E-Pedigrees: a large-scale automatic family pedigree prediction application.

MOTIVATION: The use and functionality of Electronic Health Records (EHR) have increased rapidly in the past few decades. EHRs are becoming an important depository of patient health information and can capture family data. Pedigree analysis is a longstanding and powerful approach that can gain insight into the underlying genetic and environmental factors in human health, but traditional approaches to identifying and recruiting families are low-throughput and labor-intensive. Therefore, high-throughput methods to automatically construct family pedigrees are needed. RESULTS: We developed a stand-alone application: Electronic Pedigrees, or E-Pedigrees, which combines two validated family prediction algorithms into a single software package for high throughput pedigrees construction. The convenient platform considers patients' basic demographic information and/or emergency contact data to infer high-accuracy parent-child relationship. Importantly, E-Pedigrees allows users to layer in additional pedigree data when available and provides options for applying different logical rules to improve accuracy of inferred family relationships. This software is fast and easy to use, is compatible with different EHR data sources, and its output is a standard PED file appropriate for multiple downstream analyses. AVAILABILITY AND IMPLEMENTATION: The Python 3.3+ version E-Pedigrees application is freely available on: https://github.com/xiayuan-huang/E-pedigrees.

Kidney failure in Bardet-Biedl syndrome.

The aim of this study was to explore kidney failure (KF) in Bardet-Biedl syndrome (BBS), focusing on high-risk gene variants, demographics, and morbidity. We employed the Clinical Registry Investigating BBS (CRIBBS) to identify 44 (7.2%) individuals with KF out of 607 subjects. Molecularly confirmed BBS was identified in 37 KF subjects and 364 CRIBBS registrants. KF was concomitant with recessive causal variants in 12 genes, with BBS10 the most predominant causal gene (26.6%), while disease penetrance was highest in SDCCAG8 (100%). Two truncating variants were present in 67.6% of KF cases. KF incidence was increased in genes not belonging to the BBSome or chaperonin-like genes (p < 0.001), including TTC21B, a new candidate BBS gene. Median age of KF was 12.5 years, with the vast majority of KF occurring by 30 years (86.3%). Females were disproportionately affected (77.3%). Diverse uropathies were identified, but were not more common in the KF group (p = 0.672). Kidney failure was evident in 11 of 15 (73.3%) deaths outside infancy. We conclude that KF poses a significant risk for premature morbidity in BBS. Risk factors for KF include female sex, truncating variants, and genes other than BBSome/chaperonin-like genes highlighting the value of comprehensive genetic investigation.

Medical Records-Based Genetic Studies of the Complement System.

BACKGROUND: Genetic variants in complement genes have been associated with a wide range of human disease states, but well-powered genetic association studies of complement activation have not been performed in large multiethnic cohorts. METHODS: We performed medical records-based genome-wide and phenome-wide association studies for plasma C3 and C4 levels among participants of the Electronic Medical Records and Genomics (eMERGE) network. RESULTS: In a GWAS for C3 levels in 3949 individuals, we detected two genome-wide significant loci: chr.1q31.3 (CFH locus; rs3753396-A; ß=0.20; 95% CI, 0.14 to 0.25; P=1.52x10-11) and chr.19p13.3 (C3 locus; rs11569470-G; ß=0.19; 95% CI, 0.13 to 0.24; P=1.29x10-8). These two loci explained approximately 2% of variance in C3 levels. GWAS for C4 levels involved 3998 individuals and revealed a genome-wide significant locus at chr.6p21.32 (C4 locus; rs3135353-C; ß=0.40; 95% CI, 0.34 to 0.45; P=4.58x10-35). This locus explained approximately 13% of variance in C4 levels. The multiallelic copy number variant analysis defined two structural genomic C4 variants with large effect on blood C4 levels: C4-BS (ß=-0.36; 95% CI, -0.42 to -0.30; P=2.98x10-22) and C4-AL-BS (ß=0.25; 95% CI, 0.21 to 0.29; P=8.11x10-23). Overall, C4 levels were strongly correlated with copy numbers of C4A and C4B genes. In comprehensive phenome-wide association studies involving 102,138 eMERGE participants, we cataloged a full spectrum of autoimmune, cardiometabolic, and kidney diseases genetically related to systemic complement activation. CONCLUSIONS: We discovered genetic determinants of plasma C3 and C4 levels using eMERGE genomic data linked to electronic medical records. Genetic variants regulating C3 and C4 levels have large effects and multiple clinical correlations across the spectrum of complement-related diseases in humans.

Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis.

OBJECTIVES: To test the hypothesis that genetic predisposition to systemic lupus erythematosus (SLE) increases the risk of cardiometabolic disorders. METHODS: Using 41 single nucleotide polymorphisms (SNPs) associated with SLE, we calculated a weighted genetic risk score (wGRS) for SLE. In a large biobank we tested the association between this wGRS and 9 cardiometabolic phenotypes previously associated with SLE: atrial fibrillation, ischemic stroke, coronary artery disease, type 1 and type 2 diabetes, obesity, chronic kidney disease, hypertension, and hypercholesterolemia. Additionally, we performed a phenome-wide association analysis (pheWAS) to discover novel clinical associations with a genetic predisposition to SLE. Findings were replicated in the Electronic Medical Records and Genomics (eMERGE) Network. To further define the association between SLE-related risk alleles and the selected cardiometabolic phenotypes, we performed an inverse variance weighted regression (IVWR) meta-analysis. RESULTS: The wGRS for SLE was calculated in 74,759 individuals of European ancestry. Among the pre-selected phenotypes, the wGRS was significantly associated with type 1 diabetes (OR [95%CI] =1.11 [1.06, 1.17], P-value = 1.05x10-5). In the PheWAS, the wGRS was associated with several autoimmune phenotypes, kidney disorders, and skin neoplasm; but only the associations with autoimmune phenotypes were replicated. In the IVWR meta-analysis, SLE-related risk alleles were nominally associated with type 1 diabetes (P = 0.048) but the associations were heterogeneous and did not meet the adjusted significance threshold. CONCLUSION: A weighted GRS for SLE was associated with an increased risk of several autoimmune-related phenotypes including type I diabetes but not with cardiometabolic disorders.

Association of Genetic Risk of Obesity with Postoperative Complications Using Mendelian Randomization.

BACKGROUND: The extent to which obesity and genetics determine postoperative complications is incompletely understood. METHODS: We performed a retrospective study using two population cohorts with electronic health record (EHR) data. The first included 736,726 adults with body mass index (BMI) recorded between 1990 and 2017 at Vanderbilt University Medical Center. The second cohort consisted of 65,174 individuals from 12 institutions contributing EHR and genome-wide genotyping data to the Electronic Medical Records and Genomics (eMERGE) Network. Pairwise logistic regression analyses were used to measure the association of BMI categories with postoperative complications derived from International Classification of Disease-9 codes, including postoperative infection, incisional hernia, and intestinal obstruction. A genetic risk score was constructed from 97 obesity-risk single-nucleotide polymorphisms for a Mendelian randomization study to determine the association of genetic risk of obesity on postoperative complications. Logistic regression analyses were adjusted for sex, age, site, and race/principal components. RESULTS: Individuals with overweight or obese BMI (≥25 kg/m2) had increased risk of incisional hernia (odds ratio [OR] 1.7-5.5, p < 3.1 × 10-20), and people with obesity (BMI ≥ 30 kg/m2) had increased risk of postoperative infection (OR 1.2-2.3, p < 2.5 × 10-5). In the eMERGE cohort, genetically predicted BMI was associated with incisional hernia (OR 2.1 [95% CI 1.8-2.5], p = 1.4 × 10-6) and postoperative infection (OR 1.6 [95% CI 1.4-1.9], p = 3.1 × 10-6). Association findings were similar after limitation of the cohorts to those who underwent abdominal procedures. CONCLUSIONS: Clinical and Mendelian randomization studies suggest that obesity, as measured by BMI, is associated with the development of postoperative incisional hernia and infection.

Ancestry-specific polygenic scores and SNP heritability of 25(OH)D in African- and European-ancestry populations.

Vitamin D inadequacy, assessed by 25-hydroxyvitamin D [25(OH)D], affects around 50% of adults in the United States and is associated with numerous adverse health outcomes. Blood 25(OH)D concentrations are influenced by genetic factors that may determine how much vitamin D intake is required to reach optimal 25(OH)D. Despite large genome-wide association studies (GWASs), only a small portion of the genetic factors contributing to differences in 25(OH)D has been discovered. Therefore, knowledge of a fuller set of genetic factors could be useful for risk prediction of 25(OH)D inadequacy, personalized vitamin D supplementation, and prevention of downstream morbidity and mortality. Using PRSice and weights from published African- and European-ancestry GWAS summary statistics, ancestry-specific polygenic scores (PGSs) were created to capture a more complete set of genetic factors in those of European (n = 9569) or African ancestry (n = 2761) from three cohort studies. The PGS for African ancestry was derived using all input SNPs (a p value cutoff of 1.0) and had an R2 of 0.3%; for European ancestry, the optimal PGS used a p value cutoff of 3.5 × 10-4 in the target/tuning dataset and had an R2 of 1.0% in the validation cohort. Those with highest genetic risk had 25(OH)D that was 2.8-3.0 ng/mL lower than those with lowest genetic risk (p = 0.0463-3.2 × 10-13), requiring an additional 467-500 IU of vitamin D intake to maintain equivalent 25(OH)D. PGSs are a powerful predictive tool that could be leveraged for personalized vitamin D supplementation to prevent the negative downstream effects of 25(OH)D inadequacy.

Applying family analyses to electronic health records to facilitate genetic research.

Motivation: Pedigree analysis is a longstanding and powerful approach to gain insight into the underlying genetic factors in human health, but identifying, recruiting and genotyping families can be difficult, time consuming and costly. Development of high throughput methods to identify families and foster downstream analyses are necessary. Results: This paper describes simple methods that allowed us to identify 173 368 family pedigrees with high probability using basic demographic data available in most electronic health records (EHRs). We further developed and validate a novel statistical method that uses EHR data to identify families more likely to have a major genetic component to their diseases risk. Lastly, we showed that incorporating EHR-linked family data into genetic association testing may provide added power for genetic mapping without additional recruitment or genotyping. The totality of these results suggests that EHR-linked families can enable classical genetic analyses in a high-throughput manner. Availability and implementation: Pseudocode is provided as supplementary information. Contact: HEBBRING.SCOTT@marshfieldresearch.org. Supplementary information: Supplementary data are available at Bioinformatics online.

Pilot screening study of targeted genetic polymorphisms for association with seasonal influenza hospital admission.

Host response to influenza is highly variable, suggesting a potential role of host genetic variation. To investigate the host genetics of severe influenza in a targeted fashion, 32 single nucleotide polymorphisms (SNPs) within viral immune response genes were evaluated for association with seasonal influenza hospitalization in an adult study population with European ancestry. SNP allele and genotype frequencies were compared between hospitalized influenza patients (cases) and population controls in a case-control study that included a discovery group (26 cases and 993 controls) and two independent, validation groups (1 with 84 cases and 4076 controls; the other with 128 cases and 9187 controls). Cases and controls had similar allele frequencies for variant rs12252 in interferon-inducible transmembrane protein 3 (IFITM3) (P > 0.05), and the study did not replicate the previously reported association of rs12252 with hospitalized influenza. In the discovery group, the preliminary finding of an association with a nonsense polymorphism (rs8072510) within the schlafen family member 13 (SFLN13) gene (P = 0.0099) was not confirmed in either validation group. Neither rs12252 nor rs8072510 showed an association according to the presence of clinical risk factors for influenza complications (P > 0.05), suggesting that these factors did not modify associations between the SNPs and hospitalized influenza. No other SNPs showed a statistically significant association with hospitalized influenza. Further research is needed to identify genetic factors involved in host response to seasonal influenza infection and to assess whether rs12252, a low-frequency variant in Europeans, contributes to influenza severity in populations with European ancestry.

Phenome-wide association study maps new diseases to the human major histocompatibility complex region.

BACKGROUND: Over 160 disease phenotypes have been mapped to the major histocompatibility complex (MHC) region on chromosome 6 by genome-wide association study (GWAS), suggesting that the MHC region as a whole may be involved in the aetiology of many phenotypes, including unstudied diseases. The phenome-wide association study (PheWAS), a powerful and complementary approach to GWAS, has demonstrated its ability to discover and rediscover genetic associations. The objective of this study is to comprehensively investigate the MHC region by PheWAS to identify new phenotypes mapped to this genetically important region. METHODS: In the current study, we systematically explored the MHC region using PheWAS to associate 2692 MHC-linked variants (minor allele frequency ≥0.01) with 6221 phenotypes in a cohort of 7481 subjects from the Marshfield Clinic Personalized Medicine Research Project. RESULTS: Findings showed that expected associations previously identified by GWAS could be identified by PheWAS (eg, psoriasis, ankylosing spondylitis, type I diabetes and coeliac disease) with some having strong cross-phenotype associations potentially driven by pleiotropic effects. Importantly, novel associations with eight diseases not previously assessed by GWAS (eg, lichen planus) were also identified and replicated in an independent population. Many of these associated diseases appear to be immune-related disorders. Further assessment of these diseases in 16 484 Marshfield Clinic twins suggests that some of these diseases, including lichen planus, may have genetic aetiologies. CONCLUSIONS: These results demonstrate that the PheWAS approach is a powerful and novel method to discover SNP-disease associations, and is ideal when characterising cross-phenotype associations, and further emphasise the importance of the MHC region in human health and disease.

Application of clinical text data for phenome-wide association studies (PheWASs).

MOTIVATION: Genome-wide association studies (GWASs) are effective for describing genetic complexities of common diseases. Phenome-wide association studies (PheWASs) offer an alternative and complementary approach to GWAS using data embedded in the electronic health record (EHR) to define the phenome. International Classification of Disease version 9 (ICD9) codes are used frequently to define the phenome, but using ICD9 codes alone misses other clinically relevant information from the EHR that can be used for PheWAS analyses and discovery. RESULTS: As an alternative to ICD9 coding, a text-based phenome was defined by 23 384 clinically relevant terms extracted from Marshfield Clinic's EHR. Five single nucleotide polymorphisms (SNPs) with known phenotypic associations were genotyped in 4235 individuals and associated across the text-based phenome. All five SNPs genotyped were associated with expected terms (P<0.02), most at or near the top of their respective PheWAS ranking. Raw association results indicate that text data performed equivalently to ICD9 coding and demonstrate the utility of information beyond ICD9 coding for application in PheWAS.

Regulatory polymorphisms in human DBH affect peripheral gene expression and sympathetic activity.

RATIONALE: Dopamine ß-hydroxylase (DBH) catalyzes the conversion of dopamine to norepinephrine in the central nervous system and peripherally. DBH variants are associated with large changes in circulating DBH and implicated in multiple disorders; yet causal relationships and tissue-specific effects remain unresolved. OBJECTIVE: To characterize regulatory variants in DBH, effect on mRNA expression, and role in modulating sympathetic tone and disease risk. METHODS AND RESULTS: Analysis of DBH mRNA in human tissues confirmed high expression in the locus coeruleus and adrenal gland, but also in sympathetically innervated organs (liver>lung>heart). Allele-specific mRNA assays revealed pronounced allelic expression differences in the liver (2- to 11-fold) attributable to promoter rs1611115 and exon 2 rs1108580, but only small differences in locus coeruleus and adrenals. These alleles were also associated with significantly reduced mRNA expression in liver and lung. Although DBH protein is expressed in other sympathetically innervated organs, mRNA levels were too low for analysis. In mice, hepatic Dbh mRNA levels correlated with cardiovascular risk phenotypes. The minor alleles of rs1611115 and rs1108580 were associated with sympathetic phenotypes, including angina pectoris. Testing combined effects of these variants suggested protection against myocardial infarction in 3 separate clinical cohorts. CONCLUSIONS: We demonstrate profound effects of DBH variants on expression in 2 sympathetically innervated organs, liver and lung, but not in adrenals and brain. Preliminary results demonstrate an association of these variants with clinical phenotypes responsive to peripheral sympathetic tone. We hypothesize that in addition to endocrine effects via circulating DBH and norepinephrine, the variants act in sympathetically innervated target organs.

SeqHBase: a big data toolset for family based sequencing data analysis.

BACKGROUND: Whole-genome sequencing (WGS) and whole-exome sequencing (WES) technologies are increasingly used to identify disease-contributing mutations in human genomic studies. It can be a significant challenge to process such data, especially when a large family or cohort is sequenced. Our objective was to develop a big data toolset to efficiently manipulate genome-wide variants, functional annotations and coverage, together with conducting family based sequencing data analysis. METHODS: Hadoop is a framework for reliable, scalable, distributed processing of large data sets using MapReduce programming models. Based on Hadoop and HBase, we developed SeqHBase, a big data-based toolset for analysing family based sequencing data to detect de novo, inherited homozygous, or compound heterozygous mutations that may contribute to disease manifestations. SeqHBase takes as input BAM files (for coverage at every site), variant call format (VCF) files (for variant calls) and functional annotations (for variant prioritisation). RESULTS: We applied SeqHBase to a 5-member nuclear family and a 10-member 3-generation family with WGS data, as well as a 4-member nuclear family with WES data. Analysis times were almost linearly scalable with number of data nodes. With 20 data nodes, SeqHBase took about 5 secs to analyse WES familial data and approximately 1 min to analyse WGS familial data. CONCLUSIONS: These results demonstrate SeqHBase's high efficiency and scalability, which is necessary as WGS and WES are rapidly becoming standard methods to study the genetics of familial disorders.

Use of an electronic medical record to create the marshfield clinic twin/multiple birth cohort.

Population-based genetic analyses, such as the Genome-Wide Association Study (GWAS), have proven powerful for describing the genetic complexities of common disease in epidemiologic research. However, the significant challenges faced by population-based study designs have resulted in revitalization of family-based approaches, including twin studies. Twin studies are unique in their ability to ascertain both heritable and environmental contributions to human disease. Several regional and national twin registries have been constructed using a variety of methods to identify potential twins. A significant challenge in constructing these large twin registries includes the substantial resources required to recruit participants, collect phenotypic data, and update the registries as time progresses. Here we describe the use of the Marshfield Clinic electronic medical record (EMR) to identify a cohort of 19,226 patients enriched for twins or multiples. This cohort defines the Marshfield Clinic Twin/Multiple Birth Cohort (MCTC). An EMR system provides both a mechanism to identify potential twins and a source of detailed phenotypic data in near real time without the need for patient contact outside standard medical care. To demonstrate that the MCTC can be used for genetic-based epidemiologic research, concordance rates for muscular dystrophy (MD) and fragile-X syndrome-two highly heritable diseases-were assessed. Observations indicate that both MD and fragile-X syndrome are highly correlated among affected twins in the MCTC (P ≅ 3.7 × 10(-6) and 1.1 × 10(-4) , respectively). These findings suggest that EMR systems may not only be an effective resource for predicting families of twins, but can also be rapidly applied to epidemiologic research.

The challenges, advantages and future of phenome-wide association studies.

Over the last decade, significant technological breakthroughs have revolutionized human genomic research in the form of genome-wide association studies (GWASs). GWASs have identified thousands of statistically significant genetic variants associated with hundreds of human conditions including many with immunological aetiologies (e.g. multiple sclerosis, ankylosing spondylitis and rheumatoid arthritis). Unfortunately, most GWASs fail to identify clinically significant associations. Identifying biologically significant variants by GWAS also presents a challenge. The GWAS is a phenotype-to-genotype approach. As a complementary/alternative approach to the GWAS, investigators have begun to exploit extensive electronic medical record systems to conduct a genotype-to-phenotype approach when studying human disease - specifically, the phenome-wide association study (PheWAS). Although the PheWAS approach is in its infancy, this method has already demonstrated its capacity to rediscover important genetic associations related to immunological diseases/conditions. Furthermore, PheWAS has the advantage of identifying genetic variants with pleiotropic properties. This is particularly relevant for HLA variants. For example, PheWAS results have demonstrated that the HLA-DRB1 variant associated with multiple sclerosis may also be associated with erythematous conditions including rosacea. Likewise, PheWAS has demonstrated that the HLA-B genotype is not only associated with spondylopathies, uveitis, and variability in platelet count, but may also play an important role in other conditions, such as mastoiditis. This review will discuss and compare general PheWAS methodologies, describe both the challenges and advantages of the PheWAS, and provide insight into the potential directions in which PheWAS may lead.

Assessment of the current status of real-world pharmacogenomic testing: informed consent, patient education, and related practices.

Introduction: The practice of informed consent (IC) for pharmacogenomic testing in clinical settings varies, and there is currently no consensus on which elements of IC to provide to patients. This study aims to assess current IC practices for pharmacogenomic testing. Methods: An online survey was developed and sent to health providers at institutions that offer clinical germline pharmacogenomic testing to assess current IC practices. Results: Forty-six completed surveys representing 43 clinical institutions offering pharmacogenomic testing were received. Thirty-two (74%) respondents obtain IC from patients with variability in elements incorporated. Results revealed that twenty-nine (67%) institutions discuss the benefits, description, and purpose of pharmacogenomic testing with patients. Less commonly discussed elements included methodology and accuracy of testing, and laboratory storage of samples. Discussion: IC practices varied widely among survey respondents. Most respondents desire the establishment of consensus IC recommendations from a trusted pharmacogenomics organization to help address these disparities.

The impact of glucocorticoid polymorphisms on markers of neonatal respiratory disease after antenatal betamethasone administration.

OBJECTIVE: We previously demonstrated that maternal and fetal genotypes are associated independently with neonatal respiratory distress syndrome. The objective of the current study was to determine the impact of maternal and fetal single-nucleotide polymorphisms (SNPs) in key betamethasone pathways on respiratory outcomes that serve as markers for severity of disease. STUDY DESIGN: DNA was obtained from women who were given betamethasone and from their infants. Samples were genotyped for 73 exploratory drug metabolism and glucocorticoid pathway SNPs. Clinical variables and neonatal outcomes were obtained. Logistic regression analysis that controlled for relevant clinical variables to determine SNP impact on bronchopulmonary dysplasia (BPD), the need for respiratory support, and surfactant therapy use was performed. RESULTS: Data from 109 women who delivered 117 infants were analyzed: 14.5% of the infants experienced BPD; 70.8% of the infants needed some respiratory support after birth, and 27.5% of the infants needed surfactant therapy. In a multivariable regression analysis, gestational age at delivery was associated with most neonatal respiratory outcomes (P ≤ .01), and chorioamnionitis was associated with BPD (P < .03). The following genotypes were associated with respiratory severity outcomes: BPD-fetal Importin 13 gene (IPO13; rs4448553; odds ratio [OR], 0.01; 95% confidence interval [CI], 0.00-0.92); surfactant use-maternal IPO13 (rs2428953 and 2486014; OR, 13.8; 95% CI, 1.80-105.5; and OR, 35.5; 95% CI, 1.71-736.6, respectively). CONCLUSION: Several discrete maternal and fetal SNPs in the IPO13 family may be associated with neonatal respiratory outcomes after maternal antenatal corticosteroid treatment for anticipated preterm birth.

Estimating the efficacy of pharmacogenomics over a lifetime.

It is well known that common variants in specific genes influence drug metabolism and response, but it is currently unknown what fraction of patients are given prescriptions over a lifetime that could be contraindicated by their pharmacogenomic profiles. To determine the clinical utility of pharmacogenomics over a lifetime in a general patient population, we sequenced the genomes of 300 deceased Marshfield Clinic patients linked to lifelong medical records. Genetic variants in 33 pharmacogenes were evaluated for their lifetime impact on drug prescribing using extensive electronic health records. Results show that 93% of the 300 deceased patients carried clinically relevant variants. Nearly 80% were prescribed approximately three medications on average that may have been impacted by these variants. Longitudinal data suggested that the optimal age for pharmacogenomic testing was prior to age 50, but the optimal age is greatly influenced by the stability of the population in the healthcare system. This study emphasizes the broad clinical impact of pharmacogenomic testing over a lifetime and demonstrates the potential application of genomic medicine in a general patient population for the advancement of precision medicine.

Familial Hypercholesterolemia in the Electronic Medical Records and Genomics Network: Prevalence, Penetrance, Cardiovascular Risk, and Outcomes After Return of Results.

BACKGROUND: The implications of secondary findings detected in large-scale sequencing projects remain uncertain. We assessed prevalence and penetrance of pathogenic familial hypercholesterolemia (FH) variants, their association with coronary heart disease (CHD), and 1-year outcomes following return of results in phase III of the electronic medical records and genomics network. METHODS: Adult participants (n=18 544) at 7 sites were enrolled in a prospective cohort study to assess the clinical impact of returning results from targeted sequencing of 68 actionable genes, including LDLR, APOB, and PCSK9. FH variant prevalence and penetrance (defined as low-density lipoprotein cholesterol >155 mg/dL) were estimated after excluding participants enrolled on the basis of hypercholesterolemia. Multivariable logistic regression was used to estimate the odds of CHD compared to age- and sex-matched controls without FH-associated variants. Process (eg, referral to a specialist or ordering new tests), intermediate (eg, new diagnosis of FH), and clinical (eg, treatment modification) outcomes within 1 year after return of results were ascertained by electronic health record review. RESULTS: The prevalence of FH-associated pathogenic variants was 1 in 188 (69 of 13,019 unselected participants). Penetrance was 87.5%. The presence of an FH variant was associated with CHD (odds ratio, 3.02 [2.00-4.53]) and premature CHD (odds ratio, 3.68 [2.34-5.78]). At least 1 outcome occurred in 92% of participants; 44% received a new diagnosis of FH and 26% had treatment modified following return of results. CONCLUSIONS: In a multisite cohort of electronic health record-linked biobanks, monogenic FH was prevalent, penetrant, and associated with presence of CHD. Nearly half of participants with an FH-associated variant received a new diagnosis of FH and a quarter had treatment modified after return of results. These results highlight the potential utility of sequencing electronic health record-linked biobanks to detect FH.

Serine hydroxymethyltransferase 1 and 2: gene sequence variation and functional genomic characterization.

Serine hydroxymethyltransferase (SHMT) catalyzes the transfer of a ß-carbon from serine to tetrahydrofolate to form glycine and 5,10-methylene-tetrahydrofolate. This reaction plays an important role in neurotransmitter synthesis and metabolism. We set out to resequence SHMT1 and SHMT2, followed by functional genomic studies. We identified 87 and 60 polymorphisms in SHMT1 and SHMT2, respectively. We observed no significant functional effect of the 13 non-synonymous single-nucleotide polymorphism (SNPs) in these genes, either on catalytic activity or protein quantity. We imputed additional variants across the two genes using '1000 Genomes' data, and identified 14 variants that were significantly associated (p<1.0E-10) with SHMT1 messenger RNA expression in lymphoblastoid cell lines. Many of these SNPs were also significantly correlated with basal SHMT1 protein expression in 268 human liver biopsy samples. Reporter gene assays suggested that the SHMT1 promoter SNP, rs669340, contributed to this variation. Finally, SHMT1 and SHMT2 expression were significantly correlated with those of other Folate and Methionine Cycle genes at both the messenger RNA and protein levels. These experiments represent a comprehensive study of SHMT1 and SHMT2 gene sequence variation and its functional implications. In addition, we obtained preliminary indications that these genes may be co-regulated with other Folate and Methionine Cycle genes.

Human liver methionine cycle: MAT1A and GNMT gene resequencing, functional genomics, and hepatic genotype-phenotype correlation.

The "methionine cycle" plays a critical role in the regulation of concentrations of (S)-adenosylmethionine (AdoMet), the major biological methyl donor. We set out to study sequence variation in genes encoding the enzyme that synthesizes AdoMet in liver, methionine adenosyltransferase 1A (MAT1A) and the major hepatic AdoMet using enzyme, glycine N-methyltransferase (GNMT), as well as functional implications of that variation. We resequenced MAT1A and GNMT using DNA from 288 subjects of three ethnicities, followed by functional genomic and genotype-phenotype correlation studies performed with 268 hepatic biopsy samples. We identified 44 and 42 polymorphisms in MAT1A and GNMT, respectively. Quantitative Western blot analyses for the human liver samples showed large individual variation in MAT1A and GNMT protein expression. Genotype-phenotype correlation identified two genotyped single-nucleotide polymorphisms (SNPs), reference SNP (rs) 9471976 (corrected p = 3.9 × 10(-10)) and rs11752813 (corrected p = 1.8 × 10(-5)), and 42 imputed SNPs surrounding GNMT that were significantly associated with hepatic GNMT protein levels (corrected p values < 0.01). Reporter gene studies showed that variant alleles for both genotyped SNPs resulted in decreased transcriptional activity. Correlation analyses among hepatic protein levels for methionine cycle enzymes showed significant correlations between GNMT and MAT1A (p = 1.5 × 10(-3)) and between GNMT and betaine homocysteine methyltransferase (p = 1.6 × 10(-7)). Our discovery of SNPs that are highly associated with hepatic GNMT protein expression as well as the "coordinate regulation" of methionine cycle enzyme protein levels provide novel insight into the regulation of this important human liver biochemical pathway.