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Probabilistic genotyping approaches are increasingly used for the interpretation of DNA mixtures. To explore the specificity of one of these systems (STRmix™), we conducted an extensive study using 24 complex mixtures: all were known or apparent 4-person mixtures with at least one contributor representing less than 20% of total DNA, and all mixtures had at least one contributor with suboptimal DNA quantity. Those mixtures were either generated in-house or from casework. All the mixtures were compared to 300,000 virtual non-contributors, resulting in a dataset of 7.2 million comparisons. The great majority of the non-contributor comparisons led to a LR lower than 1 for a specificity of 99.1%. The effect of using replicate amplifications to calculate the LR of non-contributors was also assessed as triplicates were used and led to an increased specificity of 99.8%. The very large extent of the analyzed data shows that STRmix™ has an excellent ability to discriminate non-contributors from complex DNA mixtures.

Background The interactive effect of the IGF pathway genes with the environment may contribute to childhood obesity. Such gene-environment interactions can take on complex forms. Detecting those relationships using longitudinal family studies requires simultaneously accounting for correlations within individuals and families. Methods We studied three methods for detecting interaction effects in longitudinal family studies. The twin model and the nonparametric partition-based score test utilized individual outcome averages, whereas the linear mixed model used all available longitudinal data points. Simulation experiments were performed to evaluate the methods’ power to detect different gene-environment interaction relationships. These methods were applied to the Quebec Newborn Twin Study data to test for interaction effects between the IGF pathway genes (IGF-1, IGFALS) and environmental factors (physical activity, daycare attendance and sleep duration) on body mass index outcomes. Results For the simulated data, the twin model with the mean time summary statistic yielded good performance overall. Modelling an interaction as linear when the true model had a different relationship influenced power; for certain non-linear interactions, none of the three methods were effective. Our analysis of the IGF pathway genes showed suggestive association for the joint effect of IGF-1 variant at position 102,791,894 of chromosome 12 and physical activity. However, this association was not statistically significant after multiple testing correction. Conclusions The analytical approaches considered in this study were not robust to different gene-environment interactions. Methodological innovations are needed to improve the current methods’ performances for detecting non-linear interactions. More studies are needed in order to better understand the IGF pathway’s role in childhood obesity development. Electronic supplementary material The online version of this article (10.1186/s12881-018-0739-x) contains supplementary material, which is available to authorized users.

Was the past genetic contribution of women and men to the current human population equal? Was polygyny (excess of breeding women) present among hominid lineages? We addressed these questions by measuring the ratio of population recombination rates between the X chromosome and the autosomes, rho(X)/rho(A). The X chromosome recombines only in female meiosis, whereas autosomes undergo crossovers in both sexes; thus, rho(X)/rho(A) reflects the female-to-male breeding ratio, beta. We estimated beta from rho(X)/rho(A) inferred from genomic diversity data and calibrated with recombination rates derived from pedigree data. For the HapMap populations, we obtained beta of 1.4 in the Yoruba from West Africa, 1.3 in Europeans, and 1.1 in East Asian samples. These values are consistent with a high prevalence of monogamy and limited polygyny in human populations. More mutations occur during male meiosis as compared to female meiosis at the rate ratio referred to as alpha. We show that at alpha not equal 1, the divergence rates and genetic diversities of the X chromosome relative to the autosomes are complex functions of both alpha and beta, making their independent estimation difficult. Because our estimator of beta does not require any knowledge of the mutation rates, our approach should allow us to dissociate the effects of alpha and beta on the genetic diversity and divergence rate ratios of the sex chromosomes to the autosomes.

Genetic variability of the compound interrupted microsatellite DXS1238, in intron 44 of the dystrophin gene, provides evidence for a complex structure of the ancestral population that led to the emergence of modern humans. We sequenced DXS1238 in 600 X-chromosomes from all over the world. Forty four percent of African-specific chromosomes belong to the ancestral lineage that did not participate in the out-of-Africa expansion and subsequent colonization of other continents. Based on the coalescence analysis these lineages separated from those that contributed to the out-of-Africa expansion 366 +/- 136 thousands years ago (Kya). Independently, the analysis of the variance in the repeat length and of the decay of the ancestral alleles of the two DXS1238 repeats, GT and GA, dates this separation at more than 200 Kya. This suggests a complex demographic history and genetic structure of the African melting pot that led to the emergence of modern humans and their out-of-Africa migration. The subsequent subdivisions of human populations among different continents appear to be preceded by even more structured population history within Africa itself, which resulted from a restricted gene flow between lineages allowing for genetic differences to accumulate. If the transition to modern humans occurred during that time, it necessarily follows that genes associated with this transformation spread between subpopulations via gene flow. Otherwise, in spite of subsequent anatomical variation, Homo sapiens as a species could have emerged in Africa already between 300 and 200 Kya, i.e. before the mitochondrial DNA and well before the Y-chromosome most recent common ancestors.

It is likely that evolutionary differences among species are driven by sequence changes in regulatory regions. Likewise, polymorphisms in the promoter regions may be responsible for interindividual differences at the level of populations. We present an unbiased survey of genetic variation in 2-kb segments upstream of the transcription start sites of 28 protein-coding genes, characterized in five population groups of different geographic origin. On average, we found 9.1 polymorphisms and 8.8 haplotypes per segment with corresponding nucleotide and haplotype diversities of 0.082% and 58%, respectively. We characterized these segments through different summary statistics, Hardy-Weinberg equilibria fixation index (Fst) estimates, and neutrality tests, as well as by analyzing the distributions of haplotype allelic classes, introduced here to assess the departure from neutrality and examined by coalescent simulations under a simple population model, assuming recombinations or different demography. Our results suggest that genetic diversity in some of these regions could have been shaped by purifying selection and driven by adaptive changes in the other, thus explaining the relatively large variance in the corresponding genetic diversity indices loci. However, some of these effects could be also due to linkage with surrounding sequences, and the neutralists' explanations cannot be ruled out given uncertainty in the underlying demographic histories and the possibility of random effects due to the small size of the studied segments. Hum Mutat 28(5), 441–450, 2007. © 2007 Wiley-Liss, Inc.

Background Founder populations have an important role in the study of genetic diseases. Access to detailed genealogical records is often one of their advantages. These genealogical data provide unique information for researchers in evolutionary and population genetics, demography and genetic epidemiology. However, analyzing large genealogical datasets requires specialized methods and software. The GENLIB software was developed to study the large genealogies of the French Canadian population of Quebec, Canada. These genealogies are accessible through the BALSAC database, which contains over 3 million records covering the whole province of Quebec over four centuries. Using this resource, extended pedigrees of up to 17 generations can be constructed from a sample of present-day individuals. Results We have extended and implemented GENLIB as a package in the R environment for statistical computing and graphics, thus allowing optimal flexibility for users. The GENLIB package includes basic functions to manage genealogical data allowing, for example, extraction of a part of a genealogy or selection of specific individuals. There are also many functions providing information to describe the size and complexity of genealogies as well as functions to compute standard measures such as kinship, inbreeding and genetic contribution. GENLIB also includes functions for gene-dropping simulations. The goal of this paper is to present the full functionalities of GENLIB. We used a sample of 140 individuals from the province of Quebec (Canada) to demonstrate GENLIB’s functions. Ascending genealogies for these individuals were reconstructed using BALSAC, yielding a large pedigree of 41,523 individuals. Using GENLIB’s functions, we provide a detailed description of these genealogical data in terms of completeness, genetic contribution of founders, relatedness, inbreeding and the overall complexity of the genealogical tree. We also present gene-dropping simulations based on the whole genealogy to investigate identical-by-descent sharing of alleles and chromosomal segments of different lengths and estimate probabilities of identical-by-descent sharing. Conclusions The R package GENLIB provides a user friendly and flexible environment to analyze extensive genealogical data, allowing an efficient and easy integration of different types of data, analytical methods and additional developments and making this tool ideal for genealogical analysis. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0581-5) contains supplementary material, which is available to authorized users.

In genetics the ability to accurately describe the familial relationships among a group of individuals can be very useful. Recent statistical tools succeeded in assessing the degree of relatedness up to 6–7 generations with good power using dense genome-wide single-nucleotide polymorphism data to estimate the extent of identity-by-descent (IBD) sharing. It is therefore important to describe genome-wide patterns of IBD sharing for more remote and complex relatedness between individuals, such as that observed in a founder population like Quebec, Canada. Taking advantage of the extended genealogical records of the French Canadian founder population, we first compared different tools to identify regions of IBD in order to best describe genome-wide IBD sharing and its correlation with genealogical characteristics. Results showed that the extent of IBD sharing identified with FastIBD correlates best with relatedness measured using genealogical data. Total length of IBD sharing explained 85% of the genealogical kinship's variance. In addition, we observed significantly higher sharing in pairs of individuals with at least one inbred ancestor compared with those without any. Furthermore, patterns of IBD sharing and average sharing were different across regional populations, consistent with the settlement history of Quebec. Our results suggest that, as expected, the complex relatedness present in founder populations is reflected in patterns of IBD sharing. Using these patterns, it is thus possible to gain insight on the types of distant relationships in a sample from a founder population like Quebec.

For years, studies of founder populations and genetic isolates represented the mainstream of genetic mapping in the effort to target genetic defects causing Mendelian disorders. The genetic homogeneity of such populations as well as relatively homogeneous environmental exposures were also seen as primary advantages in studies of genetic susceptibility loci that underlie complex diseases. European colonization of the St-Lawrence Valley by a small number of settlers, mainly from France, resulted in a founder effect reflected by the appearance of a number of population-specific disease-causing mutations in Quebec. The purported genetic homogeneity of this population was recently challenged by genealogical and genetic analyses. We studied one of the contributing factors to genetic heterogeneity, early Native American admixture that was never investigated in this population before. Consistent admixture estimates, in the order of one per cent, were obtained from genome-wide autosomal data using the ADMIXTURE and HAPMIX software, as well as with the fastIBD software evaluating the degree of the identity-by-descent between Quebec individuals and Native American populations. These genomic results correlated well with the genealogical estimates. Correlations are imperfect most likely because of incomplete records of Native founders' origin in genealogical data. Although the overall degree of admixture is modest, it contributed to the enrichment of the population diversity and to its demographic stratification. Because admixture greatly varies among regions of Quebec and among individuals, it could have significantly affected the homogeneity of the population, which is of importance in mapping studies, especially when rare genetic susceptibility variants are in play.

International audience; Reverse transcription-polymerase chain reaction (RT-PCR) approaches have been used in a large proportion of transcriptome analyses published to date. The accuracy of the results obtained by this method strongly depends on accurate transcript normalization using stably expressed genes, known as references. Statistical algorithms have been developed recently to help validate reference genes, and most studies of gene expression in mammals, yeast and bacteria now include such validation. Surprisingly, this important approach is under-utilized in plant studies, where putative housekeeping genes tend to be used as references without any appropriate validation. Using quantitative RT-PCR, the expression stability of several genes commonly used as references was tested in various tissues of Arabidopsis thaliana and hybrid aspen (Populus tremula x Populus tremuloides). It was found that the expression of most of these genes was unstable, indicating that their use as references is inappropriate. The major impact of the use of such inappropriate references on the results obtained by RT-PCR is demonstrated in this study. Using aspen as a model, evidence is presented indicating that no gene can act as a universal reference, implying the need for a systematic validation of reference genes. For the first time, the extent to which the lack of a systematic validation of reference genes is a stumbling block to the reliability of results obtained by RT-PCR in plants is clearly shown.

The distribution of the lengths of genomic segments inverted during the evolutionary divergence of two species cannot be inferred directly from the output of genome rearrangement algorithms, due to the rapid loss of signal from all but the shortest inversions. The number of short inversions produced by these algorithms, however, particularly those involving a single gene, is relatively reliable. To gain some insight into the shape of the inversion-length distribution we first apply a genome rearrangement algorithm to each of 32 pairs of bacterial genomes. For each pair we then simulate their divergence using a test distribution to generate the inversions and use the simulated genomes as input to the reconstruction algorithm. It is the comparison between the algorithm output for the real pair of genomes and the simulated pair which is used to assess the test distribution. We find that simulations based on the exponential distribution cannot provide a good fit, but that simulations based on a gamma distribution can account for both single-gene inversions and short inversions involving at most 20 genes, and we conclude that the shape of latter distribution corresponds well to the true distribution at least for small inversion lengths.

Motivation: The biologically meaningful algorithmic study of genome rearrangement should take into account the distribution of sizes of the rearranged genomic fragments. In particular, it is important to know the prevalence of short inversions in order to understand the patterns of gene order disruption observed in comparative genomics. Results: We find a large excess of short inversions, especially those involving a single gene, in comparison with a random inversion model. This is demonstrated through comparison of four pairs of bacterial genomes, using a specially-designed implementation of the Hannenhalli–Pevzner theory, and validated through experimentation on pairs of random genomes matched to the real pairs. Availability: The main routines of the experimental software are available through consultation with the authors. Contact: sankoff@uottawa.ca Keywords: short inversions, reversals, genome rearrangement, genome evolution, comparative genomics, bacterial genomes, Hannenhalli--Pevzner algorithm, experimental algorithmics. *To whom correspondence should be addressed.

To the Editor: In this issue, Lohmueller et al. rightly noted that we doubly corrected for unequal male-female population sizes, a mistake that inadvertently perpetuated itself in subsequently derived equations. We are grateful to these authors for pointing out our mistake so quickly and thus helping us to rapidly correct our calculations. We complete the corrections made by Lohmueller et al. in their comment in our Supplemental Data, available online, where we show correct versions of the derived equations and updated resulting figures and tables. Our mistake led us to underestimate the breeding ratio β. The corrected estimates are greater but still within a range of ratios of the male-to-female reproductive variance encountered in societies characterized as monogamous or serially monogamous, although they also overlap with those characterizing polygyny.1 Our updated estimates are at the low end of the estimates obtained by Hammer et al., which ranged from 1.8 to 14,2 and thus do not strongly support the results and conclusions discussed by these authors. Importantly, in addition to capturing sex differences in the reproductive variance, β can be affected by sex differences in the generation time, by sex-biased migration or inbreeding, as well as by matrilocality or patrilocality and possibly by sex-asymmetric admixture.3,4 Furthermore, following a population bottleneck, β estimates can be skewed as a result of a faster equilibration of a genetic system of lower effective population size, such as that of the X chromosomes versus the autosomes. Therefore, estimates of β from population-diversity data have to be interpreted in the context of demographic, anthropological, evolutionary, and paleontological evidence.1,3 Our estimates of β were derived from the ratio of NeX/NeA estimated from the ratio of the population recombination rates of these chromosomal systems. Lohmueller et al. remarked that NeX/NeA is a more robust statistic than β itself. In addition, focusing first on NeX/NeA, it may be easier to partition the distinct contributions of the factors enumerated above to the overall numeric outcome of this ratio in order to eventually extract only the part influenced by the breeding ratio and use it directly to estimate β. This is, however, conditional on the data and the genetic information that can be used to evaluate distinct contributing parameters. Combining information that can be obtained from historical recombinations3 with that obtained from mutations2,4,5 should help this task, both in testing population models and in refining the resulting estimates. Using our new approach, one can extract additional information from the genetic-variability data to confront different estimates obtained independently from the analysis of the mutational diversity and to examine their consistency. Divergence of such estimates prompts additional investigations. For example, the estimate of about 5 of the ratio, α, of the male-to-female mutation rate, from ΘX/ΘA evaluated by Lohmueller et al. with the use of the CHB and JPT β of 1.4 is consistent with the “phylogenetic” estimate based on the human and ape X chromosome versus autosome divergence6 (Table S3 and Figure S8). However, estimates of α of up to 22 were obtained with the use of the β estimates at their face value for both CEU and YRI populations. Because values of α of up to 22 are unrealistic, it is plausible that the greater β values found in these two populations are inflated and that other factors additionally influenced the underlying NeX/NeA estimates.3,4 Lohmueller et al. implied that α could be estimated solely from the X chromosome versus autosome divergence. This approach works with phylogenetically distant species such that the effect of the common ancestral population size can be neglected. This is not the case in humans and apes.6 Very divergent species differ in the generation time and in the number of germ cell cycles between the sexes, both influencing α.5 Therefore, realistic α estimates are expected only when closely related species are considered, which requires correction for the size of their common ancestral population to be made separately for the X chromosome and for the autosomes and thus involves β. Indeed, when applying such a correction by considering a range of ancestral ΘX/ΘA to reflect different combinations of β and α, one obtains estimates of α in the range of 5 to 6 (Table S3 and Figure S8). With realistic α, other related estimates should fall in the realistic range, otherwise disproving the model. In our study, we extended the testing space of genetic models by including the data on historical recombinations. Considering both mutational and recombinational data can enrich historical inferences and even make them more robust. In their comment, Lohmueller et al. not only praised our approach and helped us straiten our equations, but they also positively contributed to the discussion concerning the interpretation of population parameters estimated on the basis of simplified models. These models are essential for improving our understanding of human population history, but their utility depends on careful interpretation and assessment of model assumptions and limitations. This can be best achieved through complementary approaches maximizing the use of all information and through fruitful discussion as exemplified here.

Understanding genetic variation might reveal the cause of individual susceptibility to a variety of complex diseases such as asthma, diabetes, and cancer. Current efforts to identify functional DNA variants have essentially been oriented toward single nucleotide polymorphisms (SNPs) found in coding regions of candidate genes since they have direct impact on the structure and function of the affected proteins. Abnormal expression of finely regulated genes could also lead to disequilibria in different metabolic pathways and/or biological processes. Thus investigation of SNPs in the promoter regions (pSNPs) of genes should improve our knowledge of the etiology of complex diseases. Unfortunately, little is known about the nature and the prevalence of pSNPs. We have analyzed 197 genes targeting the promoter region, arbitrarily defined as a 2-kb genomic segment upstream of the transcription initiation site, by screening by dHPLC for the presence of SNPs in a worldwide panel of 40 individuals. As a result 1838 pSNPs were detected, 75% of which modify (by either gain or loss) putative binding sites of known transcription factors. We also examined the distribution of these pSNPs among features such as conserved regions, repeats, and dinucleotides as well as Gene Ontology terms. This report supports the functional relevance of several of the pSNPs investigated and suggests a putative impact on disease susceptibility.