Hypothesis Decay?: Blood Lead–Fluoridation Link Not Confirmed

Numerous studies of various populations have shown that adding fluoride to drinking water prevents dental decay. However, a 1999 study in Massachusetts and a 2000 study in New York reported associations between the use of silicofluoride compounds in community water systems and elevated blood lead (PbB) concentrations in children. Now a team of researchers has tested the hypothesis generated by the Massachusetts and New York studies against findings from two other studies and found no cause for concern [EHP 114:130–134]. 
 
As of 2000, the Centers for Disease Control and Prevention estimated that more than 162 million Americans were receiving fluoridated water. In the United States, several agents are used for fluoridation, including silicofluoride compounds (sodium silicofluoride and hydrofluosilicic acid) and sodium fluoride. Researchers with the Massachusetts and New York studies hypothesized that the silicofluoride compounds in tap water might enhance lead leaching from pipes and increase lead absorption from the water itself. Elevated PbB concentrations in children are associated with a host of cognitive, developmental, and behavioral impairments so serious that lead-based paint was banned in the United States in 1978 and lead water pipe solder was banned in the 1980s. 
 
The current research group evaluated the relationship between water fluoridation method and PbB concentrations in children by conducting a large-scale statistical analysis of two other preexisting studies: the 1992 Fluoridation Census and the Third National Health and Nutrition Examination Survey (NHANES III). In analyzing data from NHANES III and the 1992 Fluoridation Census, the team improved on prior analyses by log-transforming raw PbB concentration and by including information on possible confounding factors missing from the Massachusetts and New York studies. These included poverty status, urbanicity, duration of residence, and year in which the dwelling was built. 
 
The NHANES III sample was comprehensive, representing more than 52 million U.S. children. This survey also oversampled young children, older adults, non-Hispanic blacks, and Mexican Americans to ensure that population estimates for these groups would be statistically reliable. 
 
The team found that, overall, the PbB concentrations of children who lived in counties receiving silicofluorides did not differ significantly from the PbB concentrations of children living in counties without fluoridated water. This was true even when researchers controlled for the year in which children’s homes were built. Given these findings, the team states there is no support for concerns that silicofluorides in community water systems cause higher PbB concentrations in children. 
 
However, the investigators acknowledge that their analysis has limitations. For example, NHANES III did not measure the lead content of drinking water consumed by study participants. Also, the team did not control for factors such as density of older housing, and they were unable to control for the solubility of lead in pipes affected by different temperatures and water hardness. 
 
Because of these limitations, the investigators cannot completely rule out a link between water fluoridation method and lead uptake in children, particularly in children living in older dwellings. They speculate that other studies, possibly those including chemical investigation and animal toxicology, could yield additional valuable data. They conclude that efforts to prevent dental decay via the use of fluoridated drinking water should continue unless a causal effect of specific fluoridation methods on PbB concentration is demonstrated by additional research.


Background
Haplotype data on dense markers contain local linkage disequilibrium information on historical recombination and mutation events, and the knowledge of haplotype structure has lead to a growing belief that haplotypes may hold the key to understanding and identifying genetic variants underlying complex traits [1]. The availability of thousands or even millions of single nucleotide polymorphisms (SNPs) on the human genome requires systematic analysis in coping with both optimal modeling and computational efficiency. Haplotype sharing methods have shown promising results in gene mapping analyses in complex settings [2][3][4][5][6]. To analyze the SNP data provided by the Collaborative Study of the Genetics of Alcoholism (COGA), we implemented an algorithm for haplotype reconstruction under the criteria of minimum recom-binants and coalescent tree, and performed haplotypebased association analysis by the haplotype-sharing correlation (HSC) method [6,7]. The purpose of this paper is to evaluate the feasibility of our haplotype reconstruction algorithm and the HSC method when applied to nuclear family data with a limited amount of missing genotypes.

Data
The original COGA data contained 143 families, with an average family size of 11.2 ± 5.4 members and 9.3 ± 4.3 of them having SNP genotype data. To evaluate the feasibility of haplotype reconstruction and HSC analysis, we chose to analyze a dataset on chromosomes 1-6 in all 93 nuclear families with genotype data for both parents and at least 3 offspring. These nuclear families had an average family size of 6.6 ± 1.7 (range from 4 to 14), and contained a low proportion of 0.1% missing SNP genotypes. The phenotype variable to be analyzed was DSM-IV alcohol dependence, which was coded as ordered values of 1 for "pure unaffected", 2 for never drank, 3 for unaffected with some symptoms, and 5 for affected, and was treated as a continuous variable in HSC analysis.

Haplotype reconstruction
Haplotypes in nuclear families were reconstructed in 2 steps using a search algorithm under the criteria of minimum recombinants and coalescent tree. In step 1, all possible minimum recombinant haplotype configurations (MRHCs) were reconstructed within each family under the criteria of minimum recombinants [8]. The number of possible MRHCs in each family depends on both the family size and the transmission process of haplotypes, and some nuclear families may have more than 100 MRHCs that are consistent with the observed genotype data.
In step 2, each MRHC in each nuclear family was evaluated by fitting the combination of its founder haplotypes and all founder haplotypes in other families to a coales-cent tree structure, where the founder haplotypes were referred to the 4 parental haplotypes in each family. The MRHC corresponding to a coalescent tree with minimum tree distance was selected as the optimal solution of haplotypes. The computation of tree distance in a set of haplotypes is as follows. First, the sharing in each pair of haplotypes is quantified as the number of identical-bystate intervals summed over all markers, and the distance between 2 haplotypes is defined as the observed sharing subtracted from the maximum possible sharing. Second, a single haplotype showing the minimum sum distance with all other haplotypes is chosen as the ancestral haplotype. And third, all haplotypes are connected one-by-one starting from the ancestral haplotype using a minimum spanning tree algorithm [9], and the tree distance is defined as the minimum distance that connects all the haplotypes.

Haplotype-sharing correlation
The HSC method evaluates the correlation between phenotype similarity and haplotype sharing at each marker m in all pairs of pedigree founder haplotypes [6,7]. The HSC statistic can be written as HSC analysis of DSM4 alcohol dependence on chromosomes 1-6 in 93 nuclear family

Results
On average, we were able to reconstruct haplotypes at all markers on a whole chromosome in 98.2% of the nuclear families. For the other 1.8%, haplotype phases on less than 1% loci could not be inferred with uncertainty conditional on the criterion of minimum recombinants, and those loci were treated as missing in reconstructed haplotypes. A haplotype at a missing locus was considered to have no sharing with any other non-missing haplotypes.
In an HSC analysis on chromosomes 1-6 in 93 nuclear families, three markers on chromosomes 3, 4, and 6, respectively, were found to have significant associations with DSM-IV alcohol dependence that exceeded the 0.05 level of chromosome-wide significance (Fig. 1). Marker rs1631833 at 109.1 cM on chromosome 4 was found to have the strongest haplotype association among the 6 chromosomes analyzed (p = 0.008). Marker rs895941 at 36.7 cM on chromosome 3 and marker rs953887 at 74.2 cM on chromosome 6 were the other two markers revealed significant haplotype association (p = 0.03 and p = 0.02, respectively).

Discussion
We have developed a 2-step algorithm for haplotype reconstruction in nuclear families that avoids the assumption of linkage equilibrium by minimizing the recombinants in within-family haplotype transmissions and fitting all parental haplotypes under a coalescent tree structure. The choice of analyzing nuclear families each with a large number of offspring was mainly under the feasibility consideration for testing the algorithm. When SNP data on chromosomes 1-6 were analyzed, haplotypes on less than 0.1% a loci in 1.8% of nuclear families could not be inferred with certainty. One possible reason for the failure of haplotype reconstruction in some nuclear families is the uncertainty in counting the number of recombinants in the presence of missing genotypes. We are currently investigating the failures and alternative approaches in order to improve the haplotyping performance in the presence of missing genotypes.
The HSC method evaluates the correlation between phenotype similarity and haplotype sharing at each study marker in all pairs of pedigree founder haplotypes. When applied to the COGA data on chromosomes 1-6, 3 markers were found to have significant haplotype associations with DSM-IV alcohol dependence. The most significant signal at 109.1 cM on chromosome 4 was consistent with the strong linkage signal found on the same region using the maximum number of drinks ever consumed in a 24hour period as an alcoholism phenotype [10]. On a different note, the HSC method is not designed for controlling population stratification, although empirical results have indicated its robustness against allele heterogeneity when compared to allelic and haplotypic family-based association test [7]. Additionally, the HSC analysis does not consider within-family phenotypic correlations, and such a treatment may have an adverse effect in detecting the true associations.
Both the haplotype reconstruction and the HSC methods employed in this study have potential applications for haplotype-association studies under settings of both family-based and case-control designs. To improve the mapping of susceptibility regions associated with complex traits, clustering approaches, such as described by Yu et al. [11], may be employed in both haplotype reconstruction and haplotype association analyses. With clustering analysis, the plausibility of a candidate haplotype pair will be evaluated not by all existing haplotypes but only those believed to have the same ancestral origin. By the same token, clustering analysis will also increase the power of association analysis by reducing the ancestral heterogeneity in haplotypes associated with the same or similar phe-