Monthly Archives: June 2021

A human DNA sequence is made of four types of nucleic acid called base pairs, each represented by their first letter: adenine (A), thymine (T), guanine (G) and cytosine (C). Altogether, a list 3.055-billion-letters long across 23 chromosomes makes up the human genome. Nearly two decades ago, the Human Genome Project set out to map the genetic makeup of the human species. In 2000, scientists completed the first draft of the human genome, but eight percent still remained, reports Matthew Herper for STAT.

The unsequenced remaining portion was a dizzying array of repeating letters. These missing gaps were almost impossible to decipher with the technology available at the time. Now, in a preprint published on May 27, a group of scientists describe the first "nearly" complete sequence of the human genome, reports Sarah Zhang for the Atlantic.

The feat was completed with scientists in the Telomere to Telomere (T2T) Consortium, a collaboration consisting of about 30 different institutions, reports Sara Reardon for Nature. Together, they found 115 new genes and added 200 million base pairs to a version of the human genome measured in 2013. They named the newly deciphered genome T2T-CHM13.

One of the most challenging regions to sequence in the human genome is centromeres. Each chromosome resembles an X-shaped tangle, and centromeres are located close to the pinched, knot-like center of each criss-cross. In these regions, DNA difficult to sequence because it is so densely packed and contains nearly endless repeating codes, the Atlantic reports.

But on five of the 23 total human chromosomes, the centromere is not precisely in the middle, instead favoring one end over the other, per the Atlantic. The asymmetrical point creates one long arm and one short arm on the chromosome. The previously unsequenced, repeating letters are located in these "short arms." Now, the team behind T2T-CHM13 has deciphered them.

The sequencing was made possible using new technologies developed by two private companies: Pacific Biosciences (PacBio) of Menlo Park, California, and Oxford Nanopore of Oxford Science Park in the United Kingdom.

Previously methods for genome-deciphering required cutting DNA into tiny pieces and then reassembling stretches of DNA later in a long, tedious process. Two new methods take different approaches. The Oxford Nanopore technology pulls the DNA into a small hole where longer sequences can be read. The PacBio tech uses lasers to examine 20,000 base pair sequences of DNA at a time repeatedly to create a highly accurate readout, reports STAT.

Using the Oxford Nanopore technology, the T2T-CHM13 consortium found that it can map where proteins attach to the centromere during cell division, per the Atlantic.

The sequenced DNA was derived using a cell line taken from tissue that forms when sperm fertilizes a non-viable egg that lacks a nucleus, also known as complete hydatidiform mole, reports Nature. (In other words, the sample was not taken from a person.) However, DNA is stored in the nucleus of an egg, so an egg without a nucleus does not contain gentic material from a mother. Instead, the "mole" only contains chromosomes from the father. Using a mole makes sequencing easier because researchers do not have to differentiate two sets of chromosomes from the parents.

But the T2T-CHM13 genome only represents one genome, so the researchers plan to team up with the Human Pangenome Reference Consortium to sequence over 300 genomes in the next three years from humans worldwide, using T2T-CHM13 as a reference. They also plan to sequence a Y chromosome next since the sperm used to create the hydatidiform mole only carried an X chromosome.

Link:
Scientists Are on the Cusp of Finally Deciphering the Entire Human Genome - Smithsonian Magazine

The release of the draft human genome sequence in 2001 was a seismic moment in our understanding of the human genome, and paved the way for advances in our understanding of the genomic basis of human biology and disease.

But sections were left unsequenced, and some sequence information was incorrect. Now, two decades later, we have a much more complete version, published as a preprint (which is yet to undergo peer review) by an international consortium of researchers.

Technological limitations meant the original draft human genome sequence covered just the euchromatic portion of the genomethe 92% of our genome where most genes are found, and which is most active in making gene products such as RNA and proteins.

The newly updated sequence fills in most of the remaining gaps, providing the full 3.055 billion base pairs (letters) of our DNA code in its entirety. This data has been made publicly available, in the hope other researchers will use it to further their research.

Much of the newly sequenced material is the heterochromatic part of the genome, which is more tightly packed than the euchromatic genome and contains many highly repetitive sequences that are very challenging to read accurately.

These regions were once thought not to contain any important genetic information but they are now known to contain genes that are involved in fundamentally important processes such as the formation of organs during embryonic development. Among the 200 million newly sequenced base pairs are an estimated 115 genes predicted to be involved in producing proteins.

Two key factors made the completion of the human genome possible:

1. Choosing a very special cell type

The newly published genome sequence was created using human cells derived from a very rare type of tissue called a complete hydatidiform mole, which occurs when a fertilized egg loses all the genetic material contributed to it by the mother.

Most cells contain two copies of each chromosome, one from each parent and each parents chromosome contributing a different DNA sequence. A cell from a complete hydatidiform mole has two copies of the fathers chromosomes only, and the genetic sequence of each pair of chromosomes is identical. This makes the full genome sequence much easier to piece together.

2. Advances in sequencing technology

After decades of glacial progress, the Human Genome Project achieved its 2001 breakthrough by pioneering a method called shotgun sequencing, which involved breaking the genome into very small fragments of about 200 base pairs, cloning them inside bacteria, deciphering their sequences, and then piecing them back together like a giant jigsaw.

This was the main reason the original draft covered only the euchromatic regions of the genomeonly these regions could be reliably sequenced using this method.

The latest sequence was deduced using two complementary new DNA-sequencing technologies. One was developed by PacBio, and allows longer DNA fragments to be sequenced with very high accuracy. The second, developed by Oxford Nanopore, produces ultra-long stretches of continuous DNA sequence. These new technologies allow the jigsaw pieces to be thousands or even millions of base pairs long, making them easier to assemble.

The new information has the potential to advance our understanding of human biology including how chromosomes function and maintain their structure. It is also going to improve our understanding of genetic conditions such as Down syndrome that have an underlying chromosomal abnormality.

Well, no. An obvious omission is the Y chromosome, because the complete hydatidiform mole cells used to compile this sequence contained two identical copies of the X chromosome. However, this work is underway and the researchers anticipate their method can also accurately sequence the Y chromosome, despite it having highly repetitive sequences.

Even though sequencing the (almost) complete genome of a human cell is an extremely impressive landmark, it is just one of several crucial steps towards fully understanding humans genetic diversity.

The next job will be to study the genomes of diverse populations (the complete hydatidiform mole cells were European). Once the new technology has matured sufficiently to be used routinely to sequence many different human genomes, from different populations, it will be better positioned to make a more significant impact on our understanding of human history, biology, and health.

Both care and technological development are needed to ensure this research is conducted with a full understanding of the diversity of the human genome to prevent exacerbation of health disparities by limiting discoveries to specific populations.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: Arek Socha /Pixabay

Read the rest here:
Why It Took 20 Years to 'Finish' the Human Genomeand Why There's Still More to Do - Singularity Hub

NEW YORK Personal Genome Diagnosticssaid on Tuesday that it has signed an agreement to provide its Elio Tissue Complete next-generation sequencing test platform to the Duke University Health System Clinical Molecular Diagnostics Laboratory.

Elio Tissue Complete is a US Food and Drug Administration-cleared 507-gene panel kit that identifies single-nucleotide variants, small insertions and deletions, amplifications, rearrangements, microsatellite instability, and tumor molecular burden in DNA from patient tissue samples.

According to Personal Genome Diagnostics, or PGDx, the platform will be integrated into the Duke lab and be used as its primary technology for clinical oncology genomic testing initiatives. As part of the deal, the Baltimore-based company and DUHS will collaborate on a data integration system to join the health system's electronic health records with other local systems.

Financial and other terms of the collaboration were not disclosed.

"We believe strongly that the ability to provide accurate, rapid genomic profiling data and insights that can be easily accessed and incorporated into existing cancer care pathways will allow our clinicians to further optimize treatments for the benefit of our patients," Michael Datto, associate VP of DUHS Clinical Laboratories, said in a statement.

Earlier this year, PGDx raised $103 million in Series C funding that it has been usingto expand its commercial infrastructure and forge new agreements with diagnostic and pharmaceutical partners.

See original here:
Personal Genome Diagnostics Inks Cancer Genomic Profiling Deal with Duke University Health System - GenomeWeb

INTRODUCTION

Can evolutionary outcomes be predicted? Biologists have long been fascinated with this question, including Darwin and Wallaces anticipation of the existence of Morgans sphinx moth based on orchid morphology (1, 2), Vavilovs prediction of the types of morphological variants likely to occur in plants (3), and Goulds gedankenexperiment about replaying the tape of life (4). Natural examples of recurrent evolution provide a particularly favorable opportunity to study the mechanisms that influence evolutionary predictability, including molecular patterns (5, 6).

Although the predictability of evolution may appear to be in conflict with the unpredictability of historical contingency, understanding the past can yield important insights into future evolution. For example, vertebrate populations frequently harbor large reservoirs of standing genetic variation (SGV) (7) that give independent populations access to similar raw genetic material to respond to environmental challenges, as observed in diverse species including songbirds, cichlid fishes, and the threespine stickleback (Gasterosteus aculeatus) (811). SGV is often apparent in divergent species or populations where it is pretested by natural selection and then distributed by hybridization to related populations. Thus filtered and capable of leaping up fitness landscapes, SGV can also drive rapid evolution (12), helping address a very real practical challenge to testing evolutionary predictions: time.

Longitudinal studies of evolving populations have been used to estimate the tempo and strength of selection on a variety of traits in different species (1318). Rapid phenotypic evolution over contemporary time scales has enabled hypothesis testing against detailed observations at every step in the process. There is an increasing and impressive body of research examining the genomic consequences of these phenotypic changes in microbial, invertebrate, and vertebrate systems (1926).

Stickleback fish provide an outstanding system for further study of the genomic basis of recurrent evolution. At the end of the last Ice Age, threespine stickleback, including anadromous populations that migrate from the ocean to freshwater environments to breed, colonized and adapted to countless newly exposed freshwater environments created in the wake of retreating glaciers around the northern hemisphere (27, 28). This massively parallel adaptive radiation was facilitated by natural selection acting on extensive ancient SGV (8, 11). Under the transporter hypothesis, these variants are maintained at low frequencies in the marine populations by low levels of gene flow from freshwater populations (29). Reuse of ancient standing variants has enabled identification of genomewide sets of loci that are repeatedly differentiated among long-established stickleback populations (8, 3035). In addition, SGV enables new freshwater stickleback populations to evolve markedly within decades (17, 3638), including conspicuous phenotypic changes in armor plates (17) and body shape (39).

The rapidity of stickleback evolution has made it possible to begin characterizing genomic and allele frequency changes seen in very young or newly established populations under intense directional selection on multiple traits (18, 3638, 4043). Here, we identify key molecular features that underlie repeated and rapid evolution of freshwater stickleback by comparing genomes from diverse extant populations with the earliest generation-by-generation changes in a detailed genomic time series from three newly founded populations. We identify several basic genomic and genetic features that can be used to predict evolutionary outcomes in stickleback and show that they can predict genomic responses to selection in distantly related cichlids and Darwins finches.

Previous whole-genome sequencing (WGS) of threespine stickleback identified 174 loci covering 1.2 Mb with alleles shared by common descent repeatedly selected in freshwater populations around the world (8). Just as human genetic diversity is greatest in Africa, where Homo sapiens arose (44), we hypothesized that the north Pacific region where stickleback originated (27) may contain a particularly rich pool of ancient adaptive alleles. To test this hypothesis, we generated whole-genome sequence data with 76base pair (bp) paired-end Illumina reads for 38 new marine and 110 new freshwater stickleback, respectively (mean coverage of 5.5) (sections S2, S4, S6, and S7). Combined with previous stickleback sequencing (8, 41), our dataset includes 227 individual genomes: 135 genomes from 70 northeast Pacific populations in Alaska, Haida Gwaii, British Columbia, and Washington and 92 genomes from 62 populations in California, Japan, and the Atlantic coasts of North America, Iceland, and northern Europe (Fig. 1A and section S8).

(A) Marine (red) and freshwater (blue) stickleback from the locations shown were used for various analyses (table S2). (B) Detail of part of chrIV for single-nucleotide polymorphism (SNP)based analysis of differential allele distribution between marine and freshwater ecotypes in the northeast Pacific basin. SNPs within specific-threshold EcoPeaks are red. A subset of regions overlap the globally shared peaks of marine-freshwater differentiation indicated by blue-colored bars [cluster separation score (CSS), 5% false discovery rate (FDR) identified by Jones et al. (8)]. (C) As in (B), but for the whole chromosome [dashed lines from (B) to (C)]. (D) Same whole chromosome as in (C), but with genetic (not physical) distance along the x axis. (E and F) Genomewide SNP divergence between marine and freshwater ecotypes globally and in the northeastern Pacific basin, with specific-threshold EcoPeaks in red. (G) Many differentiated regions overlap the location of major quantitative trait loci (QTLs) controlling various morphological, physiological, and behavioral traits in previous genetic crosses [percent variance explained (PVE) > 20, interval < 5 Mb from Peichel and Marques (53)].

We used two methods to identify loci repeatedly differentiated in freshwater populations, both based on the expectation that variants recurrently selected from SGV will be more similar among geographically separated freshwater populations than neutral loci (section S9). First, we used a genetic distancebased approach within overlapping 2500-bp windows tiled across the genome [as in the study by Jones et al. (8)]. While statistically powerful, this approach may miss younger loci with few differences between alleles and exhibits spatial resolution dependent on window size. Second, we analyzed the distribution of variants at individual bases across the genome, which has base pairlevel resolution and less bias against younger loci, though at the cost of statistical power. After calling P valuebased peaks of ecotypic (freshwater- or marine-associated) differentiation using both methods, we accepted calls at two stringency levels, either requiring agreement between the two analyses at 1% false discovery rate (FDR) (specific) or support from either at 5% FDR (sensitive). We refer to these peaks of ecotypic differentiation as EcoPeaks. We called EcoPeaks for different geographic sets of samples to find alleles that were either shared globally, within the northeast Pacific, or within other geographic regions.

Although results of the global analysis largely matched a previous report [79 of 81 most stringent calls from Jones et al. (8) in sensitive EcoPeaks (P = 4.2 1021; table S3)], both the sensitive and specific call sets identified approximately five times as many Pacific EcoPeaks as global EcoPeaks, spanning sevenfold more of the genome (Fig. 1, E and F, and Table 1). In addition, many northeast Pacific EcoPeaks not overlapping the globally shared regions identified by Jones et al. (8) exhibit even more consistent ecotypic differentiation (assessed by P values) than others shared around the world (Fig. 1, B and C). Much smaller sets of non-global EcoPeaks were identified in the North Atlantic, subglacial Pacific, and supraglacial geographic regions (fig. S5), consistent with other reports (8, 35).

The comparisons by Jones et al. (8) are with the cluster separation score 5% FDR set (8).

As theoretical studies indicate that SGV is immediately available for evolution and may show an increased likelihood of large-effect alleles being advantageous compared to de novo mutations (12, 45), the rich genetic reservoir observed in the northeast Pacific provides a favorable system for studying the dynamics and predictability of rapid evolutionary change (section S10). Previous studies suggest that stickleback in the northeast Pacific can adapt to freshwater environments within decades (36). However, thus far, studies have lacked temporal resolution of genome evolution in the critical early years of adaptation.

To characterize the earliest stages of evolution after the establishment of new freshwater populations, we analyzed annual samples from populations that were recently founded by anadromous stickleback in three lakes in Alaska (Fig. 2A and section S1). In 1982, stickleback in Loberg Lake (LB) were exterminated to improve recreational fishing (17). Sometime between 1983 and 1988, LB was invaded by completely plated (~33 plates per side) anadromous stickleback [most likely from neighboring Rabbit Slough (RS)]. The characteristic freshwater, armor-reduced phenotype increased rapidly from ~16% in 1991 to ~50% by 1995 and to ~95% by 2017 (Fig. 2B) (17), with similarly rapid changes in overall body shape (39) and reproductive patterns (46). So as to more systematically examine even earlier generations of freshwater adaptation, Bell et al. (47) introduced ~3000 anadromous RS fish into each of two other Cook Inlet lakes without outlets that had been similarly treated to exterminate fish: Cheney Lake (CH) in 2009 and Scout Lake (SC) in 2011. Lowarmor-plated (~5 to 7 plates per side) stickleback began to appear in the second and third generation after founding in CH and SC respectively, and, by 2017, they had increased to 20 to 30% (Fig. 2B).

(A) The timing (years since founding) and approximate size of subsequent sequencing sample pools from lake populations [Loberg Lake (LB), Cheney Lake (CH), and Scout Lake (SC)] founded recently by anadromous stickleback (left) and the scenario for divergence of anadromous populations after colonizing the lakes (right). Red and blue fish represent the complete armor-plated and armor-reduced phenotypes, respectively. (B) Frequency of armor-reduced morphological phenotype across our CH, SC, and LB time series overlaid with the frequency squared for the freshwater (FW) Eda allele. LB data are based on a combination of individual genotypes and pool-seq frequencies, while CH and SC are based only on pool-seq frequencies. (C) Allele frequency trajectories for eight SNPs found within TempoPeaks on distinct chromosomes with the highest Cochran-Mantel-Haenszel (CMH) scores (except for chrIV:12823875, the Eda-plate regulatory region SNP). (D) Genomewide distribution of window-based CMH scores across chrIV for different combinations of transplant lakes discussed in the main text. Black, dark red, and teal bars above figure represent specific CH + SC + LB TempoPeaks, northeast Pacific EcoPeaks, and significant loci from Jones et al. (8) identified using CSS [5% FDR (8)], respectively.

To obtain genomewide allele frequencies across our time series, we performed pooled WGS (pool-seq) on all seven available annual samples from CH and SC since founding and eight from LB distributed between 1999 and 2017 (Fig. 2A and sections S3, S4, S7, and S13). Each freshwater pool-seq experiment consisted of 100 individuals (with three exceptions), with mean coverage of 223 per pool. In addition, we resequenced a pool of 200 anadromous RS individuals used to found the CH population in 2009 (RS2009) to 585.

We identified single-nucleotide polymorphisms (SNPs) with significant allele frequency changes, indicating directional selection, using a modified Cochran-Mantel-Haenszel (CMH) test optimized for pool-seq data (48), followed by an approach analogous to our EcoPeak analysis to define both a permissive sensitive and a stringent specific set of loci that we term TempoPeaks (sections S16 to S18). Combining all three populations into a single CMH analysis (CH + SC + LB) and using RS2009 as a proxy for the founders of LB, we identified 524 sensitive and 344 specific TempoPeaks. Despite operating over very different time spans, the visual correspondence between the Pacific EcoPeaks in long-established populations and the TempoPeaks in recently established populations is notable, particularly for the specific TempoPeaks, of which 323 of 344 (94%) overlap with the sensitive Pacific EcoPeaks (Fig. 2D and section S18). In contrast, even the most lenient set of global EcoPeaks and regions from Jones et al. (8) overlap only 96 of 344 (28%) and 47 of 344 (14%) specific TempoPeaks, respectively (tables S9 and S10), emphasizing the importance of understanding the locally available SGV. Even analyzing only CH + SC (thus focusing on <10 years of freshwater adaptation), we identified 271 sensitive and 86 specific TempoPeaks, 73% and 99% of which, respectively, overlap the sensitive Pacific EcoPeaks. This marked congruity strongly suggests that the ancient SGV represented by Pacific EcoPeaks is the primary genomic feature enabling extremely fast evolution of freshwater phenotypes in stickleback from the northeast Pacific basin.

The Eda SNP associated with armor plate variability (chrIV:12,823,875 T>G (49)) is within the second most significant specific TempoPeak on chrIV. In both CH and SC, the G allele increases rapidly from an initial frequency of <1% to over 50% within 8 years, while approaching fixation in LB by 15 years. Notably, the square of G-allele frequencies (i.e., the expected number of GG homozygotes) tracks closely with frequencies of the lowarmor plate phenotype, consistent with almost complete recessiveness (h = 0.0) for the G allele for this phenotype (Fig. 2B). Nonetheless, to fit the allele frequency trajectory of this SNP, and, in particular, the extremely rapid increase in CH and SC, it was necessary to impose a dominance coefficient (h) of 1.0 along with a very large selection coefficient (s) of 0.55, as in a recent paper focusing on this locus (18).

Like Eda, most TempoPeaks display similarly sharp left-shifted sigmoidal allele frequency trajectories, indicating very strong and dominant-positive selection (Fig. 2C and section S20). When modeling each peak SNP as independent, we find an extremely high mean s of 0.30 (5th, 95th percentile 0.08 to 0.53) and h of 0.98 (5th, 95th percentile 0.95 to 1.0) for the 344 specific TempoPeaks found in CH + SC + LB. The estimated s values for chrIV, where there are 69 TempoPeaks, are particularly high (mean s = 0.38), consistent with the accelerated evolution of this whole chromosome observed via a chromosome-wide FST analysis comparing the founding generation of CH, SC, and LB to all subsequent years (section S15).

The remarkable speed at which northeast Pacific stickleback adapt to new freshwater environments suggests that analysis of EcoPeaks may provide unique insights into optimal genomic properties for evolution. Using Gasterosteus nipponicus, Gasterosteus wheatlandi, and Pungitius pungitius for calibration, we estimated molecular divergence time between a pair of freshwater (Little Campbell upstream) and marine (Little Campbell downstream) stickleback in windows tiled across the genome (section S11). We find that EcoPeaks as a whole are significantly older than the rest of the genome [1600 thousand years (ka) versus 700 ka, P < 5 10324]. Although peaks shared globally trend older than those found just within the northeast Pacific (1800 ka versus 1600 ka, P = 0.18), the imputed ages overlap considerably (Fig. 3A). We estimate that the majority (161 of 209) are over a million years old and have cycled between freshwater and marine environments many times during this long history, likely persisting at high frequency in freshwater habitats south of the zone of glaciation during the Ice Ages and at more northerly latitudes during previous interglacials and the Holocene.

(A) Distribution of estimated molecular age for those EcoPeaks either shared worldwide (orange) or within the northeast Pacific (blue). Ma, million years. (B) EcoPeaks with older estimated molecular ages tend to be larger. (C) Estimated ages decline with distance on either side of EcoPeaks. Each dot represents mean age in 1-kb windows flanking the EcoPeak centers (gray bars, 1 SE). (D) Recombination rates tend to be lower within EcoPeaks compared to the rest of the genome, 1 SE. (E) Recombination rates and distances to nearest 20 recombination hotspots, plotted for randomly subsampled 1-kb windows tiled across the genome, with marginal histograms of all windows. Locations overlapping EcoPeaks (red) are shifted to both smaller hotspot distances and lower recombination rates compared to other genomic regions (gray). (F) Observed haploblock size in marine fish carrying freshwater EcoPeaks on the indicated chromosomes across three marine populations. For all, specific northeast Pacific EcoPeaks are used.

Contrary to our expectations that recombination would disassemble regions over time, we found that older EcoPeaks are larger than younger ones (Fig. 3B). This signature is strongest at the most significant markers within each EcoPeak, which are typically older than more distal sequences (Fig. 3C). This suggests that individual regions may grow over time, with alleles originally based on an initial beneficial mutation accumulating additional linked favorable mutations, snowballing over time to form a finely tuned haplotype with multiple adaptive changes. This is consistent with work in other species identifying examples of evolution through multiple linked mutations that together modify function of a gene (5052) and implies that progressive allelic improvement may be common.

We also observed that EcoPeaks frequently overlap major quantitative trait loci (QTLs) in stickleback [73 of 209 overlaps observed versus 32 of 209 expected, P < 1 1015; Fig. 1G (53)], suggesting that these variants underlie many mapped phenotypic traits. Just as the QTLs cluster in supergene complexes (54), so too do EcoPeaks (median observed interpeak distance 192 kb versus 795 kb expected, P = 4.88 1010). One particularly large complex (chrIV: 8 to 17 Mb) contains 22 EcoPeaks and the major QTLs controlling many aspects of both defensive armor and trophic morphology (e.g., the length of dorsal and pelvic spines, the number of armor plates through Eda, gill rakers, and teeth). Thus, clustering may have important functional effects by allowing multiple traits and underlying EcoPeaks to be selected and inherited as a single unit, especially when in tight linkage. A fine-scale recombination map of RS stickleback (generated with LDhelmet (55)) shows that EcoPeaks are highly enriched in regions of low average recombination, forming tightly linked haploblocks (Fig. 3D, compare Fig. 1, C and D; section S14). EcoPeaks are also enriched near local recombination hotspots within their neighborhood (Fig. 3E), potentially facilitating reassembly of larger haplotype blocks upon freshwater colonization (also see section S19).

To further examine the frequency and size of haploblocks in individual fish, we surveyed 1643 stickleback from three Alaskan marine populations by SNP array genotyping (sections S5 and S12). While most marine fish heterozygous for freshwater alleles carry a relatively small haploblock, some carry multi-megabase haploblocks containing multiple EcoPeaks (Fig. 3F). Thus, a proper treatment of rapid stickleback evolution needs to account for the complex linkage of EcoPeaks rather than treating them independently.

Modeling the genomic landscape of contemporary evolution. To estimate a more realistic distribution of fitness effects (DFE) that incorporates the genomes recombination landscape, we developed a deep neural network (DNN) approach that uses forward simulations (section S21). Our simulations, which are conceptually similar to those of Galloway et al. (56), attempted to replicate the dynamics of the transporter model (29), with one large (Ne = 10,000) anadromous population connected independently by gene flow to 10 smaller (Ne = 1000) established freshwater populations. After 1000 generations, we founded three new freshwater populations from the anadromous population, thus generating simulated allele frequency trajectories that reflect our annual LB, CH, and SC samples (Fig. 4A).

(A) Schematic showing evolutionary model of forward simulations under the transporter hypothesis. Red horizontal bars, anadromous (AN) ancestor; blue circles, descendant freshwater isolates; red to blue shaded circles, three adapting freshwater populations (i.e., LB, CH, and SC) founded recently by anadromous stickleback; and arrows, gene flow or founding events. (B) Genotypes across chrIV for freshwater-associated SNPs in RS (n = 750), LB in 1999 (n = 25), and LB in 2013 for (left) observed and (right) simulated data under best-fit DNN model. anadromous homozygous, red; heterozygous, yellow; and freshwater homozygous genotypes, blue; respectively. (C). Allele frequency trajectories for LB, CH, and SC in 100 simulations under the best-fit DNN model for five randomly selected SNPs. Larger points, observed data. (D) Distribution of average CMH scores in windows of 2500 bp across chrIV for (top) observed and (bottom) simulated data under best-fit DNN model. Red dotted lines, locations of SNPs under selection and used to fit DNN.

Focusing our DNN analysis on a subset of 19 specific TempoPeak SNPs separated by 0.4 cM (~100 kb) along chrIV, we closely replicated observed allele trajectories of positively selected freshwater alleles across all SNPs simultaneously using a beta distributionshaped DFE, for which the mean s across the 19 TempoPeaks was 0.063 and the standard deviation was 0.030, with reciprocal fitness costs implemented in the marine population (Fig. 4C). The estimated s from our DNN was thus substantially smaller than the mean of 0.48 when each SNP was considered independently. In addition, 18 of 19 SNPs were predicted to be fully dominant and none fully recessive under the best model.

We validated our best-fit DNN model by simulating the 19 selected TempoPeaks SNPs with the estimated DFE along with ~400k neutral SNPs distributed randomly along chrIV. Despite the neutral SNPs not being used in training the DNN, we were able to mimic the overall topology of the CMH scores across the entire genome, suggesting that our model was capturing the overall genomic architecture of freshwater adaptation (Fig. 4D). Our best-fit DNN model also appeared to recapitulate much of the haplotype structure of the array data from individuals from RS, LB1999, and LB2013 (Fig. 4B). Notably, the transition to freshwater alleles appears to be somewhat slower on the right half of chrIV, where there are fewer EcoPeaks, TempoPeaks, and QTLs, and this difference was observable in both the empirical and simulated data.

Overall, our model suggests that extremely rapid and replicable allele frequency increases on chrIV in LB, CH, and SC are mostly driven by multiple linked (primarily) dominant alleles, each with relatively smaller s values that act in concert, with recombination hotspots between them (section S19) allowing rapid reassembly of optimum freshwater haplotypes, consistent with the transporter hypothesis. The lower individual s values may allow these dominant alleles to persist in the marine environment at low frequency after being disassembled by recombination, especially if some act in epistasis.

Biological features with predictive power. Given the genomewide dynamism of the earliest stages of freshwater adaptation, we attempted to identify genomic features that predict the speed of evolution at TempoPeaks and understand why some peaks are consistently selected more rapidly than others (section S22). We used CMH scores as a proxy of evolutionary speed for each TempoPeak in CH + SC + LB and regressed these against a variety of sequence features.

The best predictor for the speed of evolution is the degree of ecotypic differentiation between marine and long-established freshwater populations (Pacific EcoPeak P value), with variants more commonly differentiated in the northeast Pacific being selected more quickly (Fig. 5A and fig. S81). Fishers geometric model indicates that alleles with large effects are usually disfavored; however, the prefiltering of ancient SGV that counters this tendency (12) largely benefits alleles that are broadly positively selected, possibly explaining this result.

(A) Variance in the speed of TempoPeak selection explained by different underlying genomic features, including colored bars: empirical recurrence of marine-freshwater differentiation (peak Pacific ecotypic P value), number of additional Pacific EcoPeaks within 10 cM, number of major QTLs overlapped, sequence divergence, and recombination rate; gray bars: genomic size of EcoPeak, total number of variable nucleotides, elevated Ka/Ks in coding regions, overlap with genic sequences, overlap with conserved noncoding sequence (PhastCons nonexonic), and carrier frequency of freshwater alleles in marine populations. (B) Precision-recall curve for predicting the locations of selected loci in CH + SC + LB lakes by either individual genomic features (dotted lines), a composite model trained with these basic predictors, or the empirical expectation of recurrence based on many extant populations. Precision is the fraction of predictions that are accurate, while recall is the fraction of true positives that are correctly predicted. No skill refers to the performance expected by random chance. (C) Performance above chance of the composite model applied to stickleback, cichlids, and two representative pairs of species of Darwins finches (ground finches: Geospiza magnirostris versus Geospiza propinqua; tree finches: Camarhynchus pauper versus Camarhynchus psittacula).

We also found that larger TempoPeaks are typically selected more rapidly. Similarly, greater TempoPeak density predicts more rapid divergence, suggesting that our simulation accurately reflects how nearby loci mutually reinforce their collective selection. Overlap with major QTLs also has a strong association with rapid evolution, while other variables such as increased sequence divergence, decreased recombination rate, increased gene overlap, increased sequence conservation, increased Ka/Ks, and decreased ancestral marine frequency have smaller contributions to predictive power for speed of selection (Fig. 5A).

We also tested whether underlying sequence characteristics could predict not only the speed of selection in CH + SC + LB but also the location of the selected regions themselves (section S23). Recombination rate, QTL overlap, allelic age, and an integrated genomic context score (section S23) that incorporate the previous features are all useful predictors (Fig. 5B). By combining these fundamental features into a logistic model trained on the survey of extant populations, the most confident predictions of selected regions in the rest of the genome achieve 85% precision. This model performs 67% as well as predictions based only on empirical repeatability in extant populations in the northeast Pacific (Fig. 5B). Thus, our understanding of underlying principles reflects an incomplete yet substantial proportion of evolutionary repeatability.

To test the generality of these predictive factors, we applied the stickleback-trained model to a dataset of 12 pairs of species of Darwins finches (section S23) (57). Darwins finches have undergone adaptive radiation in the Galpagos Islands over the last several hundred thousand years, are ~435 million years divergent from stickleback, and face very different selective pressures. As in stickleback, however, the islands of divergence of all 12 analyzed pairs of species of Darwins finches (sensu Han et al.) are enriched for ancient alleles overlapping mapped QTLs with low recombination rates. The top 100 windows predicted by the stickleback model recover a median of 28-fold more previously identified islands of divergence than expected by chance (P < 1 1010; Fig. 5C), including the Alx1 and Hmga2 loci implicated in beak morphology in multiple species pairs (even without QTL input). The model also recovers a substantial proportion of differentiated loci in a recent case of cichlid speciation (58). Thus, a handful of basic genomic properties allow strong quantitative predictions of the location of key evolutionary loci, even across widely separated branches of life.

Originally posted here:
Predicting future from past: The genomic basis of recurrent and rapid stickleback evolution - Science Advances

Patients across the UK will benefit from better healthcare, treatments and faster diagnosis as the government sets out how it will continue to deliver world-leading genomic healthcare.

Genomics is the study of genetic information and can help diagnose diseases earlier and more accurately, reduce some invasive procedures and enable tailored treatments. Building on the success of the 100,000 Genomes Project, the UK government has committed to sequence one million whole genomes 500,000 genomes in the NHS and 500,000 in UK Biobank which will transform healthcare in the UK and create jobs.

In addition, genomics has also been used to better understand Covid-19 and the variants that have increasingly become one of the biggest concerns of the pandemic.

Each variant is made up of a collection of mutations. The majority of mutations dont change how the virus behaves. However, some mutations can change the properties of the virus, and potentially give rise to a new variant. Many of these mutations of interest occur in the spike protein, which is what gives the virus its ability to target, latch onto and enter the cells that it infects.

Working with key partners across the genomics community, the bold new Genome UK implementation plan 2021 to 2022, published in May, sets out 27 commitments to deliver over the next year, including five high-priority actions: faster diagnosis; whole genome sequencing for patients with rare diseases; engagement closely with different communities to ensure diverse datasets; recruitment of up to five million people representative of the UK population; to develop global standards and policies for sharing genomic and related health data.

Faster diagnosis and treatment of cancer using genomics through a partnership between Genomics England and NHS England will help researchers and healthcare professionals identify technologies that could be used to provide faster and more comprehensive genomic testing for cancer.

Whole genome sequencing for patients with rare diseases and cancer, as part of the NHS Genomic Medicine Service, will build on the success of the 100,000 Genomes Project, and aims to increase the amount of genomic data available to researchers.

The drive for larger and more diverse datasets from different communities aims to ensure that everyone across the UK can benefit from genomic healthcare and genomic databases that are representative of such a diverse population. This is essential for equitable access to new techniques, such as polygenic risk scores (PRS), which compares a persons risk to others with a different genetic makeup, and pharmacogenomics, which examines the role of the genome in the bodys response to drugs.

Developing global standards and policies for sharing genomic and related health data ensures accurate and quick sharing of research data, which will help to benefit the entire genomics community.

The National Institute for Health Research, Medical Research Council and Wellcome Trust will, over the next five years, provide 4.5m of funding to the Global Alliance for Genomics and Health, ensuring standards are easily accessible and usable by global genomic programmes and data-sharing initiatives, placing the UK at the forefront of secure sharing of international genomic and health-related data.

Matt Hancock, the UKs Health Secretary, said: We will transform the UK into a life sciences superpower. Well build on the success story of our life sciences during the pandemic, which has led the world in everything from vaccine development, to finding effective treatments that work, to genomic sequencing.

Today weve published our Genome UK implementation plan for how we can build on this even further, including our commitment to sequence one million whole genomes. Because genomics saves lives, and Im determined the UK stays at the forefront of this vital new technology, Hancock continued. If we draw on ingenuity like this, we can keep up the fight against Covid-19, and also tackle the other things that stop us living healthier lives like cancer, dementia and heart disease.

So, were increasing UK investment in research and development, bringing much more of the supply chain onshore, sparing no effort to attract the brightest innovators and the best manufacturers, he concluded.

Minister for Innovation Lord Bethell said: The UK has a proud history in developing genetic and genomic technologies which improve the lives of patients across the country and globally.

This implementation plan demonstrates the great strides we have already made since the launch of Genome UK, and outlines the actions we are taking to progress key commitments over the next year.

It is vital that we continue to maintain and develop our global leadership in this field, to realise the full potential offered by genomics, Lord Bethell added.

This first phase implementation plan follows on from Genome UK: the Future of Healthcare published in 2020, which set out a vision to create the most advanced genomic healthcare system in the world, to deliver better healthcare at lower cost.

Genomics is just one example of the governments commitment to driving forward health innovation in the UK, which will be central to the future health resilience, the growth of the UKs life sciences sector and improving patient care.

Chris Wigley, Genomics England CEO, said: Since the days of Darwin, Rosalind Franklin, Crick and Watson, and Fred Sanger, the UK has been at the forefront of genomic science. With this publication its exciting to see the next chapter of that story coming to life. Our ecosystem has come together as never before through the difficult times of the pandemic and this implementation plan will allow us to build on this collaboration between all of the world-leading genomics institutions in the UK.

Professor Dame Sue Hill, NHS Englands Chief Scientific Officer, said: The NHS is already a global leader in genomics and has introduced a range of new cutting-edge tests for people with rare diseases and cancer over the last year, despite the pandemic.

Genomics can truly transform the way patient care is delivered, helping to predict and prevent disease, personalise treatments and ultimately save lives.

In February 2021, the UKs Covid-19 Genomics UK Consortium (COG UK) launched the COG-UK Mutation Explorer (COG-UK-ME) an interface that provides access to data on Sars-CoV-2 mutations and variants of interest in the COG-UK genome sequence dataset. COG-UK-ME allows anyone to view information about important changes in the Sars-CoV-2 genome over time.

The tool is updated twice weekly, and largely focuses on spike gene mutations of potential or known importance; providing information on cumulative frequency and data for the last 28 days, to give an approximate assessment of recent changes.

COG-UK-ME draws UK genome data from the MRC-CLIMB database. This data visualisation tool allows anyone to follow information over time on important changes in the Sars-CoV-2 genome.

Selecting the Mutational Explorer tab takes you to three tables. Table 1 lists mutations in the spike gene that have led to an amino acid change (called a substitution, which is concentrated on because it may change the way that the virus interacts with humans).

Mutations are ranked by frequency in the MRC-CLIMB database (the most common mutations first). A search function allows individual mutations to be selected, and a file downloaded containing a list of COG-UK identifiers, dates and lineages. For example, selection of E484K provides links to information for each genome that carries this mutation, the date of the sample, and the lineage the isolate belongs to.

Data can also be visualised for each mutation in a graph by clicking the visualiser tab. This shows the number of times the selected mutation has been detected over time.

COG-UK-ME also displays mutations that could affect the way that the virus interacts with the human immune response based on laboratory studies (Antigenic information tab).

Scientific evidence is graded. High confidence is applied when a mutation is found by multiple independent studies using multiple different approaches, including studies using polyclonal (convalescent or post-vaccine) antisera; medium confidence means this has been found by multiple independent studies; and lower confidence indicates this has been found by a single study only. Mutations with an antigenic role can also be filtered by domains of the spike protein.

The Explorer will be updated with new functions over time, based on scientific observations and ways of describing and thinking about variants. The current Covid-19 pandemic, caused by Sars-CoV-2, represents a major threat to health. The Covid-19 Genomics UK (COG-UK) consortium has been created to deliver large-scale and rapid whole-genome virus sequencing to local NHS centres and the UK government.

Led by Professor Sharon Peacock of Cambridge University, COG-UK is made up of an innovative partnership of NHS organisations, the four Public Health Agencies of the UK, the Wellcome Sanger Institute and 12 academic partners providing sequencing and analysis capacity. Professor Peacock is also on a part-time secondment to PHE as director of science, where she focuses on the development of pathogen sequencing through COG-UK.

COG-UK was established in April 2020 supported by 20m funding from the Covid-19 rapid-research-response fighting fund from the UK government, and administered by the National Institute for Health Research, UK Research and Innovation and the Wellcome Sanger Institute.

The consortium was also backed by the Department of Health and Social Cares Testing Innovation Fund in November last year to facilitate the genome sequencing capacity needed to meet the increasing number of Covid-19 cases in the UK over the winter.

More here:
UK at the forefront of genomics research - Scientific Computing World

The move to study genome sequences of the virus that infected children is part of Karnataka's effort to prepare for the third wave of infections.

June 17, 2021 / 01:52 PM IST

Karnataka has started genome sequencing of the SARS-CoV-2 virus among children who tested positive during the second wave to determine whether the infections are being caused by the newer variants of the virus.

The state has tasked Prof V Ravi, a former professor of virology, to study the gene sequences in samples of children who tested positive for COVID-19, as per an Indian Express report.

Track this LIVE blog for latest updates on coronavirus pandemic

Ravi said that samples are being collected and they need to be processed. "We should have data and information in about 15 days, he said, as quoted by the publication.

The move to study genome sequences of the virus that infected children is part of the state's effort to prepare for the third wave of infections.

"We have discussed the issue of infections among children and some additional work is required to study the genomic sequencing of viruses from samples of children," said Prof MK Sudarshan, the chairman of the state technical advisory committee, as per the report.

The state government had earlier in June formed a Genomic Surveillance Committee to identify the emerging strains early so that scientists can establish the transmissibility of the new strain.

The committee, under the leadership of Dr V Ravi, will assist the Karnataka COVID-19 Task Force in taking decisions towards controlling the COVID-19 pandemic.

The panel has also been tasked with "Genome sequencing to study virus variations/mutations and conduct an in-depth analysis of genome surveillance and vaccination to identify immune escape versions of virus and their spread".

Meanwhile, Karnataka reported 7,345 new cases of COVID-19 and 148 fatalities on June 16, taking the total number of infections to 27,84,355 and the deaths to 33,296, the Health Department said. The total number of active cases in the state is 1,51,566.

Excerpt from:
Coronavirus third wave: Karnataka begins genome sequencing of children to prepare - Moneycontrol

Most are familiar with the field of genomics in relation to ancestry mapping such as 23andMe and Ancestry.com uncovering relatives and identifying genetic risk factors for disease. However, genomics has also widely been applied to tracking infectious disease through sequencing the genomes of bacteria, parasites and viruses.

Now, it is helping us to better understand how to combat the coronavirus.

Assistant Professor Taj Azarian, an infectious-disease epidemiologist, is heading up genomic surveillance of SARS-CoV-2 at UCF ahead of the universitys plans for a full return to face-to-face instruction in the fall.

One component to help us with a safe return in the fall is vaccination of as many people as possible. Another is incorporating genomic surveillance of SARS-CoV-2 so were able to track the spread of the virus, Azarian says. What we want to do is build a ring of protection around the students and employees at the school so that basically were identifying cases as early as possible and implementing public health measures. Well also be able to assess the effectiveness of those measures using viral sequencing data.

Azarians lab takes samples with individuals identities redacted from positive COVID-19 tests and isolates the virus RNA, or genetic code. The samples are obtained from random testing on campus as well as symptomatic patients at the Student Health Center and the Parking Garage A testing site.

The lab prepares the RNA sample and then inputs it into a genome sequencing platform that enables hundreds of samples to be simultaneously analyzed with the aid of the high-performance computing cluster at UCF. This allows the lab to compare viral sequences among individuals with COVID-19 to look for similarities or differences.

For instance, Azarian says, this analysis could determine if a set of cases is linked to one residence hall, or a social event, which would help officials identify additional cases and determine appropriate public health interventions.

I like to describe this as building family trees of pathogens, he says. By looking at the relatedness between strains, we can infer a number of things like how fast an epidemic is spreading, how fast it is evolving and whether it is developing resistance to any of our interventions such as vaccines.

By looking at the relatedness between strains, we can infer a number of things like how fast an epidemic is spreading, how fast it is evolving and whether it is developing resistance to any of our interventions such as vaccines. Taj Azarian, assistant professor

Azarian says he was drawn to working at UCF because of its genomics and bioinformatics faculty cluster, which was created to inspire cross-cutting research that leverages UCFs strengths in biomedical sciences, evolution and ecology, and computer science.That faculty cluster has played a role in aiding his genomic surveillance work during the pandemic.

Since the beginning of the pandemic, he has been working with the Florida Department of Health to monitor the emergence and spread to COVID-19 in the community.

Now his focus will shift to doing the same for UCF, where his lab will be able to compare viral sequences from UCF to those collected throughout Florida and abroad.

Its part of a broader initiative led by the Centers for Disease Control and Prevention, which works closely with researchers and public health labs in the United States to generate, share and analyze viral sequencing data. Researchers and public health officials can analyze and compare the data in a larger context to better understand the virus, detect a potential outbreaks of related cases, develop interventions, and monitor emerging variants.Recently, Azarian and a colleague at the University of Florida were appointed to collaborate on a project funded by The Rockefeller Foundation to become part of a U.S. Regional Accelerators for Genomic Surveillance program.

Monitoring variants has become increasingly crucial due to their potential to spread easier or cause more severe disease. Azarian says that there is a growing concern for variants for which the vaccine is less effective.

Azarian emphasizes that ensuring individuals privacy and personal information through the process is a priority and that no ones DNA is retained anywhere.

Were only interested in the RNA of the virus, he says. In any of the work that we do, we are not looking for the human DNA that may be present in the sample. Were only focusing on the virus sequence, and once we perform the viral RNA extraction, those samples are discarded. Furthermore, when our results are uploaded to the online repository, we do a screening to make sure there is no human DNA data being uploaded.

Azarians work in his lab is assisted by two undergraduate students, a recent graduate and two post-doctoral fellows at UCF.

As an aspiring physician, being involved in such a project is an honor and a once-in-a-lifetime opportunity, says Anita Samadabadi 20, a Burnett Honors Scholar who graduated in December with a bachelors degree in biomedical sciences. This opporutnity allows me to apply what I learned in the classroom about biomedical research to real-world scenarios. My hope is that we all will learn valuable lessons from the COVID-19 pandemic so we never have to face another pandemic again in the future. But if that unfortunately happens, I am sure that the lessons and experiences I am acquiring by being part of this project will help me be an asset for my community.

Azarian says this field will continue to be critical even as more of the worlds population is vaccinated.

We as a community are starting to see the light at the end of the tunnel because of the vaccine, and I think the No. 1 question on everyones mind is, Did we win the fight? he says.

Right now, I feel like we are in a race between getting people immunized and the spread of some of these new variants. Even as immunization rates are climbing, its imperative we continue this surveillance so we can identity whether those variants continue to emerge and what that means for the future of the vaccine whether it will need to be updated at some point to be more like the seasonal influenza vaccine, or if this one is going to work against the strains now and the ones that may emerge in the future. Genomic surveillance will help us answer these questions.

Continued here:
What Is Genomic Surveillance and Why Is It Being Done at UCF? - UCF

A ball of 4,000-year-old hair frozen in time tangled around a whalebone comb led to the first ever reconstruction of an ancient human genome just over a decade ago.

The hair, which was preserved in arctic permafrost in Greenland, was collected in the 1980s and stored at a museum in Denmark. It wasn't until 2010 that evolutionary biologist Professor Eske Willerslev was able to use pioneering shotgun DNA sequencing to reconstruct the genetic history of the hair.

He found it came from a man from the earliest known people to settle in Greenland known as the Saqqaq culture. It was the first time scientists had recovered an entire ancient human genome.

Now a review of the first decade of ancient genomics of the Americas published in Nature today (June 16 2021) written by Professor Willerslev a Fellow of St John's College, University of Cambridge, and director of The Lundbeck Foundation GeoGenetics Centre, University of Copenhagen, with one of his longstanding collaborators Professor David Meltzer, an archaeologist based at Southern Methodist University, Texas, shows how the world's first analysis of an ancient genome sparked an incredible 'decade of discovery'.

Professor Willerslev said: "The last ten years has been full of surprises in the understanding of the peopling of the Americas - I often feel like a child at Christmas waiting to see what exciting DNA present I am about to unwrap! What has really blown my mind is how resilient and capable the early humans we have sequenced DNA from were - they occupied extremely different environments and often populated them in a short space of time.

"We were taught in school that people would stay put until the population grew to a level where the resources were exhausted. But we found people were spreading around the world just to explore, to discover, to have adventures.

"The last 10 years have shown us a lot about our history and what it means to be human. We won't ever see that depth of human experience on this planet again - people entered new areas with absolutely no idea of what was in front of them. It tells us a lot about human adaptability and how humans behave."

For decades, scientists relied on archaeological findings to reconstruct the past and theories weren't always accurate. It was previously thought, that there were early non-Native American people in the Americas but the ancient DNA analysis so far has shown that all of the ancient remains found are more closely related to contemporary Native Americans than to any other population anywhere else in the world.

Professor Meltzer, who worked on the review with Professor Willerslev while the former was at St John's College as a Beaufort Visiting Scholar added: "Genomic evidence has shown connections that we didn't know existed between different cultures and populations and the absence of connections that we thought did exist. Human population history been far more complex than previously thought.

"A lot of what has been discovered about the peopling of the Americas could not have been predicted. We have seen how rapidly people were moving around the world when they have a continent to themselves, there was nothing to hold them back. There was a selective advantage to seeing what was over the next hill."

In 2013, scientists mapped the genome of a four-year-old boy who died in south-central Siberia 24,000 years ago. The burial of an Upper Palaeolithic Siberian child was discovered in the 1920s by Russian archaeologists near the village of Mal'ta, along the Belaya river. Sequencing of the Mal'ta genome was key as it showed the existence of a previously unsampled population that contributed to the ancestry of Siberian and Native American populations.

Two years later, Professor Willerslev and his team published the first ancient Native American genome, sequenced from the remains of a baby boy ceremonially buried more than 12,000 years ago in Anzick, Montana.

In 2015, their ancient genomic analysis was able to solve the mystery of Kennewick Man, one of the oldest and most complete skeletons ever found in the Americas, and one of the most controversial.

The 9,000-year-old remains had been surrounded by a storm of controversy when legal jurisdiction over the skeleton became the focus of a decade of lawsuits between five Native American tribes, who claimed ownership of the man they called Ancient One, and the United States Army Corps of Engineers.

Professor Willerslev, who has rightly learnt to be mindful of cultural sensitivities when searching for ancient DNA, has spent much of the past decade talking to tribal community members to explain his work in detail and seek their support.

This meant he was able to agree with members of the Colville Tribe, based in Washington State where the remains were found, that they would donate some of their DNA to allow Professor Willerslev and his team to establish if there was a genetic link between them and Kennewick Man.

Jackie Cook, a descendant of the Colville Tribe and the repatriation specialist for the Confederated Tribes of the Colville Reservation, said: "We had spent nearly 20 years trying to have the Ancient One repatriated to us. There has been a long history of distrust between scientists and our Native American tribes but when Eske presented to us about his DNA work on the Anzick child, the hair on my arms stood up.

"We knew we shouldn't have to agree to DNA testing, and there were concerns that we would have to do it every time to prove cultural affiliation, but our Council members discussed it with the elders and it was agreed that any tribal member who wanted to provide DNA for the study could."

The Kennewick Man genome, like the Anzick baby, revealed the man was a direct ancestor of living Native Americans. The Ancient One was duly returned to the tribes and reburied.

Cook added: "We took a risk but it worked out. It was remarkable to work with Eske and we felt honoured, relieved and humbled to be able to resolve such an important case. We had oral stories that have passed down through the generations for thousands of years that we call coyote stories - teaching stories. These stories were from our ancestors about living alongside woolly mammoths and witnessing a series of floods and volcanoes erupting. As a tribe, we have always embraced science but not all history is discovered through science."

Work led by Professor Willerslev was also able to identify the origins of the world's oldest natural mummy called Spirit Cave. Scientists discovered the ancient human skeleton back in 1940 but it wasn't until 2018 that a striking discovery was made that unlocked the secrets of the Ice Age tribe in the Americas.

The revelation came as part of a study that genetically analysed the DNA of a series of famous and controversial ancient remains across North and South America including Spirit Cave, the Lovelock skeletons, the Lagoa Santa remains, an Inca mummy, and the oldest remains in Chilean Patagonia.

Scientists sequenced 15 ancient genomes spanning from Alaska to Patagonia and were able to track the movements of the first humans as they spread across the Americas at 'astonishing' speed during the Ice Age and also how they interacted with each other in the following millennia.

The team of academics not only discovered that the Spirit Cave remains was a Native American but they were able to dismiss a longstanding theory that a group called Paleoamericans existed in North America before Native Americans. Spirit Cave was returned to The Fallon Paiute-Shoshone Tribe, a group of Native Americans based in Nevada, for burial.

Professor Willerslev added: "Over the past decade human history has been fundamentally changed thanks to ancient genomic analysis - and the incredible findings have only just begun."

Reference:Willerslev E, Meltzer DJ. Peopling of the Americas as inferred from ancient genomics. Nature. 2021;594(7863):356-364. doi:10.1038/s41586-021-03499-y

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.

Original post:
Ancient Genomics Has Taught Us What It Means To Be Human - Technology Networks

NEW YORK A team led by researchers at the University of North Carolina at Chapel Hill has tracked down more than 150 parts of the genome that seem to influence the microstructure of white matter in the human brain, including loci that overlap with sites previously linked to brain diseases and other traits or conditions.

For a study published in Science on Thursday, the researchers performed a genome-wide association study that included more than 43,800 individuals who had diffusion magnetic resonance imaging, or dMRI,of their brain done, focusing in on variants at 151 new or known loci that were significantly linked to white matter microstructure.

Along with loci previously implicated in glioma or other brain conditions, they saw ties between white matter microstructure and dozens of other traits or diseasesand found genes linked to white matter structure that are targeted by existing drugs.

"The targets of many drugs commonly used for disabling cognitive disorders have genetic associations with white matter, which suggests that the neuropharmacology of many disorders can potentially be improved by studying how these medications work in the brain white matter," senior author Hongtu Zhu, a researcher at UNC Chapel Hill, and his colleagues wrote.

The team's various analyses clarified some of the implications of these genetic contributors. For example, the associations highlighted the importance of glial cells, such as oligodendrocytes, in white matter architecture, as common variants influencing the regulation of these brain cells tended to be overrepresented among white matter-related variants detected in the study.

In a related perspectives article in Science, University of Colorado researcher Christopher Filley, who was not involved in the study, emphasized that a "complete portrait of the structural basis of cognition and emotion cannot neglect the white matter because it interacts so intimately with its gray matter counterpart."

For the discovery stage of the GWAS, Zhu and colleagues considered dMRI and genotyping data for more than 34,000 UK Biobank participants, focusing on five diffusion tensor imaging-based microstructure metrics that offer a look at 21 specific white matter tracts in the cerebral cortex.

From these data, the researchers narrowed in on 42 loci linked to white matter tract structure in the past, along with 109 new loci associated with the diffusion tensor imaging metrics. They noted that 30 of those novel loci structures were found through analyses centered on specific white matter tracts.

"Our results illuminate the broad genetic control of white matter microstructural differences and the contribution of tract-specific [fractional anisotropy principal components] in identifying genetic variants associated with white matter tracts," the authors reported, adding that the genetic effects detected "are spread across a large number of genomic regions, consistent with the observed polygenic genetic architecture of many brain-related traits."

After validating suspicious variants in another 17,700 individuals from nine prior studies, the team performed meta-analyses that included discovery and validation cohort participants from European and non-European ancestry groups, along with gene-centered and drug target analyses that pointed to more than a dozen white matter-related genes that are targeted by existing antipsychotic, antidepressant, antidementia, and other neuropsychiatric drugs.

The team cautioned that the current findings largely stemmed from genetic data for individuals with European ancestry, and centered on diffusion tensor imaging parameters, leaving untapped genetic insights for other white matter metrics and populations.

"The emerging recognition of white matter and its contribution to human behavior will advance medicine as well as neuroscience," Filley wrote in his perspectives article. "Considering both environmental and genetic factors clarifies the structure and function of normal and abnormal tracts, and this knowledge promises in turn to improve the diagnosis and treatment of people in whom white matter dysfunction may be disturbing neurobehavioral capacity."

Read more here:
GWAS Reveals New Loci Linked to Brain White Matter Microstructure - GenomeWeb