Manuel García-Magariños

Resolving relationship tests that show ambiguous STR results using autosomal SNPs as supplementary markers

When using a standard battery of STRs for relationship testing a small proportion of analyses can give ambiguous results - where the claimed relationship cannot be confirmed by a high enough paternity index or excluded with fully incompatible genotypes. The majority of such cases arise from unknowingly testing a brother of the true father and observing only a small number of exclusions that can each be interpreted as one- or two-step mutations. Although adding extra STRs might resolve a proportion of cases, there are few properly validated extra STRs available, while the commonly added hypervariable SE33 locus is four times more mutable than average, increasing the risk of ambiguous results. We have found SNPs in large multiplexes are much more informative for both low initial probabilities or ambiguous exclusions and at the same time provide a more reliable genotyping approach for the highly degraded DNA encountered in many identification cases. Eight relationship cases are outlined where the addition of SNP data resolved analyses that had remained ambiguous even with extended STR typing. In addition we have made simulations to ascertain the frequency of failing to obtain exclusions or conclusive probabilities of paternity with different marker sets when a brother of the true father is tested. Results indicate that SNPs are statistically more efficient than STRs in resolving cases that distinguish first-degree relatives in deficient pedigrees.

Analysis of global variability in 15 established and 5 new European Standard Set (ESS) STRs using the CEPH human genome diversity panel

The CEPH human genome diversity cell line panel (CEPH-HGDP) of 51 globally distributed populations was used to analyze patterns of variability in 20 core human identification STRs. The markers typed comprised the 15 STRs of Identifiler, one of the most widely used forensic STR multiplexes, plus five recently introduced European Standard Set (ESS) STRs: D1S1656, D2S441, D10S1248, D12S391 and D22S1045. From the genotypes obtained for the ESS STRs we identified rare, intermediate or off-ladder alleles that had not been previously reported for these loci. Examples of novel ESS STR alleles found were characterized by sequence analysis. This revealed extensive repeat structure variation in three ESS STRs, with D12S391 showing particularly high variability for tandem runs of AGAT and AGAC repeat units. The global geographic distribution of the CEPH panel samples gave an opportunity to study in detail the extent of substructure shown by the 20 STRs amongst populations and between their parent population groups. An assessment was made of the forensic informativeness of the new ESS STRs compared to the loci they will replace: CSF1PO, D5S818, D7S820, D13S317 and TPOX, with results showing a clear enhancement of discrimination power using multiplexes that genotype the new ESS loci. We also measured the ability of Identifiler and ESS STRs to infer the ancestry of the CEPH-HGDP samples and demonstrate that forensic STRs in large multiplexes have the potential to differentiate the major population groups but only with sufficient reliability when used with other ancestry-informative markers such as single nucleotide polymorphisms. Finally we checked for possible association by linkage between the two ESS multiplex STRs closely positioned on chromosome-12: vWA and D12S391 by examining paired genotypes from the complete CEPH data set.

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.

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

BackgroundA 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.MethodsAnalyses 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.ResultsBy 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.ConclusionsEvery 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.

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.

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.

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.

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.

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.

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

Abstract 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.