Cheryl D. Cropp

Linkage analysis identifies a locus for plasma von Willebrand factor undetected by genome-wide association

The plasma glycoprotein von Willebrand factor (VWF) exhibits fivefold antigen level variation across the normal human population determined by both genetic and environmental factors. Low levels of VWF are associated with bleeding and elevated levels with increased risk for thrombosis, myocardial infarction, and stroke. To identify additional genetic determinants of VWF antigen levels and to minimize the impact of age and illness-related environmental factors, we performed genome-wide association analysis in two young and healthy cohorts (n = 1,152 and n = 2,310) and identified signals at ABO (P < 7.9E-139) and VWF (P < 5.5E-16), consistent with previous reports. Additionally, linkage analysis based on sibling structure within the cohorts, identified significant signals at chromosome 2q12–2p13 (LOD score 5.3) and at the ABO locus on chromosome 9q34 (LOD score 2.9) that explained 19.2% and 24.5% of the variance in VWF levels, respectively. Given its strong effect, the linkage region on chromosome 2 could harbor a potentially important determinant of bleeding and thrombosis risk. The absence of a chromosome 2 association signal in this or previous association studies suggests a causative gene harboring many genetic variants that are individually rare, but in aggregate common. These results raise the possibility that similar loci could explain a significant portion of the “missing heritability” for other complex genetic traits.

Whole Exome Sequencing of Distant Relatives in Multiplex Families Implicates Rare Variants in Candidate Genes for Oral Clefts

A dozen genes/regions have been confirmed as genetic risk factors for oral clefts in human association and linkage studies, and animal models argue even more genes may be involved. Genomic sequencing studies should identify specific causal variants and may reveal additional genes as influencing risk to oral clefts, which have a complex and heterogeneous etiology. We conducted a whole exome sequencing (WES) study to search for potentially causal variants using affected relatives drawn from multiplex cleft families. Two or three affected second, third, and higher degree relatives from 55 multiplex families were sequenced. We examined rare single nucleotide variants (SNVs) shared by affected relatives in 348 recognized candidate genes. Exact probabilities that affected relatives would share these rare variants were calculated, given pedigree structures, and corrected for the number of variants tested. Five novel and potentially damaging SNVs shared by affected distant relatives were found and confirmed by Sanger sequencing. One damaging SNV in CDH1, shared by three affected second cousins from a single family, attained statistical significance (P = 0.02 after correcting for multiple tests). Family-based designs such as the one used in this WES study offer important advantages for identifying genes likely to be causing complex and heterogeneous disorders.

Genome-Wide Linkage Scan for Prostate Cancer Susceptibility in Finland: Evidence for a Novel Locus on 2q37.3 and confirmation of signal on 17q21-q22

Genome‐wide linkage studies have been used to localize rare and highly penetrant prostate cancer (PRCA) susceptibility genes. Linkage studies performed in different ethnic backgrounds and populations have been somewhat disparate, resulting in multiple, often irreproducible signals because of genetic heterogeneity and high sporadic background of the disease. Our first genome‐wide linkage study and subsequent fine‐mapping study of Finnish hereditary prostate cancer (HPC) families gave evidence of linkage to one region. Here, we conducted subsequent scans with microsatellites and SNPs in a total of 69 Finnish HPC families. GENEHUNTER‐PLUS was used for parametric and nonparametric analyses. Our microsatellite genome‐wide linkage study provided evidence of linkage to 17q12‐q23, with a heterogeneity LOD (HLOD) score of 3.14 in a total of 54 of the 69 families. Genome‐wide SNP analysis of 59 of the 69 families gave a highest HLOD score of 3.40 at 2q37.3 under a dominant high penetrance model. Analyzing all 69 families by combining microsatellite and SNP maps also yielded HLOD scores of > 3.3 in two regions (2q37.3 and 17q12‐q21.3). These significant linkage peaks on chromosome 2 and 17 confirm previous linkage evidence of a locus on 17q from other populations and provide a basis for continued research into genetic factors involved in PRCA. Fine‐mapping analysis of these regions is ongoing and candidate genes at linked loci are currently under analysis.

Racial Disparities in the Association Between Variants on 8q24 and Prostate Cancer: A Systematic Review and Meta-Analysis

Recent studies implicate single nucleotide polymorphisms (SNPs) within the 8q24 region as a risk factor for prostate cancer (PCa). New developments suggest that 8q24 encodes regulators of the nearby MYC gene, a known oncogene. In order to better understand the implications of SNPs in this region, we performed meta-analyses, stratified by race, of seven SNPs and one microsatellite marker previously identified as risk loci on the 8q24 region of the genome. In addition, we reviewed the literature examining the possible associations between these polymorphisms and clinicopathological features of PCa. The results of the meta-analyses indicate that rs6983267, rs1447295, rs6983561, rs7837688, rs16901979, and DG8S737 are significantly associated with a higher risk for PCa for at least one race, whereas the variants rs13254738 and rs7000448 are not. The degree of association and frequency of the causative allele varied among men of different races. Though several studies have demonstrated an association between certain 8q24 SNPs and clinicopathological features of the disease, review of this topic revealed conflicting results.

Chromosomes 4 and 8 implicated in a genome wide SNP linkage scan of 762 prostate cancer families collected by the ICPCG.

In spite of intensive efforts, understanding of the genetic aspects of familial prostate cancer (PC) remains largely incomplete. In a previous microsatellite‐based linkage scan of 1,233 PC families, we identified suggestive evidence for linkage (i.e., LOD ≥ 1.86) at 5q12, 15q11, 17q21, 22q12, and two loci on 8p, with additional regions implicated in subsets of families defined by age at diagnosis, disease aggressiveness, or number of affected members.

SLCO1B1 Variants and Urine Arsenic Metabolites in the Strong Heart Family Study

Arsenic species patterns in urine are associated with risk for cancer and cardiovascular diseases. The organic anion transporter coded by the gene SLCO1B1 may transport arsenic species, but its association with arsenic metabolites in human urine has not yet been studied. The objective of this study is to evaluate associations of urine arsenic metabolites with variants in the candidate gene SLCO1B1 in adults from the Strong Heart Family Study. We estimated associations between % arsenic species biomarker traits and 5 single-nucleotide polymorphisms (SNPs) in the SLCO1B1 gene in 157 participants, assuming additive genetics. Linear regression models for each SNP accounted for kinships and were adjusted for sex, body mass index, and study center. The minor allele of rs1564370 was associated with lower %MMA (p = .0003) and higher %DMA (p = .0002), accounting for 8% of the variance for %MMA and 9% for %DMA. The rs1564370 minor allele homozygote frequency was 17% and the heterozygote frequency was 43%. The minor allele of rs2291075 was associated with lower %MMA (p = .0006) and higher %DMA (p = .0014), accounting for 7% of the variance for %MMA and 5% for %DMA. The frequency of rs2291075 minor allele homozygotes was 1% and of heterozygotes was 15%. Common variants in SLCO1B1 were associated with differences in arsenic metabolites in a preliminary candidate gene study. Replication of this finding in other populations and analyses with respect to disease outcomes are needed to determine whether this novel candidate gene is important for arsenic-associated disease risks.

Comparison of results from tests of association in unrelated individuals with uncollapsed and collapsed sequence variants using tiled regression

Tiled regression is an approach designed to determine the set of independent genetic variants that contribute to the variation of a quantitative trait in the presence of many highly correlated variants. In this study, we evaluate the statistical properties of the tiled regression method using the Genetic Analysis Workshop 17 data in unrelated individuals for traits Q1, Q2, and Q4. To increase the power to detect rare variants, we use two methods to collapse rare variants and compare the results with those from the uncollapsed data. In addition, we compare the tiled regression method to traditional tests of association with and without collapsed rare variants. The results show that collapsing rare variants generally improves the power to detect associations regardless of method, although only variants with the largest allelic effects could be detected. However, for traditional simple linear regression, the average estimated type I error is dependent on the trait and varies by about three orders of magnitude. The estimated type I error rate is stable for tiled regression across traits.

8q24 risk alleles and prostate cancer in African-Barbadian men.

African American men (AA) exhibit a disproportionate share of prostate cancer (PRCA) incidence, morbidity, and mortality. Several genetic association studies have implicated select 8q24 loci in PRCA risk in AA. The objective of this investigation is to evaluate the association between previously reported 8q24 risk alleles and PRCA in African‐Barbadian (AB) men known to have high rates of PRCA.

Genetic heterogeneity in Finnish hereditary prostate cancer using ordered subset analysis.

Prostate cancer (PrCa) is the most common male cancer in developed countries and the second most common cause of cancer death after lung cancer. We recently reported a genome-wide linkage scan in 69 Finnish hereditary PrCa (HPC) families, which replicated the HPC9 locus on 17q21-q22 and identified a locus on 2q37. The aim of this study was to identify and to detect other loci linked to HPC. Here we used ordered subset analysis (OSA), conditioned on nonparametric linkage to these loci to detect other loci linked to HPC in subsets of families, but not the overall sample. We analyzed the families based on their evidence for linkage to chromosome 2, chromosome 17 and a maximum score using the strongest evidence of linkage from either of the two loci. Significant linkage to a 5-cM linkage interval with a peak OSA nonparametric allele-sharing LOD score of 4.876 on Xq26.3-q27 (ΔLOD=3.193, empirical P=0.009) was observed in a subset of 41 families weakly linked to 2q37, overlapping the HPCX1 locus. Two peaks that were novel to the analysis combining linkage evidence from both primary loci were identified; 18q12.1-q12.2 (OSA LOD=2.541, ΔLOD=1.651, P=0.03) and 22q11.1-q11.21 (OSA LOD=2.395, ΔLOD=2.36, P=0.006), which is close to HPC6. Using OSA allows us to find additional loci linked to HPC in subsets of families, and underlines the complex genetic heterogeneity of HPC even in highly aggregated families.

Performance of random forests and logic regression methods using mini-exome sequence data

Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways.