The incidence of dementia in Black, Asian and minority ethnic (BAME) groups is increasing in the UK, with concern about underdiagnosis and late presentation.By reviewing referrals to memory clinics from Leicester City we examined whether the following differed by ethnicity: the proportion with a diagnosis of dementia, type of dementia and severity at presentation.n = 131) and a random sample of 260 Asian and 259 White British referrals. Severity of dementia was assessed by record review. Odds ratios (ORs) were adjusted for general practice, age, gender and year of referral.We examined referrals between 2010 and 2017: all those whose ethnicity was recorded as Black (A diagnosis of dementia was recorded in 193 (74.5%) White British, 96 (73.3%) Black and 160 (61.5%) Asian referrals. Compared with Asians, White British had twice the adjusted odds of a dementia diagnosis (OR = 1.99 (1.23–3.22). Of those with dementia, Alzheimer's disease was more common in White British (57.0%) than in Asian (43.8%) and Black referrals (51.0%): adjusted OR White British versus Asian 1.76 (1.11–2.77). Of those with dementia, the proportion with moderate/severe disease was highest in White British (66.8%), compared with 61.9% in Asian and 45.8% in Black groups. The adjusted OR for the White versus Black groups was 2.03 (1.10–3.72), with no significant difference between Asian and White British groups.Differences in confirmed dementia suggest general practitioners have a lower threshold for referral for possible dementia in some BAME groups. Unlike other centres, we found no evidence of greater severity at presentation in Asian and Black groups. It was found that in Asians the incidence of diagnosis was lower than in the White population (adjusted incidence rate ratio [IRR] = 0.82 for women and IRR = 0.88 for men). Rates of diagnosis were higher in the Black than the White population . The authors concluded that it was unclear whether lower rates in the Asian population were because of lower incidence or underdiagnosis, but that the rate of diagnosis in the Black population, despite being higher, still suggested underdiagnosis because of the higher levels of risk factors for dementia,  in this group.4 To our knowledge, only two UK studies have examined severity of dementia at presentation to memory clinics by ethnicity. In a large study of over 10 000 patients referred to two mental health trusts, Mukadam et al found Black and Asian patients had lower cognitive score at diagnosis,5 and a smaller study found lower scores at diagnosis in Black patients.6Internationally, there is evidence for underdiagnosis of dementia in BAME groups. A systematic review (drawing largely on US studies) concluded these groups accessed diagnostic services later (i.e. symptoms were more severe at presentation), and were less likely to be prescribed cholinesterase inhibitor drugs.12There are several steps in the pathway to diagnosis of dementia. First, the patient has to seek help from a healthcare professional, usually their general practitioner (GP). Second, the healthcare professional has to assess cognition, and if dementia is suspected refer the patient for a specialist to confirm or refute the diagnosis. Both these steps may be more problematic for some people from BAME groups.13Leicester is one of the most ethnically diverse cities in the UK. In the 2011 census, 50.5% of the population was White (45.1% White British), 37.1% Asian or Asian British  and 6.2% Black or Black British .14In this study we examined referrals from Leicester City Clinical Commissioning Group to a district wide memory service to establish whether the following differed by broad ethnic group: the proportion with a diagnosis of dementia confirmed, type of dementia, severity at presentation and use of cholinesterase inhibitor drugs when indicated. It was beyond the scope of this project to estimate referral rates by ethnicity as we did not have access to age-stratified population data by ethnicity. In a related study using 2011 census data we found that, in Leicester City, after adjusting for age, there were no differences between Asians and White British in their odds of referral to the memory clinic from 2011 to 2013, but from 2014 to 2017, members of Asian groups had higher odds of being referred.15 other demographic details, clinical findings and diagnosis. Ethnicity was self-ascribed, either in the GP practice before referral or at the memory clinic if this information was not provided in the referral letter. The main categories of ethnicity are: White , Mixed, Asian or Asian British, Black or Black British, and other ethnic groups (including Chinese).Leicestershire Partnership NHS Trust maintains a database of all referrals to its memory clinic. This was started in 2010 and fields include ethnicity  codes based on UK census categories),In 6.4% of records ethnicity was recorded as ‘not known’ and in 9.6% as ‘not stated’. We extracted data collected on all 3602 patients whose ethnicity was recorded as White British, Asian/Asian British or Black/Black British from Leicester City Clinical Commissioning Group from January 2010 to December 2017. Other ethnic categories, including, for example, Mixed, Chinese and Irish were not examined.P = 0.8). We therefore decided not to use this variable, so there was no need to assess missing values in White British. Therefore, to assess severity, two trained senior psychiatric nurses (one band 8 and another band 6) went through the clinical information in the case notes and electronic records together (including the MMSE score if present) and jointly agreed on a combined clinical dementia severity score .We had planned to use the Mini-Mental State Examination (MMSE) score to document severity of dementia but in too many cases this was missing. Missing values were 41% in Asian groups and 39% in Black groups .Analysing all records was beyond our financial and clinical resources. We included all 131 referrals with Black ethnicity and selected a random sample of 259/2525 patients with White British ethnicity and a random sample of 260/946 patients with Asian ethnicity. We originally sampled 260 White British but 1 patient was later excluded as they were not referred by a practice in Leicester. Using ordinal regression, this gave 80% power to detect an odds ratio of 1.7 in severity between Black and White British groups and Black and Asian groups, with greater power to detect differences between White and Asian groups.Simple randomisation was used to derive each sample separately and was carried out using Stata v14.The authors assert that all procedures contributing to this work comply with the ethical standards of 147 the relevant national and institutional committees on human experimentation and with the Helsinki 148 Declaration of 1975, as revised in 2008. All procedures involving patients were 149 approved by University of Leicester Ethics Sub-Committee for Medicine and Biological Sciences, 150 reference: 13597-sa144-healthsciences.n, %), and proportions with dementia, type of dementia and other diagnoses by ethnicity.We present mean age (s.d.) and age bands and gender  was the amended significance level. Analyses were carried out in Stata 14.Multivariable multilevel logistic regression models were used to adjust for age (continuous), gender and year, in order to generate adjusted odds ratios  to compare ethnic groups. Patients were clustered within general practices, so we adjusted for this by fitting multilevel models with random intercepts and slopes. Year was modelled as a linear function with a quadratic check for nonlinearity. 16In those with a specialist diagnosis of dementia, there will be no difference between ethnic groups in the odds of being diagnosed with more severe dementia versus less severe dementia. Moderate and severe categories were combined owing to sparse data leading to some empty cells.In those with any diagnosis, there will be no difference between ethnic groups in the odds of being diagnosed with dementia versus any other diagnosisIn those with a dementia diagnosis, there will be no difference between ethnic groups in the odds of being diagnosed with Alzheimer's disease versus any other dementiaWe assessed three null hypotheses:n = 8, 1%), 2011 , 2012  2013  2014  2015  2016  2017 . Patients were referred by 53 practices (reduced to 49 after covariate attrition), the number per practice ranging from 1 to 51.A total of 650 records from 2010 to 2017 were examined, comprising all available referrals of Black groups and random samples of White British and Asians. The total sample comprised 370 females (57%), 279 males (43%), with 1 missing value for gender. Mean age for the whole sample was 77.1 years (s.d. = 9.8). Age and gender by ethnicity are shown in 2 = 1.11 (2 d.f.), P = 0.57.The last row in P=0.005) but compared with Black groups the difference was not significant . Comparing Black and Asian groups, the former had a higher proportion diagnosed with dementia, but this was not statistically significant (odds ratio 1.65 (0.93–2.91), P = 0.08. Age and year of referral were significant predictors (with lower odds of confirmed dementia in more recent years).As shown in On univariable analysis, compared with the White British population (5.7%), other or unstated dementia was more common in Asian (18.1%) and Black groups (29.2%), and the proportion of dementias diagnosed as Alzheimer's disease correspondingly lower. The proportion with vascular dementia was similar across groups but mixed dementia was lower in Black groups.P = 0.015) but the overall ethnic group effect was borderline non-significant, so this result should be treated with caution. The other two pairwise ethnic differences did not reach statistical significance. In this model, earlier referrals (closer to 2012) were more likely to be classified as Alzheimer's disease than later ones. Year of referral was a significant predictor of a diagnosis of Alzheimer's disease, with lower odds in more recent years.As shown in In order to model predictors of severity of diagnosis, moderate and severe categories were combined because of very low numbers of severe diagnoses in the Black group, which led to some empty cells in the regression matrix. Results are shown in P = 0.051): post hoc tests showed that White British had 103% higher odds than Black groups, and Asian groups had 101% higher odds than Black groups, although with a Bonferroni correction these differences were not quite significant. There was no significant difference in severity at diagnosis between Asian and White British groups. With the passing of each extra year between 2010 and 2017, the odds of being in a higher severity category decreased by 17%. The effect was linear, the quadratic effect being non-significant when tested,Increasing age did not significantly increase the odds of being in a higher severity (moderate/severe) category compared with a mild category. Gender affected the odds of being in a higher severity category: women had 67% higher odds of being in a higher severity category. Ethnicity also appeared to significantly affect the odds of being in a higher severity category, although the overall effect of the ethnic group variable was non-significant . In Asian groups, however, this was somewhat lower (61.5%) with higher proportions being diagnosed with mild cognitive impairment and depression. Compared with Asians, White British had twice the adjusted odds of being diagnosed with dementia. Although the number of Black patients was small, the proportion diagnosed with psychosis was highest in this group, with this difference reaching statistical significance.Of those with dementia, unstated or other dementia was less common in White British (5.7%) than Asian (18.1%) and Black (29.2%) groups. A diagnosis of Alzheimer's disease was more common in White British (57.0%) than in Asian (43.8%), adjusted OR = 1.76 (95% CI 1.1–2.8). In total, 51% of Black patients were diagnosed with Alzheimer's disease, not significantly different from White British patients. It is reassuring that the use of Alzheimer's drugs for those who could benefit from them (i.e. with Alzheimer's disease or mixed dementia) did not differ by ethnicity.5 and Black groups.6 This apparent discrepancy is likely to be because of differences within broad ethnic groups between Leicester and London.After controlling for age, gender and year of diagnosis, we found no differences between Asian and White British groups in severity at presentation, but Black groups were less likely to have severe or moderate dementia compared with each of the other two groups, although after applying a Bonferroni correction this was not statistically significant. These findings are in contrast to two studies from London cited in the introduction, which found lower cognitive scores at presentation in Black and Asian groups17These findings suggest GPs may respond to the difficulty of assessing cognition by referring more people from some BAME groups where there is uncertainty. The data on type of dementia suggests that specialists may also have more difficulty in distinguishing type of dementia in some BAME groups. This could explain why vascular and mixed dementia were not more often diagnosed in Black and Asian groups, despite higher prevalence of vascular risk factors in these populations.Our paper highlights the difficulties in differentiating the clinical heterogeneities in presentation at primary care, the difficulties in relying on standardised cognitive screening tools (including the widely used MMSE) in ethnic populations and the difficulties in delivering culturally appropriate interventions. Most services are agnostic to the cultural complexities of assessing and providing care and support to these groups. Arguably such issues may be found to a greater extent in other regions of the country where BAME groups may be less represented. Our study emphasises the need to raise awareness of such issues and possibly the need to provide ongoing training to address them even if this may seem to be only of limited scale and scope.Further research is needed to further explore the reasons for the differences we found in rates of dementia in referrals from Asian and Black groups, including qualitative work examining GPs' decision to refer and the referral process from the perspective of patients and their families. Taken together this work will allow the development of guidelines to assist GPs in their assessment of cognition in these ethnic groups.18 especially in comparisons between ethnic groups. Second, the reviewers could not be masked to ethnicity as they retrieved the clinical record using names. Third, we used broad ethnic groups to allow quantitative comparisons, which may have masked important differences within groups, for example in Asians between those from a Pakistani and Indian background. Fourth, we were unable to control for deprivation, as postcode for individuals was not reliably recorded and we felt that using GP postcode as a proxy would not be sufficiently sensitive.The major strength of the study is that we examined a large number of referrals to a memory clinic in an ethnically diverse city over several years. We were able to assess not only severity of dementia but also type of dementia and the prevalence of other diagnoses. However, the study has several limitations. First, MMSE scores were not recorded consistently in the NHS database used and so we had to rely on clinical review of the records, which could be considered less objective. It should be noted, however, that clinical assessment of severity may be more meaningful than cognitive scores,More generally, a limitation of the whole study is that this is a single centre and so results cannot be generalised to places where the composition of the BAME population is different from in Leicester. This could explain why our findings differ from studies in London, where the composition of ethnic groups differs from Leicester. A number of results were non-significant, in spite of large OR (>1.65), suggesting the study was underpowered.The higher proportion of non-dementia diagnoses in Asian and Black groups suggest that GPs may respond to difficulties in assessment of cognition by having a lower threshold of referral. The higher proportion of unclassified dementia in these groups may be because specialists also find assessment more difficult. Unlike studies from other centres, we found no statistically significant evidence of greater severity at presentation in Asian and Black groups, although the large OR  strongly suggest that Black groups have lower severity than each of the other two ethnic groups, with the non-significant results only occurring because the study was underpowered. An OR > 2 is highly likely to be clinically important.


Globally, farmers cultivate and maintain crop landraces . Landraces contain unique diversity shaped in part by natural and human‐mediated selection and are an indispensable resource for farmers. Since environmental conditions change with elevation, crop landraces grown along elevational gradients have provided ideal locations to explore patterns of local adaptation. To further probe traits underlying this differentiation, transcriptome signatures can help provide a foundation for understanding the ways in which functional genetic diversity may be shaped by environment. In this study, we returned to an elevational gradient in Chiapas, Mexico, to assess transcriptional differentiation of genes underlying UV‐B protection in locally adapted maize landraces from multiple elevations. We collected and planted landraces from three elevational zones  in a common garden at 1,531 m. Using RNA‐seq data derived from leaf tissue, we performed differential expression analysis between maize from these distinct elevations. Highland and lowland landraces displayed differential expression in phenylpropanoid and flavonoid biosynthesis genes involved in the production of UV‐B protectants and did so at a rate greater than expected based on observed background transcriptional differentiation across the genome. These findings provide evidence for the differentiation of suites of genes involved in complex ecologically relevant pathways. Thus, while neutral evolutionary processes may have played a role in the observed patterns of differentiation, UV‐B may have also acted as a selective pressure to differentiate maize landraces in the region. Studies of the distribution of functional crop genetic diversity across variable landscapes can aid us in understanding the response of diversity to abiotic/biotic change and, ultimately, may facilitate its conservation and utilization. Crop landraces  grown by farmers possess unique functional genetic diversity that is of great use, while also enhancing our understanding of evolutionary and agroecological processes. In situ  conservation of landraces can contribute to long‐term agricultural sustainability in the face of change  increases with increasing elevation Godar,  and incrC‐glycosyl flavone content after UV‐B exposure in the former suggesting flavones aid highland landraces in coping with UV‐B  genes. Additionally, two flavonol synthase (FLS) genes, the entry point into flavonol biosynthesis, have been described in maize and respond to UV‐B  and the border of Guatemala (~600 m) is accompanied by abiotic and biotic gradients . Of the three elevations, the highland zone had the largest variation of collection elevations, reflected in the fact that two of the highland landraces had to be collected just outside that elevational band (Table 2.2matas (or planting locations), each of which contained three maize seeds for 30 seeds per landrace per block. We randomized the elevational main plots in each of three blocks  through RNA‐seq analysis due to differential insect herbivory later in the growing season. We collected leaf tissue between 9:00 a.m. and 1:00 p.m. from the youngest mature leaf with a collar  from three randomly selected individuals per landrace, per block; the tissue was collected from plants at the V2 to V4 leaf stage. From this total of nine individuals per landrace, we immediately froze collected leaf tissue in liquid nitrogen, stored in a −80 C freezer until transport, and transported it in a liquid nitrogen dry shipper to ensure RNA integrity.2.3As described in Kost et al. , we perf2.4We sequenced the 45 RNA‐seq libraries in four flow cell lanes of the Illumina HiSeq 2500 at the Genomics Resources Core Facility located at the Weill Cornell Medical College. We performed paired‐end sequencing at 50 bp. Since HiSeq 2500 normally produces ~120–130 million paired‐end reads per lane, we multiplexed 12 libraries per lane to produce ~10–11 million reads per library. In order to ensure balanced sequencing lanes, we ran 12 libraries in each lane. Since we only had 45 libraries, we sequenced one library in all four lanes, bringing the number of libraries in each lane to 12.FastQC version 0.10.1 . We kept a majority of the TopHat2 settings as default with the following exceptions. First, we set the mean inner distance between mate pairs and the standard deviation for distance between mate pairs to 150 bp. Second, we set the minimum and maximum intron lengths to 70 bp and 20,000 bp, respectively. Finally, we set the minimum and maximum intron length during split‐segment searches to 50 bp and 20,000 bp. We left mismatches set to the default setting of two. To ensure that mapping percentage did not differ among landraces from different elevations, which would indicate differences in divergence from the B73 RefGen_v2 5b.60 maize genome, we performed a second mapping run where we allowed zero mismatches. Any observed differences when allowing zero mismatches would indicate differential mapping, which would complicate data interpretation. Once we mapped reads to the maize B73 RefGen_v2 5b.60 maize genome, we used htseq‐count version 0.6.1  p‐values. We maintained loci having FDR p‐values of 0.05 or less; we considered them DE and retained gene lists containing DE genes for subsequent analysis. Observed differential expression among elevational zones is indicative of genetic differentiation, which could be due to adaptation or neutral evolutionary processes.We used 2.6http://cellwall.genomics.purdue.edu, genes obtained by NCBI BLAST searches, and those recently identified in the literature. In addition, we identified several genes putatively involved in flavonoid biosynthesis while visualizing our data on the MaizeCyc metabolome  to perform hierarchical clustering analyses on variance stabilized and normalized counts. We began by performing hierarchical clustering analysis on all 45 libraries using count data from 179 of the 190 genes from the full gene list; genes with no expression values across samples were excluded. Settings were adjusted to one minus Spearman rank correlation distances, 18 clusters, 1,000 sampling iterations, and average linkage, and clustering was visualized through heat map and tree generation. We also performed hierarchical clustering and heat map generation on the DE genes identified during the three pairwise comparisons of elevation origin. For these analyses, we used one minus Pearson correlation distances with remaining settings set to default to visualize the overarching patterns of gene co‐expression at the level of elevational zone of landrace origin. In addition to elucidating patterns of gene co‐expression, these latter heat maps provide the maize GRMZM gene IDs (Gene_Name) to facilitate the cross‐referencing of our results to the major maize databases  for those seeking additional gene‐specific information.First, in order to gain an overall view of the expression profiles of these genes across the landraces and among elevational zones, we used Second, we performed enrichment analyses to see if our phenolic biosynthesis gene set was overrepresented in our DE gene lists when compared to what would be expected when looking at all genes expressed in leaf tissue. Overrepresentation of focal gene sets in this type of analysis is consistent with, but does not confirm, that local adaptation may underlie the observed patterns. As our differential expression analysis was performed on leaf tissue spanning the V2 to V4 stages, we generated a list of genes expressed during these developmental stages. To do so, we accessed tissue‐specific expression data from the gene atlas presented in Hoopes et al. ; data acIn preparation for our enrichment analyses, we ensured the distributions of normalized gene counts of our leaf background gene set matched that of our phenolic biosynthesis candidate set by following the subsampling procedure described in Steige, Laenen, Reimegård, Scofield, and Slotte . In prepWe performed the enrichment analyses using Yates’ chi‐square tests in the statistical software R. In the analyses, we tested whether or not the phenolic compound biosynthesis gene set was enriched in our DE gene lists when compared to what would be expected in the leaf background. For example, in our highland–lowland experimental comparison, there were 29 DE genes from our phenolic compound biosynthesis gene list consisting of 175 genes (see below). In the highland–lowland comparison, there were 813 genes DE and the leaf background consisted of 11,656 genes (see below). Hence, we performed a Yates chi‐square test on the ratios of 175/11,656 to 29/813 to see if there were differences between what was observed and expected. We employed the same approach for the other two pairwise comparisons (highland–midland and midland–lowland).OMIX Visualization . The three pathways visualized were general phenylpropanoid, flavonoid, and lignin biosynthesis. Our OMIX Visualization pathways consisted of genes underlying enzymatic steps and metabolic intermediates. Genes up‐regulated in highland landraces are represented by circles with blue while those up‐regulated in the lowlands are represented by red. A fully filled circle represents a log2fold change of 2.11 .Third, to visualize the pathway‐specific phenolic compound biosynthesis DE patterns among landraces from the three distinct elevational zones, we used 33.1FastQC analyses on raw reads confirmed reads were of high quality. Trimming adapter sequences and poly‐A/poly‐T tails and removing low‐quality bases eliminated overrepresented sequences and stabilized Kmer content without influencing read counts. After trimming reads, paired‐end reads ranged from five million to 20 million , between 70 and 80 percent of the initial raw reads from each library uniquely mapped to the maize genome when we allowed a two base pair mismatch . This similarity in distributions can be seen in the spread of data before and after subsampling  and that the number of genes in the leaf background for all three pairwise comparisons was substantially reduced. The average normalized counts for the 179 candidate genes across all landraces ranged from 0.02 to 7,600, 156 (89%) of the candidate genes were located in the first bin (0.02–760.01 counts), and 15 (9%) were in the second bin .Our enrichment analyses demonstrated that genes involved in biosynthesis of UV‐B protectants were overrepresented or enriched in the DE gene lists obtained during all pairwise elevational zone comparisons when compared to what would be expected in the leaf background  and a gene putatively encoding a flavonoid‐3‐beta‐hydroxylases (F3H) were DE between highland and lowland landraces. The former showed greater expression in the lowland landraces while the later showed greater expression in landraces from the highlands  also showed greater expression in the lowland landraces when compared to highland landraces  gene in the midland landraces when compared to those from the highlands , were all highly expressed in the lowland landraces when compared to those from the highlands  genes in both lowland and midland landraces when compared to those from highland locations , one of the two DE genes putatively encoding cinnamoyl‐CoA reductase (CCR), one putatively encoding a cinnamyl alcohol dehydrogenase (CAD), and a gene putatively encoding a caffeoyl‐coenzyme A  Figures  and 5. W Figures  and S11. Figures  and S8. s Figure . No diff3.5FNSI‐1) (PMID: 26269546) involved in O‐glycosyl flavone biosynthesis when comparing highland landraces to those collected from midland and lowland elevations. In both comparisons, landraces from the lower elevations displayed greater expression of this gene  putatively involved in O‐glycosyl flavone biosynthesis in the midland landraces  (PMID: 27221383) when compared to those from the highlands  biosynthesis.We observed differential expression of a flavone synthase gene (1) PMID: 6269546 ie Figure  and S3. FLS gene (FLS2) (PMID: 22654889) more highly expressed in highland landraces and a predicted FGT gene more highly expressed in the midland landraces  showed increased expression in the highland landraces Figures  and 4. WS gene FL  (PMID: 27221383) encoding a rhamnose synthase (RHM) and another gene encoding a putative UDP‐D‐xylose synthase (UXS) were both more highly expressed in lowland landraces when compared to those from the highlands  were more highly expressed in the midland landraces  PMID: 7221383 es Figure . Howevers Figure .4An extensive body of literature has documented maize landrace genetic diversity; however, few studies have documented the distribution of functional genetic diversity in maize landraces across the landscape at a regional scale. Here, we employed RNA‐seq on maize landraces collected along an elevational gradient in Chiapas, Mexico, but grown at an intermediate elevation. We determined that highland, midland, and lowland landraces exhibited transcriptional differentiation in the general phenylpropanoid, flavonoid, and lignin biosynthesis pathways, as well as in genes involved in nucleotide‐sugar interconversion—all of which contribute to UV‐B protection. Hierarchical clustering suggested that highland and midland maize landraces are more like each other than they are to lowland landraces for this functional diversity. Our differential expression and enrichment analyses of landraces from different elevational zones indicate that expression differences are consistent with UV‐B acting as an agent of natural selection along our study elevation gradient, though neutral processes could also be involved. To our knowledge, this is a unique case of documenting differentiation across a complex pathway within a wild or cultivated plant species along a natural environmental gradient.4.1 are indicative of genetic differentiation , which potentially encode enzymes involved in generating sugar building blocks for carbohydrates bound for the cell wall  gene expression in the highland landraces when compared to those from the midlands suggests that the quantity of flavonols may also be relevant in counteracting the detrimental effects of UV‐B intensity in the region.In comparison with lowland landraces, highland maize landraces showed greater expression of genes putatively involved in flavonol and HCA biosynthesis, further suggesting that maize landraces in the region are transcriptionally differentiated in key pathways involved in reducing the negative effects of UV‐B irradiance. Our finding of greater expression of several putative  Graham, ; flavonoC4H gene in the highland landraces and increased expression of putative 4Cl genes in the lowland landraces suggests that highland and lowland maize landraces could be genetically differentiated in HCA biosynthesis. We observed a similar trend when comparing midland and lowland landraces—greater expression of a putative C4H gene in midland landraces and increased expression of a putative 4Cl gene in lowland landraces. Since 4Cl enzymes are needed to convert HCAs to lignin, increased expression of genes encoding these enzymes in the lowland landraces suggests that the regulatory mechanisms underlying the conversion is different in landraces from different elevational zones , and not flavonols, to be involved in the ability of highland maize landraces from Mexico  and the Andes  to cope with increased UV‐B when compared to inbred lines. Our findings are like those of Casati and Walbot , when compared to highland landraces, indicating landraces from particular elevational zones are also transcriptionally differentiated for C‐glycosyl flavones (maysin) production. Here, we also observed the differential expression of FNSI‐1 between highland and lowland landraces , a gene involved in O‐glycosyl flavone (apigenin) biosynthesis; FNSI‐1 has been shown to protect against UV‐B induced damage when expressed in Arabidopsis (Righini et al., FNSI‐1 in midland landraces when compared to those from the highland, and of FNSI‐2 in midland landraces when compared to those from the lowlands, further supports this claim.This potential trade‐off is particularly interesting in light of the study by Casati and Walbot  who idend Walbot  in that C‐glycosyl flavone production unless it was constitutively expressed rather than induced by high UV‐B. Second, other selective pressures may have shaped the phenolic biosynthesis pathway of lowland landraces in our study. As C‐glycosyl flavones retard the growth of lepidopteran pests (Waiss et al., C‐glycosyl flavone to minimize insect herbivory. Finally, lowland landraces may not have the capacity to induce flavonols and instead flavone production is increased when they experience higher than normal levels of UV‐B light, which would have been the case in a midland environment. Combined, these observations suggest that there may not be fixed patterns of how maize landraces from different regions adapt or respond to UV‐B and that other selective pressures they encounter can also shift their gene expression profiles.Apparent differences between our study and the findings reported in Casati and Walbot  are like4.3Importantly, some of the differential expression we observed in this study could have been caused by genetic drift and not local adaptation. A novel study by Kryvokhyzha et al.  suggestsWe identified DE genes involved in the biosynthesis of phenolic compounds relevant to UV‐B protection, which appear to indicate genetic differentiation and potential signals of natural selection. However, given that our data were generated under only one environmental condition, we were unable to determine the extent that phenotypic plasticity may have been affecting our findings. Phenotypic plasticity, the ability of an organism to alter its phenotype in different environments Sultan, , allows Finally, selective pressures other than UV‐B may have contributed to the observed patterns of genetic differentiation. In addition to UV‐B, other abiotic and biotic stressors such as herbivory, rainfall, salinity, and temperature have been shown to induce and/or be buffered by the phenolic compounds discussed in this study (Chalker‐Scott, 4.4To increase agricultural resiliency and sustainability in the face of unprecedented global climate change, it is imperative that we conserve and utilize the diversity contained within the crop landraces and crop wild relatives of the world Bellon, . Many smRegional transcriptional differentiation studies, as the one reported here set a foundation for DNA‐based studies looking at the spatial distribution of functional genetic diversity in crop landraces across the landscape. Identifying DNA level differences within the promoters of DE genes involved in local adaptation and the transcription factors regulating them is a logical next step toward the execution of landscape level allelic diversity studies aimed at informing in situ conservation and utilization efforts. For instance, allelic diversity information could be employed in the Weitzman approach to diversity conservation where information on rare and endangered diversity is used to determine the most efficient allocation of resources to strengthen in situ conservation efforts (Weitzman, None declared.Supplementary MaterialClick here for additional data file.


Bartonella mayotimonensis and Candidatus Bartonella rousetti, were recently identified as emerging and potential zoonotic agents, respectively. However, there is no report of bat-associated bartonellae in Zambia. Thus, we aimed to isolate and characterize Bartonella spp. from bats and bat flies captured in Zambia by culturing and PCR. Overall, Bartonella spp. were isolated from six out of 36 bats (16.7%), while Bartonella DNA was detected in nine out of 19 bat flies (47.3%). Subsequent characterization using a sequence of five different genes revealed that three isolates obtained from Egyptian fruit bats (Rousettus aegyptiacus) were Ca. B. rousetti. The isolates obtained from insectivorous bats (Macronycteris vittatus) were divided into two previously unclassified bat-associated bartonellae. A phylogenetic analysis of the six genotypes of Bartonella gltA sequences from nine pathogen-positive bat flies revealed that three genotypes belonged to the same clades as bat-associated bartonellae, including Ca. B. rousetti. The other three genotypes represented arthropod-associated bartonellae, which have previously been isolated only from ectoparasites. We demonstrated that Ca. B. rousetti is maintained between bats (R. aegyptiacus) and bat flies in Zambia. Continuous surveillance of Bartonella spp. in bats and serological surveys in humans in Africa are warranted to evaluate the public health importance of bat-associated bartonellae.Bat-associated bartonellae, including  Bartonella are Gram-negative, hemotropic, and primary vector-borne bacteria that colonize mammalian endothelial and red blood cells. To date, the genus Bartonella consists of more than 30 species, many of which have been described recently . I. IEucampectively ,23. Sequectively . Sequencigeria MH1070 and X655838) . The seqgltA, sequence type 1 clustered together with Bartonella spp. from ectoparasites, such as flea and louse in Peru, Tunisia, and Thailand [Bartonella spp. isolated from Eucampsipoda sp. from South Africa [Eucampsipoda theodori of Madagascar [Candidatus Bartonella rousetti, which was detected in Rousettus aegyptiacus from Kenya [Bartonella sp. from Rhinolophus ferrumequinum of China. Sequence type 6 clustered together with Bartonella sp. from Miniopterus sp. of Kenya and Bartonella sp. from Miniopterus schreibersii of Georgia [In the phylogenetic tree based on the partial sequences of Thailand ,26  and detected from bat flies (Eucampsipoda africana) using PCR. These results indicated that Ca. B. rousetti is distributed widely in the African continent and is maintained between bats (R. aegyptiacus) and bat flies (Eu. aficana) in a cave in Zambia. Local people frequently enter the cave and collect bat feces to use it as manure for their vegetable farms, increasing the risk of Ca. B. rousetti infection in the local population, as is the case in Nigeria. Considering that individuals who entered the same cave developed a febrile illness caused by a novel Borrelia sp. [ in 2010 . Thereaffestival . The stuenotypes . In the elia sp. , they shMacronycteris vittatus). One isolate obtained from ZB17-113 showed high sequence identities with Bartonella sp. H-556 obtained from Macronycteris commersoni of Kenya  and long-fingered bats (Miniopterus spp.) were found to be positive for three and four different Bartonella genotypes, respectively [Bartonella genotype can be detected from several different bat species [Bartonella spp. and their host bats. Our results provide information regarding the genetic diversity and geographic distribution of bat-associated bartonellae in the African continent.In the present investigation, three isolates were obtained from insectivorous bats . A phylogenetic analysis revealed that the sequence types 4, 5, and 6 belonged to the same clades as bat-associated bartonellae, including Candidatus Bartonella rousetti (Bartonella spp. which were detected only in bat flies [Bartonella spp. was reported elsewhere [Bartonella apis was isolated from the gut of the honeybee (Apis mellifera), which might provide honeybees with resistance to diseases [Bartonella spp. were also detected from Ponerine ants (Hymenoptera: Formicidae: Ponerinae) [Bartonella spp. than bats. Bat flies are specially adapted for a nearly permanent ectoparasitic relationship with their host bats. In the present study, we detected six different sequence types of the rousetti . In contat flies . In anotlsewhere ,30,31,32nerinae) . This hyBartonella in bats and their ectoparasites in Zambia. Although cases of human bartonellosis caused by bat-associated Bartonella have not been reported so far, further studies such as continuous surveillance of Bartonella spp. in bats and serological surveys in humans are warranted to evaluate their potential as zoonotic agents in Africa.This present study used a random sampling procedure and samples were collected in one location, which are the limitations of this study. Therefore, the study is not enough to describe the whole picture of bat-associated bartonellae in Zambia. However, the present study revealed the presence of diverse groups of The capturing of bats was approved by the Department of National Parks and Wildlife (DNPW) and the Ministry of Tourism and Arts of the Republic of Zambia (Act No. 14 of 2015). The approval for placing traps and entering the cave was obtained from the village head and residents. Rousettus aegyptiacus, four Macronycteris vittatus, and one Rhinolophus sp.) in vacutainer ethylenediaminetetraacetic acid (EDTA) tubes. Individually, 25 µL of uncoagulated whole blood were spread onto sheep blood agar plates. The plates were incubated at 34 °C with 5.0% CO2. In 2017 and 2018, bats were captured in the Leopard’s Hill cave , Chongwe district, Lusaka province, using a harp trap as a part of a surveillance program of filovirus infection in bats in Zambia, as described in a previous study . Whole bBartonella were selected and used for DNA extraction using the alkali–heat lysis method. Briefly, a colony was resuspended in 17 µL of 50 mM NaOH and the suspension was heated at 99 °C for 10 min. To adjust pH, 3 µL of 1 M Tris-HCl (pH 7.0) was added. The solutions were used as DNA templates for subsequent genetic characterization.After two weeks of incubation, bacterial colonies morphologically identified as To confirm the identity of bacterial species, we amplified a fragment of 16S rDNA via PCR using the primers fD1 and Rp2 as described previously . PCRs wegltA, nuoG, ftsZ, ssrA, and rpoB were performed as described previously [For further molecular characterization, PCRs targeting eviously ,38,39,40COI [gltA was used to screen Bartonella. PCR was performed and the products were analyzed as described above.To identify vector arthropods, 19 bat flies were collected from the surface of bat guano in the cave in 2018. The bat flies were morphologically identified to the genus level according to the keys described previously , and speCOI . DNA washttp://blast.ncbi.nlm.nih.gov/Blast.cgi). A phylogenetic analysis was conducted using MEGA version 6.05 [Bartonella spp. isolated from bats using the maximum likelihood method. The neighbor-joining method was used to construct a phylogenetic tree based on the gltA sequence for the analysis of Bartonella spp. from bat flies and bats. The reference sequences used in the phylogenetic inference are listed in gltA, LC460839-LC460844 for rpoB, LC460845-LC460850 for ssrA, LC460851-LC460856 for ftsZ, LC460857-LC4608622 for nuoG, and LC536586- LC536588 for COI of Eucampsipoda africana).All amplicons were sequenced using the BigDye Terminator version 3.1 cycle sequencing kit  and an ABI Prism 3130x genetic analyzer (Applied Biosystems) according to the manufacturer’s instructions. The sequences were analyzed using GENETYX version 9.1  and trimmed on both the 5’ and 3’ ends. The resulting sequences were compared to those in public databases using BLASTn (ion 6.05 . Clustal


Multiple factors may contribute to the decision to initiate methylphenidate treatment in children such as maternal sociodemographic factors of which relatively little is known. The objective was to investigate the association between these factors and methylphenidate initiation. The study population included 4243 children from the Generation R Study in the Netherlands. Maternal sociodemographic characteristics were tested as determinants of methylphenidate initiation through a time-dependent Cox regression analysis. Subsequently, we stratified by mother-reported ADHD symptoms (present in 4.2% of the study population). When ADHD symptoms were absent, we found that girls  and children born to a mother with a non-western ethnicity (compared to Dutch-Caucasian)  were less likely to receive methylphenidate. They were more likely to receive methylphenidate when their mother completed a low  or secondary  education. In conclusion, boys and children born to a mother of Dutch-Caucasian ethnicity were more likely to receive methylphenidate, irrespective of the presence of ADHD symptoms.The online version of this article (10.1007/s10578-020-01016-2) contains supplementary material, which is available to authorized users. Worldwide, 5% of children will develop or have symptoms of attention deficit hyperactivity disorder (ADHD) . StimulaNot every child diagnosed with ADHD receives pharmacological treatment . While sThe objective of our study was to investigate the association between maternal sociodemographic and prenatal lifestyle factors in relation to child methylphenidate treatment initiation. Subsequently, we performed analyses stratified by the presence of (mother reported) clinically relevant ADHD symptoms in children using the determinants that were significantly associated with child methylphenidate prescription. We hypothesized that maternal sociodemographic factors are associated with the initiation of methylphenidate treatment in children. These maternal factors would account for variance in the treatment initiation, beyond the individual child characteristics. Apart from gender and the indication, family factors may have a large impact on the decision to start therapy with methylphenidate.The study was conducted within The Generation R Study, which is a large prospective population-based cohort study investigating children’s health from fetal life onwards in Rotterdam, the Netherlands . PregnanPharmacy records of Generation R participants were collected based on the living area of their mothers . This information was used to determine at which pharmacies they may collect their medication. Pharmacists were contacted and asked for consent to retrieve the pharmacy records. Children may collect their medication at more than one pharmacy and these pharmacy records were all linked to one particular child. Pharmacy records of children may not be available because either their pharmacy or their parents did not give consent to retrieve these records or because the child’s pharmacy could not be found.For 4243 individuals in The Generation R Study, all prescriptions which were filled at their pharmacy during the entire study period were gathered starting at birth. For each prescription we had the product name, anatomical therapeutic chemical (ATC) code , date ofrd percentile [The Child Behavior Checklist (CBCL/1.5–5 and CBCL/6–18) was used to obtain information about behavioral and emotional problems in children . The CBCrcentile , 30. ForThe following maternal lifestyle and demographic characteristics were considered as potential determinants for starting treatment with methylphenidate: maternal age at intake, ethnicity , househChild and family characteristics were presented for all children with pharmacy records, which also included the type of prescriber, age of first methylphenidate prescription and the life style factors of mothers. Second, we investigated which maternal lifestyle and demographic characteristics were related to methylphenidate initiation. For the main analyses, we calculated the hazard ratio (HR), with 95% confidence interval (CI) for each determinant associated with initiation of methylphenidate. We used a time-dependent Cox regression model, where non-methylphenidate users can serve as a control more than one time . A time-First, we performed complete-case analyses. In the non-response analysis, we explored differences between mothers and children of responders and non-responders. In the sensitivity analysis, we performed the same analysis using multiple imputation of the covariates (where less than 30% was missing) using the expectation maximization algorithm to deal with missing data. Results were considered statistically significant at p < 0.05. Statistical analyses were performed using IBM SPSS Statistics for Windows, version 21.0 .Pharmacy records were available for 4243 children. A total of 295 children were prescribed an ADHD medication, of whom 291 (99%) received methylphenidate. Due to the small number of children with other ADHD medication (dexamphetamine n = 3 and atomoxetine n = 1), we only analyzed data of children who started their treatment with methylphenidate. The average age of receiving the first methylphenidate prescription was 9.4 years. Of these children, 207 received their first methylphenidate prescription from a specialist (71%) and they were more often boys .In Table In the multivariable analysis Table , we founTable When child ADHD symptoms above the cut-off were reported, we found that girls were less likely to receive a methylphenidate prescription than boys   or when they had a mother with a non-western background . Children were more likely to receive a methylphenidate prescription when they had ADHD symptoms above the cut-off reported by their mothers . However, the association between methylphenidate initiation and a low maternal education was non-significant .For the variables that were included in the analyses, less than 30% were missing, except for SSRI use during pregnancy (56.8%) and the reported clinically relevant ADHD symptoms (51.2%). The non-response analysis showed no significant differences between children with and without information for the maternal characteristics. However, we found that 2173 women did not complete the CBCL questionnaires at all or did not complete the questionnaire prior to the first prescription of methylphenidate. A dropout analysis on this variable showed that the missing of the CBCL questionnaire was not significantly associated with sex, but it was associated with ethnicity (P < 0.001), maternal education (P < 0.001) and methylphenidate initiation (P < 0.001).The analysis in the group without information on ADHD symptoms (missing information) showed similar results for sex  and ethnicity . For maternal education, we found that a lower education was significantly associated with a decreased risk of methylphenidate use compared to higher education .Pharmacy records were not available for half of the cohort participants (results not shown in table). For children of whom no pharmacy records were available, we found that their mothers were significantly younger , had a lower household income , were lower educated , more often did not have a partner , used less alcohol during pregnancy , smoked more during pregnancy  and had a lower caffeine intake during pregnancy (61.9% vs 58.9%).In the current study, we found that several child and maternal sociodemographic factors were related to methylphenidate treatment initiation. In our study, we were able to examine these determinants stratified by the presence and absence of clinically relevant ADHD symptoms. Our findings show that methylphenidate was more frequently prescribed to boys than girls. It also shows that children of mothers with a non-western background were less likely to receive a methylphenidate treatment than children of mothers with a Dutch-Caucasian or other western background. These findings are both in line with results that have been shown in previous studies , 10, 37.First, sex differences with regard to use of methylphenidate or other stimulants is probably related to the diagnosis of ADHD, which is more common in boys than girls . This coSecond, we showed that children without ADHD symptoms of mothers who only had limited education  were more likely to receive a methylphenidate prescription. On the other hand, no significant association with maternal education was observed in children with ADHD symptoms. This finding might be interpreted that as long as the child has ADHD, there is no problem with inequity. When it comes to children without a diagnosis, there is inequity in treatment. However, these results should be carefully interpreted due to the association of missing information of ADHD symptoms and maternal education. Nevertheless, a previously published study showed that a low maternal education was associated with less involvement in the decision-making of medication initiation in children. Parents may not have sufficient knowledge about ADHD and feel that it is necessary to initiate methylphenidate when this is not thoroughly discussed with them . FurtherThird, children with ADHD problems and parents from ethnic minority groups may have less access to healthcare or less communication with healthcare professionals due to a language barrier , 48–51. These characteristics may be associated with certain medication types , 59, 60.Strengths of our study are the relatively large population-based cohort, its prospective design, independent registration of dispensed medicines, and the multi-ethnic nature of the sample which limit the chance of selection and information bias. Treatment initiation was based on pharmacy dispensing records, which is more accurate in terms of dispensation date than information on prescription medication from medical records or questionnaires as medication can be prescribed but not collected at pharmacies. However, our study also has some limitations. One of the limitations is that we had to rely on questionnaires filled out by mothers to assess the presence of ADHD symptoms. These mother reports are considered valuable as they provide more insight into the perspective of the mothers with regard to their child’s behavior as ADHD symptoms are not always recognized as such across different demographic groups. However, not all mothers completed the CBCL questionnaire, which is also considered as an important limitation as we were not able to assess the presence of ADHD symptoms in these groups. Information bias may have occurred if the association between maternal characteristics or sociodemographic factors and treatment initiation is different for responders and non-responders, but this is difficult to ascertain. Nevertheless, the stratified analysis in the group without information on ADHD symptoms showed similar results for sex and ethnicity, except for maternal education. Further research is needed to assess the role of maternal education in the treatment initiation of methylphenidate, in particular in relation to the presence of ADHD symptoms. Another limitation is that pharmacy records were not available for half of the participants of The Generation R study as not all pharmacists provided consent to obtain the dispensing records from their pharmacies. As shown in the results, we found that mothers of children without pharmacy records differed on several aspects. Despite this selection bias, we observed similar results in our study compared to the available literature showing an association between initiation of methylphenidate and ethnicity, maternal education level and smoking during pregnancy , 61. FurWe assessed the association between child and maternal factors and the initiation of methylphenidate treatment. Additionally, we also assessed the association in the stratified analysis with mother-reported ADHD symptoms. The results of the study showed that apart from the child’s sex, the mother also plays an important role in the treatment initiation. We also found that even when no ADHD symptoms were reported, boys and children born to a mother of Dutch-Caucasian ethnicity or a low maternal education were still more likely to receive methylphenidate treatment. The difference in symptom profile among boys and girls may affect the decision to start pharmacological treatment. Furthermore, the decision of parents to start treatment with methylphenidate may depend on their knowledge and perceptions about ADHD and pharmacological treatment, which may vary among different ethnic groups. Considering these findings, it is important for healthcare professionals to be aware of these differences and take these into account when deciding on initiating treatment with methylphenidate. As parents play an important role, they should always be involved in the decision-making process of treatment initiation with methylphenidate. Reasons for treatment initiation in children without ADHD symptoms should be further investigated.Electronic supplementary material 1 (DOCX 43 kb)Below is the link to the electronic supplementary material.


Measuring national progress towards the Sustainable Development Goals (SDGs) enables the identification of gaps which need to be filled to end poverty, protect the planet and improve lives. Progress is typically calculated using indicators stemming from published methodologies. South Africa tracks progress towards the SDGs at a national scale, but aggregated data may mask progress, or lack thereof, at local levels.To assess the progress towards achievement of the SDGs in four low-income, rural villages (Giyani) in South Africa and to relate the findings to national SDG indicators.Using data from a cross-sectional environmental health study, the global indicator framework for the SDGs was applied to calculate indicators for Giyani. Local progress towards SDG achievement was compared with national progress, to contextualize and supplement national scale tracking.Village scores were mostly in line with country scores for those indices which were computable, given the available data. Low data availability prevented a complete local progress assessment. Higher levels of poverty prevail in the study villages compared to South Africa as a whole (17.7% compared to 7.4%), high unemployment (49.0% compared to 27.3%) and lack of access to information via the Internet (only 4.2% compared to 61.8%) were indicators in the villages identified as falling far short of the South African averages.Understanding progress towards the SDGs at a local scale is important when trying to unpack national progress. It shines a light upon issues that are not picked up by national composite assessments yet require most urgent attention. Gaps in data required to measure progress towards targets represents a serious stumbling block, preventing the creation of a true reflection of local and national scale progress. The United Nations Sustainable Development Goals (SDGs) are international guidelines which aim to move the world towards a sustainable future, balancing economic growth, social development, and environmental protection 2. They bFor regions, countries, districts, cities, and towns to meet the SDGs, specific and locally relevant strategies, policies and programs need to be designed and implemented . The AfrTable 1) illustrating the relevance of the SDG process for South Africa [In South Africa, the National Development Plan (NDP) was adopted in 2012 and defines national development priorities for the country. Despite the NDP pre-dating the SDG process by several years, there is nevertheless around 74% convergence between the two frameworks  [A similar exercise was conducted for Africa specifically, by Table 2). In 2019, South Africa did not fully achieve any of the SDGs. This may be attributed to a lack of actual progress towards goal achievement or to inadequate sources of information or data to assess real progress.South Africa’s dashboard in 2019 reported the country’s average performance  on each of the SDGs , 73% of the economic targets SDGs 7–10 and 12), 57% of the environmental targets (SDGs 6 and 13–15), 73% of the peace and security targets (SDG 16) and 29% of the targets grouped under the 17th SDG which is associated with the means of overall implementation towards achieving the goals [ and 12, These statistics are useful to understand how well certain locations in South Africa are faring towards SDG achievement especially when compared to data nationally and from other countries. The country’s first full-scale SDG progress tracking report was compiled by Statistics South Africa in 2017 and outlined a National SDG Indicator Framework. The report illustrated progress towards selected 2030 targets as specified by the SDGs and their published indices 3. Most of11Success towards meeting goals and targets is typically measured at a national scale to enable a comparison towards global progress. However, tracking smaller scale, local progress assists in better identifying and understanding gaps that need to be addressed to meet the targets within a country’s own borders. When considered at a global scale, African countries frequently appear as “red”  in the global SDG tracking dashboards, even though work is being done in those countries to progress. A higher resolution assessment at local scale, instead of a national scale, could help highlight progress made towards SDG target achievement which would ordinarily be masked by an amalgamated national index. Showcasing local government efforts could help illustrate whether countries are on the appropriate track towards meeting the SDGs and provide a basis for shared experiences and best practices .The aim of this study was to assess progress towards achievement of the SDGs in four low-income, rural villages in South Africa. Findings are compared with national indicators where possible. Results of this exercise help to identify data gaps, areas for potential research and development, as well as to stimulate discussion about the types of data needed for SDG target and index computation for South Africa. This work provides a model for how local-scale assessments can be approached and how the results can then be contextualized within national progress.Figure 1). The municipality has a population size of 247,657 inhabitants, living in 57,537 households, the majority of which are clustered in hamlets or villages. Dwellings for the study were randomly selected from four villages. A cluster sampling method was used to select 100 households in each village. Ethics clearance for the study was granted by the South African Medical Research Council Research Ethics Committee  and permission was sought from the provincial, local and traditional leadership of the study areas.A cross-sectional survey was used to collect data from 400 dwellings in the greater Giyani local municipality located in Limpopo Province, South Africa in September 2016 . Questionnaire data were double-data-entered into Microsoft Excel and analyzed for descriptive frequencies using STATA version 14.relative indices, e.g. percentage of population living below the international poverty line. Indicative comparisons were made for absolute indices, e.g. total number of social grants received. An overview of the model used is illustrated in Figure 2.Responses to questions in the Giyani study questionnaire which aligned with SDG indicators were used to calculate a Giyani-specific index which was then compared to the corresponding index listed in the South African Country Report . Existin811Table 3).Data collected from four villages in rural Giyani were compared to 20 SDG indicators using the model illustrated above . Of these 20 indicators, seven were directly calculated according to officially recognized Tier I and Tier II computation methods and 13 were used for contextualization purposes only (Table 4). The proportion of the Giyani population (17.7%) with an income below the international poverty line exceeds the national proportion which is 7.4%. Compared to South Africa as a whole (25.2%), a smaller proportion of people lives with an income below the national poverty line (17.7%). The unemployment rate in Giyani (49.0%) was higher than the figure reported for South Africa (27.3%) [The income of nearly one third of Giyani’s population ranks below the international and the national poverty lines ( (27.3%) . Given t (27.3%) 13. GenerTwo supplementary tables illustrate progress towards the SDGs in Giyani villages in relation to the rest of the country. Table S1 lists those indices for which data from the Giyani survey were available for comparison with SDG target indices. Results from the Giyani villages were also compared to South Africa’s outcomes, where the colors red, orange or green were allocated to each score (Table S1). The colors translate to “Giyani is doing worse than the rest of South Africa in this index”, “Giyani is on par with the rest of South Africa in this index” and “Giyani scores better than South Africa in this index”, respectively.Table S2 highlights instances in which the Giyani survey results did not provide enough information to officially calculate a given index, or where there is no existing South African score available to make a comparison, but where results are nevertheless of contextual value in relation to the SDGs. Table S2 is useful as a broad indication of how Giyani is faring compared to the rest of the country in relation to a particular index. For instance, the assessment illustrated that more than two thirds of households sampled received income through a state grant .Giyani is grappling with many of the same socio-economic issues which persist in other parts of South Africa. Poverty affects almost a third of households. Access to drinking water and sanitation, though “present”, is not always reliable or safe, and requires urgent attention . UnemploHere we underline the challenges associated with holistic, local and national assessments of SDG progress, including masking of extreme under-development in a society as unequal as South Africa. Data collected in Giyani yielded insights on various socio-economic issues covered by the SDGs. The original study survey was not designed for the primary purpose of an SDG progress assessment, and so the data were mostly not useful according to the globally accepted official calculation methodologies. This is frequently a pitfall in project-specific surveys: they are often not created with other assessments in mind. As data gaps represent one of the main reasons why progress towards the SDGs in RSA cannot be adequately computed and expressed, it is advisable that researchers be mindful of the opportunity for data compatibility between the SDG framework and survey data, for example in respect of socio-economic and human health. If this is done, survey data can contribute to the understanding of local, as well as national, progress towards SDG achievement. This is particularly important in countries where the lack resources focused on socio-economic surveys may also represent a limiting factor into why large data gaps exist.This exercise aimed to consider data that were collected in four rural villages in Giyani in relation to the SDG indicators. The results were then contextualized by comparing them to South Africa’s national indicator results. It was found that Giyani villages fared worse than the rest of the country on average, in relation to levels of internationally defined poverty, unemployment, and access to the internet. Giyani villages however, are faring better than the national status quo in terms of nationally defined poverty levels, access to water  and sanitation, as well as in terms of the number of people living in informal dwellings.It should be brought to the attention of researchers and survey teams that surveys could be formulated in such a way that the survey results can be directly fed into the calculations of relevant SDG target indices. This would contribute to the improvement of overall data availability for the accurate assessment of how South Africa is progressing towards meeting the SDGs, at both macro- and micro-levels. It would also highlight very specific gaps which need to be targeted at a local level to ensure the SDGs are achieved. South Africa, and indeed small communities like Giyani, are still far from reaching the SDGs, however, tracking their progress using reliable data, provides useful information to guide tailored interventions and awareness campaigns.The additional files for this article can be found as follows:10.5334/aogh.3139.s1Table S1.Detrended Oscillation and Clock Parameters.10.5334/aogh.3139.s2Table S2.Comparing Giyani and South Africa SDG scores for targets for which indices could not be computed for official/direct comparison but were unofficially calculated for contextualization.


GALC, CERS6, SMPD1, SMPDL3B, CERS2, FADS3, ELOVL5, and CERK). Successful local replication for associations with variants in GALC, SMPD1, and CERS6 was demonstrated in both replication cohorts. Variants rs35785620, rs143078230, rs398607, and rs1805078, associated with ADHD in the discovery or replication cohorts, correspond to missense mutations with predicted deleterious effects. Expression quantitative trait loci analysis revealed an association between rs398607 and increased GALC expression in the cerebellum.Attention deficit hyperactivity disorder (ADHD) is the most prevalent neurodevelopmental disorder in children, with genetic factors accounting for 75–80% of the phenotypic variance. Recent studies have suggested that ADHD patients might present with atypical central myelination that can persist into adulthood. Given the essential role of sphingolipids in myelin formation and maintenance, we explored genetic variation in sphingolipid metabolism genes for association with ADHD risk. Whole-exome genotyping was performed in three independent cohorts from disparate regions of the world, for a total of 1520 genotyped subjects. Cohort 1  sample, 371 subjects) was analyzed as the discovery cohort, while cohorts 2  and 3  were used for replication. A set of 58 genes was manually curated based on their roles in sphingolipid metabolism. A targeted exploration for association between ADHD and 137 markers encoding for common and rare potentially functional allelic variants in this set of genes was performed in the screening cohort. Single- and multi-locus additive, dominant and recessive linear mixed-effect models were used. During discovery, we found statistically significant associations between ADHD and variants in eight genes ( It is the most common neuro-behavioral disorder in childhood, affecting 5.29–7.1% of children and adolescents2. Prevalence in adults is also high, with best estimates between 2.5 and 2.8% worldwide4.Attention deficit hyperactivity disorder (ADHD) is defined as a neurodevelopmental condition characterized by persistent, cross-situational and developmentally inappropriate levels of inattention, hyperactivity, and impulsiveness that leads to various degrees of functional impairment7. Interesting results have emerged from studies of candidate genes involved in the monoamine neurotransmitter systems, which had been implicated in the pathophysiology of ADHD by the mechanisms of action of drugs used in clinical management. Family-based and case–control studies of candidate genes have replicated significant linkage and/or association between ADHD and variants in dopamine receptors , dopamine transporter (SLC6A3), serotonin transporter (SLC6A4), serotonin receptor (HTR1B), and proteins involved in synaptic transmission 16, all of them contributing to small- to medium-sized effects. The first 12 genome-wide significant ADHD risk loci were published recently17. Several of the identified loci are located in or near genes  that implicate neurodevelopmental processes likely to be relevant to ADHD pathogenesis. Historically, the lack of success in identifying genome-wide significant variants supports the complex multifactorial etiology of ADHD and likely reflects important biases in patient ascertainment and phenotyping strategies. Therefore, continued efforts are required to elucidate the missing heritability of ADHD.Genetic factors account for ~75–80% of the phenotypic variance of the ADHD phenotype20. Longitudinal neuroimaging studies targeting white and gray matter alterations have led to the proposition that ADHD involves a lag in brain maturation that eventually normalizes21. Additional evidence suggests that atypical myelination and gray matter anomalies might persist into adulthood in patients with ADHD23. Based on this evidence, myelination and neurogenesis appears to be highly attractive novel targets for genomic/metabolomic studies in ADHD.Neuroimaging studies have suggested white/gray matter anomalies in the prefrontal cortex, temporo-parietal regions, the striatum, and the cerebellum in ADHD patients24. Sphingolipids are important structural and signaling molecules that affect processes such as neuronal and glial proliferation, differentiation and apoptosis, nerve impulse generation and propagation, and neurotransmitter release27. Cell and animal models underscore the key function of sphingolipids in neurite growth and myelination in the central nervous system (CNS)30. Deficiency of ceramide synthase-2, an enzyme that catalyzes the synthesis of sphingolipids with very long acyl chains (C20–C26), results in 50% loss of compacted myelin and 80% loss of CNS myelin basic protein30. Similarly, mice with ceramide synthase-1 deficiency (enzyme specific for C18:0 acyl chains) show a 60% reduction in the levels of neuronal gangliosides and oligodendrocytic myelin-associated glycoprotein in the cerebellum and forebrain28.Sphingolipids encompass a complex range of membrane lipids in which a fatty acid is linked to a sphingosine carbon backbone. Depending on the sphingosine head group, they can be further classified into ceramides (no head group), phosphosphingolipids (mostly sphingomyelins), or glycosphingolipids (cerebrosides and the more complex gangliosides)31, opening the field to the search of new genetic risk variants in genes participating in sphingolipid metabolism. Here, we investigated 1520 genotyped individuals from three independent and geographically disparate populations to target potentially functional variants in 58 genes participating in sphingolipid metabolism. Our results provide the first evidence of a link between sphingolipid metabolism and ADHD susceptibility.The role of sphingolipids in ADHD pathogenesis has not been explored. Recently, a pilot study characterizing the serum sphingolipid profiles of ADHD patients revealed decreased levels of sphingomyelins and specific long-chain ceramides. These preliminary results also suggested that sphingolipids might eventually become an endophenotype for ADHD32 criteria for ADHD using the Diagnostic Interview Schedule for Children-Parent Version (DISC-P), supplemented with teacher report of symptoms. The DISC-P was administered at entry (in childhood) and at each of the prospective follow-up assessments, including the 6-year follow-up when the participants were between 13.0 and 15.9 years of age. After the initial 14-month treatment-by-protocol phase, the study continued as an observational follow-up into early adulthood, in which self-selected use of treatments and other variables were monitored33. The clinical and demographical characteristics of the sample, along with the recruitment procedures, have been extensively described34. Mean age of children at recruitment was 8.5 years (range 7–10 years) and 80.3% were males (n = 465). Ethnic composition of the sample included 61.5% Caucasian, 17.5% African American, 10.6% Hispanic, 1.5% Asian, and 8.9% of other race and ethnic minorities. Exclusion criteria for the MTA cohort are reported in the original study34. These criteria were limited to situations that would prevent families’ full participation in assessments or treatment, or that might require additional treatments incompatible with study treatments. The presence of comorbid conditions, such as oppositional defiant disorder (ODD), conduct disorder (CD), internalizing disorders, or specific learning disabilities, did not lead to exclusions per se as an important aim of previous studies was to examine their interactions with treatment outcomes37.The Multimodal Treatment study of children with ADHD (MTA) was designed to evaluate long-term effects of treatments for ADHD in a 14-month randomized controlled trial of 579 children meeting the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV)38. For the current study, only 371 subjects  were available for genotyping, consisting of 232/579 from the MTA group and 139/289 from the LNCG group.A local normative comparison group (LNCG) of 289 randomly selected classmates matched for grade and sex was added when participants were between 9 and 12 years old. Participants were diagnosed in childhood using the Diagnostic Interview Schedule for Children-Parent Version (DISC-P), which was administered at entry and at the prospective follow-up assessments. Outcomes in childhood , adolescence (6 and 8 years after baseline), and adulthood (up to 16 years after baseline) have been reportedThe MTA study is a cooperative effort of six independent research teams in collaboration with the Division of Services and Intervention Research, National Institute of Mental Health, and the Office of Special Education Programs, US Department of Education, Washington, DC. Research was conducted in accordance with the ethical guidelines of local Institutional Review Boards (IRBs) and the National Institutes of Health’s Office for Protection from Research Risks, Bethesda, MD. Patients were recruited under clinicaltrials.gov registration NCT00000388.39. Parents underwent a full psychiatric structured interview regarding their offspring 40. In addition, adult participants were assessed using the Composite International Diagnostic Interview as well as the Disruptive Behavior Disorders module from the DICA-IV-P modified for retrospective use40. ADHD status was defined by the best estimate method . ADHD in these extended Paisa families is highly comorbid with CD, ODD, and nicotine and alcohol abuse39. The comorbidity pattern and the large dense pedigrees of the sample have been particularly useful to identify genes conferring susceptibility to ADHD in previous molecular genetic studies43. Studies in the Paisa cohort were approved by the Ethics Committee of the University of Antioquia  and the National Human Genome Research Institute’s IRB office , and informed consent was obtained from all subjects in accordance with the Declaration of Helsinki. Patients were recruited under NHGRI protocol 00-HG-0058 .This cohort consists of 1176 persons (adults and children) from 18 extended multigenerational and 136 nuclear Paisa families inhabiting the Medellin metropolitan area in the State of Antioquia, Colombia . The detailed clinical and demographic description of the sample and the recruitment procedures have been published elsewhere44. Briefly, participants were recruited by advertising in national ADHD-related publications in the USA and on the NIH/NHGRI web page (https://www.genome.gov). Eligible families included a proband with a diagnosis of ADHD who was between 7 and 18 years of age at enrollment with at least one sibling (either affected or not). Additionally, at least one parent had to be available to participate with information accessible regarding both parents. Interested families underwent an exhaustive screening evaluation comprising questions regarding pregnancy and birth history for the proband and siblings and rating scales: the Vanderbilt Assessment Scale for Parents45, used for all family members; the Wender Utah Rating Scale46 and Conners Adult ADHD Rating Scale47, applied exclusively in adults; the Strengths and Weaknesses of Attention and Normal Behavior48, for children and adolescents. Additionally, parents underwent a full structured psychiatric interview regarding each offspring (DICA-IV-P)40 and all siblings 18 years or older responded to the Structured Clinical Interview for DSM-IV49. Questionnaires and eligibility criteria were reviewed by a clinical team consisting of a registered nurse coordinator, two registered nurses, and a clinical social worker, all with extensive training in behavioral conditions and ADHD research. Pedigrees were obtained from all families. Exclusion criteria included the following: (i) Bilineal families (both parents affected with ADHD), (ii) families with probands that met the DSM-IV32 criteria for Tourette’s disorder, obsessive compulsive disorder, pervasive developmental disorders, psychotic disorders, mood disorders with psychotic features, post-traumatic stress disorder, or (iii) prior diagnosis of lead toxicity, neurological conditions, known genetic syndromes, mental retardation, hydrocephaly, known prenatal drug exposure, cardiac surgery, or prematurity (birth weight below 2500 g). The total sample consisted of 1010 individuals . The study and consent forms were reviewed and approved by the National Human Genome Research Institute’s IRB office. Patients were recruited under NHGRI protocol 00-HG-0058 .Clinical and demographic characteristics of this sample, along with a detailed description of the recruitment protocol, have been published elsewherehttps://www.ncbi.nlm.nih.gov/geo, accession no. GSE112652).DNA for genotyping was extracted from whole blood. Whole-exome genotyping was performed in 371 subjects  from the MTA cohort (232/579 subjects from MTA and 139/289 subjects from LNCG). From the Paisa cohort, whole-exome genotyping comprised 298 participants, consisting of 159/1176 (13.5%) ADHD affected and 139/1176 (11.8%) unaffected controls. From the US cohort, whole-genome genotyping comprised 851 individuals. Genomic DNA was whole-exome genotyped using the Illumina® HumanExome BeadChip-12v1_A. This single-nucleotide polymorphism (SNP) chip covers >240,000 putatively functional exonic variants from over 12,000 individuals representing diverse populations  and a range of common conditions, such as type 2 diabetes, cancer, metabolic, and psychiatric disorders. In addition to coding variation, the HumanExome BeadChip-12v1_A chip covers SNPs in canonical splice sites  and promoter regions (7012). No 3′-untranslated region variants are represented. To test genotyping reliability and quality, one individual sample was duplicated. Processed and raw intensity signals for the array data can be accessed at GEO  fitting to Hardy–Weinberg equilibrium with P values >0.05/m ; (ii) a minimum genotype call rate of 90%, that is, at least 90% of individuals in the sample have available genotypes; (iii) and presence of two alleles. Markers not meeting any of these criteria were excluded from analyses. Genotype and allelic frequencies were estimated by maximum likelihood. Variants with a minor allele frequency (MAF) ≥ 0.01 were classified as common and rare otherwise, according to previous recommendations52. Exonic variants with potential functional effect were identified using the annotations in the database for nonsynonymous SNPs’ functional predictions 53. This filter uses SIFT, Provean, PolyPhen-2, Mutation Taster, Mutation Assessor, Gerp++, and PhyloP to predict a variant’s deleterious effect58 and is fully implemented in the SVS 8.3.0 Variant Classification module.Genetic data were imported to Golden Helix61. Associations/trends between ADHD and regions containing some of the genes included in the set have been observed in previous genome-wide association study (GWAS)/copy number variation (CNV) studies , no other candidate–gene studies have explored possible associations between ADHD risk and the remaining 56 genes examined in this study.A set of 58 genes was manually curated based on their roles in sphingolipid metabolism Table . The sel70. We allowed up to 20 steps in the backward/forward optimization algorithm. We used persistent ADHD cases (after a 3-year follow-up)36 as the affected phenotype in all models. LMEMs allow the inclusion of both fixed  and random effects (family or population structure), with the latter accounting for potential inbreeding by including a kinship matrix  pruning). A single-locus LMEM assumes that all loci have a small effect on the trait, while a multi-locus LMEM assumes that several loci have a large effect on the trait70. These models are implemented in SVS 8.3.0. The optimal model was selected using a comprehensive exploration of multiple criteria, including the extended Bayes information criteria, the modified Bayes information criteria, and the multiple posterior probability of association. P values were corrected for multiple testing using the false discovery rate (FDR) method.We conducted a targeted exploration for association between ADHD and allelic variants in the 58 previously curated genes, using single- and multi-locus additive, dominant, and recessive linear mixed-effect models (LMEMs)73. Complex phenotypes are assessed by FBAT (as implemented in the PBAT module of SVS 8.4.0) allowing testing of a combination of phenotypes  to boost the FBAT power in predicting structures and substructures of new-composed phenotypes defined by the parents’ genotypes transmission to children. Thus far, following a sequential ascertainment strategy, we explore complex structures in the Paisa sample and evaluated our initial positive findings in additional samples75. A P value of 0.1 was set as the significance level for replication76. Thus, we expect that maximum 10% of genetic variants associated with ADHD are false positives78.The genes included in the replication analysis were selected under a disjunctive-inclusive criterion based on the significant associations found in the discovery cohort. All the models applied to the discovery cohort were also used in this cohort. The analyses used family-based association tests (FBATs) under the “no linkage, no association” hypothesis, with age and gender as co-variates. In the case of the analysis for the Paisa cohort of families, an extreme, multivariate phenotype consisting of comorbid ADHD, CD, and ODD was used to define the “affected” phenotype. This combination of phenotypes offers a higher statistical power compared with permutation tests and with using separate tests for each outcome with adjustment for multiple testingP values of the discovery and replication cohorts for each gene. We used the FDR-corrected P values from the single-locus LMEMs for the MTA cohort, and the PBAT-based P values for the Paisa and US cohorts. Further, we explored the performance of the 58 sphingolipid gene set in the most recent Psychiatric Genomics Consortium (PGC) ADHD GWAS meta-analysis , which includes 11 PGC samples and 23 iPSYCH genotyping batches17. This represents the largest ADHD data set available to date, with a total number of markers of 8,047,421 included in the GWAS meta-analysis. SNPs within the targeted genes found to be associated with ADHD in our study  were extracted, for a total of 2012. For each gene, P values for these SNPs were jointly plotted with those reported in our study. P values for SNPs within each particular target gene were combined using the Stouffer’s method79.We performed a gene-based meta-analysis using the resulting 80 under default settings and the Knowledge-based mining system for Genome-wide Genetic (KGG) studies v4.081 (http://grass.cgs.hku.hk/limx/kgg/index.html), as implemented in the gene-based association test using extended Simes (GATES), effective chi-squared (ECS), and univariate gene-based tests82. Gene set-based analysis was also performed in KGG 4.0 as implemented in the LDRT procedure83.Gene-based analysis was performed using VEGAS-2GALC, rs4668077 in CERS6, rs35785620 in SMPD1, rs143078230 in SMPDL3B, rs139609178 in CERS2, rs200333847 in FADS3, and rs41273880 in ELOVL5)   Table . Four of2) Table . Additioly Table . OptimizGALC, CERS6, SMPD1, SMPDL3B, CERS2, FADS3, ELOVL5, and CERK) were selected according to a disjunctive-inclusive criterion to be sequentially analyzed in the replication cohorts. Note that replication cohorts in this study correspond to independent samples differing in ethnic composition, recruitment strategy, and investigation timeframe. Thus, these cohorts are not intended for “exact replication,” but for a validation of the genetic associations in the discovery cohort under modified influencing factors , which includes the markers originally identified plus other markers in the same region that were not necessarily part of the original experiment . “Local replication” is considered to confer stronger evidence regarding the generalizability of genetic associations87.Eight genes . For GALC, associations with variant rs398607 and rs1805078 were observed in the Paisa and US cohort, respectively. Variant rs1805078, associated with ADHD in the US replication cohort, present evidence of LD  with variant rs7407370, associated with ADHD in the discovery cohort, and with variant rs398607, associated with ADHD in the Paisa replication cohort . Note that Paisa population is geographically isolated and were genetically originated from the admixture of Caucasian men Amerindian women. Variant rs7407370, originally associated with ADHD in the discovery cohort, is monoallelic in European population (1000 genomes) and present MAF = 0.001 in Admixed American population. For SMPD1, variant rs7951904 was found to be associated with ADHD in both replication cohorts. This variant present evidence for LD with variant rs35785620, originally associated with ADHD in the discovery cohort (D′ = 1 in African and Admixed American populations). Variant rs35785620, originally associated with ADHD in the discovery cohort, is monoallelic in European population (1000 genomes) and present MAF = 0.006 in admixed American population.The results of the analysis performed in the Paisa and US cohorts are shown in Table P values for the GALC, CERS6, and SMPD1 genes were obtained based on both the Stouffer’s79 and Fisher’s88P value combination methods. Thus, the combined P value for the GALC, CERS6, and SMPD1 genes were 1.44 × 10–6, 1.15 × 10–3, and 8.1 × 10–3, respectively, which all are significant at 5% even after correcting for multiple testing using the FDR method. These results suggest that the association between variants within these genes and ADHD is not unique to the MTA cohort, but can also be expanded to the Paisa and US cohorts.Meta-analysis of GALC, CERS6, SMPD1, SMPDL3B, CERS2, FADS3, ELOVL5, and CERK) that were found to be associated with ADHD in our study were extracted from the PGC data set, as described in the “Methods” section . Combined analysis of SNPs in each gene in the PCG data set and in our study using the Stouffer’s method (which does not correct for markers in LD detected a strong association between ADHD and markers in CERS6 (P < 0.0001), SMPD1 (P = 0.0130), and SMPDL3B (P = 0.0034) from the PGC data set , but significance was lost after LD correction   revealed a significant association between the rs398607 GG risk genotype and increased GALC expression in the cerebellum  catalyze the acylation of sphinganine to produce dihydroceramides. Six types of CerS exist in mammals (CERS1–6), all of which are expressed in the brain, except for CERS392. Because CerSs are length specific for fatty acyl-CoAs, they determine the length of downstream sphingolipids, including ceramides themselves, sphingomyelins and glycosphingolipids. Our targeted analysis on the genetic data from the MTA cohort found significant associations between ADHD (persistent phenotype) and variants rs4668077  (Supplementary Table SMPD1 encodes for sphingomyelin phosphodiesterase 1 (acid sphingomyelinase), enzyme that has been implicated in the pathology of Niemann–Pick types A and B lysosomal storage disorders (MIM 257200 and 607616), inherited as autosomal recessive traits. Both disorders present with severe neurological involvement. In addition, in vitro and in vivo models have demonstrated a role of SMPD1 in the pathogenesis of common complex neurologic disorders, such as depression and Alzheimer’s disease, highlighting the importance of acid sphingomyelinase in neurocognitive functioning in humans95. SMPD1 marker rs35785620, significantly associated with ADHD in the MTA/discovery cohort, corresponds to a rare variant with a minor allele frequency (MAF) of 0.4%. This variant corresponds to a missense change leading to a threonine-to-methionine substitution at position 588 (NP_000534.3) and is predicted by Mutation Taster to disrupt the formation of a disulfide bond between cysteines at positions 586 and 590. The variant was predicted to have a neutral effect by PROVEAN. Variant rs143078230 in SMPDL3B, significantly associated with ADHD in the MTA cohort, corresponds to a rare missense variant (MAF = 0.2%), leading to a tyrosine-to-histidine substitution at position 186 (NP_001291508.1). The T186H change is predicted to be deleterious by both Mutation Assessor and PROVEAN.In the recycling salvage pathway, ceramides are generated from sphingomyelins and other complex sphingolipids (glycosphingolipids). Since sphingomyelins are the most abundant complex sphingolipids in human cell membranes, regulation of its metabolism is essential for cellular homeostasis. Breakdown of sphingomyelin occurs through the hydrolysis of phosphocholine head groups by enzymes from the sphingomyelinase family3B Table  genes frGALC, which codes for galactosylceramidase, enzyme responsible for the breakdown of galactosyl- and lactosylceramide, galactosylsphingosine, and galactocerebrosides. GALC defects lead to the accumulation of cytotoxic galactosylsphingosine (psychosine) in Krabbe disease96, an autosomal recessive disorder that results in demyelination and severe progressive motor neuron degeneration. Of note, two missense variants were detected in association with ADHD in the Paisa (rs398607) and US (rs1805078) Cohorts. Variant rs398607 leads to an isoleucine-to-threonine substitution at position 562; and variant rs1805078 leads to an arginine-to-cysteine substitution at position 184. The functional impact of rs398607 (MAF = 38% in the Paisa cohort) is predicted as moderately deleterious by Mutation Assessor and as deleterious by PROVEAN. The predicted functional impact of variant rs1805078 (MAF = 5.8% in the US cohort) is low according to Mutation Assessor and deleterious according to PROVEAN. The neuroanatomic and neurofunctional correlates of these variants are unknown.Also, in the recycling pathway, targeted analysis of the MTA genetic data detected significant associations between ADHD and 17, as they did not genotype for rare variants. In order to validate the replicability of our results, we performed a meta-analysis using SNPs from the PGC data set that were harbored in our ADHD-associated genes. To our satisfaction, we were able to detect moderate associations at the marker level in CERS6, SMPD1, and SMPDL3B, and at the gene level in CERS6, SMPDL3B, and CERK. Although genome-wide significance was not achieved, this result supports the findings of our study. It is important to mention, however, that while genome-wide association studies are a useful tool for discovering novel risk variants (as it involves a hypothesis-free interrogation of the entire genome) any lack of genetic association may just reflect the polygenic, multifactorial nature of ADHD, with both common and rare variants likely contributing to small genetic effects99. In addition, an important factor is the genetic heterogeneity of ADHD subtypes, which may have different underlying genetic mechanisms. Therefore, genome-wide significance may be achieved only for loci with larger genetic effects, while others with smaller effects remain undetected for a given population size.The markers identified here are not represented in the most recent genome-wide significant ADHD study done by the PGCGALC expression in the cerebellum. This makes sense functionally as more GALC activity would be intuitively associated with increased cerebellar myelin breakdown. Brain imaging studies have implicated cerebellar structural abnormalities in ADHD101. In addition to its role in motor control, the cerebellum contributes to a wide range of cognitive and affective processes. Lesion studies demonstrate important roles for the cerebellum in motor and perceptual tasks in which events span milliseconds—thus requiring fine temporal control102—in the orientation of spatial attention103, in verbal working memory and language processing104, and in affective regulation105.Interestingly, the rs398607 risk allele was associated with increased 106. The sensitivity of DTI to detect subtle changes in white matter integrity can provide a useful technique to investigate white matter tracts longitudinally in patients with ADHD in the context of sphingolipid genetic variation. Such research would provide new prospects and challenges for future research into the pathophysiology of ADHD.Additional research on sphingolipid metabolism may shed light into the pathogenesis of ADHD in the context of detailed brain imaging evaluation of affected individuals. Although more research is needed, diffusion tensor imaging (DTI) has proven to be a promising technique for the diagnosis of white matter structural abnormalities in ADHD, consistent with fronto-striatal-cerebellar deficitsGALC, CERS6, SMPD1, SMPDL3B, CERS2, FADS3, ELOVL5, and CERK) in the discovery cohort, with “local replication” for associations with variants in CERS6, SMPD1, and GALC genes. Some of these variants correspond to missense mutations with predicted damaging effects. This is the first piece of evidence linking genetic variation in sphingolipid metabolism genes to ADHD pathophysiology.Sphingolipids are highly abundant in CNS and crucial to glial and neuronal function and development. To date, an association between ADHD and variation in sphingolipid metabolism genes had not been explored. Here we present the results from a targeted analysis of 58 genes directly involved in sphingolipid metabolism performed on three different cohorts from disparate geographical regions. We found an association between ADHD and variants in eight of these genes (Supplementary Material