306 research outputs found
Conflation of short identity-by-descent segments bias their inferred length distribution
Identity-by-descent (IBD) is a fundamental concept in genetics with many
applications. In a common definition, two haplotypes are said to contain an IBD
segment if they share a segment that is inherited from a recent shared common
ancestor without intervening recombination. Long IBD segments (> 1cM) can be
efficiently detected by a number of algorithms using high-density SNP array
data from a population sample. However, these approaches detect IBD based on
contiguous segments of identity-by-state, and such segments may exist due to
the conflation of smaller, nearby IBD segments. We quantified this effect using
coalescent simulations, finding that nearly 40% of inferred segments 1-2cM long
are results of conflations of two or more shorter segments, under demographic
scenarios typical for modern humans. This biases the inferred IBD segment
length distribution, and so can affect downstream inferences. We observed this
conflation effect universally across different IBD detection programs and human
demographic histories, and found inference of segments longer than 2cM to be
much more reliable (less than 5% conflation rate). As an example of how this
can negatively affect downstream analyses, we present and analyze a novel
estimator of the de novo mutation rate using IBD segments, and demonstrate that
the biased length distribution of the IBD segments due to conflation can lead
to inflated estimates if the conflation is not modeled. Understanding the
conflation effect in detail will make its correction in future methods more
tractable
Modelling the influence of foot-and-mouth disease vaccine antigen stability and dose on the bovine immune response
KLFDAPC : a supervised machine learning approach for spatial genetic structure analysis
CSC-University of St Andrews Joint Scholarship (to X.Q.); International Postdoctoral Exchange Fellowship Program (Talent-Introduction Program) from China Postdoc Council (to X.Q.); National Institute of General Medical Sciences (NIGMS) of the National Institute of Health (grant R35GM142783 to C.W.K.C.). Part of the computation for this work is supported by USC’s Center for Advanced Research Computing (https://carc.usc.edu).Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect and describe them is principal component analysis (PCA) or the supervised linear discriminant analysis of principal components (DAPC). However, genetic features produced from both approaches could fail to correctly characterize population structure for complex scenarios involving admixture. In this study, we introduce Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC), a supervised non-linear approach for inferring individual geographic genetic structure that could rectify the limitations of these approaches by preserving the multimodal space of samples. We tested the power of KLFDAPC to infer population structure and to predict individual geographic origin using neural networks. Simulation results showed that KLFDAPC has higher discriminatory power than PCA and DAPC. The application of our method to empirical European and East Asian genome-wide genetic datasets indicated that the first two reduced features of KLFDAPC correctly recapitulated the geography of individuals and significantly improved the accuracy of predicting individual geographic origin when compared to PCA and DAPC. Therefore, KLFDAPC can be useful for geographic ancestry inference, design of genome scans and correction for spatial stratification in GWAS that link genes to adaptation or disease susceptibility.Publisher PDFPeer reviewe
What Would It Take to Overcome the Damaging Effects of Structural Racism and Ensure a More Equitable Future?
This report calls on civic leaders, advocates, elected officials, and philanthropists to address the legacy of structural racism in the United States and advance racial equity by taking steps to close four large equity gaps between people of color and white people.Based on interviews and discussions with experts, advocates, practitioners, and policy makers in the fields of wealth building, public education, employment, and justice policy, the report outlines solutions for each of the four interrelated disparities — in wealth, education, employment and earnings, and policing practices — arguing that greater equity in one area could lead to gains in others
Unifying Parsimonious Tree Reconciliation
Evolution is a process that is influenced by various environmental factors,
e.g. the interactions between different species, genes, and biogeographical
properties. Hence, it is interesting to study the combined evolutionary history
of multiple species, their genes, and the environment they live in. A common
approach to address this research problem is to describe each individual
evolution as a phylogenetic tree and construct a tree reconciliation which is
parsimonious with respect to a given event model. Unfortunately, most of the
previous approaches are designed only either for host-parasite systems, for
gene tree/species tree reconciliation, or biogeography. Hence, a method is
desirable, which addresses the general problem of mapping phylogenetic trees
and covering all varieties of coevolving systems, including e.g., predator-prey
and symbiotic relationships. To overcome this gap, we introduce a generalized
cophylogenetic event model considering the combinatorial complete set of local
coevolutionary events. We give a dynamic programming based heuristic for
solving the maximum parsimony reconciliation problem in time O(n^2), for two
phylogenies each with at most n leaves. Furthermore, we present an exact
branch-and-bound algorithm which uses the results from the dynamic programming
heuristic for discarding partial reconciliations. The approach has been
implemented as a Java application which is freely available from
http://pacosy.informatik.uni-leipzig.de/coresym.Comment: Peer-reviewed and presented as part of the 13th Workshop on
Algorithms in Bioinformatics (WABI2013
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Evidence of Widespread Selection on Standing Variation in Europe at Height-Associated SNPs
Strong signatures of positive selection at newly arising genetic variants are well-documented in humans, but this form of selection may not be widespread in recent human evolution. Because many human traits are highly polygenic and partly determined by common, ancient genetic variation, an alternative model for rapid genetic adaptation has been proposed: weak selection acting on many pre-existing (standing) genetic variants, or polygenic adaptation. By studying height, a classic polygenic trait, we demonstrate the first human signature of widespread selection on standing variation. We show that frequencies of alleles associated with increased height, both at known loci and genome-wide, are systematically elevated in Northern Europeans compared with Southern Europeans . This pattern mirrors intra-European height differences and is not confounded by ancestry or other ascertainment biases. The systematic frequency differences are consistent with the presence of widespread weak selection (selection coefficients per allele) rather than genetic drift alone
Ethnic Comparison of Weight Loss in Trial of Nonpharmacologic Interventions in the Elderly
https://onlinelibrary.wiley.com/doi/epdf/10.1038/oby.2002.1
HIV-1 gp120 N-linked glycosylation differs between plasma and leukocyte compartments
<p>Abstract</p> <p>Background</p> <p>N-linked glycosylation is a major mechanism for minimizing virus neutralizing antibody response and is present on the Human Immunodeficiency Virus (HIV) envelope glycoprotein. Although it is known that glycosylation changes can dramatically influence virus recognition by the host antibody, the actual contribution of compartmental differences in N-linked glycosylation patterns remains unclear.</p> <p>Methodology and Principal Findings</p> <p>We amplified the <it>env </it>gp120 C2-V5 region and analyzed 305 clones derived from plasma and other compartments from 15 HIV-1 patients. Bioinformatics and Bayesian network analyses were used to examine N-linked glycosylation differences between compartments. We found evidence for cellspecific single amino acid changes particular to monocytes, and significant variation was found in the total number of N-linked glycosylation sites between patients. Further, significant differences in the number of glycosylation sites were observed between plasma and cellular compartments. Bayesian network analyses showed an interdependency between N-linked glycosylation sites found in our study, which may have immense functional relevance.</p> <p>Conclusion</p> <p>Our analyses have identified single cell/compartment-specific amino acid changes and differences in N-linked glycosylation patterns between plasma and diverse blood leukocytes. Bayesian network analyses showed associations inferring alternative glycosylation pathways. We believe that these studies will provide crucial insights into the host immune response and its ability in controlling HIV replication <it>in vivo</it>. These findings could also have relevance in shielding and evasion of HIV-1 from neutralizing antibodies.</p
The role of caretakers in disease dynamics
One of the key challenges in modeling the dynamics of contagion phenomena is
to understand how the structure of social interactions shapes the time course
of a disease. Complex network theory has provided significant advances in this
context. However, awareness of an epidemic in a population typically yields
behavioral changes that correspond to changes in the network structure on which
the disease evolves. This feedback mechanism has not been investigated in
depth. For example, one would intuitively expect susceptible individuals to
avoid other infecteds. However, doctors treating patients or parents tending
sick children may also increase the amount of contact made with an infecteds,
in an effort to speed up recovery but also exposing themselves to higher risks
of infection. We study the role of these caretaker links in an adaptive network
models where individuals react to a disease by increasing or decreasing the
amount of contact they make with infected individuals. We find that pure
avoidance, with only few caretaker links, is the best strategy for curtailing
an SIS disease in networks that possess a large topological variability. In
more homogeneous networks, disease prevalence is decreased for low
concentrations of caretakers whereas a high prevalence emerges if caretaker
concentration passes a well defined critical value.Comment: 8 pages, 9 figure
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Exome sequencing of Finnish isolates enhances rare-variant association power.
Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power
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