Global geographic patterns of heterospecific pollen receipt help uncover potential ecological and evolutionary impacts across plant communities worldwide.

Species interactions are known to be key in driving patterns of biodiversity across the globe. Plant-plant interactions through heterospecific pollen (HP) transfer by their shared pollinators is common and has consequences for plant reproductive success and floral evolution, and thus has the potential to influence global patterns of biodiversity and plant community assembly. The literature on HP transfer is growing and it is therefore timely to review patterns and causes of among-species variation in HP receipt at a global scale, thus uncovering its potential contribution to global patterns of biodiversity. Here we analyzed published data on 245 species distributed across five continents to evaluate latitudinal and altitudinal patterns of HP receipt. We further analyzed the role of floral symmetry and evolutionary history in mediating patterns of HP receipt. Latitude and elevation affected the likelihood and intensity of HP receipt indicating that HP transfer increases in species-rich communities and in areas with high abundance of vertebrate pollinators. Floral symmetry and evolutionary history determined HP load size across plant communities worldwide. Overall, our results suggest that HP receipt may have the potential to contribute to global geographic patterns of plant diversity by imposing strong selective pressures in species-rich areas across the globe.

Cycloalkyl-aminomethylrhodamines: pH dependent photophysical properties tuned by cycloalkane ring size.

A series of fluorescent pH probes based on the spiro-cyclic rhodamine core, aminomethylrhodamines (AMR), was synthesized and the effect of cycloalkane ring size on the acid/base properties of the AMR system was explored. The study involved a series of rhodamine 6G (cAMR6G) and rhodamine B (cAMR) pH probes with cycloalkane ring sizes from C-3 to C-6 on the spiro-cyclic amino group. It is known that the pKa value of cycloalkylamines can be tuned by different ring sizes in accordance with the Baeyer ring strain theory. Smaller ring amines have lower pKa value, i.e., they are less basic, such that the relative order in cycloalkylamine basicity is: cyclohexyl > cyclopentyl > cyclobutyl > cyclopropyl. Herein, it was found that the pKa values of the cAMR and cAMR6G systems can also be predicted by Baeyer ring strain theory. The pKa values for the cAMR6G series were shown to be higher than the cAMR series by a value of approximately 1.

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.

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.

Identifying rare variants from exome scans: the GAW17 experience.

Genetic Analysis Workshop 17 (GAW17) provided a platform for evaluating existing statistical genetic methods and for developing novel methods to analyze rare variants that modulate complex traits. In this article, we present an overview of the 1000 Genomes Project exome data and simulated phenotype data that were distributed to GAW17 participants for analyses, the different issues addressed by the participants, and the process of preparation of manuscripts resulting from the discussions during the workshop.

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.

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.

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.

A search for non-chromosome 6 susceptibility loci contributing to rheumatoid arthritis.

We conducted a search for non-chromosome 6 genes that may increase risk for rheumatoid arthritis (RA). Our approach was to retrospectively ascertain three "extreme" subsamples from the North American Rheumatoid Arthritis Consortium. The three subsamples are: 1) RA cases who have two low-risk HLA-DRB1 alleles (N = 18), 2) RA cases who have two high-risk HLA-DRB1 alleles (N = 163), and 3) controls who have two low-risk HLA-DRB1 alleles (N = 652). We hypothesized that since Group 1's RA was likely due to non-HLA related risk factors, and because Group 3, by definition, is unaffected, comparing Group 1 with Group 2 and Group 1 with Group 3 would result in the identification of candidate susceptibility loci located outside of the MHC region. Accordingly, we restricted our search to the 21 non-chromosome 6 autosomes. The case-case comparison of Groups 1 and 2 resulted in the identification of 17 SNPs with allele frequencies that differed at p < 0.0001. The case-control comparison of Groups 1 and 3 identified 23 SNPs that differed in allele frequency at p < 0.0001. Eight of these SNPs (rs10498105, rs2398966, rs7664880, rs7447161, rs2793471, rs2611279, rs7967594, and rs742605) were common to both lists.

Power and false-positive rates for the restricted partition method (RPM) in a large candidate gene data set.

Many phenotypes of public health importance (e.g., diabetes, coronary artery disease, major depression, obesity, and addictions to alcohol and nicotine) involve complex pathways of action. Interactions between genetic variants or between genetic variants and environmental factors likely play important roles in the functioning of these pathways. Unfortunately, complex interacting systems are likely to have important interacting factors that may not readily reveal themselves to univariate analyses. Instead, detecting the role of some of these factors may require analyses that are sensitive to interaction effects.In this study, we evaluate the sensitivity and specificity of the restricted partition method (RPM) to detect signals related to coronary artery disease in the Genetic Analysis Workshop 16 Problem 3 data using the 50,000 k candidate gene single-nucleotide polymorphism set. Power and false-positive rates were evaluated using the first 100 replicate datasets. This included an exploration of the utility of using of all genotyped family members compared with selecting one member per family.

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.

Genome-wide association and replication studies identify four variants associated with prostate cancer susceptibility.

We report a prostate cancer genome-wide association follow-on study. We discovered four variants associated with susceptibility to prostate cancer in several European populations: rs10934853[A] (OR = 1.12, P = 2.9 x 10(-10)) on 3q21.3; two moderately correlated (r2 = 0.07) variants, rs16902094[G] (OR = 1.21, P = 6.2 x 10(-15)) and rs445114[T] (OR = 1.14, P = 4.7 x 10(-10)), on 8q24.21; and rs8102476[C] (OR = 1.12, P = 1.6 x 10(-11)) on 19q13.2. We also refined a previous association signal on 11q13 with the SNP rs11228565[A] (OR = 1.23, P = 6.7 x 10(-12)). In a multivariate analysis using 22 prostate cancer risk variants typed in the Icelandic population, we estimated that carriers in the top 1.3% of the risk distribution are at a 2.5 times greater risk of developing the disease than members of the general population.

DICER1 mutations in familial pleuropulmonary blastoma.

Pleuropulmonary blastoma (PPB) is a rare pediatric lung tumor that is often part of an inherited cancer syndrome. PPBs consist of mesenchymal cells that are susceptible to malignant transformation and cysts lined by epithelial cells. In a subset of patients, overgrowth of the cysts by mesenchymal cells leads to sarcoma formation. Here, we show that 11 multiplex PPB families harbor heterozygous germline mutations in DICER1, a gene encoding an endoribonuclease critical to the generation of small noncoding regulatory RNAs. Expression of DICER1 protein was undetectable in the epithelial component of PPB tumors but was retained in the malignant mesenchyme (sarcoma). We hypothesize that loss of DICER1 in the epithelium of the developing lung alters the regulation of diffusible factors that promote mesenchymal proliferation.

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.

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

BACKGROUND: 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. METHODS: Nine previously implicated SNPs in six cell cycle genes were evaluated in a European-American cohort of 186 patients with advanced PCa and 222 cancer-free controls. All patients received hormone ablation and had either a PSA>50 ng/ml or documented metastatic disease. Controls were all 75 years of age or older, had a negative DRE and had a PSA<4.0 ng/ml. All genotypes were determined using Pyrosequencing assays. RESULTS: One of nine (CDKN1A c10791t) was statistically different (P<0.05) and an additional two of nine (CCND1 a870g and MDM2 tSNP309g) approached significance (P<0.1). Analysis of genotypes revealed that presence of at least one copy of the t allele of MDM2 tSNP309g was associated with an increased risk of advanced PCa (OR 2.26: 95% CI=1.15-4.46) which was particularly strong in androgen-independent disease (OR 2.28: 95% CI=1.01-5.12) and younger age of diagnosis (OR 2.61: 95% CI=1.05-6.46). CONCLUSION: These results suggest that in a European-American population, SNPs within cell cycle genes are promising markers for aggressive PCa. Larger studies will be needed to confirm these findings.

Using linkage and association to identify and model genetic effects: summary of GAW15 Group 4.

Group 4 at Genetic Analysis Workshop 15 focused on methods that exploited both linkage and association information to map disease loci. All contributions considered the dichotomous trait of rheumatoid arthritis, using either affected sibpairs and/or unrelated controls. While one contribution investigated linkage and association approaches separately in genome-wide analyses, the remaining others focused on joint linkage and association methods in specific genomic regions. The latter contributions proposed new methods and/or examined existing methods that addressed whether one or more polymorphisms partially or fully explained a linkage signal, particularly the methods proposed by Li et al. that are implemented in the computer program Linkage and Association Modeling in Pedigrees (LAMP). Using simulated SNP data under linkage peaks, several contributions found that existing family-based association approaches such as those of Martin et al. and Lake et al. had power similar to LAMP and to several methods proposed by the contributors for testing that a single nucleotide polymorphism partially explains a linkage peak. In evaluating methods for identifying if a polymorphism or a set of polymorphisms fully accounted for a linkage signal, several contributions found that it was important to understand that these methods may be subject to low power in some situations and thus, a non-significant result was not necessarily indicative of the polymorphism(s) being fully responsible for the linkage signal. Finally, modeling the disease using association evidence conditional on linkage may improve understanding of the etiology of disease.

Gene by environment interactions.

This paper summarizes the contributions of group 8 to the Genetic Analysis Workshop 15. Group 8 focused on ways to address the possibility that genetic and environmental effects on phenotype may not be independent, but instead may interact in ways that could play important roles in determining phenotype. Among the eight contributors to this group, all three data sets (expression data, rheumatoid arthritis data, and simulated data) were analyzed. Contributions to this section fell into the two broad categories of refining the data (e.g. stratifying or weighting based on a covariate value) and explicitly modeling the interactions. The contributions also illustrate that there are at least two possible goals for such studies. One goal is simply to identify factors contributing to phenotype in the presence of interactions that might mask the signal to univariate methods. A related but distinct goal is to characterize an interaction (e.g. to determine if the interaction is significant).