Carol H. Jin

Genomewide Linkage Scan of 409 European-Ancestry and African American Families with Schizophrenia: Suggestive Evidence of Linkage at 8p23.3-p21.2 and 11p13.1-q14.1 in the Combined Sample

We report the clinical characteristics of a schizophrenia sample of 409 pedigrees--263 of European ancestry (EA) and 146 of African American ancestry (AA)--together with the results of a genome scan (with a simple tandem repeat polymorphism interval of 9 cM) and follow-up fine mapping. A family was required to have a proband with schizophrenia (SZ) and one or more siblings of the proband with SZ or schizoaffective disorder. Linkage analyses included 403 independent full-sibling affected sibling pairs (ASPs) (279 EA and 124 AA) and 100 all-possible half-sibling ASPs (15 EA and 85 AA). Nonparametric multipoint linkage analysis of all families detected two regions with suggestive evidence of linkage at 8p23.3-q12 and 11p11.2-q22.3 (empirical Z likelihood-ratio score [Z(lr)] threshold >/=2.65) and, in exploratory analyses, two other regions at 4p16.1-p15.32 in AA families and at 5p14.3-q11.2 in EA families. The most significant linkage peak was in chromosome 8p; its signal was mainly driven by the EA families. Z(lr) scores >2.0 in 8p were observed from 30.7 cM to 61.7 cM (Center for Inherited Disease Research map locations). The maximum evidence in the full sample was a multipoint Z(lr) of 3.25 (equivalent Kong-Cox LOD of 2.30) near D8S1771 (at 52 cM); there appeared to be two peaks, both telomeric to neuregulin 1 (NRG1). There is a paracentric inversion common in EA individuals within this region, the effect of which on the linkage evidence remains unknown in this and in other previously analyzed samples. Fine mapping of 8p did not significantly alter the significance or length of the peak. We also performed fine mapping of 4p16.3-p15.2, 5p15.2-q13.3, 10p15.3-p14, 10q25.3-q26.3, and 11p13-q23.3. The highest increase in Z(lr) scores was observed for 5p14.1-q12.1, where the maximum Z(lr) increased from 2.77 initially to 3.80 after fine mapping in the EA families.

Analysis of Candidate Genes for Prostate Cancer

Considerable evidence demonstrates that genetic factors are important in the development and aggressiveness of prostate cancer. To identify genetic variants that predispose to prostate cancer we tested candidate SNPs from genomic regions that show linkage to prostate cancer susceptibility and/or aggressiveness, as well as genes that show a significant difference in mRNA expression level between tumor and normal tissue. Cases had histologically verified prostate cancer. Controls were at least 65 years old, never registered a PSA above 2.5 ng/ml, always had digital rectal examinations that were not suspicious for cancer, and have no known family history of prostate cancer. Thirty-nine coding SNPs and nine non-coding SNPs were tested in up to 590 cases and 556 controls resulting in over 40,000 SNP genotypes. Significant differences in allele frequencies between cases and controls were observed for ID3 (inhibitor of DNA binding), p = 0.05, HPN (hepsin), p = 0.009, BCAS1 (breast carcinoma amplified sequence 1), p = 0.007, CAV2 (caveolin 2), p = 0.007, EMP3 (epithelial membrane protein 3), p < 0.0001, and MLH1 (mutL homolog 1), p < 0.0001. SNPs in three of these genes (BCAS1, EMP3 and MLH1) remained significant in an age-matched subsample.

Variants in the HEPSIN gene are associated with prostate cancer in men of European origin

There is considerable evidence that genetic factors are involved in prostate cancer susceptibility. We have studied the association of 11 single nucleotide polymorphisms (SNPs) in the HEPSIN gene (HPN) with prostate cancer in men of European ancestry. HPN is a likely candidate in prostate cancer susceptibility, as it encodes a transmembrane cell surface serum protease, which is overexpressed in prostate cancer; HPN is also located on 19q11–q13.2, where linkage is found with prostate cancer susceptibility. In this case-control association study (590 men with histologically verified prostate cancer and 576 unrelated controls, all of European descent), we find significant allele frequency differences between cases and controls at five SNPs that are located contiguously within the gene. A major 11-locus haplotype is significantly associated, which provides further support that HPN is a potentially important candidate gene involved in prostate cancer susceptibility. Association of one of the SNPs with Gleason score is also suggestive of a plausible role of HPN in tumor aggressiveness.

Association between polymorphisms in cell cycle genes and advanced prostate carcinoma.

Single nucleotide polymorphisms (SNPs) have been associated with a variety of malignancies including prostate carcinoma (PCa). Since a high percentage of PCa patients have low risk disease, of particular interest is not whether SNPs are associated with localized PCa, but whether they are associated with aggressive, potentially lethal disease. Herein, we explored the role of SNPs in cell cycle genes to determine if they were associated with advanced PCa.

Association of CASP8 D302H Polymorphism with Reduced Risk of Aggressive Prostate Carcinoma

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.

TGFBR1*6A is not associated with prostate cancer in men of European ancestry.

The TGFBR1*6A (*6A) variant in exon 1 of the TGFBR1 gene has been postulated as a putative tumor susceptibility allele in several studies. We have performed a case–control study in 537 men with histologically verified prostate cancer and in 488 unrelated controls to investigate the association of *6A with prostate cancer. Our results revealed that the frequency of the *6A allele does not differ in men with prostate cancer compared to healthy controls, even in a subset of age-matched cases and controls. There is no compelling evidence for an association of the *6A variant with prostate cancer.

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.

Gene × gene and gene × environment interactions for complex disorders

The restricted partition method (RPM) provides a way to detect qualitative factors (e.g. genotypes, environmental exposures) associated with variation in quantitative or binary phenotypes, even if the contribution is predominantly an interaction displaying little or no signal in univariate analyses. The RPM provides a model (possibly non-linear) of the relationship between the predictor covariates and the phenotype as well as measures of statistical and clinical significance for the model.Blind to the generating model, we used the RPM to screen a data set consisting 1500 unrelated cases and 2000 unrelated controls from Replicate 1 of the Genetic Analysis Workshop 15 Problem 3 data for genetic and environmental factors contributing to rheumatoid arthritis (RA) risk. Both univariate and pair-wise analyses were performed using sex, smoking, parental DRB1 HLA microsatellite alleles, and 9187 single-nucleotide polymorphisms genotypes from across the genome. With this approach we correctly identified three genetic loci contributing directly to RA risk, and one quantitative trait locus for the endophenotype IgM level. We did not mistakenly identify any factors not in the generating model. All the factors we found were detectable with univariate RPM analyses. We failed to identify two genetic loci modifying the risk of RA. After breaking the blind, we examined the true modeling factors in the first 50 data replicates and found that we would not have identified the additional factors as important even had we combined all the data from the first 50 replicates in a single data set.

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 Xba I site (the restriction enzyme used in the Affymetrix platform), resulting in a fragment too long (>1,000 bp) to be amplified.