NBSTRN Tools to Advance Newborn Screening Research and Support Newborn Screening Stakeholders.

Rapid advances in the screening, diagnosis, and treatment of genetic disorders have increased the number of conditions that can be detected through universal newborn screening (NBS). However, the addition of conditions to the Recommended Uniform Screening Panel (RUSP) and the implementation of nationwide screening has been a slow process taking several years to accomplish for individual conditions. Here, we describe web-based tools and resources developed and implemented by the newborn screening translational research network (NBSTRN) to advance newborn screening research and support NBS stakeholders worldwide. The NBSTRN's tools include the Longitudinal Pediatric Data Resource (LPDR), the NBS Condition Resource (NBS-CR), the NBS Virtual Repository (NBS-VR), and the Ethical, Legal, and Social Issues (ELSI) Advantage. Research programs, including the Inborn Errors of Metabolism Information System (IBEM-IS), BabySeq, EarlyCheck, and Family Narratives Use Cases, have utilized NBSTRN's tools and, in turn, contributed research data to further expand and refine these resources. Additionally, we discuss ongoing tool development to facilitate the expansion of genetic disease screening in increasingly diverse populations. In conclusion, NBSTRN's tools and resources provide a trusted platform to enable NBS stakeholders to advance NBS research and improve clinical care for patients and their families.

Digital assay for rapid electronic quantification of clinical pathogens using DNA nanoballs.

Fast and accurate detection of nucleic acids is key for pathogen identification. Methods for DNA detection generally rely on fluorescent or colorimetric readout. The development of label-free assays decreases costs and test complexity. We present a novel method combining a one-pot isothermal generation of DNA nanoballs with their detection by electrical impedance. We modified loop-mediated isothermal amplification by using compaction oligonucleotides that self-assemble the amplified target into nanoballs. Next, we use capillary-driven flow to passively pass these nanoballs through a microfluidic impedance cytometer, thus enabling a fully compact system with no moving parts. The movement of individual nanoballs is detected by a change in impedance providing a quantized readout. This approach is flexible for the detection of DNA/RNA of numerous targets (severe acute respiratory syndrome coronavirus 2, HIV, ß-lactamase gene, etc.), and we anticipate that its integration into a standalone device would provide an inexpensive (<$5), sensitive (10 target copies), and rapid test (<1 hour).

Nucleic Acid Quantification by Multi-Frequency Impedance Cytometry and Machine Learning.

Determining nucleic acid concentrations in a sample is an important step prior to proceeding with downstream analysis in molecular diagnostics. Given the need for testing DNA amounts and its purity in many samples, including in samples with very small input DNA, there is utility of novel machine learning approaches for accurate and high-throughput DNA quantification. Here, we demonstrated the ability of a neural network to predict DNA amounts coupled to paramagnetic beads. To this end, a custom-made microfluidic chip is applied to detect DNA molecules bound to beads by measuring the impedance peak response (IPR) at multiple frequencies. We leveraged electrical measurements including the frequency and imaginary and real parts of the peak intensity within a microfluidic channel as the input of deep learning models to predict DNA concentration. Specifically, 10 different deep learning architectures are examined. The results of the proposed regression model indicate that an R_Squared of 97% with a slope of 0.68 is achievable. Consequently, machine learning models can be a suitable, fast, and accurate method to measure nucleic acid concentration in a sample. The results presented in this study demonstrate the ability of the proposed neural network to use the information embedded in raw impedance data to predict the amount of DNA concentration.

A systems biology approach identifies the role of dysregulated PRDM6 in the development of hypertension.

Genetic variants in the third intron of the PRDM6 gene have been associated with BP traits in multiple GWAS. By combining fine mapping, massively parallel reporter assays, and gene editing, we identified super enhancers that drive the expression of PRDM6 and are partly regulated by STAT1 as the causal variants for hypertension. The heterozygous disruption of Prdm6 in mice expressing Cre recombinase under the control of mouse smooth muscle cell protein 22-α promoter (Prdm6fl/+ SM22-Cre) exhibited a markedly higher number of renin-producing cells in the kidneys at E18.5 compared with WT littermates and developed salt-induced systemic hypertension that was completely responsive to the renin inhibitor aliskiren. Strikingly, RNA-Seq analysis of the mouse aortas identified a network of PRDM6-regulated genes that are located in GWAS-associated loci for blood pressure, most notably Sox6, which modulates renin expression in the kidney. Accordingly, the smooth muscle cell-specific disruption of Sox6 in Prdm6fl/+ SM22-Cre mice resulted in a dramatic reduction of renin. Fate mapping and histological studies also showed increased numbers of neural crest-derived cells accompanied by increased collagen deposition in the kidneys of Prdm6fl/+ Wnt1Cre-ZsGreen1Cre mice compared with WT mice. These findings establish the role of PRDM6 as a regulator of renin-producing cell differentiation into smooth muscle cells and as an attractive target for the development of antihypertensive drugs.

Validation of a targeted metabolomics panel for improved second-tier newborn screening.

Improved second-tier assays are needed to reduce the number of false positives in newborn screening (NBS) for inherited metabolic disorders including those on the Recommended Uniform Screening Panel (RUSP). We developed an expanded metabolite panel for second-tier testing of dried blood spot (DBS) samples from screen-positive cases reported by the California NBS program, consisting of true- and false-positives from four disorders: glutaric acidemia type I (GA1), methylmalonic acidemia (MMA), ornithine transcarbamylase deficiency (OTCD), and very long-chain acyl-CoA dehydrogenase deficiency (VLCADD). This panel was assembled from known disease markers and new features discovered by untargeted metabolomics and applied to second-tier analysis of single DBS punches using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a 3-min run. Additionally, we trained a Random Forest (RF) machine learning classifier to improve separation of true- and false positive cases. Targeted metabolomic analysis of 121 analytes from DBS extracts in combination with RF classification at a sensitivity of 100% reduced false positives for GA1 by 83%, MMA by 84%, OTCD by 100%, and VLCADD by 51%. This performance was driven by a combination of known disease markers (3-hydroxyglutaric acid, methylmalonic acid, citrulline, and C14:1), other amino acids and acylcarnitines, and novel metabolites identified to be isobaric to several long-chain acylcarnitine and hydroxy-acylcarnitine species. These findings establish the effectiveness of this second-tier test to improve screening for these four conditions and demonstrate the utility of supervised machine learning in reducing false-positives for conditions lacking clearly discriminating markers, with future studies aimed at optimizing and expanding the panel to additional disease targets.

Association of Maternal Age and Blood Markers for Metabolic Disease in Newborns.

Pregnancy at an advanced maternal age is considered a risk factor for adverse maternal, fetal, and neonatal outcomes. Here we investigated whether maternal age could be associated with differences in the blood levels of newborn screening (NBS) markers for inborn metabolic disorders on the Recommended Universal Screening Panel (RUSP). Population-level NBS data from screen-negative singleton infants were examined, which included blood metabolic markers and covariates such as age at blood collection, birth weight, gestational age, infant sex, parent-reported ethnicity, and maternal age at delivery. Marker levels were compared between maternal age groups (age range: 1544 years) using effect size analyses, which controlled for differences in group sizes and potential confounding from other covariates. We found that 13% of the markers had maternal age-related differences, including newborn metabolites with either increased (Tetradecanoylcarnitine [C14], Palmitoylcarnitine [C16], Stearoylcarnitine [C18], Oleoylcarnitine [C18:1], Malonylcarnitine [C3DC]) or decreased (3-Hydroxyisovalerylcarnitine [C5OH]) levels at an advanced maternal age (≥35 years, absolute Cohen's d > 0.2). The increased C3DC levels in this group correlated with a higher false-positive rate in newborn screening for malonic acidemia (p-value < 0.001), while no significant difference in screening performance was seen for the other markers. Maternal age is associated with inborn metabolic differences and should be considered together with other clinical variables in genetic disease screening.

Metabolic diversity in human populations and correlation with genetic and ancestral geographic distances.

DNA polymorphic markers and self-defined ethnicity groupings are used to group individuals with shared ancient geographic ancestry. Here we studied whether ancestral relationships between individuals could be identified from metabolic screening data reported by the California newborn screening (NBS) program. NBS data includes 41 blood metabolites measured by tandem mass spectrometry from singleton babies in 17 parent-reported ethnicity groupings. Ethnicity-associated differences identified for 71% of NBS metabolites (29 of 41, Cohen's d > 0.5) showed larger differences in blood levels of acylcarnitines than of amino acids (P < 1e-4). A metabolic distance measure, developed to compare ethnic groupings based on metabolic differences, showed low positive correlation with genetic and ancient geographic distances between the groups' ancestral world populations. Several outlier group pairs were identified with larger genetic and smaller metabolic distances (Black versus White) or with smaller genetic and larger metabolic distances (Chinese versus Japanese) indicating the influence of genetic and of environmental factors on metabolism. Using machine learning, comparison of metabolic profiles between all pairs of ethnic groupings distinguished individuals with larger genetic distance (Black versus Chinese, AUC = 0.96), while genetically more similar individuals could not be separated metabolically (Hispanic versus Native American, AUC = 0.51). Additionally, we identified metabolites informative for inferring metabolic ancestry in individuals from genetically similar populations, which included biomarkers for inborn metabolic disorders (C10:1, C12:1, C3, C5OH, Leucine-Isoleucine). This work sheds new light on metabolic differences in healthy newborns in diverse populations, which could have implications for improving genetic disease screening.

dbRUSP: An Interactive Database to Investigate Inborn Metabolic Differences for Improved Genetic Disease Screening.

The Recommended Uniform Screening Panel (RUSP) contains more than forty metabolic disorders recommended for inclusion in universal newborn screening (NBS). Tandem-mass-spectrometry-based screening of metabolic analytes in dried blood spot samples identifies most affected newborns, along with a number of false positive results. Due to their influence on blood metabolite levels, continuous and categorical covariates such as gestational age, birth weight, age at blood collection, sex, parent-reported ethnicity, and parenteral nutrition status have been shown to reduce the accuracy of screening. Here, we developed a database and web-based tools (dbRUSP) for the analysis of 41 NBS metabolites and six variables for a cohort of 500,539 screen-negative newborns reported by the California NBS program. The interactive database, built using the R shiny package, contains separate modules to study the influence of single variables and joint effects of multiple variables on metabolite levels. Users can input an individual's variables to obtain metabolite level reference ranges and utilize dbRUSP to select new candidate markers for the detection of metabolic conditions. The open-source format facilitates the development of data mining algorithms that incorporate the influence of covariates on metabolism to increase accuracy in genetic disease screening.

Population-Based Screening of Newborns: Findings From the NBS Expansion Study (Part One).

Each year, through population-based newborn screening (NBS), 1 in 294 newborns is identified with a condition leading to early treatment and, in some cases, life-saving interventions. Rapid advancements in genomic technologies to screen, diagnose, and treat newborns promise to significantly expand the number of diseases and individuals impacted by NBS. However, expansion of NBS occurs slowly in the United States (US) and almost always occurs condition by condition and state by state with the goal of screening for all conditions on a federally recommended uniform panel. The Newborn Screening Translational Research Network (NBSTRN) conducted the NBS Expansion Study to describe current practices, identify expansion challenges, outline areas for improvement in NBS, and suggest how models could be used to evaluate changes and improvements. The NBS Expansion Study included a workshop of experts, a survey of clinicians, an analysis of data from online repositories of state NBS programs, reports and publications of completed pilots, federal committee reports, and proceedings, and the development of models to address the study findings. This manuscript (Part One) reports on the design, execution, and results of the NBS Expansion Study. The Study found that the capacity to expand NBS is variable across the US and that nationwide adoption of a new condition averages 9.5 years. Four factors that delay and/or complicate NBS expansion were identified. A companion paper (Part Two) presents a use case for each of the four factors and highlights how modeling could address these challenges to NBS expansion.

A multipurpose panel of microhaplotypes for use with STR markers in casework.

A small panel of highly informative loci that can be genotyped on the same equipment as the standard CODIS short tandem repeat (STR) markers has strong potential for application in forensic casework. Single nucleotide polymorphisms (SNPs) can be typed by a couple of methods on capillary electrophoresis (CE) machines and on sequencers, but the amount of information relative to the laboratory effort has hindered use of SNPs in actual casework. Insertion-deletion markers (InDels) suffer from similar problems. Microhaplotypes (MHs) are much more informative per locus but have similar technical difficulties unless they are typed by massively parallel sequencing (MPS). As forensic labs are acquiring sequencing machines, MHs become more likely to be used in casework, especially if multiplexed with STRs. Here we present the details of a multipurpose panel of 24 MHs with the highest effective number of alleles (Ae) from previous work. An augmented STR panel of 24 loci (20 CODIS markers plus four commonly typed STRs) is also considered. The Ae and ancestry informativeness (In) distributions of these two datasets are compared. The MH panel is shown to have better individualization and population distinction than the augmented CODIS STRs. We note that the 24 MHs should be better for mixture analyses than the STRs. Finally, we suggest that a commercial kit including both the standard CODIS markers and this set of 24 MH would greatly improve the discrimination power over that of current commercial assays.

The population genetics characteristics of a 90 locus panel of microhaplotypes.

Single-nucleotide polymorphisms (SNPs) and small genomic regions with multiple SNPs (microhaplotypes, MHs) are rapidly emerging as novel forensic investigative tools to assist in individual identification, kinship analyses, ancestry inference, and deconvolution of DNA mixtures. Here, we analyzed information for 90 microhaplotype loci in 4009 individuals from 79 world populations in 6 major biogeographic regions. The study included multiplex microhaplotype sequencing (mMHseq) data analyzed for 524 individuals from 16 populations and genotype data for 3485 individuals from 63 populations curated from public repositories. Analyses of the 79 populations revealed excellent characteristics for this 90-plex MH panel for various forensic applications achieving an overall average effective number of allele values (Ae) of 4.55 (range 1.04-19.27) for individualization and mixture deconvolution. Population-specific random match probabilities ranged from a low of 10-115 to a maximum of 10-66. Mean informativeness (In) for ancestry inference was 0.355 (range 0.117-0.883). 65 novel SNPs were detected in 39 of the MHs using mMHseq. Of the 3018 different microhaplotype alleles identified, 1337 occurred at frequencies > 5% in at least one of the populations studied. The 90-plex MH panel enables effective differentiation of population groupings for major biogeographic regions as well as delineation of distinct subgroupings within regions. Open-source, web-based software is available to support validation of this technology for forensic case work analysis and to tailor MH analysis for specific geographical regions.

Multi-frequency impedance sensing for detection and sizing of DNA fragments.

Electronic biosensors for DNA detection typically utilize immobilized oligonucleotide probes on a signal transducer, which outputs an electronic signal when target molecules bind to probes. However, limitation in probe selectivity and variable levels of non-target material in complex biological samples can lead to nonspecific binding and reduced sensitivity. Here we introduce the integration of 2.8 µm paramagnetic beads with DNA fragments. We apply a custom-made microfluidic chip to detect DNA molecules bound to beads by measuring Impedance Peak Response (IPR) at multiple frequencies. Technical and analytical performance was evaluated using beads containing purified Polymerase Chain Reaction (PCR) products of different lengths (157, 300, 613 bp) with DNA concentration ranging from 0.039 amol to 7.8 fmol. Multi-frequency IPR correlated positively with DNA amounts and was used to calculate a DNA quantification score. The minimum DNA amount of a 300 bp fragment coupled on beads that could be robustly detected was 0.0039 fmol (1.54 fg or 4750 copies/bead). Additionally, our approach allowed distinguishing beads with similar molar concentration DNA fragments of different lengths. Using this impedance sensor, purified PCR products could be analyzed within ten minutes to determine DNA fragment length and quantity based on comparison to a known DNA standard.

Massively parallel discovery of human-specific substitutions that alter enhancer activity.

Genetic changes that altered the function of gene regulatory elements have been implicated in the evolution of human traits such as the expansion of the cerebral cortex. However, identifying the particular changes that modified regulatory activity during human evolution remain challenging. Here we used massively parallel enhancer assays in neural stem cells to quantify the functional impact of >32,000 human-specific substitutions in >4,300 human accelerated regions (HARs) and human gain enhancers (HGEs), which include enhancers with novel activities in humans. We found that >30% of active HARs and HGEs exhibited differential activity between human and chimpanzee. We isolated the effects of human-specific substitutions from background genetic variation to identify the effects of genetic changes most relevant to human evolution. We found that substitutions interacted in both additive and nonadditive ways to modify enhancer function. Substitutions within HARs, which are highly constrained compared to HGEs, showed smaller effects on enhancer activity, suggesting that the impact of human-specific substitutions is buffered in enhancers with constrained ancestral functions. Our findings yield insight into how human-specific genetic changes altered enhancer function and provide a rich set of candidates for studies of regulatory evolution in humans.

Validation of novel forensic DNA markers using multiplex microhaplotype sequencing.

Microhaplotypes (MH) are comprised of multiple single nucleotide polymorphisms (SNPs) that are located within 300 bases of genomic sequence. Improved tools are needed to facilitate broader application of microhaplotypes in a diverse range of populations and forensic settings. We designed an assay for multiplex sequencing of 90 microhaplotypes (mMHseq) that include 46 MH loci with high Effective Number of Alleles (Ae) from previous studies [1], and 44 high Ae MH loci containing between four to fourteen SNPs that were identified from the 1000 Genomes (1KG) Project. The unique design of mMHseq integrates a novel method for multiplex amplification from small DNA amounts, and multiplex sequencing of 48 samples in a single MiSeq run to detect all relevant MH variation. Assay performance was evaluated in a cohort of 156 individuals from seven different world populations from Africa, Asia, and Europe. Three of those populations from East Africa (Chagga, Sandawe, and Zaramo) and one from Eastern Europe (Adygei) had sufficient individuals sequenced by the assay to be included in statistical analyses with the 26 1KG populations. For those 30 populations the mean global average Ae was 5.08 (range: 2.7-11.54) and mean informativeness for biogeographic variation (In) was 0.30 (range: 0.08-0.70). Eighty-five novel SNPs were detected in 58 of the 90 microhaplotypes. Open-source, web-based software was developed to visualize haplotype phase data for each microhaplotype and individual. Our approach for multiplex microhaplotype sequencing can be customized and expanded as novel loci are being discovered.

Ethnic variability in newborn metabolic screening markers associated with false-positive outcomes.

Newborn screening (NBS) programmes utilise information on a variety of clinical variables such as gestational age, sex, and birth weight to reduce false-positive screens for inborn metabolic disorders. Here we study the influence of ethnicity on metabolic marker levels in a diverse newborn population. NBS data from screen-negative singleton babies (n = 100 000) were analysed, which included blood metabolic markers measured by tandem mass spectrometry and ethnicity status reported by the parents. Metabolic marker levels were compared between major ethnic groups (Asian, Black, Hispanic, White) using effect size analysis, which controlled for group size differences and influence from clinical variables. Marker level differences found between ethnic groups were correlated to NBS data from 2532 false-positive cases for four metabolic diseases: glutaric acidemia type 1 (GA-1), methylmalonic acidemia (MMA), ornithine transcarbamylase deficiency (OTCD), and very long-chain acyl-CoA dehydrogenase deficiency (VLCADD). In the result, 79% of the metabolic markers (34 of 43) had ethnicity-related differences. Compared to the other groups, Black infants had elevated GA-1 markers (C5DC, Cohen's d = .37, P < .001), Hispanics had elevated MMA markers (C3, Cohen's d = .13, P < .001, and C3/C2, Cohen's d = .27, P < .001); and Whites had elevated VLCADD markers (C14, Cohen's d = .28, P < .001, and C14:1, Cohen's d = .22, P < .001) and decreased OTCD markers (citrulline, Cohen's d = -.26, P < .001). These findings correlated with the higher false-positive rates in Black infants for GA-1, in Hispanics for MMA, and in Whites for OTCD and for VLCADD. Web-based tools are available to analyse ethnicity-related changes in newborn metabolism and to support developing methods to identify false-positives in metabolic screening.

Reducing False-Positive Results in Newborn Screening Using Machine Learning.

Newborn screening (NBS) for inborn metabolic disorders is a highly successful public health program that by design is accompanied by false-positive results. Here we trained a Random Forest machine learning classifier on screening data to improve prediction of true and false positives. Data included 39 metabolic analytes detected by tandem mass spectrometry and clinical variables such as gestational age and birth weight. Analytical performance was evaluated for a cohort of 2777 screen positives reported by the California NBS program, which consisted of 235 confirmed cases and 2542 false positives for one of four disorders: glutaric acidemia type 1 (GA-1), methylmalonic acidemia (MMA), ornithine transcarbamylase deficiency (OTCD), and very long-chain acyl-CoA dehydrogenase deficiency (VLCADD). Without changing the sensitivity to detect these disorders in screening, Random Forest-based analysis of all metabolites reduced the number of false positives for GA-1 by 89%, for MMA by 45%, for OTCD by 98%, and for VLCADD by 2%. All primary disease markers and previously reported analytes such as methionine for MMA and OTCD were among the top-ranked analytes. Random Forest's ability to classify GA-1 false positives was found similar to results obtained using Clinical Laboratory Integrated Reports (CLIR). We developed an online Random Forest tool for interpretive analysis of increasingly complex data from newborn screening.

Timing of Newborn Blood Collection Alters Metabolic Disease Screening Performance.

Blood collection for newborn genetic disease screening is preferably performed within 24-48 h after birth. We used population-level newborn screening (NBS) data to study early postnatal metabolic changes and whether timing of blood collection could impact screening performance. Newborns were grouped based on their reported age at blood collection (AaBC) into early (12-23 h), standard (24-48 h), and late (49-168 h) collection groups. Metabolic marker levels were compared between the groups using effect size analysis, which controlled for group size differences and influence from the clinical variables of birth weight and gestational age. Metabolite level differences identified between groups were correlated to NBS data from false-positive cases for inborn metabolic disorders including carnitine transport defect (CTD), isovaleric acidemia (IVA), methylmalonic acidemia (MMA), and phenylketonuria (PKU). Our results showed that 56% of the metabolites had AaBC-related differences, which included metabolites with either decreasing or increasing levels after birth. Compared to the standard group, the early-collection group had elevated marker levels for PKU (phenylalanine, Cohen's d = 0.55), IVA (C5, Cohen's d = 0.24), MMA (C3, Cohen's d = 0.23), and CTD (C0, Cohen's d = 0.23). These findings correlated with higher false-positive rates for PKU (P < 0.05), IVA (P < 0.05), and MMA (P < 0.001), and lower false-positive rate for CTD (P < 0.001) in the early-collection group. Blood collection before 24 h could affect screening performance for some metabolic disorders. We have developed web-based tools integrating AaBC and other variables for interpretive analysis of screening data.

Elevated methylmalonic acidemia (MMA) screening markers in Hispanic and preterm newborns.

Analysis of California newborn screening (NBS) data revealed a high prevalence of Hispanic infants testing positive for methylmalonic acidemia (MMA), a trend seen for both true- and false-positive cases. Here we show that Hispanic infants have significantly higher levels of MMA screening markers than non-Hispanics. Preterm birth and increased birth weight were found to be associated with elevated MMA marker levels but could not entirely explain these differences. While the preterm birth rate was higher in Blacks than Hispanics, Black infants had on average the lowest MMA marker levels. Preterm birth was associated with lower birth weight and increased MMA marker levels suggesting that gestational age is the stronger predictive covariate compared to birth weight. These findings could help explain why MMA false-positive results are more likely in Hispanic than in Black infants, which could inform screening and diagnostic procedures for MMA and potentially other disorders in newborns.

Combining newborn metabolic and DNA analysis for second-tier testing of methylmalonic acidemia.

PURPOSE: Improved second-tier tools are needed to reduce false-positive outcomes in newborn screening (NBS) for inborn metabolic disorders on the Recommended Universal Screening Panel (RUSP). METHODS: We designed an assay for multiplex sequencing of 72 metabolic genes (RUSPseq) from newborn dried blood spots. Analytical and clinical performance was evaluated in 60 screen-positive newborns for methylmalonic acidemia (MMA) reported by the California Department of Public Health NBS program. Additionally, we trained a Random Forest machine learning classifier on NBS data to improve prediction of true and false-positive MMA cases. RESULTS: Of 28 MMA patients sequenced, we found two pathogenic or likely pathogenic (P/LP) variants in a MMA-related gene in 24 patients, and one pathogenic variant and a variant of unknown significance (VUS) in 1 patient. No such variant combinations were detected in MMA false positives and healthy controls. Random Forest-based analysis of the entire NBS metabolic profile correctly identified the MMA patients and reduced MMA false-positive cases by 51%. MMA screen-positive newborns were more likely of Hispanic ethnicity. CONCLUSION: Our two-pronged approach reduced false positives by half and provided a reportable molecular finding for 89% of MMA patients. Challenges remain in newborn metabolic screening and DNA variant interpretation in diverse multiethnic populations.