Laboratory testing for preconception/prenatal carrier screening: A technical standard of the American College of Medical Genetics and Genomics (ACMG).

Carrier screening has historically assessed a relatively small number of autosomal recessive and X-linked conditions selected based on frequency in a specific subpopulation and association with severe morbidity or mortality. Advances in genomic technologies enable simultaneous screening of individuals for several conditions. The American College of Medical Genetics and Genomics recently published a clinical practice resource that presents a framework when offering screening for autosomal recessive and X-linked conditions during pregnancy and preconception and recommends a tier-based approach when considering the number of conditions to screen for and their frequency within the US population in general. This laboratory technical standard aims to complement the practice resource and to put forth considerations for clinical laboratories and clinicians who offer preconception/prenatal carrier screening.

Variant Classification Concordance using the ACMG-AMP Variant Interpretation Guidelines across Nine Genomic Implementation Research Studies.

Harmonization of variant pathogenicity classification across laboratories is important for advancing clinical genomics. The two CLIA-accredited Electronic Medical Record and Genomics Network sequencing centers and the six CLIA-accredited laboratories and one research laboratory performing genome or exome sequencing in the Clinical Sequencing Evidence-Generating Research Consortium collaborated to explore current sources of discordance in classification. Eight laboratories each submitted 20 classified variants in the ACMG secondary finding v.2.0 genes. After removing duplicates, each of the 158 variants was annotated and independently classified by two additional laboratories using the ACMG-AMP guidelines. Overall concordance across three laboratories was assessed and discordant variants were reviewed via teleconference and email. The submitted variant set included 28 P/LP variants, 96 VUS, and 34 LB/B variants, mostly in cancer (40%) and cardiac (27%) risk genes. Eighty-six (54%) variants reached complete five-category (i.e., P, LP, VUS, LB, B) concordance, and 17 (11%) had a discordance that could affect clinical recommendations (P/LP versus VUS/LB/B). 21% and 63% of variants submitted as P and LP, respectively, were discordant with VUS. Of the 54 originally discordant variants that underwent further review, 32 reached agreement, for a post-review concordance rate of 84% (118/140 variants). This project provides an updated estimate of variant concordance, identifies considerations for LP classified variants, and highlights ongoing sources of discordance. Continued and increased sharing of variant classifications and evidence across laboratories, and the ongoing work of ClinGen to provide general as well as gene- and disease-specific guidance, will lead to continued increases in concordance.

Molecular diagnostic yield of genome sequencing versus targeted gene panel testing in racially and ethnically diverse pediatric patients.

PURPOSE: Adoption of genome sequencing (GS) as a first-line test requires evaluation of its diagnostic yield. We evaluated the GS and targeted gene panel (TGP) testing in diverse pediatric patients (probands) with suspected genetic conditions. METHODS: Probands with neurologic, cardiac, or immunologic conditions were offered GS and TGP testing. Diagnostic yield was compared using a fully paired study design. RESULTS: A total of 645 probands (median age 9 years) underwent genetic testing, and 113 (17.5%) received a molecular diagnosis. Among 642 probands with both GS and TGP testing, GS yielded 106 (16.5%) and TGPs yielded 52 (8.1%) diagnoses (P < .001). Yield was greater for GS vs TGPs in Hispanic/Latino(a) (17.2% vs 9.5%, P < .001) and White/European American (19.8% vs 7.9%, P < .001) but not in Black/African American (11.5% vs 7.7%, P = .22) population groups by self-report. A higher rate of inconclusive results was seen in the Black/African American (63.8%) vs White/European American (47.6%; P = .01) population group. Most causal copy number variants (17 of 19) and mosaic variants (6 of 8) were detected only by GS. CONCLUSION: GS may yield up to twice as many diagnoses in pediatric patients compared with TGP testing but not yet across all population groups.

Identification of copy number variants with genome sequencing: Clinical experiences from the NYCKidSeq program.

Copy number variations (CNVs) play a significant role in human disease. While chromosomal microarray has traditionally been the first-tier test for CNV detection, use of genome sequencing (GS) is increasing. We report the frequency of CNVs detected with GS in a diverse pediatric cohort from the NYCKidSeq program and highlight specific examples of its clinical impact. A total of 1052 children (0-21 years) with neurodevelopmental, cardiac, and/or immunodeficiency phenotypes received GS. Phenotype-driven analysis was used, resulting in 183 (17.4%) participants with a diagnostic result. CNVs accounted for 20.2% of participants with a diagnostic result (37/183) and ranged from 0.5 kb to 16 Mb. Of participants with a diagnostic result (n = 183) and phenotypes in more than one category, 5/17 (29.4%) were solved by a CNV finding, suggesting a high prevalence of diagnostic CNVs in participants with complex phenotypes. Thirteen participants with a diagnostic CNV (35.1%) had previously uninformative genetic testing, of which nine included a chromosomal microarray. This study demonstrates the benefits of GS for reliable detection of CNVs in a pediatric cohort with variable phenotypes.

Detection of mosaic variants using genome sequencing in a large pediatric cohort.

The increased use of next-generation sequencing has expanded our understanding of the involvement and prevalence of mosaicism in genetic disorders. We describe a total of eleven cases: nine in which mosaic variants detected by genome sequencing (GS) and/or targeted gene panels (TGPs) were considered to be causative for the proband's phenotype, and two of apparent parental mosaicism. Variants were identified in the following genes: PHACTR1, SCN8A, KCNT1, CDKL5, NEXMIF, CUX1, TSC2, GABRB2, and SMARCB1. In addition, we identified one large duplication including three genes, UBE3A, GABRB3, and MAGEL2, and one large deletion including deletion of ARFGAP1, EEF1A2, CHRNA4, and KCNQ2. All patients were enrolled in the NYCKidSeq study, a research program studying the communication of genomic information in clinical care, as well as the clinical utility and diagnostic yield of GS for children with suspected genetic disorders in diverse populations in New York City. We observed variability in the correlation between reported variant allele fraction and the severity of the patient's phenotype, although we were not able to determine the mosaicism percentage in clinically relevant tissue(s). Although our study was not sufficiently powered to assess differences in mosaicism detection between the two testing modalities, we saw a trend toward better detection by GS as compared with TGP testing. This case series supports the importance of mosaicism in childhood-onset genetic conditions and informs guidelines for laboratory and clinical interpretation of mosaic variants detected by GS.

Re-scaling and small area estimation of behavioral risk survey guided by social vulnerability data.

BACKGROUND: Local governments and other public health entities often need population health measures at the county or subcounty level for activities such as resource allocation and targeting public health interventions, among others. Information collected via national surveys alone cannot fill these needs. We propose a novel, two-step method for rescaling health survey data and creating small area estimates (SAEs) of smoking rates using a Behavioral Risk Factor Surveillance System survey administered in 2015 to participants living in Allegheny County, Pennsylvania, USA. METHODS: The first step consisted of a spatial microsimulation to rescale location of survey respondents from zip codes to tracts based on census population distributions by age, sex, race, and education. The rescaling allowed us, in the second step, to utilize available census tract-specific ancillary data on social vulnerability for small area estimation of local health risk using an area-level version of a logistic linear mixed model. To demonstrate this new two-step algorithm, we estimated the ever-smoking rate for the census tracts of Allegheny County. RESULTS: The ever-smoking rate was above 70% for two census tracts to the southeast of the city of Pittsburgh. Several tracts in the southern and eastern sections of Pittsburgh also had relatively high (> 65%) ever-smoking rates. CONCLUSIONS: These SAEs may be used in local public health efforts to target interventions and educational resources aimed at reducing cigarette smoking. Further, our new two-step methodology may be extended to small area estimation for other locations and health outcomes.

Consensus interpretation of the p.Met34Thr and p.Val37Ile variants in GJB2 by the ClinGen Hearing Loss Expert Panel.

PURPOSE: Pathogenic variants in GJB2 are the most common cause of autosomal recessive sensorineural hearing loss. The classification of c.101T>C/p.Met34Thr and c.109G>A/p.Val37Ile in GJB2 are controversial. Therefore, an expert consensus is required for the interpretation of these two variants. METHODS: The ClinGen Hearing Loss Expert Panel collected published data and shared unpublished information from contributing laboratories and clinics regarding the two variants. Functional, computational, allelic, and segregation data were also obtained. Case-control statistical analyses were performed. RESULTS: The panel reviewed the synthesized information, and classified the p.Met34Thr and p.Val37Ile variants utilizing professional variant interpretation guidelines and professional judgment. We found that p.Met34Thr and p.Val37Ile are significantly overrepresented in hearing loss patients, compared with population controls. Individuals homozygous or compound heterozygous for p.Met34Thr or p.Val37Ile typically manifest mild to moderate hearing loss. Several other types of evidence also support pathogenic roles for these two variants. CONCLUSION: Resolving controversies in variant classification requires coordinated effort among a panel of international multi-institutional experts to share data, standardize classification guidelines, review evidence, and reach a consensus. We concluded that p.Met34Thr and p.Val37Ile variants in GJB2 are pathogenic for autosomal recessive nonsyndromic hearing loss with variable expressivity and incomplete penetrance.

Excess of homozygosity in the major histocompatibility complex in schizophrenia.

Genome-wide association studies (GWAS) in schizophrenia have focused on additive allelic effects to identify disease risk loci. In order to examine potential recessive effects, we applied a novel approach to identify regions of excess homozygosity in an ethnically homogenous cohort: 904 schizophrenia cases and 1640 controls drawn from the Ashkenazi Jewish (AJ) population. Genome-wide examination of runs of homozygosity identified an excess in cases localized to the major histocompatibility complex (MHC). To refine this signal, we used the recently developed GERMLINE algorithm to identify chromosomal segments shared identical-by-descent (IBD) and compared homozygosity at such segments in cases and controls. We found a significant excess of homozygosity in schizophrenia cases compared with controls in the MHC (P-value = 0.003). An independent replication cohort of 548 schizophrenia cases from Japan and 542 matched healthy controls demonstrated similar effects. The strongest case-control recessive effects (P = 8.81 × 10(-8)) were localized to a 53-kb region near HLA-A, in a segment encompassing three poorly annotated genes, TRIM10, TRIM15 and TRIM40. At the same time, an adjacent segment in the Class I MHC demonstrated clear additive effects on schizophrenia risk, demonstrating the complexity of association in the MHC and the ability of our IBD approach to refine localization of broad signals derived from conventional GWAS. In sum, homozygosity in the classical MHC region appears to convey significant risk for schizophrenia, consistent with the ecological literature suggesting that homozygosity at the MHC locus may be associated with vulnerability to disease.

Genome-wide mapping of IBD segments in an Ashkenazi PD cohort identifies associated haplotypes.

The recent series of large genome-wide association studies in European and Japanese cohorts established that Parkinson disease (PD) has a substantial genetic component. To further investigate the genetic landscape of PD, we performed a genome-wide scan in the largest to date Ashkenazi Jewish cohort of 1130 Parkinson patients and 2611 pooled controls. Motivated by the reduced disease allele heterogeneity and a high degree of identical-by-descent (IBD) haplotype sharing in this founder population, we conducted a haplotype association study based on mapping of shared IBD segments. We observed significant haplotype association signals at three previously implicated Parkinson loci: LRRK2 (OR = 12.05, P = 1.23 × 10(-56)), MAPT (OR = 0.62, P = 1.78 × 10(-11)) and GBA (multiple distinct haplotypes, OR > 8.28, P = 1.13 × 10(-11) and OR = 2.50, P = 1.22 × 10(-9)). In addition, we identified a novel association signal on chr2q14.3 coming from a rare haplotype (OR = 22.58, P = 1.21 × 10(-10)) and replicated it in a secondary cohort of 306 Ashkenazi PD cases and 2583 controls. Our results highlight the power of our haplotype association method, particularly useful in studies of founder populations, and reaffirm the benefits of studying complex diseases in Ashkenazi Jewish cohorts.

Brain white matter development is associated with a human-specific haplotype increasing the synthesis of long chain fatty acids.

The genetic and molecular pathways driving human brain white matter (WM) development are only beginning to be discovered. Long chain polyunsaturated fatty acids (LC-PUFAs) have been implicated in myelination in animal models and humans. The biosynthesis of LC-PUFAs is regulated by the fatty acid desaturase (FADS) genes, of which a human-specific haplotype is strongly associated with ω-3 and ω-6 LC-PUFA concentrations in blood. To investigate the relationship between LC-PUFA synthesis and human brain WM development, we examined whether this FADS haplotype is associated with age-related WM differences across the life span in healthy individuals 9-86 years of age (n = 207). Diffusion tensor imaging was performed to measure fractional anisotropy (FA), a putative measure of myelination, of the cerebral WM tracts. FADS haplotype status was determined with a single nucleotide polymorphism (rs174583) that tags this haplotype. Overall, normal age-related WM differences were observed, including higher FA values in early adulthood compared with childhood, followed by lower FA values across older age ranges. However, individuals homozygous for the minor allele (associated with lower LC-PUFA concentrations) did not display these normal age-related WM differences (significant age × genotype interactions, p(corrected) < 0.05). These findings suggest that LC-PUFAs are involved in human brain WM development from childhood into adulthood. This haplotype and LC-PUFAs may play a role in myelin-related disorders of neurodevelopmental origin.

Independent evidence for an association between general cognitive ability and a genetic locus for educational attainment.

Cognitive deficits and reduced educational achievement are common in psychiatric illness; understanding the genetic basis of cognitive and educational deficits may be informative about the etiology of psychiatric disorders. A recent, large genome-wide association study (GWAS) reported a genome-wide significant locus for years of education, which subsequently demonstrated association to general cognitive ability ("g") in overlapping cohorts. The current study was designed to test whether GWAS hits for educational attainment are involved in general cognitive ability in an independent, large-scale collection of cohorts. Using cohorts in the Cognitive Genomics Consortium (COGENT; up to 20,495 healthy individuals), we examined the relationship between g and variants associated with educational attainment. We next conducted meta-analyses with 24,189 individuals with neurocognitive data from the educational attainment studies, and then with 53,188 largely independent individuals from a recent GWAS of cognition. A SNP (rs1906252) located at chromosome 6q16.1, previously associated with years of schooling, was significantly associated with g (P = 1.47 × 10(-4) ) in COGENT. The first joint analysis of 43,381 non-overlapping individuals for this a priori-designated locus was strongly significant (P = 4.94 × 10(-7) ), and the second joint analysis of 68,159 non-overlapping individuals was even more robust (P = 1.65 × 10(-9) ). These results provide independent replication, in a large-scale dataset, of a genetic locus associated with cognitive function and education. As sample sizes grow, cognitive GWAS will identify increasing numbers of associated loci, as has been accomplished in other polygenic quantitative traits, which may be relevant to psychiatric illness.

Disaggregation of Green Space Access, Walkability, and Behavioral Risk Factor Data for Precise Estimation of Local Population Characteristics.

BACKGROUND: Social and Environmental Determinants of Health (SEDH) provide us with a conceptual framework to gain insights into possible associations among different human behaviors and the corresponding health outcomes that take place often in and around complex built environments. Developing better built environments requires an understanding of those aspects of a community that are most likely to have a measurable impact on the target SEDH. Yet data on local characteristics at suitable spatial scales are often unavailable. We aim to address this issue by application of different data disaggregation methods. METHODS: We applied different approaches to data disaggregation to obtain small area estimates of key behavioral risk factors, as well as geospatial measures of green space access and walkability for each zip code of Allegheny County in southwestern Pennsylvania. RESULTS: Tables and maps of local characteristics revealed their overall spatial distribution along with disparities therein across the county. While the top ranked zip codes by behavioral estimates generally have higher than the county's median individual income, this does not lead them to have higher than its median green space access or walkability. CONCLUSION: We demonstrated the utility of data disaggregation for addressing complex questions involving community-specific behavioral attributes and built environments with precision and rigor, which is especially useful for a diverse population. Thus, different types of data, when comparable at a common local scale, can provide key integrative insights for researchers and policymakers.

Genetic variants associated with breast cancer risk for Ashkenazi Jewish women with strong family histories but no identifiable BRCA1/2 mutation.

The ability to establish genetic risk models is critical for early identification and optimal treatment of breast cancer. For such a model to gain clinical utility, more variants must be identified beyond those discovered in previous genome-wide association studies (GWAS). This is especially true for women at high risk because of family history, but without BRCA1/2 mutations. This study incorporates three datasets in a GWAS analysis of women with Ashkenazi Jewish (AJ) homogeneous ancestry. Two independent discovery cohorts comprised 239 and 238 AJ women with invasive breast cancer or preinvasive ductal carcinoma in situ and strong family histories of breast cancer, but lacking the three BRCA1/2 founder mutations, along with 294 and 230 AJ controls, respectively. An independent, third cohort of 203 AJ cases with familial breast cancer history and 263 healthy controls of AJ women was used for validation. A total of 19 SNPs were identified as associated with familial breast cancer risk in AJ women. Among these SNPs, 13 were identified from a panel of 109 discovery SNPs, including an FGFR2 haplotype. In addition, six previously identified breast cancer GWAS SNPs were confirmed in this population. Seven of the 19 markers were significant in a multivariate predictive model of familial breast cancer in AJ women, three novel SNPs [rs17663555(5q13.2), rs566164(6q21), and rs11075884(16q22.2)], the FGFR2 haplotype, and three previously published SNPs [rs13387042(2q35), rs2046210(ESR1), and rs3112612(TOX3)], yielding moderate predictive power with an area under the curve (AUC) of the ROC (receiver-operator characteristic curve) of 0.74. Population-specific genetic variants in addition to variants shared with populations of European ancestry may improve breast cancer risk prediction among AJ women from high-risk families without founder BRCA1/2 mutations.

De novo copy number variants are associated with congenital diaphragmatic hernia.

BACKGROUND: Congenital diaphragmatic hernia (CDH) is a common birth defect with significant morbidity and mortality. Although the aetiology of CDH remains poorly understood, studies from animal models and patients with CDH suggest that genetic factors play an important role in the development of CDH. Chromosomal anomalies have been reported in CDH. METHODS: In this study, the authors investigated the frequency of chromosomal anomalies and copy number variants (CNVs) in 256 parent-child trios of CDH using clinical conventional cytogenetic and microarray analysis. The authors also selected a set of CDH related training genes to prioritise the genes in those segmental aneuploidies and identified the genes and gene sets that may contribute to the aetiology of CDH. RESULTS: The authors identified chromosomal anomalies in 16 patients (6.3%) of the series including three aneuploidies, two unbalanced translocation, and 11 patients with de novo CNVs ranging in size from 95 kb to 104.6 Mb. The authors prioritised the genes in the CNV segments and identified KCNA2, LMNA, CACNA1S, MYOG, HLX, LBR, AGT, GATA4, SOX7, HYLS1, FOXC1, FOXF2, PDGFA, FGF6, COL4A1, COL4A2, HOMER2, BNC1, BID, and TBX1 as genes that may be involved in diaphragm development. Gene enrichment analysis identified the most relevant gene ontology categories as those involved in tissue development (p=4.4×10(-11)) or regulation of multicellular organismal processes (p=2.8×10(-10)) and 'receptor binding' (p=8.7×10(-14)) and 'DNA binding transcription factor activity' (p=4.4×10(-10)). CONCLUSIONS: The present findings support the role of chromosomal anomalies in CDH and provide a set of candidate genes including FOXC1, FOXF2, PDGFA, FGF6, COL4A1, COL4A2, SOX7, BNC1, BID, and TBX1 for further analysis in CDH.

Multivariate small area modelling of undernutrition prevalence among under-five children in Bangladesh.

District-representative data are rarely collected in the surveys for identifying localised disparities in Bangladesh, and so district-level estimates of undernutrition indicators - stunting, wasting and underweight - have remained largely unexplored. This study aims to estimate district-level prevalence of these indicators by employing a multivariate Fay-Herriot (MFH) model which accounts for the underlying correlation among the undernutrition indicators. Direct estimates (DIR) of the target indicators and their variance-covariance matrices calculated from the 2019 Bangladesh Multiple Indicator Cluster Survey microdata have been used as input for developing univariate Fay-Herriot (UFH), bivariate Fay-Herriot (BFH) and MFH models. The comparison of the various model-based estimates and their relative standard errors with the corresponding direct estimates reveals that the MFH estimator provides unbiased estimates with more accuracy than the DIR, UFH and BFH estimators. The MFH model-based district level estimates of stunting, wasting and underweight range between 16 and 43%, 15 and 36%, and 6 and 13% respectively. District level bivariate maps of undernutrition indicators show that districts in north-eastern and south-eastern parts are highly exposed to either form of undernutrition, than the districts in south-western and central parts of the country. In terms of the number of undernourished children, millions of children affected by either form of undernutrition are living in densely populated districts like the capital district Dhaka, though undernutrition indicators (as a proportion) are comparatively lower. These findings can be used to target districts with a concurrence of multiple forms of undernutrition, and in the design of urgent intervention programs to reduce the inequality in child undernutrition at the localised district level.

Food insecurity in the Eastern Indo-Gangetic plain: Taking a closer look.

OBJECTIVE: Food security is an important policy issue in India. As India recently ranked 107th out of 121 countries in the 2022 Global Hunger Index, there is an urgent need to dissect, and gain insights into, such a major decline at the national level. However, the existing surveys, due to small sample sizes, cannot be used directly to produce reliable estimates at local administrative levels such as districts. DESIGN: The latest round of available data from the Household Consumer Expenditure Survey (HCES 2011-12) done by the National Sample Survey Office of India used stratified multi-stage random sampling with districts as strata, villages as first stage and households as second stage units. SETTING: Our Small Area Estimation approach estimated food insecurity prevalence, gap, and severity of each rural district of the Eastern Indo-Gangetic Plain (EIGP) region by modeling the HCES data, guided by local covariates from the 2011 Indian Population Census. PARTICIPANTS: In HCES, 5915 (34429), 3310 (17534) and 3566 (15223) households (persons) were surveyed from the 71, 38 and 18 districts of the EIGP states of Uttar Pradesh, Bihar and West Bengal respectively. RESULTS: We estimated the district-specific food insecurity indicators, and mapped their local disparities over the EIGP region. By comparing food insecurity with indicators of climate vulnerability, poverty and crop diversity, we shortlisted the vulnerable districts in EIGP. CONCLUSIONS: Our district-level estimates and maps can be effective for informed policy-making to build local resiliency and address systemic vulnerabilities where they matter most in the post-pandemic era. ADVANCES: Our study computed, for the Indian states in the EIGP region, the first area-level small area estimates of food insecurity as well as poverty over the past decade, and generated a ranked list of districts upon combining these data with measures of crop diversity and climatic vulnerability.

Implementation of Rapid Genome Sequencing for Critically Ill Infants With Complex Congenital Heart Disease.

BACKGROUND: Rapid genome sequencing (rGS) has been shown to improve care of critically ill infants. Congenital heart disease (CHD) is a leading cause of infant mortality and is often caused by genetic disorders, yet the utility of rGS has not been prospectively studied in this population. METHODS: We conducted a prospective evaluation of rGS to improve the care of infants with complex CHD in our cardiac neonatal intensive care unit. RESULTS: In a cohort of 48 infants with complex CHD, rGS diagnosed 14 genetic disorders in 13 (27%) individuals and led to changes in clinical management in 8 (62%) cases with diagnostic results. These included 2 cases in whom genetic diagnoses helped avert intensive, futile interventions before cardiac neonatal intensive care unit discharge, and 3 cases in whom eye disease was diagnosed and treated in early childhood. CONCLUSIONS: Our study provides the first prospective evaluation of rGS for infants with complex CHD to our knowledge. We found that rGS diagnosed genetic disorders in 27% of cases and led to changes in management in 62% of cases with diagnostic results. Our model of care depended on coordination between neonatologists, cardiologists, surgeons, geneticists, and genetic counselors. These findings highlight the important role of rGS in CHD and demonstrate the need for expanded study of how to implement this resource to a broader population of infants with CHD.

Molecular diagnostic yield of genome sequencing versus targeted gene panel testing in racially and ethnically diverse pediatric patients.

Purpose: Adoption of genome sequencing (GS) as a first-line test requires evaluation of its diagnostic yield. We evaluated the GS and targeted gene panel (TGP) testing in diverse pediatric patients (probands) with suspected genetic conditions. Methods: Probands with neurologic, cardiac, or immunologic conditions were offered GS and TGP testing. Diagnostic yield was compared using a fully paired study design. Results: 645 probands (median age 9 years) underwent genetic testing, and 113 (17.5%) received a molecular diagnosis. Among 642 probands with both GS and TGP testing, GS yielded 106 (16.5%) and TGPs yielded 52 (8.1%) diagnoses ( P < .001). Yield was greater for GS vs . TGPs in Hispanic/Latino(a) (17.2% vs . 9.5%, P < .001) and White/European American (19.8% vs . 7.9%, P < .001), but not in Black/African American (11.5% vs . 7.7%, P = .22) population groups by self-report. A higher rate of inconclusive results was seen in the Black/African American (63.8%) vs . White/European American (47.6%; P = .01) population group. Most causal copy number variants (17 of 19) and mosaic variants (6 of 8) were detected only by GS. Conclusion: GS may yield up to twice as many diagnoses in pediatric patients compared to TGP testing, but not yet across all population groups.

Multivariate Small Area Modelling for Measuring Micro Level Earning Inequality: Evidence from Periodic Labour Force Survey of India.

The economy of India is growing continuously with its gross domestic product increasing rapidly than most of the developing countries. Nonetheless an increase in national gross domestic product is not revealing the earning parity at micro level in the country. The earning inequality in a country like India has adversely obstructed under privileged in accessing basic needs such as health and education. The Periodic labour force survey (PLFS) conducted by the National Statistical Office of India generates estimates on earning status at state and national level for both rural and urban sectors separately. However, due to a small sample size problem that leads to high sampling variability, these surveys cannot be used directly to produce reliable estimates at micro level such as district or further disaggregate levels. As earnings are often unevenly distributed among the subgroups of comparatively small areas, disaggregate level statistics are inevitably needed in the country for target specific policy planning and monitoring to reduce the earning disparity. Nonetheless, owing to unavailability of estimates at district level, the analysis and spatial mapping related to earning inequality are limited to the national and state level. As a result, the existing variability in disaggregate level earning distribution are often unavailable. This article describes multivariate small area estimation (SAE) to generate precise and representative district-wise model-based estimates of inequality in earning distribution in rural and urban areas of Uttar Pradesh state in India by linking the latest round of PLFS 2018-2019 data and the 2011 Indian Population Census data. The diagnostic measures demonstrate that the district-wise estimates of earning generated by multivariate SAE method are reliable and representative. The spatial maps produced in this analysis reveal district level inequality in earning distribution in the state of Uttar Pradesh. These disaggregate level estimates and spatial mapping of earning distribution are directly pertinent to measuring and monitoring the sustainable development goal 10 of inequality reduction within countries. These expected to offer evidence to executive policy-makers and experts for recognizing the areas demanding additional consideration. This study will definitely provide added advantage to the newly launched schemes of Government of India for fund distribution along with the better monitoring of these schemes.