Skip to main content
Erschienen in: BMC Oral Health 1/2018

Open Access 01.12.2018 | Research article

Novel caries loci in children and adults implicated by genome-wide analysis of families

verfasst von: Manika Govil, Nandita Mukhopadhyay, Daniel E. Weeks, Eleanor Feingold, John R. Shaffer, Steven M. Levy, Alexandre R. Vieira, Rebecca L. Slayton, Daniel W. McNeil, Robert J. Weyant, Richard J. Crout, Mary L. Marazita

Erschienen in: BMC Oral Health | Ausgabe 1/2018

Abstract

Background

Dental caries is a common chronic disease among children and adults alike, posing a substantial health burden. Caries is affected by multiple genetic and environmental factors, and prior studies have found that a substantial proportion of caries susceptibility is genetically inherited.

Methods

To identify such genetic factors, we conducted a genome-wide linkage scan in 464 extended families with 2616 individuals from Iowa, Pennsylvania and West Virginia for three dental caries phenotypes: (1) PRIM: dichotomized as zero versus one or more affected primary teeth, (2) QTOT1: age-adjusted quantitative caries measure for both primary and permanent dentitions including pre-cavitated lesions, and (3) QTOT2: age-adjusted quantitative caries excluding pre-cavitated lesions. Genotyping was conducted for approximately 600,000 SNPs on an Illumina platform, pruned to 127,511 uncorrelated SNPs for the analyses reported here.

Results

Multipoint non-parametric linkage analyses generated peak LOD scores exceeding 2.0 for eight genomic regions, but no LOD scores above 3.0 were observed. The maximum LOD score for each of the three traits was 2.90 at 1q25.3 for PRIM, 2.38 at 6q25.3 for QTOT1, and 2.76 at 5q23.3 for QTOT2. Some overlap in linkage regions was observed among the phenotypes. Genes with a potential role in dental caries in the eight chromosomal regions include CACNA1E, LAMC2, ALMS1, STAMBP, GXYLT2, SLC12A2, MEGF10, TMEM181, ARID1B, and, as well as genes in several immune gene families. Our results are also concordant with previous findings from association analyses on chromosomes 11 and 19.

Conclusions

These multipoint linkage results provide evidence in favor of novel chromosomal regions, while also supporting earlier association findings for these data. Understanding the genetic etiology of dental caries will allow designing personalized treatment plans based on an individual’s genetic risk of disease.
Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s12903-018-0559-6) contains supplementary material, which is available to authorized users.
Manika Govil and Nandita Mukhopadhyay contributed equally to this work.
Abkürzungen
bp
basepair
cM
centiMorgan
COHRA
Center for Oral Health Research in Appalachia
D1MFT
Count of decayed, missing, and filled permanent teeth including white spot lesions
D2MFT
Count of decayed, missing, and filled permanent teeth excluding white spot lesions
dbGaP
The database of Genotypes and Phenotypes
dft
Count of decayed and/or filled primary teeth (dft) score
DRDR
University of Pittsburgh Dental Registry and DNA Repository
GENEVA
Gene Environment Association Studies Initiative
GMI
Genetic Map Interpolator
HP
Heritability Parameter
HWE
Hardy-Weinberg Equilibrium
IBD
Identical-By-Descent
IFS
Iowa Fluoride Study
IHS
Iowa Head Start
LD
Linkage Disequilibrium
LOD
Logarithm of the odds of linkage
MARDD
Myopathy, areflexia, respiratory distress, and dysphagia
Mb
106 (or 1 million) bp
NPL
Non-Parametric Linkage
PRIM
New binary dental caries phenotype based on dft
QT
Quantitative Trait
QTOT1
New age-adjusted quantitative caries phenotype based on the sum of the dft score and D1MFT score
QTOT2
New age-adjusted quantitative caries phenotype based on the sum of the dft score and D2MFT score
r2
A measure of LD based on the square of the correlation coefficient between loci)
SNP
Single Nucleotide Polymorphism

Background

Dental caries is one of the most common chronic diseases among children and adults alike. Childhood caries is associated with failure to thrive, and it can affect self-esteem and school performance [1]. For both children and adults, caries is associated with pain and loss of teeth, and caries may adversely impact growth and weight gain in children, as well as nutrition among adults, thereby negatively affecting quality of life.
Caries is known to have a genetic component. Detection of genetic factors is complicated by the fact that numerous diverse environmental factors influence the incidence and severity of this disease, including the microbiome, dietary habits, fluoride exposure, salivary factors and tooth structure.
Prior studies have shown that caries experience in humans is determined by genetic causes with heritability values between 20 and 60% [27]. To date, there have also been numerous studies investigating association of dental caries with candidate genes or with whole-genome Single Nucleotide Polymorphism (SNP) panels [8, 9]. The only previous genome-wide linkage study of caries was conducted using a panel of 392 microsatellite markers, on 46 extended Filipino families with 642 total individuals [10]. This study found suggestive linkage of low caries experience to chromosome regions 5q13.3, 14q11.2, and 13q27.1, and high caries experience to 13q31.1 and 14q24.3 However, results of previous studies have, in general, not been extensively replicated, possibly due to relatively small sample sizes [8, 9] and the enumeration of genetic factors is far from complete.
Our present study is the first to apply genome-wide multipoint linkage analysis to explore the genetic etiology of caries (whether in childhood or adulthood) using densely spaced SNPs on a population previously analyzed by genome-wide association. Genome-wide linkage analysis is a complementary strategy to genome-wide association analysis for gene-discovery. Whereas association identifies specific marker alleles correlated with the caries phenotype, linkage analysis strategies identify genomic regions shared between related individuals who show similar disease characteristics. The advantage of linkage analysis is that it makes full use of familial inheritance, is less sensitive to allelic heterogeneity, and, unlike association, can be used to detect rare disease-causing mutations. Furthermore, multipoint linkage utilizes genotypes from SNPs neighboring the test location, while association conducts tests at each location independently.
In this study, multipoint non-parametric linkage analysis was conducted, i.e., no assumptions were made with respect to the mode of inheritance of dental caries [11], and the analysis was, therefore, robust to uncertainty about the underlying genetic model. Empirical significance of the linkage signals was assessed across the genome by simulating multiple sets of genome-wide data such that the SNP genotypes were unlinked to caries status.

Methods

Study subjects and genotype data

The families and individuals included in this study are from western Pennsylvania, West Virginia, and Iowa. Subjects from Pennsylvania and West Virginia were ascertained through the Center for Oral Health Research in Appalachia (COHRA; [12]). Additional subjects from Pennsylvania were recruited under the University of Pittsburgh Dental Registry and DNA Repository (DRDR; [13]). Subjects from Iowa were recruited under two University of Iowa projects, the Iowa Fluoride Study (IFS; [1417]) and Iowa Head Start (IHS; [18]). All subject recruitment and data collection was approved by site-specific Institutional Review Boards. Genotyping was conducted under the Gene Environment Association Studies Initiative (GENEVA) for approximately 600,000 SNPs on an Illumina platform (Human 610_Quadv1_B; Illumina, Inc., San Diego, CA, USA). All genotype and phenotype data is available on dbGaP (The database of Genotypes and Phenotypes; https://​www.​ncbi.​nlm.​nih.​gov/​gap; accession number phs000095.v3.p1). Details on genotyping and quality control protocols are also presented on dbGaP, or can be found in earlier studies [19, 20]. Table 1 summarizes the different subsets of data in terms of the sample available for this study. This study utilizes complete families, in other words, non-genotyped and non-phenotyped individuals also contribute to various aspects of the analysis. Prior studies primarily utilized unrelated individuals for conducting association analysis. The starting study sample comprised a total of 4727 self-reported non-Hispanic white individuals, of which 437 were unrelated, 1674 were in 558 two-parent and single offspring (trio) families, and 2616 were in 464 non-trio families. Approximately 76% of individuals were genotyped (Table 1).
Table 1
Starting sample size
Site
Current study starting sample
Unrelated
Triosa (Individuals)
Non-trio pedigreesb
(Individuals)
Genotyped/Total
COHRA
29
162 (486)
452 (2549)
2209/3064
IFS
394 (1182)
4 (32)
964/1214
IHS
169
1 (3)
7 (29)
183/201
DRDR
239
1 (3)
1 (6)
235/248
Total
437
558 (1674)
464 (2616)
3591/4727
Note: aTrios: Family structure of two parents and one child
bNon-trio pedigrees: Families with four or more members

Definition of dental caries phenotypes

Caries scores were assessed on the COHRA, IFC, and IHS subjects in accordance with the COHRA study protocol [12]. For subjects in the DRDR study, we used caries scores abstracted from clinical records by dental students trained by Dr. Alexandre R. Vieira, who is a co-author on this manuscript.
We defined three dental caries phenotypes, one based on primary dentition (PRIM), and two that combine primary and permanent dentitions (QTOT1, QTOT2). PRIM was coded as a binary primary dentition caries phenotype based on the count of decayed and/or filled primary teeth (dft) score. An individual with a dft score of 1 or more was designated as being affected. The primary teeth from all subjects with primary or mixed dentition were assessed for PRIM. These individuals included adults with over retained primary teeth. QTOT1, an age-adjusted quantitative caries phenotype, is based on the sum of the dft score (primary teeth) and D 1 MFT score (count of decayed, missing, and filled permanent teeth including white spot lesions). QTOT1 scores were generated by adjusting this raw sum for age and age-squared effects using locally fitted splines. Scores for 113 individuals below 2 years of age and 5 individuals above 60 years, were excluded from linkage analysis due to a very low caries experience in the 0–2 years age group, and the presence of very few subjects above 60 years of age. Age-adjusted QTOT2, the second quantitative caries phenotype, is based on the sum of the dft score and the D 2 MFT score (count of decayed, missing, and filled permanent teeth excluding white spot lesions). Age-adjustment was performed as for the QTOT1 phenotype; and QTOT2 scores for 115 individuals between 0 and 2 years of age and 44 individuals above 60 years were set to missing.

Data cleaning and preparation

Genetic map positions were generated for all SNPs. These genetic markers were filtered based on genotyping rates and Hardy-Weinberg proportions. The SNPs were then pruned for linkage disequilibrium (LD). SNPs with residual LD were clustered into super-markers. The procedures used for filtering SNPs, map creation, and LD-based SNP pruning and clustering are described below.

Genetic map creation

The Genetic Map Interpolator (GMI) program, [21] was used to calculate genetic map positions for all SNPs. Sex-averaged map positions were created for SNPs on chromosomes 1–22, and female map positions were created for SNPs on the X chromosome. In the GMI program, the physical basepair (bp) position of each SNP per March 2006 Build NCBI36/hg18 was transformed to the corresponding centiMorgan (cM) scale genetic map distance based on interpolation into the Rutgers Combined Linkage-Physical Map [22].

SNP filtering

In addition to the quality control and cleaning steps detailed on dbGap, we filtered SNPs on the basis of low genotyping success rate and deviation from Hardy-Weinberg equilibrium (HWE) proportions using the software PLINK [23]. SNPs with genotyping success rates below 95%, calculated using genotype data for all individuals, were excluded from analysis. Known genotypes of founders (i.e. those individuals in a family whose parents are not included in the study) and unrelated individuals were used to test SNPs for HWE proportions. The HWE proportions significance threshold was set at 10− 5 for rejecting the null hypothesis of no deviation from HWE proportions.

Linkage disequilibrium-based SNP pruning and clustering

The genotyping panel available to this study was designed for genome-wide association analysis. When conducting linkage analysis on densely spaced SNP marker loci, the presence of substantial marker-to-marker LD is known to inflate linkage signals, especially if parental genotypes are missing [24]. In this study, LD was removed in two stages. First, the set of quality-filtered SNPs were pruned using PLINK such that the LD r2 (a measure of LD based on the square of the correlation coefficient between loci) value among remaining SNPs fell below 20%. In PLINK, LD pruning consists of creating blocks of 50 consecutive SNPs followed by recursive removal of SNPs within blocks, until the LD r2 value among the remaining SNPs is below the desired threshold (20% in our case). Only the unrelated genotyped individuals in our data – pedigree founders and unrelated cases/controls -- were used to calculate LD in this step. Any remaining LD was then accounted for using LD-based clustering in Merlin [25]. In clustering, each block of consecutive SNPs that shows an r2 value greater than a specified threshold (in our case 10%), is analyzed collectively as a super-marker.
Table 2 summarizes the data processing steps undertaken to select SNPs for linkage analysis, and the samples that contributed information to specific parts of this data cleaning. After HWE filtering and LD-based pruning, 127,511 SNPs in low LD (pairwise r2 ≤ 20%) were retained. LD-based SNP clustering combined 92,495 SNPs into 20,634 super-markers, leaving 35,016 SNPs to be analyzed individually. The average genetic map distance between the final set of markers (super-marker index and singleton SNPs) is approximately 0.07 cM on the autosomes and 0.13 cM on the X-chromosome. Super-marker and singleton SNP allele frequencies were generated as maximum likelihood estimates using Merlin. The SNP clustering and allele frequency estimation steps utilized 1022 informative families.
Table 2
Sample for data cleaning
Procedure
Data
Low genotype rate filtering (PLINK)
3591 genotyped individuals
HWE testing (PLINK)
1839 genotyped (foundersa + unrelated)b
LD-based pruning (PLINK)
1839 genotyped (foundersa + unrelated)b
LD-based clustering and super-marker creation (Merlin)
1022 families (trios + non-trio pedigrees)c
Super-marker and SNP allele frequency estimation (Merlin)
1022 families (trios + non-trio pedigrees)c
Note: aFounders: Individuals in a pedigree or trio whose parents are not included in the study. For example, both parents in a trio are founders. Also note that some of the larger multigenerational pedigrees may have more than two founders
bThe counts of individuals differs from totals provided in Table 1 since not all founders and unrelated individuals were genotyped for this study
cTrios: Family structure of two parents and one child; non-trio pedigrees: families with four or more members
Table 3
Linkage analysis final sample
 
PRIM
QTOT1
QTOT2
Total Non-trio Pedigrees
108
376
385
COHRA
106
372
373
IFS
1
4
4
IHS
1
7
DRDR
1
Total Individuals
687
2200
2243
Phenotyped
243 affected
1738
1756
Genotyped
483
1582
1604
Total informative relative pairsa
160
1026
1038
Median [Min, Max] pairs/pedigree
1 [1–6]
1 [1–24]
1 [1–24]
Sibling-pairs
100
599
609
Half-sibling pairs
39
228
228
Cousin pairs
21
73
73
Grandparent-grandchild
0
28
28
Avuncular
0
98
100
Note: aPRIM: Affected relative pairs; QTOT1, QTOT2: phenotyped relative pairs

Linkage analysis

Table 3 summarizes the sample of individuals used within the linkage analysis for the three traits. In the table are presented the number of informative pedigrees, individuals, and phenotyped relative pairs by relationship type that were included in NPL and QT linkage. A total of 160 relative pairs were informative for PRIM NPL. For QT linkage, the corresponding informative relative pair counts were 1026 and 1038 for QTOT1 and QTOT2.

Genome-wide linkage of PRIM

In non-parametric linkage (NPL) analysis, affected individuals within each pedigree are examined to detect whether affected relatives share genomic regions identical-by-descent (IBD) more often than expected due to their relatedness alone. This IBD sharing is tested at locations along each chromosome. Genome-wide NPL analysis was carried out for the PRIM phenotype using the SAll statistic [26] as implemented in Merlin [25].

Genome-wide linkage of QTOT1 and QTOT2

The quantitative trait (QT) regression-based linkage method, Merlin-regress, [27] was utilized to carry out analyses of the two quantitative phenotypes across autosomes. The QT linkage method is based on regressing estimated IBD sharing between relative pairs on the squared sums and differences of their phenotypes. It does not handle X-linked SNPs, hence the X chromosome was not analyzed for the two quantitative traits. Merlin-regress analyses required specification of a heritability parameter (set at 50% based on published estimates for DMFT) and sample-based means and variances for QTOT1 and QTOT2. All results, NPL and QT linkage, are reported as LOD (logarithm of the odds of linkage) scores.

Empirical significance of observed linkage signals

The most commonly used LOD score threshold of 3.0 used to test for significant linkage (Morton) was derived for parametric linkage analysis of a single locus on a binary trait phenotype. Subsequent research (e.g. those reviewed in [28]) that address newer linkage methods such as whole-genome analysis, affected-relative pairs and multipoint calculations are also based on assumptions on the study data, that are rarely true in real-life. Therefore, to correct for multiple testing, we carried out a simulation study to assess the genome-wide significance thresholds for the NPL and QT regression LOD scores. In general, for a null simulation, hundreds of simulated genetic data sets are generated and analyzed to produce an empirical distribution of LOD scores. Since this process would be prohibitively time consuming given the study data, we used an adaptive approach to generate null distributions. The replicate pool method, Pseudo [29] was used to derive the empirical null distribution of NPL scores for PRIM. An initial pool of 100 simulated genotype data sets was generated for this study using Merlin (simulate option) followed by the pseudo-simulation of 100,000 NPL genome-scans to create the empirical distribution of unlinked NPL LOD scores. Pseudo was not utilized for the quantitative data simulations since QT-regression does not produce pedigree-specific LOD scores. For QTOT1 and QTOT2, 5000 data sets each were simulated and analyzed using Merlin.

Selection of linkage peak regions and etiologic genes

For super-markers, the NPL and QT LOD scores correspond to the index (first) SNP of each cluster. In the linkage scan for each phenotype, maximizing markers in regions with LOD score ≥ 2.0 were identified as linkage peaks. A support interval of one LOD drop was used for exploring genes under selected linkage peaks. The one LOD drop support interval is the interval where the LOD score is within one unit of its maximum.
Regions with LOD scores ≥1.0 were identified for trait. Overlap of linkage signal among the three traits was determined based on overlap of peak support intervals or secondary peaks(s) of at least 1.0 LODs, lying within the primary peak support interval. In the event peaks for multiple phenotypes overlapped, the resulting support intervals were reduced to the region of overlap.
Genes within these support intervals were examined for a potential etiologic role in dental caries incidence. Genes identified as causal would include, for example, genes related to blood glucose levels, secretory function of the salivary glands, and host immune response. Proximity of genes to SNPs corresponding to linkage peaks was determined by physical map positions obtained from UCSC Genome Browser corresponding to the March 2006 (NCBI36/hg18) Assembly [30]. When no genes were identified as potentially contributing to caries risk, we instead listed the gene closest to the SNP with the maximum observed LOD.

Comparison with prior published findings

A systematic search of literature was conducted to compile caries risk-conferring genes and genomic regions from previous studies utilizing some portion of our data, as well as from studies of other populations. Physical positions for these genes and genomic regions were then mapped to our linkage scans. Linkage regions with a LOD score of 1.0 or greater have been reported as indicative of concordance or replication, as appropriate.

Sensitivity analysis

Effect of variation in parameter values on NPL statistics

For multifactorial diseases such as caries, the true underlying genetic model for disease is difficult to ascertain. In this study, model-free linkage methods were used to detect linkage. The QT methods are sensitive to the misspecification of the required programmatic input values. We conducted a sensitivity analysis for the heritability parameter (HP), since published literature provides a wide range of heritability values (40–60%), and our work utilized HP = 50%. In the sensitivity analysis, HP values were set at 40, and 60% for QTOT1 and QTOT2.
Mega2 [31] was used to re-format and create input files for all the software used in data cleaning, LD-based pruning and clustering, genetic map creation, linkage analysis and data simulation.

Results

Study sample characteristics

Figure 1 provides detailed information on the distribution of the three phenotypes, PRIM (panels A and B), QTOT1 (panels C, D, E and I), and QTOT2 (panels F, G, H and I). There were 287 individuals with known PRIM phenotypes (panel A), of which 243 individuals were affected for PRIM. Of these 243 subjects, 242 were 18 years or younger in age (panel B). Subjects with primary or mixed dentition included in the PRIM NPL analysis ranged from 15 months to 22.5 years of age, with a mean of 7.4 years. These subjects with primary dentition caries constitute mainly the youngest generation. The distribution of the raw caries index by decade, age-adjusted index by decade, and age-adjusted caries index within all phenotyped individuals compared to those between 2 and 60 years of age are shown for QTOT1 (panels C, D, and E) and QTOT2 (panels F, G, and H). The number of phenotyped individuals, range, mean and standard deviation are presented in panel I for both quantitative traits. A larger number of individuals were phenotyped for D 2 MFT as compared to D 1 MFT in this study. For QTOT1 and QTOT2, there were 2484, and 2868 phenotyped individuals in the 2–60 age range. Both of the age-adjusted phenotypes follow an approximately normal distribution, with a mean of zero. The QTOT1 and QTOT2 mean and standard deviations for the 2–60 age group were included as distribution parameters within quantitative trait linkage.

Linkage analysis

Figure 2 shows genome-wide LOD scores by SNP (or super-marker index SNP) for PRIM, QTOT1, and QTOT2. The empirical 5% genome-wide significance level, indicated as a solid horizontal line, was 3.48, 3.61, and 3.76 for PRIM, QTOT1, and QTOT2, respectively. Overlapping LOD score peaks for multiple phenotypes were observed in a few regions.

Highest LOD Score Regions

Table 4 presents peak LOD scores and 1-LOD support intervals. The SNP (or index SNP) with the maximum LOD value in each peak is identified along with its genomic location. Regions with maximum LOD ≥ 2.0 are shown ordered by chromosome, along with secondary peak(s) of at least 1.0 LODs, if observed for other traits. The highest LOD scores by trait were 2.90 for PRIM, 2.38 for QTOT1, and 2.76 for QTOT2. Detailed results for all SNPs that lie within the support region for peaks reported in Table 4 with a LOD score of 2.0 or more are provided in supplementary material [see Additional File 1].
Table 4
Linkage peaks in highest LOD score regions
Chr
Trait
Peaka
Support Interval (Mb)b for Peak with LOD ≥ 2
Closest Genes within Support Intervalc
SNP
bp
LOD
Left
Right
 
1
QTOT1
rs12096999
178,046,412
1.19
  
CACNA1E; LAMC2
1
PRIM
rs1281317
180,232,077
2.90
174.78
182.03
2
QTOT2
rs7572396
59,893,993
2.30
58.63
64.29
BCL11A d
2
QTOT1
rs13420242
71,117,276
1.55
  
ALMS1; STAMBP
2
QTOT2
rs831535
73,976,537
2.10
65.23
79.72
3
PRIM
rs9842115
20,378,197
2.50
15.06
22.19
KAT2B d
3
QTOT1
rs2044594
74,474,447
2.12
67.65
76.08
GXYLT2
5
QTOT1
rs11748635
123,232,224
1.24
  
SLC12A2; MEGF10; IL gene family
5
QTOT2
rs6866597
128,905,516
2.76
122.43
133.84
6
QTOT1
rs240642
158,117,314
2.38
156.81
159.48
TMEM181; ARID1B
6
QTOT2
rs9295289
158,387,494
1.96
  
19
QTOT1
rs11084325
59,424,868
1.12
  
NLRP2; NLRP7; NLRP, KIR, and LILR gene families
19
QTOT2
rs1671133
60,198,861
2.15
59.42
61.47
Note: aNovel regions with LOD ≥ 2.00, and secondary peaks ≥1.0 observed for the other phenotypes; peak LOD ≥ 2.00 shown in bold
bSupport interval for LOD ≥ 2.00; Mb: 106 (or 1 million) bp
cGenes with a potential role in caries incidence. If no such gene is identified, then the closest gene to the peak reported
dUnknown role in caries incidence; closest gene to the linkage peak SNP
For each linkage peak, the table also reports the closest gene, if found, with a potential role in caries incidence. For two of these peaks, one on chromosome 2 (QTOT2; LOD 2.30) and the other on chromosome 3 (PRIM; LOD 2.50), no such etiologic genes were identified within the support intervals. In these intervals, genes BCL11A (60.538–60.634 Mb) and KAT2B (20.056–20.171 Mb) were found to be closest to the respective LOD score peak SNPs. The genes within linkage peak regions that may play an etiologic role in dental caries are described in the sections below.
Chromosome 1
The highest LOD 2.90 across all three traits was observed on chromosome 1 for PRIM (Table 4). Under this peak, the CACNA1E (179.719–180.037 Mb) gene has been shown to be involved in glucose-evoked insulin secretion in mice [32]. Poor glycemic control has potential implications for increased caries risk in humans. Mutations in the LAMC2 (181.422–181.481 Mb) laminin gene are known to cause non-Herlitz form of junctional epidermolysis bullosa, which includes hypodontia and dental caries among its phenotypes [33].
Chromosome 2
The second QTOT2 peak on chromosome 2 includes the ALMS1 (73.466–73.691 Mb) gene. Mutations in this gene causes Alström syndrome, where gingivitis and discolored enamel are two clinical phenotypes [34]. Individuals with mutations in the STAMBP (73.910–73.944 Mb) gene have been reported to have cleft palate and facial dysmorphology [35].
Chromosome 3
The GXYLT2 (73.020–73.107 Mb) gene is located within the chromosome 3 QTOT1 peak. GXYLT2 acts on epidermal growth factor, which is expressed in human submandibular and parotid glands, and important for the maintenance of oroesophageal and gastric tissue.
Chromosome 5
The highest genome-wide quantitative trait LOD was observed for QTOT2. This QTOT2 peak contains the SLC12A2 (127.447–127.553 Mb) gene, whose protein product helps the movement of chloride ions in saliva, thereby assisting in salivary function. Also within the support interval are genes from the IL family, which code for cytokines involved in blood production and immune system function. Defects in these genes result in autoimmune diseases and immune deficiency. A third gene, MEGF10 (126.654–126.825 Mb) has been implicated in MARDD (Myopathy, areflexia, respiratory distress, and dysphagia), with cleft palate as an associated phenotype [36].
Chromosome 6
The TMEM181 (158.877–158.976 Mb) gene under the QTOT1 linkage peak codes for a putative G-coupled protein receptor which mediates reaction to cytolethal distending toxins secreted by many pathogenic bacteria. ARID1B (157.141–157.572 Mb) mutations result in mental retardation along with minor teeth anomalies [37].
Chromosome 19
This region harbors several genes from the NLRP, KIR, and LILR immune gene families that code for various receptors within immune cells. NLRP2 (60.170–60.204 Mb), and NLRP7 (60.127–60.151 Mb) were closest to the peak.

Comparison with previous relevant signals

Table 5 shows regions reported by previous studies, where our current LOD score is 1.0 or greater. Two regions were found to contain genes reported in prior studies.
Table 5
Linkage signals with LOD ≥ 1 concordant with published findings
Study, Gene, phenotype
Highest observed LOD Score
SNP
bp
LOD
Trait
Genome-wide association, MPPED2, dichotomized d 1 fta in US children aged 3–12 (Shaffer et al. 2011)
rs1447267
30,643,586
1.23
PRIM
Association with gene set enrichment, NLRP12, dichotomized d 1 ft1 in children aged 3–12 (Wang et al. [38])
rs11084325
59,424,868
1.12
QTOT1
rs1671133
60,198,861
2.15
QTOT2
Note: aDichotomized d 1 ft as used in our study
Chromosome 11
A PRIM LOD of 1.23 was observed 8500 bp from the MPPED2 (30.338–30.558 Mb) gene. A suggestive association of primary teeth caries was reported by a previous study on 1305 children aged 3–12, some of whom are also part of this analysis (Shaffer et al., 2011). The phenotype was defined similarly to our PRIM phenotype.
Chromosome 19
A gene-set enrichment analysis study [38] reported an association of primary teeth caries to NLRP12 (58.989–59.019 Mb) in 1142 children aged 3–13, a subset of whom are also included in our study. QTOT1 and QTOT2 LOD scores ≥1.0 were observed 0.4–1.2 Mb from this gene.

Sensitivity analysis

For each of the two quantitative traits, QTOT1 and QTOT2, Fig. 3, panels A, B, C, and D show the percentage deviation of LODs obtained using HPs of 40% or 60% from baseline LODs produced with an HP of 50%. These deviations are plotted on the y-axis against the corresponding baseline LOD (x-axis). The red points indicate SNPs for which baseline LODs of 2.0 or greater dropped below 2.0 when the HP value was changed. Conversely, the green points show SNP positions with baseline LODs below 2.0, which subsequently switched to a score of 2.0 or more with a change in HP. Percentage deviations where the baseline LODs were between 0 and 0.05 are not presented in panels A through D. Within this range, the change in LOD combined across HP = 40% and HP = 60% ranges from − 0.04 to 0.11 LOD for QTOT1. For QTOT2, the corresponding range is − 0.05 to 0.1 LOD. Although in percentage terms they represent exponential changes as compared to the baseline, none of the deviations in the 0 to 0.5 baseline LOD score range result in the LOD score approaching significance. Panels E and F break down for each trait, the percentage of all SNPs that drop below—or exceed—the 2.0 LOD score threshold with a change in HP. For both traits, a decrease in HP to 40% results in a minimal percentage of SNPs changing status (be it an increase or decrease in LOD score). In contrast, SNPs with LOD scores of 2.0 or greater at HP 50% are more likely to drop below the 2.0 LOD threshold when the HP is increased to 60%.
Table 6 presents the change in QTOT1 and QTOT2 LOD scores due to a change in HP for only the linkage peaks reported in Table 4. All LOD score peaks, except for one, remain above 2.0 despite changes in HP.
Table 6
Comparison of reported peaks in HP sensitivity analysis
Trait
Chr
SNP
A
HP = 50%a
B
HP = 40% [B-A]
C
HP = 60% [C-A]
QTOT1
3
rs2044594
2.12
2.14 [0.02]
2.04 [−0.08]
6
rs240642
2.38
2.62 [0.24]
2.10 [−0.28]
QTOT2
2
rs7572396
2.30
2.20 [−0.10]
2.36 [0.06]
2
rs831535
2.10
2.16 [0.06]
2.00 [−0.10]
5
rs6866597
2.76
2.64 [−0.12]
2.86 [0.10]
19
rs1671133
2.15
1.95 [−0.20]
2.28 [0.13]
Note: aThese values are the peak LOD score results reported in Table 4 for QTOT1 and QTOT2

Discussion

To our knowledge, this study was the first to apply genome-wide multipoint linkage analysis to explore the genetic etiology of caries using densely spaced SNPs.
We defined two new quantitative phenotypes which combine childhood and adulthood caries indices while also accounting for variability by age. The linkage findings in this study nominated genes on six chromosomes (1, 2, 3, 5, 6, and 19) with potential involvement in caries etiology. Some of the genes are known to cause syndromes with a dental or oral phenotype, while others have a role to play in human immune and host defense response, blood glucose levels, and secretory function of the salivary glands all of which may have a potential impact on incidence of dental caries (see, for example, Carneiro et al. (2015) for the relationship between diabetes and dental caries). After a comprehensive review of the literature, we also detected linkage to regions on chromosomes 11 and 19 previously reported as associated to caries.
As expected, we do not recapitulate all findings from all prior association studies published by our group although this linkage study and the previous association studies utilized data from the same sources (i.e., COHRA, IHS, DRDR, IFS). As mentioned previously, linkage and association are complementary strategies for gene-discovery. In linkage, similarities and differences in pairs of phenotypes are modeled in terms of genetic similarity over related pairs from families. In association, this modeling is performed at the level of individuals. Our linkage uses multi-point analysis, i.e., the LOD score at any specific location is influenced by linkage at neighboring loci. Association generally uses a set of independent one-locus tests. Finally, as described in methods, this study differs from prior published work, both in the number, and the type (in terms of family composition) of individuals included in the analysis. Linkage utilizes family data and all related pairs (affected or phenotyped) within a pedigree whereas association generally is conducted on unrelated cases and controls, or at most parent-offspring trios.
The genotyping panel was designed for association analyses, and therefore, is far denser than a linkage SNP panel. Although dense bi-allelic SNP panels may allow extraction of more information, a concern for this study was existing linkage disequilibrium between SNPs. We pruned SNPs based on marker-to-marker LD, and then exploited any remaining LD among the pruned set to create clusters which served as polymorphic markers. Despite the pruning and clustering, our analysis was conducted on a much denser set of markers (35,016 SNPs and 20,634 super-markers) compared to a typical linkage panel with 6000 SNPs. The use of multi-allelic super-markers also had the potential of increasing power of linkage studies in such a setting.
Genome-wide significance for each phenotype was empirically assessed through a series of simulations, which provides an approximation of the true underlying distribution of a statistic since not all features of the data can be completely replicated. In an exploratory study, adhering strictly to genome-wide significance thresholds may be overly conservative. Furthermore, of the 4727 subjects, only 2616 contributed to the linkage analysis, providing a comparatively small number of relative pairs given the large sample size.
The sensitivity analysis conducted for the parameter HP explores the impact of parameter value selection on a model-free QT method. The results from this analysis indicated that the non-parametric quantitative trait linkage method, as implemented in Merlin, was robust to variation in HP, and that changing the HP parameter had a minimal impact on LOD scores. Even more importantly, the linkage peaks were insensitive to parameter misspecification.
Environmental factors are not accounted for in this study due to unavailability of such data on many of our subjects, which would have drastically reduced the cohort size. We also did not attempt to analyze gene-by-gene interaction. The available methods for detection of gene-gene interaction that are applicable to our study design are computationally complex, thus making whole-genome interaction analysis beyond the scope of the current work (e.g. see the review of the various classes of interaction detection methods by Li [39]).

Conclusions

This study presents two new quantitative measures for dental caries which combine both the primary and permanent dentition, while adjusting for age effects. Genes identified in peak linkage regions underline the importance of exploring potential relationships between caries and other traits. We did not include environmental factors in this study. The interaction between putative caries risk conferring genes and factors including fluoride exposure, dietary habits, and the microbiome need to be investigated, as do interactions between the genes themselves. From a clinical perspective, individuals would be at an elevated lifetime risk of developing caries in both primary and permanent dentition, given increased genetic susceptibility. Understanding the genetic etiology of dental caries will allow health providers to design personalized treatment plans based on an individual’s genetic risk of disease.

Acknowledgements

Many thanks are due to the study participants, and also to the dedicated research staff at all of the research sites, without whom these studies would be impossible.
Support for analyses was provided by the National Institute of Dental and Craniofacial Research (NIDCR) as a Pathway to Independence Award (R00-DE018085). Funding for genotyping was provided by NIDCR as part of the trans-NIH Genes, Environment and Health Initiative [GEI] (U01-DE018903). Genotyping was done by the Johns Hopkins University (JHU) Center for Inherited Disease Research (CIDR), with funding from the NIDCR, contract number HHSN268200782096C. Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the GENEVA Coordinating Center (U01-HG004446) and by NCBI. Data and samples were provided by (1) the Center for Oral Health Research in Appalachia (a collaboration of the University of Pittsburgh and West Virginia University funded by NIDCR R01-DE 014899); (2) the Iowa Fluoride Study and the Iowa Bone Development Study, funded by NIDCR R01-DE09551and R01-DE12101, respectively); (3) the Iowa Comprehensive Program to Investigate Craniofacial and Dental Anomalies (funded by NIDCR, P60-DE-13076. The Dental Registry and DNA Repository project is supported by the University of Pittsburgh School of Dental Medicine. All analyses were conducted on the Centos 276-node cluster, Indy, also supported by the School of Dental Medicine at the University of Pittsburgh.

Funding

National Institute of Dental and Craniofacial Research (NIDCR), Award Numbers: R00-DE018085, U01-DE018903, HHSN268200782096C, R01-DE 014899, R01-DE09551, R01-DE12101, P60-DE-13076.
GENEVA Coordinating Center Award Number: U01-HG004446.
Neither NIDCR nor the GENEVA Coordinating Center played a role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Availability of data and materials

All genotype and phenotype data for the datasets analyzed during the current study are available on dbGaP repository, [ https://www.ncbi.nlm.nih.gov/gap ; accession number phs000095.v3.p1].
All subject recruitment and data collection was approved by the site-specific Institutional Review Boards listed below. At each site, written consent to participate was obtained from all study subjects.
COHRA participants: University of Pittsburgh Institutional Review Board and West Virginia University Institutional Review Board.
IHS participants: University of Iowa Institutional Review Board.
IFS participants: University of Iowa Institutional Review Board.
DRDR participants: University of Pittsburgh Institutional Review Board.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://​creativecommons.​org/​licenses/​by/​4.​0/​), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://​creativecommons.​org/​publicdomain/​zero/​1.​0/​) applies to the data made available in this article, unless otherwise stated.
Literatur
1.
Zurück zum Zitat Chou R, Cantor A, Zakher B, Mitchell JP, Pappas M: Prevention of Dental Caries in Children Younger Than 5 Years Old: Systematic Review to Update the U.S. Preventive Services Task Force Recommendation. Evidence Synthesis No. 104. ; 2014. Chou R, Cantor A, Zakher B, Mitchell JP, Pappas M: Prevention of Dental Caries in Children Younger Than 5 Years Old: Systematic Review to Update the U.S. Preventive Services Task Force Recommendation. Evidence Synthesis No. 104. ; 2014.
2.
Zurück zum Zitat Boraas JC, Messer LB, Till MJ. A genetic contribution to dental caries, occlusion, and morphology as demonstrated by twins reared apart. J Dent Res. 1988;67(9):1150–5.CrossRefPubMed Boraas JC, Messer LB, Till MJ. A genetic contribution to dental caries, occlusion, and morphology as demonstrated by twins reared apart. J Dent Res. 1988;67(9):1150–5.CrossRefPubMed
3.
Zurück zum Zitat Bretz WA, Corby PM, Schork NJ, Robinson MT, Coelho M, Costa S, Melo Filho MR, Weyant RJ, Hart TC. Longitudinal analysis of heritability for dental caries traits. J Dent Res. 2005;84(11):1047–51.CrossRefPubMedPubMedCentral Bretz WA, Corby PM, Schork NJ, Robinson MT, Coelho M, Costa S, Melo Filho MR, Weyant RJ, Hart TC. Longitudinal analysis of heritability for dental caries traits. J Dent Res. 2005;84(11):1047–51.CrossRefPubMedPubMedCentral
4.
Zurück zum Zitat Morrison J, Laurie CC, Marazita ML, Sanders AE, Offenbacher S, Salazar CR, Conomos MP, Thornton T, Jain D, Laurie CA, et al. Genome-wide association study of dental caries in the Hispanic communities health study/study of Latinos (HCHS/SOL). Hum Mol Genet. 2016;25(4):807–16.CrossRefPubMed Morrison J, Laurie CC, Marazita ML, Sanders AE, Offenbacher S, Salazar CR, Conomos MP, Thornton T, Jain D, Laurie CA, et al. Genome-wide association study of dental caries in the Hispanic communities health study/study of Latinos (HCHS/SOL). Hum Mol Genet. 2016;25(4):807–16.CrossRefPubMed
5.
Zurück zum Zitat Shaffer JR, Wang X, Desensi RS, Wendell S, Weyant RJ, Cuenco KT, Crout R, McNeil DW, Marazita ML. Genetic susceptibility to dental caries on pit and fissure and smooth surfaces. Caries Res. 2012;46(1):38–46.CrossRefPubMedPubMedCentral Shaffer JR, Wang X, Desensi RS, Wendell S, Weyant RJ, Cuenco KT, Crout R, McNeil DW, Marazita ML. Genetic susceptibility to dental caries on pit and fissure and smooth surfaces. Caries Res. 2012;46(1):38–46.CrossRefPubMedPubMedCentral
6.
Zurück zum Zitat Shaffer JR, Wang X, McNeil DW, Weyant RJ, Crout R, Marazita ML. Genetic susceptibility to dental caries differs between the sexes: a family-based study. Caries Res. 2015;49(2):133–40.CrossRefPubMedPubMedCentral Shaffer JR, Wang X, McNeil DW, Weyant RJ, Crout R, Marazita ML. Genetic susceptibility to dental caries differs between the sexes: a family-based study. Caries Res. 2015;49(2):133–40.CrossRefPubMedPubMedCentral
7.
Zurück zum Zitat Wang X-J, Shaffer JR, Weyant RJ, Cuenco KT, DeSensi RH, Crout RJ, McNeil DW, Marazita ML. Genes and their effects on dental caries may differ between primary and permanent dentitions. Caries Res. 2010;44(3):277–84.CrossRefPubMedPubMedCentral Wang X-J, Shaffer JR, Weyant RJ, Cuenco KT, DeSensi RH, Crout RJ, McNeil DW, Marazita ML. Genes and their effects on dental caries may differ between primary and permanent dentitions. Caries Res. 2010;44(3):277–84.CrossRefPubMedPubMedCentral
9.
Zurück zum Zitat Opal S, Garg S, Jain J, Walia I. Genetic factors affecting dental caries risk. Aust Dent J. 2015;60(1):2–11.CrossRefPubMed Opal S, Garg S, Jain J, Walia I. Genetic factors affecting dental caries risk. Aust Dent J. 2015;60(1):2–11.CrossRefPubMed
10.
Zurück zum Zitat Vieira AR, Marazita ML, Goldstein-McHenry T. Genome-wide scan finds suggestive caries loci. J Dent Res. 2008;87(5):435–9.CrossRefPubMed Vieira AR, Marazita ML, Goldstein-McHenry T. Genome-wide scan finds suggestive caries loci. J Dent Res. 2008;87(5):435–9.CrossRefPubMed
11.
Zurück zum Zitat Kruglyak L, Daly MJ, Reeve-Daly MP, Lander ES. Parametric and nonparametric linkage analysis: a unified multipoint approach. Am J Hum Genet. 1996;58(6):1347–63.PubMedPubMedCentral Kruglyak L, Daly MJ, Reeve-Daly MP, Lander ES. Parametric and nonparametric linkage analysis: a unified multipoint approach. Am J Hum Genet. 1996;58(6):1347–63.PubMedPubMedCentral
12.
Zurück zum Zitat Polk DE, Weyant RJ, Crout RJ, McNeil DW, Tarter RE, Thomas JG, Marazita ML. Study protocol of the Center for Oral Health Research in Appalachia (COHRA) etiology study. BMC oral health. 2008;8:18.CrossRefPubMedPubMedCentral Polk DE, Weyant RJ, Crout RJ, McNeil DW, Tarter RE, Thomas JG, Marazita ML. Study protocol of the Center for Oral Health Research in Appalachia (COHRA) etiology study. BMC oral health. 2008;8:18.CrossRefPubMedPubMedCentral
14.
Zurück zum Zitat Franzman MR, Levy SM, Warren JJ, Broffitt B: Tooth-brushing and dentifrice use among children ages 6 to 60 months. Pediatr Dent 2004, 26(1):87–92. Franzman MR, Levy SM, Warren JJ, Broffitt B: Tooth-brushing and dentifrice use among children ages 6 to 60 months. Pediatr Dent 2004, 26(1):87–92.
15.
Zurück zum Zitat Levy SM, Warren JJ, Broffitt B, Hillis SL, Kanellis MJ. Fluoride, beverages and dental caries in the primary dentition. Caries Res. 2003;37(3):157–65.CrossRefPubMed Levy SM, Warren JJ, Broffitt B, Hillis SL, Kanellis MJ. Fluoride, beverages and dental caries in the primary dentition. Caries Res. 2003;37(3):157–65.CrossRefPubMed
16.
Zurück zum Zitat Levy SM, Warren JJ, Davis CS, Kirchner HL, Kanellis MJ, Wefel JS. Patterns of fluoride intake from birth to 36 months. J Public Health Dent. 2001;61(2):70–7.CrossRefPubMed Levy SM, Warren JJ, Davis CS, Kirchner HL, Kanellis MJ, Wefel JS. Patterns of fluoride intake from birth to 36 months. J Public Health Dent. 2001;61(2):70–7.CrossRefPubMed
17.
Zurück zum Zitat Marshall TA, Levy SM, Broffitt B, Warren JJ, Eichenberger-Gilmore JM, Burns TL, Stumbo PJ. Dental caries and beverage consumption in young children. Pediatrics. 2003;112(3 Pt 1):e184–91.CrossRefPubMed Marshall TA, Levy SM, Broffitt B, Warren JJ, Eichenberger-Gilmore JM, Burns TL, Stumbo PJ. Dental caries and beverage consumption in young children. Pediatrics. 2003;112(3 Pt 1):e184–91.CrossRefPubMed
18.
Zurück zum Zitat Slayton RL, Cooper ME, Marazita ML. Tuftelin, mutans streptococci, and dental caries susceptibility. J Dent Res. 2005;84(8):711–4.CrossRefPubMed Slayton RL, Cooper ME, Marazita ML. Tuftelin, mutans streptococci, and dental caries susceptibility. J Dent Res. 2005;84(8):711–4.CrossRefPubMed
19.
Zurück zum Zitat Wang X, Shaffer JR, Zeng Z, Begum F, Vieira AR, Noel J, Anjomshoaa I, Cuenco KT, Lee MK, Beck J, et al. Genome-wide association scan of dental caries in the permanent dentition. BMC oral health. 2012;12:57.CrossRefPubMedPubMedCentral Wang X, Shaffer JR, Zeng Z, Begum F, Vieira AR, Noel J, Anjomshoaa I, Cuenco KT, Lee MK, Beck J, et al. Genome-wide association scan of dental caries in the permanent dentition. BMC oral health. 2012;12:57.CrossRefPubMedPubMedCentral
20.
Zurück zum Zitat Shaffer JR, Wang X, Feingold E, Lee M, Begum F, Weeks DE, Cuenco KT, Barmada MM, Wendell SK, Crosslin DR, et al. Genome-wide association scan for childhood caries implicates novel genes. J Dent Res. 2011;90(12):1457–62.CrossRefPubMedPubMedCentral Shaffer JR, Wang X, Feingold E, Lee M, Begum F, Weeks DE, Cuenco KT, Barmada MM, Wendell SK, Crosslin DR, et al. Genome-wide association scan for childhood caries implicates novel genes. J Dent Res. 2011;90(12):1457–62.CrossRefPubMedPubMedCentral
22.
Zurück zum Zitat Matise TC, Chen F, Chen W, De La Vega FM, Hansen M, He C, Hyland FC, Kennedy GC, Kong X, Murray SS, et al. A second-generation combined linkage physical map of the human genome. Genome Res. 2007;17(12):1783–6.CrossRefPubMedPubMedCentral Matise TC, Chen F, Chen W, De La Vega FM, Hansen M, He C, Hyland FC, Kennedy GC, Kong X, Murray SS, et al. A second-generation combined linkage physical map of the human genome. Genome Res. 2007;17(12):1783–6.CrossRefPubMedPubMedCentral
23.
Zurück zum Zitat Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75.CrossRefPubMedPubMedCentral Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75.CrossRefPubMedPubMedCentral
24.
25.
Zurück zum Zitat Abecasis GR, Cherny SS, Cookson WO, Cardon LR. Merlin--rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet. 2002;30(1):97–101.CrossRefPubMed Abecasis GR, Cherny SS, Cookson WO, Cardon LR. Merlin--rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet. 2002;30(1):97–101.CrossRefPubMed
26.
Zurück zum Zitat Whittemore AS, Halpern J. A class of tests for linkage using affected pedigree members. Biometrics. 1994;50(1):118–27.CrossRefPubMed Whittemore AS, Halpern J. A class of tests for linkage using affected pedigree members. Biometrics. 1994;50(1):118–27.CrossRefPubMed
27.
Zurück zum Zitat Sham PC, Purcell S, Cherny SS, Abecasis GR. Powerful regression-based quantitative-trait linkage analysis of general pedigrees. Am J Hum Genet. 2002;71(2):238–53.CrossRefPubMedPubMedCentral Sham PC, Purcell S, Cherny SS, Abecasis GR. Powerful regression-based quantitative-trait linkage analysis of general pedigrees. Am J Hum Genet. 2002;71(2):238–53.CrossRefPubMedPubMedCentral
29.
Zurück zum Zitat Wigginton JE, Abecasis GR. An evaluation of the replicate pool method: quick estimation of genome-wide linkage peak p-values. Genet Epidemiol. 2006;30(4):320–32.CrossRefPubMed Wigginton JE, Abecasis GR. An evaluation of the replicate pool method: quick estimation of genome-wide linkage peak p-values. Genet Epidemiol. 2006;30(4):320–32.CrossRefPubMed
31.
Zurück zum Zitat Baron RV, Kollar C, Mukhopadhyay N, Weeks DE. Mega2: validated data-reformatting for linkage and association analyses. Source Code Biol Med. 2014;9(1):26.CrossRefPubMedPubMedCentral Baron RV, Kollar C, Mukhopadhyay N, Weeks DE. Mega2: validated data-reformatting for linkage and association analyses. Source Code Biol Med. 2014;9(1):26.CrossRefPubMedPubMedCentral
32.
Zurück zum Zitat Jing X, Li DQ, Olofsson CS, Salehi A, Surve VV, Caballero J, Ivarsson R, Lundquist I, Pereverzev A, Schneider T, et al. CaV2.3 calcium channels control second-phase insulin release. J Clin Invest. 2005;115(1):146–54.CrossRefPubMedPubMedCentral Jing X, Li DQ, Olofsson CS, Salehi A, Surve VV, Caballero J, Ivarsson R, Lundquist I, Pereverzev A, Schneider T, et al. CaV2.3 calcium channels control second-phase insulin release. J Clin Invest. 2005;115(1):146–54.CrossRefPubMedPubMedCentral
33.
Zurück zum Zitat Bircher AJ, Lang-Muritano M, Pfaltz M, Bruckner-Tuderman L. Epidermolysis bullosa junctionalis progressiva in three siblings. Br J Dermatol. 1993;128(4):429–35.CrossRefPubMed Bircher AJ, Lang-Muritano M, Pfaltz M, Bruckner-Tuderman L. Epidermolysis bullosa junctionalis progressiva in three siblings. Br J Dermatol. 1993;128(4):429–35.CrossRefPubMed
34.
Zurück zum Zitat Ozgul RK, Satman I, Collin GB, Hinman EG, Marshall JD, Kocaman O, Tutuncu Y, Yilmaz T, Naggert JK. Molecular analysis and long-term clinical evaluation of three siblings with Alstrom syndrome. Clin Genet. 2007;72(4):351–6.CrossRefPubMed Ozgul RK, Satman I, Collin GB, Hinman EG, Marshall JD, Kocaman O, Tutuncu Y, Yilmaz T, Naggert JK. Molecular analysis and long-term clinical evaluation of three siblings with Alstrom syndrome. Clin Genet. 2007;72(4):351–6.CrossRefPubMed
35.
Zurück zum Zitat Carter MT, Geraghty MT, De La Cruz L, Reichard RR, Boccuto L, Schwartz CE, Clericuzio CL. A new syndrome with multiple capillary malformations, intractable seizures, and brain and limb anomalies. Am J Med Genet A. 2011;155a(2):301–6.CrossRefPubMed Carter MT, Geraghty MT, De La Cruz L, Reichard RR, Boccuto L, Schwartz CE, Clericuzio CL. A new syndrome with multiple capillary malformations, intractable seizures, and brain and limb anomalies. Am J Med Genet A. 2011;155a(2):301–6.CrossRefPubMed
36.
Zurück zum Zitat Hartley L, Kinali M, Knight R, Mercuri E, Hubner C, Bertini E, Manzur AY, Jimenez-Mallebrera C, Sewry CA, Muntoni F. A congenital myopathy with diaphragmatic weakness not linked to the SMARD1 locus. Neuromuscul Disord. 2007;17(2):174–9.CrossRefPubMed Hartley L, Kinali M, Knight R, Mercuri E, Hubner C, Bertini E, Manzur AY, Jimenez-Mallebrera C, Sewry CA, Muntoni F. A congenital myopathy with diaphragmatic weakness not linked to the SMARD1 locus. Neuromuscul Disord. 2007;17(2):174–9.CrossRefPubMed
37.
Zurück zum Zitat Hoyer J, Ekici AB, Endele S, Popp B, Zweier C, Wiesener A, Wohlleber E, Dufke A, Rossier E, Petsch C, et al. Haploinsufficiency of ARID1B, a member of the SWI/SNF-a chromatin-remodeling complex, is a frequent cause of intellectual disability. Am J Hum Genet. 2012;90(3):565–72.CrossRefPubMedPubMedCentral Hoyer J, Ekici AB, Endele S, Popp B, Zweier C, Wiesener A, Wohlleber E, Dufke A, Rossier E, Petsch C, et al. Haploinsufficiency of ARID1B, a member of the SWI/SNF-a chromatin-remodeling complex, is a frequent cause of intellectual disability. Am J Hum Genet. 2012;90(3):565–72.CrossRefPubMedPubMedCentral
38.
Zurück zum Zitat Wang Q, Jia P, Cuenco KT, Zeng Z, Feingold E, Marazita ML, Wang L, Zhao Z. Association signals unveiled by a comprehensive gene set enrichment analysis of dental caries genome-wide association studies. PLoS One. 2013;8(8):e72653.CrossRefPubMedPubMedCentral Wang Q, Jia P, Cuenco KT, Zeng Z, Feingold E, Marazita ML, Wang L, Zhao Z. Association signals unveiled by a comprehensive gene set enrichment analysis of dental caries genome-wide association studies. PLoS One. 2013;8(8):e72653.CrossRefPubMedPubMedCentral
39.
Zurück zum Zitat Li C: Detecting gene-gene interaction in linkage analysis. Current protocols in human genetics 2005, Chapter 1:Unit 1.15. Li C: Detecting gene-gene interaction in linkage analysis. Current protocols in human genetics 2005, Chapter 1:Unit 1.15.
Metadaten
Titel
Novel caries loci in children and adults implicated by genome-wide analysis of families
verfasst von
Manika Govil
Nandita Mukhopadhyay
Daniel E. Weeks
Eleanor Feingold
John R. Shaffer
Steven M. Levy
Alexandre R. Vieira
Rebecca L. Slayton
Daniel W. McNeil
Robert J. Weyant
Richard J. Crout
Mary L. Marazita
Publikationsdatum
01.12.2018
Verlag
BioMed Central
Erschienen in
BMC Oral Health / Ausgabe 1/2018
Elektronische ISSN: 1472-6831
DOI
https://doi.org/10.1186/s12903-018-0559-6

Weitere Artikel der Ausgabe 1/2018

BMC Oral Health 1/2018 Zur Ausgabe

Newsletter

Bestellen Sie unseren kostenlosen Newsletter Update Zahnmedizin und bleiben Sie gut informiert – ganz bequem per eMail.