Introduction

Prostate cancer (PCa) is the most frequently diagnosed cancer among men in the western world (World Health Organization 2003). In Finland, where the incidence of PCa has been rising in the last decade, it is estimated that in the year 2006, there will be 5,485 newly diagnosed PCa cases with the age-adjusted incidence rate of 115.4/100,000 inhabitants (Finnish Cancer Registry 2006).

Familial clustering of PCa was observed as early as the 1950s (Gianferrari et al. 1956), and in about 10% of all cases there is a clear positive family history of the disease. Carter et al. (1992) reported that for 40–50% of PCa cases, familial clustering was associated with multiple affected relatives, especially in families of early-onset probands. Hereditary prostate cancer (HPC), which accounts for 5–10% of all PCa, is an etiologically complex disease with several genes implicated in determining risk (Grönberg et al. 1997). In a large Nordic consortium study of twins, Lichtenstein et al. (2000) reported an unusually high heritability of 42% for PCa. The clinical phenotype of PCa is complex and heterogeneous, and the arrival of the prostate specific antigen (PSA) era has further complicated the genetic analysis of PCa by allowing the early diagnosis of disease that might remain latent or clinically unimportant. The International Consortium for Prostate Cancer Genetics (ICPCG), which seeks to improve the mapping of PCa genes, has emphasized that one of the major difficulties in studying PCa is genetic heterogeneity, possibly due to multiple, incompletely penetrant PCa-susceptibility genes (Xu et al. (2005). Using parametric (dominant and recessive) and nonparametric analyses on 1,233 families, Xu et al. (2005) identified five distinct chromosomal regions with “suggestive” linkage (LOD score > 1.86) to PCa, namely 5q12, 8p21, 15q11, 17q21, and 22q12. Subsets of the analyzed group of families characterized by large numbers of early-onset (≤65 years) PCa, which are more likely to segregate highly penetrant mutations, provided stronger evidence of linkage in several regions (including the 22q12 locus, with a LOD score of 3.57). Additional PCa susceptibility loci reported to date (Schaid 2004) also include the three cloned genes: HPC1/RNASEL, HPC2/ELAC2 and MSR1 (Rebbeck et al. 2000; Tavtigian et al. 2001; Carpten et al. 2002; Xu et al. 2002).

The Finnish population of 5 million inhabitants represents a genetically isolated population with a unique gene pool useful for the study of genetic susceptibility to cancer and other complex diseases (de la Chapelle 1993; Peltonen 1997). Reliable population data are obtainable from various linked registries and the population-based Finnish Cancer Registry (FCR) covers virtually all histologically confirmed cancer diagnoses over almost 50 years. In addition, church and parish records enable the identification of familial relationships for individuals over several centuries. In Finland, HPC1/RNASEL, HPC2/ELAC2 and MSR1 loci explain only a small fraction of PCa cases (Rökman et al. 2001, 2002; Seppälä et al. 2003). Instead, three additional major susceptibility loci have been mapped in Finnish families including the HPCX (Xq27–28), 3p25–26 and 11q14 regions (Xu et al. 1998; Schleutker et al. 2003). Even so, a large proportion of Finnish HPC remains unexplained.

The purpose of this study was to assess the nature of familial aggregation of PCa in a sample of 1,546 Finnish nuclear families using regressive models as employed in complex segregation analysis. Segregation analysis is a statistical method for testing compatibility with Mendelian expectations by estimating the parameters of a given model of inheritance from family data. Previous segregation analyses in diverse populations have suggested that familial aggregation of PCa follows autosomal dominance, multifactorial, recessive or X-linked inheritance, but remain inconclusive. Six reports suggest a dominant inheritance mode (Carter et al. 1992; Grönberg et al. 1997; Schaid et al. 1998; Verhage et al. 2001; Conlon et al. 2003; Valeri et al. 2003). Cui (2001) reported a mixture of models including autosomal dominant inheritance in younger onset families with recessive or X-linked inheritance in older-onset families. A multifactorial model has been suggested by Gong et al. (2002). Families of Icelandic breast cancer probands with PCa-affected men yielded a codominant model (Baffoe-Bonnie et al. 2002). To account for the possibility of different modes of inheritance in families of early-onset probands (<61 years) versus late-onset families (≥61 years), we performed segregation analyses on these two separate cohorts and also analyzed the complete, combined dataset to determine the most parsimonious model for explanation of the familial aggregation of the disease in Finland.

Subjects and methods

Data sources

The nation-wide population based Finnish Cancer Registry (FCR) was founded in 1952 and reporting of cancer to the FCR was made obligatory in 1961. Currently physicians, hospitals and pathology laboratories send their reports to the registry independently. In addition, the FCR receives information from every death certificate in which cancer is mentioned, registering over 99% of all solid tumors diagnosed in Finland (Teppo et al. 1994). The FCR files can be linked to the registry of deaths and of immigrants issued by the Population Register Center in Finland. Population registration in Finland has traditions dating back to the sixteenth century and is considered to be of excellent quality. Since 1964 a centralized, nation-wide, computer-based population registry has been maintained by the National Population Registry Center and is based on unique personal identifiers, which are now used as main keys in every major person registry including the Finnish Cancer Registry.

Probands and relatives

We chose the pre-PSA time period between 1 January 1988 and 31 December 1993 and identified 9,142 men with newly diagnosed PCa nationwide from the Finnish Cancer Registry. Two non-overlapping cohorts were identified with 557 early-onset probands (diagnosed at <61 years of age) and 989 late-onset probands (diagnosed at ≥61 years of age). The cut-off of 61 years was selected so that the cohort of early-onset PCa would be informative, i.e., has enough cases. Although all cut-offs are arbitrary, ours is in line with the one used by previous PSA screening trial in Finland (Mäkinen et al. 2002). Details of the collection of population-based PCa families and the analyses for other cancers among first-degree relatives have been published elsewhere (Matikainen et al. 2001). Briefly, information on the birthplaces of probands was obtained from the Central Population Registry. The local registries (parishes and local authorities) of the communities where probands were born were contacted to obtain the names and birth dates of their parents, siblings, spouses and children. Family members were successfully traced for 94% of the probands, giving a total of 10,650 first-degree relatives out of the 11,427 identified. Descriptive statistics of these two non-overlapping cohorts and after combining them are shown in Table 1.

Table 1 Descriptive statistics for prostate cancer cohorts in Finland
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Segregation analysis

To test specifically for Mendelian inheritance of PCa in these Finnish pedigrees, maximum likelihood segregation analyses were performed on the age at diagnosis expressed as a censored trait using the REGTL module of the Statistical Analysis for Genetic Epidemiology program (SAGE 3.1. 1997). Under model 1 of this program, employing class A regressive models (Bonney 1986), the “type” or “ousiotype” (Cannings et al. 1978) influences age at diagnosis of PCa through the location and scale parameters of the logistic distribution, but does not influence susceptibility. Specifically, some constant proportion (γ) of the male population is assumed to be at a risk of PCa. The PCa phenotype is defined as a dichotomous variable (Y), where Y = 1 if affected and Y = 0 if unaffected (censored). Parameters estimated in the analysis include: q A, the frequency of the putative high-risk allele ‘A’, β i baseline parameters, where i represents an individual’s type (AA, AB, BB); α i the age coefficients and γ i the susceptibilities (Elston and George 1989). The logistic function describing the probability that an individual is affected by age “a” is given as γ i [1/(1 + e−Φ], where

$$ \Phi = \beta _{\text{i} } + \alpha _{i} {\text{(a)}} + \delta _{{\text{F}}} {{(Y}}_{{\text{F}}} {\text{)}} $$
(1)

The coefficient δF reflects familial influence on risk corresponding to having an affected father.

Positive values of δF mean that the individual with an affected father is more likely to have an earlier age at diagnosis, while negative values mean that the individual with an affected father is more likely to have a later age at diagnosis. Nonzero values of δF indicate the effects of polygenic and/or unmeasured shared-familial environmental risk factors on PCa risk.

Age at diagnosis for prostate cancer phenotype is assumed to follow a logistic distribution described by two parameters α and β, with the probability distribution function according to Elston and George (1989)

$$ {f(age)} = {\text{[}}\alpha {\text{e}}^{{\beta \text{i} + \alpha {\text{(age)}}}} {\text{]}}/{\text{(}}1 + {\text{e}}^{{\beta \text{i} + \alpha {\text{(age)}}}} {\text{)}}^{2} $$
(2)

This symmetric distribution is similar to a normal distribution and has a mean −β/α, and variance, π2/3α2, where π has a value of 3.1416. Based on the logistic distribution, the cumulative distribution function (CDF) is given by

$$ {\text{F}(age)} = {\text{[}}\alpha {\text{e}}^{{\beta \text{i} + \alpha {\text{(age)}}}} {\text{]}}/{\text{[}}1 + {\text{e}}^{{\beta \text{i} + \alpha {\text{(age)}}}} {\text{]}}. $$
(3)

The CDF represents the probability that a susceptible person will be affected by a given age. Age-specific penetrances were calculated for each genotype as

$$ {\text{P(Y}}|{\text{genotype }}{i} ,{\text{ age)}} = {\text{[e}}^{{\beta {i} + {\text{a(age)}}}} {\text{]}}/{\text{[}}1 + {\text{e}}^{{\beta {i} + {\text{a(age)}}}} {\text{]}}. $$
(4)

If the observed sex-specific ages at diagnosis do not follow a logistic distribution, this model may still be appropriate after transformation. A transformation equation equivalent to: aG1 × ln (age) was considered here, where aG1 is the geometric mean age at diagnosis for prostate cancer, computed from the observed ages at diagnosis among the 160 affected non-probands with PCa, 51 for the early-onset and 109 for the late-onset cohorts (Table 1).

Tests for genetic contribution to disease risk were implemented by postulating three types of individuals (AA, AB, BB) with three corresponding transmission parameters (τ AA, τ AB, τ BB) describing the probability that a parent of a given type transmits the disease producing factor ‘A’ to his/her offspring (Elston and Stewart 1971; Elston and Yelverton 1975; Elston 1981). Under the hypothesis of genetic transmission, these τ parameters are constrained to the Mendelian values of τ AA = 1.0, τ AB = 0.5, τ BB = 0.0. Five sub-models of disease transmission were tested against a general model, where the transmission probabilities are estimated but with the restriction of homogeneity of trait distribution across generations to identify the best model for these data (Elston 1981). The “no major gene” model assumes that baseline risk is not influenced by “type” therefore all persons would come from a single distribution of age-specific risk for PCa. Single-locus Mendelian models assume that a major locus with two alleles should act in codominant, dominant or recessive fashion. The dominant and recessive models are special cases of the codominant model, where each genotype has a distinct age at diagnosis distribution. An environmental model with potentially distinct types of individuals was also tested, but here the transmission probability was held constant for all individuals.

We present results from the maximum likelihood segregation analyses performed on the log-transformed age at diagnosis of PCa expressed as a censored trait using the REGTL program (SAGE 3.1. 1997). Log-transformation of ages at diagnosis and ages at examination for all individuals with non-zero ages led to a final model that estimated genotypic baseline parameters (β i ) and age coefficient (α i ) and lifetime susceptibility for PCa for males along with the frequency (qA) of the high-risk allele A.

Hypothesis testing

The likelihood ratio test (LRT) was used to test each sub-model against the general model, and was computed as minus twice the natural log likelihood [−2ln(L)] of the general model subtracted from that for a restricted sub-model. This difference is asymptotically distributed as a χ2 distribution with degrees of freedom equal to the difference in the number of independent parameters estimated in the two models.

Another method to compare models uses Akaike’s information criteria (AIC), defined as: AIC = −2ln(L) + 2(number of parameters estimated). The most parsimonious model has the minimum AIC value (Akaike 1974). To correct for ascertainment bias, the likelihood of each pedigree was conditioned on the proband’s affection status, using his age at diagnosis as recorded in the Cancer Registry (Cannings and Thompson 1977; Elston and Sobel 1979).

Results

Cohort-1 with 557 early-onset PCa families

As shown in Table 2, the no major gene model gave a very poor fit to the data in the early-onset PCa cohort and was thus rejected against the general unrestricted model in which all parameters were estimated based on the likelihood ratio test (LRT) (model 1 vs. model 6, χ2 = 18.36, P < 0.005 for 6df). The dominant (P < 0.0014), codominant (P < 0.036) and environmental (P < 0.0082) models were all rejected compared to the general model. The final general model reported is almost identical to the recessive Mendelian model (model 4 vs. model 6, χ2 = 1.54, P < 0.83 for 4df). The AIC, which takes into account the number of parameters estimated also, confirmed that the recessive model was the most parsimonious model. The estimated frequency ±SE (standard error) for the high-risk allele q A was 0.054 (±0.01) for the recessive model. Figure 1 presents the predicted cumulative distribution function curves for log-transformed ages at diagnosis for the early-onset families under the recessive model. High-risk homozygous carriers of the putative risk allele AA have predicted age-specific cumulative probabilities greater than the heterozygous AB and BB non-carriers. The predicted mean age at diagnosis (i.e., 50% cumulative risk in Fig. 1) for the AA individuals is 60 and 64.6 years for the non-carriers. The susceptibility parameter γ was estimated at 1.0 for all male carriers of the risk allele, suggesting that 100% of the male population if they lived to infinity and did not die of competing causes, would express PCa if they were homozygous carriers of the allele A. Under this Mendelian recessive model, the cumulative probability that a male in Finland would be affected by PCa by age 70 was 0.92 for carriers and 0.79 for non-carriers, thus implying that if carriers and non-carriers did not die from competing causes, the estimated risk of being diagnosed with PCa at age 70 years for the homozygote carriers of the deleterious allele (q = 0.054), would be 2.7 per 1,000 among a hypothetical cohort of 100,000 men.

Table 2 Parameter estimates from segregation analysis of prostate cancer in 557 early-onset Finnish families ascertained through a single prostate cancer proband aged <61 years
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Fig. 1
figure 1

Predicted cumulative risks for recessive AA (carriers) and AB/BB (non-carriers) with an affected father: 557 early-onset Finnish prostate cancer families

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Cohort-2 with 989 late-onset PCa families

Table 3 shows the parameter estimates from the segregation analysis for the 989 families ascertained through late-onset probands diagnosed at ≥61 years of age. Compared to the unrestricted general model, the no major gene, the Mendelian dominant and environmental models did not fit the data and were rejected at P < 0.001. The Mendelian codominant model was also rejected by the LRT with a χ2 of 12.64 and a P value of 0.006. The recessive model was the most parsimonious model according to the LRT (model 4 vs. model 6, χ2 = 6.82, P < 0.15 for 4df), and it also had the lowest AIC = 1,331.10. Under this recessive model, inheritance of a putative high-risk allele A with an allele frequency (±SE) of 0.086 (±0.006) had predicted mean ages of onset of 65.6 years for men with the AA genotype and 72.2 years for AB/BB males, respectively. The lifetime risk of being diagnosed with PCa under this model was 5.0 per 1,000 among a hypothetical cohort of 100,000 men.

Table 3 Parameter estimates from segregation analysis of prostate cancer in 989 late-onset Finnish families ascertained through a single prostate cancer proband
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Figure 2 shows the predicted cumulative distribution function curves for log-transformed ages at diagnosis for this late-onset cohort in which the AA genotype has a distinctly different mean age at diagnosis of PCa.

Fig. 2
figure 2

Predicted cumulative risks for recessive AA (carriers) and AB/BB (non-carriers) of high-risk allele A with an affected father for 989 late-onset Finnish prostate cancer families

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Combined Cohort-1 and Cohort-2 with 1,546 PCa families

From Table 4, the combined cohorts with 1,546 probands gave parameter estimates very similar to those obtained from Cohort-2 with 989 probands. All other models except the Mendelian recessive model were significantly rejected when compared with the unrestricted general model. The recessive model was the most parsimonious model according to the LRT (model 4 vs. model 6, χ2 = 8.78, P < 0.07 for 4df), and it also had the lowest AIC = 1,795.12. Under this recessive model, inheritance of a putative high-risk allele A with an allele frequency (±SE) of 0.0903 (±0.005) had predicted mean ages of onset of 63.6 years for men with the AA genotype and 71.0 years for AB/BB genotype males, respectively. Figure 3 shows that under the recessive model, the predicted cumulative risks for PCa are distinctly different for the AA compared to the AB/BB genotypes. The estimated mean age at diagnosis for the male homozygous carriers of the putative, high-risk allele A is 63.6 and it is 71.0 years for AB/BB genotype males. With a cumulative risk of 0.80 for homozygote carriers of the A allele at age 70 years, the estimated risk of being diagnosed with PCa in the absence of competing causes of death was 6.5 per 1,000 among a hypothetical cohort of 100,000 men.

Table 4 Parameter estimates from segregation analysis of prostate cancer in combined analysis of two cohorts of 1,546 Finnish families ascertained through a single prostate cancer proband
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Fig. 3
figure 3

Predicted cumulative risks for recessive AA (carriers) and AB/BB (non-carriers) of the putative high-risk allele A with an affected father for the 1,546 combined Finnish prostate cancer families

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All of these models (in each cohort and in the combined cohorts) included a residual paternal regressive coefficient, since inclusion of this coefficient significantly improved the fit of these models. The impact of genotype alone versus residual effect of having an affected father can be measured by computing the log odds of various combinations.

In effect, among men of the same age, born in the same cohort, and having the same affected father status, the log odds for being a homozygous carrier of the high risk allele A is computed as the difference between the genotypic baseline coefficients of the homozygous carriers and of the heterozygote and homozygous non-carriers. Using the parameters of the recessive model in the combined cohort as an example:

$$ \begin{aligned}{} & {\text{(}}\beta _{{{\text{AA}}}} + \alpha a' + \delta _{{F}} {\text{(YF))}} - {\text{(}}\beta _{{{\text{AB/BB}}}} + \alpha a' + \delta _{{F}} {\text{(}}Y_{{F}} {\text{))}} \\ & = \,\beta _{{{\text{AA}}}} - {\text{ }}\beta _{{{\text{AB/BB}}}} \\ & = \, - 63.34 - {\text{(}} - 71.23{\text{)}} = 7.89. \\ \end{aligned} $$

The odds of PCa in homozygous carriers of the A allele compared to the AB/BB non-carriers is 2,670, i.e., the exp (7.89). The log-odds due to an affected father between two individuals with the same genotype is 3.90 and the corresponding odds ratio is 49.40. The increase in log odds for a homozygote for the high-risk allele A with an affected father compared to the heterozygote carrier of the same age born in the same cohort but having an affected father would therefore be (βAA−βAB/BB) + δF(Y F)) = 7.89 + 3.90 = 11.79 leading to high odds ratio.

Discussion

In the present segregation analysis of our 1,546 population-based PCa families in Finland with ages at diagnosis of probands ranging between 41.8 and 96 years, a Mendelian recessive model with significant paternal regressive coefficient was shown to fit the homogeneous Finnish population best when the families were not separated into early- and late-onset cohorts. Recessive models with significant paternal regressive coefficients were also the most parsimonious models in both the 557 families of the early-onset cohort and in the 989 late-onset families. Under Hardy–Weinberg equilibrium, the estimated allele frequency of 0.09 for the combined cohort implies that 0.81% of the population in Finland would carry this rare putative high-risk allele. However, being a gender limited disease the susceptibility parameter γ of 1.0 obtained in the analysis suggested that 100% of the homozygous carrier male population at risk would develop PCa if they lived to infinity and did not die of competing causes.

Our results suggest that individuals carrying the risk allele get PCa at younger ages (<66 years) compared to non-carriers, whether they belong to the 557 early-onset or 989 late-onset groups or when the two cohorts are combined. Homozygous carriers of the risk allele (AA genotype) in the three groups above have a mean age at diagnosis of 60.1, 65.6 and 63.6 years, respectively, given that only about 6 years separate the ages at diagnosis of the homozygotes for the risk allele in the early-onset from the late-onset cohorts. Since the residual paternal effect was positive, those with affected fathers were at a higher risk for earlier onset PCa with polygenic and/or unmeasured shared-familial environmental risk factors compared to those with unaffected fathers. These findings are quite different from the previously reported evidence for the segregation of a rare autosomal dominant gene with high penetrance among different populations that included some series of prostatectomy patient families (Carter et al. 1992; Grönberg et al. 1997; Schaid et al. 1998; Verhage et al. 2001; Conlon et al. 2003; Valeri et al. 2003). Likewise a Swedish study by Grönberg et al. (1997) that was carried out on a population-based sample of 2,857 families selected through an affected father diagnosed with PCa in 1959–1963 and identified from the nationwide Cancer Registry, revealed that the observed clustering of PCa was best explained by a high risk allele inherited in a dominant mode, with a high population frequency (1.67%) and a moderate lifetime penetrance (63%). Similarly, the segregation analysis by Valeri et al. (2003) of families identified through 691 PCa patients recruited from three hospitals reported evidence for autosomal dominant gene inheritance of a high risk allele (frequency of 0.03%) with brother–brother dependence.

Our analyses suggest that there are likely to be multiple loci behind PCa and similar results have also been reported previously. A segregation study performed in Australia on 1,476 population-based pedigrees whose probands were diagnosed with PCa before the age of 70 in 1994–1997 suggested that a two-locus model fitted better than single-locus models and included a dominantly inherited risk that was greater at younger ages and a recessively inherited or X-linked increased risk which was greater at older ages (Cui et al. 2001). In a study of 1,719 first degree relatives in American and Canadian families by Gong et al. (2002), it was also observed that the good fit of the multifactorial model suggests that multiple genes, each having low penetrance, may be responsible for most inherited PCa susceptibility, and that the contribution of rare highly penetrant mutations might be small. In a segregation analysis of 389 Icelandic pedigrees that included both breast and prostate cancer, Baffoe-Bonnie et al. (2002), reported that the most parsimonious model was a Mendelian codominant model.

Previously, a recessive mode of inheritance has been reported in only a few studies. Cui et al. (2001) suggested that recessively inherited or X-linked inheritance increased risk at older ages, which was also seen in our linkage analyses of the HPCX locus (Xu et al. 1998; Schleutker et al. 2003). For an adult onset, sex-limited cancer such as PCa, recessive inheritance with incomplete penetrance and sporadic cases is consistent with X-linked PCa, which we previously mapped to the Xq27–28 region using Finnish families characterized by “no-male-to-male transmission” (NMM). A follow-up linkage disequilibrium study utilizing familial/sporadic PCa cases and appropriate healthy controls identified an associated haplotype in the HPCX region (Baffoe-Bonnie et al. 2005). The results of this segregation analysis study are therefore consistent with the X-linked PCa transmission described previously (Schleutker et al. 2000). It has also been shown that there is a presence of residual brother–brother dependence (Valeri et al. 2003). In the study, by Narod et al. (1995) the prevalence of PCa was increased in those men with any first-degree relative affected. Most of the increase in relative risk was contributed by affected brothers, thus alluding to recessive or X-linked inheritance of the disease. In the study, by Monroe et al. (1995) an excess risk of PCa in men with affected brothers compared to those with affected fathers was also observed, consistent with the hypothesis of an X-linked, or recessive model of inheritance. Moreover, the prostate cancer risk was higher in probands’ brothers than in probands’ fathers in the Mayo Clinic Study (Schaid et al. 1998). These observations can be interpreted as evidence of recessive or X-linked effects in the risk of PCa.

Probands in the four American studies (Carter et al. 1992; Schaid et al. 1998; Verhage et al. 2001; Conlon et al. 2003) were part of a radical prostatectomy series for primary clinically localized PCa, and thus corresponded to a subgroup of patients not representative of all prostate cancer cases. This, as conceded by Schaid et al. (1998), could limit the power to assess heterogeneity of transmission across different age groups and represent a selection bias due to phenotypic characteristics. The particular strength of our study is the large population-based data composed of a homogenous Finnish population with registry-based approaches that provide unbiased information of malignancies in families. Prostate cancer was histologically confirmed in all cases in our families, and did not rely on PSA screening. The homogeneity of the Finnish population increases our chances of identifying loci, which may be less, pronounced in ethnically more diverse populations. Thus, linkage and association analyses of HPC conducted on Finnish families have found loci that are different from those reported in studies from other countries and populations (Schleutker et al. 2003; Seppälä et al. 2003a, b). Also, the present study was based on all prostate cancer cases in a population in a certain time window, not just known prostate cancer families, where genetic components may contribute to other cancer types and also be biased by specific family collection criteria.

The International Consortium for Prostate Cancer Genetics (ICPCG) recently announced that even though evidence of the existence of major PCa-susceptibility genes has been provided by multiple segregation analyses, genome-wide screens have not yielded conclusive chromosomal regions due to major difficulties that include genetic heterogeneity (Xu et al. 2005). The ICPCG employed parametric (dominant and recessive) and nonparametric analyses of 1,233 families world wide to identify several regions “indicative” of linkage. The main subsets of families likely to segregate highly penetrant mutations include families with large numbers of affected individuals or early age at diagnosis, leading to stronger evidence of linkage in several regions. Linkage and the association analysis of HPC conducted on Finnish families have found loci that are different from those reported in studies from other countries and populations (Schleutker et al. 2003; Seppälä et al. 2003a, b). We therefore believe that the results of these segregation analyses will be applied to defining a new model(s) for improving linkage analyses of the multiplex PCa families collected in Finland.

In conclusion, our findings suggest that the inheritance of PCa in the Finnish population is best explained by a Mendelian recessive model with a significant paternal regressive coefficient that is indicative of a polygenic multifactorial component. The rising incidence of PCa in Finland is possibly due to a combination of factors that include socio-cultural and lifestyle changes, environmental factors and the on-going PSA screening (Finnish Cancer Registry, Mäkinen et al. 2003).