A parametric approach to kinship hypothesis testing using identity-by-descent parameters.

There is a large number of applications where family relationships need to be determined from DNA data. In forensic science, competing ideas are in general verbally formulated as the two hypotheses of a test. For the most common paternity case, the null hypothesis states that the alleged father is the true father against the alternative hypothesis that the father is an unrelated man. A likelihood ratio is calculated to summarize the evidence. We propose an alternative framework whereby a model and the hypotheses are formulated in terms of parameters representing identity-by-descent probabilities. There are several advantages to this approach. Firstly, the alternative hypothesis can be completely general. Specifically, the alternative does not need to specify an unrelated man. Secondly, the parametric formulation corresponds to the approach used in most other applications of statistical hypothesis testing and so there is a large theory of classical statistics that can be applied. Theoretical properties of the test statistic under the null hypothesis are studied. An extension to trios of individuals has been carried out. The methods are exemplified using simulations and a real dataset of 27 Spanish Romani individuals.

Models and implementation for relationship problems with dropout.

Allelic dropout in relationship problems may commonly appear in areas such as disaster victim identification and the identification of missing persons. If dropout is not accounted for, the results may be incorrect interpretation of profiles, loss of valuable information and biased results. In this paper, we explore different models for dropout in kinship cases and present an efficient implementation for one of the models. The implementation allows for dropout to be handled simultaneously with phenomena like silent alleles and mutations that may also cause discordances in relationship data, in addition to subpopulation correction. The implemented dropout model is freely available in the new version of the Familias software. The concepts and methods are illustrated on real and simulated data.

"New turns from old STaRs": enhancing the capabilities of forensic short tandem repeat analysis.

The field of research and development of forensic STR genotyping remains active, innovative, and focused on continuous improvements. A series of recent developments including the introduction of a sixth dye have brought expanded STR multiplex sizes while maintaining sensitivity to typical forensic DNA. New supplementary kits complimenting the core STRs have also helped improve analysis of challenging identification cases such as distant pairwise relationships in deficient pedigrees. This article gives an overview of several recent key developments in forensic STR analysis: availability of expanded core STR kits and supplementary STRs, short-amplicon mini-STRs offering practical options for highly degraded DNA, Y-STR enhancements made from the identification of rapidly mutating loci, and enhanced analysis of genetic ancestry by analyzing 32-STR profiles with a Bayesian forensic classifier originally developed for SNP population data. As well as providing scope for genotyping larger numbers of STRs optimized for forensic applications, the launch of compact next-generation sequencing systems provides considerable potential for genotyping the sizeable proportion of nucleotide variation existing in forensic STRs, which currently escapes detection with CE.

The saga of the many studies wrongly associating mitochondrial DNA with breast cancer.

BACKGROUND: A large body of genetic research has focused on the potential role that mitochondrial DNA (mtDNA) variants might play on the predisposition to common and complex (multi-factorial) diseases. It has been argued however that many of these studies could be inconclusive due to artifacts related to genotyping errors or inadequate design. METHODS: Analyses of the data published in case-control breast cancer association studies have been performed using a phylogenetic-based approach. Variation observed in these studies has been interpreted in the light of data available on public resources, which now include over >27,000 complete mitochondrial sequences and the worldwide phylogeny determined by these mitogenomes. Complementary analyses were carried out using public datasets of partial mtDNA sequences, mainly corresponding to control-region segments. RESULTS: By way of example, we show here another kind of fallacy in these medical studies, namely, the phenomenon of SNP-SNP interaction wrongly applied to haploid data in a breast cancer study. We also reassessed the mutually conflicting studies suggesting some functional role of the non-synonymous polymorphism m.10398A>G (ND3 subunit of mitochondrial complex I) in breast cancer. In some studies, control groups were employed that showed an extremely odd haplogroup frequency spectrum compared to comparable information from much larger databases. Moreover, the use of inappropriate statistics signaled spurious "significance" in several instances. CONCLUSIONS: Every case-control study should come under scrutiny in regard to the plausibility of the control-group data presented and appropriateness of the statistical methods employed; and this is best done before potential publication.

Development of a novel forensic STR multiplex for ancestry analysis and extended identity testing.

There is growing interest in developing additional DNA typing techniques to provide better investigative leads in forensic analysis. These include inference of genetic ancestry and prediction of common physical characteristics of DNA donors. To date, forensic ancestry analysis has centered on population-divergent SNPs but these binary loci cannot reliably detect DNA mixtures, common in forensic samples. Furthermore, STR genotypes, forming the principal DNA profiling system, are not routinely combined with forensic SNPs to strengthen frequency data available for ancestry inference. We report development of a 12-STR multiplex composed of ancestry informative marker STRs (AIM-STRs) selected from 434 tetranucleotide repeat loci. We adapted our online Bayesian classifier for AIM-SNPs: Snipper, to handle multiallele STR data using frequency-based training sets. We assessed the ability of the 12-plex AIM-STRs to differentiate CEPH Human Genome Diversity Panel populations, plus their informativeness combined with established forensic STRs and AIM-SNPs. We found combining STRs and SNPs improves the success rate of ancestry assignments while providing a reliable mixture detection system lacking from SNP analysis alone. As the 12 STRs generally show a broad range of alleles in all populations, they provide highly informative supplementary STRs for extended relationship testing and identification of missing persons with incomplete reference pedigrees. Lastly, mixed marker approaches (combining STRs with binary loci) for simple ancestry inference tests beyond forensic analysis bring advantages and we discuss the genotyping options available.

SNPs as Supplements in Simple Kinship Analysis or as Core Markers in Distant Pairwise Relationship Tests: When Do SNPs Add Value or Replace Well-Established and Powerful STR Tests?

BACKGROUND: Genetic tests for kinship testing routinely reach likelihoods that provide virtual proof of the claimed relationship by typing microsatellites-commonly consisting of 12-15 standard forensic short tandem repeats (STRs). Single nucleotide polymorphisms (SNPs) have also been applied to kinship testing but these binary markers are required in greater numbers than multiple-allele STRs. However SNPs offer certain advantageous characteristics not found in STRs, including, much higher mutational stability, good performance typing highly degraded DNA, and the ability to be readily up-scaled to very high marker numbers reaching over a million loci. This article outlines kinship testing applications where SNPs markedly improve the genetic data obtained. In particular we explore the minimum number of SNPs that will be required to confirm pairwise relationship claims in deficient pedigrees that typify missing persons' identification or war grave investigations where commonly few surviving relatives are available for comparison and the DNA is highly degraded. METHODS: We describe the application of SNPs alongside STRs when incomplete profiles or allelic instability in STRs create ambiguous results, we review the use of high density SNP arrays when the relationship claim is very distant, and we outline simulations of kinship analyses with STRs supplemented with SNPs in order to estimate the practical limit of pairwise relationships that can be differentiated from random unrelated pairs from the same population. RESULTS: The minimum number of SNPs for robust statistical inference of parent-offspring relationships through to those of second cousins (S-3-3) is estimated for both simple, single multiplex SNP sets and for subsets of million-SNP arrays. CONCLUSIONS: There is considerable scope for resolving ambiguous STR results and for improving the statistical power of kinship analysis by adding small-scale SNP sets but where the pedigree is deficient the pairwise relationships must be relatively close. For more distant relationships it is possible to reduce chip-based SNP arrays from the million+ markers down to ∼7,000. However, such numbers indicate that current genotyping approaches will not be able to deliver sufficient data to resolve distant pairwise relationships from the limited DNA typical of the most challenging identification cases.

Lasso logistic regression, GSoft and the cyclic coordinate descent algorithm: application to gene expression data.

Statistical methods generating sparse models are of great value in the gene expression field, where the number of covariates (genes) under study moves about the thousands while the sample sizes seldom reach a hundred of individuals. For phenotype classification, we propose different lasso logistic regression approaches with specific penalizations for each gene. These methods are based on a generalized soft-threshold (GSoft) estimator. We also show that a recent algorithm for convex optimization, namely, the cyclic coordinate descent (CCD) algorithm, provides with a way to solve the optimization problem significantly faster than with other competing methods. Viewing GSoft as an iterative thresholding procedure allows us to get the asymptotic properties of the resulting estimates in a straightforward manner. Results are obtained for simulated and real data. The leukemia and colon datasets are commonly used to evaluate new statistical approaches, so they come in useful to establish comparisons with similar methods. Furthermore, biological meaning is extracted from the leukemia results, and compared with previous studies. In summary, the approaches presented here give rise to sparse, interpretable models that are competitive with similar methods developed in the field.

Population stratification in Argentina strongly influences likelihood ratio estimates in paternity testing as revealed by a simulation-based approach.

A simulation-based analysis was carried out to investigate the potential effects of population substructure in paternity testing in Argentina. The study was performed by evaluating paternity indexes (PI) calculated from different simulated pedigree scenarios and using 15 autosomal short tandem repeats (STRs) from eight Argentinean databases. The results show important statistically significant differences between PI values depending on the dataset employed. These differences are more dramatic when considering Native American versus urban populations. This study also indicates that the use of Fst to correct for the effect of population stratification on PI might be inappropriate because it cannot account for the particularities of single paternity cases.

Evaluating the ability of tree-based methods and logistic regression for the detection of SNP-SNP interaction.

Most common human diseases are likely to have complex etiologies. Methods of analysis that allow for the phenomenon of epistasis are of growing interest in the genetic dissection of complex diseases. By allowing for epistatic interactions between potential disease loci, we may succeed in identifying genetic variants that might otherwise have remained undetected. Here we aimed to analyze the ability of logistic regression (LR) and two tree-based supervised learning methods, classification and regression trees (CART) and random forest (RF), to detect epistasis. Multifactor-dimensionality reduction (MDR) was also used for comparison. Our approach involves first the simulation of datasets of autosomal biallelic unphased and unlinked single nucleotide polymorphisms (SNPs), each containing a two-loci interaction (causal SNPs) and 98 'noise' SNPs. We modelled interactions under different scenarios of sample size, missing data, minor allele frequencies (MAF) and several penetrance models: three involving both (indistinguishable) marginal effects and interaction, and two simulating pure interaction effects. In total, we have simulated 99 different scenarios. Although CART, RF, and LR yield similar results in terms of detection of true association, CART and RF perform better than LR with respect to classification error. MAF, penetrance model, and sample size are greater determining factors than percentage of missing data in the ability of the different techniques to detect true association. In pure interaction models, only RF detects association. In conclusion, tree-based methods and LR are important statistical tools for the detection of unknown interactions among true risk-associated SNPs with marginal effects and in the presence of a significant number of noise SNPs. In pure interaction models, RF performs reasonably well in the presence of large sample sizes and low percentages of missing data. However, when the study design is suboptimal (unfavourable to detect interaction in terms of e.g. sample size and MAF) there is a high chance of detecting false, spurious associations.

Revealing latitudinal patterns of mitochondrial DNA diversity in Chileans.

The territory of Chile is particularly long and narrow, which combined with its mountainous terrain, makes it a unique scenario for human genetic studies. We obtained 995 control region mitochondrial DNA (mtDNA) sequences from Chileans representing populations living at different latitudes of the country from the North to the southernmost region. The majority of the mtDNA profiles are of Native American origin (∼88%). The remaining haplotypes are mostly of recent European origin (∼11%), and only a minor proportion is of recent African ancestry (∼1%). While these proportions are relatively uniform across the country, more structured patterns of diversity emerge when examining the variation from a phylogeographic perspective. For instance, haplogroup A2 reaches ∼9% in the North, and its frequency decreases gradually to ∼1% in the southernmost populations, while the frequency of haplogroup D (sub-haplogroups D1 and D4) follows the opposite pattern: 36% in the southernmost region, gradually decreasing to 21% in the North. Furthermore, there are remarkable signatures of founder effects in specific sub-clades of Native American (e.g. haplogroups D1j and D4p) and European (e.g. haplogroups T2b3 and K1a4a1a+195) ancestry. We conclude that the magnitude of the latitudinal differences observed in the patterns of mtDNA variation might be relevant in forensic casework.

Evaluating methods to correct for population stratification when estimating paternity indexes.

The statistical interpretation of the forensic genetic evidence requires the use of allelic frequency estimates in the reference population for the studied markers. Differences in the genetic make up of the populations can be reflected in statistically different allelic frequency distributions. One can easily figure out that collecting such information for any given population is not always possible. Therefore, alternative approaches are needed in these cases in order to compensate for the lack of information. A number of statistics have been proposed to control for population stratification in paternity testing and forensic casework, Fst correction being the only one recommended by the forensic community. In this study we aimed to evaluate the performance of Fst to correct for population stratification in forensics. By way of simulations, we first tested the dependence of Fst on the relative sizes of the sub-populations, and second, we measured the effect of the Fst corrections on the Paternity Index (PI) values compared to the ones obtained when using the local reference database. The results provide clear-cut evidence that (i) Fst values are strongly dependent on the sampling scheme, and therefore, for most situations it would be almost impossible to estimate real values of Fst; and (ii) Fst corrections might unfairly correct PI values for stratification, suggesting the use of local databases whenever possible to estimate the frequencies of genetic profiles and PI values.

Th17-related genes and celiac disease susceptibility.

Th17 cells are known to be involved in several autoimmune or inflammatory diseases. In celiac disease (CD), recent studies suggest an implication of those cells in disease pathogenesis. We aimed at studying the role of genes relevant for the Th17 immune response in CD susceptibility. A total of 101 single nucleotide polymorphisms (SNPs), mainly selected to cover most of the variability present in 16 Th17-related genes (IL23R, RORC, IL6R, IL17A, IL17F, CCR6, IL6, JAK2, TNFSF15, IL23A, IL22, STAT3, TBX21, SOCS3, IL12RB1 and IL17RA), were genotyped in 735 CD patients and 549 ethnically matched healthy controls. Case-control comparisons for each SNP and for the haplotypes resulting from the SNPs studied in each gene were performed using chi-square tests. Gene-gene interactions were also evaluated following different methodological approaches. No significant results emerged after performing the appropriate statistical corrections. Our results seem to discard a relevant role of Th17 cells on CD risk.

The 'Pokemon' (ZBTB7) Gene: No Evidence of Association with Sporadic Breast Cancer.

It has been proposed that the excess of familiar risk associated with breast cancer could be explained by the cumulative effect of multiple weakly predisposing alleles. The transcriptional repressor FBI1, also known as Pokemon, has recently been identified as a critical factor in oncogenesis. This protein is encoded by the ZBTB7 gene. Here we aimed to determine whether polymorphisms in ZBTB7 are associated with breast cancer risk in a sample of cases and controls collected in hospitals from North and Central Spanish patients. We genotyped 15 SNPs in ZBTB7, including the flanking regions, with an average coverage of 1 SNP/2.4 Kb, in 360 sporadic breast cancer cases and 402 controls. Comparison of allele, genotype and haplotype frequencies between cases and controls did not reveal associations using Pearson's chi-square test and a permutation procedure to correct for multiple test. In this, the first study of the ZBTB7 gene in relation to, sporadic breast cancer, we found no evidence of an association.