Distributions of pairwise differences often called "mismatch distributions" have been extensively used to estimate the demographic parameters of past population expansions. However, these estimations relied on the assumption that all mutations occurring in the ancestry of a pair of genes lead to observable differences (the infinite-sites model). This mutation model may not be very realistic, especially in the case of the control region of mitochondrial DNA, where this methodology has been mostly applied. In this article, we show how to infer past demographic parameters by explicitly taking into account a finite-sites model with heterogeneity of mutation rates. We also propose an alternative way to derive confidence intervals around the estimated parameters, based on a bootstrap approach. By checking the validity of these confidence intervals by simulations, we find that only those associated with the timing of the expansion are approximately correctly estimated, while those around the population sizes are overly large. We also propose a test of the validity of the estimated demographic expansion scenario, whose proper behavior is verified by simulation. We illustrate our method with human mitochondrial DNA, where estimates of expansion times are found to be 10-20% larger when taking into account heterogeneity of mutation rates than under the infinite-sites model.

https://www.genetics.org/content/genetics/152/3/1079.full.pdf

Estimation of Past Demographic Parameters From the Distribution of Pairwise Differences When the Mutation Rates Vary Among Sites: Application to Human Mitochondrial DNA

Analysis of variation in the hypervariable region of mitochondrial DNA (mtDNA) has emerged as an important tool for studying human evolution and migration. However, attempts to reconstruct optimal intraspecific mtDNA phylogenies frequently fail because parallel mutation events partly obscure the true evolutionary pathways. This makes it inadvisable to present a single phylogenetic tree at the expense of neglecting equally acceptable ones. As an alternative, we propose a novel network approach for portraying mtDNA relationships. For small sample sizes (< approximately 50), an unmodified median network contains all most parsimonious trees, displays graphically the full information content of the sequence data, and can easily be generated by hand. For larger sample sizes, we reduce the complexity of the network by identifying parallelisms. This reduction procedure is guided by a compatibility argument and an additional source of phylogenetic information: the frequencies of the mitochondrial haplotypes. As a spin-off, our approach can also assist in identifying sequencing errors, which manifest themselves in implausible network substructures. We illustrate the advantages of our approach with several examples from existing data sets.

https://www.genetics.org/content/genetics/141/2/743.full.pdf

Mitochondrial portraits of human populations using median networks.

All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from this is the last candidate. next esc will revert to uncompleted text. he publisher. Overview Dataset shift is a challenging situation where the joint distribution of inputs and outputs differs between the training and test stages. Covariate shift is a simpler particular case of dataset shift where only the input distribution changes (covariate denotes input), while the conditional distribution of the outputs given the inputs p(y|x) remains unchanged. Dataset shift is present in most practical applications for reasons ranging from the bias introduced by experimental design, to the mere irreproducibility of the testing conditions at training time. For example, in an image classification task, training data might have been recorded under controlled laboratory conditions, whereas the test data may show different lighting conditions. In other applications, the process that generates data is in itself adaptive. Some of our authors consider the problem of spam email filtering: successful " spammers " will try to build spam in a form that differs from the spam the automatic filter has been built on. Dataset shift seems to have raised relatively little interest in the machine learning community until very recently. Indeed, many machine learning algorithms are based on the assumption that the training data is drawn from exactly the same distribution as the test data on which the model will later be evaluated. Semi-supervised learning and active learning, two problems that seem very similar to covariate shift have received much more attention. How do they differ from covariate shift? Semi-supervised learning is designed to take advantage of unlabeled data present at training time, but is not conceived to be robust against changes in the input distribution. In fact, one can easily construct examples of covariate shift for which common SSL strategies such as the " cluster assumption " will lead to disaster. In active learning the algorithm is asked to select from the available unlabeled inputs those for which obtaining the label will be most beneficial for learning. This is very relevant in contexts where labeling data is very costly, but active learning strategies 2 Contents are not specifically design for dealing with covariate shift. This book attempts to give an overview of the different recent efforts that are being …

Adaptive computation and machine learning

Founder analysis is a method for analysis of nonrecombining DNA sequence data, with the aim of identification and dating of migrations into new territory. The method picks out founder sequence types in potential source populations and dates lineage clusters deriving from them in the settlement zone of interest. Here, using mtDNA, we apply the approach to the colonization of Europe, to estimate the proportion of modern lineages whose ancestors arrived during each major phase of settlement. To estimate the Palaeolithic and Neolithic contributions to European mtDNA diversity more accurately than was previously achievable, we have now extended the Near Eastern, European, and northern-Caucasus databases to 1,234, 2,804, and 208 samples, respectively. Both back-migration into the source population and recurrent mutation in the source and derived populations represent major obstacles to this approach. We have developed phylogenetic criteria to take account of both these factors, and we suggest a way to account for multiple dispersals of common sequence types. We conclude that (i) there has been substantial back-migration into the Near East, (ii) the majority of extant mtDNA lineages entered Europe in several waves during the Upper Palaeolithic, (iii) there was a founder effect or bottleneck associated with the Last Glacial Maximum, 20,000 years ago, from which derives the largest fraction of surviving lineages, and (iv) the immigrant Neolithic component is likely to comprise less than one-quarter of the mtDNA pool of modern Europeans.

/pdf/tracing-european-founder-lineages-in-the-near-eastern-mtdna-1ihqn57qrw.pdf

Tracing European founder lineages in the Near Eastern mtDNA pool.

Mitochondrial DNA (mtDNA) sequence variation was examined in Finns, Swedes and Tuscans by PCR amplification and restriction analysis. About 99% of the mtDNAs were subsumed within 10 mtDNA haplogroups (H, I, J, K, M, T, U, V, W, and X) suggesting that the identified haplogroups could encompass virtually all European mtDNAs. Because both hypervariable segments of the mtDNA control region were previously sequenced in the Tuscan samples, the mtDNA haplogroups and control region sequences could be compared. Using a combination of haplogroup-specific restriction site changes and control region nucleotide substitutions, the distribution of the haplogroups was surveyed through the published restriction site polymorphism and control region sequence data of Caucasoids. This supported the conclusion that most haplogroups observed in Europe are Caucasoid-specific, and that at least some of them occur at varying frequencies in different Caucasoid populations. The classification of almost all European mtDNA variation in a number of well defined haplogroups could provide additional insights about the origin and relationships of Caucasoid populations and the process of human colonization of Europe, and is valuable for the definition of the role played by mtDNA backgrounds in the expression of pathological mtDNA mutations

https://www.genetics.org/content/genetics/144/4/1835.full.pdf

Classification of European Mtdnas from an Analysis of Three European Populations

Nine skeletons found in a shallow grave in Ekaterinburg, Russia, in July 1991, were tentatively identified by Russian forensic authorities as the remains of the last Tsar, Tsarina, three of their five children, the Royal Physician and three servants. We have performed DNA based sex testing and short tandem repeat (STR) analysis and confirm that a family group was present in the grave. Analysis of mitochondrial (mt) DNA reveals an exact sequence match between the putative Tsarina and the three children with a living maternal relative. Amplified mtDNA extracted from the remains of the putative Tsar has been cloned to demonstrate heteroplasmy at a single base within the mtDNA control region. One of these sequences matches two living maternal relatives of the Tsar. We conclude that the DNA evidence supports the hypothesis that the remains are those of the Romanov family.

Identification of the remains of the Romanov family by DNA analysis

Mitochondrial DNA (mtDNA) from 100 unrelated British White Caucasians was extracted, amplified and directly sequenced. Sequences of approximately 800 nucleotides were obtained from 2 hypervariable segments within the non-coding region of the mitochondrial genome. A total of 91 different sequences were observed with an average nucleotide diversity of 1.1%. The most diverse pair of sequences differed at 3.6% of their nucleotide (nt) sites. Comparison to a consensus reference sequence showed that each region was polymorphic to a similar extent. Different methods of genetic analysis were used to examine the variation in each region, including pairwise comparisons, which demonstrated that although the data did not fit a Poisson distribution, the fit was closer to a Negative Binomial distribution.

Nicola Benson

Papers

Identification of the remains of the Romanov family by DNA analysis

The application of mitochondrial DNA typing to the study of white Caucasian genetic identification.