Cleft Lip & Palate: From Origin to Treatment, 1st Edition

21. Locating genes for oral clefts in humans

Diego F. Wyszynski

The identification of disease genes (genes whose aberrant alleles are responsible for defined clinical syndromes) has been a major focus of human genetics over the past 30 years as a natural consequence of significant improvements in DNA technology and genetic resources. To identify genes involved in inherited disease or predispositions, two general strategies are commonly applied: positional cloning and the candidate-gene approach. In the case of positional cloning, a physical map of the target region must be developed. All genes within the mapped region are considered candidates for the disease gene and are subject to screening for mutations in constitutional DNA from patients. In the candidate-gene approach, prior knowledge of specific genes is exploited by analyzing these genes for mutations (Groden and Albertsen, 1996). A subset of genes already shown to play an important role in the development of the head, with particular relevance to the development of the lip and palate, is listed in Table 21.1. Additional growth, signaling, and transcription factors that play a role in facial development include JAGGED 1, sonic hedgehog, patched, cAMP response element (CRE)-binding protein, GLI3, fibroblast growth factor receptor 1 (FGFR1), calcium/calmodulindependent serine protein kinase (CASK), treacle, fibroblast grown factor receptor 2 (FGFR2), distalless homeo box (DLX)5/6, and paired box gene 3 (PAX3) (Schutte and Murray, 1999).

Many traits are not associated with cytogenetically detectable chromosomal abnormalities, and in these cases, linkage analysis on family material has to be performed to determine the chromosomal localization of the underlying mutant gene. Since chromosomal anomalies and linkage analysis are the mainstays of mapping technologies, they will be detailed and illustrated with relevant studies in the orofacial field.

Chromosomal Anomalies to Determine the Location of Disease Genes

Chromosomal anomalies present in germline DNA, such as translocations, duplications, expansions, and deletions, are found either as de novo mutations or as transmissions in ova or sperm from parents to their offspring (Groden and Albertsen, 1996; Shaffer and Lupski, 2000). De novo, apparently balanced reciprocal translocation occurs in 1:2000 births, and close to 6% of these will be associated with major congenital malformations (Warburton, 1991). In a completely ascertained cohort of cases with oral clefting, apparently balanced chromosomal rearrangements were found in 1.05% (95% confidence interval 0.98%-1.12%) of patients (FitzPatrick et al., 1994).

As of November 2000, the Mendelian Cytogenetics Network database (http://www.mcndb.imbg.ku.dk/) contained information on 75 breakpoints in 25 individuals with cleft palate (CP). Among these cases, there were eight bands involved in more than one case (Ip31, 4q21, 6p24, 7q36, 9pl3, 16q24, 17q23, and 17p25). Table 21.2 presents published translocations and inversions associated with orofacial clefting. Systematic cloning of breakpoints associated with specific non-Mendelian developmental pathologies is now feasible and being undertaken by several investigators (Wirth et al., 1999).

TABLE 21.1. Genes Regulating Development of the Head, in Particular of the Lip and Palate*

Gene Type Feature Mouse Human References TGFα GF L/P LD Ardinger et al. (1989), Mitchell (1997) END1 SF M KO Linkage Kurihara et al. (1994) RARα SF P/M TG/EXP LD/linkage Chenevix-Trench et al. (1992) TGF/β GF L/P KO, EXP LD Lidral et al. (1998), Maestri et al. (1997) SKI GF L/P/M KO, EXP Berk et al. (1997) MSX1 HD L/P KO, EXP LD, linkage Lidraj et al. (1998), van den Boogaard et al. (2000) DLX1/2 HD P/M EXP Qiu et al. (1997) PITX2 HD P/M KO, EXP Rieger Semina et al. (1996) PAX9 HD P/M KO, EXP Peters et al. (1998) AP2 TF L/P/M KO, EXP Linkage Nottoli et al. (1998) TTF2 TF P KO thyroid dysgenesis De Felice et al. (1998), Clifton-Bligh et al. (1998) PVRL1 PVR L/P PC/CLP, ectodermal dysplasia Suzuki et al. (2000), Sozen et al, (2001) *GF, growth factor; HD, homeodomain; SF, signaling factor; TF, transcription factor; PVR, orthologue of the gene encoding the poliovirus receptor; lip; P, palate; M, maxilla and/or mandible; KO, knockout; TG, transgene; EXP, expression; LD, linkage disequilibrium; PC, positional cloning; CL/P, cleft lip with or without palate. Source: Schutte and Murray (1999).

TABLE 21.2. Reported Translocations and Inversions Associated with Orofacial Clefting*

Reference BP1 BP2 De novo? Clinical Details Cleft lip with/without cleft palate Yoshiura et al. (1998) 2qll.2 19ql3.3† Familial Davies et al. (1998) 6p23 7q36.1 ? Bilateral CLP Donnai et al. (1992) 6p23‡ 9q22.3 Familial Oral clefts and features of ectodermal dysplasia Hasegawa et al. (1991) 7pll.2 9ql2 Familial Familial case ectrodactyly, ectodermal dysplasia, and CL/P Cowchock (1989) 2q23 10pl3 ? Unilateral CLP Cowchock (1989) 10pl3 14q24 Familial Father has bilateral CLP, son has VSD Tinning et al. (1975) Iq23 6q27 ? MR and congenital glaucoma Masuno et al. (1997) Xq28 16qll.2 De novo CL, pedunculated skin masses, short stature, MR Akita et al. (1993) 7q22.1 7q36.3 De novo Sparse hair, CLP, bilateral ectrodactyly of hands and feet Viljoen and Smart (1993) 6q21 13ql2 ? Severe MR, ectrodactyly of both feet, bilateral microphthalmia, bilateral CLP Ikeuchi et al. (1991) Iq31.2 7pl5 De novo CLP, hypertelorism, microtia Cleft palate only Wallace et al. (1994) 7q32.1 20ql3.2 Quest; Cataracts, ptosis, CP, thickened alveolar ridges Conte et al. (1992) 9p21.2 Ilpl4.2 Quest; Dysmorphisms and CP Brewer et al. (1999) 2q33‡ 7p21 De novo CP and mild MR Brewer et al. (1999) 2q33‡ Ilpl4 De novo C P and mild MR Pfeiffer et al. (1973) Yq 15p ? CP and MR Davies et al. (1998) 6p24 9p23 ? CP and MR Riccardi and Holmquist (1979) 13ql2 13q22 ? *BP, breakpoint; CLP, cleft lip and palate; CP, cleft palate; VSD, ventricular septal defect; MR, mental retardation. †Breakpoint cloned and a gene identified. ‡Breakpoint currently being cloned by collaborating laboratories. Source: D. David FitzPatrick (personal communication).

FIG. 21.1. In pedigree 1, the disease cosegregates with an A allele of marker. In 2, initially a B allele until there is a recombination, after which it cosegregates with a C allele.

Linkage Analysis

Genetic linkage analysis refers to the ordering of genetic loci on a chromosome and to estimating genetic distances among them, where these distances are determined on the basis of a statistical phenomenon (i.e., the crossover frequency occurring between two points in a gamete) (Ott, 1999). Linkage analysis is a powerful methodology to help elucidate the underlying genetic mechanisms for inherited disorders (traits) and to find chromosomal locations for genes controlling susceptibility.

Genetic linkage is the phenomenon whereby alleles at loci close together on the same chromosome tend to be inherited together in a family, thus departing from Mendel's law of independent assortment. The extent of linkage is a function of the distance between the two loci, which can be measured by the number of crossovers between them among the observed meioses (Xu et al., 1998).

In Figure 21.1, pedigree 1, the disease cosegregates with the A allele of the marker in all affected individuals. In pedigree 2, the disease cosegregates with the B allele of the marker. In this family, however, there is an affected individual who carries the genotype AC. This situation is referred to as recombination. A non-recombinant is an individual who inherited a haplotype (i.e., alleles at the marker locus and the disease locus) identical to that received by the parent from one grandparent. A recombinant is an individual who inherited a haplotype not identical to that inherited from his or her grandparent. All individuals in pedigree 1 are non-recombinants, having received a paternal haplotype intact (i.e., A allele at the marker and disease allele at the trait locus). Individuals who inherited the A allele and are not affected are called nonpenetrant. In pedigree 2, there are two affected people who descend from the recombinant individual, both carriers of the C allele. This situation originated as a consequence of a genetic recombination during meiosis in the father of the recombinant individual.

FIG. 21.2. DNA haplotypes shared by descent in siblings of affected individuals.

The closer together two loci are, the less likely crossovers will be and the fewer recombinants will be observed. If loci are far apart or on different chromosomes, then recombination will occur by chance in 50% of meioses. The recombination fraction, also known as 6, ranges from 0 (complete linkage) to 0.5 (no linkage) and is a measure of genetic distance.

Linkage can be used to map disease genes by typing polymorphic DNA markers and seeing if their alleles cosegregate with disease among related subjects. Linkage can be studied in multiplex families, in which case the strength of evidence in favor of linkage can be measured as the log-odds or, as it is more frequently termed, the lod score. The lod score is the log₁₀ of the ratio of the likelihood of the observed genotypes given linkage (θ < 0.5) compared with the likelihood under nonlinkage (i.e., θ = 0.5). Traditionally, a lod of 3.0 or more is taken as significant evidence for linkage, while a lod score of -2.0 or less is taken as evidence against linkage (Morton, 1955). These critical values correspond to 1000:1 odds for linkage and 100:1 odds against linkage, respectively, at some specified value of θ (Meyers, 1993). The actual rate of type I error (i.e., rejecting the true hypothesis that θ = 0.5) using these values is very close to the conventional 5% level of significance, once the prior probability of two loci being linked is considered (Morton, 1955). Table 21.3 presents alternative approaches to judging significance when evaluating lod scores.

Xu et al. (1998) described four major advantages of lod score analysis over the genetic analysis methods described previously:

1. Statistically, it is a more powerful approach than any nonparametric method (see below).

2. It utilizes every family member's phenotypic and genotypic information.

3. It provides an estimate of the recombination fraction (genetic distance).

4. It provides a statistical test for linkage and for genetic heterogeneity.

It can be difficult to apply the lod score method to so-called complex diseases where there is nonMendelian inheritance because the likelihood calculations require that an exact mode of inheritance and other parameters be specified and, of course, this may be unknown. When these parameters are correctly specified, linkage analysis remains a powerful approach for detecting genes. However, if the assumed genetic model is wrong, the true picture can be disguised, leading to either false-positive or false-negative evidence of linkage (Xu et al., 1998).

The MOD score analysis (also called maximized lod score), in which lod scores are maximized over several genetic parameters, not just 0, provides an alternative to lod score analysis when mode of inheritance is unknown. Simulation studies have shown that, in at least some cases, MOD score analyses have good power to detect linkage when the true mode of inheritance is complex (i.e., a single disease locus with reduced penetrance or two additive disease loci), but fairly simple genetic models are still assumed (Hodge and Elston, 1994; Greenberg et al., 1998).

A further limitation of the lod score method for the study of oral clefts is that it requires families with at least two available affected individuals and DNA from both affected and unaffected family members. As most individuals with nonsyndromic cleft lip with or without cleft palate (CL/P) and CP do not have any family history of clefting, it is difficult to identify families that are suitable for lod score analyses. Hence, this approach is limited to investigators who have access to very large patient cohorts or who participate in multicenter collaborations (Mitchell et al., 2002).

TABLE 21.3. Proposed p Values for a Genomic Screen

Thomson (1994) Nominal P value (adjusted P value [lod score]] Lander and Kruglyak (1995) P value (lod score) Haines (1998) P value (lod score) Weak 3 data sets with p <0.05 (0.0001) [2.9] Suggestive 0.0007(2.2) Interesting 0.03 (1.0) Moderate 2 data sets with P <0.01 (0.0001) [3.0] Significant 0.00002(3.6) Very interesting 0.0009 (2.0) Strong 1 data set with P <0.001 (0.001) [4.1] Highly significant 0.0000003 (5.4) Provisional linkage Confirmed linkage 0.00003 (3.0) 0.0000008 (4.0) (from at least 2 independent data sets) Source: Haines (1998).

Sib-Pair Methods of Analysis

An alternative approach to the lod score method is to examine allele sharing between pairs of affected relatives, and the simplest example of this is the sib-pair method. This approach was first described by Penrose (1935), who reasoned that if linkage existed, it would be reflected in the nonrandom association of two traits (i.e., a disease and a marker or two markers) in independent pairs of sibs.

In the absence of linkage between the unknown susceptibility locus and the marker locus, full sibs are expected to share two alleles at any marker 25% of the time, one allele 50% of the time, and zero alleles 25% of the time. Some terminology should be explained first. Two alleles are said to be identical by state (IBS) if they cannot be distinguished by means of a particular method of detection (Goldgar, 1998). Identity by descent (IBD), however, depends not only on whether the alleles appear the same but also on whether they were derived or inherited from a common ancestor (Goldgar, 1998). While IBD alleles must also be IBS, the converse is not necessarily true. Table 21.4 shows the expected proportion of alleles IBD for a number of other common relationships. Figure 21.2 illustrates the mating of two individuals with genotypes A/B and C/D. The proband carries the genotype A/C. He shares by descent both alleles with the first sibling, one allele with the following two sibs, and no alleles with his youngest sib, in perfect agreement with Mendelian expectation. If the marker is linked to the disease gene, however, alleles will be shared between affected sib pairs more often than expected. If the parents are genotyped, the inheritance of the marker alleles can be studied directly (IBD analysis). If the parents are unavailable, one can use population allele frequencies to test for increased allele sharing (IBS analysis). The strength of evidence in favor of linkage can be given by a x² statistic or by a maximum likelihood score, the latter being on the same scale as a lod score. These allele-sharing statistics are commonly nonparametric approaches, primarily because no specific assumptions have been made about the mode of inheritance.

TABLE 21.4. Expected Percentage of Affected Pairs Showing 0, 1, or 2 Alleles Identical by Descent (IBD) at a Marker Locus if No Linkage Is Present

Pair Type Alleles IBD 0 1 2 Monozygotic twins 0 0 100 Siblings 25 50 25 Parent-child 0 100 0 Grandparent—grandchild 50 50 0 Half-siblings 50 50 0 Uncle-nephew, etc. 50 50 0 Cousins 75 25 0 Double first cousins 62 38 0

Nonparameteric Linkage Analysis

The nonparametric linkage (NPL) statistic is a measure of allele sharing among affected individuals within a pedigree. Introduced in 1996 by Kruglyak et al. (1996), this method is the only affected relative pair test that considers all affected relatives simultaneously, rather than as a combination of all possible pairs. This approach tests for excess allele sharing, however, as done in affected sib-pair analysis. This means that, e.g., if five affected individuals in a pedigree share the same allele IBD, this information should carry more weight than if each of the 15 possible pairs in the same pedigree shared some allele IBD but not necessarily the same allele.

While the major disadvantage of using the NPL method is that it is limited to relatively small and simple pedigrees, the NPL statistic can be used in several situations:

1. When pedigrees of moderate size are available

2. When many relatives other than siblings are available for a complex trait where the exact model of inheritance is unknown

3. When a large number of linked markers are being examined simultaneously (in this case, the NPL approach can be readily extended to multipoint analysis)

Relative to the lod score method, the main advantage of allele-sharing methods is that they provide valid tests of linkage without the need specifying details of the mode of inheritance for the phenotype inter est. The main disadvantage of this method is that it can require a very large number (i.e., several hundred) of affected relative pairs (Mitchell et al., 2002).

The allele-sharing methods require DNA from pairs of affected relatives. To establish IBD, DNA is also required from additional relatives (e.g., for affected sib pairs, parental DNA samples are also required). As the recurrence rates for nonsyndromic CL/P and CP are relatively low, the accumulation of an adequate sample of affected relatives is likely to be restricted to investigators who have access to large patient cohorts or who participate in multicenter collaborations.

Multipoint Linkage Analysis

The term multipoint mapping refers to linkage analysis of more than two loci at a time. Considering multiple loci simultaneously gives substantial increases in information for both estimating the recombination fraction and establishing the order of linked loci (Meyers, 1993). Thus, linkage results are less sensitive to the uninformative or missing genotype at any single marker. Additionally, the multipoint mapping approach may be very useful to pinpoint the disease gene location when fine mapping. This technique may be used when applying the lod score method as well as the NPL statistic (Kruglyak and Lander, 1995), for both qualitative and quantitative traits, and in more general analyses of extended pedigrees (Kruglyak et al., 1996).

Linkage Analysis of Nonsyndromic Oral Clefts

For the past 20 years, several research groups have evaluated multiplex families with CL/P using linkage analysis. The results, however, remain conflicting and non definitive. Table 21.5 presents some features of published linkage studies. No single locus has been shown conclusively to be etiologically related to nonsyndromic oral clefting in multiple studies.

It is widely accepted that nonsyndromic oral clefts are complex birth defects characterized by an uncertain mode of inheritance, incomplete penetrance, and heterogeneity both within and among populations (Maestri et al., 1997). Other non-mutually exclusive circumstances may produce conflicting linkage results as well:

· Clinical and etiological heterogeneity. Difference in clinical characteristics of oral cleft patients between studies could be a cause of discrepant linkage results if genetic markers are linked with different trait loci. A relatively large proportion of individuals with oral clefting have an underlying genetic or developmental syndrome (Jones, 1988; Gorlin, 1990; Saal, 1998; Stoll et al., 2000). Thus, it is necessary to distinguish between individuals with syndromic and nonsyndromic clefts when conducting studies of potential risk factors. Similarly, some oral clefts may be due to maternal exposures to teratogens. Identification of these cases is a challenge when conducting population-based research since both family history information and direct physical examination of affected individuals are needed and often unavailable (Mitchell et al., 2002).

· Ethnic variability. As shown by Wyszynski et al. (1997b) and Maestri et al. (1997), ethnic background may act as an effect modifier in the relationship between genetic polymorphisms and oral clefting.

· Insufficient statistical power. Attaining an adequate sample size to conduct definitive linkage analysis may be difficult in some populations. This situation is particularly critical if the genetic marker under study is not highly informative. An example is BCL3, on chromosome 19ql3, which has a heterozygosity of only 0.47 (Wyszynski et al., 1997b). Also, large sample sizes are required if the contribution of the susceptibility locus to familial clustering of the trait is small, if oral clefts are determined by several susceptibility loci, and if the researcher is evaluating a modifier rather than a major gene. Results of analyses performed using an insufficient sample may produce ambiguous results (i.e., neither for nor against evidence of linkage).

In spite of these limitations and applying a biologically driven “complex disease approach,” candidate genes are now used to test for interactions among genes and between genes and selected environmental factors (see Chapter 23). Sophisticated statistical methods to distinguish maternal genotypic effects from those of the fetus have been developed as well (Weinberg et al., 1998; Wilcox et al., 1998; Weinberg, 1999a,b; Wyszynski and Diehl, 2001). Finally, the growing number of robust statistical methods of linkage analysis incorporated in user-friendly computer software packages will allow researchers to overcome or at least minimize some of the limitations mentioned above.

TABLE 21.5. Results of Linkage Analyses on Candidate Genes for Nonsyndromic Cleft Lip and Cleft Palate

Chromosome Region Locus Name Population Results* References Ip36 Multipoint 92 UK affected sib pairs MLS = 1.34 Prescott et al. (2000) Iq21 D1S104 3 European Caucasian families Z = 0.09 at θ = 0.40 Pierpont et al. (1995) Iq32 D1S245 19 Swedish families Z < —2 for all markers (multipoint) Wong et al. (2000) D1S471 D1S491 D1S3753 D1S205 2pl3 TGF-α 12 Families, ethnicity not specified Z = -2.1 at θ = 0 Hecht et al. (1993) TGF-α 8 Families, ethnicity not specified Z = 0.448 at θ = 0 Vintiner et al. (1993) TGF-α 14 West Bengal Indian families Z = 0.13 at θ = 0.2 Field et al. (1994) TGF-α 40 Caucasian Italian families Z = 0.22 at θ = 0.3 Scapoli et al. (1999) D2S443 32 U.S families Z = 0.14 at θ = 0.05 Wyszynski et al. (1997a) D2S443 22 Mexican families Z = -0.008 at θ = 0.05 Wyszynski et al. (1997a) D2S380 38 Italian Caucasian families Z= 1.15 at θ = 0.3 Pezzetti et al. (1998) D2S378 46 Italian Caucasian families Z = 0.001 Scapoli et al. (1999) D2S123 19 Swedish families Z < —2 for all markers (multipoint) Wong et al. (2000) D2S378 D2S337 D2S380 Multipoint 92 UK affected sib pairs MLS = 0.66 Prescott et al. (2000) 2q37 PAX3 3 European Caucasian families Z = -0.03 at θ = 0.40 Pierpont et al. (1995) Multipoint 92 UK affected sib pairs MLS = 0.9 Prescott et al. (2000) 4q31 D4S175 1 U.S. Caucasian family Z = 2.27 at θ = 0 Beiraghi et al. (1994) D4S175 3 European Caucasian families Z = -0.64 at θ = 0 Pierpont et al. (1995) 6p23–24 F13A 49 Danish Caucasian families Z = 2.016 at θ = 0.01 for males Eiberg et al. (1987) F13A 8 British Caucasian families Z = 0.224 at θ = 0.001 Vintiner et al. (1993) F13A 9 Families, different ethnic groups Z = -0.29 at θ = 0.3 Hecht et al. (1993) F13A 19 Swedish families Z < 1 (multipoint) Wong et al. (2000) D6S89 14 Families from northeastern Italy Z = 4.48 at θ = 0.001 Carinci et al. (1995) D6S89 12 Families, different ethnic groups Z = -0.41 at θ = 0.3 Hecht et al. (1993) D6S89 33 Families, different ethnic groups Z = -2 at 6 θ <=0.3 Blanton et al. (1996) D6S259 38 Italian Caucasian families Z = 3.6 at 1 cM, α = 0.6 Scapoli et al. (1997) D6S259 46 Italian Caucasian families Z = 2.1 at θ = 0.098 Scapoli et al. (1999) D6S89+F13A 14 Families from northeastern Italy Z = 2.03 at θ = 0.001 for males Carinci et al. (1995) D6S89+EDN1 14 Families from northeastern Italy Z = 2.31 at θ = 0.1 Carinci et al. (1995) EDN1 19 Swedish families Z < 1 (multipoint) Wong et al. (2000) Multipoint 92 UK affected sib pairs MLS = 1.34 Prescott et al. (2000) 6p23+2pl3 D6S259-D2S378 30 Italian Caucasian families Z = 3.79 at θ = 0.01 Pezzetti et al. (1998) 6p23+19ql3 D6S89-not stated 15 Families linked to 19ql3.1 Z = -0.835 at θ = 0.3 Blanton et al. (1996) 6p25 Multipoint 92 UK affected sib pairs MLS = 1.50 Prescott et al. (2000) Ilpl2-ql4 Multipoint 92 UK affected sib pairs MLS = 2.09 Prescott et al. (2000) 16 P13 Multipoint 92 UK affected sib pairs MLS = 1.65 Prescott et al. (2000) 17q21 RAR-α 8 British Caucasian families Z = 0.048 at θ = 0.05 Vintiner et al. (1993) RAR-α 17 Families, different ethnic groups Z = 1.14 at θ = 0 Stein et al. (1995) D17S579 8 West Bengal Indian families Z = -2 at θ < 0.1 Shaw et al. (1993) 19ql3 BCL3 17 Families, different ethnic groups Z = 7.00 at θ = 0 (multipoint) Stein et al. (1995) BCL3 30 U.S. and 11 Mexican families Z = 0.89 at θ = 0.2 Wyszynski et al. (1997b) BCL3 38 Italian Caucasian families Z = 0.23 at θ = 0.3 Martinelli et al. (1998) BCL3 19 Swedish families Z < 1 (multipoint) Wong et al. (2000) 22qll.2 D22S156 3 European Caucasian families Z = -0.04 at θ =0.40 Pierpont et al. (1995) D22S264 3 European Caucasian families Z = 0.43 at θ =0.20 Pierpont et al. (1995) Xcen Multipoint 92 UK affected sib pairs MLS = 2.89 Prescott et al. (2000) *MLS, Maximum lod score from sib-pair analysis; Z, highest lod score from linkage analysis; θ, recombination fraction.

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