Clumpak: a program for identifying clustering modes and packaging population structure inferences across K

The identification of the genetic structure of populations from multilocus genotype data has become a central component of modern population‐genetic data analysis. Application of model‐based clustering programs often entails a number of steps, in which the user considers different modelling assumptions, compares results across different predetermined values of the number of assumed clusters (a parameter typically denoted K), examines multiple independent runs for each fixed value of K, and distinguishes among runs belonging to substantially distinct clustering solutions. Here, we present Clumpak (Cluster Markov Packager Across K), a method that automates the postprocessing of results of model‐based population structure analyses. For analysing multiple independent runs at a single K value, Clumpak identifies sets of highly similar runs, separating distinct groups of runs that represent distinct modes in the space of possible solutions. This procedure, which generates a consensus solution for each distinct mode, is performed by the use of a Markov clustering algorithm that relies on a similarity matrix between replicate runs, as computed by the software Clumpp . Next, Clumpak identifies an optimal alignment of inferred clusters across different values of K, extending a similar approach implemented for a fixed K in Clumpp and simplifying the comparison of clustering results across different K values. Clumpak incorporates additional features, such as implementations of methods for choosing K and comparing solutions obtained by different programs, models, or data subsets. Clumpak , available at http://clumpak.tau.ac.il , simplifies the use of model‐based analyses of population structure in population genetics and molecular ecology.     

Ancient mitochondrial DNA from the northern fringe of the Neolithic farming expansion in Europe sheds light on the dispersion process

The European Neolithization process started around 12 000 years ago in the Near East. The introduction of agriculture spread north and west throughout Europe and a key question has been if this was brought about by migrating individuals, by an exchange of ideas or a by a mixture of these. The earliest farming evidence in Scandinavia is found within the Funnel Beaker Culture complex (Trichterbecherkultur, TRB) which represents the northernmost extension of Neolithic farmers in Europe. The TRB coexisted for almost a millennium with hunter–gatherers of the Pitted Ware Cultural complex (PWC). If migration was a substantial part of the Neolithization, even the northerly TRB community would display a closer genetic affinity to other farmer populations than to hunter–gatherer populations. We deep-sequenced the mitochondrial hypervariable region 1 from seven farmers (six TRB and one Battle Axe complex, BAC) and 13 hunter–gatherers (PWC) and authenticated the sequences using postmortem DNA damage patterns. A comparison with 124 previously published sequences from prehistoric Europe shows that the TRB individuals share a close affinity to Central European farmer populations, and that they are distinct from hunter–gatherer groups, including the geographically close and partially contemporary PWC that show a close affinity to the European Mesolithic hunter–gatherers.     

Prostaglandin E2 synthesizing enzymes in rheumatoid arthritis B cells and the effects of B cell depleting therapy on enzyme expression

Introduction

B cells may play an important role in promoting immune activation in the rheumatoid synovium and can produce prostaglandin E₂ (PGE₂) when activated. In its turn, PGE₂ formed by cyclooxygenase (COX) and microsomal prostaglandin E₂ synthase 1 (MPGES1) contributes to the rheumatoid arthritis (RA) pathological process. Therapeutic depletion of B cells results in important improvement in controlling disease activity in rheumatoid patients. Therefore we investigated the expression of PGE₂ pathway enzymes in RA B cells and evaluated the effects of B cell depleting therapy on their expression in RA tissue.

Methods

B cells expressing MPGES1 and COX-2 were identified by flow cytometry in in vitro stimulated and control mononuclear cells isolated from synovial fluid and peripheral blood of RA patients. Synovial biopsies were obtained from 24 RA patients before and at two consecutive time points after rituximab therapy. Expression of MPGES1, COX-1 and COX-2, as well as interleukin (IL)-1β and IL-6, known inducers of MPGES1, was quantified in immunostained biopsy sections using computerized image analysis.

Results

Expression of MPGES1 or COX-2 was significantly upregulated upon stimulation of B cells from blood and synovial fluid while control cells displayed no detectable enzymes. In synovial biopsy sections, the expression of MPGES1, COX-1 or COX-2 was resistant to rituximab therapy at 8 or 16 weeks after start of treatment. Furthermore expression of IL-1β in the synovial tissue remained unchanged, while IL-6 tended to decrease after therapy.

Conclusions

Therapy with B cell depleting agents, although efficient in achieving good clinical and radiographic response in RA patients, leaves important inflammatory pathways in the rheumatoid synovium essentially unaffected.     

The Relationship Between FST and the Frequency of the Most Frequent Allele

F_ST is frequently used as a summary of genetic differentiation among groups. It has been suggested that F_ST depends on the allele frequencies at a locus, as it exhibits a variety of peculiar properties related to genetic diversity: higher values for biallelic single-nucleotide polymorphisms (SNPs) than for multiallelic microsatellites, low values among high-diversity populations viewed as substantially distinct, and low values for populations that differ primarily in their profiles of rare alleles. A full mathematical understanding of the dependence of F_ST on allele frequencies, however, has been elusive. Here, we examine the relationship between F_ST and the frequency of the most frequent allele, demonstrating that the range of values that F_ST can take is restricted considerably by the allele-frequency distribution. For a two-population model, we derive strict bounds on F_ST as a function of the frequency M of the allele with highest mean frequency between the pair of populations. Using these bounds, we show that for a value of M chosen uniformly between 0 and 1 at a multiallelic locus whose number of alleles is left unspecified, the mean maximum F_ST is ∼0.3585. Further, F_ST is restricted to values much less than 1 when M is low or high, and the contribution to the maximum F_ST made by the most frequent allele is on average ∼0.4485. Using bounds on homozygosity that we have previously derived as functions of M, we describe strict bounds on F_ST in terms of the homozygosity of the total population, finding that the mean maximum F_ST given this homozygosity is 1 − ln 2 ≈ 0.3069. Our results provide a conceptual basis for understanding the dependence of F_ST on allele frequencies and genetic diversity and for interpreting the roles of these quantities in computations of F_ST from population-genetic data. Further, our analysis suggests that many unusual observations of F_ST, including the relatively low F_ST values in high-diversity human populations from Africa and the relatively low estimates of F_ST for microsatellites compared to SNPs, can be understood not as biological phenomena associated with different groups of populations or classes of markers but rather as consequences of the intrinsic mathematical dependence of F_ST on the properties of allele-frequency distributions.DIFFERENTIATION among groups is one of the fundamental subjects of the field of population genetics. Comparisons of the level of variation among subpopulations with the level of variation in the total population have been employed frequently in population-genetic theory, in statistical methods for data analysis, and in empirical studies of distributions of genetic variation. Wright’s (Wright 1951) fixation indices, and F_ST in particular, have been central to this effort.Wright’s F_ST was originally defined as the correlation between two randomly sampled gametes from the same subpopulation when the correlation of two randomly sampled gametes from the total population is set to zero. Several definitions of F_ST or F_ST-like quantities are now available, relying on a variety of different conceptual formulations but all measuring some aspect of population differentiation (e.g., Charlesworth 1998; Holsinger and Weir 2009). Many authors have claimed that one or another formulation of F_ST is affected by levels of genetic diversity or by allele frequencies, either because the range of F_ST is restricted by these quantities or because these quantities affect the degree to which F_ST reflects population differentiation (e.g., Charlesworth 1998; Nagylaki 1998; Hedrick 1999, 2005; Long and Kittles 2003; Jost 2008; Ryman and Leimar 2008; Long 2009; Meirmans and Hedrick 2011). For example, Nagylaki (1998) and Hedrick (1999) argued that measures of F_ST may be poor measures of genetic differentiation when the level of diversity is high. Charlesworth (1998) suggested that F_ST can be inflated when diversity is low, arguing that F_ST might not be appropriate for comparing loci with substantially different levels of variation. In a provocative recent article, Jost (2008) used the diversity dependence of forms of F_ST to question their utility as differentiation measures at all.One definition that is convenient for mathematical assessment of the relationship of an F_ST-like quantity and allele frequencies is the quantity labeled G_ST by Nei (1973), which for a given locus measures the difference between the heterozygosity of the total (pooled) population, h_T, and the mean heterozygosity across subpopulations, h_S, divided by the heterozygosity of the total population:GST=hT−hShT.(1)In terms of the homozygosity of the total population, H_T = 1 − h_T, and the mean homozygosity across subpopulations, H_S = 1 − h_S, we can writeGST=HS−HT1−HT.(2)The Wahlund (1928) principle guarantees that H_S ≥ H_T and, therefore, because H_S ≤ 1 and for a polymorphic locus with finitely many alleles, 0 < H_T < 1, G_ST lies in the interval [0,1].Using G_ST for their definition of F_ST, Hedrick (1999, 2005) and Long and Kittles (2003) pointed out that because h_T < 1, F_ST cannot exceed the mean homozygosity across subpopulations, H_S:F_ST = 1 ? h_S/h_T < 1 ? h_S = H_S.(3)Hedrick (2005) obtained this result by considering a set of K equal-sized subpopulations, in which each allele is private to a single subpopulation. In the limit as K → ∞, a stronger upper bound on F_ST as a function of H_S and K reduces to Equation 3 (see also Jin and Chakraborty 1995 and Long and Kittles 2003).While Hedrick (1999, 2005) and Long and Kittles (2003) have clarified the relationship between F_ST and the mean homozygosity H_S across subpopulations, their approaches do not easily illuminate the connection between F_ST and allele frequencies themselves. A formal understanding of the relationship between F_ST and allele frequencies would make it possible to more fully understand the behavior of F_ST in situations where markers of interest differ substantially in allele frequencies or levels of genetic diversity. Our recent work on the relationship between homozygosity and the frequency of the most frequent allele (Rosenberg and Jakobsson 2008; Reddy and Rosenberg 2012) provides a mathematical approach for formal investigation of bounds on population-genetic statistics in terms of allele frequencies. In this article, we therefore seek to thoroughly examine the dependence of F_ST on allele frequencies by investigating the upper bound on F_ST in terms of the frequency M of the most frequent allele across a pair of populations. We derive bounds on F_ST given the frequency of the most frequent allele and bounds on the frequency of the most frequent allele given F_ST. We consider loci with arbitrarily many alleles in a pair of subpopulations. Using theory for the bounds on homozygosity given the frequency of the most frequent allele, we obtain strict bounds on F_ST given the homozygosity of the total population. Our analysis clarifies the relationships among F_ST, allele frequencies, and homozygosity, providing explanations for peculiar observations of F_ST that can be attributed to allele-frequency dependence.     

Incorporating double copies of a chromatin insulator into lentiviral vectors results in less viral integrants

Background  

Lentiviral vectors hold great promise as gene transfer vectors in gene therapeutic settings. However, problems related to the risk of insertional mutagenesis, transgene silencing and positional effects have stalled the use of such vectors in the clinic. Chromatin insulators are boundary elements that can prevent enhancer-promoter interactions, if placed between these elements, and protect transgene cassettes from silencing and positional effects. It has been suggested that insulators can improve the safety and performance of lentiviral vectors. Therefore insulators have been incorporated into lentiviral vectors in order to enhance their safety profile and improve transgene expression. Commonly such insulator vectors are produced at lower titers than control vectors thus limiting their potential use.