Category Archives: Transhuman News

We knew the Delta variant would eventually arrive in Aotearoa, but real-time sequencing, which produces full genomes from positive cases in less than 12 hours, will ensure the lockdown is as short and effective as possible.

There are now 21 cases and we can expect more to be reported over the coming days. Genome sequencing of the first case, identified on Tuesday, did not show any direct matches to cases found in managed isolation facilities, but it is linked with the current Delta outbreak in New South Wales.

But the newer cases in the cluster are a close match to a returnee from Sydney who arrived on a managed flight on August 7, tested positive two days after arrival and was transferred to hospital on August 16.

The genomes and cases we have found so far cannot tell us how many cases there are, but modelling by Te Pnaha Matatini, which takes into account the number of people with COVID-like symptoms getting tested, suggests the outbreak was already between 30 and 75 active cases by the time we discovered it. Whatever the number is, it is almost certainly still growing.

Because of its higher transmissibility, Delta has become the dominant strain in many parts of the world, including in Aotearoa. All cases found at our border over the past three months have been the Delta variant 170 full genomes found so far.

Read more: 'Genomic fingerprinting' helps us trace coronavirus outbreaks. What is it and how does it work?

While this is the first community transmission of the Delta variant weve seen in Aotearoa, that is mainly because our border detection and management has been successful in keeping it at the border until now.

Lockdown measures along with tracking, tracing and isolation will dramatically reduce the opportunity for the virus to spread and hopefully bring the R number below 1 so that the number of new cases will eventually start dropping.

As we find more cases that are not directly linked to each other, their genomes will give us some information about how large the outbreak might be. Essentially, the greater the diversity in the genomes we see, the older and larger the outbreak is likely to be.

If all the cases have identical genomes, it would mean the outbreak has not been around long enough to pick up mutations. But if there are several mutations that separate cases, it would mean there is probably a longer chain of transmission between the cases and a potentially large number of as yet undiscovered cases.

The Delta variant of SARS-CoV-2 (the virus that causes COVID-19) was first seen in India in late 2020 and is the most recent variant of concern to have been identified. Variants of concern are lineages that are either more transmissible, cause more serious disease or show greater ability to evade vaccines.

Delta is a variant of concern first and foremost because it transmits at a much higher rate than previous variants. Its basic reproduction number, R0, is estimated to be around 5 or 6. In an unvaccinated population with no other prevention measures, this means an infected person would likely infect five or six others, compared to about two or three for the variants that were dominant in 2020.

Read more: SARS-CoV-2 mutations: why the virus might still have some tricks to pull

Like other variants of concern, Delta has a large number of mutations that distinguish it from other SARS-CoV-2 lineages. It is characterised by over 20 mutations, including nine on the spike protein which enables the virus to stick to and infect cells. Essentially, these changes make the virus more sticky and more successful at infecting cells and replicating.

This results in much higher viral loads (the overall number of viral copies an infected person has) and people becoming infectious and symptomatic more quickly. Combined, this results in faster transmission and larger outbreaks.

We know that SARS-CoV-2 transmission depends on superspreading events when a small number of cases (perhaps 10-20%) are responsible for most (80%) of the transmission.

We saw this in Aotearoas first wave in 2020, which was dominated by a few large clusters. It was also evident in various lucky breaks we have had since then, when cases in the community have not transmitted the virus to household contacts.

Delta is different in that fewer Delta cases have no onward transmission but it seems likely this is just a function of the overall higher transmissibility, rather than a change in super-spreading behaviour.

The other reason Delta is of concern is because it is more able to infect vaccinated people. Such breakthrough infections remain rare, and vaccines are still very effective at preventing serious disease.

But people with breakthrough infections can pass the virus on to others, albeit at a lower rate.

Vaccines therefore give us multiple lines of protection. They make us less likely to get infected, and even if we do, much less likely to get seriously sick and less likely to transmit the virus.

The speed at which the Delta variant spreads means we cannot vaccinate fast enough to change the course of the current outbreak. But if we eliminate this outbreak and rapidly roll out the vaccine in the next few months, future outbreaks will be easier to control.

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Why rapid genome sequencing is key to finding out how long Delta has been in NZ, and how large this outbreak might be - The Conversation AU

Public labs put into action by the INSACOG for this purpose are not adequately funded and have the capacity to process only 30,000 samples a month.

Genome sequencing is a recent technique and hence focus must be put on creating awareness for both people as well as clinicians

The rollout of vaccines to tackle the spread of the novel coronavirus has brought relief to the entire world. However, inequity in vaccine distribution has created opportunities for new variants to emerge. A robust genomic surveillance program must be developed in India and then scaled up by December 2021, to rapidly detect and evaluate new variants. This will be essential to manage and mitigate threats to collective public health.

Genomic surveillance allows researchers to examine and compare the genome sequence of the viral strains infecting the population. This characterisation is necessary to pre-empt and prevent new COVID-19 wavesand future epidemics/pandemics. Public labs put into action by the Indian SARS-CoV-2 Genome Sequencing Consortia (INSACOG) for this purpose are not adequately funded. These public labs have the capacity to process 30,000 samples a month.

To achieve the five percent sequencing target, India must process 75,000 samples a month which is 2.5 times the current scale. Increasing the effort to a magnitude of over five times, will quickly use up spare capacity in the public labs. Making private sector labs a part of the genome sequencing process can help India build a better response to the impending third wave as well as future pandemics.

Sequencing the viral genome helps public health specialists track the spread and trajectory of the mutated virus, called variants, and design effective strategies. Failure to characterize the variant affects diagnosis and reduces our ability to stop the infection and treat patients in the right manner.

This partially explains negative RT-PCRs in infected people in the second wave. Vaccines, the most effective control mechanism today, can also have a reduced response to variants. With existing drugs and vaccines reducing their beneficial effects on variants, the possibility of more severe disease will increase. For it to be effective, the surveillance needs to be done at a global scale since that is how the pandemic spreads.

An open-source collaborative initiative called Global Initiative on Sharing All Influenza Data or GISAID, already collects, shares and analyses data on all viral infections including COVID. Through its efforts, GISAID has become a credible source for researchers around the world to track the trajectory of the virus and help governments create the most appropriate responses. As of 13th July, India had submitted 34,997 samples to this initiative. This amounts to 0.113 percent of the database.

In India, where less than one percent of the population is infected, sampling of five percent of its positive cases can help identify and track new mutations of the virus. With 45,000-50,000 new cases, sampling five percent requires 2,500 samples to be collected and analysed every single day. We now average 450-500 samples a day.

Increasing the speed and scale of genome sequencing in India is therefore absolutely essential. This includes identifying the right people, collecting good quality samples (through a nasal or oral swab), submitting the samples to the right labs that are equipped to carry out the test, and sequencing the viral genome. Finally, the results must be shared with INSACOG and GISAID, the Indian and global bodies tracking the mutation. This process is exhaustive but necessary, because it helps to track and control the spread of the delta- or any other emerging variant that could well result in a third wave of the pandemic.

To speed up results we should get representative samples from hotspots or areas of unusual outbreaks, particularly focusing on people getting re-infected. Samples from vaccinated people getting infected will also be required to fully understand the degree of mutation in the variant. To achieve scale, investing to increase the capacities of public genome-sequencing labs would be ideal. However, since the need is immediate and urgent, a more practical approach would be to utilise private sector capacity in collaboration with the union government. An order from the ICMR has, however, banned this for the moment.

Including the private sector, leverages untapped capacity while requiring minimal investment in infrastructure and human capital. Private lab chains say each lab can sequence between 100-2000 samples a day depending on its size. Considering that India has several such companies, large and small, this will be a welcome addition in capacity. Assuming the daily number of cases stays constant, 2500 samples a day, will not prove difficult to sequence. In the event of cases escalating, private labs can scale to two to three times their current capacity with adequate funding from the government. Since these are a handful of companies, managing the agreements and executing this plan also seems relatively simple.

A central nodal agency to ensure data is anonymised and stored securely, while not the best option, could work in the immediate term to address concerns about data storage, privacy and security. ICMR or the NCDC may be required to create a standardized protocol for processing and storing samples to ensure the highest data quality and standardization.

Regular check-ins with private players can ensure that targets are met and that costs do not escalate. One way to do this is to focus sample collection on infection hotspots, containment zones and travel hubs. This will ensure that the aim is on the quality of the samples and not the quantity.

This is by no means an all-encompassing idea. The approach will have to be multi-pronged. The government will have to invest in upscaling INSACOGs public labs in parallel while opening more of them. Adding this capacity may seem a long and tedious process, but is absolutely necessary for a coherent response to fend off future waves or other pandemics. Given the urgency in the immediate term, involving private players seems to be an effective, efficient, affordable, and quick turnaround solution.

Genome sequencing is a recent technique and hence focus must be put on creating awareness for both people as well as clinicians. Cultural issues around privacy and sharing genome samples could create barriers for this very important but niched practice, to go mainstream. As we can see, it is critical for genomic sequencing hubs to become a permanent part of Indias pandemic preparedness policy.

The author was formerly Executive Vice President at GlaxoSmithKline and a participant in the Graduate Certificate in Public Policy (Health & Lifesciences) program at the Takshashila Institution.

Read more:
Genomic sequencing hubs must be made a permanent part of Indias pandemic response - Firstpost

Results from genome sequencing could make a crucial difference to the scale of this outbreak and how long New Zealand stays in lockdown. Photo / Dom Thomas/RNZ

Results from genome sequencing could make a crucial difference to the scale of this outbreak and how long New Zealand stays in lockdown. Science reporter Jamie Morton explains.

Genome sequencing creates a "genetic fingerprint" of a virus that has infected a person, and can help public health officials untangle different cases involved in an outbreak through their genetic sequences.

We can think of a whole genome as a box of jigsaw pieces, all of which make up the genetic puzzle of any organism on the planet.

Within our own box is 22 paired chromosomes, along with a 23rd that sorts our sex.

It's formed as a double helix of DNA, or deoxyribonucleic acid, and packs about 30,000 genes, along with three billion chemical bases that help hold the strands of DNA together.

Even the SARS-CoV-2 virus that causes Covid-19 has its own jigsaw puzzle.

But it's different in that it's comprised of ribonucleic acid, or RNA, so is single-stranded rather than double-stranded like DNA.

Unsurprisingly, it's much less complex than us: it contains just 30,000 bases, making up 15 specific genes.

Scientists piece these puzzles together through a process called sequencing - or figuring out the order of bases in a genome, then assembling them at once to get a complete picture of an organism's DNA.

In New Zealand's first wave of Covid-19, for instance, scientists sequenced the genomes of 649 separate cases to reveal nearly 300 different introductions from different parts of the world.

19 Aug, 2021 01:00 AMQuick Read

Later, in Auckland's August outbreak, genome sequencing - together with other tools like contact tracing - swiftly helped officials tie together chains of infections in real-time.

Because of our relatively low number of positive cases to date, scientists have been sequencing samples of every positive case that's come into the country through MIQ.

That's helped link back any cases that have managed to get into the community.In this case, so far, it's already told us we're dealing with the Delta variant - and that the strain came from New South Wales, where much sequencing has also been done.

Scientists will also be sequencing the genomes of positive cases that emerge from this outbreak, to aid tracing.

Just three positive cases from Sydney went through MIQ in New Zealand in August and scientists have been quickly carrying out genome sequencing on samples from these cases.

For rapid or urgent samples, ESR can typically have a result within 24 hours.

If one of these cases is linked to the current outbreak, that will give officials a clear link to the border - and narrow down the search for chains of infection.

"If contact tracing and genome sequencing can identify when the virus first leaked into New Zealand from New South Wales, we'll be in a better position to estimate how far down the chain of transmission these new cases are and how many additional community cases might be out there," Te Punaha Matatini modeller Dr Rachelle Binny said.

Fellow modeller Professor Shaun Hendy said a best-case scenario would be a firm genomic link to MIQ - or to another border point like a port.

"That will tell us something about the chain of transmission and then maybe we are just looking at a handful of cases."

Otherwise, the situation would remain uncertain - potentially pointing to a tip-of-the-iceberg scenario and raising worrying questions about how the virus entered the country.

Had the original incursion been a traveller who arrived in New Zealand from New South Wales before the transtasman bubble closed last month, for instance, that would mean the virus had been circulating here since then.

Yet, so far, indicators like community testing and wastewater surveillance for Covid-19 - all showing no positives - hadn't suggested a large hidden outbreak.

The Government was meanwhile contacting all travellers from Australia into New Zealand during the relevant timeframe to find whether the first case is linked to them.

Prime Minister Jacinda Ardern said the Government would leave "no stone unturned" in identifying a case that at some point originated in Australia.

Read more:
Covid 19: Why genome sequencing is crucial to this outbreak - New Zealand Herald

The companion diagnostic (CDx) Human Neurotrophic Tyrosine Receptor Kinase (NTRK) 1/2/3 Genomic Alteration Testing Kit developed by OrigiMed in cooperation with Bayer is granted the Special Review Procedure for Innovative Medical Devices by the National Medical Products Administration (NMPA) of China.

The CDx test detects NTRK gene fusions via DNA- and RNA-based next-generation sequencing (NGS) to inform treatment decisions for larotrectinib.

Larotrectinib, a highly selective TRK inhibitor exclusively designed to treat tumors that have an NTRK gene fusion, is approved in more than 40 countries including the United States (U.S.), countries of the European Union (EU), and Japan for adult and pediatric patients with solid tumors that harbor an NTRK gene fusion and is in development in China.

SHANGHAI, Aug. 21, 2021 /PRNewswire/ -- OrigiMed announced that the Human NTRK1/2/3 Genomic Alteration Testing Kit has been granted the Special Review Procedure for Innovative Medical Devices by the Center for Medical Device Evaluation of NMPA. This testing kit is developed by OrigiMed in cooperation with Bayer.

The Human NTRK1/2/3 Genomic Alteration Testing Kit is developed to detect NTRK 1, 2 and 3 gene fusions in solid tumors. It is the first companion diagnostic specifically developed for larotrectinib in China and will help identify NTRK gene fusion-positive patients for whom treatment with larotrectinib may be appropriate.

Larotrectinib, a highly selective TRK inhibitor exclusively designed to treat tumors that have an NTRK gene fusion, is approved in more than 40 countries including the U.S., countries of the EU, and Japan for adult and pediatric patients with solid tumors that harbor an NTRK gene fusion. Additional filings in other markets, including China, are underway or planned.

The Human NTRK 1/2/3 Genomic Alteration Testing Kit is based on DNA- and RNA-based next-generation sequencing (NGS) and applies the innovative OriFusion independently patented by OrigiMed as its core technology. Fusion candidates are identified by hybrid-capture based technology. Besides the known fusion, it also can effectively detect novel fusions with high sensitivity and specificity.

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About Larotrectinib

Larotrectinib, a highly selective TRK inhibitor, was exclusively designed to treat tumors that are NTRK1/2/3 gene fusion positive (TRK fusion cancer). The compound has demonstrated high response rates and durable responses over three years in adults and children with TRK fusion cancer, including responses and a high disease control rate in central nervous system (CNS) tumors. It has the largest dataset and longest follow-up data of any TRK inhibitor. The dataset of 218 patients was presented at the ASCO Annual Meeting 2021.

Larotrectinib is approved under the brand name Vitrakvi in more than 40 countries, including the U.S., countries of the EU, Japan, and other markets around the world, for pediatric and adult patients solid tumors that harbor an NTRK gene fusion. Additional filings in other markets, including China, are underway or planned. The Human NTRK1/2/3 Genomic Alteration Detection Kit is a companion diagnostic test for larotrectinib in treating adult and pediatric patients in China with solid tumors that harbor an NTRK gene fusion once larotrectinib is approved for medical use.

About TRK fusion cancer

TRK fusion cancer is rare overall, affecting both children and adults and occurs in varying frequencies across various tumor types. TRK fusion cancer occurs when an NTRK gene fuses with another unrelated gene, producing an altered TRK protein. The altered protein, or TRK fusion protein, becomes constitutively active or overexpressed, triggering a signaling cascade. These TRK fusion proteins act as oncogenic drivers that fuel the spread and growth of the patients' cancer, regardless of where it originates in the body.

About OrigiMed

OrigiMed is a precision medicine company focusing on high-tech R&D with a global vision and the knowledge transfer in the clinical practice of cancers. With the commitment to developing hundreds of cancer genomic tests, the company strive to provide comprehensive and accurate molecular information to every patient and help doctors with their precision medicine practice. OrigiMed aims to promote the innovation in the way of clinical treatment for cancer in China. At OrigiMed, patients' molecular changes are identified by the stringently verified CGP and matched with approved targeted therapies, immunotherapies, and clinical trials. OrigiMed closely collaborates with global and domestic biopharmaceutical companies to help with new drug development and approvals. For more information, please visit: http://www.origimed.com

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The NGS based CDx Human NTRK1/2/3 Genomic Alteration Test Kit to inform treatment decisions for larotrectinib developed by OrigiMed in cooperation...

Radiocarbon dating

We sampled the petrous part of the temporal bone of samples POP23 and POP39 and obtained calibrated radiocarbon dates from the Oxford Radiocarbon Accelerator Unit for POP23 (OxA-378000; OxA-37801, ORAU) and POP39 (OxA-37999, ORAU) using IntCal13 calibration curve45 and OxCal version 4.3.246. We also report the following radiocarbon dates for individuals included in this study: from the Ruer Bokovi Institute for POP33 (Z-5732, IRB) with IntCal13 calibration curve45 and OxCal version 4.2.447, from the Penn State AMS 14C Facility for POP35 (PSUAMS-4444, PSU) with IntCal13 calibration curve45 and OxCal version 4.3.248; from University of California Irvine Keck-Carbon Cycle AMS facility for JAG34 (UCIAMS-233509, UCI KCCAMS) with IntCal20 calibration curve49 and OxCal version 4.448 (Table 1, Fig.1b, Supplementary Table S1).

Group labels generally follow the format Country_SiteName/Region_TimePeriod, with the site name contracted to a short form, for example Croatia_Pop_MN; Pop Popova zemlja, Jag Jagodnjak, Dal Dalmatia. Time periods are labelled as: BA Bronze Age, CA Copper Age, EBA Early Bronze Age, EN Early Neolithic, IA Iron Age, LBA Late Bronze Age, LCA Late Copper Age, MBA Middle Bronze Age, MN Middle Neolithic, N Neolithic and RomanP Roman Period.

We processed all samples in dedicated ancient DNA laboratories at the University College Dublin, Ireland. Petrous bones were UV irradiated for 10 to 15min on each side followed by light sandblasting of the outer surface to remove loose debris. The cochlea was then excavated from the petrous bone using a sandblaster, and UV irradiated on each side for 10min before it was finely powdered in a mixer-mill (Retsch).

DNA was extracted from about 50 to 70mg of bone powder following a modified silica column based method optimised for ancient DNA samples50. In a pre-digestion step aimed at reducing contamination51, the bone powders were digested for one hour at 56C without rotation in 1ml of extraction buffer containing 0.45M EDTA and 0.25mg/ml Proteinase K. The bone powder was spun down to a pellet by centrifugation and re-suspended in fresh extraction buffer. Samples were digested for one hour at 56C followed by 18h at 37C with rotation. Samples were centrifuged at 13,000rpm, and the 1ml supernatant added to the reservoir of a Roche High Pure extender assembly tube containing 13ml of binding buffer. Binding buffer consisted of 5M Guanidine Hydrochloride, 40% isopropanol, 90mM sodium acetate and 0.05% Tween-20. Tubes were centrifuged for four minutes at 1500 rcf, the spin column detached and placed in a collection tube and dry spun at 6000rpm for one minute to remove remaining binding buffer. After placing the spin column in a fresh collection tube, 650l of PE wash buffer was added, centrifuged for one minute at 6000rpm and the flow-through discarded. This step was repeated once followed by dry spinning at 13,000rpm to remove remaining wash buffer. The column was placed in a clean Eppendorf tube and the sample eluted with 25l TET which had been incubated at 3756C. Samples were then incubated at 37C for ten minutes followed by centrifugation for 30s at maximum speed. The elution step was repeated, resulting in a total volume of 50l DNA extract. One negative control containing only extraction buffer was processed for every seven samples.

Non-UDG-treated, double-stranded libraries were constructed following52. Blunt-end repair was carried out by adding NEBNext End- Repair module (New England Biolabs) to 12.5l of each DNA extract, which was incubated at 25C for 15min, followed by 12C for 5min. Samples were then incubated at 25C for 30min for adapter ligation with T4 DNA Ligase (ThermoFisher Scientific). Adapter fill-in was performed with Bst Polymerase (New England Biolabs) with an incubation at 37C for 30min and 80C for 20min to inactivate the enzyme. Purification steps following blunt-end repair and adapter ligation was performed with the MinElute PCR purification kit from Qiagen. A negative control was processed for every seven samples, and a final library volume of 40l obtained.

Single indexing PCR was performed by adding a unique seven-base-pair index to 3l of each library using Accuprime Pfx Supermix (Life Technology) and IS4 primer to a total reaction volume of 25l. The PCR temperature profile consisted of initial denaturation at 95C for five minutes, a further 15s of denaturation at 95C, twelve cycles of annealing at 60 C for 30s, elongation at 68 C for 30s and a final extension at 68 C for five minutes. A negative PCR control was included with every batch. The PCR amplification and subsequent clean-up steps were performed in a separate lab in a different part of the building from the clean labs. MinElute PCR purification kit spin columns were used for purification of amplified libraries following Qiagen instructions. Quantification of amplified product was performed using Qubit 2.0 Fluorometer (Thermo Fischer Scientific) and Agilent 2100 Bioanalyser DNA 1000 assay. Single-end shotgun sequencing was performed by pooling samples in equimolar amounts onto an Illumina NextSeq500 platform using 75-cycle kits for 176 cycles and 17 cycles for de-multiplexing.

Adapter sequences were trimmed from reads using Cutadapt (version 1.15)53 discarding reads under 17bp (-m 17) and allowing an overlap of 1bp between the read and adapter (-O 1). Reads were then mapped to the UCSC genome browser human reference hg19 (GRCh37) to produce BAM files with BWA aln/samse (version 0.7.15-r1140)54, replacing the mitochondrial genome with the revised Cambridge Reference Sequence (rCRS, Gen bank accession no. NC_012920.1)55. Seed length was disabled (l 1000) and the default number of differences (-n 0.04) and minimum Phred scale mapping quality of 30 (-q 30) were used. PCR duplicates were removed with SAMtools rmdup (v.0.1.19-96b5f2294a)56.

Sites overlapping the~1240k SNP capture array were used to generate a pileup file for each individual using SAMtools mpileup (version 1.3)56 with the quality flags q30, -Q30 and B. This file was used to genotype individuals whereby a single base call was chosen at random from each SNP site to produce pseudo-haploid calls with transversion SNPs only using the flag t SkipTransitions in pileupCaller (https://github.com/stschiff/sequenceTools) in order to remove variants affected by post-mortem damage in non-UDG treated samples. A second genotype dataset was produced that included all SNPs for use in functional SNP and ROH analyses (see below) (Supplementary Table S1).

We used mergeit (version 2450) from the package ADMIXTOOLS57, to merge the new genotype data to reference datasets2,30,31,35,38,39,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,101, Supplementary Table S2) containing 1250 ancient individua l s genotyped at 1,233,013 SNP sites, of which 140,159192,648 transversion-only SNP positions are covered by the newly-reported individuals (Supplementary Table S1). This was also merged with diploid genotypes of 26 present-day individuals58,59,60,61, and, for tests involving present-day comparisons, a panel of worldwide present-day populations containing 1311 individuals genotyped at 597,573 nuclear SNP positions on the Affymetrix Human Origins (HO) array30, of which 66,88094,317 transversion-only SNP positions are covered by the newly-reported individuals (Supplementary Table S1).

We produced a pileup from BAM files using SAMtools mpileup (version 1.3)56 with a minimum mapping and base quality of 30 (-Q 30, -q 30) and B to turn off base alignment quality for five phenotypically informative SNPs that are included in the 1240K panel. This included lactase persistence39,40 and skin and eye pigmentation35,36,37. Numbers of reads supporting each allele are reported in Supplementary Table S11.

Coverage on the autosomal and sex chromosomes was calculated using a script available at https://github.com/TCLamnidis/Sex.DetERRmine to determine genetic sex of each individual with standard errors. Males should have an x-rate of 0.5 and y-rate of 0.5. Females should have an x-rate of one and y-rate of zero (Table 1, Supplementary Fig. S1, Supplementary Table S1).

The presence of deamination damage patterns at the terminal bases of reads, characteristic of ancient DNA, was verified using mapDamage (version 2.0.8)62 (Supplementary Table S1).

As males have only one copy of the X chromosome, we measured contamination in males by estimating polymorphism on the X chromosome using ANGSD (version 0.910)63. Results based on new Method1 are reported for a minimum of 200 SNPs on the X chromosome that are covered at least twice (Table 1, Supplementary Table S1).

Estimates of contamination based on comparison of the mitochondrial genome with a database of potential present-day contaminant human mtDNA sequences were obtained with Schmutzi64. To do this, we used EAGER (version 1.92.55)65 to remap all reads for each individual to the mitochondrial rCRS (Gen bank accession no. NC_012920.1)55 with CircularMapper (version 1.93.4)65, filtering on a minimum mapping quality of 30 and removing duplicates. Schmutzi confidence intervals are given as est.high and est.low (Table 1, Supplementary Table S1).

We used Schmutzi64 to reconstruct consensus mtDNA sequences for each individual from the remapped reads, which we then imported into Haplogrep266 (https://haplogrep.i-med.ac.at/) for automated mitochondrial haplogroup assignment based on phylotree mtDNA tree build 17 (http://www.phylotree.org/) (Table 1, Fig.4a, Supplementary Table S1). We manually checked aligned mtDNA sequences for individuals possessing the same haplogroup, which revealed that the pair of first degree relatives, JAG58 and JAG06, possessed identical mutations for the T2b branch. Variants at unreliable polyC stretch positions were disregarded: 518, 309.1C(C), 315.1C, AC indels at 515522, 16093C, 16182C, 16183C, 16,193.1C(C) and 16,519.

We used Yleaf (version 1.0)67 to infer the Y chromosomal haplogroup in males in an automated way based on haplogroup-defining SNP positions in the ISOGG 2016 nomenclature. We filtered results on derived alleles and transversion-only SNPs, and the most downstream haplogroup was selected (Table 1, Fig.4a, Supplementary Table S1). Haplogroups were also inferred manually using SAMtools mpileup q30 Q30 with concordant results (version 1.3)56. We used Integrative Genomics Viewer (Broad Institute)68 to visually inspected reads in order to verify if defining variants were in the middle or at the end of a read to assess reliability, and to confirm mutations among individuals with shared haplotypes. Four of the Jagodnjak males share the same mutations (G2a2a-Z31430) while no reads covered the defining position for this haplotype in the fifth male, JAG82.

Consanguinity up to two degrees of relatedness was assessed by calculating pairwise mismatch rates from autosomal pseudo-haploid genotype data filtered on transversion-only SNP sites from the 1240K SNP panel, using pMMRCalculator (https://github.com/TCLamnidis/pMMRCalculator) and READ69 with default parameters. READ provides an upper and lower Z score to help assess the certainty of the results, with the upper Z score indicating the distance to a lower degree of relationship, and a lower Z score indicating the distance to a higher degree of relationship. There was a minimum overlap of 92,000 SNPs between pairs of individuals, and both methods produced comparable results (Fig.4a, Supplementary Fig. S8, Supplementary Tables S1 and S9), although READ produces slightly lower mismatch rates than pMMRCalculator, meaning individuals are estimated to be slightly more closely related than pMMRCalculator estimates. READs binned approach with sliding windows may contribute to this discrepancy. A pair of first degree relatives is expected to have a pairwise mismatch rate that is halfway between the baseline for unrelated and identical individuals. Coefficients of relatedness can be somewhat inflated for individuals with high inbreeding coefficients however. No relatedness was found between POP39 and a previously published individual I3499 from the same site and similar radiocarbon date. Where first degree relatives were identified, one individual from the pair was excluded from population-wide analyses, in this case JAG06. The same analyses were performed on a genotype dataset that included all SNP sites in order to assess the effects of damage on kinship estimates (Supplementary Fig. S8). Transversion-only genotypes shift the pairwise mismatch rates downwards, which means individuals are estimated as more closely related than when using all SNPs. This data is produced from non-UDG-treated DNA libraries, therefore this could indicate that ancient DNA damage can lead to an under-estimate of true relatedness.

Runs of Homozygosity (ROH) greater than four centimorgans (cM) were identified with the Python package hapROH34 (https://test.pypi.org/project/hapsburg/) using default parameters for the dataset containing all SNPs. A global dataset of 5008 haplotypes were used as a reference panel taken from the 1000 Genomes Project. The total sum ROH is reported for each length category of 4-8cM, 8-12cM, 12-20cM and>20cM (Fig.4a,c, Supplementary Table S10).

We used smartpca (version 16,000) in the EIGENSOFT package (version 6.0.1)70 with a set of 59 present-day west Eurasian populations from the Human Origins dataset30 to construct the first two principal components, and projected the ancient genomes with options lsqproject:YES, shrinkmode:YES and outliermode:2 (Fig.2). Present-day populations in the HO dataset used for computing the principal components included: Abkhasian, Adygei, Albanian, Armenian, Balkar, Basque, BedouinA, BedouinB, Belarusian, Bulgarian, Canary_Islander, Chechen, Chuvash, Croatian, Cypriot, Czech, Druze, English, Estonian, Finnish, French, Georgian, Greek, Hungarian, Icelandic, Iranian, Italian_North, Italian_South, Jew_Ashkenazi, Jew_Georgian, Jew_Iranian, Jew_Iraqi, Jew_Libyan, Jew_Moroccan, Jew_Tunisian, Jew_Turkish, Jew_Yemenite, Jordanian, Kumyk, Lebanese, Lezgin, Lithuanian, Maltese, Mordovian, North_Ossetian, Norwegian, Orcadian, Palestinian, Polish, Russian, Sardinian, Saudi, Scottish, Sicilian, Spanish, Spanish_North, Syrian, Turkish, Ukrainian.

UMAP was run with the R package umap (version 0.2.3.1)71 using default parameters (Fig.3c). Input was provided from the first ten principal components computed by PCA for 128 individuals from 13 present-day HO popula tions (Albanian, Romanian, Bulgarian, Cypriot, Greek, Italian_North, Italian_South, Maltese, Sicilian, Czech, Hungarian, German, Croatian) and 47 ancient individuals (Germany_Untetice_EBA, Hungary_BA, Montenegro_LBA, Romania_CA, Bulgaria_BA, Bulgaria_IA, Croatia_Dal_BA and the newly-sequenced individuals.

We performed unsupervised admixture analysis with ADMIXTURE (version 1.3.0)72 (Supplementary Fig. S2) on 2,361 ancient and present-day individuals (see Datasets section in Methods) by first using PLINK (version 1.90b5.3)73 to remove variants that had a minor allele frequency below 0.01, and to prune the dataset for strong linkage disequilibrium with parameters indep-pairwise 200 25 0.4. We then ran five replicates for K4 to K17 with a random seed and cross-validation (Supplementary Fig. S2), and the highest likelihood replicate was chosen.

We used a set of packages in ADMIXTOOLS57 for performing f-based statistics. Outgroup f3-statistics was calculated with qp3Pop (version 435) (SupplementaryFig. S3a-b, Supplementary Fig. S7, Supplementary Table S3), qpDstat (version 751) was used to calculate f4-statistics with the option f4Mode: YES (Supplementary Fig. S6, Supplementary Table S7), and qpWave (version 410) and qpAdm (version 810) were used with option allsnps: YES for estimating mixture proportions (Fig.3a-b, Fig.4b, Supplementary Fig. S4, Supplementary Table S4). The option Chr: 23 was added to qpAdm for computing results based on the X chromosome in analyses testing for sex-bias (Supplementary Table S8). Following the method outlined in2, we calculated a Z score for each ancestry component to measure the difference in admixture proportions between the autosomes and X chromosome, where a positive Z score indicates more admixture on the autosomes and therefore male-biased ancestry. Mbuti.DG was used as an outgroup for all statistics. For qpAdm, right populations included Mbuti.DG, Ust_Ishim_HG_published.DG, Ethiopia_4500BP.SG, Russia_MA1_HG.SG, Italy_Villabruna, Papuan.DG, Onge.DG, Han.DG. qpWave was used to check the outgroup populations could successfully distinguish the ancestries present in the sources. Rather than identifying the specific source populations and admixture events that occurred, qpAdm models help to ascertain the type ancestry that would have contributed to the gene pool of the target population via admixture.

We used DATES (https://github.com/priyamoorjani/DATES)74 to estimate the age of past population admixture events between two source populations by inferring time since mixture from the average size of ancestry blocks, assuming a generation time of 29years (Supplementary Table S5). Decay curves are reported in Supplementary Fig. S5. Estimates can contain some noise due to later admixture events, and this model does not take into account multiple admixture events or admixture of already admixed populations.

Read more:
Reconstructing genetic histories and social organisation in Neolithic and Bronze Age Croatia | Scientific Reports - Nature.com

DUBLIN, Aug. 20, 2021 /PRNewswire/ -- The "Global Direct-to-Consumer Genetic Testing Market: Focus on Direct-to-Consumer Genetic Testing Market by Product Type, Distribution Channel, 15 Countries Mapping, and Competitive Landscape - Analysis and Forecast, 2021-2031" report has been added to ResearchAndMarkets.com's offering.

Global direct-to-consumer genetic testing market to be one of the growing markets, which is predicted to grow at a CAGR of 17.30% during the forecast period, 2021-2031.

The direct-to-consumer genetic testing market's growth has been primarily attributed to the major drivers in this market, such as the growing amount of direct-to-consumer genetic testing, increasing research funding in the field of molecular biology, and an increase in awareness and acceptance of personalized medicine on a global level.

However, genomic data protection, ethical and social issues, and lack of regulatory standards are some of the factors expected to restrain the market growth.

Decreased cost and time required for genetic sequencing have increased the acceptance of DTC genetic testing among consumers. DTC genetic testing companies offer these genetic tests to their consumers through online channels and over-the-counter (OTC) channels, which has made these tests easily accessible to consumers around the globe.

The market is favored by the increased research activities based on next-generation sequencing-based technologies. The technology has been segmented into targeted analysis, whole genome sequencing, and single nucleotide polymorphisms. The whole genome sequencing segment is expected to grow at the highest CAGR of 17.37% during the forecast period 2021-2031.

This increase is mainly attributed to a large number of research and development being conducted due to the COVID-19 pandemic and regulatory approvals gained by key companies for genetic health risks-based tests.

Within the research report, the market is segmented on the basis of product type, technology, distribution channel, and region. Each segment covers the snapshot of the market over the projected years, the inclination of the market revenue, underlying patterns, and trends by using analytics on the primary and secondary data obtained.

Competitive Landscape

With the increasing consumer awareness and intense market penetration, companies such as 23andme, Inc., Ancestry.com, LLC, and Color Genomics have become pioneers and significant competitors in this market.

Other key players in the market are 24Genetics, Easy DNA, DNAfit, and My Heritage Ltd., among others.

The increased demand for complex and custom sequencing techniques, rising genetic testing services, and growing research to treat and diagnose genetic and infectious diseases have opened opportunities for companies to expand their product portfolios, increase automation facilitation, and develop novel consumer genetics solutions by adopting different strategic approaches.

Key Topics Covered:

1 Product Definition

2 Market Scope

3 Research Methodology

4 Market Overview4.1 Product Definition4.1.1 Ancestry Tests4.1.2 Health and Wellness Test4.1.3 Entertainment Test4.2 Direct-to-Consumer Genetic Testing Business Model4.2.1 One-to-One4.2.2 One-to-Many4.3 Future Potential4.4 COVID-19 Impact: Global Direct-to-Consumer Genetic Testing Market

5 Global Direct-to-Consumer Genetic Testing Market Regulatory Landscape5.1 Legal Requirements and Regulations5.2 Regulation in North America5.2.1 U.S.5.2.2 Canada5.3 Regulation in Europe5.3.1 Germany5.3.2 France5.3.3 U.K.5.3.4 Italy5.3.5 Spain5.4 Regulation in APAC5.4.1 China5.4.2 Regulation in Japan5.4.3 Australia5.4.4 South-Korea

6 Market Dynamics6.1 Impact Analysis6.1.1 Growing Number of Direct-to-Consumer Genetic Tests6.1.2 Increase in Awareness and Acceptance of Personalized Medicines on a Global Level6.1.3 Decreasing Cost of Sequencing6.1.4 Increasing Research Funding in the Field of Molecular Biology6.2 Market Restraints6.2.1 Genomic Data Protection6.2.2 Uncertain Regulatory Standards for Direct-to-Consumer Genetic Tests6.2.3 Ethical and Social Issues6.3 Market Opportunity6.3.1 Massive Scope for Adoption of Genomic-Based Medicine in Emerging Nations6.3.2 Capitalizing on the High Prevalence of Genetic Disorders6.3.3 Growth in Emerging Nations6.4 Key Trends6.4.1 Curiosity Among Consumers6.4.2 Increasing Public Awareness6.4.3 Mushrooming Direct-to-Consumer Genetic Testing Services Market6.4.4 Need for Precision Medicine6.4.5 Hassle-Free Model6.5 Key Strategies and Developments6.5.1 Synergistic Activities6.5.2 Approvals6.5.3 Product Launches and Expansions6.5.4 Acquisitions and Mergers6.5.5 Funding6.6 Market Share Analysis

7 Global Direct-to-Consumer Genetic Testing Market (by Technology), $Million, 2020-20317.1 Overview7.2 Targeted Analysis7.3 Single Nucleotide Polymorphism (SNPs)7.4 Whole Genome Sequencing (WGS)

8 Global Direct to Consumer Genetic Testing Market (by Distribution Channel), $Million, 2020-20318.1 Overview8.1.1 Online Channel8.1.2 Over-the-Counter (OTC) Channel

9 Global Direct-to-Consumer Genetic Testing Market (by Product Type), $Million, 2020-20319.1 Ancestry9.1.1 Genealogy9.1.2 Relationship9.2 Health and Wellness9.2.1 Predictive Tests9.2.2 Carrier Tests9.2.3 Pharmacogenomics Tests9.3 Entertainment

10 Global Direct-to-Consumer Genetic Testing Market (by Region), $Million, 2020-2031

11 Company Profiles

For more information about this report visit https://www.researchandmarkets.com/r/i3cr66

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