Investigative genetic genealogy: Current methods, knowledge and practice.

Investigative genetic genealogy (IGG) has emerged as a new, rapidly growing field of forensic science. We describe the process whereby dense SNP data, commonly comprising more than half a million markers, are employed to infer distant relationships. By distant we refer to degrees of relatedness exceeding that of first cousins. We review how methods of relationship matching and SNP analysis on an enlarged scale are used in a forensic setting to identify a suspect in a criminal investigation or a missing person. There is currently a strong need in forensic genetics not only to understand the underlying models to infer relatedness but also to fully explore the DNA technologies and data used in IGG. This review brings together many of the topics and examines their effectiveness and operational limits, while suggesting future directions for their forensic validation. We further investigated the methods used by the major direct-to-consumer (DTC) genetic ancestry testing companies as well as submitting a questionnaire where providers of forensic genetic genealogy summarized their operation/services. Although most of the DTC market, and genetic genealogy in general, has undisclosed, proprietary algorithms we review the current knowledge where information has been discussed and published more openly.

Non-CYP2D6 Variants Selected by a GWAS Improve the Prediction of Impaired Tamoxifen Metabolism in Patients with Breast Cancer.

A certain minimum plasma concentration of (Z)-endoxifen is presumably required for breast cancer patients to benefit from tamoxifen therapy. In this study, we searched for DNA variants that could aid in the prediction of risk for insufficient (Z)-endoxifen exposure. A metabolic ratio (MR) corresponding to the (Z)-endoxifen efficacy threshold level was adopted as a cutoff value for a genome-wide association study comprised of 287 breast cancer patients. Multivariate regression was used to preselect variables exhibiting an independent impact on the MR and develop models to predict below-threshold MR values. In total, 15 single-nucleotide polymorphisms (SNPs) were significantly associated with below-threshold MR values. The strongest association was with rs8138080 (WBP2NL). Two alternative models for MR prediction were developed. The predictive accuracy of Model 1, including rs7245, rs6950784, rs1320308, and the CYP2D6 genotype, was considerably higher than that of the CYP2D6 genotype alone (AUC 0.879 vs 0.758). Model 2, which was developed using the same three SNPs as for Model 1 plus rs8138080, appeared as an interesting alternative to the full CYP2D6 genotype testing. In conclusion, the four novel SNPs, tested alone or in combination with the CYP2D6 genotype, improved the prediction of impaired tamoxifen-to-endoxifen metabolism, potentially allowing for treatment optimization.

Differences between chronic lymphocytic leukaemia and small lymphocytic lymphoma cells by proteomic profiling and SNP microarray analysis.

The majority of malignant cells in chronic lymphocytic leukaemia (CLL) circulate in the peripheral blood whereas small lymphocytic lymphoma (SLL) cells reside in tissues. The aim of this study was to detect differences in chemokine receptor expression, DNA single nucleotide polymorphism (SNP) microarray analysis and proteomic profiling to help elucidate why the cells remain in their respective environments. We identified by flow cytometric studies of chemokine receptors and DNA SNP microarray analysis significant differences between cells from CLL and SLL patients. Proteomic analysis revealed two potential markers (m/z 3091 and 8707) to distinguish the two disorders. There was a significantly greater expression of leucocyte trafficking receptor CXCR3 (CD183) and migration and homing receptor CXCR4 (CD184), and significantly lower expression of cell adhesion molecule integrin α4 chain (CD49d), on CLL cells, compared with SLL cells. Conversely, SNP microarrays revealed greater numbers of copy-neutral loss of heterozygosity chromosomal aberrations, as well as gross chromosomal aberrations, in the SLL group, compared with the CLL group. These findings revealed that there was a significantly greater expression of trafficking, migration and homing receptors and significantly lower expression of adhesion molecules on CLL cells than on SLL cells, and that SLL may be a more progressive disease than CLL, with a more complex genotype.

Genetic characterization of Polish ccRCC patients: somatic mutation analysis of PBRM1, BAP1 and KDMC5, genomic SNP array analysis in tumor biopsy and preliminary results of chromosome aberrations analysis in plasma cell free DNA.

BACKGROUND: Mutation analysis and cytogenetic testing in clear cell renal cell carcinoma (ccRCC) is not yet implemented in a routine diagnostics of ccRCC. MATERIAL AND METHODS: We characterized the chromosomal alterations in 83 ccRCC tumors from Polish patients using whole genome SNP genotyping assay. Moreover, the utility of next generation sequencing of cell free DNA (cfDNA) in patients plasma as a potential tool for non-invasive cytogenetic analysis was tested. Additionally, tumor specific somatic mutations in PBRM1, BAP1 and KDM5C were determined. RESULTS: We confirmed a correlation between deletions at 9p and higher tumor size, and deletion of chromosome 20 and the survival time. In Fuhrman grade 1, only aberrations of 3p and 8p deletion, gain of 5q and 13q and gains of chromosome 7 and 16 were present. The number of aberrations increased with Fuhrman grade, all chromosomes displayed cytogenetic changes in G3 and G4. ccRCC specific chromosome aberrations were observed in cfDNA, although discrepancies were found between cfDNA and tumor samples. In total 12 common and 94 rare variants were detected in PBRM1, BAP1 and KDM5C, with four potentially pathogenic variants. We observed markedly lower mutation load in PBRM1. CONCLUSIONS: Cytogenetic analysis of cfDNA may allow more accurate diagnosis of tumor aberrations and therefore the correlation between the chromosome aberrations in cfDNA and clinical outcome should be studied in larger cohorts. The functional studies on in BAP1, KDM5C, PBRM1 mutations in large, independent sample set would be necessary for the assessment of their prognostic and diagnostic potential.

argyle: An R Package for Analysis of Illumina Genotyping Arrays.

Genotyping microarrays are an important and widely-used tool in genetics. I present argyle, an R package for analysis of genotyping array data tailored to Illumina arrays. The goal of the argyle package is to provide simple, expressive tools for nonexpert users to perform quality checks and exploratory analyses of genotyping data. To these ends, the package consists of a suite of quality-control functions, normalization procedures, and utilities for visually and statistically summarizing such data. Format-conversion tools allow interoperability with popular software packages for analysis of genetic data including PLINK, R/qtl and DOQTL. Detailed vignettes demonstrating common use cases are included as supporting information. argyle bridges the gap between the low-level tasks of quality control and high-level tasks of genetic analysis. It is freely available at https://github.com/andrewparkermorgan/argyle and has been submitted to Bioconductor.

Genome-wide screening of DNA copy number alterations in cervical carcinoma patients with CGH+SNP microarrays and HPV-FISH.

Alterations in the genome that lead to changes in DNA sequence copy number are characteristic features of solid tumors. We used CGH+SNP microarray and HPV-FISH techniques for detailed screening of copy number alterations (CNAs) in a cohort of 26 patients with cervical carcinoma (CC). This approach identified CNAs in 96.2% (25/26) of tumors. Array-CGH discovered CNAs in 73.1% (19/26) of samples, HPV-FISH experiments revealed CNAs in additional 23.1% (6/26) of samples. Common gains of genetic sequences were observed in 3q (50.0%), 1q (42.4%), 19q (23.1%), while losses were frequently found in 11q (30.8%), 4q (23.1%) and 13q (19.2%). Chromosomal regions involved in loss of heterozygosity were observed in 15.4% of samples in 8q21, 11q23, 14q21 and 18q12.2. Incidence of gain 3q was associated with HPV 16 and HPV 18 positive samples and simultaneous presence of gain 1q (P = 0.033). We did not found a correlation between incidence of CNAs identified by array-CGH and HPV strain infection and incidence of lymph node metastases. Subsequently, HPV-FISH was used for validation of array-CGH results in 23 patients for incidence of hTERC (3q26) and MYC (8q24) amplification. Using HPV-FISH, we found chromosomal lesions of hTERC in 87.0% and MYC in 65.2% of specimens. Our findings confirmed the important role of HPV infection and specific genomic alterations in the development of invasive cervical cancer. This study also indicates that CGH+SNP microarrays allow detecting genome-wide CNAs and copy-neutral loss of heterozygosity more precisely, however, it may be less sensitive than FISH in samples with low level clonal CNAs.

Amelioration of the typical cognitive phenotype in a patient with the 5pter deletion associated with Cri-du-chat syndrome in addition to a partial duplication of CTNND2.

Cri-du-chat is a rare congenital syndrome characterized by intellectual disability, severe speech/developmental delay, dysmorphic features, and additional syndromic findings. The etiology of this disorder is well known, and is attributed to a large deletion on chromosome 5 that typically ranges from band 5p15.2 to the short arm terminus. This region contains CTNND2, a gene encoding a neuronal-specific protein, delta-catenin, which plays a critical role in cellular motility and brain function. The exact involvement of CTNND2 in the cognitive functionality of individuals with Cri-du-chat has not been fully deciphered, but it is thought to be significant. This report describes an 8-year-old African-American female with a complex chromosome 5 abnormality and a relatively mild case of cri-du-chat syndrome. Because of the surprisingly mild cognitive phenotype, although a karyotype had confirmed the 5p deletion at birth, an oligo-SNP microarray was obtained to further characterize her deletion. The array revealed a complex rearrangement, including a breakpoint in the middle of CTNND2, which resulted in a partial deletion and partial duplication of that gene. The array also verified the expected 5p terminal deletion. Although the patient has a significant deletion in CTNND2, half of the gene (including the promoter region) is not only preserved, but is duplicated. The patient's milder cognitive and behavioral presentation, in conjunction with her atypical 5p alteration, provides additional evidence for the role of CTNND2 in the cognitive phenotype of individuals with Cri-du-chat.

Genome-wide analysis of the role of copy-number variation in pancreatic cancer risk.

Although family history is a risk factor for pancreatic adenocarcinoma, much of the genetic etiology of this disease remains unknown. While genome-wide association studies have identified some common single nucleotide polymorphisms (SNPs) associated with pancreatic cancer risk, these SNPs do not explain all the heritability of this disease. We hypothesized that copy number variation (CNVs) in the genome may play a role in genetic predisposition to pancreatic adenocarcinoma. Here, we report a genome-wide analysis of CNVs in a small hospital-based, European ancestry cohort of pancreatic cancer cases and controls. Germline CNV discovery was performed using the Illumina Human CNV370 platform in 223 pancreatic cancer cases (both sporadic and familial) and 169 controls. Following stringent quality control, we asked if global CNV burden was a risk factor for pancreatic cancer. Finally, we performed in silico CNV genotyping and association testing to discover novel CNV risk loci. When we examined the global CNV burden, we found no strong evidence that CNV burden plays a role in pancreatic cancer risk either overall or specifically in individuals with a family history of the disease. Similarly, we saw no significant evidence that any particular CNV is associated with pancreatic cancer risk. Taken together, these data suggest that CNVs do not contribute substantially to the genetic etiology of pancreatic cancer, though the results are tempered by small sample size and large experimental variability inherent in array-based CNV studies.

Imputation-based genomic coverage assessments of current human genotyping arrays.

Microarray single-nucleotide polymorphism genotyping, combined with imputation of untyped variants, has been widely adopted as an efficient means to interrogate variation across the human genome. "Genomic coverage" is the total proportion of genomic variation captured by an array, either by direct observation or through an indirect means such as linkage disequilibrium or imputation. We have performed imputation-based genomic coverage assessments of eight current genotyping arrays that assay from ~0.3 to ~5 million variants. Coverage was determined separately in each of the four continental ancestry groups in the 1000 Genomes Project phase 1 release. We used the subset of 1000 Genomes variants present on each array to impute the remaining variants and assessed coverage based on correlation between imputed and observed allelic dosages. More than 75% of common variants (minor allele frequency > 0.05) are covered by all arrays in all groups except for African ancestry, and up to ~90% in all ancestries for the highest density arrays. In contrast, less than 40% of less common variants (0.01 < minor allele frequency < 0.05) are covered by low density arrays in all ancestries and 50-80% in high density arrays, depending on ancestry. We also calculated genome-wide power to detect variant-trait association in a case-control design, across varying sample sizes, effect sizes, and minor allele frequency ranges, and compare these array-based power estimates with a hypothetical array that would type all variants in 1000 Genomes. These imputation-based genomic coverage and power analyses are intended as a practical guide to researchers planning genetic studies.

Optimizing SNP microarray probe design for high accuracy microbial genotyping.

Microarrays to characterize single nucleotide polymorphisms (SNPs) provide a cost-effective and rapid method (under 24h) to genotype microbes as an alternative to sequencing. We developed a pipeline for SNP discovery and microarray design that scales to 100's of microbial genomes. Here we tested various SNP probe design strategies against 8 sequenced isolates of Bacillus anthracis to compare sequence and microarray data. The best strategy allowed probe length to vary within 32-40 bp to equalize hybridization free energy. This strategy resulted in a call rate of 99.52% and concordance rate of 99.86% for finished genomes. Other probe design strategies averaged substantially lower call rates (94.65-96.41%) and slightly lower concordance rates (99.64-99.80%). These rates were lower for draft than finished genomes, consistent with higher incidence of sequencing errors and gaps. Highly accurate SNP calls were possible in complex soil and blood backgrounds down to 1000 copies, and moderately accurate SNP calls down to 100 spiked copies. The closest genome to the spiked strain was correctly identified at only 10 spiked copies. Discrepancies between sequence and array data did not alter the SNP-based phylogeny, regardless of the probe design strategy, indicating that SNP arrays can accurately place unsequenced isolates on a phylogeny.

An Efficient Gatekeeper Algorithm for Detecting GxE.

The risk for many complex diseases is believed to be a result of the interactive effects of genetic and environmental factors. Developing efficient techniques to identify gene-environment interactions (GxE) is important for unraveling the etiologic basis of many modern day diseases including cancer. The problem of false positives and false negatives continues to pose significant roadblocks to detecting GxE and informing targeted public health screening and intervention. A heuristic gatekeeper method is presented to guide the selection of single nucleotide polymorphisms (SNPs) in the design phase of a GxE study.