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American Journal of Human Genetics logoLink to American Journal of Human Genetics
. 2002 Jun 4;71(1):45–55. doi: 10.1086/341095

A Major Susceptibility Locus for Specific Language Impairment Is Located on 13q21

Christopher W Bartlett 1, Judy F Flax 1, Mark W Logue 3, Veronica J Vieland 3, Anne S Bassett 4, Paula Tallal 1, Linda M Brzustowicz 1,2,5
PMCID: PMC384992  PMID: 12048648

Abstract

Children who fail to develop language normally—in the absence of explanatory factors such as neurological disorders, hearing impairment, or lack of adequate opportunity—are clinically described as having specific language impairment (SLI). SLI has a prevalence of ∼7% in children entering school and is associated with later difficulties in learning to read. Research indicates that genetic factors are important in the etiology of SLI. Studies have consistently demonstrated that SLI aggregates in families. Increased monozygotic versus dizygotic twin concordance rates indicate that heredity, not just shared environment, is the cause of the familial clustering. We have collected five pedigrees of Celtic ancestry that segregate SLI, and we have conducted genomewide categorical linkage analysis, using model-based LOD score techniques. Analysis was conducted under both dominant and recessive models by use of three phenotypic classifications: clinical diagnosis, language impairment (spoken language quotient <85) and reading discrepancy (nonverbal IQ minus non-word reading >15). Chromosome 13 yielded a maximum multipoint LOD score of 3.92 under the recessive reading discrepancy model. Simulation to correct for multiple models and multiple phenotypes indicated that the genomewide empirical P value is < .01. As an alternative measure, we also computed the posterior probability of linkage (PPL), obtaining a PPL of 53% in the same region. One other genomic region yielded suggestive results on chromosome 2 (multipoint LOD score 2.86, genomic P value <.06 under the recessive language impairment model). Our findings underscore the utility of traditional LOD-score–based methods in finding genes for complex diseases, specifically, SLI.

Introduction

Specific language impairment (SLI) is clinically defined as failure to develop language normally, given adequate environment for learning language and the absence of hearing deficits, mental retardation, oral motor/structural abnormalities, and neurological or psychiatric impairments affecting language acquisition. This disorder affects ∼7% of children entering school (Tomblin et al. 1997), and, although some children will successfully learn to compensate as adults, many do not (Bishop and Adams 1990; Stothard et al. 1998). Individuals with SLI tend to perform poorly on general assessments of language and reading (Reed 1989; Bishop and Adams 1990; Catts 1993; Snowling et al. 2000, 2001). Research also indicates that many, but not all, have difficulty with higher level phonological processing necessary for the development of both language and reading and also demonstrate concomitant difficulties in processing dynamic (rapidly changing) sensory information within a very brief time range (Tallal and Piercy 1973a, 1973b; Wright et al. 1997, 2000; Witton et al. 1998; Talcott et al. 1999). Many children identified early in life as having SLI will subsequently develop characteristics of dyslexia when entering school (Bishop and Adams 1990; Scarborough 1990; Catts 1993; Stothard et al. 1998; Snowling et al. 2000).

Familial aggregation studies, twin studies, and prospective studies, taken together, suggest that SLI has a genetic component. Several case-control familial aggregation studies of SLI have been reported (Tallal et al. 1989, 2001; Tomblin 1989; van der Lely and Stollwerck 1996; Rice et al. 1998; for review of all familial aggregation studies, see Stromswold 1998). In studies of this type, incidence of SLI will vary depending on the definition of affection status. However, all of the above studies show a significantly increased frequency of impairment in first-degree relatives in families containing a proband (18%–42%) versus control families (3%–26%)

Genetic influences on language delay, a risk factor for SLI, were examined in two-year-old children in a sample of 3,000 twins (Dale et al. 1998). By consideration of both the variance of the control group and the variance of the group with language delay as having separate distributions, genetic factors were found to contribute 73% of the variance for the group with language delay, compared with 25% when all individuals in the sample were considered together. This indicates that individuals with language delay may have some unique genetic component that influences language acquisition, as compared with the general population.

Twin studies using the categorical diagnosis of SLI demonstrate near 100% concordance for MZ twins and ∼50%–70% concordance for DZ twins (Bishop et al. 1995; Tomblin and Buckwalter 1998), indicating that SLI as defined by categorical affection status does have a genetic component. Another twin study systematically explored pre- and perinatal hazards in children with SLI (Bishop 1997). This report examined medical records for relationships with birth weight, Apgar scores, and other obstetrical factors. Although no decisive evidence was found for an association between any of these factors and SLI, suggestive associations of SLI with toxemia of pregnancy, and hypertension were reported. Although it appears that these types of environmental factors are not essential in the development of SLI, this does not exclude the importance of other interactions with the environment over the course of development.

Prospective studies that compare infants who have a positive family history of reading and language problems with infants who have a negative family history may help in the identification of very early stages of the abnormal phenotype. One example of this kind of work used auditory temporal processing measures in infants (Benasich and Tallal 1996). Temporal processing, the ability to discriminate rapid and successive frequency changes in brief intervals, correlates with later language outcomes in infants who have a family history of language problems (Benasich and Spitz 1998). These studies indicate that even before expressive language has developed into easily recognizable words, children at genetic risk for difficulties in learning language perform differently, as a group, from control children on sensory-processing measures that may subsequently be important for phonological development.

Recently, a genome scan for SLI-susceptibility loci performed by use of quantitative-trait analyses based on a sib-pair design was completed by use of a combined clinical/epidemiological sample (SLI Consortium 2002). The authors found genomewide suggestive evidence for loci on 16q and 19q before correction for multiple phenotypes and tests. The locus on chromosome 16 was identified using a children’s test of phonological memory (Gathercole et al. 1994), whereas the locus on 19q was identified using an expressive language score. Although SLI and dyslexia have been postulated to be genetically related, this study did not find any evidence for linkage in regions previously implicated in dyslexia on chromosomes 2, 6, 15, or 18 (Cardon et al. 1994; Grigorenko et al. 1997; Fisher et al. 1999, 2002; Gayán et al. 1999). Furthermore, there was no evidence for linkage to 7q near FOXP2, a gene that is implicated in a severe speech impairment (Lai and Fisher et al. 2001) and is located in a region that may also include a major locus for autism (International Molecular Genetic Study of Autism Consortium 1998; Collaborative Linkage Study of Autism (CLSA) 2001a [originally published in 1999]).

The present study reports the results of a genome scan for SLI-susceptibility loci, using an extended family design. Three different phenotypic classifications were tested for linkage by use of traditional LOD-score–based methods and extensions to this basic approach. Although complex diseases, such as SLI, do not segregate in an apparent Mendelian framework, parametric analysis using a single-locus model has been shown to be an effective method for detection of linkage to oligogenic disorders (for a review of the relevant literature, see, e.g., Vieland 1998; Hodge 2001).

Subjects and Methods

Families and Phenotype Assignment

The sample consisted of branches of five Canadian families of Celtic ancestry that were originally identified during a linkage study of schizophrenia (Brzustowicz et al. 2000) and were noted to have a history of language or reading impairments. A total of 73 subjects were phenotyped with language/reading measures, and these plus 13 additional subjects (86 total) had DNA available. The largest family (n=34 phenotypes and DNA) was not directly part of the schizophrenia study, because they are related to a branch of a family segregating schizophrenia only by a marriage, which should preclude any subject for this study from sharing a schizophrenia locus by descent. A speech/language pathologist screened families, by telephone interview, for a history of language impairment segregating in the family.

Families with a strong history of language impairment were scheduled for assessment. All subjects received a comprehensive battery of tests administered by an experienced tester in their own homes. Assessment tools included the following:

  • 1. 

    The age-appropriate version of the Test of Language Development, which is a comprehensive test of language functioning that addresses specific subtypes of language processes, including comprehension, expression, syntax, grammar, and phonology (either the Test of Adolescent Language [TOAL:2; see Hammill et al. 1987], the Test of Language Development–Primary, 2nd edition [TOLDP:2] [see Newcomer and Hammill 1988], or the Test of Language Development–Intermediate, 2nd edition [TOLDI:2] [see Hammill and Newcomer 1988]).

  • 2. 

    Performance portions of the age-appropriate Wechsler Intelligence Test: either the Wechsler Intelligence Scale for Children (WISC) (Wechsler 1974), the Wechlser Intelligence Scale for Adults (WAIS) (Wechsler 1981), the Wechsler Preschool and Primary Scale of Intelligence (WIPPSI) (Wechsler 1989), or the Wechsler Abbreviated Scale for Intelligence (WASI) (Wechsler 1999).

  • 3. 

    Self report or parental report questionnaire to assess history of hearing difficulties.

  • 4. 

    Word Indentification (single word reading) and Word Attack (single non-word reading) subtests from the Woodcock Reading Mastery Test (Woodcock 1987).

  • 5. 

    The age-appropriate version of the Token Test, which measures a subject’s ability to perform increasingly complex directions (DiSimoni 1978; for the modified version for adults, see Tomblin et al. 1992; for the adult version standardized to the children’s scale, see Tallal et al. 2001).

  • 6. 

    Test of diadochokinesis from the Oral Speech Mechanism Screening Examination (St. Louis and Ruscello 1987), to assess oral structure and motor function.

Subjects were classified as an SLI proband if they met the following inclusionary/exclusionary criteria:

  • 1. 

    Spoken Language Quotient Standard Score (SLQ) ⩽85 on the age-appropriate version of the Test of Language Development.

  • 2. 

    Performance Intelligence Quotient (PIQ) ⩾80 on the age-appropriate version of the Wechsler Intelligence Test, as well as PIQ ⩾ SLQ.

  • 3. 

    Hearing within normal limits (no history of recurrent ear infection or abnormal hearing screen) as assessed by self-report or parental report questionaire.

  • 4. 

    No motor impairments or oral structural deviations affecting speech or non-speech movement of the articulators.

  • 5. 

    No comorbid diagnosis of autism, schizophrenia, psychoses, or neurological disorders.

After all family members who agreed to participate were tested, families were included in the study if at least two members met the criteria for an SLI proband. All subjects were enrolled and tested after giving informed consent that conformed to the guidelines for treatment of human subjects approved by Rutgers University.

Three diagnostic classifications of impairment were employed. The classifications were not mutually exclusive; an individual subject could meet the criteria for more than one of the classifications that follow (see table 1 for the extent of overlap). A subject was classified as language impaired if his or her SLQ was ⩽85. A subject was classified as reading impaired if his or her single nonword reading score (word attack) was 1 SD below their performance IQ (reading discrepancy score). Finally, a subject was classified as clinically impaired if one or more of the following three criteria were met:

  • 1. 

    The subject was language impaired, defined by SLQ ⩽85, or the subject was reading impaired, defined by word identification and/or word attack ⩽85.

  • 2. 

    The subject’s overall SLQ was >85, but the subject scored 1 SD below the mean (⩽7) on three individual subtests of TOLD or scored ⩽85 on the Token Test. This criterion is designed to identify adults who have compensated for their deficit but still show residual language difficulty.

  • 3. 

    The subject had a history of language difficulty defined by at least 2 years of speech/language therapy and/or reading intervention with the label of “dyslexic.”

Table 1.

Overlap Between Phenotypic Classifications

Phenotypea n
LI only 0
RI only 4
CI only 11
CI+LI 18
CI+RI 6
LI+RI+CI 7
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a

LI = language impairment, RI = reading impairment, and CI = clinical impairment. Note that, by definition, individuals classified as LI will also be CI, but the opposite is not necessarily true. The CI-only group represents individuals who were identified by low subtest scores or self-reported history (as outlined in the “Subjects and Methods” section).

All individuals with schizophrenia or schizophrenia spectrum disorders (n=7) were coded as having an unknown phenotype. It was not necessary to exclude any subject from analysis because of mental retardation, abnormal hearing, or oral motor or structural defects.

Genotyping

All family members who were willing to submit DNA samples (n=86) were genotyped. DNA was extracted from peripheral blood samples by the GenePure system (Gentra Systems). Buccal-swab DNA was extracted by use of cell lysis buffer and incubation as described by Laird et al. (1991), followed by NH4OAc precipitation and suspension in tris ethylenediaminetetraacetic acid (TE). Genotyping was conducted in our laboratory and the laboratories of the Center for Inherited Disease Research (CIDR) at Johns Hopkins University in Baltimore. Initial genotyping of 381 markers from the Weber Screening Set, version 6.0, spanning the genome at an average spacing of 9 cM and average heterozygosity of 0.76 was conducted by CIDR by use of automated fluorescent microsatellite analysis (see the CIDR Web site for further details) on 69 subjects. Follow-up genotyping was performed in our laboratory with these and 17 additional subjects, as described elsewhere (Brzustowicz et al. 1997). Two additional markers on chromosome 13 (D13S1317 and D13S1306), one marker on chromosome 2 (D2S352), and one marker on chromosome 17 (D17S809), were also genotyped. PCR primers were ordered from Research Genetics as part of the Human Map Pairs set or were redesigned from the Genome Database locus sequence with the assistance of the Primer 3 program.

Statistical Analysis

Parametric analysis was performed with FASTLINK version 4.1P programs (Cottingham et al. 1993; Schäffer et al. 1994) and the LINKAGE version 5.2 programs (Lathrop and Lalouel 1984; Lathrop et al. 1984). The language impairment, reading impairment, and clinical impairment phenotypes were each analyzed under both a dominant and a recessive model of inheritance, for a total of six analyses. For the dominant models, penetrance for individuals with one or two copies of the susceptibility allele was set to 0.5. For the recessive models, penetrance for individuals with two copies of the susceptibility allele was set to 0.8, and penetrance for individuals with one copy of the susceptibility allele was set to 0.01. For both dominant and recessive models, the penetrance of individuals with no susceptibility alleles was set to 0.001. The disease-allele frequency was set to 0.08 for the dominant model and 0.3 for the recessive model. These models correspond to an ∼7% rate of affection in the population, which is one estimate of the population prevalence of SLI (Tomblin et al. 1997). Although the parameters in our genetic models are almost certainly not correct, it has been demonstrated that use of arbitrary penetrance values with both dominant and recessive modes of inheritance provides a sufficiently powerful test for linkage in complex diseases (Greenberg et al. 1998; Abreu et al. 1999). Two-point linkage analysis was performed by use of the MLINK program, multipoint linkage analysis by use of the LINKMAP program, and heterogeneity testing by use of the HOMOG program. Marker allele frequencies were estimated by use of all available unrelated individuals. Recombination fractions (θ) between markers were taken from the Marshfield map supplied with the screening set. For additional markers, values of θ were also taken from the Marshfield map as D13S1317–.04–D13S800–.04–D13S1306; D2S405–.028–D2S352–.037–D2S1788; D17S2180–.064–D17S809–.057–D17S1290. Haplotypes were generated by a Markov chain–Monte Carlo approach using simulated annealing algorithms implemented in SimWalk2 version 2.82 (Sobel and Lange 1996). Files were analyzed several times by use of slightly different parameters and random number seeds to ensure convergence on a stable solution.

Simulations are useful to determine the proper significance of linkage results either when a sample is unique or when multiple correlated tests have been performed, as in this study. Empirical P values for the complete data set were obtained by simulation of 1,500 sets of 400 markers (representing a genome scan) not linked to a susceptibility gene generated by the SIMULATE program (Terwilliger and Ott 1994). Simulated markers had four alleles of equal frequency, for a heterozygosity of 0.75. Markers were analyzed by the program MSIM and were evaluated for heterogeneity by the program ElodHET. These programs were modified to accommodate analysis of six genetic models and report the maximum homogeneity LOD score and the maximum heterogeneity LOD score across all six models per simulated genome scan. The best homogeneity and heterogeneity LOD scores over each simulated genome scan were extracted and compiled into a single distribution. LOD scores from the real analysis were compared to this distribution from the simulated data sets, to see how often a given result would be expected by chance from an unlinked data set. This is reported as the empirical P value. Pedigree structures, phenotypic classifications, and genetic models were the same as those used in the actual analysis, but marker information was generated without regard to affection status.

To establish the statistical cutoff for follow-up analysis on the initial family set with additional pedigree members, 1,000 replicates were simulated under the assumption of no linkage for the initial sample and were analyzed under all six models, as described above. A LOD score of 1.74 was expected to occur by chance approximately once in every two genome scans, and this score was used as the cutoff for follow-up genotyping with the additional DNA samples.

We have also calculated the posterior probability of linkage (PPL), using the general form of Vieland (1998), which employs two-point LOD scores in lieu of constituent likelihoods (see also Wang et al. 1999, 2000; Vieland et al. 2001). The PPL differs from the LOD score, first, because it directly measures the probability that the genetic distance between the marker and a putative disease gene is <50 cM; and second, because it explicitly incorporates the prior distribution of θ, including the small prior probability of linkage between a trait gene and a random marker. Here we have used a prior probability of linkage of 2% (Elston 1975; Morton 1998) and have modeled the prior density of θ, given linkage, in terms of the random distance of a trait gene to its closest marker on a fixed marker map (Vieland et al. 2001).

We have also implemented a new feature in computing PPLs for the SLI data: rather than fixing the trait parameters at arbitrary values, we have included them as nuisance parameters in the model by assigning them independent uniform prior distributions and then integrating them out, to obtain a marginal posterior density in θ alone (see Appendix B of Vieland et al. 2001). The posterior marginal density of θ was approximated via direct numerical evaluation, by discretizing each parameter, computing two-point LOD scores at each possible combination of parameter values, and then averaging the resultant set of LOD scores (likelihoods) for each value of θ (M. W. Logue, unpublished data). The three penetrances (for the AA, Aa, and aa genotypes) were independently varied from 0 to 1, in increments of 0.10 (but the degenerate case of all penetrances being equal was skipped, and 0.999 was substituted for 1); θ was varied from 0 to .5 in increments of 0.01; and the admixture parameter (α) was varied from 0 to 1, in increments of 0.05. The grid for the disease-gene frequency was 0.001, 0.01, 0.1, 0.3, 0.5, and 0.8. The PPL was computed from the posterior marginal density of θ integrating over θ<.5 by numerical approximation, as described above.

Results

Two-point and three-point analysis was conducted on the initial subject set with follow-up genotyping on markers that produced multipoint heterogeneity LOD (HLOD) scores greater than 1.74. Figure 1 summarizes the two point results for all screening set markers and includes results of follow-up genotyping (see full table of LOD scores in online-only supplementonline-only supplement). After follow-up genotyping, three areas gave two point results over 1.74 (D13S800, 3.62, recessive reading discrepancy; D2S405, 2.28, recessive language impairment; D17S1290, 1.92, dominant reading discrepancy). Two additional markers flanking D13S800 (∼4 cM on each side) also gave positive two-point LOD scores under the recessive reading model (D13S1317, 2.99; D13S1306, 1.00). Four-point analysis done by use of D13S800 and these flanking markers produced a maximum LOD score of 3.92 at 0.9 cM telomeric to D13S800 (fig. 2). Haplotypes generated by SimWalk2 version 2.82 indicated recombination events occur in affected individuals between D13S788 and D13S1317, as well as between D13S800 and D13S1306. Because of the relatively large spacing between the adjacent markers used for this study, the placement of these flanking recombination events cannot be defined with much precision. On the basis of the Marshfield Comprehensive Human Genetic Map, the flanking recombination events are separated by a genetic distance of 4–14 cM. According to the December 2001 assembly of the Human Genome Project Working Draft, this corresponds to physical distance of 6.8–25.9 Mb.

Figure 1.

Figure 1

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Maximum two-point heterogeneity LOD scores for all six models, summarized over the entire genome. The three highest peaks are labeled by marker and model. A list of two-point results for all markers and models is located in the online-only supplementonline-only supplement. C = clinical diagnosis; R = reading discrepant; L = language impaired; “Rec” and “Dom” are recessive and dominant modes of inheritance, respectively.

Figure 2.

Figure 2

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Four-point analysis of the recessive reading model using markers D13S1317, D13S800, and D13S1306. This graph shows the overlap between our observed linkage and the CLSA (2001b; originally published as Bradford et al. 2001). The horizontal bar indicates the Zmax-1 interval from the CLSA data set.

Since our sample size is relatively small but does have complex pedigree structure and since our phenotypes are moderately correlated, the true false-positive rate may differ from that suggested by traditional guidelines. For a full assessment of the significance of our finding, 1,500 simulated genome scans of unlinked markers were tested under all six models, to determine the empirical significance level. A score ⩾3.92 occurred <1% of the time under homogeneity and heterogeneity analysis, indicating that our genomewide empirical significance level is P<.01.

Figure 3 summarizes the PPL results over the length of chromosome 13 for linkage with reading discrepancy. D13S1317 gave a PPL of 0.53%, D13S800 gave a PPL of 27%, and D13S1306 gave a PPL of 9%. Markers that appeared to be unlinked on the basis of LOD scores failed to produce a PPL >0.045.

Figure 3.

Figure 3

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PPL across the length of chromosome 13 when the reading discrepancy phenotype is used. Note the height of the linkage peak relative to the background level of linkage across the chromosome.

The region with the next highest LOD score was in 2p22 at marker D2S405 (θ=0) with a maximum two-point LOD score of 2.28 under the recessive language model. Markers near D2S405 produced positive LOD scores (1.57 at D2S352; 1.31 at D21788). Four-point analysis of the region that used these three markers gave a maximum LOD score of 2.79 at 0.05 cM distal to D2S405. Overlapping three-point analysis of this region is shown in figure 4. A LOD score of 2.79 corresponds to an empirical P value <.06 in our sample, as determined by simulation. PPL analysis of the markers was only very slightly higher than the prior probability of linkage (for D2S405, PPL = 0.058; for D2S352, PPL = 0.037; for D2S1788, PPL = 0.024). The other region exceeding our screening criteria had a peak near D17S1290 (θ=.01), with a maximum two-point LOD score of 1.92 under the dominant reading model after follow-up genotyping (PPL = 0.036 for reading impairment). Four-point analysis of the region including the two flanking markers (D17S2180 and D17S809), performed at ∼11 cM resolution, gave a maximum LOD score of 2.19, corresponding to an empirical P value of .20.

Figure 4.

Figure 4

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Overlapping three-point analysis with the recessive language model of a selected region of chromosome 2. For the purposes of this graph, LOD scores were plotted by use of successive analyses anchored by each marker. The potential localization of DYX3 is shown by the black horizontal bar (Fagerheim et al. 1999).

Discussion

This study has demonstrated significant evidence for linkage between 13q21 and susceptibility to SLI, by use of a reading-based phenotype. Our analysis also suggests two additional loci, on 2p22 and 17q23, that may play a role in the overall phenotype associated with SLI. Although the families in this study were initially ascertained for schizophrenia, all persons with a diagnosis of schizophrenia (present in three of five families), were coded as phenotypically unknown. Furthermore, the largest pedigree in our data set, which contributed ∼50% of the linkage signal on 13q21 and 2p22, was connected to an original family with schizophrenia only by a marriage so the portion of the family in our data set should not segregate a schizophrenia locus by descent. As schizophrenia and SLI are two common disorders, it would be expected to find families that segregate both these disorders independently. Thus it seems most likely that our results relate to SLI and not schizophrenia.

The locus on 13q21 was identified using a reading discrepancy phenotype that might be considered a dyslexia phenotype. To date, no studies have strongly implicated 13q21 or the surrounding region in dyslexia. One possible explanation for this lack of overlap may lie within the ascertainment definitions of SLI and dyslexia. Traditionally, for a subject to be classified with dyslexia or specific reading disability, language (receptive and expressive grammar, syntax, and vocabulary) would have to be within the developmentally normal range. The diagnosis of SLI, however, requires difficulty in the acquisition of language skills that, by necessity, would make learning to read more difficult and thus reduce reading scores. Our reading phenotype in a population selected for SLI is most likely measuring the resultant reading outcome of an underlying language deficit as opposed to a reading deficit in isolation. It is interesting to note that reading discrepancy as a quantitative trait was found to have significant heritability (h2g=.46; SE=.15) in twins selected for reading disability (Pennington et al. 1992). Although the relationship between a similar, categorical reading discrepancy and SLI are still unclear, our data suggest that further work should be done to clarify why such a discrepancy shows utility for finding genes in SLI, whereas language-IQ discrepancy, as demonstrated by Bishop et al. (1995), does not (h2g=0–0.17±0.5–0.91 with four language measures in 90 twin pairs).

The 13q21 region has also been suggestively implicated in autism (MIM 209850) by the CLSA (2001a, 2001b [originally published in 1999, 2001]). When the authors of this study divided their autism sample in subsets, on the basis of language delay in the probands (onset of phrase speech >36 mo), and coded the parents as affected/unaffected according to questionnaire information on the parents’ history of language, reading, and spelling ability, the evidence for linkage was increased. Virtually all of the linkage signal came from the group with language-delayed probands. The maximum multipoint HLOD score was 2.54 at D13S800, the same marker where the peak LOD score occurs in the present study. Figure 2 shows the extent of overlap between our study and the study by the CLSA (2001b, originally published in 2001). Both autism and SLI appear to follow complex patterns of inheritance, so, if these disorders do share a common gene, then it would not be responsible for the entire phenotypic presentation of either disorder. Furthermore, since human disease genes have been documented to have many separate mutations, even implication of the same gene in both disorders does not necessarily imply the same molecular mechanism (Noone and Knowles 2001). A recent report by Kjelgaard and Tager-Flusberg (2001) indicates that a subset of children with autism show language deficits that are very similar to SLI. Since SLI is a common disorder, genes that negatively modulate language may be segregating independently from autism but still having an impact on the phenotypic presentation of that disorder. The use of special populations with circumscribed deficits may prove useful for the elucidation of other complex disorders, through determination of the genetic mechanisms for phenotypic components, such as the language component of autism.

PPL analysis has been used to further characterize our higher LOD scores. The PPL differs from the HLOD in a few notable respects. First, it already has incorporated into it the small prior probability of linkage (which is not used in calculation of the HLOD, although it is often invoked as a consideration in judgment of whether the observed HLOD is large enough to constitute strong evidence). Second, it incorporates a prior probability distribution for θ (again, the HLOD does not; whereas the PPL is integrated over θ, the HLOD is maximized over it). Third, whereas the HLOD has been calculated under the assumption of a particular trait model, the PPL is written as a function of the parameters of a (marginal) single-locus trait model, including admixture, and then these parameters are integrated out of the final statistic (so, again, the PPL is integrated over the trait parameter-space, whereas the HLOD, in this case, is taken at two particular fixed points in that space, one dominant and one recessive). Although all of these features introduce some differences between the statistics, it is this last item that is most likely to explain the fact that the HLODs maximize at a slightly different location than does the PPL. Localization on the basis of PPL would indicate that the disease gene is closer to D13S1317 than to D13S800, in contrast to the multipoint results. Calculation of the PPL is based solely on two-point analysis, whereas multipoint analysis has the ability to recover some of the power lost to marker uninformativeness, which is especially important in small data sets. Since the two localizations differ only by <5 cM, it is reasonable to conclude that the 13q21 region is implicated in the etiology of SLI, but further analysis will be required to refine the location estimate in this sample.

The PPL has an additional advantage over LOD scores in that it can be interpreted directly as a measure of the probability of linkage, given the data. A 53% PPL means that, more likely than not, there is a susceptibility gene at this location, and failure to follow up on this result is likely to miss a valid finding. It is important to note as well that the prior distribution for all nuisance parameters was taken to be uniform, which is likely to be conservative. The uniform distribution for penetrance will weight a model that has a 90% phenocopy rate equally with one that has a very low phenocopy rate. However, a 90% phenocopy rate would not be consistent with the behavioral genetics of SLI, which suggests a strong genetic component and therefore would indicate use of a very small prior distribution for such unlikely models. Yet, definition of the genetically likely parameter space is not a straightforward undertaking, and thus the observed PPL can be considered conservative. Figure 3 graphically demonstrates the signal-to-noise ratio of the PPL across chromosome 13, which indicates both the conservative nature of the statistic and the relative strength of a 53% PPL.

Our findings on 2p22 are plotted in figure 4, with the location of the dyslexia locus, DYX3, indicated (Fagerheim et al. 1999). The maximum LOD score in our sample is ∼40 cM from the location of DYX3. Since 40 cM approaches the approximate limit of detectable linkage, it is very unlikely that our locus represents linkage to DYX3. The PPLs in this region were also low (3.7% and 2.4%) but were higher than the prior probability of linkage. Thus, it would be premature to entirely exclude this locus for SLI. The locus on 2p22 was suggested only with the language impairment model, which is analogous to the 13q21 locus being found only under the reading impairment model. Although the two models do correlate to a modest extent (see table 1), they are not derived from the same phenotypic measures and are likely to have somewhat differential sensitivities for the respective domains of reading and language, which may account for our results. Further, the clinical impairment classification may have been unconservative in that it would increase the phenocopy rate for either language or reading impairments, since it also included self-reported history information, which—although increasing sensitivity to detect compensated adults—may not be as reliable as direct testing (Tallal et al. 2001).

This study has used a population with Celtic ancestry to map genes that either are involved in a limited but distinct disruption of language acquisition (SLI) or represent part of the lower tail of normal variation in language ability. There remains an unresolved controversy in the field of language learning with regard to the exact etiology of SLI (Aram 1991; Johnston 1991; Leonard 1991; Bishop 1994, 2001; Fitch et al. 1997; Dale et al. 1998; Leonard 1998). This debate may be resolvable, within limits, once the gene at 13q21 and other SLI-susceptibility genes are successfully found and characterized.

Lai and Fisher et al. (2001) recently cloned the FOXP2 gene (MIM 606354) at the SPCH1 locus (MIM 602081) responsible for a more severe and complex speech/language abnormality. The presumably causal gene was shown to have a point mutation in one extended pedigree (the KE family) and not in 364 ethnically matched controls. The only other case this group reports resulted from a disruption of FOXP2 due to a de novo translocation event. As a result, the rather unique phenotype of the KE kindred (severe oral motor dyspraxia and, in some cases, very low IQ), which encompassed more than the isolated language deficits defining SLI, does not appear to be part of normal genetic variation in language ability. Conducting linkage studies in samples that show only language and reading deficits may prove to be more useful in further definition of the etiology of SLI and may illuminate molecular mechanisms that are part of normal genetic variation.

The present study differs from the genome scan by the SLI Consortium (2002) in several ways. The SLI Consortium presented suggestive evidence for linkage on 16q and 19q, whereas our study did not. Such differences may reflect the statistical difficulty of the replication of loci or may reflect locus heterogeneity. It is possible that susceptibility alleles within the fairly homogeneous sample of Celtic ancestry that we studied (although it was not a population isolate) do not segregate within the United Kingdom as a whole with great enough frequency for the SLI Consortium study to detect the 13q21 locus, particularly because nuclear families do not provide very much power to detect admixture. Furthermore, the statistical approach used by the SLI Consortium was based solely on quantitative genetic analysis. Although quantitative traits in sib pairs/nuclear families overcomes certain constraints inherent to research of disorders based on quantitative measures and have been proven useful by the SLI Consortium’s suggestion of SLI loci on chromosomes 16 and 19, our study represents another demonstration of the utility of using categorical phenotypes with the traditional LOD-score–based method for implication of loci in complex diseases. It is important to keep in mind that different statistical methods require different assumptions and require different data structures/dependencies. This study indicates that using categorical techniques in extended pedigrees may reveal important genetic factors in disease etiology that could potentially be missed by sib-pair analysis.

Acknowledgments

We would like to thank the participating families, who contributed their time and patience to make this study possible; Linda Hirsch, Teresa Realpe, and Jason Nawyn, for managing the phenotype database in Newark; Jared Hayter, for technical assistance in the laboratory; the experienced testers associated with the laboratory of Paula Tallal; and Dawn Waterworth, for useful comments on earlier revisions. This research was funded by research grant 12-FY98-0008 from the March of Dimes Birth Defects Foundation (support to L.M.B.) and the National Alliance for Autism Research (with research partner The Sidgmore Family Foundation; support to C.W.B. and L.M.B.). P.T., L.M.B., and J.F.F. were supported by National Institute on Deafness and Other Communication Disorders grant RO1 DC01654. V.J.V. is supported by National Institutes of Health grants K02-MH01432 and MH52841. M.W.L. is supported by National Institutes of Health grant 5 T32 MH14620 (support to Dr. Raymond Crowe). A.S.B. is supported by the Medical Council of Canada and The Bill Jefferies Schizophrenia Endowment Fund. Genotyping services were provided by CIDR. CIDR is fully funded through contract N01-HG-65403 from the National Institutes of Health to The Johns Hopkins University.

Supplemental Online-Only Table.

Clinical Impairment
 Reading Impairment
 Language Impairment
Dominant
 Recessive
 Dominant
 Recessive
 Dominant
 Recessive
Chromosomeand Marker cM LOD θ LOD θ LOD θ LOD θ LOD θ LOD θ
Chromosome 1:
 D1S468 4 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D1S2660 11 .616 .05 0 .5 .569 0 0 .5 0 .5 0 .5
 D1S1612 16 0 .5 0 .5 .287 0 .0242 0 0 .5 .009 .4
 D1S1597 30 0 .5 0 .5 0 .5 .0272 0 0 .5 .022 .4
 D1S3669 37 .0725 0 0 .5 .413 0 0 .5 .225 0 0 .5
 D1S552 45 .308 .1 0 .5 .08 .2 .002 .4 0 .5 0 .5
 D1S1622 57 .478 .05 .0023 .1 .113 .3 .051 .3 0 .5 0 .5
 D1S255 65 .003 .4 0 .5 0 .5 .001 .4 0 .5 0 .5
 D1S3721 73 0 .5 0 .5 .239 .1 .002 .3 .111 .1 .028 .3
 D1S2134 76 .398 .1 .107 .2 0 .5 .029 .2 .1512 0 .298 .1
 D1S3728 89 0 .5 0 .5 .052 0 .213 .1 .352 0 .516 .05
 D1S1665 102 .093 .3 .048 .3 0 .5 0 .5 .123 .1 .326 .2
 D1S1728 109 .0313 .05 0 .5 0 .5 .013 .3 .0091 0 .028 .3
 D1S551 114 .021 .3 0 .4 0 .5 .093 .2 .268 0 .051 .3
 D1S1588 126 .5 0 .0775 0 0 .5 0 .5 0 .5 0 .5
 D1S1631 137 0 .5 0 .5 0 .5 0 .5 .005 .4 .012 .4
 D1S3723 141 .0163 0 0 .5 .176 .1 0 .5 .0103 0 0 .5
 D1S534 152 .019 .2 0 .5 0 .5 0 .5 .583 .1 .03 .4
 D1S1653 164 .5764 0 0 .5 0 .5 0 .5 .333 .2 .005 .4
 D1S1679 171 .1687 .05 .005 .4 0 .5 0 .5 .254 .2 0 .5
 D1S1677 176 0 .5 .003 .4 0 .5 0 .5 .158 .3 .05 .4
 D1S1619 189 .0024 0 .01 .4 .0001 0 0 .5 .069 .2 .011 .4
 D1S1589 193 .022 .3 .0293 0 0 .5 0 .5 0 .5 0 .5
 D1S518 203 .538 0 .717 .1 0 .5 0 .5 .374 .2 .228 .3
 D1S1660 213 .2475 .1 .718 .2 .139 .1 0 .5 .45 .2 .181 .3
 D1S1647 217 0 .5 0 .5 .306 0 0 .5 0 .5 0 .5
 GATA124F08 227 .303 0 .1982 .1 .227 0 0 .5 .146 .2 .038 .3
 D1S2141 234 0 .5 0 .5 0 .5 0 .5 .231 .2 .192 .3
 D1S549 240 .01 .3 .153 .3 .129 .05 .746 0 .435 .2 1.3046 0
 D1S3462 248 0 .5 0 .5 .262 0 0 .5 0 .5 0 .5
 D1S235 255 0 .5 0 .5 .0133 0 0 .5 0 .5 0 .5
 D1S547 268 .002 .4 .17 .2 0 .5 0 .5 0 .5 0 .5
 D1S1609 275 0 .5 .159 .2 0 .5 0 .5 0 .5 .035 .4
Chromosome 2:
 D2S2976 4 0 .5 0 .5 0 .5 .5264 0 0 .5 0 .5
 D2S2952 18 .0492 0 0 .5 0 .5 0 .5 0 .5 0 .5
 D2S1400 28 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D2S1360 38 0 .5 0 .5 0 .5 0 .5 .088 .3 .417 .2
 D2S405 48 .015 .3 .0878 .3 .2172 .5 .061 .3 .38 .1 2.285 0
 D2S1788 56 .1206 .1 .076 .3 0 .5 .2575 .05 .5 .2 .391 .05
 D2S1356 64 0 .5 .0021 .4 0 .5 0 .5 .814 .1 .775 .1
 D2S2739 74 0 .5 0 .5 0 .5 0 .5 .209 .2 .452 .1
 D2S441 87 0 .5 .003 .4 .042 .4 0 .5 0 .5 .051 .3
 D2S1394 91 0 .5 0 .5 0 .5 0 .5 0 .5 .0472 .05
 D2S1777 99 0 .5 .058 .4 .016 .4 0 .5 0 .5 .044 .3
 D2S1790 103 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D2S2972 114 0 .5 .038 0 .156 0 .022 .4 0 .5 0 .5
 D2S410 125 0 .5 .0231 0 .006 .4 0 .5 0 .5 0 .5
 D2S1328 133 .0052 0 .719 .1 0 .5 0 .5 0 .5 0 .5
 D2S1334 145 .011 .4 .014 .4 0 .5 .026 .3 0 .5 0 .5
 D2S442 147 .004 .4 .367 .2 0 .5 0 .5 0 .5 .03 .4
 D2S1399 152 0 .5 .0228 0 .005 .4 .009 .4 0 .5 0 .5
 D2S1353 165 0 .5 .045 0 0 .5 0 .5 0 .5 0 .5
 D2S1776 173 .132 .2 .202 0 0 .5 0 .5 1.312 0 0 .5
 D2S1391 186 .441 .1 .684 .05 0 .5 0 .5 .0583 0 .161 0
 D2S1384 200 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D2S2944 210 0 .5 .076 .2 .4643 0 .606 0 0 .5 .096 .3
 D2S434 216 0 .5 .325 .1 .5442 0 .4098 0 0 .5 0 .5
 D2S1363 227 0 .5 .004 .4 .002 .4 .083 .2 0 .5 .5838 0
 D2S427 237 .778 0 .667 .05 .0003 0 0 .5 .443 .01 .591 0
 D2S2968 252 .028 .3 .013 .4 0 .5 0 .5 .014 .4 .012 .4
 D2S125 261 .003 .4 .013 0 0 .5 0 .5 0 .5 .304 .2
Chromosome 3:
 D3S2387 6 0 .5 0 .5 0 .5 .008 .4 0 .5 0 .5
 D3S1560 19 .386 0 .195 .2 .053 0 .085 0 .002 .4 0 .5
 D3S1304 22 .064 .1 0 .5 0 .5 .111 0 .0067 0 .08 0
 D3S4545 26 .094 .2 .0077 .3 0 .5 0 .5 .076 .3 0 .5
 D3S1259 37 0 .4 0 .5 0 .5 0 .5 .052 .3 .007 .4
 D3S3038 45 0 .5 .168 .2 0 .5 0 .5 .456 .1 .128 .3
 D3S2432 58 .328 .1 .0125 .1 0 .5 0 .4 .0391 .1 .0991 .05
 D3S1768 62 0 .5 .019 .4 .205 .2 .0721 .2 0 0 .826 .05
 D3S2409 71 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D3S1766 79 .009 .4 .006 .4 0 .5 0 .5 0 .5 0 .5
 D3S4542 90 .005 .4 .032 .4 0 .5 0 .5 0 .5 0 .5
 D3S2406 103 0 .5 .0097 .2 .091 0 0 .5 0 .5 0 .5
 D3S4529 112 0 .5 .005 .4 0 .5 0 .5 0 .5 0 .5
 D3S2459 119 0 .5 0 .5 .188 0 0 .5 0 .5 0 .5
 D3S3045 124 0 .5 0 .5 .058 .2 0 .5 0 .5 0 .5
 D3S2460 135 0 .5 0 .5 .033 .2 .001 .4 0 .5 0 .5
 D3S4523 138 .0024 0 .1068 0 .043 .2 .131 .2 .0141 .05 .1003 .2
 D3S1764 153 .105 .1 .0141 0 0 .5 1.201 0 0 .5 .0012 0
 D3S1744 161 .031 .3 .0262 .05 .2954 0 .648 .1 .024 .4 1.117 0
 D3S1763 177 .114 .1 .264 .1 0 .5 .048 .2 .235 0 .1802 0
 D3S3053 182 0 .5 0 .5 .0083 0 .1155 0 .0382 0 .311 .1
 D3S2427 188 .03 .3 .032 .4 .002 .4 0 .5 0 .5 0 .5
 D3S1262 201 1.257 0 .203 .2 .093 .3 .024 .3 .469 .1 .152 .3
 D3S2398 209 .911 0 .661 .01 .15 .1 .087 .2 .246 0 .1488 .1
 D3S2418 216 .768 0 .08 .3 .115 .3 .012 .3 .0053 0 .318 .2
 D3S1311 225 .164 .2 .13 .3 .001 .4 .032 .3 .306 .1 .163 .3
Chromosome 4:
 D4S403 26 .734 .05 .508 .1 .346 .1 0 .5 .127 .2 .164 .2
 D4S2639 33 .1849 .2 .1679 0 .769 0 .1882 .1 .03 .4 0 .5
 D4S2397 43 .009 .4 0 .5 .321 .05 0 .5 .004 .4 0 .5
 D4S2632 51 .603 0 0 .5 0 .5 0 .5 0 .5 0 .5
 D4S1627 60 .9102 0 .8404 0 0 .5 0 .5 0 .5 0 .5
 D4S3248 73 .6675 .01 .023 .3 0 .5 0 .5 0 .5 0 .5
 D4S2367 78 .384 0 .019 .3 0 .5 .17 0 0 .5 0 .5
 D4S3243 88 .0476 0 .3 .2 0 .5 0 .5 0 .5 .013 .4
 D4S2361 93 .023 .3 .2973 0 0 .5 0 .5 0 .5 0 .5
 D4S1647 105 .005 .4 0 .5 0 .5 0 .5 0 .5 0 .5
 D4S2623 114 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D4S2394 130 .15 .3 .014 .4 .452 0 .0043 .2 .38 .1 .242 .2
 D4S1644 143 0 .5 .012 .4 .008 .4 0 .5 .235 0 .03 .3
 D4S1625 146 0 .5 0 .5 .025 .3 0 .5 0 .5 .036 .3
 D4S1629 158 .475 0 0 .5 0 .5 .31 .05 0 .5 .318 0
 D4S2368 168 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D4S2431 176 .119 .2 .023 .4 0 .5 .272 .2 .038 .4 0 .5
 D4S2417 182 .106 .1 0 .5 0 .5 .025 .2 .114 .2 .023 .4
 D4S408 195 .18 .2 0 .5 0 .5 0 .5 .0288 0 0 .5
 D4S1652 208 0 .5 .0164 0 .0004 0 .117 0 .0476 0 .0511 0
Chromosome 5:
 D5S2488 0 0 .5 0 .5 .059 0 1 0 0 .5 .334 .2
 D5S2849 8 0 .5 0 .5 .046 .2 .025 .3 0 .5 0 .5
 D5S2505 14 .593 .05 .395 .2 0 .5 .021 .3 .017 .4 .656 .1
 D5S807 19 .0346 0 .3502 0 0 .5 .12 .2 .0555 0 .4249 0
 D5S817 23 0 .5 .1 .2 0 .5 .223 .1 0 .5 0 .5
 D5S2845 36 0 .5 0 .5 0 .5 0 .5 .012 .4 0 .5
 D5S2848 40 0 .5 .011 .4 .0011 0 .114 .2 .022 .4 .006 .4
 D5S1470 45 0 .5 .025 .4 0 .5 .121 .2 0 .5 .026 .4
 D5S1457 59 .0429 0 .006 .4 0 .5 0 .5 .015 .4 .1052 0
 D5S2500 69 0 .5 .05 .3 .026 .3 0 .5 0 .5 .276 .1
 D5S424 82 .078 .01 .038 .1 .235 0 .272 0 .086 .01 0 .5
 D5S641 92 .125 .1 .0429 0 1.083 0 .085 0 0 .5 .0658 0
 D5S1725 98 .178 .1 1.016 .05 1.284 0 .3219 0 .0498 0 .5417 0
 D5S1503 108 0 .5 0 .5 .001 .4 0 .5 .09 0 0 .5
 D5S1453 115 .397 .1 .993 0 .682 0 0 .5 .1406 0 .6107 0
 D5S2501 117 .3336 .05 .4989 0 0 .5 .002 .3 .3264 0 1.066 0
 D5S1505 130 .664 0 .2807 0 .604 0 0 .5 1.004 0 .195 .2
 D5S816 139 .103 .2 0 .5 0 .5 0 .5 0 .5 .181 .2
 D5S1480 147 0 .5 .002 .4 .05 .3 .039 .3 0 .5 .434 .2
 D5S820 160 .0929 0 .1802 .05 0 .5 .004 .4 .0018 0 0 .5
 D5S1471 172 .0297 .2 .237 .1 0 .5 0 .5 .213 0 .0253 .05
 D5S1456 175 0 .5 .0016 .3 .002 .4 0 .5 .0508 0 0 .3
 D5S211 183 .016 .4 .0857 0 .116 .1 .015 .2 0 .5 .0013 0
 D5S408 195 0 .5 0 .5 .2647 0 .0296 .05 0 .5 0 .5
Chromosome 6:
 F13A1 9 .0062 .01 0 .5 .0019 .2 .0137 .2 0 .5 0 .5
 D6S2434 25 .0011 .3 .0073 0 0 .5 0 .5 .012 .4 0 .5
 D6S1959 34 .004 .4 0 .5 0 .5 0 .5 .004 .4 0 .5
 D6S2439 42 0 .5 0 .5 .004 .3 .003 .3 0 .5 0 .5
 D6S2427 54 .002 .4 0 .5 0 .5 0 .5 .002 .4 .031 .4
 D6S1017 63 0 .5 0 .5 0 .5 .008 .3 0 .5 0 .5
 D6S2410 73 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D6S1053 80 0 .5 0 .5 0 .5 .022 .3 0 .5 0 .5
 D6S1031 89 0 .5 0 .5 .099 .2 .2547 0 0 .5 .003 .4
 D6S1056 103 0 .5 0 .5 .088 .3 .128 .2 .009 .4 .02 .4
 D6S1021 112 0 .5 0 .5 .088 .1 .191 .2 0 .5 0 .5
 D6S474 119 0 .5 .0881 0 .003 .4 .406 .05 .941 0 .819 .1
 D6S1040 129 .1336 0 .4865 0 0 .5 .577 .1 .99 0 .882 .1
 D6S1009 138 .075 .2 .147 .3 0 .5 .155 .2 1.175 0 .903 .1
 GATA184A08 146 0 .5 0 .5 .1312 0 .533 .05 .234 .01 .0108 .3
 D6S2436 155 .221 .05 .482 .1 .0123 0 0 .5 0 .5 .058 .3
 D6S1277 173 .012 .4 0 .5 0 .5 0 .5 0 .5 0 .5
 D6S1027 187 .088 .3 .024 .4 0 .5 0 .5 .167 .2 .197 .3
Chromosome 7:
 D7S3056 7 .826 .05 1.671 0 0 .5 .076 .2 .764 .1 1.525 0
 D7S513 17 .4321 0 .7441 0 0 .5 0 .5 .504 .1 .7377 0
 D7S3051 29 .005 .4 0 .5 0 .5 0 .5 .454 0 .1495 0
 D7S1802 33 0 .5 0 .5 .146 0 0 .5 .007 .4 0 .5
 D7S1808 42 .1398 0 .0462 .2 0 .5 0 .5 .454 .2 .3048 0
 D7S817 50 .346 .1 .174 .2 .012 .4 .013 .4 .356 .2 .419 .05
 D7S2846 58 .0096 0 .071 .2 0 .5 0 .5 .771 0 0 .5
 D7S1818 70 .098 .2 0 .5 0 .5 0 .5 .529 .05 .333 .1
 D7S3046 79 .0039 0 .004 .4 0 .5 0 .5 .099 .3 .041 .4
 D7S2204 91 .829 0 .096 0 .0004 0 .003 .4 .093 .2 0 .5
 D7S2212 95 .0275 0 .524 0 0 .5 0 .5 .033 .2 .303 0
 D7S821 109 0 .5 .02 .3 0 .5 0 .5 .021 .4 .021 .4
 D7S1799 114 0 .5 .003 .4 .183 .2 .002 .4 .033 .4 .024 .4
 D7S3061 128 0 .5 0 .5 .26 .1 0 .5 .009 .4 0 .5
 D7S1804 137 0 .5 0 .5 .174 .2 0 .5 0 .5 0 .5
 D7S1824 150 0 .5 0 .5 .321 0 .005 .4 0 .5 0 .5
 D7S2195 155 0 .5 0 .5 .175 .1 0 .5 .004 .4 0 .5
 D7S3070 163 0 .5 0 .5 1.184 0 .448 0 .64 .05 0 .5
 D7S3058 174 .078 .2 0 .5 1.012 0 .375 0 .221 .2 0 .5
 D7S559 182 .004 .4 0 .5 .431 .05 .0021 .3 .126 .2 0 .5
Chromosome 8:
 D8S264 1 .012 .4 0 .5 .202 .2 .022 .3 0 .5 0 .5
 D8S1469 16 .007 .3 .0001 .4 .1 0 0 .5 0 .5 0 .5
 D8S1130 22 .1274 0 0 .5 .1117 0 .005 .4 .089 .05 0 .5
 D8S1106 26 0 .5 .014 .4 .4089 0 0 .5 0 .5 .029 .4
 D8S1145 37 0 .5 0 .5 .348 .05 .115 .2 0 .5 0 .5
 D8S136 44 .193 .2 .0732 0 1.209 0 .006 .3 0 .5 0 .5
 D8S1771 50 .106 0 0 .5 .061 .05 0 .5 0 .5 0 .5
 D8S1477 60 0 .5 0 .5 .372 0 .1145 0 0 .5 0 .5
 D8S1110 67 .024 .3 0 .5 .806 0 0 .5 0 .5 0 .5
 D8S1113 78 .018 .4 0 .5 .473 .05 0 .5 0 .5 0 .5
 D8S1136 82 .3946 0 0 .5 .901 0 .647 0 .084 .3 0 .5
 D8S2324 94 .027 .3 0 .5 .873 0 .161 .1 .195 .2 0 .5
 D8S1119 101 .364 .1 .0004 .4 .418 .05 0 .5 .5153 0 0 .5
 GAAT1A4 110 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D8S1132 119 0 .5 0 .5 .2665 0 0 .5 .026 .3 0 .5
 D8S592 125 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D8S1179 135 0 .5 0 .4 0 .5 0 .5 0 .5 0 .5
 D8S1128 140 .023 .2 0 .5 1 0 .131 0 .456 .1 0 .5
 D8S256 148 .0347 .2 .038 .3 0 .5 0 .5 .184 .05 0 .5
 D8S373 164 .027 .2 0 .5 0 .5 0 .5 .431 0 0 .5
Chromosome 9:
 D9S2169 14 0 .2 .459 .01 .133 .1 .007 .4 .136 .2 .434 0
 D9S921 22 .002 .5 .016 .5 .42 .5 .185 .5 0 .5 .297 .5
 D9S925 32 0 .5 0 0 0 .05 0 .2 0 .5 0 .5
 D9S1121 44 0 .1 .1518 .2 .383 .01 .07 .5 0 .2 0 .5
 D9S1118 58 .227 .2 .201 .3 .644 .5 0 .5 .097 0 0 .5
 D9S301 66 .036 .05 .003 .05 0 0 0 .2 .145 .5 0 .5
 D9S1122 76 .184 .2 .704 .4 .604 0 .138 0 0 .1 0 .01
 D9S922 80 .425 0 .47 .05 .508 0 .657 0 .502 0 .585 .01
 D9S283 95 0 .5 0 .5 .673 0 .017 .4 .143 .1 0 .5
 D9S910 104 .534 0 .442 .1 .784 0 .01 .3 .732 0 0 .5
 D9S938 111 .285 0 0 .5 .394 0 .073 .3 0 .5 0 .5
 D9S930 120 .462 0 .3326 0 .557 .05 .036 .3 .138 .2 .0595 0
 D9S934 128 .047 .2 .0542 .2 0 .5 0 .5 .031 .2 0 .5
 D9S1825 136 1.412 0 .3428 .05 .457 .01 .99 0 .2383 0 .751 .1
 D9S2157 147 .0481 0 0 .5 0 .5 0 .5 .0091 .2 0 .5
 D9S1826 160 0 .5 0 .5 0 .5 .006 .4 0 .5 0 .5
 D9S1838 164 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
Chromosome 10:
 D10S1435 4 .407 0 .533 .2 .01 .4 0 .5 .394 .1 1.029 .1
 D10S189 19 .157 .2 .695 0 .607 0 .2287 0 .702 0 .789 .1
 D10S1412 28 .167 .2 .277 .2 .599 .1 .777 0 .183 .1 .787 .05
 D10S2325 33 1.186 0 .344 .2 .016 .3 .137 .2 .0433 0 .303 .1
 D10S1423 46 0 .5 0 .5 .966 0 .758 0 .448 0 .0443 0
 D10S1426 59 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D10S1208 63 0 .5 0 .5 0 .5 0 .5 0 .5 .04 .3
 D10S1221 76 0 .5 0 .5 0 .5 0 .5 .0012 0 0 .5
 D10S1225 81 .002 .4 0 .5 0 .5 0 .5 0 .5 0 .5
 GATA121A08 88 .062 .3 .0051 .3 0 .5 .02 .2 .143 .2 0 .5
 D10S1432 94 0 .5 0 .5 0 .5 0 .5 .028 .4 0 .5
 D10S2327 101 .081 .3 0 .5 0 .5 0 .5 .059 .4 .041 .4
 D10S2470 113 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D10S677 117 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D10S1239 125 .033 .3 0 .5 .45 0 .07 0 .005 .4 0 .5
 D10S1237 135 0 .5 .0021 0 0 .5 0 .5 0 .5 0 .5
 D10S1230 143 0 .5 0 .5 .027 .4 0 .5 0 .5 0 .5
 D10S1213 148 0 .5 0 .5 .017 .4 0 .5 0 .5 0 .5
 D10S217 158 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D10S212 171 .319 0 .1348 0 .067 .05 .068 .1 0 .5 0 .5
Chromosome 11:
 D11S1984 2 .014 .4 0 .5 0 .5 .122 .2 0 .5 0 .5
 D11S2362 9 .0003 0 .028 .4 0 .5 0 .5 .213 .2 .064 .3
 D11S1999 17 0 .5 .031 .4 0 .5 .088 .2 .3 .2 .113 .3
 D11S1981 21 0 .5 .023 .4 .0178 0 .006 .3 .378 .1 .042 .4
 ATA34E08 33 .0555 0 0 .5 0 .5 .007 .4 0 .5 0 .5
 D11S1392 43 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D11S1344 58 .196 .2 .452 .4 0 .5 0 .5 .305 .1 0 .5
 D11S2371 76 0 .5 0 .5 0 .5 .2 .1 .096 .3 0 .5
 D11S2002 85 0 .5 0 .5 .021 .4 .02 .4 0 .5 0 .5
 D11S2000 101 .547 .01 .3651 0 .739 0 1.246 0 .033 .3 .574 .1
 D11S1391 105 0 .5 0 .5 .058 .1 .064 .2 .062 .2 .139 .3
 D11S1998 113 .1686 0 0 .5 .591 0 .291 .1 .0601 0 0 .5
 D11S4464 123 .1517 .1 .209 .2 0 .5 .0658 0 .494 0 .729 0
 D11S912 131 0 .5 0 .5 .519 .05 0 .5 .167 .2 .286 .2
 D11S968 148 .193 0 .2124 0 .04 .3 0 .5 0 .5 0 .5
Chromosome 12:
 D12S372 6 0 .5 0 .5 .012 .4 .02 .4 0 .5 .0022 .4
 GATA49D12 18 0 .5 .049 .4 0 .5 0 .5 0 .5 .452 .2
 D12S391 26 0 .5 .151 .3 .15 .05 0 .5 .05 .3 .551 .2
 D12S373 36 .0896 0 .1548 .1 0 .5 0 .5 .0529 0 .1474 0
 D12S1042 49 0 .5 0 .5 0 .5 0 .5 .0018 0 0 .5
 GATA91H06 56 .009 .4 0 .5 .0316 .2 0 .5 0 .5 0 .5
 D12S398 68 0 .5 0 .5 0 .5 0 .5 0 .5 .0607 .2
 D12S1294 78 .0988 .2 .2458 0 0 .5 0 .5 .049 .3 .6717 0
 D12S375 81 .002 .4 0 .5 .015 .3 0 .5 .066 .3 0 .5
 D12S1052 83 .0766 0 0 .5 .339 0 .0093 0 .078 .1 0 .5
 D12S1064 95 0 .5 .036 .3 0 .5 .016 .4 0 .5 0 .5
 D12S1300 104 0 .5 0 .5 .005 .4 0 .5 0 .5 0 .5
 PAH 109 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D12S2070 125 0 .5 0 .5 .038 .4 0 .5 0 .5 0 .5
 D12S395 137 0 .5 .0484 .2 0 .5 0 .5 0 .5 0 .5
 D12S2078 150 0 .5 0 .5 0 .5 .007 .4 0 .5 0 .5
 D12S1045 161 0 .5 0 .5 0 .5 .5869 0 .048 .4 0 .5
 D12S392 166 0 .5 0 .5 0 .5 .0173 0 .041 .4 0 .5
Chromosome 13:
 D13S787 9 0 .5 0 .5 .017 .4 0 .5 0 .5 0 .5
 D13S217 17 .005 .3 .193 .01 .744 0 .915 0 .018 .2 .0001 .4
 D13S1493 26 .147 .05 .0264 .1 .645 0 .6034 0 0 .5 0 .5
 D13S894 33 .01 .4 0 .5 .203 .2 .647 0 0 .5 0 .5
 D13S325 39 .1728 .1 .1936 0 .284 .2 .725 .05 0 .5 .0124 0
 D13S788 46 .472 .1 0 .5 .083 .2 .844 0 .052 .3 .265 .2
 D13S800 56 .144 .1 0 .5 .4999 0 3.624 0 0 .5 0 .3
 D13S317 64 0 .5 0 .5 0 .5 .077 .2 0 .5 0 .5
 D13S793 76 .005 .4 .013 .4 0 .5 .112 .2 0 .5 0 .5
 D13S779 83 .1 .2 .089 .3 .01 .3 .011 .4 0 .5 .0001 .4
 D13S796 94 0 .5 0 .5 .017 .2 0 .5 0 .5 0 .5
 D13S1265 99 0 .5 .0697 .2 0 .5 0 .5 .084 .3 .078 .1
 D13S285 111 0 .5 .168 .2 .1838 0 0 .5 0 .5 0 .5
Chromosome 14:
 D14S742 12 0 .5 0 .5 0 .5 .0161 .3 0 .5 0 .5
 D14S1280 26 .402 .05 .2977 .01 .43 .05 .153 .2 .0089 0 0 .5
 D14S608 28 0 .5 .046 .2 .37 0 0 .5 0 .5 0 .5
 D14S599 41 .025 .3 0 .5 0 .5 0 .5 .017 .4 0 .5
 D14S306 44 .067 .3 .006 .4 .323 0 .023 0 0 .5 0 .5
 D14S587 56 0 .5 0 .5 0 .5 .101 .2 0 .5 0 .5
 D14S592 67 .0107 .01 0 .5 .0255 0 .005 .4 0 .5 0 .5
 D14S588 76 0 .5 0 .5 0 .5 .3441 0 0 .5 .1154 .2
 D14S53 86 0 .5 0 .5 0 .5 .157 0 0 .5 .004 .4
 D14S606 92 .011 .3 .039 .3 0 .5 .1168 .2 0 .5 .065 .3
 GATA193A07 96 0 .5 0 .5 .005 .4 .3533 0 .003 .4 .019 .4
 D14S617 106 0 .5 0 .5 .005 .4 .035 .4 0 .5 0 .5
 D14S1434 113 0 .5 .017 .4 0 .5 .0024 0 0 .5 0 .5
 D14S1426 126 .0057 .3 .0749 0 .0478 0 0 .5 0 .5 0 .5
Chromosome 15:
 D15S822 12 .018 .3 0 .5 0 .5 0 .5 0 .5 0 .5
 D15S165 20 .303 0 0 .5 .106 0 .091 .05 0 .5 0 .5
 D15S1012 36 .017 .3 .023 .3 0 .5 .02 .1 .005 .4 .735 0
 D15S659 43 0 .5 0 .5 0 .5 0 .5 .116 .2 0 .5
 D15S643 52 0 .5 0 .5 0 .5 0 .5 .016 .4 0 .5
 D15S1507 60 0 .5 0 .5 0 .5 0 .5 .3091 0 0 .5
 D15S980 72 .144 .2 0 .5 .306 .05 0 .5 .17 .2 0 .5
 D15S655 83 0 .5 0 .5 0 .5 0 .5 .043 .4 0 .5
 D15S652 90 .357 .1 .298 .2 0 .5 0 .5 .495 .05 .031 .4
 D15S816 101 .0003 .2 .12 .2 0 .5 0 .5 0 .5 0 .5
 D15S657 105 0 .5 .037 .3 0 .5 0 .5 .076 .3 0 .5
 D15S966 112 0 .5 .016 .2 0 .5 0 .5 0 .5 .051 .2
 D15S642 121 .022 .3 .353 .2 0 .5 0 .5 .001 .4 0 .5
Chromosome 16:
 D16S2616 11 0 .5 0 .5 .086 0 .0033 0 .0882 0 .193 0
 D16S748 23 0 .5 .2414 0 0 .5 0 .5 0 .5 0 .5
 D16S764 30 0 .5 .0001 .2 0 .5 0 .5 0 .5 0 .5
 D16S403 44 0 .5 0 .5 0 .5 .005 .4 0 .5 0 .5
 D16S769 51 0 .5 0 .5 .046 .3 0 .5 0 .5 0 .5
 D16S540 58 0 .5 .013 .4 0 .5 0 .5 0 .5 0 .5
 D16S3396 64 .021 .4 .081 .3 0 .5 0 .5 .092 .2 .673 .1
 D16S3253 72 0 .5 0 .5 .431 .01 .05 .3 0 .5 0 .5
 D16S2620 81 0 .5 0 .5 .099 .1 .039 .2 0 .5 0 .5
 D16S2624 88 0 .5 .63 0 .515 0 .198 0 0 .5 .351 0
 D16S516 100 .097 .2 .358 .2 .201 0 0 .5 0 .5 .004 .4
 D16S3091 111 .592 0 .33 .1 .023 .4 .035 .3 .0539 .1 .08 .3
 D16S539 125 .556 0 .015 .3 .581 0 .824 0 0 .5 0 .5
 D16S2621 130 .0029 0 .292 .1 .1019 0 .3937 0 .075 .2 .083 .3
Chromosome 17:
 D17S1308 1 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D17S1298 11 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D17S974 22 .152 .2 0 .5 .685 0 .054 .2 .051 .3 0 .5
 D17S1303 24 .054 .3 0 .5 .1337 0 0 .5 0 .5 0 .5
 D17S969 28 .366 0 .371 0 0 .5 0 .5 .187 0 .176 .05
 D17S2196 45 .019 .4 0 .5 1.152 0 .039 .3 .004 .4 0 .5
 D17S975 51 0 .5 0 .4 0 .5 0 .5 0 .5 0 .4
 D17S1293 56 0 .5 0 .5 .604 0 .263 0 0 .5 0 .5
 D17S1299 62 0 .5 .194 .2 .81 0 .002 .4 .39 .05 .284 .2
 D17S2180 67 .069 .2 0 .5 1.368 0 .055 0 .602 .05 .12 .1
 D17S1290 82 0 .5 0 .5 1.922 .01 .496 0 .816 0 0 .5
 D17S2193 89 0 .5 0 .5 .181 .1 .055 .4 0 .5 0 .5
 D17S1301 100 .137 .2 .413 .1 1.178 0 .085 .1 .419 .1 .079 .3
 D17S784 117 .678 0 .296 .1 .6709 0 .007 .4 .495 0 .066 .2
 D17S928 126 .237 .2 .119 .2 .277 .2 .201 .2 .05 .3 .826 .05
Chromosome 18:
 GATA178F11 3 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D18S976 13 .151 .2 .0501 0 .099 .2 .25 .1 .0572 0 .3514 0
 D18S843 28 .0042 0 0 .5 0 .5 .0006 .4 0 .5 0 .5
 D18S542 41 .005 .4 .595 .1 .002 .4 .103 0 .1585 0 .536 .1
 D18S877 54 .023 .3 .92 .1 0 .5 0 .5 .052 0 .307 .2
 D18S535 64 .0281 0 .152 .2 0 .5 0 .5 .451 0 .1025 0
 D18S851 75 .14 .2 .641 .1 0 .5 0 .5 .0914 0 .4932 0
 D18S858 80 .126 .2 .355 .2 .004 .4 .0392 .1 .0007 0 .452 .2
 D18S862 89 .012 .3 .6984 0 .002 .4 0 .5 .011 .3 .452 .1
 D18S1364 99 0 .5 .0388 .3 0 .5 0 .5 0 .5 0 .5
 ATA82B02 107 0 .5 .4442 0 0 .5 0 .5 0 .5 .094 .3
 D18S1371 116 0 .5 0 .5 0 .5 0 .5 .039 .3 0 .5
 D18S70 126 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
Chromosome 19:
 D19S591 10 0 .5 0 .5 0 .5 0 .5 .059 .4 .0136 .2
 D19S1034 21 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D19S586 33 .556 0 .41 0 0 .5 0 .5 .243 0 .025 .3
 D19S714 42 .016 0 .17 .3 0 .5 0 .5 .1933 0 .313 .2
 D19S433 52 0 .5 0 .5 0 .5 .005 .4 0 .5 0 .5
 D19S245 59 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 D19S178 68 .324 .05 .529 .1 0 .5 0 .5 .083 .2 .1 .3
 D19S246 78 0 .5 .0177 .3 0 .5 .274 0 0 .5 0 .5
 D19S589 88 .874 .05 .588 .2 0 .5 .1997 0 .1175 0 .1168 .05
 D19S254 101 .2067 0 .3065 0 .007 .4 0 .5 .288 .1 .5383 0
Chromosome 20:
 D20S103 2 .518 .05 .16 .2 .5608 0 0 .5 .933 0 0 .5
 D20S482 12 0 .5 0 .5 0 .5 0 .5 .293 0 0 .5
 D20S851 25 .1709 .1 0 .5 .018 .4 0 .5 1.531 0 .032 .4
 D20S604 33 .852 0 1.266 0 0 .5 0 .5 .3322 0 .4561 0
 D20S470 39 .689 .1 .51 .1 .005 .4 .009 .3 .539 .05 1.64 0
 D20S477 48 .388 .1 .259 .2 .013 .4 .0001 .4 .4579 0 .4196 0
 D20S478 54 .384 .05 .515 .2 0 .5 0 .5 .496 0 .392 .2
 D20S481 62 0 .5 .059 .3 0 .5 0 .5 0 .5 0 .5
 D20S480 80 .915 .05 1.228 .1 .017 .4 0 .5 .893 0 1.278 .1
 D20S171 96 .1482 0 .2114 0 .004 .4 0 .5 0 .5 0 .5
Chromosome 21:
 D21S1432 3 .0024 .3 .011 .4 0 .5 0 .5 .038 .3 .127 .3
 D21S1437 13 0 .5 0 .5 0 .5 .0136 0 .052 .3 0 .5
 D21S2052 25 0 .5 0 .5 .003 .4 .001 .4 0 .5 0 .5
 D21S1440 37 0 .5 .07 .2 0 .5 0 .5 .378 0 .755 0
 D21S2055 40 0 .5 0 .5 0 .5 .0022 0 0 .5 0 .5
 D21S1446 58 0 .5 .0695 .2 .028 .2 .118 .1 0 .5 0 .5
Chromosome 22:
 D22S420 4 .0353 0 .1392 0 0 .5 0 .5 .037 .4 .079 .4
 MFD313 19 0 .5 0 .5 0 .5 0 .5 .003 .4 0 .5
 D22S689 29 .011 .4 .011 .4 .016 .4 .111 .2 .163 .2 .055 .3
 D22S685 32 .0571 0 .0193 .01 0 .5 0 .5 .496 0 .0454 .1
 D22S683 36 .007 .4 0 .5 .023 .4 .1768 0 .159 .3 0 .5
 D22S445 46 .0432 0 .0434 0 .0003 0 .101 .1 .294 .1 .0156 0
Chromosome X:
 DXS9900 4 .112 .1 .49 .1 .32 0 .0093 0 0 .5 .4469 .1
 DXS9895 16 0 .5 .2048 .1 0 .5 0 .5 .0439 0 .3789 0
 DXS9902 22 .026 .4 .039 .4 0 .5 0 .5 .387 0 .049 .3
 DXS9896 31 .0023 0 0 .5 0 .5 0 .5 0 .5 0 .5
 DXS1068 37 0 .5 0 .5 .009 .4 0 .5 0 .5 0 .5
 DXS6810 43 .885 0 .0908 .1 0 .5 0 .5 .0277 0 .018 .4
 GATA144D04 47 .1472 0 .38 .2 0 .5 0 .5 .1825 .1 .095 .3
 DXS7132 53 0 .5 .183 .3 0 .5 0 .5 0 .5 0 .5
 DXS6800 58 0 .5 .0165 0 0 .5 0 .5 .006 .4 0 .5
 DXS6789 63 .001 .4 .166 .3 0 .5 0 .5 0 .5 0 .5
 DXS6797 67 0 .5 0 .5 0 .5 0 .5 0 .5 0 .5
 GATA172D05 69 0 .5 .027 .4 0 .5 0 .5 0 .5 0 .5
 GATA165B12 77 .086 .3 .259 .3 0 .5 0 .5 .008 .4 .025 .4
 DXS1047 82 .013 .4 .219 .3 .056 .3 0 .5 .023 .4 .016 .4
 CXS318 88 .1232 0 .0525 0 0 .5 0 .5 .126 0 .0929 0
 DXS9908 93 0 .5 0 .5 .031 .2 0 .5 .189 .1 .287 .01
 DXYS154 105 0 .5 0 .5 .005 .3 0 .5 0 .5 0 .5
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Electronic-Database Information

Accession numbers and URLs for data in this article are as follows:

  1. CIDR, http://www.cidr.jhmi.edu/ (for genotyping protocols)
  2. Genome Database, http://www.gdb.org/ (for initial STS sequences used to redesign primers)
  3. Human Genome Project Working Draft, http://genome.ucsc.edu
  4. Marshfield Medical Center for Medical Genetics, http://research.marshfieldclinic.org/genetics/ (for order and genetic distances of microsatellite markers)
  5. National Center for Biotechnology Information (NCBI) Genetic Analysis Software, ftp://fastlink.nih.gov/pub/fastlink/ (for FASTLINK versions of MLINK and LINKMAP)
  6. Online Mendelian Inheritance in Man (OMIM), http://www.ncbi.nlm.nih.gov/Omim/ (for autistic disorder [MIM 209850], FOXP2 [MIM 606354], and SPCH1 [MIM 602081])
  7. Primer 3, http://www-genome.wi.mit.edu/genome_software/other/primer3.html (for designing primers)
  8. Rockefeller University, ftp://linkage.rockefeller.edu/software/ (for SIMULATE, MSIM, ElodHet, HOMOG, and linkage utility programs)
  9. University of Pittsburgh, http://watson.hgen.pitt.edu/register/soft_doc.html (for SimWalk2 version 2.82)

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