Gene variants of the phosphatidylcholine synthesis pathway do not contribute to RDS in the Chinese population.

BACKGROUND: To determine population-based prevalence and disease contribution of phosphatidylcholine synthetic pathway-associated gene variants in a native southern Chinese cohort. METHODS: We used bloodspots from 2010 that were obtained from the Guangxi Neonatal Screening Center in Nannning China and included the Han (n = 443) and Zhuang (n = 313) ethnic groups. We sequenced the exons of cholinephosphate cytidylyltransferase (PCYT1B) lysophospholipid acyltransferase 1 (LPCAT1), and cholinephosphotransferase (CHPT1) genes, and analyzed both rare and common exonic variants. RESULTS: We obtained five mutations (G199D, A299V, G434C, Y490C, L312S) with eight alleles in the three candidate genes. The collapsed minor allele frequency for candidate genes was not significantly different between the Han and Zhuang populations (0.0045 vs. 0.0064, respectively, P = 0.725). The combined Han and Zhuang pool collapsed carrier frequency of rare mutation allele was found to be 1.06%, which is much higher than previously reported for the Missouri population (0.1%). Further, we detected six exonic common variants (three in LPCAT1 and three in CHPT1), with three non-synonymous variants (F162S, F341L, M427K) among them. Two of the non-synonymous exonic variants (F341L, M427K) were not found in CHB; F341L was also not previously reported in exome sequencing project. CONCLUSIONS: The population-based frequency of mutations in the phosphatidylcholine synthesis pathway-associated genes PCYT1B LPCAT1, CHPT1 is low in southern Chinese newborns and there is no evidence of contribution to population-based disease burden of respiratory distress syndrome. As a population-based study of rare mutations and common variants, this work is valuable in directing future research.

Outcomes of Lung Transplantation for Infants and Children with Genetic Disorders of Surfactant Metabolism.

OBJECTIVE: To compare outcomes of infants and children who underwent lung transplantation for genetic disorders of surfactant metabolism (SFTPB, SFTPC, ABCA3, and NKX2-1) over 2 epochs (1993-2003 and 2004-2015) at St Louis Children's Hospital. STUDY DESIGN: We retrospectively reviewed clinical characteristics, mortality, and short- and long-term morbidities of infants (transplanted at <1 year; n = 28) and children (transplanted >1 year; n = 16) and compared outcomes by age at transplantation (infants vs children) and by epoch of transplantation. RESULTS: Infants underwent transplantation more frequently for surfactant protein-B deficiency, whereas children underwent transplantation more frequently for SFTPC mutations. Both infants and children underwent transplantation for ABCA3 deficiency. Compared with children, infants experienced shorter times from listing to transplantation (P = .014), were more likely to be mechanically ventilated at the time of transplantation (P < .0001), were less likely to develop bronchiolitis obliterans post-transplantation (P = .021), and were more likely to have speech and motor delays (P ≤ .0001). Despite advances in genetic diagnosis, immunosuppressive therapies, and supportive respiratory and nutritional therapies, mortality did not differ between infants and children (P = .076) or between epochs. Kaplan-Meier analyses demonstrated that children transplanted in epoch 1 (1993-2003) were more likely to develop systemic hypertension (P = .049) and less likely to develop post-transplantation lymphoproliferative disorder compared with children transplanted in epoch 2 (2004-2015) (P = .051). CONCLUSION: Post-lung transplantation morbidities and mortality remain substantial for infants and children with genetic disorders of surfactant metabolism.

Protein-structure-guided discovery of functional mutations across 19 cancer types.

Local concentrations of mutations are well known in human cancers. However, their three-dimensional spatial relationships in the encoded protein have yet to be systematically explored. We developed a computational tool, HotSpot3D, to identify such spatial hotspots (clusters) and to interpret the potential function of variants within them. We applied HotSpot3D to >4,400 TCGA tumors across 19 cancer types, discovering >6,000 intra- and intermolecular clusters, some of which showed tumor and/or tissue specificity. In addition, we identified 369 rare mutations in genes including TP53, PTEN, VHL, EGFR, and FBXW7 and 99 medium-recurrence mutations in genes such as RUNX1, MTOR, CA3, PI3, and PTPN11, all mapping within clusters having potential functional implications. As a proof of concept, we validated our predictions in EGFR using high-throughput phosphorylation data and cell-line-based experimental evaluation. Finally, mutation-drug cluster and network analysis predicted over 800 promising candidates for druggable mutations, raising new possibilities for designing personalized treatments for patients carrying specific mutations.

Candidate gene resequencing to identify rare, pedigree-specific variants influencing healthy aging phenotypes in the long life family study.

BACKGROUND: The Long Life Family Study (LLFS) is an international study to identify the genetic components of various healthy aging phenotypes. We hypothesized that pedigree-specific rare variants at longevity-associated genes could have a similar functional impact on healthy phenotypes. METHODS: We performed custom hybridization capture sequencing to identify the functional variants in 464 candidate genes for longevity or the major diseases of aging in 615 pedigrees (4,953 individuals) from the LLFS, using a multiplexed, custom hybridization capture. Variants were analyzed individually or as a group across an entire gene for association to aging phenotypes using family based tests. RESULTS: We found significant associations to three genes and nine single variants. Most notably, we found a novel variant significantly associated with exceptional survival in the 3' UTR OBFC1 in 13 individuals from six pedigrees. OBFC1 (chromosome 10) is involved in telomere maintenance, and falls within a linkage peak recently reported from an analysis of telomere length in LLFS families. Two different algorithms for single gene associations identified three genes with an enrichment of variation that was significantly associated with three phenotypes (GSK3B with the Healthy Aging Index, NOTCH1 with diastolic blood pressure and TP53 with serum HDL). CONCLUSIONS: Sequencing analysis of family-based associations for age-related phenotypes can identify rare or novel variants.

Genetic Factors Contribute to Risk for Neonatal Respiratory Distress Syndrome among Moderately Preterm, Late Preterm, and Term Infants.

OBJECTIVE: To determine the genetic contribution to risk for respiratory distress syndrome (RDS) among moderately preterm, late preterm, and term infants (estimated gestational age ≥32 weeks) of African- and European-descent. STUDY DESIGN: We reviewed clinical records for 524 consecutive twin pairs ≥32 weeks gestation. We identified pairs in which at least 1 twin had RDS (n = 225) and compared the concordance of RDS between monozygotic and dizygotic twins. Using mixed-effects logistic regression, we identified covariates that increased disease risk. We performed additive genetic, common environmental, and residual effects modeling to estimate genetic variance and used the ratio of genetic variance to total variance to estimate genetic contribution to RDS disease risk. RESULTS: Monozygotic twins were more concordant for RDS than dizygotic twins (P = .0040). Estimated gestational age, European-descent, male sex, delivery by cesarean, and 5-minute Apgar score each independently increased risk for RDS. After adjusting for these covariates, genetic effects accounted for 58% (P = .0002) of the RDS disease risk variance for all twin pairs. CONCLUSIONS: In addition to environmental factors, genetic factors may contribute to RDS risk among moderately preterm, late preterm, and term infants. Discovery of risk alleles may be important for prediction and management of RDS risk.

Population-based frequency of surfactant dysfunction mutations in a native Chinese cohort.

BACKGROUND: Rare mutations in surfactant-associated genes contribute to neonatal respiratory distress syndrome. The frequency of mutations in these genes in the Chinese population is unknown. METHODS: We obtained blood spots from the Guangxi Neonatal Screening Center in Nanning, China that included Han (n=443) and Zhuang (n=313) ethnic groups. We resequenced all exons of the surfactant proteins-B (SFTPB), -C (SFTPC), and the ATP-binding cassette member A3 (ABCA3) genes and compared the frequencies of 5 common and all rare variants. RESULTS: We found minor differences in the frequencies of the common variants in the Han and Zhuang cohorts. We did not find any rare mutations in SFTPB or SFTPC, but we found three ABCA3 mutations in the Han [minor allele frequency (MAF)=0.003] and 7 in the Zhuang (MAF=0.011) cohorts (P=0.10). The ABCA3 mutations were unique to each cohort; five were novel. The collapsed carrier rate of rare ABCA3 mutations in the Han and Zhuang populations combined was 1.3%, which is significantly lower than that in the United States (P<0.001). CONCLUSION: The population-based frequency of mutations in ABCA3 in south China newborns is significantly lower than that in United States. The contribution of these rare ABCA3 mutations to disease burden in the south China population is still unknown.

Patterns and functional implications of rare germline variants across 12 cancer types.

Large-scale cancer sequencing data enable discovery of rare germline cancer susceptibility variants. Here we systematically analyse 4,034 cases from The Cancer Genome Atlas cancer cases representing 12 cancer types. We find that the frequency of rare germline truncations in 114 cancer-susceptibility-associated genes varies widely, from 4% (acute myeloid leukaemia (AML)) to 19% (ovarian cancer), with a notably high frequency of 11% in stomach cancer. Burden testing identifies 13 cancer genes with significant enrichment of rare truncations, some associated with specific cancers (for example, RAD51C, PALB2 and MSH6 in AML, stomach and endometrial cancers, respectively). Significant, tumour-specific loss of heterozygosity occurs in nine genes (ATM, BAP1, BRCA1/2, BRIP1, FANCM, PALB2 and RAD51C/D). Moreover, our homology-directed repair assay of 68 BRCA1 rare missense variants supports the utility of allelic enrichment analysis for characterizing variants of unknown significance. The scale of this analysis and the somatic-germline integration enable the detection of rare variants that may affect individual susceptibility to tumour development, a critical step toward precision medicine.

CD4+ and CD8+ T cells have opposing roles in breast cancer progression and outcome.

The Cancer Immunoediting concept has provided critical insights suggesting dual functions of immune system during the cancer initiation and development. However, the dynamics and roles of CD4+ and CD8+ T cells in the pathogenesis of breast cancer remain unclear. Here we utilized two murine breast cancer models (4T1 and E0771) and demonstrated that both CD4+ and CD8+ T cells were increased and involved in immune responses, but with distinct dynamic trends in breast cancer development. In addition to cell number increases, CD4+ T cells changed their dominant subsets from Th1 in the early stages to Treg and Th17 cells in the late stages of the cancer progression. We also analyzed CD4+ and CD8+ T cell infiltration in primary breast cancer tissues from cancer patients. We observed that CD8+ T cells are the key effector cell population mediating effective anti-tumor immunity resulting in better clinical outcomes. In contrast, intra-tumoral CD4+ T cells have negative prognostic effects on breast cancer patient outcomes. These studies indicate that CD4+ and CD8+ T cells have opposing roles in breast cancer progression and outcomes, which provides new insights relevant for the development of effective cancer immunotherapeutic approaches.

Admixture mapping of coronary artery calcification in African Americans from the NHLBI family heart study.

BACKGROUND: Coronary artery calcification (CAC) is an imaging biomarker of coronary atherosclerosis. In European Americans, genome-wide association studies (GWAS) have identified several regions associated with coronary artery disease. However, few large studies have been conducted in African Americans. The largest meta-analysis of CAC in African Americans failed to identify genome-wide significant variants despite being powered to detect effects comparable to effects identified in European Americans. Because CAC is different in prevalence and severity in African Americans and European Americans, admixture mapping is a useful approach to identify loci missed by GWAS. RESULTS: We applied admixture mapping to the African American cohort of the Family Heart Study and identified one genome-wide significant region on chromosome 12 and three potential regions on chromosomes 6, 15, and 19 that are associated with CAC. Follow-up studies using previously reported GWAS meta-analysis data suggest that the regions identified on chromosome 6 and 15 contain variants that are possibly associated with CAC. The associated region on chromosome 6 contains the gene for BMP-6, which is expressed in vascular calcific lesions. CONCLUSIONS: Our results suggest that admixture mapping can be a useful hypothesis-generating tool to identify genomic regions that contribute to complex diseases in genetically admixed populations.

Vasohibin-1 suppresses colon cancer.

Vasohibin-1 (VASH1) is an endogenous angiogenesis inhibitor.However, the clinical relevance of VASH1 in colon cancer and its regulations on cancer angiogenesis and cancer cell biological characteristics are still unknown. Here we showed that stromal VASH1 levels were negatively correlated with tumor size, advanced clinical stage and distant metastases in colon cancer patients. Overexpression of VASH1 in colon cancer cells induced apoptosis and senescence, inhibiting cancer cell growth and colony formation in vitro and tumor growth in vivo. In addition, knockdown of VASH1 in cancer cells promoted cell growth, adhesion and migration in vitro, and enhanced tumorigenesis and metastasis in vivo.

Genome-Wide Association Study and Linkage Analysis of the Healthy Aging Index.

BACKGROUND: The Healthy Aging Index (HAI) is a tool for measuring the extent of health and disease across multiple systems. METHODS: We conducted a genome-wide association study and a genome-wide linkage analysis to map quantitative trait loci associated with the HAI and a modified HAI weighted for mortality risk in 3,140 individuals selected for familial longevity from the Long Life Family Study. The genome-wide association study used the Long Life Family Study as the discovery cohort and individuals from the Cardiovascular Health Study and the Framingham Heart Study as replication cohorts. RESULTS: There were no genome-wide significant findings from the genome-wide association study; however, several single-nucleotide polymorphisms near ZNF704 on chromosome 8q21.13 were suggestively associated with the HAI in the Long Life Family Study (p < 10(-) (6)) and nominally replicated in the Cardiovascular Health Study and Framingham Heart Study. Linkage results revealed significant evidence (log-odds score = 3.36) for a quantitative trait locus for mortality-optimized HAI in women on chromosome 9p24-p23. However, results of fine-mapping studies did not implicate any specific candidate genes within this region of interest. CONCLUSIONS: ZNF704 may be a potential candidate gene for studies of the genetic underpinnings of longevity.

Burden of disease variants in participants of the Long Life Family Study.

Case control studies of nonagenarians and centenarians provide evidence that long-lived individuals do not differ in the rate of disease associated variants compared to population controls. These results suggest that an enrichment of novel protective variants, rather than a lack of disease associated variants, determine the genetic predisposition to exceptionally long lives. Using data from the Long Life Family Study (LLFS), we sought to replicate these findings and extend them to include a larger number of disease-specific risk alleles. To accomplish this goal, we built a genetic risk score for each of four age-related disease groups: Alzheimer's disease, cardiovascular disease and stroke, type 2 diabetes, and various cancers and compared the distribution of these scores between older participants of the LLFS, their offspring and their spouses. The analyses showed no significant differences in distribution of the genetic risk scores for cardiovascular disease and stroke, type 2 diabetes, or cancer between the groups, while participants of the LLFS appeared to carry an average 1% fewer risk alleles for Alzheimer's disease compared to spousal controls and, while the difference may not be clinically relevant, it was statistically significant. However, the statistical significance between familial longevity and the Alzheimer's disease genetic risk score was lost when a more stringent linkage disequilibrium threshold was imposed to select independent genetic variants.

Identification of functional variants for cleft lip with or without cleft palate in or near PAX7, FGFR2, and NOG by targeted sequencing of GWAS loci.

Although genome-wide association studies (GWASs) for nonsyndromic orofacial clefts have identified multiple strongly associated regions, the causal variants are unknown. To address this, we selected 13 regions from GWASs and other studies, performed targeted sequencing in 1,409 Asian and European trios, and carried out a series of statistical and functional analyses. Within a cluster of strongly associated common variants near NOG, we found that one, rs227727, disrupts enhancer activity. We furthermore identified significant clusters of non-coding rare variants near NTN1 and NOG and found several rare coding variants likely to affect protein function, including four nonsense variants in ARHGAP29. We confirmed 48 de novo mutations and, based on best biological evidence available, chose two of these for functional assays. One mutation in PAX7 disrupted the DNA binding of the encoded transcription factor in an in vitro assay. The second, a non-coding mutation, disrupted the activity of a neural crest enhancer downstream of FGFR2 both in vitro and in vivo. This targeted sequencing study provides strong functional evidence implicating several specific variants as primary contributory risk alleles for nonsyndromic clefting in humans.

Selection of models for the analysis of risk-factor trees: leveraging biological knowledge to mine large sets of risk factors with application to microbiome data.

MOTIVATION: Establishment of a statistical association between microbiome features and clinical outcomes is of growing interest because of the potential for yielding insights into biological mechanisms and pathogenesis. Extracting microbiome features that are relevant for a disease is challenging and existing variable selection methods are limited due to large number of risk factor variables from microbiome sequence data and their complex biological structure. RESULTS: We propose a tree-based scanning method, Selection of Models for the Analysis of Risk factor Trees (referred to as SMART-scan), for identifying taxonomic groups that are associated with a disease or trait. SMART-scan is a model selection technique that uses a predefined taxonomy to organize the large pool of possible predictors into optimized groups, and hierarchically searches and determines variable groups for association test. We investigate the statistical properties of SMART-scan through simulations, in comparison to a regular single-variable analysis and three commonly-used variable selection methods, stepwise regression, least absolute shrinkage and selection operator (LASSO) and classification and regression tree (CART). When there are taxonomic group effects in the data, SMART-scan can significantly increase power by using bacterial taxonomic information to split large numbers of variables into groups. Through an application to microbiome data from a vervet monkey diet experiment, we demonstrate that SMART-scan can identify important phenotype-associated taxonomic features missed by single-variable analysis, stepwise regression, LASSO and CART.

Adjusting family relatedness in data-driven burden test of rare variants.

Family data represent a rich resource for detecting association between rare variants (RVs) and human traits. However, most RV association analysis methods developed in recent years are data-driven burden tests which can adaptively learn weights from data but require permutation to evaluate significance, thus are not readily applicable to family data, because random permutation will destroy family structure. Direct application of these methods to family data may result in a significant inflation of false positives. To overcome this issue, we have developed a generalized, weighted sum mixed model (WSMM), and corresponding computational techniques that can incorporate family information into data-driven burden tests, and allow adaptive and efficient permutation test in family data. Using simulated and real datasets, we demonstrate that the WSMM method can be used to appropriately adjust for genetic relatedness among family members and has a good control for the inflation of false positives. We compare WSMM with a nondata-driven, family-based Sequence Kernel Association Test (famSKAT), showing that WSMM has significantly higher power in some cases. WSMM provides a generalized, flexible framework for adapting different data-driven burden tests to analyze data with any family structures, and it can be extended to binary and time-to-onset traits, with or without covariates.

Estimating and testing pleiotropy of single genetic variant for two quantitative traits.

Along with the accumulated data of genetic variants and biomedical phenotypes in the genome era, statistical identification of pleiotropy is of growing interest for dissecting and understanding genetic correlations between complex traits. We proposed a novel method for estimating and testing pleiotropic effect of a genetic variant on two quantitative traits. Based on a covariance decomposition and estimation, our method quantifies pleiotropy as the portion of between-trait correlation explained by the same genetic variant. Unlike most multiple-trait methods that assess potential pleiotropy (i.e., whether a variant contributes to at least one trait), our method formulates a statistic that tests exact pleiotropy (i.e., whether a variant contributes to both of two traits). We developed two approaches (a regression approach and a bootstrapping approach) for such test and investigated their statistical properties, in comparison with other potential pleiotropy test methods. Our simulation shows that the regression approach produces correct P-values under both the complete null (i.e., a variant has no effect on both two traits) and the incomplete null (i.e., a variant has effect on only one of two traits), but requires large sample sizes to achieve a good power, when the bootstrapping approach has a better power and produces conservative P-values under the complete null. We demonstrate our method for detecting exact pleiotropy using a real GWAS dataset. Our method provides an easy-to-implement tool for measuring, testing, and understanding the pleiotropic effect of a single variant on the correlation architecture of two complex traits.

Persistent gut microbiota immaturity in malnourished Bangladeshi children.

Therapeutic food interventions have reduced mortality in children with severe acute malnutrition (SAM), but incomplete restoration of healthy growth remains a major problem. The relationships between the type of nutritional intervention, the gut microbiota, and therapeutic responses are unclear. In the current study, bacterial species whose proportional representation define a healthy gut microbiota as it assembles during the first two postnatal years were identified by applying a machine-learning-based approach to 16S ribosomal RNA data sets generated from monthly faecal samples obtained from birth onwards in a cohort of children living in an urban slum of Dhaka, Bangladesh, who exhibited consistently healthy growth. These age-discriminatory bacterial species were incorporated into a model that computes a 'relative microbiota maturity index' and 'microbiota-for-age Z-score' that compare postnatal assembly (defined here as maturation) of a child's faecal microbiota relative to healthy children of similar chronologic age. The model was applied to twins and triplets (to test for associations of these indices with genetic and environmental factors, including diarrhoea), children with SAM enrolled in a randomized trial of two food interventions, and children with moderate acute malnutrition. Our results indicate that SAM is associated with significant relative microbiota immaturity that is only partially ameliorated following two widely used nutritional interventions. Immaturity is also evident in less severe forms of malnutrition and correlates with anthropometric measurements. Microbiota maturity indices provide a microbial measure of human postnatal development, a way of classifying malnourished states, and a parameter for judging therapeutic efficacy. More prolonged interventions with existing or new therapeutic foods and/or addition of gut microbes may be needed to achieve enduring repair of gut microbiota immaturity in childhood malnutrition and improve clinical outcomes.

Re-sequencing expands our understanding of the phenotypic impact of variants at GWAS loci.

Genome-wide association studies (GWAS) have identified >500 common variants associated with quantitative metabolic traits, but in aggregate such variants explain at most 20-30% of the heritable component of population variation in these traits. To further investigate the impact of genotypic variation on metabolic traits, we conducted re-sequencing studies in >6,000 members of a Finnish population cohort (The Northern Finland Birth Cohort of 1966 [NFBC]) and a type 2 diabetes case-control sample (The Finland-United States Investigation of NIDDM Genetics [FUSION] study). By sequencing the coding sequence and 5' and 3' untranslated regions of 78 genes at 17 GWAS loci associated with one or more of six metabolic traits (serum levels of fasting HDL-C, LDL-C, total cholesterol, triglycerides, plasma glucose, and insulin), and conducting both single-variant and gene-level association tests, we obtained a more complete understanding of phenotype-genotype associations at eight of these loci. At all eight of these loci, the identification of new associations provides significant evidence for multiple genetic signals to one or more phenotypes, and at two loci, in the genes ABCA1 and CETP, we found significant gene-level evidence of association to non-synonymous variants with MAF<1%. Additionally, two potentially deleterious variants that demonstrated significant associations (rs138726309, a missense variant in G6PC2, and rs28933094, a missense variant in LIPC) were considerably more common in these Finnish samples than in European reference populations, supporting our prior hypothesis that deleterious variants could attain high frequencies in this isolated population, likely due to the effects of population bottlenecks. Our results highlight the value of large, well-phenotyped samples for rare-variant association analysis, and the challenge of evaluating the phenotypic impact of such variants.