Revisiting Schizophrenia Linkage Data in the NIMH Repository: Reanalysis of Regularized Data Across Multiple Studies

Data for all multiplex pedigrees with available genome-wide genotyping available as of April 2011 were downloaded from the HGI. Families in the download came from seven different studies (2–9), referred to here as studies 1–7. An extensive data regularization protocol was applied to both genotypic and phenotypic information as downloaded from the HGI. This included applying strict criteria to the DSM diagnostic codes provided to the HGI by the individual studies to define a narrow schizophrenia phenotype and a schizophrenia/affective phenotype, which included schizoaffective disorder or any schizophrenia disorder and a significant affective disorder. (See online data supplements SA1 and SA2 for details of genotype and phenotype processing, respectively.)

Families were included in these analyses if they contained at least one case of schizophrenia, at least one additional case of either schizophrenia or schizophrenia/affective diagnoses, and at least two affected genotyped individuals. This left 970 multiplex families for analysis, containing 4,023 phenotyped individuals (average, 4.1 per pedigree) and 4,208 genotyped individuals (average, 4.3 per pedigree). (See online data supplement ST1 for an annotated list of families included and excluded from these analyses.) We elected to use stringent diagnostic and inclusion criteria in order to minimize clinical heterogeneity within data subsets, and especially in order to minimize the effects of clinical differences across studies. Such differences may tend to be more pronounced for broad-spectrum conditions than for the core diagnoses.

We grouped families into subsets by study and population (African American, European American, Han Chinese, and Hispanic). Each of the original studies either contained a single group or, in the cases of study 1 (2, 3) and study 2 (4), divided their own analyses into European American and African American families in published analyses. Thus, these subgroupings match previous treatments of the data sets.

Additionally, because schizophrenia with a significant affective component is now considered to be a potentially genetically distinct form of schizophrenia, we further subdivided the data based on the presence or absence of any individuals with schizophrenia/affective diagnoses within the pedigree. (When referring to families, “schizophrenia” refers to families whose affected members are all classified as having schizophrenia; and “schizophrenia/affective” refers to families in which at least one affected individual is classified as having a schizophrenia/affective diagnosis.) While it is impossible to distinguish a family that would never have produced a case meeting schizophrenia/affective criteria from a family that, by chance, failed to manifest this affective phenotype among the small number of affected individuals present, even an imperfect classifier can be helpful in detecting linkage in one but not the other group (10).

Several of the studies had multiple data collection sites, and “site” itself might demarcate more homogeneous subsets of the data. However, further subdivision by site results in sample sizes that are too small for meaningful analysis (<10 small families/data set), so we did not subdivide by site. (See online data supplement ST2 for sample sizes by subset.)

All analyses were conducted using the software package KELVIN (1), which implements the PPL class of models for measuring the strength of genetic evidence. The specific statistic employed here was the PPL itself (posterior probability of linkage). The PPL is essentially a straightforward application of Bayes’s theorem. Letting L represent “linkage” to a given genomic position and D be the data, the PPL is calculated asThe two likelihoods appearing in this equation, P(D|L) and P(D|no L), are the numerator and denominator, respectively, of the exponentiated ordinary LOD score. They are functions of the parameters of the unknown trait model, and what distinguishes the PPL as computed by KELVIN is the way in which these parameters are handled within and across data subsets, as described below.

The PPL as applied here represents the probability of a schizophrenia gene at each location, given the available data. For the microsatellite data, four-point (three-marker) multipoint calculations were run; for the single-nucleotide polymorphism data, full multipoint analysis using all markers was used. (Technical details and all primary references to the supporting theoretical literature may be found in reference 1; examples of previous applications may be found in references 11–13.)

The PPL is based on an approximating single-locus likelihood allowing for within-sample heterogeneity using Smith’s admixture parameter α (14). All parameters of the trait model, including α, are integrated out of the PPL using (independent) uniform priors on each parameter (see reference 1 for details), implicitly allowing for dominant, recessive, or intermediate models. This provides a robust approximation for mapping complex traits in terms of the marginal model at each locus, and because the parameters are integrated out, no specific assumptions regarding their values are required.

The PPL framework accumulates evidence across data subsets by integrating the trait parameters out of the likelihood separately for each subset (allowing their values to differ across subsets), then using Bayesian sequential updating to carry the posterior linkage evidence from previously analyzed data forward as prior evidence as new data subsets are analyzed at each genomic position in turn. This allows for differences in allele frequencies, penetrances (due to background genetic or environmental differences across subsets), and chance fluctuations in the proportion of “linked” pedigrees at a given locus. The order in which data sets are analyzed does not affect the final result. In the presence of appreciable heterogeneity, sequential updating is far more robust in retaining true signals originating from individual subsamples than analyses that simply combine subsets for a single analysis (10, 15, 16). It is also substantially more sensitive in detecting true genetic effects across heterogeneous disorders than standard meta-analyses (17, 18). Moreover, the PPL accumulates evidence against linkage as well as in favor of linkage. Thus, inspection of subset-specific contributions to the omnibus signal can distinguish among subsets that are (perhaps only weakly) supporting linkage and subsets that are actually contributing evidence against linkage, as we illustrate below. Here we sequentially updated across data subsets defined by study (studies 1–7), major population group (African American, European American, Han Chinese, Hispanic), and clinical type (presence or absence of any individual meeting schizophrenia/affective criteria within the family).

The PPL is on the probability scale, and its interpretation is therefore straightforward: e.g., a PPL of 40% means that there is an estimated 40% probability of a trait gene at the given location based on these data. Based on Elston and Lange’s analysis (19), the prior probability at each location is set to 2%, so that PPLs >2% indicate (some degree of) evidence in favor of a trait gene at the locus, while PPLs <2% represent evidence against the location. This prior probability is based on empirical data (19), and because it is calculated as the probability of linkage between a random marker location and a single disease gene, it is conservative for multilocus disorders. The PPL is a measure of statistical evidence, not a decision-making procedure; therefore, there are no “significance levels” associated with it, and it is not interpreted in terms of associated error probabilities (20, 21). By the same token, no multiple testing corrections are applied to the PPL, just as one would not “correct” a measure of the temperature made in one location for readings taken at different locations (22). Nevertheless, it may assist readers to have some sense of scale relative to more familiar frequentist test statistics. In simulations of 10,000 replicates of sets of 1,000 affected sib pairs (the predominant data structure in these analyses) under the null hypothesis (no linkage), “significance” cutoffs of PPLs of 5%, 10%, 15%, and 25% were associated with type I error probabilities (the rate at which the PPL crossed the given threshold under the null) of 0.0070, 0.0011, 0.0004, and 0.0001, respectively. In the Results section, we highlight loci with PPL ≥25%; complete results are presented in online data supplement ST3.

Figure 1 presents omnibus linkage results, sequentially updated across all data subsets. Overall, 76% of the genome showed evidence against linkage (PPL<0.02), while only 10% showed PPL>0.05 and 4% showed PPL>0.1. There were twenty distinct loci with PPL>0.1, 13 with PPL>0.15, and four with PPL>0.25. These last occurred at 2q36.1 (PPL=0.41), 3q28 (PPL=0.27), 12q23.1 (PPL=0.26), and 15q23 (PPL=0.27) (Table 1).

Figure 2 presents population-specific results. There are four population-specific loci with PPL>0.25 (Table 1). Two of these come from Han Chinese (10q22.3, PPL=0.36; 10q26.12, PPL=0.34), and at both these loci all three of the other population groups show evidence against linkage. Similarly, European American data support 1p32.3 (PPL=0.27), with data from all other population groups providing evidence against linkage at this locus. By contrast, the Hispanic-specific peak at 5p14.1 (PPL=0.26) receives modest support from African American data (PPL=0.04) but evidence against linkage in the other two sets. The four omnibus peaks (see above) are each supported by at least three population groups, in each case with the remaining group being neutral, that is, showing evidence neither for nor against linkage (Table 1).

Figure 3 presents results by clinical subgroup. There are five loci with PPL>0.25 in either of the two clinical subgroups (Table 1), and in every case the other subgroup provides evidence against linkage (in each case, PPL<0.01). Notably, three of these peaks (11p15.3, 11p11.2, 23q26.1) are supported by the far smaller schizophrenia/affective subset, suggesting that this really does constitute a more homogeneous classification of the families. However, there is possible confounding by population group here, since each clinical subset includes all four population groups, but in differing proportions (see the Discussion section).

Figure 4 illustrates salient differences between the omnibus results and results extracted from the original published linkage analyses of the individual studies. The 10% PPL threshold was chosen based on qualitative visual separation of salient signals from background noise in Figure 1 and is intended to mimic the criterion of “suggestive” evidence as shown in the figure based on the original publications. There is no rigorous way to precisely compare the magnitude of results across data-analytic methods or to derive and apply exactly comparable interval estimates. Thus, the figure is intended to convey in broad strokes differences in the overall genomic landscape between the individual original reports and our omnibus analyses rather than to present precise quantitative differences.

Of the 20 intervals with PPL>0.10, 12 line up in or near an originally reported interval. This could be coincidental, however, since all but three chromosomes are implicated (generally at the level of “suggestive” linkage) by at least one of the original publications, with little overlap in reported loci across the original studies. Figure 4 also shows the large portions of the genome showing evidence against linkage using the PPL. The PPL’s ability to accumulate evidence against linkage has no correlate in either the original nonparametric linkage or meta-analyses.

Of the four omnibus loci with PPL over 25%, all are in regions of previous schizophrenia linkage reports. Chromosome 2q36.1 is in a region that has been implicated in several previous linkage reports, including a significant finding in a Finnish sample (23), and has received suggestive support from other reports (5, 24, 25) that include families found in study 2 and study 3. SCG2, the gene encoding secretogranin II, is located under the center of this peak and has been reported to have increased expression in the dorsolateral prefrontal cortex in individuals with schizophrenia (26). Chromosome 3q28 is supported by suggestive linkage results in studies in Finnish (23), Palauan (27), and Dagestani (28) samples that are not part of the NIMH-HGI collection. Chromosome 12q23.1 has been implicated by a suggestive linkage report with a negative symptom factor score (29). While the 12q candidate gene DAO is located beyond the boundaries of this peak, in an area where the omnibus analysis produced evidence against linkage, the gene PAH is located near the edge of the omnibus peak, in a region with PPL scores slightly above 2%. PAH, the gene for phenylalanine hydroxylase, while traditionally associated with phenylketonuria, has been suggested as a schizophrenia candidate gene in multiple studies (29–33). Of further interest is the 12q linkage peak observed in the African American subset; this peak has a maximum PPL score of 25.4% located 4 centimorgans (cM) telomeric to the omnibus peak, very near PAH. The omnibus peak on chromosome 15q23 overlays a suggestive linkage peak that was originally reported in the Hispanic samples included in this analysis (8).

Four additional peaks over 25% were observed in specific population groups. The 10q22.3 and 10q26.12 peaks in the Han Chinese sample overlap the linked regions from the original report on these families (5). Similarly, the Hispanic-specific peak at 5p14.1 overlaps a suggestive linkage peak that was previously reported in these families (8). By contrast, the 1p32.3 locus seen in the European American subset was not suggested by the individual studies contributing to this subset (4, 34, 35). Having data from one population group provide evidence for linkage and data from the other groups provide combined evidence against is consistent with either separate sets of genes operating in the different population groups or, more likely, dependence of the salience of genetic effects on the background (ancestral) genome or environment. It is also consistent, however, with the subset-specific PPLs being overestimates of the actual probability of linkage, which is given by the omnibus PPL. Differences in sample sizes across groups (139 African American, 187 European American, 483 Han Chinese, and 161 Hispanic) also complicate interpretation.

Dividing the data by clinical subgroup (families containing individuals with schizophrenia/affective diagnoses versus families with only narrow schizophrenia) yielded four additional loci with PPL>25%, and in every case the locus was supported by data from one group while data from the other group provided evidence against linkage. This strongly suggests that the genetics of schizophrenia in families characterized by the presence of schizoaffective disorder or schizophrenia with a comorbid affective disorder may be distinct from the genetics of schizophrenia in individuals without a significant mood disorder. On the other hand, there is considerable confounding by population, since the preponderance of families with individuals with a schizophrenia/affective diagnosis varies by group as well. The proportions of groups (African American, European American, Han Chinese, Hispanic) within schizophrenia-only families and families that include schizophrenia/affective diagnoses are 0.11, 0.15, 0.59, 0.15 and 0.28, 0.41, 0.04, 0.26, respectively.

Interestingly, however, all three locations identified in the families with schizophrenia/affective diagnoses (11p15.3, 11p11.2, and Xq26.1) have been previously implicated in linkage or association studies using samples with bipolar disorder or mixed samples with schizophrenia and bipolar disorder (36–40). The 11p15.3 region was also reported as a main finding in the original publication for study 7 (9), and indeed, the African American-specific PPL reaches 15% at 28 cM. Of particular note is that study 7 reported (9) a marked strengthening of the linkage signal at 11p15.3 when using a phenotype that included schizophrenia and schizoaffective disorder rather than schizophrenia only. Finally, the schizophrenia subset produced a peak of 46% at 6q14.1 that overlaps a peak of 26% in the European American subgroup but was not reported by any of the original European American studies. In aggregate, these results have significant implications for the inclusion of individuals with schizoaffective disorder in linkage or association studies of schizophrenia. (See also online data supplement SA3 for a comparison with results in the National Human Genome Research Institute genome-wide association study database.)

Insofar as population is a major source of heterogeneity in these analyses, it is noteworthy that, with the exception of the Han Chinese sample, the remaining sample sizes for the different groups remain quite small. It is not surprising, therefore, that even the largest PPLs remain moderate in size. Similarly, dividing the data based on the presence of affective illness results in a far smaller subset for the group containing schizophrenia/affective diagnoses. Strikingly, however, this group also produces some of the highest PPLs. Our results suggest that both population and clinical subgroup are key classifiers for the purposes of gene mapping. Ideally, we would like to use subsets simultaneously on both criteria. However, the extremely small subsets that would result preclude this approach.

It is also of interest to compare our results with the meta-analyses of Nato et al. (41) and Ng et al. (42), both of which included studies from the HGI collection as well as other studies. Nato et al. identified 22 regions of interest, while Ng et al. reported eight regions with aggregate genome-wide evidence for linkage, all of which overlap with regions identified by Nato et al. While the majority of the Nato et al. (13 of 22) and Ng et al. (five of eight) regions produced some level of evidence in favor of linkage under the omnibus analysis, only two of our four omnibus peaks >25% (2q36.1 and 3q28) overlapped a region of interest from either study, with our largest peak of 41% overlapping regions of interest from both studies. Of our eight peaks >25% that were specific to an ethnic or clinical subset, only two (5p14.1 and 10q26.12) overlapped a region of interest from either study, again with the larger peak of 34% overlapping regions of interest from both studies. (See online data supplement ST4 for detailed comparisons.)