Deep Learning in Population Genetics DOI Creative Commons
Kevin Korfmann, Oscar E. Gaggiotti, Matteo Fumagalli

et al.

Genome Biology and Evolution, Journal Year: 2023, Volume and Issue: 15(2)

Published: Jan. 23, 2023

Abstract Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and need study increasingly complex evolutionary scenarios. With likelihood Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, in particular deep algorithms are emerging as popular techniques for population genetic inferences. These rely on that learn non-linear relationships between input model parameters being estimated through representation learning from training sets. Deep currently employed field comprise discriminative generative models with fully connected, convolutional, recurrent layers. Additionally, wide range powerful simulators generate under scenarios now available. The application empirical sets mostly replicates previous findings demography reconstruction signals natural selection organisms. To showcase feasibility tackle new challenges, we designed branched architecture detect recent balancing temporal haplotypic data, which exhibited good predictive performance simulated data. Investigations interpretability neural networks, their robustness uncertain creative will provide further opportunities technological advancements field.

Language: Английский

Long-Read DNA Sequencing: Recent Advances and Remaining Challenges DOI Creative Commons

Peter E. Warburton,

Robert Sebra

Annual Review of Genomics and Human Genetics, Journal Year: 2023, Volume and Issue: 24(1), P. 109 - 132

Published: April 19, 2023

DNA sequencing has revolutionized medicine over recent decades. However, analysis of large structural variation and repetitive DNA, a hallmark human genomes, been limited by short-read technology, with read lengths 100-300 bp. Long-read (LRS) permits routine fragments tens to hundreds kilobase pairs in size, using both real-time synthesis nanopore-based direct electronic sequencing. LRS haplotypic phasing genomes enabled the discovery characterization rare pathogenic variants repeat expansions. It also recently assembly complete, gapless genome that includes previously intractable regions, such as highly centromeres homologous acrocentric short arms. With addition protocols for targeted enrichment, epigenetic modification detection, long-range chromatin profiling, promises launch new era understanding genetic diversity mutations populations.

Language: Английский

Citations

68

Assembly of 43 human Y chromosomes reveals extensive complexity and variation DOI
Pille Hallast, Peter Ebert, Mark Loftus

et al.

Nature, Journal Year: 2023, Volume and Issue: 621(7978), P. 355 - 364

Published: Aug. 23, 2023

Language: Английский

Citations

66

Recent advances in Forensic DNA Phenotyping of appearance, ancestry and age DOI Creative Commons
Manfred Kayser, Wojciech Branicki, Walther Parson

et al.

Forensic Science International Genetics, Journal Year: 2023, Volume and Issue: 65, P. 102870 - 102870

Published: April 7, 2023

Forensic DNA Phenotyping (FDP) comprises the prediction of a person's externally visible characteristics regarding appearance, biogeographic ancestry and age from crime scene samples, to provide investigative leads help find unknown perpetrators that cannot be identified with forensic STR-profiling. In recent years, FDP has advanced considerably in all its three components, which we summarize this review article. Appearance broadened beyond eye, hair skin color additionally comprise other traits such as eyebrow color, freckles, structure, loss men, tall stature. Biogeographic inference progressed continental sub-continental detection resolving co-ancestry patterns genetically admixed individuals. Age estimation widened blood more somatic tissues saliva bones well new markers tools for semen. Technological progress allowed forensically suitable technology largely increased multiplex capacity simultaneous analysis hundreds predictors targeted massively parallel sequencing (MPS). Forensically validated MPS-based predicting i) several appearance traits, ii) multi-regional ancestry, iii) together iv) different tissue types, are already available. Despite advances will likely increase impact criminal casework near future, moving reliable level detail accuracy police investigators may desire, requires further intensified scientific research technical developments validations necessary funding.

Language: Английский

Citations

57

A concerted neuron–astrocyte program declines in ageing and schizophrenia DOI Creative Commons
Emi Ling,

James Nemesh,

Melissa Goldman

et al.

Nature, Journal Year: 2024, Volume and Issue: 627(8004), P. 604 - 611

Published: March 6, 2024

Abstract Human brains vary across people and over time; such variation is not yet understood in cellular terms. Here we describe a relationship between people’s cortical neurons astrocytes. We used single-nucleus RNA sequencing to analyse the prefrontal cortex of 191 human donors aged 22–97 years, including healthy individuals with schizophrenia. Latent-factor analysis these data revealed that, whose more strongly expressed genes encoding synaptic components, astrocytes distinct functions for synthesizing cholesterol, an astrocyte-supplied component membranes. call this neuron astrocyte program (SNAP). In schizophrenia ageing—two conditions that involve declines cognitive flexibility plasticity 1,2 —cells divested from SNAP: astrocytes, glutamatergic (excitatory) GABAergic (inhibitory) all showed reduced SNAP expression corresponding degrees. The astrocytic neuronal components both involved which genetic risk factors were concentrated. SNAP, varies quantitatively even among similar age, may underlie many aspects normal interindividual differences be important point convergence multiple kinds pathophysiology.

Language: Английский

Citations

56

Deep Learning in Population Genetics DOI Creative Commons
Kevin Korfmann, Oscar E. Gaggiotti, Matteo Fumagalli

et al.

Genome Biology and Evolution, Journal Year: 2023, Volume and Issue: 15(2)

Published: Jan. 23, 2023

Abstract Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and need study increasingly complex evolutionary scenarios. With likelihood Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, in particular deep algorithms are emerging as popular techniques for population genetic inferences. These rely on that learn non-linear relationships between input model parameters being estimated through representation learning from training sets. Deep currently employed field comprise discriminative generative models with fully connected, convolutional, recurrent layers. Additionally, wide range powerful simulators generate under scenarios now available. The application empirical sets mostly replicates previous findings demography reconstruction signals natural selection organisms. To showcase feasibility tackle new challenges, we designed branched architecture detect recent balancing temporal haplotypic data, which exhibited good predictive performance simulated data. Investigations interpretability neural networks, their robustness uncertain creative will provide further opportunities technological advancements field.

Language: Английский

Citations

53