirm it (Table 20.2). An association of TGFA with CP has also been reported, although most studies of TGFA and CP have failed to find an association (Table 20.2).
There are many possible reasons for the conflicting TGFA association study results. Although the majority of the studies have been in Caucasian populations, the data sets are heterogeneous in several ways. There are differing proportions of cases with a family history of clefting: some studies included only familial cases, some studies included only sporadic cases, and many studies included both familial and sporadic cases in varying proportions, If there are different etiologic factors that are important in familial vs. sporadic clefting, then it is not surprising that association study results differ. Furthermore, there were different proportions of CL vs. CLP cases, different types of data (case-control vs. nuclear triads vs. nuclear families vs. extended kindreds), different types of analysis, and different TGFAmarkers employed. Sample size may also be a factor, although most of the published studies were sufficiently large for valid conclusions.
Any of these factors could account for the inconsistent results. However, there is no consistent correlation between any of the above factors and association; i.e., there are positive and negative associations with TGFA in each possible study design. In general, studies with fewer familial cases were less likely to exhibit a positive association with TGFA. There were also no positive results in Asians, although there were only two studies [Filipinos studied by Lidral et al. (1997) and Chinese studied by Marazita et al. (2001)], too few to draw any conclusion about TGFA and ethnicity. A meta-analysis of pre-1996 studies (Mitchell, 1996) concluded that there was positive evidence of association between CL/P and TGFA in Caucasians (odds ratio 1.43, 95% confidence interval 1.12-1.80). The meta-analysis found significant heterogeneity between Caucasian studies in the allele frequencies of cases but not controls. Thus, heterogeneity between studies is unlikely to be due to ethnicity differences; it is more likely to be due to the differing proportions of familial and/or severe cases of clefting (Mitchell, 1996).
There are additional etiologic clues from association studies of TGFA and clefting. A few studies were consistent, with TGFA modifying cleft severity, i.e., with significantly different TGFA allele patterns between CL and CLP cases. Also, some association studies reported an interaction between maternal smoking and TGFA, which increased the risk of clefts, although other studies failed to find such an interaction (see Chapter 23 for a summary of gene-environment interaction studies). Finally, as noted earlier, different TGFA markers have been assessed in different studies. Of possible etiologic significance, some studies tested multiple TGPA markers, with usually only one of the markers showing an association with clefting.
Machida et al. (1999) characterized the intron-exon boundaries in TGFA, as well as a substantial portion of additional untranslated sequence. They identified five regions of human-mouse homology outside of the coding sequence, with a particularly high degree of homology scattered throughout the 3′-untranslated region (UTR). The 3′-UTR regions play a role in mRNA stability and are sites for RNA-binding proteins (Siomi and Dreyfuss, 1997). Machida et al. (1999) also found five rare variants (three in the 3′-UTR) among 250 nonsyndromic oral-facial cleft cases; none of these variants was found in any of 270 control samples. This may be an example of a rare mutation in control regions as at least one component of TGFA cleft susceptibility. Also of note is that TGFA knockout mice do not have a cleft (Luetteke et al., 1993). This result does not necessarily eliminate a role for TGFA in clefting because alterations in TGFA expression, rather than complete gene inactivation, could be involved.
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TABLE 20.2. Summary of Association Studies of Oral-Facial Clefting for Chromosomal Regions with at Least One Study Showing Positive Evidence of Association |
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In addition to TGFA, alleles at loci in several other chromosomal regions have shown positive association results with oral-facial clefting, although, like TGFA, none of these loci has given consistent results across all studies. Homeobox 7 (MSX1, chromosome 4pl6) has one report of an association with CL/P and CP in Caucasians and two negative reports in Asians. Anonymous markers on 4q31 have one report of a positive association with CL/P in Caucasians and one negative report in Asians. Transforming growth factor-/:β 3 (TGFB3, 14q24) has one positive association reported with CP (in Caucasians) and two negative reports (one Caucasian, one Asian). Retinoic acid receptor α (RARA, 17q21) has multiple reports of a positive association with CL/P (Caucasians) and negative reports (Caucasians and Asians), as well as one negative report with CP in Caucasians. Protooncogene BCL3 (19ql3) has multiple positive and negative reports with CL/P (Caucasians and Asians) and two negative reports with CP.
Association analysis in human genetics is coming of age, and the use of association analysis methods will have increasing utility in studies of oral-facial clefts. To clarify the inconsistent association study results, several study designs will be important, as will independent replication of any positive findings. To remove any possible biases due to unknown population stratification, family-based association studies, such as the TDT, should be pursued. Covariates such as gender, cleft severity, and pregnancy history should be included, e.g., using logistic regression methods (Schaid, 1996; Maestri et al., 1997). Additional studies should be done in ethnic groups other than Caucasians to clarify possible differences in association patterns. Future association studies of clefting should also focus on methods to incorporate gene-gene interactions, to assess the simultaneous effects of alleles at multiple loci on the risk of clefts. Statistical analysis of recurrence risk patterns is consistent with oligogenic models for oral-facial clefting, in which there are four to seven different etiologic genes (Farrall and Holder, 1992; Mitchell and Risch, 1992).
In addition, with the development of extremely dense marker maps, genomewide scans for cleft loci via linkage disequilibrium (at genetic distances much less than 1 cM) are becoming feasible. The major weakness of population-based analysis, that it has the potential to generate artifactual associations due to population stratification, has begun to be addressed by the use of genomic controls (Devlin and Roeder, 1999; Bacanu et al., 2000). Risch and Teng have compared the power of various population-based and family-based association methods, both when DNA samples are pooled (Risch and Teng, 1998) and for individual genotyping (Teng and Risch, 1999), and provide conditions under which different approaches will have the most power. Finally, methods that combine elements of both population and family-based data are being proposed (Whittemore and Tu, 2000). With the development of new markers and new methods, progress in detecting cleft-susceptibility genes with small to moderate effects can be expected in the near future.
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