Significance: Uniquely human biology is the result of genetic differences between humans and other primates, but identifying the critical changes still poses a major challenge. We screened >32,000 human-specific substitutions in two classes of putative transcriptional enhancers implicated in human evolution for their effects on enhancer activity in human neural stem cells, a cell type fundamental for cortical development and expansion. We identify hundreds of substitutions that modify enhancer activity either alone or in combination with other variants. We find different patterns of substitution effects in different enhancer types. Our data support substitutions often interacting by modifying the same transcription factor binding sites. The functional substitutions we identify provide a rich set of candidate loci for studies of human-specific regulatory evolution.

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.

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 (A_e) from previous studies [1], and 44 high A_e 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 A_e was 5.08 (range: 2.7-11.54) and mean informativeness for biogeographic variation (I_n) 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.

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). 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. 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. 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.

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.

Newborn screening for cystic fibrosis enables early detection and management of this debilitating genetic disease. Implementing comprehensive CFTR analysis using Sanger sequencing as a component of confirmatory testing of all screen-positive newborns has remained impractical due to relatively lengthy turnaround times and high cost. Here, we describe CFseq, a highly sensitive, specific, rapid (2000 CFTR variants. Validation studies across 190 dried blood spots demonstrated 100% sensitivity and a positive predictive value of 100% for single-nucleotide variants and insertions and deletions and complete concordance across the polymorphic poly-TG and consecutive poly-T tracts. Additionally, we accurately detected both a known exon 2,3 deletion and a previously undetected exon 22,23 deletion. CFseq is thus able to replace all existing CFTR molecular assays with a single robust, definitive assay at significant cost and time savings and could be adapted to high-throughput screening of other inherited conditions.