Genome-wide association study identifies a second prostate cancer susceptibility variant at 8q24.

Prostate cancer is the most prevalent noncutaneous cancer in males in developed regions, with African American men having among the highest worldwide incidence and mortality rates. Here we report a second genetic variant in the 8q24 region that, in conjunction with another variant we recently discovered, accounts for about 11%-13% of prostate cancer cases in individuals of European descent and 31% of cases in African Americans. We made the current discovery through a genome-wide association scan of 1,453 affected Icelandic individuals and 3,064 controls using the Illumina HumanHap300 BeadChip followed by four replication studies. A key step in the discovery was the construction of a 14-SNP haplotype that efficiently tags a relatively uncommon (2%-4%) susceptibility variant in individuals of European descent that happens to be very common (approximately 42%) in African Americans. The newly identified variant shows a stronger association with affected individuals who have an earlier age at diagnosis.

A common variant associated with prostate cancer in European and African populations.

With the increasing incidence of prostate cancer, identifying common genetic variants that confer risk of the disease is important. Here we report such a variant on chromosome 8q24, a region initially identified through a study of Icelandic families. Allele -8 of the microsatellite DG8S737 was associated with prostate cancer in three case-control series of European ancestry from Iceland, Sweden and the US. The estimated odds ratio (OR) of the allele is 1.62 (P = 2.7 x 10(-11)). About 19% of affected men and 13% of the general population carry at least one copy, yielding a population attributable risk (PAR) of approximately 8%. The association was also replicated in an African American case-control group with a similar OR, in which 41% of affected individuals and 30% of the population are carriers. This leads to a greater estimated PAR (16%) that may contribute to higher incidence of prostate cancer in African American men than in men of European ancestry.

Incorporating linkage information into a common disease/rare variant framework.

Recent developments in sequencing technology have allowed the investigation of the common disease/rare variant hypothesis. In the Genetic Analysis Workshop 17 data set, we have sequence data on both unrelated individuals and eight large extended pedigrees with simulated quantitative and qualitative phenotypes. Group 11, whose focus was incorporating linkage information, considered several different ways to use the extended pedigrees to identify causal genes and variants. The first issue was the use of standard linkage or identity-by-descent information to identify regions containing causal rare variants. We found that rare variants of large effect segregating through pedigrees were precisely the bailiwick of linkage analysis. For a common disease, we anticipate many risk loci, so a heterogeneity linkage analysis or an analysis of a single pedigree at a time may be useful. The second issue was using pedigree data to identify individuals for sequencing. If one can identify linked regions and even carriers of risk haplotypes, the sequencing will be substantially more efficient. In fact, sequencing only 2.5% of the genome in carefully selected individuals can detect 52% of the risk variants that would be detected through whole-exome sequencing in a large number of unrelated individuals. Finally, we found that linkage information from pedigrees can provide weights for case-control association tests. We also found that pedigree-based association tests have the same issues of binning variants and variant counting as those in tests of unrelated individuals. Clearly, when pedigrees are available, they can provide great assistance in the search for rare variants that influence common disorders.

Fine-mapping of prostate cancer aggressiveness loci on chromosome 7q22-35.

BACKGROUND: Deciphering the genetic basis of prostate cancer aggressiveness could provide valuable information for the screening and treatment of this common but complex disease. We previously detected linkage between a broad region on chromosome 7q22-35 and Gleason score-a strong predictor of prostate cancer aggressiveness. To further clarify this finding and focus on the potentially causative gene, we undertook a fine-mapping study across the 7q22-35 region. METHODS: Our study population encompassed 698 siblings diagnosed with prostate cancer. 3,072 single nucleotide polymorphisms (SNPs) spanning the chromosome 7q22-35 region were genotyped using the Illumina GoldenGate assay. The impact of SNPs on Gleason scores were evaluated using affected sibling pair linkage and family-based association tests. RESULTS: We confirmed the previous linkage signal and narrowed the 7q22-35 prostate cancer aggressiveness locus to a 370 kb region. Centered under the linkage peak is the gene KLRG2 (killer cell lectin-like receptor subfamily G, member 2). Association tests indicated that the potentially functional non-synonymous SNP rs17160911 in KLRG2 was significantly associated with Gleason score (P = 0.0007). CONCLUSIONS: These findings suggest that genetic variants in the gene KLRG2 may affect Gleason score at diagnosis and hence the aggressiveness of prostate cancer.

Population stratification and patterns of linkage disequilibrium.

Although the importance of selecting cases and controls from the same population has been recognized for decades, the recent advent of genome-wide association studies has heightened awareness of this issue. Because these studies typically deal with large samples, small differences in allele frequencies between cases and controls can easily reach statistical significance. When, unbeknownst to a researcher, cases and controls have different substructures, the number of false-positive findings is inflated. There have been three recent developments of purely statistical approaches to assessing the ancestral comparability of case and control samples: genomic control, structured association, and multivariate reduction analyses. The widespread use of high-throughput technology has allowed the quick and accurate genotyping of the large number of markers required by these methods. Group 13 dealt with four population stratification issues: single-nucleotide polymorphism marker selection, association testing, nonstandard methods, and linkage disequilibrium calculations in stratified or mixed ethnicity samples. We demonstrated that there are continuous axes of ethnic variation in both data sets of Genetic Analysis Workshop 16. Furthermore, ignoring this structure created P-value inflation for a variety of phenotypes. Principal-components analysis (or multidimensional scaling) can control inflation as covariates in a logistic regression. One can weigh for local ancestry estimation and allow the use of related individuals. Problems arise in the presence of extremely high association or unusually strong linkage disequilibrium (e.g., in chromosomal inversions). Our group also reported a method for performing an association test controlling for substructure, when genome-wide markers are not available, to explicitly compute stratification.

Association of CASP8 D302H polymorphism with reduced risk of aggressive prostate carcinoma.

BACKGROUND: Because of the dramatically different clinical course of aggressive and indolent prostate carcinoma (PCa), markers that distinguish between these phenotypes are of critical importance. Apoptosis is an important protective mechanism for unrestrained cellular growth and metastasis. Therefore, dysfunction in this pathway is a key step in cancer progression. As such, genetic variants in apoptosis genes are potential markers of aggressive PCa. Recent work in breast carcinoma has implicated the histidine variant of CASP8 D302H (rs1045485) as a protective risk allele. METHODS: We tested the hypothesis that the H variant was protective for aggressive PCa in a pooled analysis of 796 aggressive cases and 2,060 controls. RESULTS: The H allele was associated with a reduced risk of aggressive PCa (OR(per allele) = 0.67, 95% CI: 0.54-0.83, P(trend) = 0.0003). The results were similar for European-Americans (OR(per allele) = 0.68; 95% CI: 0.54-0.86) and African-Americans (OR(per allele) = 0.61; 95% CI: 0.34-1.10). We further determined from the full series of 1,160 cases and 1,166 controls in the Prostate, Lung, Colorectal, Ovarian (PLCO) population that the protective effect of the H allele tended to be limited to high-grade and advanced PCa (all cases OR(per allele) = 0.94; 95% CI: 0.79-1.11; localized, low-grade disease OR(per allele) = 0.98; 95% CI: 0.79-1.23; and aggressive disease OR(per allele) = 0.73; 95% CI: 0.50-1.07). CONCLUSION: These results suggest that histidine variant of CASP8 D302H is a protective allele for aggressive PCa with potential utility for identification of patients at differential risk for this clinically significant phenotype.

Common variants in 8q24 are associated with risk for prostate cancer and tumor aggressiveness in men of European ancestry.

BACKGROUND: Recent whole genome association studies have independently identified multiple prostate cancer (PC) risk variants on 8q24. We have evaluated association of common variants in this region with PC susceptibility and tumor aggressiveness in a sample of European American men. METHODS: Forty-nine tagging SNPs including three previously reported significant variants (rs1447295, rs6983267, rs16901979) and seven variants in the 5' upstream region of the MYC proto-oncogene were tested for association with susceptibility to PC and tumor aggressiveness in 596 histologically verified PC cases and 567 ethnically matched controls. RESULTS: Significant associations with susceptibility to PC were found at 17 SNPs, four of which (rs1016342, rs1378897, rs871135, and rs6470517) remained significant after adjusting for multiple corrections. One of the associated SNPs, rs871135, is located in the putative gene POU5F1P1 within the 8q24 region. An in slico analysis showed that the associated variant of this SNP alters a transcription factor implicating a plausible regulatory role. Additionally, one of the significantly associated SNPs, rs6470517, with PC susceptibility showed a significant over-representation of the G allele in cases with aggressive tumor. CONCLUSIONS: Although this study does not directly confirm associations of the three specific SNPs (cited above), it corroborates reported signals of association in 8q24 reaffirming that genetic variation on 8q24 influences susceptibility to PC in men of European ancestry. Although our study did not confirm the allelic association of rs1447295, meta-analysis of this SNP provided support to previous reported associations. Further, this study implicates the 8q24 region with aggressive forms of PC.

CDKN1A and CDKN1B polymorphisms and risk of advanced prostate carcinoma.

A multigenic model of prostate cancer susceptibility has been proposed, in which common polymorphic variants of genes, such as the androgen and vitamin D receptor, contribute to tumorigenesis. The discovery of additional genetic factors that contribute to prostate cancer risk should provide opportunities for new approaches to the detection and treatment of this common malignancy. Herein, we examined single nucleotide polymorphic variants in the 3'-untranslated region of CDKN1A (p21(cip1)) and in codon 109 of CDKN1B (p27(kip1)) for association with advanced prostate cancer in a European-American population. Ninety-six cases and 106 controls were analyzed using PCR amplification and restriction digestion assays. CDKN1A genotype was scored as CC, CT, and TT on the basis of the digestion products. The CDKN1A genotypes CT and TT were associated with an increased risk of advanced prostate carcinoma compared with the CC genotype [odds ratio (OR), 2.24; 95% confidence interval (CI), 1.02-4.95]. The CDKN1B genotype was scored as VV, VG, or GG, again on the basis of the digestion products. The CDKN1B genotype VV was also associated with an increased risk of advanced prostate carcinoma (OR, 1.95; 95% CI, 1.09-3.47). These associations were particularly strong in those patients with androgen-independent disease [OR = 2.88 (95% CI, 1.19-6.97) and 2.11 (95% CI, 1.05-4.22) for high-risk genotypes of CDKN1A and CDKN1B, respectively]. In addition, the association of CDKN1B was particularly strong in the cohort of patients under the median age of diagnosis (OR, 2.23; 95% CI, 1.08-4.59). These results suggest that in a European-American population, CDKN1A and CDKN1B variants are associated with advanced prostate cancer. Analysis of CDKN1A and/or CDKN1B genotypes may prove useful in determining which patients are at risk for developing advanced prostate carcinoma and therefore would gain the most from aggressive screening, prophylaxis, and/or treatment.

A common polymorphism in the upstream promoter region of the hepatocyte nuclear factor-4 alpha gene on chromosome 20q is associated with type 2 diabetes and appears to contribute to the evidence for linkage in an ashkenazi jewish population.

Variants in hepatocyte nuclear factor-4 alpha (HNF4 alpha), a transcription factor that influences the expression of glucose metabolic genes, have been correlated with maturity-onset diabetes of the young, a monogenic form of diabetes. Previously, in a genome scan of Ashkenazi Jewish type 2 diabetic families, we observed linkage to the chromosome 20q region encompassing HNF4 alpha. Here, haplotype-tag single nucleotide polymorphisms (htSNPs) were identified across a 78-kb region around HNF4 alpha and evaluated in an association analysis of Ashkenazi Jewish type 2 diabetic (n = 275) and control (n = 342) subjects. We found that two of nine htSNPs were associated with type 2 diabetes: a 3' intronic SNP, rs3818247 (29.2% case subjects vs. 21.7% control subjects; P = 0.0028, odds ratio [OR] 1.49) and a 5' htSNP located approximately 3.9 kb upstream of P2, rs1884614 (26.9% case subjects vs. 20.3% control subjects; P = 0.0078, OR 1.45). Testing of additional SNPs 5' of rs1884614 revealed a >10-kb haplotype block that was associated with type 2 diabetes. Conditioning on the probands' rs1884614 genotype suggested that the chromosomal region identified by the htSNP accounted for the linkage signal on chromosome 20q in families in which the proband carried at least one risk allele. Notably, the associations and the partitioned linkage profiles near P2 were independently observed in a Finnish sample, suggesting the presence of potential regulatory element(s) that may contribute to the risk for type 2 diabetes.

Microsatellites versus single-nucleotide polymorphisms in linkage analysis for quantitative and qualitative measures.

BACKGROUND: Genetic maps based on single-nucleotide polymorphisms (SNP) are increasingly being used as an alternative to microsatellite maps. This study compares linkage results for both types of maps for a neurophysiology phenotype and for an alcohol dependence phenotype. Our analysis used two SNP maps on the Illumina and Affymetrix platforms. We also considered the effect of high linkage disequilibrium (LD) in regions near the linkage peaks by analysing a "sparse" SNP map obtained by dropping some markers in high LD with other markers in those regions. RESULTS: The neurophysiology phenotype at the main linkage peak near 130 MB gave LOD scores of 2.76, 2.53, 3.22, and 2.68 for the microsatellite, Affymetrix, Illumina, and Illumina-sparse maps, respectively. The alcohol dependence phenotype at the main linkage peak near 101 MB gave LOD scores of 3.09, 3.69, 4.08, and 4.11 for the microsatellite, Affymetrix, Illumina, and Illumina-sparse maps, respectively. CONCLUSION: The linkage results were stronger overall for SNPs than for microsatellites for both phenotypes. However, LOD scores may be artificially elevated in regions of high LD. Our analysis indicates that appropriately thinning a SNP map in regions of high LD should give more accurate LOD scores. These results suggest that SNPs can be an efficient substitute for microsatellites for linkage analysis of both quantitative and qualitative phenotypes.

An analysis of identical single-nucleotide polymorphisms genotyped by two different platforms.

The overlap of 94 single-nucleotide polymorphisms (SNP) among the 4,720 and 11,120 SNPs contained in the linkage panels of Illumina and Affymetrix, respectively, allows an assessment of the discrepancy rate produced by these two platforms. Although the no-call rate for the Affymetrix platform is approximately 8.6 times greater than for the Illumina platform, when both platforms make a genotypic call, the agreement is an impressive 99.85%. To determine if disputed genotypes can be resolved without sequencing, we studied recombination in the region of the discrepancy for the most discrepant SNP rs958883 (typed by Illumina) and tsc02060848 (typed by Affymetrix). We find that the number of inferred recombinants is substantially higher for the Affymetrix genotypes compared to the Illumina genotypes. We illustrate this with pedigree 10043, in which 3 of 7 versus 0 of 7 offspring must be double recombinants using the genotypes from the Affymetrix and the Illumina platforms, respectively. Of the 36 SNPs with one or more discrepancies, we identified a subset that appears to cluster in families. Some of this clustering may be due to the presence of a second segregating SNP that obliterates a XbaI site (the restriction enzyme used in the Affymetrix platform), resulting in a fragment too long (>1,000 bp) to be amplified.

Multipoint identity-by-descent computations for single-point polymorphism and microsatellite maps.

We used the LOKI software to generate multipoint identity-by-descent matrices for a microsatellite map (with 31 markers) and two single-nucleotide polymorphism (SNP) maps to examine information content across chromosome 7 in the Collaborative Study on the Genetics of Alcoholism dataset. Despite the lower information provided by a single SNP, SNP maps overall had higher and more uniform information content across the chromosome. The Affymetrix map (578 SNPs) and the Illumina map (271 SNPs) provided almost identical information. However, increased information has a computational cost: SNP maps require 100 times as many iterations as microsatellites to produce stable estimates.

The efficacy of short tandem repeat polymorphisms versus single-nucleotide polymorphisms for resolving population structure.

Accurately resolving population structure in a sample is important for both linkage and association studies. In this study we investigated the power of single-nucleotide polymorphisms (SNPs) in detecting population structure in a sample of 286 unrelated individuals. We varied the number of SNPs to determine how many are required to approach the degree of resolution obtained with the Collaborative Study on the Genetics of Alcoholism (COGA) short tandem repeat polymorphisms (STRPs). In addition, we selected SNPs with varying minor allele frequencies (MAFs) to determine whether low or high frequency SNPs are more efficient in resolving population structure. We conclude that a set of at least 100 evenly spaced SNPs with MAFs of 40-50% is required to resolve population structure in this dataset. If SNPs with lower MAFs are used, then more than 250 SNPs may be required to obtain reliable results.

Prostate cancer aggressiveness locus on chromosome 7q32-q33 identified by linkage and allelic imbalance studies.

The biologic aggressiveness of prostate tumors is an important indicator of prognosis. Chromosome 7q32-q33 was recently reported to show linkage to more aggressive prostate cancer, based on Gleason score, in a large sibling pair study. We report confirmation and narrowing of the linked region using finer-scale genotyping. We also report a high frequency of allelic imbalance (AI) defined within this locus in a series of 48 primary prostate tumors from men unselected for family history or disease status. The highest frequency of AI was observed with adjacent markers D7S2531 (52%) and D7S1804 (36%). These two markers delineated a common region of AI, with 24 tumors exhibiting interstitial AI involving one or both markers. The 1.1-Mb candidate region contains relatively few transcripts. Additionally, we observed positive associations between interstitial AI at D7S1804 and early age at diagnosis (P=.03) as well as a high combined Gleason score and tumor stage (P=.06). Interstitial AI at D7S2531 was associated with a positive family history of prostate cancer (P=.05). These data imply that we have localized a prostate cancer tumor aggressiveness loci to chromosome 7q32-q33 that is involved in familial and nonfamilial forms of prostate cancer.

Are there mappable genes for family resemblance for the magnitude of intra-individual variation in systolic blood pressure?

BACKGROUND: The genetic regulation of variation in intra-individual fluctuations in systolic blood pressure over time is poorly understood. Analysis of the magnitude of the average fluctuation of a person's systolic blood pressure around his or her age-adjusted trend line, however, shows moderate, albeit significant, family resemblance in Cohort 1 of the Framingham Heart Study. To determine whether genomic regions affecting this phenotype could be identified, we pursued a "model-free" multipoint quantitative linkage analysis. RESULTS: Two different linkage methods revealed multiple nominally significant signals, two to four of which are "replicated" in Cohort 2. When both cohorts are assembled into extended pedigrees, three linkage signals remain nominally significant by one or both methods. CONCLUSION: Any or all of the genomic regions in the vicinity of D5S1456, D11S2359, and D20S470 may contain elements that regulate systolic blood pressure homeostasis.

Identifying cryptic population structure in multigenerational pedigrees in a Mexican American sample.

Cryptic population structure can increase both type I and type II errors. This is particularly problematic in case-control association studies of unrelated individuals. Some researchers believe that these problems are obviated in families. We argue here that this may not be the case, especially if families are drawn from a known admixed population such as Mexican Americans. We use a principal component approach to evaluate and visualize the results of three different approaches to searching for cryptic structure in the 20 multigenerational families of the Genetic Analysis Workshop 18 (GAW18). Approach 1 uses all family members in the sample to identify what might be considered "outlier" kindreds. Because families are likely to differ in size (in the GAW18 families, there is about a 4-fold difference in the number of typed individuals), approach 2 uses a weighting system that equalizes pedigree size. Approach 3 concentrates on the founders and the "marry-ins" because, in principle, the entire pedigree can be reconstructed with knowledge of the sequence of these unrelated individuals and genome-wide association study (GWAS) data on everyone else (to identify the position of recombinations). We demonstrate that these three approaches can yield very different insights about cryptic structure in a sample of families.

Genotypic discrepancies arising from imputation.

The ideal genetic analysis of family data would include whole genome sequence on all family members. A strategy of combining sequence data from a subset of key individuals with inexpensive, genome-wide association study (GWAS) chip genotypes on all individuals to infer sequence level genotypes throughout the families has been suggested as a highly accurate alternative. This strategy was followed by the Genetic Analysis Workshop 18 data providers. We examined the quality of the imputation to identify potential consequences of this strategy by comparing discrepancies between GWAS genotype calls and imputed calls for the same variants. Overall, the inference and imputation process worked very well. However, we find that discrepancies occurred at an increased rate when imputation was used to infer missing data in sequenced individuals. Although this may be an artifact of this particular instantiation of these analytic methods, there may be general genetic or algorithmic reasons to avoid trying to fill in missing sequence data. This is especially true given the risk of false positives and reduction in power for family-based transmission tests when founders are incorrectly imputed as heterozygotes. Finally, we note a higher rate of discrepancies when unsequenced individuals are inferred using sequenced individuals from other pedigrees drawn from the same admixed population.

Linkage analysis merging replicate phenotypes: an application to three quantitative phenotypes in two African samples.

We report two approaches for linkage analysis of data consisting of replicate phenotypes. The first approach is specifically designed for the unusual (in human data) replicate structure of the Genetic Analysis Workshop 17 pedigree data. The second approach consists of a standard linkage analysis that, although not specifically tailored to data consisting of replicate genotypes, was envisioned as providing a sounding board against which our novel approach could be assessed. Both approaches are applied to the analysis of three quantitative phenotypes (Q1, Q2, and Q4) in two sets of African families. All analyses were carried out blind to the generating model (i.e., the "answers"). Using both methods, we found numerous significant linkage signals for Q1, although population colocalization was absent for most of these signals. The linkage analysis of Q2 and Q4 failed to reveal any strong linkage signals.

Stratify or adjust? Dealing with multiple populations when evaluating rare variants.

The unrelated individuals sample from Genetic Analysis Workshop 17 consists of a small number of subjects from eight population samples and genetic data composed mostly of rare variants. We compare two simple approaches to collapsing rare variants within genes for their utility in identifying genes that affect phenotype. We also compare results from stratified analyses to those from a pooled analysis that uses ethnicity as a covariate. We found that the two collapsing approaches were similarly effective in identifying genes that contain causative variants in these data. However, including population as a covariate was not an effective substitute for analyzing the subpopulations separately when only one subpopulation contained a rare variant linked to the phenotype.

Detecting population stratification using related individuals.

Although identification of cryptic population stratification is necessary for case/control association analyses, it is also vital for linkage analyses and family-based association tests when founder genotypes are missing. However, including related individuals in an analysis such as EIGENSTRAT can result in bias; using only founders or one individual per pedigree results in loss of data and inaccurate estimates of stratification. We examine a generalization of principal-component analyses to allow for the inclusion of related individuals by down-weighting the significance of individual comparisons.