Why does the X chromosome lag behind autosomes in GWAS findings?

The X-chromosome is among the largest human chromosomes. It differs from autosomes by a number of important features including hemizygosity in males, an almost complete inactivation of one copy in females, and unique patterns of recombination. We used data from the Catalog of Published Genome Wide Association Studies to compare densities of the GWAS-detected SNPs on the X-chromosome and autosomes. The density of GWAS-detected SNPs on the X-chromosome is 6-fold lower compared to the density of the GWAS-detected SNPs on autosomes. Differences between the X-chromosome and autosomes cannot be explained by differences in the overall SNP density, lower X-chromosome coverage by genotyping platforms or low call rate of X-chromosomal SNPs. Similar differences in the density of GWAS-detected SNPs were found in female-only GWASs (e.g. ovarian cancer GWASs). We hypothesized that the lower density of GWAS-detected SNPs on the X-chromosome compared to autosomes is not a result of a methodological bias, e.g. differences in coverage or call rates, but has a real underlying biological reason-a lower density of functional SNPs on the X-chromosome versus autosomes. This hypothesis is supported by the observation that (i) the overall SNP density of X-chromosome is lower compared to the SNP density on autosomes and that (ii) the density of genic SNPs on the X-chromosome is lower compared to autosomes while densities of intergenic SNPs are similar.

Genome-wide interaction analysis identified low-frequency variants with sex disparity in lung cancer risk.

Differences by sex in lung cancer incidence and mortality have been reported which cannot be fully explained by sex differences in smoking behavior, implying existence of genetic and molecular basis for sex disparity in lung cancer development. However, the information about sex dimorphism in lung cancer risk is quite limited despite the great success in lung cancer association studies. By adopting a stringent two-stage analysis strategy, we performed a genome-wide gene-sex interaction analysis using genotypes from a lung cancer cohort including ~ 47 000 individuals with European ancestry. Three low-frequency variants (minor allele frequency < 0.05), rs17662871 [odds ratio (OR) = 0.71, P = 4.29×10-8); rs79942605 (OR = 2.17, P = 2.81×10-8) and rs208908 (OR = 0.70, P = 4.54×10-8) were identified with different risk effect of lung cancer between men and women. Further expression quantitative trait loci and functional annotation analysis suggested rs208908 affects lung cancer risk through differential regulation of Coxsackie virus and adenovirus receptor gene expression in lung tissues between men and women. Our study is one of the first studies to provide novel insights about the genetic and molecular basis for sex disparity in lung cancer development.

Identification of genetically predicted DNA methylation markers associated with non-small cell lung cancer risk among 34,964 cases and 448,579 controls.

BACKGROUND: Although the associations between genetic variations and lung cancer risk have been explored, the epigenetic consequences of DNA methylation in lung cancer development are largely unknown. Here, the genetically predicted DNA methylation markers associated with non-small cell lung cancer (NSCLC) risk by a two-stage case-control design were investigated. METHODS: The genetic prediction models for methylation levels based on genetic and methylation data of 1595 subjects from the Framingham Heart Study were established. The prediction models were applied to a fixed-effect meta-analysis of screening data sets with 27,120 NSCLC cases and 27,355 controls to identify the methylation markers, which were then replicated in independent data sets with 7844 lung cancer cases and 421,224 controls. Also performed was a multi-omics functional annotation for the identified CpGs by integrating genomics, epigenomics, and transcriptomics and investigation of the potential regulation pathways. RESULTS: Of the 29,894 CpG sites passing the quality control, 39 CpGs associated with NSCLC risk (Bonferroni-corrected p ≤ 1.67 × 10-6 ) were originally identified. Of these, 16 CpGs remained significant in the validation stage (Bonferroni-corrected p ≤ 1.28 × 10-3 ), including four novel CpGs. Multi-omics functional annotation showed nine of 16 CpGs were potentially functional biomarkers for NSCLC risk. Thirty-five genes within a 1-Mb window of 12 CpGs that might be involved in regulatory pathways of NSCLC risk were identified. CONCLUSIONS: Sixteen promising DNA methylation markers associated with NSCLC were identified. Changes of the methylation level at these CpGs might influence the development of NSCLC by regulating the expression of genes nearby. PLAIN LANGUAGE SUMMARY: The epigenetic consequences of DNA methylation in lung cancer development are still largely unknown. This study used summary data of large-scale genome-wide association studies to investigate the associations between genetically predicted levels of methylation biomarkers and non-small cell lung cancer risk at the first time. This study looked at how well larotrectinib worked in adult patients with sarcomas caused by TRK fusion proteins. These findings will provide a unique insight into the epigenetic susceptibility mechanisms of lung cancer.

Effects of in ovo feeding of vitamin E or vitamin C on egg hatchability, performance, carcass traits and immunity in broiler chickens.

The effect of in ovo feeding of different levels of vitamins C and E on egg hatchability, immune response, growth and carcass traits of broiler chickens were investigated. A total of 672 fertilized eggs were assigned to one of eight experimental groups having three replicates with 28 eggs as follows: (1) negative control (not injected); (2) positive control (injected with 0.2 mL deionized water); (3) vitamin C at 1 mg; (4) vitamin C at 3 mg; (5) vitamin C at 6 mg; (6) vitamin E at 0.5 IU; (7) vitamin E at 0.75 IU; and (8) vitamin E at 1.0 IU. At the end of incubation, the number of chicks hatched, and their individual body weight were recorded. Among hatched birds, a total of 240 mixed chicks were randomly selected (30 subject per group equally shared in three pen floors). Chicks were vaccinated against Avian Influenza, Gumboro, Bronchitis, and Newcastle disease virus. Performance parameters were weekly evaluated until 42 days of age. At days 28 and 42, broiler serum and spleen and Bursa of Fabricius relative weight were assessed as well as on day 42 the carcass traits. From results, in ovo injection with 3 mg of vitamin C or 0.75 IU of vitamin E, increased significantly (p < .05) the embryos hatchability when compared to the negative control. However, body weight at hatch and growth performance parameters showed no differences among treatments. Similarly, in ovo concentrations of vitamins C or E showed no differences on carcass traits, immunity-related organs weight or immune response for anti-Newcastle disease hemagglutination-inhibition and total immunoglobulins against sheep red blood cells (SRBC) when compared to the control groups. Based on findings, it can be concluded that in ovo feeding vitamins E and C supported positively chicken embryos hatchability demonstrating the key-role as antioxidant agents; however, further studies are currently being evaluated.

The effects of peppermint (Mentha piperita L.) and chicory (Cichorium intybus L.) in comparison with a prebiotic on productive performance, blood constituents, immunity and intestinal microflora in broiler chickens.

A total of 320 one-day-old broiler chickens were used in a 42-day feeding trial to evaluate the effects of peppermint (Mentha piperita L.) and chicory (Cichorium intybus L.) in comparison with a prebiotic on-growth performance, blood constitutes, immunity and intestinal microflora. The dietary treatments were as follows: basal diet (control); control + prebiotic (Fermacto™); control + 0.1% peppermint; control + 0.1% chicory, respectively. A significant (p < 0.05) body weight gain and feed intake was found at 21 and 42 days of growth period in broilers fed diet supplemented with 0.1% chicory compared with other groups. Feeding of prebiotic or chicory led to higher (p < 0.05) feed intake. Chickens fed control diet had higher (p < 0.05) abdominal fat compared with the other groups. Serum blood constituents indicated that broilers fed prebiotic or supplemented with peppermint or chicory had reduced (p < 0.05) levels of cholesterol, triglycerides and low-density lipoprotein than control group. Immunity-related parameters showed that chicken fed chicory had lower (p < 0.05) heterophil-to-lymphocyte ratio compared with the other groups. Intestinal microflora revealed that chickens fed prebiotic or herbals had higher count of Lactobacillus and lower E. coli than control. Thus, it can be concluded that broiler dietary supplementation with prebiotic or chicory can improve performance supporting positively health status.

SNP characteristics and validation success in genome wide association studies.

Genome wide association studies (GWASs) have identified tens of thousands of single nucleotide polymorphisms (SNPs) associated with human diseases and characteristics. A significant fraction of GWAS findings can be false positives. The gold standard for true positives is an independent validation. The goal of this study was to identify SNP features associated with validation success. Summary statistics from the Catalog of Published GWASs were used in the analysis. Since our goal was an analysis of reproducibility, we focused on the diseases/phenotypes targeted by at least 10 GWASs. GWASs were arranged in discovery-validation pairs based on the time of publication, with the discovery GWAS published before validation. We used four definitions of the validation success that differ by stringency. Associations of SNP features with validation success were consistent across the definitions. The strongest predictor of SNP validation was the level of statistical significance in the discovery GWAS. The magnitude of the effect size was associated with validation success in a non-linear manner. SNPs with risk allele frequencies in the range 30-70% showed a higher validation success rate compared to rarer or more common SNPs. Missense, 5'UTR, stop gained, and SNPs located in transcription factor binding sites had a higher validation success rate compared to intergenic, intronic and synonymous SNPs. There was a positive association between validation success and the level of evolutionary conservation of the sites. In addition, validation success was higher when discovery and validation GWASs targeted the same ethnicity. All predictors of validation success remained significant in a multivariate logistic regression model indicating their independent contribution. To conclude, we identified SNP features predicting validation success of GWAS hits. These features can be used to select SNPs for validation and downstream functional studies.

Effect of dietary flaxseed meal supplemented with dried tomato and grape pomace on performance traits and antioxidant status of laying hens.

This study was carried out to determine the effect of dietary flaxseed meal (FSM) supplemented with dried tomato pomace (DTP) and dried grape pomace (DGP) on performance, egg quality, biochemical parameters traits and antioxidant status of laying hens. Birds (1825 ± 87 g of body weight) were divided into 12 dietary groups with six replicates per group (eight birds per replicate), under a completely randomized design with factorial arrangement 2 × 3 × 2 consisted of two levels of DTP (0 and 15%), three FSM levels (0, 4 and 8%) and two levels of DGP (0 and 5%). As a result of this study, there were no significant differences in egg production and weight as well in feed conversion ratio (FCR) among treatments (p > 0.05). Feeding of DGP reduced significantly feed intake and egg mass when compared to control group (p < 0.05). There was no effect (p > 0.05) of dietary treatment on shell thickness and strength, shape index, Haugh unit and egg specific gravity. Hens consuming 15% DTP and 5% DGP revealed a significantly higher yolk color compared to the other dietary treatments (p < 0.05). Moreover, there was no difference among dietary treatments in terms of serum low-density lipoprotein cholesterol (LDL) and high-density lipoprotein cholesterol (HDL) cholesterol, atherogenic index, triglycerides, total cholesterol levels (p > 0.05). Serum antioxidant parameters as glutathione peroxidase (GSH-Px) activity, malondialdehyde (MDA), total superoxide dismutase (TSOD) and total antioxidant capacity (T-AOC) were not influenced by treatments (p > 0.05). Based on findings, FSM and DTP supplements did not significantly impact most of hens' performance indicators and egg quality parameters, whereas significant improvements were observed by feeding of 15% DTP and 5% DGP on egg traits, in particular on egg-yolk color that plays a key-role in consumer's choice. However, the supplementation of FSM and DTP or DGP even in laying hen diet is still controversial and further research is needed.

Effects of different levels of dietary black cumin (Nigella sativa L.) and fenugreek (Trigonella foenum-graecum L.) and their combination on productive traits, selected blood constituents, microbiota and immunity of broilers.

The effects of inclusion of powdered seeds of black cumin (B) (Nigella sativa L.) and fenugreek (F) (Trigonella foenum-graecum L.) on productive traits, selected blood constituents, microbiota and immunity of broilers were studied. A total of 648 day-old chicks were randomly assigned to nine treatments, with four pen replicates, each with 18 birds, including three levels of B seed powder (BSP; 0, 5 or 10 g/kg) and three levels of F seed powder (FSP; 0, 5 or 10 g/kg) in a 3 × 3 factorial arrangement. Neither powder affected feed intake. The FSP increased (p = 0.048) feed conversion ratio (FCR), but decreased daily BW gain (p = 0.02) between days 0 and 21, while BSP increased daily gain between days 22 and 42 and overall (both p = 0.005). Abdominal fat was decreased (p = 0.003) by BSP. Blood constituents were unaffected by either powder, but ileal Escherichia coli were decreased (p = 0.039) at day 42. The BSP increased a range of immunological titers, where BSP affected positively the measured variables. The interactions between BSP and FSP, specifically on broiler carcass cuts, suggested that where BSP is included at 10 g/kg, the inclusion of FSP at the same level may provide no additional benefit. Thus, while either powder could be included separately, the co-inclusion of both at 10 g/kg is not recommended.

Human genes differ by their UV sensitivity estimated through analysis of UV-induced silent mutations in melanoma.

We hypothesized that human genes differ by their sensitivity to ultraviolet (UV) exposure. We used somatic mutations detected by genome-wide screens in melanoma and reported in the Catalog Of Somatic Mutations In Cancer. As a measure of UV sensitivity, we used the number of silent mutations generated by C>T transitions in pyrimidine dimers of a given transcript divided by the number of potential sites for this type of mutations in the transcript. We found that human genes varied by UV sensitivity by two orders of magnitude. We noted that the melanoma-associated tumor suppressor gene CDKN2A was among the top five most UV-sensitive genes in the human genome. Melanoma driver genes have a higher UV-sensitivity compared with other genes in the human genome. The difference was more prominent for tumor suppressors compared with oncogene. The results of this study suggest that differential sensitivity of human transcripts to UV light may explain melanoma specificity of some driver genes. Practical significance of the study relates to the fact that differences in UV sensitivity among human genes need to be taken into consideration whereas predicting melanoma-associated genes by the number of somatic mutations detected in a given gene.

Tumor somatic mutations also existing as germline polymorphisms may help to identify functional SNPs from genome-wide association studies.

We hypothesized that a joint analysis of cancer risk-associated single-nucleotide polymorphism (SNP) and somatic mutations in tumor samples can predict functional and potentially causal SNPs from GWASs. We used mutations reported in the Catalog of Somatic Mutations in Cancer (COSMIC). Confirmed somatic mutations were subdivided into two groups: (1) mutations reported as SNPs, which we call mutational/SNPs and (2) somatic mutations that are not reported as SNPs, which we call mutational/noSNPs. It is generally accepted that the number of times a somatic mutation is reported in COSMIC correlates with its selective advantage to tumors, with more frequently reported mutations being more functional and providing a stronger selective advantage to the tumor cell. We found that mutations reported ≥10 times in COSMIC-frequent mutational/SNPs (fmSNPs) are likely to be functional. We identified 12 cancer risk-associated SNPs reported in the Catalog of published GWASs at least 10 times as confirmed somatic mutations and therefore deemed to be functional. Additionally, we have identified 42 SNPs that are tightly linked (R2 ≥ 0.8) to SNPs reported in the Catalog of published GWASs as cancer risk associated and that are also reported as fmSNPs. As a result, 54 candidate functional/potentially causal cancer risk associated SNPs were identified. We found that fmSNPs are more likely to be located in evolutionarily conserved regions compared with cancer risk associated SNPs that are not fmSNPs. We also found that fmSNPs also underwent positive selection, which can explain why they exist as population polymorphisms.

Downstream targets of GWAS-detected genes for breast, lung, and prostate and colon cancer converge to G1/S transition pathway.

Genome-wide association studies (GWASs) identified over 500 single nucleotide polymorphisms (SNPs) influencing cancer risk. It is logical to expect the cancer-associated genes to cluster in pathways directly involved in carcinogenesis, e.g. cell cycle. Nevertheless, analyses of the GWAS-detected cancer risk genes usually show no or weak enrichment by known cancer genes.We hypothesized that GWAS-detected cancer risk-associated genes function as upstream regulators of the genes directly involved in carcinogenesis. We have analyzed four common cancers: breast, colon, lung, and prostate. To identify downstream targets of GWAS-detected cancer risk genes we used MedScan, which is a text mining tool offered by PathwayStudio. We also used data on protein/protein interactions reported by BioGRID database. Among all identified targets we have selected common downstream targets. A gene was considered a common downstream target if it was a downstream target for at least three GWAS-detected genes for a given cancer type. Common downstream targets were identified separately for each cancer type. We found that common downstream targets for all four cancer types were enriched by cell cycle genes, more specifically, the genes involved in G1/S transition. Common downstream targets for bipolar disorder, Crohn's disease, and type 2 diabetes did not show G1/S transition enrichment.The results of this analysis suggest that many cancer risk genes function as upstream regulators of the genes directly involved in G1/S transition and exert their risk effects by reducing threshold for G1/S transition, elevating the background level of cell proliferation and cancer risk.

SNP eQTL status and eQTL density in the adjacent region of the SNP are associated with its statistical significance in GWA studies.

BACKGROUND: Over the relatively short history of Genome Wide Association Studies (GWASs), hundreds of GWASs have been published and thousands of disease risk-associated SNPs have been identified. Summary statistics from the conducted GWASs are often available and can be used to identify SNP features associated with the level of GWAS statistical significance. Those features could be used to select SNPs from gray zones (SNPs that are nominally significant but do not reach the genome-wide level of significance) for targeted analyses. METHODS: We used summary statistics from recently published breast and lung cancer and scleroderma GWASs to explore the association between the level of the GWAS statistical significance and the expression quantitative trait loci (eQTL) status of the SNP. Data from the Genotype-Tissue Expression Project (GTEx) were used to identify eQTL SNPs. RESULTS: We found that SNPs reported as eQTLs were more significant in GWAS (higher -log10p) regardless of the tissue specificity of the eQTL. Pan-tissue eQTLs (those reported as eQTLs in multiple tissues) tended to be more significant in the GWAS compared to those reported as eQTL in only one tissue type. eQTL density in the ±5 kb adjacent region of a given SNP was also positively associated with the level of GWAS statistical significance regardless of the eQTL status of the SNP. We found that SNPs located in the regions of high eQTL density were more likely to be located in regulatory elements (transcription factor or miRNA binding sites). When SNPs were stratified by the level of statistical significance, the proportion of eQTLs was positively associated with the mean level of statistical significance in the group. The association curve reaches a plateau around -log10p ≈ 5. The observed associations suggest that quasi-significant SNPs (10- 5 < p < 5 × 10- 8) and SNPs at the genome wide level of statistical significance (p < 5 × 10- 8) may have a similar proportions of risk associated SNPs. CONCLUSIONS: The results of this study indicate that the SNP's eQTL status, as well as eQTL density in the adjacent region are positively associated with the level of statistical significance of the SNP in GWAS.

Gene characteristics predicting missense, nonsense and frameshift mutations in tumor samples.

BACKGROUND: Because driver mutations provide selective advantage to the mutant clone, they tend to occur at a higher frequency in tumor samples compared to selectively neutral (passenger) mutations. However, mutation frequency alone is insufficient to identify cancer genes because mutability is influenced by many gene characteristics, such as size, nucleotide composition, etc. The goal of this study was to identify gene characteristics associated with the frequency of somatic mutations in the gene in tumor samples. RESULTS: We used data on somatic mutations detected by genome wide screens from the Catalog of Somatic Mutations in Cancer (COSMIC). Gene size, nucleotide composition, expression level of the gene, relative replication time in the cell cycle, level of evolutionary conservation and other gene characteristics (totaling 11) were used as predictors of the number of somatic mutations. We applied stepwise multiple linear regression to predict the number of mutations per gene. Because missense, nonsense, and frameshift mutations are associated with different sets of gene characteristics, they were modeled separately. Gene characteristics explain 88% of the variation in the number of missense, 40% of nonsense, and 23% of frameshift mutations. Comparisons of the observed and expected numbers of mutations identified genes with a higher than expected number of mutations- positive outliers. Many of these are known driver genes. A number of novel candidate driver genes was also identified. CONCLUSIONS: By comparing the observed and predicted number of mutations in a gene, we have identified known cancer-associated genes as well as 111 novel cancer associated genes. We also showed that adding the number of silent mutations per gene reported by genome/exome wide screens across all cancer type (COSMIC data) as a predictor substantially exceeds predicting accuracy of the most popular cancer gene predicting tool - MutsigCV.

Allelic Spectra of Risk SNPs Are Different for Environment/Lifestyle Dependent versus Independent Diseases.

Genome-wide association studies (GWAS) have generated sufficient data to assess the role of selection in shaping allelic diversity of disease-associated SNPs. Negative selection against disease risk variants is expected to reduce their frequencies making them overrepresented in the group of minor (<50%) alleles. Indeed, we found that the overall proportion of risk alleles was higher among alleles with frequency <50% (minor alleles) compared to that in the group of major alleles. We hypothesized that negative selection may have different effects on environment (or lifestyle)-dependent versus environment (or lifestyle)-independent diseases. We used an environment/lifestyle index (ELI) to assess influence of environmental/lifestyle factors on disease etiology. ELI was defined as the number of publications mentioning "environment" or "lifestyle" AND disease per 1,000 disease-mentioning publications. We found that the frequency distributions of the risk alleles for the diseases with strong environmental/lifestyle components follow the distribution expected under a selectively neutral model, while frequency distributions of the risk alleles for the diseases with weak environmental/lifestyle influences is shifted to the lower values indicating effects of negative selection. We hypothesized that previously selectively neutral variants become risk alleles when environment changes. The hypothesis of ancestrally neutral, currently disadvantageous risk-associated alleles predicts that the distribution of risk alleles for the environment/lifestyle dependent diseases will follow a neutral model since natural selection has not had enough time to influence allele frequencies. The results of our analysis suggest that prediction of SNP functionality based on the level of evolutionary conservation may not be useful for SNPs associated with environment/lifestyle dependent diseases.

Ancestry inference using principal component analysis and spatial analysis: a distance-based analysis to account for population substructure.

BACKGROUND: Accurate inference of genetic ancestry is of fundamental interest to many biomedical, forensic, and anthropological research areas. Genetic ancestry memberships may relate to genetic disease risks. In a genome association study, failing to account for differences in genetic ancestry between cases and controls may also lead to false-positive results. Although a number of strategies for inferring and taking into account the confounding effects of genetic ancestry are available, applying them to large studies (tens thousands samples) is challenging. The goal of this study is to develop an approach for inferring genetic ancestry of samples with unknown ancestry among closely related populations and to provide accurate estimates of ancestry for application to large-scale studies. METHODS: In this study we developed a novel distance-based approach, Ancestry Inference using Principal component analysis and Spatial analysis (AIPS) that incorporates an Inverse Distance Weighted (IDW) interpolation method from spatial analysis to assign individuals to population memberships. RESULTS: We demonstrate the benefits of AIPS in analyzing population substructure, specifically related to the four most commonly used tools EIGENSTRAT, STRUCTURE, fastSTRUCTURE, and ADMIXTURE using genotype data from various intra-European panels and European-Americans. While the aforementioned commonly used tools performed poorly in inferring ancestry from a large number of subpopulations, AIPS accurately distinguished variations between and within subpopulations. CONCLUSIONS: Our results show that AIPS can be applied to large-scale data sets to discriminate the modest variability among intra-continental populations as well as for characterizing inter-continental variation. The method we developed will protect against spurious associations when mapping the genetic basis of a disease. Our approach is more accurate and computationally efficient method for inferring genetic ancestry in the large-scale genetic studies.

FastPop: a rapid principal component derived method to infer intercontinental ancestry using genetic data.

BACKGROUND: Identifying subpopulations within a study and inferring intercontinental ancestry of the samples are important steps in genome wide association studies. Two software packages are widely used in analysis of substructure: Structure and Eigenstrat. Structure assigns each individual to a population by using a Bayesian method with multiple tuning parameters. It requires considerable computational time when dealing with thousands of samples and lacks the ability to create scores that could be used as covariates. Eigenstrat uses a principal component analysis method to model all sources of sampling variation. However, it does not readily provide information directly relevant to ancestral origin; the eigenvectors generated by Eigenstrat are sample specific and thus cannot be generalized to other individuals. RESULTS: We developed FastPop, an efficient R package that fills the gap between Structure and Eigenstrat. It can: 1, generate PCA scores that identify ancestral origins and can be used for multiple studies; 2, infer ancestry information for data arising from two or more intercontinental origins. We demonstrate the use of FastPop using 2318 SNP markers selected from the genome based on high variability among European, Asian and West African (African) populations. We conducted an analysis of 505 Hapmap samples with European, African or Asian ancestry along with 19661 additional samples of unknown ancestry. The results from FastPop are highly consistent with those obtained by Structure across the 19661 samples we studied. The correlations of the results between FastPop and Structure are 0.99, 0.97 and 0.99 for European, African and Asian ancestry scores, respectively. Compared with Structure, FastPop is more efficient as it finished ancestry inference for 19661 samples in 16 min compared with 21-24 h required by Structure. FastPop also provided scores based on SNP weights so the scores of reference population can be applied to other studies provided the same set of markers are used. We also present application of the method for studying four continental populations (European, Asian, African, and Native American). CONCLUSIONS: We developed an algorithm that can infer ancestries on data involving two or more intercontinental origins. It is efficient for analyzing large datasets. Additionally the PCA derived scores can be applied to multiple data sets to ensure the same ancestry analysis is applied to all studies.

Implementation of a Molecular Tumor Board: The Impact on Treatment Decisions for 35 Patients Evaluated at Dartmouth-Hitchcock Medical Center.

BACKGROUND: Although genetic profiling of tumors is a potentially powerful tool to predict drug sensitivity and resistance, its routine use has been limited because clinicians are often unfamiliar with interpretation and incorporation of the information into practice. We established a Molecular Tumor Board (MTB) to interpret individual patients' tumor genetic profiles and provide treatment recommendations. PATIENTS AND METHODS: DNA from tumor specimens was sequenced in a Clinical Laboratory Improvement Amendments-certified laboratory to identify coding mutations in a 50-gene panel (n = 34) or a 255-gene panel (n = 1). Cases were evaluated by a multidisciplinary MTB that included pathologists, oncologists, hematologists, basic scientists, and genetic counselors. RESULTS: During the first year, 35 cases were evaluated by the MTB, with 32 presented for recommendations on targeted therapies, and 3 referred for potential germline mutations. In 56.3% of cases, MTB recommended treatment with a targeted agent based on evaluation of tumor genetic profile and treatment history. Four patients (12.5%) were subsequently treated with a MTB-recommended targeted therapy; 3 of the 4 patients remain on therapy, 2 of whom experienced clinical benefit lasting >10 months. CONCLUSION: For the majority of cases evaluated, the MTB was able to provide treatment recommendations based on targetable genetic alterations. The most common reasons that MTB-recommended therapy was not administered stemmed from patient preferences and genetic profiling at either very early or very late stages of disease; lack of drug access was rarely encountered. Increasing awareness of molecular profiling and targeted therapies by both clinicians and patients will improve acceptance and adherence to treatments that could significantly improve outcomes. IMPLICATIONS FOR PRACTICE: Case evaluation by a multidisciplinary Molecular Tumor Board (MTB) is critical to benefit from individualized genetic data and maximize clinical impact. MTB recommendations shaped treatment options for the majority of cases evaluated. In the few patients treated with MTB-recommended therapy, disease outcomes were positive and support genetically informed treatment.

Genes with a large intronic burden show greater evolutionary conservation on the protein level.

BACKGROUND: The existence of introns in eukaryotic genes is believed to provide an evolutionary advantage by increasing protein diversity through exon shuffling and alternative splicing. However, this eukaryotic feature is associated with the necessity of exclusion of intronic sequences, which requires considerable energy expenditure and can lead to splicing errors. The relationship between intronic burden and evolution is poorly understood. The goal of this study was to analyze the relationship between the intronic burden and the level of evolutionary conservation of the gene. RESULTS: We found a positive correlation between the level of evolutionary conservation of a gene and its intronic burden. The level of evolutionary conservation was estimated using the conservation index (CI). The CI value was determined on the basis of the most distant ortholog of the human protein sequence and ranged from 0 (the gene was unique to the human genome) to 9 (an ortholog of the human gene was detected in plants). In multivariable model, both the number of introns and total intron size remained significant predictors of CI. We also found that the number of alternative splice variants was positively correlated with CI.The expression level of a gene was negatively correlated with the number of introns and total size of intronic region. Genes with a greater intronic burden had lower density of missense and nonsense mutations in the coding regions of the gene, which suggests that they are under a stronger pressure from purifying selection. CONCLUSIONS: We identified a positive association between intronic burden and CI. One of the possible explanations of this is the idea of a cost-benefits balance. Evolutionarily conserved (functionally important) genes can "afford" the negative consequences of maintaining multiple introns because these consequences are outweighed by the benefit of maintaining the gene. Evolutionarily conserved and functionally important genes may use introns to create novel splice variants to tune the gene function to developmental stage and tissue type.

How to get the most from microarray data: advice from reverse genomics.

BACKGROUND: Whole-genome profiling of gene expression is a powerful tool for identifying cancer-associated genes. Genes differentially expressed between normal and tumorous tissues are usually considered to be cancer associated. We recently demonstrated that the analysis of interindividual variation in gene expression can be useful for identifying cancer associated genes. The goal of this study was to identify the best microarray data-derived predictor of known cancer associated genes. RESULTS: We found that the traditional approach of identifying cancer genes--identifying differentially expressed genes--is not very efficient. The analysis of interindividual variation of gene expression in tumor samples identifies cancer-associated genes more effectively. The results were consistent across 4 major types of cancer: breast, colorectal, lung, and prostate. We used recently reported cancer-associated genes (2011-2012) for validation and found that novel cancer-associated genes can be best identified by elevated variance of the gene expression in tumor samples. CONCLUSIONS: The observation that the high interindividual variation of gene expression in tumor tissues is the best predictor of cancer-associated genes is likely a result of tumor heterogeneity on gene level. Computer simulation demonstrates that in the case of heterogeneity, an assessment of variance in tumors provides a better identification of cancer genes than does the comparison of the expression in normal and tumor tissues. Our results thus challenge the current paradigm that comparing the mean expression between normal and tumorous tissues is the best approach to identifying cancer-associated genes; we found that the high interindividual variation in expression is a better approach, and that using variation would improve our chances of identifying cancer-associated genes.

SNP characteristics predict replication success in association studies.

Successful independent replication is the most direct approach for distinguishing real genotype-disease associations from false discoveries in genome-wide association studies (GWAS). Selecting SNPs for replication has been primarily based on P values from the discovery stage, although additional characteristics of SNPs may be used to improve replication success. We used disease-associated SNPs from more than 2,000 published GWASs to identify predictors of SNP reproducibility. SNP reproducibility was defined as a proportion of successful replications among all replication attempts. The study reporting association for the first time was considered to be discovery and all consequent studies targeting the same phenotype replications. We found that -Log(P), where P is a P value from the discovery study, is the strongest predictor of the SNP reproducibility. Other significant predictors include type of the SNP (e.g., missense vs intronic SNPs) and minor allele frequency. Features of the genes linked to the disease-associated SNP also predict SNP reproducibility. Based on empirically defined rules, we developed a reproducibility score (RS) to predict SNP reproducibility independently of -Log(P). We used data from two lung cancer GWAS studies as well as recently reported disease-associated SNPs to validate RS. Minus Log(P) outperforms RS when the very top SNPs are selected, while RS works better with relaxed selection criteria. In conclusion, we propose an empirical model to predict SNP reproducibility, which can be used to select SNPs for validation and prioritization.