In Vitro Functional Analysis Can Aid Precision Diagnostics of HNF1B-MODY.

Precision medicine relies on accurate and consistent classification of sequence variants. A correct diagnosis of hepatocyte nuclear factor (HNF) 1B maturity-onset diabetes of the young, caused by pathogenic variants in the HNF1B gene, is important for optimal disease management and prognosis, and it has implications for genetic counseling and follow-up of at-risk family members. We hypothesized that the functional characterization could provide valuable information to assist the interpretation of pathogenicity of HNF1B variants. Using different in vitro functional assays, variants identified among 313 individuals, suspected to have monogenic diabetes with or without kidney disease, were characterized. The data from the functional assays were subsequently conjugated with obtained clinical, biochemical, and in silico data. Two variants (p.A167P, p.H336Pfs∗22) showed severe loss of function due to impaired transactivation, reduced DNA binding (p.A167P), and mRNA instability (p.A167P). Although both these variant carriers were diagnosed with diabetes, the p.H336Pfs∗22 carrier also had congenital absence of a kidney, which is a characteristic trait for HNF1B maturity-onset diabetes of the young. Functional analysis of the p.A167P variant revealed damaging effects on HNF-1B protein function, which may warrant imaging of the kidneys and/or pancreas. In addition, the current study has generated important data, including evidence supporting the benign functional impact of five variants (p.D82N, p.T88A, p.N394D, p.V458G, and p.T544A), and piloting new approaches that will prove critical for the growth of HNF1B-diabetes diagnosis.

Genetic architecture and biology of youth-onset type 2 diabetes.

The prevalence of youth-onset type 2 diabetes (T2D) and childhood obesity has been rising steadily1, producing a growing public health concern1 that disproportionately affects minority groups2. The genetic basis of youth-onset T2D and its relationship to other forms of diabetes are unclear3. Here we report a detailed genetic characterization of youth-onset T2D by analysing exome sequences and common variant associations for 3,005 individuals with youth-onset T2D and 9,777 adult control participants matched for ancestry, including both males and females. We identify monogenic diabetes variants in 2.4% of individuals and three exome-wide significant (P < 2.6 × 10-6) gene-level associations (HNF1A, MC4R, ATXN2L). Furthermore, we report rare variant association enrichments within 25 gene sets related to obesity, monogenic diabetes and ß-cell function. Many youth-onset T2D associations are shared with adult-onset T2D, but genetic risk factors of all frequencies-and rare variants in particular-are enriched within youth-onset T2D cases (5.0-fold increase in the rare variant and 3.4-fold increase in common variant genetic liability relative to adult-onset cases). The clinical presentation of participants with youth-onset T2D is influenced in part by the frequency of genetic risk factors within each individual. These findings portray youth-onset T2D as a heterogeneous disease situated on a spectrum between monogenic diabetes and adult-onset T2D.

Recommendations for risk allele evidence curation, classification, and reporting from the ClinGen Low Penetrance/Risk Allele Working Group.

PURPOSE: Genetic variants at the low end of the penetrance spectrum have historically been challenging to interpret because their high population frequencies exceed the disease prevalence of the associated condition, leading to a lack of clear segregation between the variant and disease. There is currently substantial variation in the classification of these variants, and no formal classification framework has been widely adopted. The Clinical Genome Resource Low Penetrance/Risk Allele Working Group was formed to address these challenges and promote harmonization within the clinical community. METHODS: The work presented here is the product of internal and community Likert-scaled surveys in combination with expert consensus within the Working Group. RESULTS: We formally recognize risk alleles and low-penetrance variants as distinct variant classes from those causing highly penetrant disease that require special considerations regarding their clinical classification and reporting. First, we provide a preferred terminology for these variants. Second, we focus on risk alleles and detail considerations for reviewing relevant studies and present a framework for the classification these variants. Finally, we discuss considerations for clinical reporting of risk alleles. CONCLUSION: These recommendations support harmonized interpretation, classification, and reporting of variants at the low end of the penetrance spectrum.

Rare variant association analysis in 51,256 type 2 diabetes cases and 370,487 controls informs the spectrum of pathogenicity of monogenic diabetes genes.

We meta-analyzed array data imputed with the TOPMed reference panel and whole-genome sequence (WGS) datasets and performed the largest, rare variant (minor allele frequency as low as 5×10-5) GWAS meta-analysis of type 2 diabetes (T2D) comprising 51,256 cases and 370,487 controls. We identified 52 novel variants at genome-wide significance (p<5 × 10-8), including 8 novel variants that were either rare or ancestry-specific. Among them, we identified a rare missense variant in HNF4A p.Arg114Trp (OR=8.2, 95% confidence interval [CI]=4.6-14.0, p = 1.08×10-13), previously reported as a variant implicated in Maturity Onset Diabetes of the Young (MODY) with incomplete penetrance. We demonstrated that the diabetes risk in carriers of this variant was modulated by a T2D common variant polygenic risk score (cvPRS) (carriers in the top PRS tertile [OR=18.3, 95%CI=7.2-46.9, p=1.2×10-9] vs carriers in the bottom PRS tertile [OR=2.6, 95% CI=0.97-7.09, p = 0.06]. Association results identified eight variants of intermediate penetrance (OR>5) in monogenic diabetes (MD), which in aggregate as a rare variant PRS were associated with T2D in an independent WGS dataset (OR=4.7, 95% CI=1.86-11.77], p = 0.001). Our data also provided support evidence for 21% of the variants reported in ClinVar in these MD genes as benign based on lack of association with T2D. Our work provides a framework for using rare variant imputation and WGS analyses in large-scale population-based association studies to identify large-effect rare variants and provide evidence for informing variant pathogenicity.

The use of precision diagnostics for monogenic diabetes: a systematic review and expert opinion.

BACKGROUND: Monogenic diabetes presents opportunities for precision medicine but is underdiagnosed. This review systematically assessed the evidence for (1) clinical criteria and (2) methods for genetic testing for monogenic diabetes, summarized resources for (3) considering a gene or (4) variant as causal for monogenic diabetes, provided expert recommendations for (5) reporting of results; and reviewed (6) next steps after monogenic diabetes diagnosis and (7) challenges in precision medicine field. METHODS: Pubmed and Embase databases were searched (1990-2022) using inclusion/exclusion criteria for studies that sequenced one or more monogenic diabetes genes in at least 100 probands (Question 1), evaluated a non-obsolete genetic testing method to diagnose monogenic diabetes (Question 2). The risk of bias was assessed using the revised QUADAS-2 tool. Existing guidelines were summarized for questions 3-5, and review of studies for questions 6-7, supplemented by expert recommendations. Results were summarized in tables and informed recommendations for clinical practice. RESULTS: There are 100, 32, 36, and 14 studies included for questions 1, 2, 6, and 7 respectively. On this basis, four recommendations for who to test and five on how to test for monogenic diabetes are provided. Existing guidelines for variant curation and gene-disease validity curation are summarized. Reporting by gene names is recommended as an alternative to the term MODY. Key steps after making a genetic diagnosis and major gaps in our current knowledge are highlighted. CONCLUSIONS: We provide a synthesis of current evidence and expert opinion on how to use precision diagnostics to identify individuals with monogenic diabetes.

Some diabetes types, called monogenic diabetes, are caused by changes in a single gene. It is important to know who has this kind of diabetes because treatment can differ from that of other types of diabetes. Some treatments also work better than others for specific types, and some people can for example change from insulin injections to tablets. In addition, relatives can be offered a test to see if they are at risk. Genetic testing is needed to diagnose monogenic diabetes but is expensive, so it's not possible to test every person with diabetes for it. We evaluated published research on who should be tested and what test to use. Based on this, we provide recommendations for doctors and health care providers on how to implement genetic testing for monogenic diabetes.

Genetic counseling in diabetes mellitus: A practice resource of the National Society of Genetic Counselors.

Diabetes mellitus is a group of diseases characterized by hyperglycemia and its consequences, affecting over 34 million individuals in the United States and 422 million worldwide. While most diabetes is polygenic and is classified as type 1 (T1D), type 2 (T2D), or gestational diabetes (GDM), at least 0.4% of all diabetes is monogenic in nature. Correct diagnosis of monogenic diabetes has important implications for glycemic management and genetic counseling. We provide this Practice Resource to familiarize the genetic counseling community with (1) the existence of monogenic diabetes, (2) how it differs from more common polygenic/complex diabetes types, (3) the advantage of a correct diagnosis, and (4) guidance for identifying, counseling, and testing patients and families with suspected monogenic diabetes. This document is intended for genetic counselors and other healthcare professionals providing clinical services in any setting, with the goal of maximizing the likelihood of a correct diagnosis of monogenic diabetes and access to related care.

Association of parity with body mass index and cardiometabolic risk in high-parous women.

OBJECTIVE: Postpregnancy weight retention contributes to obesity, but the long-term effect of parity on body mass index (BMI) and other cardiometabolic risk factors is unclear. We aimed to evaluate the relationship between parity and BMI among highly parous Amish women, both before and after menopause, and to evaluate the associations of parity with glucose, blood pressure, and lipids. METHODS: We conducted a cross-sectional study among 3,141 Amish women 18 years or older from Lancaster County, PA, who participated in our community-based Amish Research Program between 2003 and 2020. We evaluated the association between parity and BMI across different age groups, both before and after the menopausal transition. We further assessed associations between parity and cardiometabolic risk factors among the 1,128 postmenopausal women. Finally, we evaluated the association of change in parity with change in BMI in 561 women followed longitudinally. RESULTS: Approximately 62% of women in this sample (mean age, 45.2 y) reported having four or more children, and 36% reported having seven or more. A one-child increase in parity was associated with increased BMI in both premenopausal women (estimate [95% confidence interval], 0.4 kg/m 2 [0.2-0.5]) and to a lesser degree in postmenopausal women (0.2 kg/m 2 [0.02-0.3], Pint = 0.02), suggesting that the impact of parity on BMI decreases over time. Parity was not associated with glucose, blood pressure, total cholesterol, low-density lipoprotein, or triglycerides ( Padj > 0.05). CONCLUSIONS: Higher parity was associated with increased BMI in both premenopausal and postmenopausal women, but more so in younger/premenopausal women. Parity was not associated with other indices of cardiometabolic risk.

A Systematic Review of the use of Precision Diagnostics in Monogenic Diabetes.

Monogenic forms of diabetes present opportunities for precision medicine as identification of the underlying genetic cause has implications for treatment and prognosis. However, genetic testing remains inconsistent across countries and health providers, often resulting in both missed diagnosis and misclassification of diabetes type. One of the barriers to deploying genetic testing is uncertainty over whom to test as the clinical features for monogenic diabetes overlap with those for both type 1 and type 2 diabetes. In this review, we perform a systematic evaluation of the evidence for the clinical and biochemical criteria used to guide selection of individuals with diabetes for genetic testing and review the evidence for the optimal methods for variant detection in genes involved in monogenic diabetes. In parallel we revisit the current clinical guidelines for genetic testing for monogenic diabetes and provide expert opinion on the interpretation and reporting of genetic tests. We provide a series of recommendations for the field informed by our systematic review, synthesizing evidence, and expert opinion. Finally, we identify major challenges for the field and highlight areas for future research and investment to support wider implementation of precision diagnostics for monogenic diabetes.

Genome-wide significant risk loci for mood disorders in the Old Order Amish founder population.

Genome-wide association studies (GWAS) of mood disorders in large case-control cohorts have identified numerous risk loci, yet pathophysiological mechanisms remain elusive, primarily due to the very small effects of common variants. We sought to discover risk variants with larger effects by conducting a genome-wide association study of mood disorders in a founder population, the Old Order Amish (OOA, n = 1,672). Our analysis revealed four genome-wide significant risk loci, all of which were associated with >2-fold relative risk. Quantitative behavioral and neurocognitive assessments (n = 314) revealed effects of risk variants on sub-clinical depressive symptoms and information processing speed. Network analysis suggested that OOA-specific risk loci harbor novel risk-associated genes that interact with known neuropsychiatry-associated genes via gene interaction networks. Annotation of the variants at these risk loci revealed population-enriched, non-synonymous variants in two genes encoding neurodevelopmental transcription factors, CUX1 and CNOT1. Our findings provide insight into the genetic architecture of mood disorders and a substrate for mechanistic and clinical studies.

Enrollment of underrepresented racial and ethnic groups in the Rare and Atypical Diabetes Network (RADIANT).

Introduction: Diabetes mellitus in underrepresented racial and ethnic groups (URG) is rapidly increasing in incidence and has worse outcomes than diabetes in non-Hispanic White individuals. Rare and Atypical Diabetes Network (RADIANT) established recruitment targets based on the racial and ethnic distribution of the USA to enroll a diverse study population. We examined participation of URG across RADIANT study stages and described strategies to enhance recruitment and retention of URG. Materials and Methods: RADIANT is a multicenter NIH-funded study of people with uncharacterized forms of atypical diabetes. RADIANT participants consent online and progress through three sequential study stages, as eligible. Results: We enrolled 601 participants with mean age 44 ± 16.8 years, 64.4% female. At Stage 1, 80.6% were White, 7.2% African American (AA), 12.2% other/more than one race, and 8.4% Hispanic. Enrollment of URG was significantly below preset targets across most stages. Referral sources differed by race (p < 0.001) but not ethnicity (p = 0.15). Most AA participants were referred by RADIANT investigators (58.5% vs. 24.5% in Whites), whereas flyers, news, social media, and family or friends were more frequent referral sources for White individuals (26.4% vs. 12.2% in AA). Ongoing initiatives to increase enrollment of URG in RADIANT include engaging with clinics/hospitals serving URG, screening electronic medical records, and providing culturally competent study coordination and targeted advertisement. Conclusions: There is low participation of URG in RADIANT, potentially limiting the generalizability of its discoveries. Investigations into barriers and facilitators for recruitment and retention of URG in RADIANT, with implications for other studies, are ongoing.

Identification of Genetic Variation Influencing Metformin Response in a Multiancestry Genome-Wide Association Study in the Diabetes Prevention Program (DPP).

Genome-wide significant loci for metformin response in type 2 diabetes reported elsewhere have not been replicated in the Diabetes Prevention Program (DPP). To assess pharmacogenetic interactions in prediabetes, we conducted a genome-wide association study (GWAS) in the DPP. Cox proportional hazards models tested associations with diabetes incidence in the metformin (MET; n = 876) and placebo (PBO; n = 887) arms. Multiple linear regression assessed association with 1-year change in metformin-related quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-treatment interaction. No significant associations emerged for diabetes incidence. We identified four genome-wide significant variants after correcting for correlated traits (P < 9 × 10-9). In the MET arm, rs144322333 near ENOSF1 (minor allele frequency [MAF]AFR = 0.07; MAFEUR = 0.002) was associated with an increase in percentage of glycated hemoglobin (per minor allele, ß = 0.39 [95% CI 0.28, 0.50]; P = 2.8 × 10-12). rs145591055 near OMSR (MAF = 0.10 in American Indians) was associated with weight loss (kilograms) (per G allele, ß = -7.55 [95% CI -9.88, -5.22]; P = 3.2 × 10-10) in the MET arm. Neither variant was significant in PBO; gene-by-treatment interaction was significant for both variants [P(G×T) < 1.0 × 10-4]. Replication in individuals with diabetes did not yield significant findings. A GWAS for metformin response in prediabetes revealed novel ethnic-specific associations that require further investigation but may have implications for tailored therapy.

Statistical evidence for high-penetrance MODY-causing genes in a large population-based cohort.

AIMS: Numerous genes have been proposed as causal for maturity-onset diabetes of the young (MODY). Scoring systems to annotate mutation pathogenicity have been widely used; however, statistical evidence for being a highly penetrant MODY gene has not been well-established. METHODS: Participants were from the UK Biobank with whole-exome sequencing data, including 14,622 with and 185,509 without diagnosis of diabetes. Pathogenic/likely pathogenic (P/LP) mutations in 14 reported and 3 possible MODY genes were annotated using American College of Medical Genetics criteria. Evidence for being a high-penetrant MODY gene used two statistical criteria: frequency of aggregate P/LP mutations in each gene are (1) significantly more common in participants with a diagnosis of diabetes than without using the SKAT-O (p < .05) and (2) lower than the maximum credible frequency in the general population. RESULTS: Among the 17 genes, 6 (GCK, HNF1A, HNF4A, NEUROD1, KCNJ11 and HNF1B) met both criteria, 7 (ABCC8, KLF11, RFX6, PCBD1, WFS1, INS and PDX1) met only one criterion, and the remaining 4 (CEL, BLK, APPL1 and PAX4) failed both criteria, and were classified as 'consistent', 'inconclusive' and 'inconsistent' for being highly penetrant diabetes genes, respectively. Diabetes participants with mutations in the 'consistent' genes had clinical presentations that were most consistent with MODY. In contrast, the 'inconclusive' and 'inconsistent' genes did not differ clinically from non-carriers in diabetes-related characteristics. CONCLUSIONS: Data from a large population-based study provided novel statistical evidence to identify 6 MODY genes as consistent with being highly penetrant. These results have potential implications for interpreting genetic testing results and clinical diagnosis of MODY.

Model for Integration of Monogenic Diabetes Diagnosis Into Routine Care: The Personalized Diabetes Medicine Program.

OBJECTIVE: To implement, disseminate, and evaluate a sustainable method for identifying, diagnosing, and promoting individualized therapy for monogenic diabetes. RESEARCH DESIGN AND METHODS: Patients were recruited into the implementation study through a screening questionnaire completed in the waiting room or through the patient portal, physician recognition, or self-referral. Patients suspected of having monogenic diabetes based on the processing of their questionnaire and other data through an algorithm underwent next-generation sequencing for 40 genes implicated in monogenic diabetes and related conditions. RESULTS: Three hundred thirteen probands with suspected monogenic diabetes (but most diagnosed with type 2 diabetes) were enrolled from October 2014 to January 2019. Sequencing identified 38 individuals with monogenic diabetes, with most variants found in GCK or HNF1A. Positivity rates for ascertainment methods were 3.1% for clinic screening, 5.3% for electronic health record portal screening, 16.5% for physician recognition, and 32.4% for self-referral. The algorithmic criterion of non-type 1 diabetes before age 30 years had an overall positivity rate of 15.0%. CONCLUSIONS: We successfully modeled the efficient incorporation of monogenic diabetes diagnosis into the diabetes care setting, using multiple strategies to screen and identify a subpopulation with a 12.1% prevalence of monogenic diabetes by molecular testing. Self-referral was particularly efficient (32% prevalence), suggesting that educating the lay public in addition to clinicians may be the most effective way to increase the diagnosis rate in monogenic diabetes. Scaling up this model will assure access to diagnosis and customized treatment among those with monogenic diabetes and, more broadly, access to personalized medicine across disease areas.

Impact of parental relatedness on reproductive outcomes among the Old Order Amish of Lancaster County.

Genetically isolated populations that arise due to recent bottleneck events have reduced genetic variation reflecting the common set of founders. Increased genetic relatedness among members of isolated populations puts them at increased risk for some recessive disorders that are rare in outbred populations. To assess the burden on reproductive health, we compared frequencies of adverse reproductive outcomes between Amish couples who were both heterozygous carriers of a highly penetrant pathogenic or likely pathogenic variant and noncarrier couples from the same Amish community. In addition, we evaluated whether overall genetic relatedness of parents was associated with reproductive outcomes. Of the 1824 couples included in our study, 11.1% were at risk of producing a child with an autosomal recessive disorder. Carrier couples reported a lower number of miscarriages compared to noncarrier couples (p = 0.02), although the number of stillbirths (p = 0.3), live births (p = 0.9), and number of pregnancies (p = 0.9) did not differ significantly between groups. In contrast, higher overall relatedness between spouses was positively correlated with number of live births (p < 0.0001), pregnancies (p < 0.0001), and stillbirths (p = 0.03), although not with the number of miscarriages (p = 0.4). These results highlight a complex association between relatedness of parents and reproductive health outcomes in this community.

Biallelic truncating variants in the muscular A-type lamin-interacting protein (MLIP) gene cause myopathy with hyperCKemia.

BACKGROUND AND PURPOSE: Muscular A-type lamin-interacting protein (MLIP) is most abundantly expressed in cardiac and skeletal muscle. In vitro and animal studies have shown its regulatory role in myoblast differentiation and in organization of myonuclear positioning in skeletal muscle, as well as in cardiomyocyte adaptation and cardiomyopathy. We report the association of biallelic truncating variation in the MLIP gene with human disease in five individuals from two unrelated pedigrees. METHODS: Clinical evaluation and exome sequencing were performed in two unrelated families with elevated creatine kinase level. RESULTS: Family 1. A 6-year-old girl born to consanguineous parents of Arab-Muslim origin presented with myalgia, early fatigue after mild-to-moderate physical exertion, and elevated creatine kinase levels up to 16,000 U/L. Exome sequencing revealed a novel homozygous nonsense variant, c.2530C>T; p.Arg844Ter, in the MLIP gene. Family 2. Three individuals from two distantly related families of Old Order Amish ancestry presented with elevated creatine kinase levels, one of whom also presented with abnormal electrocardiography results. On exome sequencing, all showed homozygosity for a novel nonsense MLIP variant c.1825A>T; p.Lys609Ter. Another individual from this pedigree, who had sinus arrhythmia and for whom creatine kinase level was not available, was also homozygous for this variant. CONCLUSIONS: Our findings suggest that biallelic truncating variants in MLIP result in myopathy characterized by hyperCKemia. Moreover, these cases of MLIP-related disease may indicate that at least in some instances this condition is associated with muscle decompensation and fatigability during low-to-moderate intensity muscle exertion as well as possible cardiac involvement.

The burden of pathogenic variants in clinically actionable genes in a founder population.

Founder populations may be enriched with certain genetic variants of high clinical impact compared to nonfounder populations due to bottleneck events and genetic drift. Using exome sequencing (ES), we quantified the load of pathogenic variants that may be clinically actionable in 6136 apparently healthy adults living in the Lancaster, PA Old Order Amish settlement. We focused on variants in 78 genes deemed clinically actionable by the American College of Medical Genetics and Genomics (ACMG) or Geisinger's MyCode Health Initiative. ES revealed 3191 total variants among these genes including 480 nonsynonymous variants. After quality control and filtering, we applied the ACMG/AMP guidelines for variant interpretation and classified seven variants, across seven genes, as either pathogenic or likely pathogenic. Through genetic drift, all seven variants, are highly enriched in the Amish compared to nonfounder populations. In total, 14.7% of Lancaster Amish individuals carry at least one of these variants, largely explained by the 13% who harbor a copy of a single variant in APOB. Other studies report combined frequencies of pathogenic/likely pathogenic (P/LP) variants in actionable genes between 2.0% and 6.2% in outbred populations. The Amish population harbors fewer actionable variants compared to similarly characterized nonfounder populations but have a higher frequency of each variant identified, offering opportunities for efficient and cost-effective targeted precision medicine.

Monogenic Diabetes in Youth With Presumed Type 2 Diabetes: Results From the Progress in Diabetes Genetics in Youth (ProDiGY) Collaboration.

OBJECTIVE: Maturity-onset diabetes of the young (MODY) is frequently misdiagnosed as type 1 or type 2 diabetes. Correct diagnosis may result in a change in clinical treatment and impacts prediction of complications and familial risk. In this study, we aimed to assess the prevalence of MODY in multiethnic youth under age 20 years with a clinical diagnosis of type 2 diabetes. RESEARCH DESIGN AND METHODS: We evaluated whole-exome sequence data of youth with a clinical diagnosis of type 2 diabetes. We considered participants to have MODY if they carried a MODY gene variant classified as likely pathogenic (LP) or pathogenic (P) according to current guidelines. RESULTS: Of 3,333 participants, 93 (2.8%) carried an LP/P variant in HNF4A (16 participants), GCK (23), HNF1A (44), PDX1 (5), INS (4), and CEL (1). Compared with those with no LP/P variants, youth with MODY had a younger age at diagnosis (12.9 ± 2.5 vs. 13.6 ± 2.3 years, P = 0.002) and lower fasting C-peptide levels (3.0 ± 1.7 vs. 4.7 ± 3.5 ng/mL, P < 0.0001). Youth with MODY were less likely to have hypertension (6.9% vs. 19.5%, P = 0.007) and had higher HDL cholesterol (43.8 vs. 39.7 mg/dL, P = 0.006). CONCLUSIONS: By comprehensively sequencing the coding regions of all MODY genes, we identified MODY in 2.8% of youth with clinically diagnosed type 2 diabetes; importantly, in 89% (n = 83) the specific diagnosis would have changed clinical management. No clinical criterion reliably separated the two groups. New tools are needed to find ideal criteria for selection of individuals for genetic testing.

Strategies to Integrate Genomic Medicine into Clinical Care: Evidence from the IGNITE Network.

The complexity of genomic medicine can be streamlined by implementing some form of clinical decision support (CDS) to guide clinicians in how to use and interpret personalized data; however, it is not yet clear which strategies are best suited for this purpose. In this study, we used implementation science to identify common strategies for applying provider-based CDS interventions across six genomic medicine clinical research projects funded by an NIH consortium. Each project's strategies were elicited via a structured survey derived from a typology of implementation strategies, the Expert Recommendations for Implementing Change (ERIC), and follow-up interviews guided by both implementation strategy reporting criteria and a planning framework, RE-AIM, to obtain more detail about implementation strategies and desired outcomes. We found that, on average, the three pharmacogenomics implementation projects used more strategies than the disease-focused projects. Overall, projects had four implementation strategies in common; however, operationalization of each differed in accordance with each study's implementation outcomes. These four common strategies may be important for precision medicine program implementation, and pharmacogenomics may require more integration into clinical care. Understanding how and why these strategies were successfully employed could be useful for others implementing genomic or precision medicine programs in different contexts.

Monogenic diabetes: a gateway to precision medicine in diabetes.

Monogenic diabetes refers to diabetes mellitus (DM) caused by a mutation in a single gene and accounts for approximately 1%-5% of diabetes. Correct diagnosis is clinically critical for certain types of monogenic diabetes, since the appropriate treatment is determined by the etiology of the disease (e.g., oral sulfonylurea treatment of HNF1A/HNF4A-diabetes vs. insulin injections in type 1 diabetes). However, achieving a correct diagnosis requires genetic testing, and the overlapping of the clinical features of monogenic diabetes with those of type 1 and type 2 diabetes has frequently led to misdiagnosis. Improvements in sequencing technology are increasing opportunities to diagnose monogenic diabetes, but challenges remain. In this Review, we describe the types of monogenic diabetes, including common and uncommon types of maturity-onset diabetes of the young, multiple causes of neonatal DM, and syndromic diabetes such as Wolfram syndrome and lipodystrophy. We also review methods of prioritizing patients undergoing genetic testing, and highlight existing challenges facing sequence data interpretation that can be addressed by forming collaborations of expertise and by pooling cases.