Biology subjects

Novembre, J.

Publications and source records attributed to Novembre, J..

23 records · Page 2Linked to original sources

bioRxiv2018

Inference and visualization of DNA damage patterns using a grade of membership model

Quality control plays a major role in the analysis of ancient DNA (aDNA). One key step in this quality control is assessment of DNA damage: aDNA contains unique signatures of DNA damage that distinguish it from modern DNA, and so analyses of damage patterns can help confirm that DNA sequences obtained are from endogenous aDNA rather than from modern contamination. Predominant signatures of DNA damage include a high frequency of cytosine to thymine substitutions (C-to-T) at the ends of fragments, and elevated rates of purines (A & G) before the 5 strand-breaks. Existing QC procedures help assess damage by simply plotting for each sample, the C-to-T mismatch rate along the read and the composition of bases before the 5 strand-breaks. Here we present a more flexible and comprehensive model-based approach to infer and visualize damage patterns in aDNA, implemented in an R package aRchaic. This approach is based on a \"grade of membership\" model (also known as \"admixture\" or \"topic\" model) in which each sample has an estimated grade of membership in each of K damage profiles that are estimated from the data. We illustrate aRchaic on data from several aDNA studies and modern individuals from 1000 Genomes Project Consortium (2012). Here, aRchaic clearly distinguishes modern from ancient samples irrespective of DNA extraction, lab and sequencing protocols. Additionally, through an in-silico contamination experiment, we show that the aRchaic grades of membership reflect relative levels of exogenous modern contamination. Together, the outputs of aRchaic provide a concise visual summary of DNA damage patterns, as well as other processes generating mismatches in the data. Availability: aRchaic is available for download from https://www.github.com/kkdey/aRchaic.\n\nContact: halasadi@uchicago.edu, kkdey@uchicago.edu

bioinformatics↗

bioRxiv2017

Genetic landscapes reveal how human genetic diversity aligns with geography

Geographic patterns in human genetic diversity carry footprints of population history1,2 and provide insights for genetic medicine and its application across human populations3,4. Summarizing and visually representing these patterns of diversity has been a persistent goal for human geneticists5-10, and has revealed that genetic differentiation is frequently correlated with geographic distance. However, most analytical methods to represent population structure11-15 do not incorporate geography directly, and it must be considered post hoc alongside a visual summary. Here, we use a recently developed spatially explicit method to estimate \"effective migration\" surfaces to visualize how human genetic diversity is geographically structured (the EEMS method16). The resulting surfaces are \"rugged\", which indicates the relationship between genetic and geographic distance is heterogenous and distorted as a rule. Most prominently, topographic and marine features regularly align with increased genetic differentiation (e.g. the Sahara desert, Mediterranean Sea or Himalaya at large scales; the Adriatic, interisland straits in near Oceania at smaller scales). In other cases, the locations of historical migrations and boundaries of language families align with migration features. These results provide visualizations of human genetic diversity that reveal local patterns of differentiation in detail and emphasize that while genetic similarity generally decays with geographic distance, there have regularly been factors that subtly distort the underlying relationship across space observed today. The fine-scale population structure depicted here is relevant to understanding complex processes of human population history and may provide insights for geographic patterning in rare variants and heritable disease risk.

evolutionary biology↗

bioRxiv2017

Detecting past and ongoing natural selection among ethnically Tibetan women at high altitude in Nepal

Adaptive evolution in humans has rarely been characterized for its whole set of components, i.e. selective pressure, adaptive phenotype, beneficial alleles and realized fitness differential. We combined approaches for detecting selective sweeps and polygenic adaptations and for mapping the genetic bases of physiological and fertility phenotypes in approximately 1000 indigenous ethnically Tibetan women from Nepal, adapted to high altitude. We performed genome-wide association analysis and tests for polygenic adaptations which showed evidence of positive selection for alleles associated with more pregnancies and live births and evidence of negative selection for those associated with higher offspring mortality. Lower hemoglobin level did not show clear evidence for polygenic adaptation, despite its strong association with an EPAS1 haplotype carrying selective sweep signals.

genetics↗

bioRxiv2017

Karp: Accurate and fast taxonomic classification using pseudoalignment

Pooled DNA from multiple unknown organisms arises in a variety of contexts, for example microbial samples from ecological or human health research. Determining the composition of pooled samples can be difficult, especially at the scale of modern sequencing data and reference databases. Here we propose the novel pooled DNA classification method Karp. Karp combines the speed and low-memory requirements of k-mer based pseudoalignment with a likelihood framework that uses base quality information to better resolve multiply mapped reads. In this text we apply Karp to the problem of classifying 16S rRNA reads, commonly used in microbiome research. Using simulations, we show Karp is accurate across a variety of read lengths and when samples contain reads originating from organisms absent from the reference. We also assess performance in real 16S data, and show that relative to other widely used classification methods Karp can reveal stronger statistical association signals and should empower future discoveries.

bioinformatics↗

bioRxiv2016

Population history of the Sardinian people inferred from whole-genome sequencing

The population of the Mediterranean island of Sardinia has made important contributions to genome-wide association studies of traits and diseases. The history of the Sardinian population has also been the focus of much research, and in recent ancient DNA (aDNA) studies, Sardinia has provided unique insight into the peopling of Europe and the spread of agriculture. In this study, we analyze whole-genome sequences of 3,514 Sardinians to address hypotheses regarding the founding of Sardinia and its relation to the peopling of Europe, including examining fine-scale substructure, population size history, and signals of admixture. We find the population of the mountainous Gennargentu region shows elevated genetic isolation with higher levels of ancestry associated with mainland Neolithic farmers and depleted ancestry associated with more recent Bronze Age Steppe migrations on the mainland. Notably, the Gennargentu region also has elevated levels of pre-Neolithic hunter-gatherer ancestry and increased affinity to Basque populations. Further, allele sharing with pre-Neolithic and Neolithic mainland populations is larger on the X chromosome compared to the autosome, providing evidence for a sex-biased demographic history in Sardinia. These results give new insight to the demography of ancestral Sardinians and help further the understanding of sharing of disease risk alleles between Sardinia and mainland populations.

genetics↗