Non-communicable diseases (NCDs), are increasing globally, causing about 60% of disability-adjusted life years and 39.8 million deaths in 2015. Risk factors often cluster and interact multiplicatively in an individual and this is strongly associated with the development and severity of NCDs. We assessed the sociodemographic factors associated with the presence of multiple NCD risk factors among individuals aged 18 years and older in the Kenyan population.We used national representative data from 4066 individuals out of 4500 who participated in the WHO STEPs study in 2015. NCD risk factor counts were derived by summing the risk factors present in an individual and categorising into 1–3, 4–6 and 7+ risk factors in any combination of the 12 assessed NCD risk factors . Ordered logistic regression was used to investigate the sociodemographic factors associated with an individual possesing multiple NCD risk factors.Majority (75.8%) of the individuals in the study possesed 4–6 and 10% had ≥7 NCDs risk factors. Nearly everyone (99.8%) had insufficient fruits and vegetable intakes, 89.5% consumed high salt in their diet and 80.3% did not engage in sufficient physical activity. Apart from NCD risk count which increased with age among both men and women, associations with other socio-demographic factors differed between men and women. A woman of Akamba ethinicity had lower odds (0.43) while Meru women had higher odds (3.58) of higher NCD risk factor count, compared to the Kalenjin women. Among men, being a Kisii or Luo was associated with lower odds (0.48 and 0.25 respectively) of higher NCD risk factor count. Women in a marital union had higher odds (1.58) of a higher NCD risk factor count.Majority of Kenyan adults possess more than four NCD risk factors; a clear indication of an emerging epidemic of NCDs in this population. Effective and multi-sectoral interventions targeting multiple risk factors in individuals are required to mitigate especially the behavioural and modifiable NCD risk factors in Kenya. Non-communicable diseases (NCDs), comprising of cardiovascular diseases (CVDs), diabetes, cancers and chronic pulmonary diseases are currently a major contributor to the burden of disease and mortality worldwide, with predictions showing that by the year 2020, NCDs will account for seven out of every ten deaths in developing countries, surpassing communicable diseases as the leading cause of death –3.Although there exist few reliable data on the prevalence and distribution of NCDs and their risk factors in sub-Saharan Africa SSA , NCDs arThe rising epidemic of NCDs in SSA is attributable to growing urbanisation, changing lifestyles, population growth, ageing and epidemiologic changes in the disease, also known as epidemiological transition , 12–16. The World Health Organisation (WHO) has since prioritised action against these four behavioural risk factors globally . A few sThe WHO STEPs survey, carried out in Kenya between April and June 2015, employed a cross-sectional household study design that targeted adult individuals aged 18 years and older. A three-stage cluster sampling was used to, in the first stage, select 100 each of urban and rural clusters from the the fifth national sample surveys and evaluation programme (NASSEP V) sampling frame by the Kenya National Bureau of Statistics. In the second stage, a sample of 30 households in each cluster were identified, while in the third stage, one adult (aged between 18 and 69 years) from each household was randomly selected to participate in the study. Further details regarding the design of the study have been published previously .Data was collected using personal digital assistants (PDA) loaded with the eSTEPS tool provided by the World Health Organisation . Data waBlood pressure (BP) was diagnosed using a validated oscillometric automated digital BP device . Using appropriate cuff sizes, three readings were taken on the left arm from a respondent in a seated position, at one minute intervals. The mean of the second and the third measurement was recorded. Raised blood pressure/hypertension was based on blood pressure readings cut-off for systolic > = 140 mmHg and/or diastolic > = 90 mmHg or if they reported to have been previously diagnosed and informed to have hypertension by a health care worker and/or were taking medication for raised blood pressure.For a small number of respondents (84/4066), random blood sugar was taken because they did not fast as instructed. For all other respondents, fasting blood glucose measurements were taken a day after the first contact with the respondent, after they confirmed to have had an overnight fast. A drop of blood from a finger prick was used to test for glucose levels using a digital meter (ACCUCHECK™ glucometer and test strips). An individual was classified to have raised blood glucose/ diabetes mellitus based on random and fasting blood capillary glucose measurements cut-offs of 11.1 mmol/L and > 7.0 mmol/L respectively, or if they reported to have been previously diagnosed and informed to have diabetes by a health care worker and/or were taking medication for diabetes.Total blood cholesterol levels were categorized either as ideal or high, with a cut off of point of 5.2 mmol/L or if an individual is currently on treatment for raised blood cholesterol. Low blood HDL cholesterol levels were categorized either as ideal or high, with a cut-off point of 1.17 mmol/L. A drop of blood from a finger prick was used to test for cholesterol levels using a digital meter (ACCUCHECK™ glucometer and test strips). A drop of blood from a finger prick was used to test for cholesterol levels using a digital meter (ACCUCHECK™ glucometer and test strips). Sugar intake was self-reported. High sugar intake was defined as regular consumption of excessive sugar in the form of sugary drinks and soda, processed foods high in sugar content on a regular, even daily, basis.2 and central obesity on waist circumference of > 94 cm for males and > 80 cm for females.BMI was computed from height and weight measures, collected in centimeters and kilograms using SECA™ height boards and calibrated digital weigh scales respectively. Height was taken while the individual stood in an upright position on a flat surface. Obesity was diagnosed based on international guidelines recommended by the WHO expert consultation for 2008 . GeneralThe other risk factors/variables were self-reported. Insufficient fruits and vegetables consumption is when an individual consumes less than the recommended five servings of fruits or vegetables daily. A serving of fruit/vegetables equates to one small fruit, ½ a cup of raw vegetables, one cup of leady greens or one banana. Insufficient physical activity is less than 150 h/week of moderate-intensive activity or less than 75 h/week of vigorous-intensive physical activities, which may include walking and cycling. An individual that spent 180 min/day sitting or reclining was also classified as physically inactive.Unhealthy cooking fats referred to the use in cooking of saturated oils and solid fats e.g. lard, margarine, butter and vegetable fat for cooking, instead of unsaturated fats and oils. High salt intake was based on an individual reporting that they consumed salty and processed foods always or often, or if they added salt to cooked food. Excessive alcohol use was defined as the consumption of more than two standard drinks per day for females and more than three standard drinks for males. One standard drink contains about 14 g of pure alcohol, equivalent to 350 ml of beer with 5% alcohol by volume , or 1 glass table wine with 12% alc/vol. Current tobacco use was defined as the daily use of smoked or smokeless tobacco, in the form of cigarettes, cigars, pipes, snuff and other local tobacco products.A proxy index for wealth was created based on household-level variables including type of dwelling unit; ownership of the dwelling unit; construction materials of the dwelling unit specifically the roof and floor; source of cooking and lighting fuel; several household possessions/goods including a wall clock, electronics like radio and television, refrigerator, and furniture; source of water for drinking and cooking; and type of sanitation facility available for use by the household. A wealth index was then generated using principal component analysis (PCA), multivariate statistical technique. Principal components are weighted averages of the variables used to construct them. The generated index was then used to categorize the households into five categories (quintiles): poorest, second, middle, fourth and richest.The analysis was done using Stata 14.1 . The outcome variable for this study is the number (count) of NCD risk factors in an individual in any combination out of the twelve described NCD risk factors. The number of NCD risk factors in an individual were categorised into three groups: 1–3, 4–6 and 7+ risk factors. Proportions and bar-graphs were used to summarize frequency/counts of NCD risk factors. Ordered logistic regression was used to assess the association between individual and household level sociodemographic factors with the presence of one or more NCD risk factors in an individual. Proportional odds assumption was tested using brant command method in Stata, with non-proportional odds model fitted wherever the assumption is violated.We first fitted bivariate (unadjusted) ordered logistic regression models for each of the sociodemographic factors: age, sex, level of education, occupation, marital status, place of residence, ethnic group and household wealth-status with the outcome variable. We then fitted a multivariate model to assess the strength of association of each of the sociodemographic factors with the number of NCD risk factors in an individual. We used the likelihood ratio test to compare the goodness of fit, comparing a null model which is a special case of the alternative model with one additional variable being tested. Odds ratios are reported together with their 95% confidence intervals. Stratified analysis by sex was done to check for differences in the magnitude and pattern of the NCD risk factors between males and females, after confirming that there existed no statistical interactions between the sociodemographic variables in question with sex. Analysis was weighted at the individual level to account for study design, and to allow for generalizability of the findings.The study protocol was reviewed and approved by the Scientific and Ethics Review Unit at Kenya Medical Research Institute (Kemri) SSC No. 2607. Informed consent was sought from each and every participant prior to enrolement in the study. The study team was introduced to concepts of research ethics and encouraged to protect the rights of human research participants. Researchers upheld justice, regard for welfare and respect for study participants at all times during the study. During analysis and reporting, personal identifiers were delinked from the data.A total of 4066 individuals  were included in this analysis. This number was 90% of the 4500 individuals reached in the STEPs survey, from 4754 households that consented to participate in the study. Demographic characteristics of the study population are shown in Table The weighted prevalence of the twelve NCD risk factors in this population is shown in Fig. Harmful alcohol use, daily tobacco use, hypertension, low high density lipoprotein (HDL) cholesterol, bad fat intake, and high sugar intake were more prevalent in males than females, 24.5% vs 3.7%, 17.9% vs 2.9%, 25.9% vs 23.7%, 66.5% vs 49.3%, 44.4% vs 35.4%, 16.5% vs 13.3%, while diabetes, obesity, and raised blood total cholesterol were more common among females, 2.9% vs 2.3%, 56.5% vs 22.4%, 12.8% vs 7.2%, respectively.The highest number of risk factors recorded in an individual was ten out of the possible 12 NCD risks factors considered in this study. The bulk of the population (75.8%) possesed between four and six NCD risk factors. The biggest proportion, 30.4%, possessed five; 25.3% four; and 20.0% six NCD risk factors. A little less than 12% possesssed three, while only about 2 % possessed two or fewer NCD risk factors. About 10% possessed seven and more NCD risk factors. Figure The number of NCD risk factors in individuals increased with their age, household wealth status, education, and employment but varied by ethnicity. Overall, men and women showed differences in the risk factor counts. Generally, men tended to have a higher number of NCD risk factors when compared to women. Whereas the NCD risk factor count increased among men by wealth status, education level, and occupation, these factors did not affect risk factor count among women. While women in union had a higher risk factor count compared to those not in union, marital status did not have any effect on risk factor count amont men. Participants in the younger age-groups, both male and female, were twice as likely to possess three or less NCD risk factors and thrice least likely to possess seven and more NCD risk factors compared the 45 years or older age-group. No differences were observed among urban and rural residents on the NCD risk factor counts. Table .Table 2DTable The factors associated with an increase in NCD risk count in the population were mainly driven by the female sex, and thus there existed some differences in socio-demographic factors for a high count of NCD risk factors observed between men and women. Men were one and half times more likely to have higher counts for NCD risk factors compared to women. Being in a marital union was also associated with higher odds (1.27) of possessing higher counts of NCD risk factors. Women in a marital union were one and half times likely to have a higher count of NCD risk factors, but not so among men.Overall (both sexes), compared to the Kalenjin ethnic group, the Kamba, Kisii, and the Luo had lower odds of having a higher counts of NCD risk factors , while the Meru had almost twice as high odds (1.92) of having a higher count of NCD risk factors compared to the Kalenjin. Among men, being a Kisii or Luo was associated with lower odds (0.48 and 0.25 respectively) of having a higher counts of NCD risk factors. However, while a woman of the Akamba ethnic group had lower odds (0.43) of having a high NCD rik factors count, a women from the Meru ethnic group had 3.58 odds of possessing higher NCD risk factor counts when compared to the Kalenjin women.Our findings reveal that up to 75% of the population in Kenya possessed between four and six NCD risk factors of any combination out of 12 assessed NCD risk factors: one in three of respondents possessed five NCD risk factors, a quarter possessed four risk factors, one in five respondents had six risk factors while about 12% possessed three NCD risk factors. The most prevalent NCD risk factors in the population, for both males and females, were insufficient consumption of fruits and vegetables, high salt consumption and insufficient physical activity, 99.8%, 89.5% and 80.3% respectively. Similar findings on low fruits and vegetable consumption were reported in a study conducted in a sub-sample of a Kenyan population , and in Some NCD risk factors were seemingly gendered. Harmful alcohol use, daily tobacco use, hypertension, low HDL cholesterol, high fat, and high sugar intake were more prevalent in males than females, while the opposite was true for diabetes, obesity, raised blood total cholesterol. These echo findings from other studies in Kenya , and in Although results from the World Health Survey on socioeconomic inequalities in risk factors for non-communicable diseases in LMICs showed that ‘physical inactivity was less prevalent in populations of low socioeconomic status, especially in low-income countries’ , and theNCD risk factors in individuals increased with age, marital status and ethnic group (more significantly among women) indicating that early screening would forestall the accumulation and effect on NCD risk factors in individuals. The association between NCD risk factor and ethnic grouping is not new, as a study conducted among minority groups in the USA reported a similar association . We postThe association between wealth and NCD risk factors has been reported in the World Health Survey . Huge inOur findings suffer from one main limitation. Data on behavioural risk factors was based on self-reports and may be affected by potential under-reporting, especially on the socially discouraged practices like smoking/tobacco use and harmful alcohol consumption. That notwithstanding, our study applies a standard chronic disease risk surveillance approach (WHO STEPs) that confers comparability of our findings with those from other setttings and countries. Our findings provide new insights on the patterns and the distribution of multiple NCD risk factors together with their sociodemographic determinants nationally.The majority (75%) of the Kenyan adults possess between four and six NCD risk factors, while still a substantial proportion (10.1%) posses seven or more NCD risk factors. This is a clear indication of an impending NCD epidemic in this population that needs to be addressed. The epidemiological assessment of key NCD risk factors, their combination/clustering and distribution across differerent sociodemographic strata can inform the design of effective, targeted multisectoral interventions, especially those targeting behavioural and modifiable NCD risk factors, for the prevention of NCDs.Global status report on non-communicable diseases 2010, has ranked the monitoring and surveillance of risk factors a top priority to tackle growing NCD epidemics in low-resource settings like Kenya. More research is needed on perceptions of the Kenyan population regarding risk factors and the accompanying risk for developing NCDs, an aspect that influences individual level actions for the prevention and control of NCDs. Because the risk factors tend to cluster, and these risk factors act in an additive and a multiplicate way to cause NCDs, a ‘comprehensive approach’ rather than one based on a single factor is important to forestall cumulative effects of multiple NCD risk factors which occur over time [The WHO, in the ver time . Effecti


Population health research is increasingly focused on the genetic determinants of healthy ageing, but there is no public resource of whole genome sequences and phenotype data from healthy elderly individuals. Here we describe the first release of the Medical Genome Reference Bank (MGRB), comprising whole genome sequence and phenotype of 2570 elderly Australians depleted for cancer, cardiovascular disease, and dementia. We analyse the MGRB for single-nucleotide, indel and structural variation in the nuclear and mitochondrial genomes. MGRB individuals have fewer disease-associated common and rare germline variants, relative to both cancer cases and the gnomAD and UK Biobank cohorts, consistent with risk depletion. Age-related somatic changes are correlated with grip strength in men, suggesting blood-derived whole genomes may also provide a biologic measure of age-related functional deterioration. The MGRB provides a broadly applicable reference cohort for clinical genetics and genomic association studies, and for understanding the genetics of healthy ageing. Healthspan and healthy aging are areas of research with potential socioeconomic impact. Here, the authors present the Medical Genome Reference Bank (MGRB) which consist of over 4,000 individuals aged 70 years and older without a history of the major age-related diseases and report on results from whole-genome sequencing and association analyses. Most developed nations face crises in health care associated with population ageing. Healthy ageing is a complex phenotype, influenced by both environmental and genetic factors. Healthy ageing—the absence of clinically significant, non-communicable disease or morbidity in old age —is distinct from longevity, which disregards quality of life. Healthy ageing captures the critical distinction between a long life with minimal impairment, and one bearing significant, costly, and potentially prolonged morbidity.3. Rare pathogenic variants that hasten the onset of common diseases associated with age, such as cancer, cardiovascular disease, and neurodegenerative disorders, might also be expected to be depleted in healthy aged individuals. While the single study using whole-genome sequencing (WGS) in 511 healthy aged individuals confirmed the link with the APOE locus, and suggested depletion of polygenic risk for Alzheimer’s disease and coronary artery disease4, no evidence was found for depletion of rare pathogenic variation. Limited by sample size, these studies have focussed on single-nucleotide variants and indels, while large-scale structural variation remains unexplored. In addition, somatic variation such as clonal haematopoiesis is known to correlate with both age and susceptibility to disease6. A synthesis of all forms of somatic and germline genomic variation is needed to inform our understanding of healthy ageing and disease susceptibility.Relatively little is known about the genetic determinants of ageing that account for the broad spectrum of health states observed in older people. Genetic variation contributes to healthy ageing through pleiotropic effects on many diseases, immune responses, anthropomorphic, and behavioural phenotypes. For example, alleles associated with behavioural phenotypes that contribute to a healthy lifestyle, such as avoidance of smoking, or propensity for regular exercise, might be anticipated to have an effect on healthy ageing. However, to date, common variation at only a relatively small number of loci has been consistently associated with lifespan or parental longevity7. The missing heritability arguably resides in the total burden of both common and rare variation, structural variation untagged by simple polymorphisms, and their interactions9. WGS enables more comprehensive characterisation of common, rare, and complex variation in human cohorts. The next few years will see the release of large-scale WGS studies in rare diseases and cancer, such as the 100,000 Genomes Project, and population studies like the UK Biobank. Maximising the analytic power of whole-genome association studies using these cohorts will require well-phenotyped and high-quality control data. The concept of extreme phenotype sampling maximises statistical power by comparing the extremes of phenotypes of interest10. We postulate that an elderly cohort depleted of the major common diseases constitutes a powerful and broadly applicable tool for genome-wide association studies of disease.The advent of WGS is driving intense interest in mapping the genetic basis of disease, less than 50% of which is currently understood11. Here we describe the first release of this resource, comprising WGS data and phenotype for 2570 well elderly individuals. For comparison, we also perform WGS of 344 young subjects, and 273 elderly individuals with cancer. We subject these cohorts to a broad spectrum, systematic analysis of germline and somatic variation within the nuclear and mitochondrial genomes, which we link to both chronologic age as well as frailty measures.With this background, we are undertaking WGS of over 4000 elderly individuals with no reported history of cancer, cardiovascular disease, or neurodegenerative diseases up to age 70, to create the Medical Genome Reference Bank (MGRB)12, and Sax Institute’s 45 and Up Study13, who lived to at least 70 years of age without any history of cancer, cardiovascular disease, or dementia, confirmed either at baseline entry or study follow-ups11. We sequenced blood of 2926 MGRB individuals by WGS, mapping to build 37 of the human reference genome, and calling variants following GATK best practices. After exclusion of 356 samples that failed quality control and relatedness checks, 2570 samples remained, forming the first release of the MGRB cohort genomic data  per individual were observed, most commonly deletions  guidelines18. Forty pathogenic or likely pathogenic heterozygous small variants were identified, with 28/2570 (1.1%) individuals carrying dominantly acting variants linked to disease . In addition to all cancers, we specifically examined colorectal cancer due to its high incidence in our case set, and well-defined genetic risk. The MGRB samples were significantly depleted for rare pathogenic variation in the APC, MLH1, MSH2, MSH6, PMS2, and SMAD4 genes, relative to colorectal cancer cases .One of the primary purposes of the MGRB is to serve as a genetic risk-depleted control cohort for studies of the common causes of morbidity and mortality. To test its utility, we compared the rates of pathogenic variants in tumour suppressor genes between the 717 MGRB individuals from the 45 and Up Study, with 269 demographically matched cancer cases from the same study . This enrichment of protective alleles was specific to the clinical phenotypes excluded from MGRB , and was not observed for negative control loci linked to anthropometric  or behavioural  traits.We next sought evidence for depletion of common disease-associated variation in the MGRB, relative to the gnomAD and UKBB datasets. Although SNP allele frequencies were highly concordant across all three cohorts . MGRB individuals were also depleted for prostate cancer risk relative to both UKBB  and prostate cancer cases , indicating that MGRB is an extreme depletion cohort for prostate cancer polygenic risk . We constructed polygenic predictors for a range of phenotypes measured or depleted in the MGRB, and compared PS between MGRB, the gnomAD non-Finnish European reference cohort, the UK Biobank (UKBB), and the 45 and Up Study cancer cohort. Since the MGRB is an Australian cohort in which allele frequencies differ slightly from the white British sample represented by the UKBB, we controlled for expected subtle differences in allele frequencies genome-wide by simulating the reference population as a derivative from the UKBB that differs due to drift by an amount typical of the MGRB-UKBB comparison. Similar comparisons were made for the gnomAD and 45 and Up comparison cohorts, and 100,000 bootstraps were performed  commonly observed in myelodysplasia and leukaemia. Low-depth WGS is a powerful tool for detecting SVs that has been applied to the identification of CHIP-associated SNVs23, but not CNV. Here we estimate the burden of cancer-associated somatic variation, at both SNV and CNV level, in peripheral blood DNA using whole-genome data in the MGRB cohort.In addition to its use as a surrogate for the germline, peripheral blood DNA carries somatic variation reflecting the life history and health state of the donor. Clonal haematopoiesis of Indeterminate Potential (CHIP) occurs in at least 10% of individuals over the age of 65 years, as evidenced by the presence of detectable somatic SNVs in blood DNATET2 , DNMT3A (23), or ASXL1 (11) , NRAS G12D (1), a dominant-negative allele in DNMT3A, R882H (1)24, and a putative loss-of-function variant in TP53, C275Y (1). JAK2 V617F is a recognised driver of myeloproliferative disorders, which are also associated with ASXL1 loss25, and TET2 and DNMT3A loss-of-function variants are frequent in CHIP5. In total, the blood of 91/2570 (3.5%) cancer-free MGRB individuals carried deleterious small variation in at least one of these four genes, and 13 individuals had multiple deleterious mutations in this gene set. We next sought evidence for subclonal CNV. Of 2570 MGRB individuals, 1975 were successfully fit to a subclonal CNV model; of these 55 (2.8%) showed evidence of subclonal CNV, as determined by the presence of an aneuploid lineage representing more than 10% of nucleated blood cells  of MGRB individuals displayed evidence of CHIP, with SNVs associated with overgrowth and neoplasia observed in more than 10% of reads , while the rate of change of the two aged cohorts was consistent across all measures . Taken together, these results are suggestive of altered kinetics of age-related somatic mutation in the elderly compared to younger populations, although we note longitudinal measurements will be necessary to definitively establish this relationship.As well as CHIP, ageing is associated with telomere shortening, somatic Y chromosome loss, decreased mitochondrial copy number, and increased mitochondrial heteroplasmyics Fig. . Compareics Fig. . Interes30. As expected, grip strength and gait speed both consistently decreased with age in both genders , no current or recurrent condition with a high risk of major bleeding, no anaemia , no current continuous use of other antiplatelet drug or anticoagulant, no systolic blood pressure ≥180 mm Hg and/or a diastolic blood pressure ≥105 mm Hg, no history of dementia or a Modified Mini‐Mental State Examination (3MS) score ≤77, and no severe difficulty or an inability to perform any one of the 6 Katz basic activities of daily living.Samples from the 45 and Up Study were from individuals with no self-reported history of cancer, heart disease, or stroke. Neurological disease was not explicitly excluded, but participants were required to correctly self-complete a health survey at enrolment. We confirmed no record of cancer diagnosis in the NSW Central Cancer Registry, and no record of cancer diagnosis in the NSW Admitted Patient Data Collection, for all 45 and Up Study individuals in the MGRB.42. UK Biobank samples were sourced from the UK Biobank Resource under Application Number 17984.Participants in the Australian Schizophrenia Research Bank (ASRB) were recruited through a national media campaign and consented to data and sample collection and genomic analyses following discussion with a clinical assessment officerThe ASPREE Biobank study was approved by the Monash University Human Research Ethics Committee, and subsequent WGS of Australian ASPREE participants was approved by the Alfred Hospital Ethics Committee. The use of 45 and Up Study samples in the MGRB is covered by ethics approvals from the University of New South Wales Human Research Ethics Committee and the NSW Population & Health Services Research Ethics Committee. The use of the ASRB data was approved by the University of Newcastle Human Ethics Research Committee.For ASPREE participants of the MGRB, peripheral blood samples were processed to buffy coat within 4 h of collection, then stored at −80 °C. DNA was later purified from buffy coat via magnetic bead extraction (Qiagen).For 45 and Up Study participants of the MGRB, peripheral blood samples were refrigerated at 4 °C and processed to buffy coat within 48 h of collection. Buffy coat was stored at −80 °C, and DNA purified via column extraction (Qiagen).ASRB participant PBMCs were extracted from whole blood by centrifugation in Lymphoprep . Genomic DNA (gDNA) was extracted from PBMCs using salt extraction and quantified by PicoGreen assay (Life Technologies). The integrity of gDNA was determined by agarose gel electrophoresis prior to sequencing.WGS of the MGRB, 45 and Up cancer, and ASRB cohorts was performed on Illumina HiSeq X sequencers at the Kinghorn Centre for Clinical Genomics (KCCG), Sydney, using paired-end Illumina TruSeq Nano DNA HT libraries and v2.5 clustering and sequencing reagents. Each sample was sequenced on one HiSeq X lane.43. We first defined a custom reference genome tailored to Illumina HiSeq X sequencers, being the 1000 Genomes Phase 3 decoyed version of build 37 of the human genome, with an added contig of NC_001422.1 to act as a decoy for the HiSeq-specific ΦX174 sequence spike-in. Reads were aligned to this reference using bwa 0.7.15 mem in paired mode, and duplicates marked with biobambam2 2.0.65 bamsormadup, with a minimum optical pixel distance of 2500. All other parameters for both bwa and bamsormadup were left at defaults. For high-depth samples run on multiple sequencing lanes, data merging was performed at this point using samtools 1.5. Indel realignment and base quality score recalibration of mapped reads were performed using GATK 3.7-0 and best practices parameters; unmapped reads were left unmodified. GATK HaplotypeCaller was used to generate g.vcfs from all single-lane realigned and recalibrated BAMs using recommended parameters. Pipeline steps were accelerated using GNU parallel 20170722 (ref. 44).All sequence data generated at the KCCG were processed following the Genome Analysis Toolkit (GATK) best practicesWe defined locus confidence tiers for WGS genotyping on the basis of prior annotations, sequence complexity, and empirical metrics on our data. Locus tiers ranged from 1 to 3, with 1 indicating the highest confidence in WGS variant detection performance and 3 the lowest.45, the lower bound was thresholded to always be at least 15. Within each sample, we defined each autosomal locus as being either in-bound (depth within the sample-specific bounds) or out-of-bound. We then calculated across all samples the rate at which each locus was out-of-bounds, considering the entire MGRB cohort. Regions for which this rate exceeded 5%  were marked as problematic. These problematic regions were smoothed by a morphological closing operation followed by an open operation, with structuring elements being centred intervals on the genome of size 131 and 11 bp, respectively, to yield a final definition of regions of unusual depth in the MGRB cohort. These regions totalled 409 Mb, 13.0% of the reference genome, 13.2% of the canonical chromosomes , and 14.9% of the CCDS coding sequence (accessed 21 Nov 2017).To specify the locus confidence tiers, we first identified regions of the genome which empirically had unusual coverage in the MGRB and 45 and Up cancer sequencing data. For each sample we defined bounds on the expected sequence coverage as the 0.001 and 0.999 quantiles of a Poisson distribution, with rate equal to the modal nonzero coverage observed across all autosomal loci within that sample. As typically 15 reads are required for high genotyping performanceWe then defined a poor-quality subset of the genome as all loci within 5 bp of the union of: the unusual depth regions, repeat regions identified by RepeatMasker, low complexity regions of the reference genome detected by mdust with default parameters, excludable regions from the ENCODE project, and poorly aligned or non-unique regions from the ENCODE project (Supplementary Data 14) it was assigned the highest confidence tier 1, else it was assigned an intermediate confidence tier of 2. In total, 81.9% of the CCDS coding genome was in confidence tier 1 or 2 , or within the poor-quality subset of the genome defined above, were assigned to the lowest confidence tier 3. For the remaining variants in canonical chromosomes, if the variant overlapped a high-confidence HG001 region identified by the GiaB consortium v3.3.2 (ref. 46). A total of 2904/3033 (95.7%) samples passed initial quality thresholds , and without a population reference cohort. Fourteen pairs of individuals related to second degree or closer were identified and excluded from the cohort. MGRB (cancer-free) and 45 and Up cancer samples were split into separate cohorts at this point, and four 45 and Up cancer samples excluded on the basis of incomplete or inconsistent clinical data. In summary 2841 unrelated samples passed all data quality requirements, comprising 2570 cancer-free MGRB individuals, 269 45 and Up cancer samples, and the reference materials RM 8391 and RM 8398.The 2890 MGRB and 45 and Up cancer samples passing initial QC were joint called in a single batch using GATK GenotypeGVCFs, and imported to Hail v0.1 for processing. A second round of QC . Merged variants were LD-pruned in Hail with an r2 threshold of 0.1, and PCA performed in Hail on biallelic variants with a combined MGRB and 1000G allele frequency in the range of 5–95%.The MGRB cohort population structure was determined using principal components analysis (PCA), with reference to the 1000 genomes (1000 G) populations. A merged dataset of all MGRB and 45 and Up cancer genotypes and the 1000G Phase 3 genotypes was generated in Hail. To ensure high genotype concordance between platforms, merged variants were restricted to autosomal strand-specific SNPs in Tier 1 regions of the genome (see Locus confidence tiers), with a 1000G allele frequency in the range of 5–95%, and no evidence of deviation from Hardy–Weinberg equilibrium within any of 17 homogeneous 1000G populations (49).A hierarchical eigenvalue decomposition discriminant analysis classifier was constructed to assign MGRB samples to 1000G populations on the basis of PCA scores. The first classifier layer predicted a sample’s 1000G superpopulation , and the second a sample’s European population , conditional on EUR being the predicted superpopulation by the first layer. Models were trained on 1000G sample scores only using PC1–4 as predictors, and then were applied to predict population source for the MGRB samples. All models were implemented using mclust v5.3 , excluding regions in the Encode DAC Mappability Consensus Excludable list (Supplementary Data Germline SVs in the MGRB and 45 and Up cancer cohorts were detected using GRIDSS v1.4.1 (ref. 51) without blacklisting existing mobile element regions. MEI calls were then processed to remove false-positive events in existing mobile element regions and to estimate variant zygosity by local realignment to the reference genome. MEIs occurring in different samples within 100 bp of each other were merged to the one call.Germline MEIs were identified using Mobster v0.2.2  in ACMG SF 2.0 cancer-associated genes were joint called across both cohorts, and each variant scored for pathogenicity by ACMG criteria, blinded to cohort. The rate of individuals carrying pathogenic variants was then directly compared by Fisher’s test. To exclude potential confounding due to source cohort, the 45 and Up component of the MGRB only was compared to the 45 and Up cancer cases.52. To minimise the influence of technical artefacts, variants were restricted to strand-specific biallelic SNPs listed in the EBI GWAS database, that were located in regions of the genome covered by the Genome in a Bottle standard, and were sequenced to a depth of at least 15 in at least 98% of samples in both the MGRB and gnomAD WGS cohorts. Further, variants which were not observed in one or more cohorts, or were genotyped at a rate of less than 97% in any cohort, were excluded. In total, 21,033 SNPs remained following this filtering, with very similar allele frequencies across all cohorts  WGS allele frequencies, and allele frequencies from a subset of the UK Biobank genotype set, consistent of participants who self-identified as white British and who had confirmed European ancestry by PCAWe tested for phenotype-linked bias in allele frequency between the cohorts as follows. For a given phenotype-associated set of variants, each variant was scored on two metrics: its variant allele frequency enrichment or depletion in MGRB vs gnomAD or UKBB, and the positive or negative association of the variant allele with the trait. A Fisher’s exact test was then used to test for dependence of variant enriched/depleted status on the trait direction of effect, with deviation from the null indicating an allele frequency bias between MGRB and gnomAD or UKBB that is specific to the phenotype considered.Three sets of variants were tested by this procedure: a test set of variants reported to be associated with phenotypes depleted in the MGRB . rs10414043 was used in preference to the more conventional rs429358 as the latter was not robustly genotyped on all platforms. All loci, alleles, and coefficients used in the PRS calculations are detailed in Supplementary Data PS GWAS coefficients were derived from each source publication by the following procedure. Firstly, all loci that were reported to be genome-wide significant in replication were selected, along with the effect allele’s association coefficient in the replication cohort. If replication results were not reported, the derivation cohort loci and coefficients were used. Regression coefficients for binary traits were then transformed to a log-odds scale; coefficients for the continuous traits of height and parental lifespan were used as-is. Loci were converted to GRCh37-centric coordinates, and where possible any loci falling within tier 3 regions (see section Locus confidence tiers) were imputed to an alternative locus falling in a tier 1 or 2 region, chosen so that the original and alternative loci had an fiCohort, denotes the variant allele frequency at locus i in cohort Cohort. di2, was calculated for all well-genotyped loci (not just those in PSs). di2, is related to the fixation index FST, and the distribution of di2, values between a cohort and MGRB reflects the genetic distance between these groups of individuals. For a given PS and comparison cohort Other, testing then proceeded as follows. A bootstrap Australian reference cohort (ARC) was generated by shifting the Other allele frequencies based on d2: d2. This simulates an unseen comparison cohort ARC which has the same mean genetic distance from MGRB as the comparison cohort Other, but which we would expect has no allelic shift due to the phenotypic depletion that we hypothesise is present in MGRB. To test this hypothesis we calculate the expected PS in both MGRB and in ARC, as j = 1...n indexes the PS loci, and estimate the MGRB score depletion as sMGRB−sOther. This depletion statistic was calculated for each comparator cohort and PS, for 100,000 bootstrap rounds.An approximate bootstrap procedure was used to test for PS shift between MGRB, gnomAD, UKBB, and the 45 and Up cancer cohort, accounting for genetic drift between populations MGRB,( and sk)Other, affecting Cancer Gene Census Tier 1 genes, with a maximum population allele frequency of less than 0.1%, a variant allele fraction (VAF) of at least 10%, and three or more reads supporting the variant. We then identified likely driver mutations from these filtered variants by the following criteria: either a variant had a HIGH consequence in a canonical tumour suppressor gene transcript or the variant was observed at least 100 times in the COSMIC database. Consequences and canonical transcripts were as defined by Ensembl VEP; tumour suppressor genes were Tier 1 genes from the COSMIC Cancer Gene Census with a TSG annotation.64). To reduce batch effects between the ASRB and MGRB cohorts, ASRB telomere length estimates were calibrated using Deming regression, fit to 85 ASRB samples sequenced both in the original ASRB batch, and contemporaneously with the MGRB.Telomere lengths were estimated using Telseq v0.0.1 , 2× Rotor-Gene SYBR Green Master Mix (Qiagen), 500 nM Tel forward [5′-CGGTTT(GTTTGG)5GTT-3′] and 500 nM Tel reverse [5′-GGCTTG(CCTTAC)5CCT-3′] or 300 nM 36B4 forward [5′-CAGCAAGTGGGAAGGTGTAATCC-3′] and 500 nM 36B4 reverse [5′-CCCATTCTATCATCAACGGGTACAA-3′] in a 25 μL reaction. Amplification was conducted in a Rotor-Gene Q qPCR cycler (Qiagen) at 95 °C for 5 min, followed by 30 cycles of 95 °C for 7 s and 58 °C for 10 s (telomere reaction) or 35 cycles of 95 °C for 15 s and 58 °C for 30 s (single copy gene reaction). Telomere content for each sample was determined by the telomere to single copy gene ratio (T/S ratio) by calculating R2 > 0.95).Telomere length estimation by Telseq was validated by qPCR on a subset of 120 samples from the ASRB and MGRB cohorts  the cumulative density function of a binomial distribution with N draws and success probability p. Variants with q < 30, high-depth variants (n > 15) with an alternate read strand bias of greater than 0.9, or variants in the highly variable locations MT:302–319 or MT:3105–3109 were discarded. The final metric of mitochondrial variant burden for a sample was defined as the number of low-frequency  variants passing all above filters in that sample.Variants in the mitochondrial genome were detected using FreeBayes, considering only reads with base quality over 24 and mapping quality over 30; all other parameters were left at defaults. Variants with fewer than 10 alternate reads, or an alternate allele fraction under 0.001, were discarded. For each variant passing these filters a Phred-like quality score Somatic SNV burden was estimated using a combination of statistical filtering and spectral denoising. Putative somatic SNVs were first identified on the basis of a variant allele frequency that was statistically inconsistent with either machine error or germline variation. The burden of these variants in each sample was then dimensionally reduced by spectral factorisation, and per-sample signature scores used as the final somatic variant estimates.nA is the number of non-reference allele reads at the locus, and cE and cH are integers which maximise:We first developed a statistical filtering procedure to identify likely somatic variants that uses dynamic thresholds to optimise sensitivity while controlling signal to noise ratio. This procedure calls a variant at a given locus as likely somatic if it satisfies the following criterion:n is the sum of reference and alternate allele depths at the locus, rH is the expected rate of heterozygous variant germline loci, rC the expected rate of somatic variant loci, ε the base read error rate, gr the minimum acceptable ratio of true positive calls to false positive, and f is the expected somatic variant allele fraction. αE and αH are test sizes corresponding to thresholds cE and cH: nA too large to be due to sequencing error (nA ≥ cE), yet too low to be from a poorly sampled heterozygous germline locus (nA ≤ cH). The derivation of this procedure and further details are available in Supplementary Data pn. These estimates are critical in normalisation of variant detection rates to account for differential coverage across variable sequencing runs, which is necessary for the accurate estimation of sample somatic variant burden.subject to:67, we calculate per-sample sensitivity-normalised somatic variant burden for each of 96 single-nucleotide variant classes, as the total number of detected somatic events of a given class in that sample, divided by the sum of pn in that sample for all loci corresponding to the given variant class. The resulting 96 × n normalised burden matrix is then reduced by non-negative matrix factorisation, using 100 random optimisation starting points per cardinality. To select the appropriate factorisation cardinality, we reduce the burden matrix by merging groups of 16 age-consecutive samples by summing burden for each variant class, and factorise this reduced matrix with 100 random restarts and cardinality ranging from 2 to 10. The lowest cardinality that gives an inflection point on the plot of explained variance vs cardinality is selected, and applied to the full burden matrix. Per-sample scores are extracted from the best of 100 random runs at this final selected cardinality.Insufficiencies of the simple error model used above result in incomplete control of the signal to noise ratio, and further filtering is required to reliably quantify somatic variant burden. Assuming that true somatic events and false-positive machine noise exhibit differential sequence context bias, we use a spectral dimensionality reduction approach to achieve additional denoising and summarise the total somatic variant burden in each sample. Extending previous cancer somatic signature workgr = 5, f = 0.2, 29, and the per-sample scores for this signature were used as the summative somatic burden measure. Signature 1 from this work was very similar to COSMIC signature 1 (cosine similarity 0.95), which has also been associated with spontaneous deamination processes and age. However, we observed substantial inter-cohort differences in score distribution for this signature, suggestive of high technical variability, and did not examine it further.We applied the above procedure to the MGRB and ASRB samples, with parameters adapted to maximise sensitivity with low-depth sequencing data: We developed a model-based strategy to identify subclonal copy number variants (CNVs), assuming a single genetically homogeneous subclone present on a background of diploid cells.rel) ∈  in both cohorts, var(DPrel) ∈  in the MGRB, and var(DPrel) ∈  in the ASRB cohort. Here DPrel is locus depth relative to mean sample depth, and statistics are calculated over all samples in each cohort. In total, 1,862,065 loci passed all filters, with a median inter-locus distance of 626 bp, and 5th and 95th percentiles of 30 and 4904 bp, respectively.We first defined a set of autosomal SNPs with highly stable sequencing characteristics on our platform. We selected loci containing autosomal biallelic SNPs in the MGRB cohort, with a variant allele fraction between 5% and 95%, and a mean GC content in the surrounding 100 bp of between 30% and 55%. These were further filtered to retain only loci with highly consistent coverage in both the MGRB and ASRB cohort data, with mean. Within each sample, the depths of reference and variant alleles at all heterozygous SNV target loci were fit to the following subclonal CNV model to produce estimates of local ploidy and global sample subclonal fraction.i in a single sample which contains fraction f of aneuploid cells, the remaining 1−f being entirely diploid (gonosomes are not modelled). We denote the copy number (ploidy) of the aneuploid cells at i with ki1, and ki2,, ki1,, ki2, =  duplication of both alleles. Our task is to estimate ki1,, ki2, for all i, and f globally, given reference and non-reference allele depths diR, and diA,.Consider a locus ki1, and ki2, affect the representation of alleles in the mixed cell population depends on the aneuploid cell fraction f. Let pi1, and pi2, represent the mean ploidy of each chromatid in the mixed cell DNA pool. As the pool is assumed to consist of only two populations, with 1−f of the cells diploid, The extent to which the aneuploid cell ploidies ci a normalising constant to account for the depth at locus i. Here di1, and di2, denote the depths of reads from chromatid 1 and 2, respectively. Unfortunately we do not have phased genotypes, and so cannot easily determine the chromatid source of each read. Instead we have unphased reference and non-reference depths diR, and diA,, and must account for the resulting phase uncertainty with a mixture model.We assume that the sequencer does not exhibit allelic bias. Then, i using a mixture: D(μ) denoting a distribution function with expected value μ. In our implementation we employ a negative binomial distribution for D, with probability mass function s captures overdispersion relative to the Poisson distribution, and is optimised per-sample in the model fit.Disregarding allele phasing we model the depths of reference and non-reference reads at ci is half the expected depth at locus i, which is itself a complex function of locus- and sample-specific factors. We model this function at the locus- and sample-level using empirical cohort depth measurements, and a sample-specific GC bias correction. Specifically, we define bi is the mean relative depth of locus i , across the sample’s cohort (either MGRB or ASRB), h is a smooth function, and gi is a vector of GC fraction in windows of various size around locus i. For this work, gi was a 5-vector of GC fraction in windows of size 100, 200, 400, 600, and 800 bp, calculated on the reference sequence centred at locus i. The sample-specific GC correction function h was implemented using a generalised additive model (GAM) with five smooth terms, and fit to all heterozygous loci for each sample as rj,i being the score of the jth principal component of the GC fraction matrix for locus i, and s denoting a penalised regression spline term. GAMs were fit using mgcv 1.8–17 (ref. 68) with default parameters.The normalising constant p value genome-wide were merged. This process was repeated until either no segment pairs remained to merge, or all Kolmogorov–Smirnov test p values were less than 0.01. Segments were never merged between chromosomes.A greedy agglomerative algorithm was used to segment the genome of each sample into regions of differing ploidy state. Initially the genome was divided into segments of 100 consecutive heterozygous loci, with segment boundaries enforced between chromosomes. Adjacent segments were tested for identical distribution of ki1, and ki2,, as well as the global aneuploid fraction f and overdispersion s, were optimised by grid search with local polishing. As very high ploidies coupled with low f result in highly expressive but likely incorrect models, the maximum allowable ploidy kmax; f, as well as local ploidies across the genome, for the single aneuploid clone assumed to be present in each sample.The above model was fit to the allele counts within each genome segment by maximum likelihood. Ploidies of each segment, 6, clonal haematopoiesis of indeterminate potential (CHIP) was defined in an individual if either: a somatic small variant  was detected with a variant allele frequency of at least 10%, or somatic CNV (see section Somatic copy number variants) indicated the presence of a clone comprising at least 10% of nucleated blood cells.Extending previous workExploratory analysis indicated that variable transformation was required for some measures. For the following analyses, telomere length and Y copy number were modelled as-is; somatic variant burden, mitochondrial load, and mitochondrial variant count were log-transformed prior to modelling; and grip strength in kg was power transformed with exponent 0.7.p values corrected for multiple testing by Holm’s step-up procedure69.Within-cohort trends in somatic measures were estimated by linear regression, with 95% Wald confidence intervals. Likelihood ratio tests of nested models were used to evaluate inter-cohort trend differences, with 2, abdominal circumference (abdocirc) in cm, and the somatic measure of interest . In this model specification, s(x) denotes a GCV-penalised thin plate spline smooth term in x as implemented in R package mgcv 1.8-17(ref. 68), with Gaussian error and identity link. This model’s deviance d was compared to the deviance di), we calculated the deviance of the following generalised additive model 68. After confirming the suitability of the model fits, cohort-specific effects were removed by calculating the quantity i, to be plotted; yi is the original measurement for individual i in cohort C; and C and the ASPREE cohort, respectively. In this manner, somatic measurements were transformed to have an intercept matching that fitted to the ASPREE cohort.We observed cohort differences in intercepts in plots of somatic measures vs age. To remove these solely for the purposes of illustration Fig. , for eaci in the ASPREE cohort, an age-local quantile of mitochondrial DNA copy number ci was defined as c in the neighbourhood of individual i, with the neighbourhood of an individual i defined as all male ASPREE individuals within ±1 year of age of i. Ages were rounded to the nearest integer for the purposes of neighbourhood definition; for the median ASPREE male age of 80 years, this neighbourhood contained 293 men with ages in  years. Given these local mtDNA copy number quantile estimates q, a generalised additive model of the form grip strength~age + s(q) was fit using the R package mgcv68, with s smooth term as above. Predictions from this model with age = 80 and varying q defined the estimated influence of age-local mtDNA copy number on grip strength for an 80 year old man. These grip strength predictions were transformed to effective age estimates assuming typical mtDNA copy number by inversion of the model predictions for s = 0.5, and used to calculate an age excess as a function of q. Variability of this relationship was estimated using 100,000 bootstrap samples, and results presented as highest posterior density intervals.We used the following procedure to illustrate the effect of mtDNA copy number on grip strength in males. For each male individual Further information on research design is available in the Supplementary InformationDataset 1Dataset 2Dataset 3Dataset 4Dataset 5Peer Review FileReporting SummaryDescription of Additional Supplementary Files


Vitis spp. acquired different mechanisms of resistance to leaf and root attack. Its appearance in European vineyards at the beginning of the 1860s, where the phylloxera-susceptible grapevine species V. vinifera L. is majorly cultivated, caused the devastation of a great number of vineyards, generating a deep crisis in the European wine production and trade industries. However, the origin and genetic structure of this pest across European vineyards still remain controversial and uncertain. Herein, we analysed the genetic structure of 1173 grape phylloxera individuals collected from 100 locations across eight European countries. Structure and phylogenetic analyses show that contemporary grape phylloxera populations in Europe are the result of at least two independent introductions from the native range that mirrors the historical records that also suggest two major outbreaks in Europe. The comparative analysis with samples from the native range trace back one of these two genetic groups to plants imported from the North East coast of North America, where the American species V. riparia and V. labrusca dominate. This study clarifies the level of genetic diversity of grape phylloxera in Europe and provides relevant information to resolve previous controversy about its origin.Grape phylloxera is native to North America, where  Daktulosphaira vitifoliae Fitch (commonly known as grape phylloxera) is the most geographically spread pest species, virtually occurring in all viticultural regions around the world1. Grape phylloxera is an obligate biotroph of Vitis spp., capable of feeding on both grapevine roots and leaves2. Its native range extends from southern Canada to northern South America, but it is more common in the eastern and central USA3. The coevolution of grape phylloxera and American grapevine Vitis spp. led to the generation of different grapevine species with different strategies of resistance to grape phylloxera leaf and root attack4. Because grape phylloxera was not present in the centre of origin of cultivated grapes and Europe before the nineteenth century, the cultivated grapevine (Vitis vinifera L.) did not develop any resistance mechanism4. In susceptible V. vinifera cultivars, grape phylloxera forms abundant root nodosities and tuberosities that occlude the vascular system of the vine, resulting in a dramatic loss of leaf surface area and yield. In addition, feeding wounds allow secondary infections by soil-borne pathogens and cause plants to die from secondary infections2. The natural capability of grape phylloxera to move among vineyards to infest healthy plants is limited5, but human-assisted dispersal can inadvertently transport insects between sites through infested plant material, machinery, clothing and footwear, postharvest grape products and winery wastes1. It is generally accepted that the insect was unintentionally introduced into Europe at the end of the nineteenth century through infected plant material from North America to fight oidium (powdery mildew), the fungus that threatened European vineyards in the 1850s6. Grape phylloxera appearance almost destroyed European viticulture, and it provoked the most radical switch in viticultural practices of the last two centuries, when grape growing changed from the use of own-rooted V. vinifera plants to their grafting onto partially-resistant American non-vinifera Vitis spp. or hybrids used as rootstocks7.Phylloxera vastatrix8. Almost simultaneously, the insect was observed in the Bordeaux region, with an outbreak registered in 1869 in the term of Floirac (Gironde)9. Soon after, grape phylloxera become ubiquitous all over southern and central vineyards of France11, where it destroyed about 40% of vines, inducing an important economic shock in a society largely dependent on agriculture12. From this main focus, grape phylloxera advanced southward towards Spain13 and Italy14, and northward towards the wine regions of Geneva and St. Gallen in Switzerland10. Between 1874 and 1900, diverse outbreaks were reported in several locations in the Rhine, Moselle, Neckar and Main river valleys (Germany)10. At the same time, two important additional grape phylloxera outbreaks were registered in very distant regions. During the 1860s, wine producers reported a new pest affecting Douro’s vines in Portugal, with withering vines detected in vineyards of the municipalities of Sabrosa, Santa Marta de Penaguião, Peso da Régua and São João da Pesqueira. In spring of 1863, the initial focus of this grape phylloxera outbreak was found in a vineyard of Gouvinhas, probably as a result of the introduction of some American vine cuttings imported by the owner of the vineyard15. Simultaneously, another major outbreak was observed in 1868 in the city of Klosterneuburg , which was officially registered in 187216. From there, grape phylloxera spread following the Danube river route, first to neighbouring Austrian viticultural areas of Lower Austria and Burgenland, and then to modern Czechia, Slovakia and Hungary10. After 1880, grape phylloxera was observed in viticultural regions of Serbia, Bulgaria and Romania, reaching Moldavia in 1887 and the Crimean Peninsula soon after10.Historical records show that grape phylloxera appeared in Europe at the beginning of the 1860s in the southern Rhône region of France , where vines began to wither and die for no previously-known causes. Nevertheless, it was not until 1868 that the presence of grape phylloxera in grapevine dead roots was certified by a commission of experts, who initially denominated the insect as 19, few studies have dealt with this issue at a multinational scale21. Nevertheless, the use of low resolution genetic markers20 or the screening of a limited number of populations from a reduced number of sampling sites21 hindered obtaining sound conclusions on the genetic structure of this pest in Europe. Better understanding of genetic diversity of grape phylloxera in Europe provides critical information to develop monitoring and control strategies for this pest in European vineyards in near future. The goal of this study is to analyse the spatial genetic structure of grape phylloxera infesting commercial and abandoned vineyards in Europe. For this purpose, we investigated the association between the insect and geography in conjunction with historical information to understand contemporary population structure patterns. We carried out extensive sampling of grape phylloxera populations from 100 locations spread over eight European countries . In addition, we included some grape phylloxera samples from the introduced regions of South Africa and Uruguay. They were jointly analysed with individuals from the native range to evaluate the point of origin of the populations nowadays present in Europe.In spite of being a matter of concern for European grape growers and scientists for more than 150 years, the genetic structure of grape phylloxera in Europe is still unclear. Although several reports dealing with grape phylloxera populations at a national level can be foundV. vinifera L. cultivars, Vitis spp. interspecific rootstocks, Vitis spp. interspecific hybrid direct-producers and Vitis spp. interspecific resistant grape varieties) were genotyped with 7 SSR markers . All the SSR markers were polymorphic among grape phylloxera samples, with a mean number of 9.1 alleles per locus. The number of alleles ranged from 5 (for DV8) to 12 (for PhyIII_55 and DVSSR4). The combination of this genetic information led to the identification of 774 unique MLGs  and Quinten (in Switzerland), separated by ca. 620 km. MLG380 was not the only case in which the same MLG was collected from sites separated by large distances. MLG113 was found in Nuβdorf ob der Traisen (Austria) and in Rudolfingen (Switzerland), separated by ca. 330 km. Similarly, we found MLG475 in Köveskál (Hungary) and in Rafz (Switzerland), locations separated by ca. 690 km. On the other hand, we found seven MLGs  feeding in both leaves and roots. In addition, several examples of the same MLG in Vitis spp. interspecific hybrid direct-producers and Vitis spp. interspecific rootstocks , in Vitis spp. interspecific hybrid direct-producers and V. vinifera L. cultivars (MLG247 and MLG381), and in Vitis spp. interspecific rootstocks and V. vinifera L. cultivars  were found  and two other introduced regions (South Africa and Uruguay) from a diverse variety of hosts . To avoid the impact of uneven sampling for these variables, STRUCTURE output was evaluated considering the MedMeaK, MaxMeaK, MedMedK and MaxMedK statistics to determine the most probable number of genetic groups. MedMedK, MaxMedK and MaxMeaK suggested K = 3 as the optimal level of structuring when using the sampling site (country) as correcting factor, whereas MedMeaK pointed out K = 2. On the other hand, the four metrics indicated K = 2 as the most probable number of genetic groups when using feeding form or host plant as co-factor  over 0.80 for group assignation, 302 MLGs (39.0%) were associated with one of the two genetic groups (denominated Eu1 and Eu2), whereas 472 MLGs (61.0%) were identified as “admixed” , Vitis spp. interspecific rootstocks (54 MLGs), V. vinifera L. cultivars (31 MLGs) and one Vitis spp. interspecific resistant grape variety (1 MLG). On the contrary, MLGs in Eu2 were mostly collected from Vitis spp. interspecific rootstocks (125 MLGs) , Germany (21 MLGs), Uruguay (19 MLGs), Hungary (14 MLGs), Italy (13 MLGs), Croatia (9 MLGs) and Austria (8 MLGs) Fig. . On the Gs) Fig. . More dePsex values (<0.01). The average number of alleles per locus is similar in Eu1 (6.14) and Eu2 (5.71). We detected 10 and 13 private alleles in Eu1 and Eu2, respectively, which were found across all the loci screened. A lower than expected level of heterozygosity was observed in Eu1 and Eu2, suggesting the effect of inbreeding forces on both populations. Lastly, FIS values were positive for most of the loci analysed in this work, but for PhyIII_36 in Eu1 and PhyIII_30 and DVSSR4 in Eu2.Genetic parameters indicated a similar level of genotypic diversity for Eu1 and Eu2 genetic groups, with values rather similar to those calculated for the whole dataset  on grape phylloxera genetic structure, as well as a minor effect of host plant . No significant effect for the feeding behaviour was observed , neither for the two-way or three-way interactions between the factors evaluated. Linear regression modelling results revealed a significant effect (p ≤ 0.05) of longitude and latitude of the sampling sites on grape phylloxera population structure. Attending to the standardised beta coefficients given by the model to each variable to compare their relative relevance on the genetic structure obtained, we observed a major effect of longitude (beta = 0.626) over latitude (beta = 0.171).The three-way ANOVA analysis revealed a major significant effect of sampling site (country)  was formed by 212 MLGs, 211 from the native range of grape phylloxera in the current states of Tennessee (42 MLGs), Missouri (34), Virginia (26), North Carolina (17), New Mexico (17) and Arizona (15) (among others), and one MLG sampled in Germany (assigned to Eu1). Group 2 (Eu) clustered 376 MLGs, mainly from Europe (140 of Eu1 and 156 MLGs of Eu2). The remaining MLGs clustering in this group corresponded to samples isolated from the native range, including, among others, isolates from the states of New York (25 MLGs), Pennsylvania (8 MLGs), Massachusetts (6 MLGs), South Dakota (6 MLGs) and Indiana (5 MLGs), as well as some MLGs from the introduced regions of Austria (7 MLGs), Hungary (5 MLGs), California (4 MLGs), Uruguay (3 MLGs), and Brazil (3 MLGs) retrieved from Lund et al.3 , but the Eu genetic group was divided into two genetic groups (Eu1’ and Eu2’). This division matched with the genetic structuring previously observed when analysing the 774 MLGs from Europe. Eu1’ grouped 188 MLGs, 122 of which were previously assigned to the Eu1 genetic group. It also clustered samples from the states of New York (22 MLGs), South Dakota (10), Pennsylvania (8), Massachusetts (7), Indiana (5), Minnesota (4) and Arkansas (4), as well as from the introduced regions of Uruguay (3 MLGs), Brazil (2) and Hungary (1). Eu2’ grouped 160 MLGs, 151 of which were previously assigned to the Eu2 genetic group. The remaining MLGs corresponded to 1 MLG from Eu1, 2 from the Arizona state, 5 from Austria and 1 from Hungary   showed three distinct groups  and Iași (eastern Romania), which represents a quantitative improvement compared to previous workset al.20, who found a loose correlation with sampling latitude. Unfortunately, authors did not test the effect of longitude. Here, we also found a significant effect of latitude on the genetic structure, but longitude had a major effect. In this regard, Eu1 is mostly formed by grape phylloxera genotypes that dominate in western regions , whereas Eu2 genotypes dominate in eastern areas . This genetic structure observed from nuclear genomic analyses is in accordance with previous mtDNA phylogenetic analyses that indicate a common origin for grape phylloxera in Germany and France, but independent to those from Hungary23. The effect of host plant on the selection of specific grape phylloxera clones has been already suggested24, especially in the native range25 as a result of centuries of plant-insect coevolution4. Here, we observed a significant effect of host plant on the genetic structure, but it can be the result of the non-random distribution of the different plant hosts in the diverse regions analysed in this work . In this line, a fine-scale exploration is needed to separate host plant- and geography-based associations.Our results strongly indicate the presence of at least two independent grape phylloxera introductions from the native range into Europe, which derived into two genetic groups (Eu1 and Eu2). The structuring of grape phylloxera populations into two groups was already suggested by Forneck 8, which spread northwards and southwards to infest Swiss and Italian vineyards, respectively14. In addition, our results indicate that this introduction might have been responsible of the outbreaks reported in the west of Germany between 1874 and 1900 too10, as no additional stratification between Swiss, German and Italian samples was observed. On the other hand, grape phylloxera genotypes of Eu2 might have directly derived from the grape phylloxera outbreak detected in 1868 in the nurseries of the formerly known as the Imperial and Royal Viticulture Research Station at Klosterneuburg (Austria)16, where vine cuttings from different regions (including the USA and France) were received26. According to historical records, vine cuttings were distributed as a solution against oidium before grape phylloxera symptoms were evident, easing the dispersal of the insect across the former Austro-Hungarian Empire26. The independence on the origin of these two outbreaks was already evidenced in the 1870s, as no grape phylloxera injuries were detected when all the plant material received from France was screened at the Klosterneuburg nurseries27.Considering the geographic distribution and historical reports, it seems plausible that genotypes of Eu1 derived from the grape phylloxera population introduced in the South of France in the 1860s23. Nevertheless, we found this statement to be exclusively true for the Eu1 genetic group, which was likely founded by grape phylloxera populations originating from the North East coast of North America, where V. riparia and V. labrusca dominate4. How grape phylloxera was introduced into France is still controversial, although it is generally accepted it might have been introduced on roots of V. labrusca and/or leaf-galled V. riparia plants4, agreeing with our results. Some historical records suggest that the French outbreak probably arose from the cultivation of infested plants of the hybrid grape cultivar Isabella à grains noirs, introduced from Pennsylvania28. Some reports additionally indicate that first grape phylloxera populations could have been introduced to France via United Kingdom, where grape leaf galls were already observed in table grape vines by 186311. The cultivation of Isabella à grains noirs vines  was rather common in the South of France due to its pleasant fragrance and raspberry-like flavour28. Unfortunately, this trend favoured the rapid dissemination of the insect across neighbouring vineyards, and caused the decease of a great number of vines throughout west and central Europe. On the other hand, historical reports indicate that the strains initially introduced into Austria (founder of the Eu2 genetic group) could have been transported within a set of 20 American varieties imported to Klosterneuburg from New Jersey (USA), which were planted in the experimental vineyard to evaluate their fitness against oidium29. Nevertheless, we could not place the point of origin of this genetic group, as genotypes in Eu2 clustered only with samples from other introduced regions and not with those of the native range. In this line, it would be of interest to cross our data with a major number of grape phylloxera samples collected in the native range, as a wide insect genetic diversity exists as the result of plant-insect coevolution4.Our analysis confirmed previous findings that indicated a close relationship between European grape phylloxera populations and north-eastern American populations30.Samples from the introduced regions of Uruguay and South Africa did not form a specific cluster, suggesting that (I) either both regions received infested plant material from Europe, or (II) the introduction came from the same source(s) as those that devastated European vineyards. Accordingly, it has been recently indicated that Uruguayan populations might have resulted from the unintended introduction of infested plant material from Europe2, which presents a low capability of colonizing new vines or vineyards5. The lack of natural resistance in V. vinifera L. and the absence of natural enemies in European vineyards favoured the fast colonization of the new environment. Genetic diversity parameters indicate that these initial populations were subjected to different selection pressures that lead to the selection of different alleles for local adaptation, generating a series of clones mainly by parthenogenesis, as suggested by significant Psex values. In fact, parthenogenesis is acknowledged as the preferable mechanism for grape phylloxera reproduction in different introduced regions33. Clonal reproduction is still capable of generating a high level of genetic variation per generation34. This reproductive mechanism likely facilitated the long-term persistence of early introduced populations until today, as this strategy favours aphid populations persistence in areas with temperate climate35, like European winemaking regions36. Unravelling the mechanisms that drove to the generation of site- and host-adapted grape phylloxera populations in Europe will aid to understand why some of the rootstocks generated more than a century ago are still useful to control grape phylloxera effects, as well as to plan effective measures of control (including the breeding of new more resistant rootstocks) to anticipate to new, more aggressive outbreaks.Our analysis revealed an important role of the first introductions that occurred in the nineteenth century in Europe. Considering historical records, it is likely that the introduced grape phylloxera populations rapidly spread across the continent by the unintended transportation of infested American vine cuttings to fight other disease-causing agents. This human-assisted dispersal counteracted the sessile nature of the insect for most of its life cycleV. vinifera L. cultivars (such as Cabernet Sauvignon and Chardonnay), Vitis spp. interspecific rootstocks , Vitis spp. interspecific hybrid direct-producers , and Vitis spp. interspecific resistant grape varieties  in 100 locations from eight European countries . It corresponded to a latitudinal gradient from 43.0°N to 50.0°N and a longitudinal gradient between 6.2°E and 27.6°E. Samples were collected from 2012 to 2019  have been analysed in this work.Leaf and soil-emerged grape phylloxera individuals were collected from et al.19 and stored at −20 °C for further use. A total of 1173 samples were genotyped with seven fluorescently labelled (6-FAM or HEX) simple sequence repeat (SSR) primers . DNA amplification, fragments selection and allele calling were performed as detailed previously38. In every set of samples, we included six control genotypes with known allele size to keep allele calling consistent between different runs. This analysis revealed the presence of 774 grape phylloxera MLGs.Whole genomic DNA was extracted from grape phylloxera individuals as indicated in Forneck et al.3 explored the population genetic diversity of grape phylloxera in its native range, using samples collected across the USA, as well as some samples obtained from Europe (Austria and Hungary) and South America . These samples were genotyped using 32 SSRs, including the SSR markers Phy_III_55, Phy_III_30, Phy_III_36 and Dvit6. We retrieved the information of these 4 markers to perform a joint analysis between these samples and our genotypes.In a previous work, Lund 39 was used to infer the number of genetic groups and to assign individuals to the inferred populations, using our dataset of 774 MLGs based on 7 SSR markers. The existence of genetic structure was tested in a number of hypothetical genetic groups (K) from 1 to 15, using a cycle of 250.000 burn-in steps followed by 500.000 Markov Chain Monte Carlo iterative steps. Ten replications per K value were run to assess the consistency of the results, and each one was performed considering an admixture model with uncorrelated allele frequencies. Aware of the uneven sampling for the factors likely affecting grape phylloxera population structure , four estimators of the most likely K value (median of means (MedMeaK), maximum of means (MaxMeaK), median of medians (MedMedK) and maximum of medians (MaxMedK)) were determined using a membership coefficient threshold of 0.5, as proposed by Puechmaille40 and implemented in STRUCTURE SELECTOR41. Thus, these four statistics were independently calculated using the different group classes obtained for sampling site , feeding form (leaf or root) or host plant  as co-factors. The statistics of the three scenarios were then compared, and the most likely number of genetic groups was determined as the most repeated K value within the 12 calculations. Once the most probable K value was set, MLGs were assigned to a genetic group considering an ancestry coefficient Q ≥ 0.80; otherwise, a MLG was considered as “admixed”. In parallel, a dissimilarity matrix was calculated between 752 grape phylloxera MLGs (22 were discarded because of the presence of missing data) using the DARwin software package v. 6.0.2142 on the basis of 10.000 bootstrap steps, which was further used for a Principal Coordinate Analysis (PCoA). As indicated by Emanuelli et al.43, a Neighbor-Joining Unweighted (NJUw) dendrogram was constructed for the subset of MLGs clustered in the genetic groups previously determined to support the consistency of STRUCTURE and PCoA clustering methods. The dendrogram was constructed on the basis of a dissimilarity matrix with 10.000 bootstrap steps, and it was drawn considering 1.000 bootstrap replicates. MLGs with missing data were excluded from the analysis. The three outcomes  were used to determine the optimum level of genetic structure of grape phylloxera in Europe.The Bayesian clustering software STRUCTURE v.2.3.4Hobs) and expected (He) heterozygosities under Hardy-Weinberg expected equilibrium, and FIS values, which were independently calculated for each locus. The genotypic diversity index (R) was estimated as R = (MLG-1)/(N-1), and the clonal diversity index (Pd) as Pd = MLGs/N, MLG being the number of multilocus genotypes identified, and N the number of sampled individuals. All calculations were performed using MLGsim 2.044 with 10.000 simulation steps. In addition, the same software was used to calculate the Psex statistic to estimate the likelihood that grape phylloxera individuals with the same MLG are products of distinct sexual reproductive events or truly clonal. Thus, a multiple MLG was considered to be a true clone if Psex < 0.01 .For each genetic group, we calculated the mean number of alleles per locus, the observed (Q ≥ 0.80 in any of the identified genetic groups (n = 302). For this analysis, three variables  were used as independent variables, defining eight levels for country, two for feeding form and four for host plant, as detailed above. As dependent variable we used the Q values given by STRUCTURE to each MLG (Q-matrix). Factors were considered to have a significant effect on the Q-matrix at p ≤ 0.05. To deepen into the effect of geography on the genetic structure, we calculated a linear regression model using the Q-matrix as dependent variable and latitude and longitude values of the sampling sites as independent variables. Factors were considered significant if p ≤ 0.05. Analyses were performed using SPSS v.24.0 .To evaluate the effect of sampling site (country), feeding form and host plant on the genetic structure of grape phylloxera in Europe, a three-way ANOVA was calculated focusing in the European MLGs with an ancestry coefficient 39 to analyse the relationship between the 302 European MLGs clustered in the final genetic groups previously identified and the MLGs reported in Lund et al.3 sampled in the native range and in diverse introduced regions. In total, 621 MLGs have been included in this analysis. To this aim, we used the procedure described above, but using the information available for four SSR loci . The most likely number of genetic groups was assessed by plotting the ΔK value of the data over 10 runs, as implemented in STRUCTURE HARVESTER45, and defined in Evanno et al.46. MLGs were assigned to a genetic group if they have a coefficient of ancestry (Q) over 0.80; otherwise, they were considered as “admixed”. A dissimilarity matrix was calculated considering genetic data on 4 SSRs for 575 MLGs as previously detailed, discarding 46 MLGs for the presence of missing data. The matrix was then used to construct an NJUw dendrogram with 1.000 bootstrap replicates to get insights into the relationship between the genetic groups identified in our dataset and those previously reported from grape phylloxera native range and different introduced regions3.A second population structure analysis was performed with STRUCTURESupplementary information


The current model, based on rodent data, proposes that thalamocortical afferents (TCA) innervate the subplate towards the end of cortical neurogenesis. This implies that the laminar identity of cortical neurons is specified by intrinsic instructions rather than information of thalamic origin. In order to determine whether this mechanism is conserved in the primates, we examined the growth of thalamocortical (TCA) and corticofugal afferents in early human and monkey fetal development. In the human, TCA, identified by secretagogin, calbindin, and ROBO1 immunoreactivity, were observed in the internal capsule of the ventral telencephalon as early as 7–7.5 PCW, crossing the pallial/subpallial boundary (PSB) by 8 PCW before the calretinin immunoreactive corticofugal fibers do. Furthermore, TCA were observed to be passing through the intermediate zone and innervating the presubplate of the dorsolateral cortex, and already by 10–12 PCW TCAs were occupying much of the cortex. Observations at equivalent stages in the marmoset confirmed that this pattern is conserved across primates. Therefore, our results demonstrate that in primates, TCAs innervate the cortical presubplate at earlier stages than previously demonstrated by acetylcholinesterase histochemistry, suggesting that pioneer thalamic afferents may contribute to early cortical circuitry that can participate in defining cortical neuron phenotypes. It is generally accepted from studies in mice that the protomap of cortical arealisation, which determines which region of the cortex is innervated by which thalamic nucleus, is driven by intrinsic programmes of gene expression  and therhttp://translatingtime.org; In the mouse, it is well established that TCA cross the diencephalic/telencephalic boundary (DTB) at embryonic day (E) 12, the pallial/subpallial boundary (PSB) by E14.5 and innervate the subplate by E15.5, where they make synaptic connections with subplate neurons . In humaThat corticothalamic neurons are born prior to the arrival of thalamic afferents is key to our current understanding of thalamocortical pathfinding to the cortex. Corticothalamic axons cross the PSB first (“handshake hypothesis”) and are required to guide the TCA across the PSB  which isThe present study examined the expression of three proteins, secretagogin (SCGN) calbindin , and ROBO1, present in both cell body and axon, in the developing human and marmoset thalamus. We were able to identify the outgrowth of axons from early born thalamic neurons and demonstrate that thalamocortical innervation occurs prior to the outgrowth of corticofugal fibers and in time to innervate the pSP from the earliest stages of CP formation.http://www.hdbr.org; Human fetal tissue from terminated pregnancies was obtained from the joint MRC/Wellcome Trust-funded Human Developmental Biology Resource  dissolved in 0.1 M phosphate-buffered saline (PBS). Once fixed, whole or half brains  were dehydrated in a series of graded ethanols before embedding in paraffin. Brain samples were cut at 8-μm section thickness in three different planes; horizontally, sagittally, and coronally, and mounted on slides.Callithrix jacchus) aged embryonic day (E) 55 (n = 1) and 60 (n = 2) were used in this study, procured from the National Nonhuman Primate Breeding and Research Facility . The animals were transcardially flushed with warm heparinised phosphate buffer 0.1 M  containing 0.1% sodium nitrite and subsequently perfused with 4% paraformaldehyde in PB 0.1 M. Cerebral tissues were postfixed overnight in the same fixative at 4°C, dehydrated in increasing concentrations of sucrose  in PB 0.1 M, frozen in isopropanol cooled at −49 °C and stored at −80 °C until cryosectioning.All experiments were conducted according to the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes and were approved by the Monash University Animals Ethics Committee, which also monitored the welfare of the animals. New World marmoset monkeys  at a thickness of 20 μm and were collected on Superfrost Plus® microscope slides (Menzel-Gläser/Thermo Fischer) and stored at −20°C. Sections were rehydrated in PBS, blocked in a solution of PBS, 0.3% Triton-X, 10% normal goat serum and incubated with rabbit anti-calretinin (diluted 1:1000 in the blocking solution) and rabbit anti-ROBO1  for 16–18 h at 4°C. Sections were then washed in PBS, incubated with a goat anti-rabbit Alexa Fluor 594 secondary antibody  in the blocking solution (1:1000) for 1 h, rinsed in PBS and incubated with Hoechst  to visualize cell nuclei.Images from immunoperoxidase stained sections were captured using a Leica slide scanner and Zeiss Axioplan 2 microscope; from immunofluorescent stained human sections with a Zeiss Axioimager Z2 apotome; from immunofluorescent stained marmoset sections with an Axio Imager Z1 microscope (Zeiss) equipped with a Zeiss Axiocam HRm digital camera using the Axiovision software (v 4.8.1.0) at a resolution of 1024 × 1024 pixels. The objectives used were Zeiss EC-Plan Neofluar 10×0.3, #420 340–9901. Filter sets used for visualizing fluorescently-labeled cells were Zeiss 49 Dapi #488 049-9901-000, and Zeiss HQ Texas Red #000 000-1114-462. Images were adjusted for brightness and sharpness using Adobe Photoshop CS6 software. Planes of sectioning are illustrated in At 7 PCW, the thalamic primordium was relatively underdeveloped and consisted principally of neural progenitors in a ventricular zone (VZ) around the third ventricle. These cells expressed PAX6 with moderate intensity in the thalamus  and are proposed to project the first corticofugal axons . Only spTo determine if the early innervation of the pSP by TCA is unique to human development or occurs in other primates, we tested for the presence of TCA in the marmoset embryonic brain. At E55, corresponding to 7.5/8 PCW in human development, we observed ROBO1+ fibers emerging from the thalamus into the telencephalon and crossing the PSB into the cortex Fig.  the numbBy 10 PCW, expression of SCGN was downregulated in the thalamus, but maintained in the epithalamus . ROBO1 aThe present study demonstrates that, in the human forebrain by 7 PCW, a population of thalamic neurons is born that extends axons towards the cortex, reaching the PSB a few days later. The pSP is innervated by 8 PCW at the earliest stages of CP formation and prior to extensive outgrowth of corticofugal fibers. This was also shown to be the case in marmoset at a similarly early stage of development. It differs markedly from observations made in rodents which suggest that thalamic and cortical neurons are generated synchronously and also extend axons at the same. time This is also considerably earlier than has been previously reported from studies in human that relied principally on AChE histochemistry to visualize thalamic neurons and their afferents. This may be because AChE is not expressed by all developing TCA . TCA areGbx2 in post-mitotic cells along its anterior–posterior axis  cluster nearer to the SVZ. Similarly, in the IC by 12 PCW, Putative CalR+ coritcofugal fibers segregate dorsally (nearer the SVZ) and TCA ventrally.Previous studies in macaque have shown that by the time of neurogenesis of the upper layers of the visual cortex, TCA lie close to the SVZ  and it hhttp://translatingtime.org; 8B). No studies of electrical activity in human subplate have been made before 16 PCW, although at this age there are subplate neurons present capable of repetitive firing of action potentials . Certainly, there is expression of human GJDR2 mRNA, the gene for connexin 36, at 7.5–9 PCW in samples from the whole cortical wall  proteins  are similar in sequence and structure, they may have different functions which would explain their differing expression patterns. SCGN has a very high affinity for calcium and acts as a calcium sensor  whereas proteins . SCGN inproteins  and coul−/− mice . Thalami−/− mice  and calc−/− mice  signalinUp until now, in both primate and rodent, all evidence has pointed towards TCA entering the cortex after CTA have left, being guided by the CTA in the process. Our evidence shows that at least a population of pioneer TCA are likely to cross the PSB before any corticofugal afferents do. Furthermore, some TCA contact the pSP by 8 PCW in the human, at least one month earlier than previously described. This creates the possibility for thalamic input to influence subplate driven early cortical network activity from a much earlier developmental stage.Supplementary DataClick here for additional data file.