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Amish

The Amish are a Christian religious group known for their traditional, plain lifestyle and avoidance of modern technology.
Originating in Europe, the Amish communities in North America are characterized by their distinctive dress, rejection of most forms of public electricity and motorized transportation, and reliance on agriculture and handcrafts.
Amish culture places a strong emphasis on family, community, and faith, with a focus on preservation of their heritage and way of life.
Researchers studying Amish populations may utilize specialized protocols to ensure reproducibility and accuracy of their findings, leveraging the unique characteristics of these communities.
PubCompare.ai's AI-driven platform can help discover and optimize Amish research protocols, locating relevant literature, preprints, and patents while using cutting-edge comparisons to identify the most effective approaches.
This innovative solution can unlock research excellence and advance our understanding of the Amish lifestyle and culture.

Most cited protocols related to «Amish»

1
Vitamin D and Cardiovascular Disease
The collaboration investigating the association of vitamin D and the risk of cardiovascular disease and related traits (D-CarDia) consists of European ancestry cohorts from the United Kingdom (UK), United States (US), Canada, Finland, Germany, and Sweden. This study comprised a meta-analysis of directly genotyped and imputed SNPs from 21 cohorts totalling 42,024 individuals (Table 1). An expanded description of the participating studies is provided in the Text S2.
To replicate our findings on the association between the vitamin D-related SNPs and allele scores with BMI, we used the data from the genome-wide meta-analyses on BMI conducted as part of the Genetic Investigation of Anthropometric Traits (GIANT) consortium [23] (link). The GIANT meta-analyses consisted of 46 studies with up to 123,865 adults of European ancestry, including the 1958 British Birth Cohort, Framingham Heart study, Nurses' Health Study, Twins UK, UK Blood Services Common Control Collection, the Amish Family Osteoporosis Study, Health2000 GENMETS sub-sample, and Northern Finland Birth Cohort 1966, which were also part of the D-CarDia collaboration.
Vimaleswaran K.S., Berry D.J., Lu C., Tikkanen E., Pilz S., Hiraki L.T., Cooper J.D., Dastani Z., Li R., Houston D.K., Wood A.R., Michaëlsson K., Vandenput L., Zgaga L., Yerges-Armstrong L.M., McCarthy M.I., Dupuis J., Kaakinen M., Kleber M.E., Jameson K., Arden N., Raitakari O., Viikari J., Lohman K.K., Ferrucci L., Melhus H., Ingelsson E., Byberg L., Lind L., Lorentzon M., Salomaa V., Campbell H., Dunlop M., Mitchell B.D., Herzig K.H., Pouta A., Hartikainen A.L., Streeten E.A., Theodoratou E., Jula A., Wareham N.J., Ohlsson C., Frayling T.M., Kritchevsky S.B., Spector T.D., Richards J.B., Lehtimäki T., Ouwehand W.H., Kraft P., Cooper C., März W., Power C., Loos R.J., Wang T.J., Järvelin M.R., Whittaker J.C., Hingorani A.D, & Hyppönen E. (2013). Causal Relationship between Obesity and Vitamin D Status: Bi-Directional Mendelian Randomization Analysis of Multiple Cohorts. PLoS Medicine, 10(2), e1001383.
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Publication 2013
865-123 Adult Alleles Amish Birth Cohort BLOOD Cardia Cardiovascular Diseases Ergocalciferol Europeans Genome Nurses Osteoporosis Single Nucleotide Polymorphism Twins
2
Rare Variant Analysis of Hemoglobin
We used data from TOPMed Freeze 4, which included 7,486 individuals from the Old Order Amish Study (N=1,102), Jackson Heart Study (JHS, N=3,251 African Americans), and Framingham Heart Study (FHS, N=3,133 European Americans from the offspring and generation 3 studies). All participants provided informed consent and the study was approved by IRBs in each of the participating institutions. Additional information about these studies is provided in the Supplementary Material. For TOPMed WGS data acquisition and QC report see ncbi.nlm.nih.gov/projects/gap/cgi-bin/GetPdf.cgi?id=phd006969.1.
We compared the performance of the SKAT Score statistic for testing the association of hemoglobin (HGB) values with sets of rare variants, under different approaches for rank-normalization and use of residuals, as described below. All analyses used linear mixed models, accounting for genetic relatedness using a genetic relationship matrix (GRM) computed using the GCTA method, (Yang, Lee, Goddard, & Visscher, 2013 (link)) where GRM was computed using all variants with MAF≥0.001. Covariates were age, sex, and study. Additionally, to account for heteroscedasticity, we used a study-specific variance model, where we estimated a separate residual variance for each study (Amish, JHS, FHS Offspring, and FHS generation 3). The SKAT test was applied on sets of genotypes formed by taking all genetic variants with alternate allele frequency in the range (0,0.01), and dividing them into non-overlapping sets, defined by running windows across the genome, of length 5, 10, and 50 kilo bases (kb). For comparison, we also report results from analysis of a single permutation phenotype. Specifically, we randomly permuted HGB across participants once, and performed the same association testing as for the unpermuted trait.
Sofer T., Zheng X., Gogarten S.M., Laurie C.A., Grinde K., Shaffer J.R., Shungin D., O’Connell J.R., Durazo-Arvizo R.A., Raffield L., Lange L., Musani S., Vasan R.S., Cupples L.A., Reiner A.P., Laurie C.C, & Rice K.M. (2019). A Fully-Adjusted Two-Stage Procedure for Rank Normalization in Genetic Association Studies. Genetic epidemiology, 43(3), 263-275.
Publication 2018
African American Amish Ethics Committees, Research Europeans Freezing Genetic Diversity Genome Genotype Hemoglobin Phenotype Reproduction
3
Genetic Determinants of Clopidogrel Response
We assessed the correlates (eg, age, sex, body mass index [BMI], lipid levels, and blood pressure) of clopidogrel response using a regression-based approach as implemented in the SOLAR version 4.07 (Southwest Foundation for Biomedical Research, San Antonio, Texas),30 (link) in which we accounted for relatedness among study participants by including a polygenic component as a random effect. Triglyceride levels were logarithm-transformed for analysis and back-transformed for presentation. Distribution analyses were generated in SAS. All statistical tests were 2-sided.
Association analyses between SNPs and ADP-stimulated platelet aggregation following clopidogrel administration were performed under a variance component model that assesses the effect of genotype as an additive effect on the quantitative trait, while simultaneously estimating the effects of age, age2 (link), sex, preclopidogrel platelet aggregation, and the aforementioned polygenic component.
The polygenic component was modeled using the relationship matrix derived from the complete Amish pedigree structure available through published genealogical records maintained by the church.31 (link) The heritability of baseline platelet aggregation and clopidogrel response corresponds to the proportion of the trait variance accounted for by the polygenic component. The genomic control λ coefficient was 1.03; thus, the P values reported are unadjusted.
A power calculation indicated 80% power to detect SNPs with allele frequencies of 0.2 to 0.4 in the initial sample (n = 429), accounting for 8% to 9% of phenotypic variation at α =10−7. To determine whether the loss-of-function CYP2C19*2 variant could account for the chromosome 10q24 association signal, we estimated the independent effects of both rs12777823— the most highly associated SNP from the genome-wide association analysis— and the CYP2C19*2 variant on platelet aggregation by including both in the model simultaneously. Pairwise linkage disequilibrium correlation statistics (|D′| and r2 (link)) were computed using Haploview (http://www.broad.mit.edu).
Shuldiner A.R., O'Connell J.R., Bliden K.P., Gandhi A., Ryan K., Horenstein R.B., Damcott C.M., Pakyz R., Tantry U.S., Gibson Q., Pollin T.I., Post W., Parsa A., Mitchell B.D., Faraday N., Herzog W, & Gurbel P.A. (2009). Association of Cytochrome P450 2C19 Genotype With the Antiplatelet Effect and Clinical Efficacy of Clopidogrel Therapy. JAMA : the journal of the American Medical Association, 302(8), 849-857.
Publication 2009
Amish Blood Pressure Chromosomes Clopidogrel CYP2C19 protein, human Genome Genome-Wide Association Study Genotype Index, Body Mass Lipids Platelet Aggregation Triglycerides
4
Genome-wide Association of Insulin Secretion Traits
Association analyses of insulin secretion and action traits were performed within 11 cohorts participating in the Meta-Analysis of Glucose- and Insulin-related traits Consortium (MAGIC) in a total of up to 10,831 individuals. In the discovery stage 1, we performed a meta-analysis of 6 GWASs (Diabetes Genetics Initiative (DGI), Amish Family Diabetes Study, Sorbs, Helsinki Birth Cohort Study (HBCS), French Obese Adults, and Relationship between Insulin Sensitivity and Cardiovascular disease Study (RISC)) for glucose-stimulated insulin secretion (GSIS) during an oral glucose-tolerance test (OGTT) at 3 time points (fasting, 30 min, 120 min) for primary traits measured as (1) insulin response to glucose after the first 30 min estimated as corrected insulin response (CIR), and (2) overall insulin response to glucose estimated as area under the curve (AUC) for insulin over a total AUC for glucose (AUCIns/AUCGluc) in up to 5,318 non-diabetic individuals (Table S1A).
As none of the traits gave genome-wide significant association, we selected the top 50 independent signals from both primary and secondary traits (see “Phenotype Definition” below) after LD pruning (r2<0.2). Signals prioritized for replication were ranked by the number of associations observed at primary traits and/or secondary traits, association p-value and number of times the signal was observed across the traits (more than 2). We selected 14 SNPs for replication genotyping and follow-up analyses, out of which 3 loci were based on biological relevance: GRB10[13] (link) (rs933360, discovery p-value (CIR) = 5.09×10−6), UCN3 (rs11253130, discovery p-value (Ins30adjBMI) = 9.46×10−7) and INADL (rs2476186, discovery p-value (AUCIns/AUCGluc) = 1.88×10−6). Replication stage 2A de novo genotyping was undertaken in five population-based studies (Botnia-PPP, ULSAM, METSIM, BPS and Haguenau; only GWAS index SNP rs933360 in the latter three; max N = 15,273) (Table S1A, S2A). Replication stage 2B in silico was undertaken using an iSelect CardioMetabochip array (CM) (Illumina, San Diego, CA, USA) to genotype data in 5 independent population-based studies (Botnia-PPP, ULSAM, Ely, DR's Extra and METSIM) including up to 5,513 individuals (Table S1A).
The GWAS/CM (stage 1 and stage 2B) data including 93,896 SNPs were pooled together with the de novo genotyping results from stage 2A for non-overlapping individuals. In this meta-analysis, we defined all independent (r2<0.2) genome-wide significant (p-value<5×10−8) association signals for insulin secretion traits at 8 genomic loci (Table 1).
Prokopenko I., Poon W., Mägi R., Prasad B R., Salehi S.A., Almgren P., Osmark P., Bouatia-Naji N., Wierup N., Fall T., Stančáková A., Barker A., Lagou V., Osmond C., Xie W., Lahti J., Jackson A.U., Cheng Y.C., Liu J., O'Connell J.R., Blomstedt P.A., Fadista J., Alkayyali S., Dayeh T., Ahlqvist E., Taneera J., Lecoeur C., Kumar A., Hansson O., Hansson K., Voight B.F., Kang H.M., Levy-Marchal C., Vatin V., Palotie A., Syvänen A.C., Mari A., Weedon M.N., Loos R.J., Ong K.K., Nilsson P., Isomaa B., Tuomi T., Wareham N.J., Stumvoll M., Widen E., Lakka T.A., Langenberg C., Tönjes A., Rauramaa R., Kuusisto J., Frayling T.M., Froguel P., Walker M., Eriksson J.G., Ling C., Kovacs P., Ingelsson E., McCarthy M.I., Shuldiner A.R., Silver K.D., Laakso M., Groop L, & Lyssenko V. (2014). A Central Role for GRB10 in Regulation of Islet Function in Man. PLoS Genetics, 10(4), e1004235.
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Publication 2014
Adult Amish Biopharmaceuticals Cardiovascular Diseases Diabetes Mellitus DNA Replication Genome Genome-Wide Association Study Glucose GRB10 protein, human Insulin Insulin Secretion Insulin Sensitivity Obesity Oral Glucose Tolerance Test Phenotype RNA-Induced Silencing Complex Single Nucleotide Polymorphism Urocortin III
5
Alzheimer's Disease Cohorts and Datasets
All cohorts are described in more detail in the supplementary material provide
online. The National Institute on Aging (NIA) Alzheimer’s Disease
Center (ADC) subjects were ascertained, evaluated, and sampled by the
clinical and neuropathology cores of the 29 NIA-funded ADCs (Table 1). Subject data data collection is
coordinated by the National Alzheimer’s Coordinating Center (NACC). DNA
from these samples for genotyping was prepared by the National Cell Repository
for Alzheimer’s Disease (NCRAD). The Alzheimer’s Disease
Neuroimaging Initiative (ADNI) subjects are AD cases and controls
ascertained for neuroimaging, biomarker, and genetic studies. Data used here
were generates as previously described8
and obtained from the ADNI database (www.loni.ucla.edu/ADNI ). The
Collaborative Aging and Memory Project (CAMP) subjects are from
the Amish communities of central Ohio and northern Indiana9 (link)–10 .
The Columbia University (CU) subjects are a Hispanic cohort
described in detail elsewhere11 . The
Framingham Heart Study (FHS) is a single-site, community-based,
ongoing cohort study described elsewhere12 (link)–14 . Phenotype and
genome-wide association study (GWAS) data were from dbGaP website (http://www.ncbi.nlm.nih.gov/gap). The Johns Hopkins
University (JHU) subjects are from the Genetic
and Environmental Risk Factors for
Alzheimer’s disease among
African Americans (GenerAAtions) Study identified
through the electronic claims database of the Henry Ford Health System. The
MIRAGE Study is a family-based genetic epidemiological study of
AD in which AD cases and unaffected sibling controls were enrolled at 17
clinical centers in the United States, Canada, Germany, and Greece15 . The NIA-LOAD Family
Study16 cohort are families
with two or more affected siblings with LOAD and unrelated, non-demented control
subjects similar in age and ethnic background. One case per family was selected
and controls were deteremined to be cognitively normal after an in-person
neurological examination and were not related to a study participant. The
Oregon Health and Science University (OHSU) were recruited from
aging research cohorts at 10 NIA-funded ADCs and do not overlap with other ADGC
samples. The TGEN dataset is a publicly available sample of AD
cases and controls (http://www.tgen.org/research/index.cfm?pageid=106517 (link). The University of
Miami/Vanderbilt University/Mt. Sinai School of Medicine (UM/VU/MSSM) were new and previously published18 (link)–22 (link) subjects
ascertained at the University of Miami, Vanderbilt University and Mt. Sinai
School of Medicine. The Wadi Ara dataset are from an inbred Arab
community in northern Israel23 (link)–26 (link).
Jun G., Naj A.C., Beecham G.W., Wang L.S., Buros J., Gallins P.J., Buxbaum J.D., Ertekin-Taner N., Fallin M.D., Friedland R., Inzelberg R., Kramer P., Rogaeva E., St George-Hyslop P., Cantwell L.B., Dombroski B.A., Saykin A.J., Reiman E.M., Bennett D.A., Morris J.C., Lunetta K.L., Martin E.R., Montine T.J., Goate A.M., Blacker D., Tsuang D.W., Beekly D., Cupples L.A., Hakonarson H., Kukull W., Foroud T.M., Haines J., Mayeux R., Farrer L.A., Pericak-Vance M.A, & Schellenberg G.D. (2010). Meta-Analysis confirms CR1, CLU, and PICALM as Alzheimer’s disease risk loci and reveals interactions with APOE genotypes. Archives of neurology, 67(12), 1473-1484.
Publication 2010
African American Alzheimer's Disease Amish Arabs Biological Markers Cells Ethnicity Genes, vif Genome-Wide Association Study Hispanics Memory Mirage porcelain Neurologic Examination Phenotype Sibling

Most recents protocols related to «Amish»

1
Automated Structural OCT Annotation for AMD
SLIVER-net [14 (link)] was used to automatically annotate OCT B-scan volumes for the following machine-read structural OCT AMD risk-progression biomarkers: high central drusen volume (hcDV), subretinal drusenoid deposits (SDD) and, or reticular pseudodrusen (RPD), intraretinal hyperreflective foci (IHRF), and hyporeflective drusen cores (hDC). The likelihood of each biomarker being present was represented as a score between 0 and 1. OCT B-scan volumes which we could not link to the EHR were not included in this analysis. Since not all OCT B-scan volumes consisted of the same number of slices, only volumes with at least 19 slices were utilized. Volumes with more than 19 slices were downsampled uniformly.
SLIVER-net was developed using the dataset described in [27 ] of 4,686 patients, and the Amish Eye Study dataset [28 (link)] of 1,007 subjects whose imaging data was manually annotated by clinician experts. The model’s performance was compared to these human expert graders [14 (link)], and it was found that SLIVER-net overperformed all clinician experts in identifying subretinal drusenoid deposits (SDD), and it overperformed 2 out of 3 clinicians in identifying intraretinal hyperreflective foci (HRF). Human graders identified hyporeflective drusen cores (hDC) with higher accuracy, however, SLIVER-net predicted high central drusen volume (HighDrusenVol) and reticular pseudodrusen (RPD), something human experts would have needed additional imaging modalities or software analytical tools in order to do.
Rudas A., Chiang J.N., Corradetti G., Rakocz N., Avram O., Halperin E, & Sadda S.R. (2023). Automated large-scale prediction of exudative AMD progression using machine-read OCT biomarkers. PLOS Digital Health, 2(2), e0000106.
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Publication 2023
Amish Biological Markers Disease Progression Homo sapiens Patients Radionuclide Imaging
2
Cognitive Assessment of Amish Individuals
Six Amish individuals between the ages of 11 and 29 with KRD (three female, three male) were psychometrically assessed using the Wechsler Intelligence Scale for Children 4th Edition (WISC-IV) (6–16 years) or the Wechsler Adult Intelligence Scale 4th Edition (WAIS-IV) (over 16 years), which assesses cognitive performance in four domains including verbal comprehension (VCI), perceptual reasoning (PRI), processing speed (PSI) and working memory (WMI) indices, and can be combined to generate a full-scale intelligence quotient (FSIQ) score. Further details are provided in the Supplementary material, ‘Methods’ section.
Levitin M.O., Rawlins L.E., Sanchez-Andrade G., Arshad O.A., Collins S.C., Sawiak S.J., Iffland PH I.I., Andersson M.H., Bupp C., Cambridge E.L., Coomber E.L., Ellis I., Herkert J.C., Ironfield H., Jory L., Kretz P.F., Kant S.G., Neaverson A., Nibbeling E., Rowley C., Relton E., Sanderson M., Scott E.M., Stewart H., Shuen A.Y., Schreiber J., Tuck L., Tonks J., Terkelsen T., van Ravenswaaij-Arts C., Vasudevan P., Wenger O., Wright M., Day A., Hunter A., Patel M., Lelliott C.J., Crino P.B., Yalcin B., Crosby A.H., Baple E.L., Logan D.W., Hurles M.E, & Gerety S.S. (2023). Models of KPTN-related disorder implicate mTOR signalling in cognitive and overgrowth phenotypes. Brain, 146(11), 4766-4783.
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Publication 2023
Adult Amish Cognition Males Memory, Short-Term Wechsler Scales Woman
3
Amish Eye Study: Genetic Factors in AMD
The details of the Amish Eye Study (supported by NEI R01EY023164 and 1R01EY030614) have been described in prior reports. Briefly, the Amish Eye Study was a longitudinal prospective observational study aimed at understanding the OCT-based risk factors and their genetic association with AMD progression.10 (link) All subjects in the study (N = 1339) underwent baseline volume OCT assessment and approximately half of the individuals (N = 666) returned for a 24-month follow-up visit including OCT imaging. All participants in this IRB-approved study signed written informed consent. The study was performed in accordance with the Health Insurance Portability and Accountability Act and adhered to the tenets of the Declaration of Helsinki.
OCT volume scans were obtained of both eyes of all subjects using the Cirrus OCT (Carl Zeiss Meditec, Dublin, CA; 512×128 macular cube; 6×6 mm scan region centered at the fovea). Deidentified OCT volumes were exported and transmitted to the Doheny Image Reading and Research Lab for analysis. To be eligible for this analysis, subjects had to have evidence of intermediate AMD (Beckman classification)21 (link) and IHRF on the baseline OCT scans. Eyes with evidence of a retinal disease other than iAMD (evidence of early or late AMD, diabetic retinopathy, epiretinal membrane, etc) were excluded. Eyes with OCT scans with gross segmentation errors which could impact retinal slab selection and poor-quality images were also excluded.
Sadda S., Verma A., Corradetti G., Nittala M., He Y., Nassisi M., Velaga S.B., Haines J., Pericak-Vance M, & Stambolian D. (2023). Longitudinal evaluation of the distribution of intraretinal hyper-reflective foci in eyes with intermediate age-related macular degeneration. Research Square.
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Publication Preprint 2023
Amish Diabetic Retinopathy Disease Progression Epiretinal Membrane Eye Macula Lutea Radionuclide Imaging Retina Retinal Diseases
4
Diverse Genetic Ancestry in Multi-Ethnic BMI Study
Our study population was racially, ethnically, geographically, and ancestrally diverse. We analyzed a multi-population sample of 88,873 adults from 36 studies in the freeze 8 TOPMed and CCDG programs (Figure 1, Supplementary Data 1). They belonged to 15 population groups, reflecting the way participants self-identified in each study. For individuals who had unreported or non-specific population memberships (e.g., “Multiple” or “Other”), we applied the Harmonized Ancestry and Race/Ethnicity (HARE) method 10 (link) to infer their group memberships using genetic data. This imputation was applied to 8,015 participants (9% of the overall population), assigning each to one of the existing population groups. In this way, our study population groups were defined based on a combination of self-reported identity and the first nine genetic principal components (PCs) (Figure 1, Supplementary Fig 1, and Supplementary Data 1).
The 15 population groups were labeled by their self-identified or primary inferred population group (e.g., predominantly African ancestry/admixed African/Black were labeled as “African”). Sample sizes for these groups ranged from 341 to over 43,000 as follows: African (N = 22,488), Amish (N = 1,106), Asian (N = 1,241), Barbadian (N = 248), Central American (N = 776), Costa Rican (N = 341), Cuban (N = 2,128), Dominican (N = 2,046), European (N = 43,434), Han Chinese (N = 1,787), Mexican (N = 4,265), Puerto Rican (N = 4,991), Samoan (N = 1,274), South American (N = 695), and Taiwanese (N = 2,053). We refer to analyses involving all 15 population groups as multi-population analysis and group-specific analyses by their primary population group.
Among the 88,873 participants, 53,109 (60%) were female and 45,439 (51%) were non-European. The mean (SD) age of the participants was 53.5 (15.1) years. Additional descriptive tables of the participants are presented in Supplementary Data 2 – 4. BMI was calculated by dividing weight in kilograms by the square of height in meters. Participants were excluded from analyses if less than 18 years of age, had known pregnancy at the time of BMI measurement, had implausible BMI values (above 100 kg/m2 without corroborating evidence), or did not provide appropriate consent. The mean (SD) of BMI varied by study, ranging from 23.4 (3.1) in GenSALT to 32.7 (6.8) in VAFAR (Supplementary Data 2), and by population group, ranging from 23.4 (3.1) in Han Chinese to 33.7 (6.8) in Samoans (Supplementary Data 3).
Zhang X., Brody J.A., Graff M., Highland H.M., Chami N., Xu H., Wang Z., Ferrier K., Chittoor G., Josyula N.S., Li X., Li Z., Allison M.A., Becker D.M., Bielak L.F., Bis J.C., Boorgula M.P., Bowden D.W., Broome J.G., Buth E.J., Carlson C.S., Chang K.M., Chavan S., Chiu Y.F., Chuang L.M., Conomos M.P., DeMeo D.L., Du M., Duggirala R., Eng C., Fohner A.E., Freedman B.I., Garrett M.E., Guo X., Haiman C., Heavner B.D., Hidalgo B., Hixson J.E., Ho Y.L., Hobbs B.D., Hu D., Hui Q., Hwu C.M., Jackson R.D., Jain D., Kalyani R.R., Kardia S.L., Kelly T.N., Lange E.M., LeNoir M., Li C., Marchand L.L., McDonald M.L., McHugh C.P., Morrison A.C., Naseri T., O’Connell J., O’Donnell C.J., Palmer N.D., Pankow J.S., Perry J.A., Peters U., Preuss M.H., Rao D.C., Regan E.A., Reupena S.M., Roden D.M., Rodriguez-Santana J., Sitlani C.M., Smith J.A., Tiwari H.K., Vasan R.S., Wang Z., Weeks D.E., Wessel J., Wiggins K.L., Wilkens L.R., Wilson P.W., Yanek L.R., Yoneda Z.T., Zhao W., Zöllner S., Arnett D.K., Ashley-Koch A.E., Barnes K.C., Blangero J., Boerwinkle E., Burchard E.G., Carson A.P., Chasman D.I., Chen Y.D., Curran J.E., Fornage M., Gordeuk V.R., He J., Heckbert S.R., Hou L., Irvin M.R., Kooperberg C., Minster R.L., Mitchell B.D., Nouraie M., Psaty B.M., Raffield L.M., Reiner A.P., Rich S.S., Rotter J.I., Shoemaker M.B., Smith N.L., Taylor K.D., Telen M.J., Weiss S.T., Zhang Y., Heard-Costa N., Sun Y.V., Lin X., Adrienne Cupples L., Lange L.A., Liu C.T., Loos R.J., North K.E, & Justice A.E. (2023). WHOLE GENOME SEQUENCING ANALYSIS OF BODY MASS INDEX IDENTIFIES NOVEL AFRICAN ANCESTRY-SPECIFIC RISK ALLELE. medRxiv.
Publication Preprint 2023
Adult Amish Asian Americans Central American People Chinese Ethnicity Europeans Females Freezing Gene Components Negroid Races Population Group Pregnancy Puerto Ricans Ribs South American People
5
MNLP Region Amplification and Sequencing
Pooled germline genomic DNA sample (NA13405DNA, sample pool (n = 62), CEPH Collection DNA pool: Amish, Utah and Venezuelan Pedigrees, (males (31) and females (31))) was purchased from Coriell and 100 ng was used to amplify (Phusion DNA polymerase, Thermo Fisher Scientific) the MNLP region using the o460 and o461 (Illumina sequencing platform compatible o458 and o459) oligonucleotide combination (Supplementary Data 6). Amplicons were purified (QIAquick PCR Purification Kit, Qiagen) and sent for deep amplicon sequencing to DFCI-MBCF. Sequencing was performed on Mini-Seq instrument (Illumina), 1 M reads were requested using 150 PE sequencing chemistry. Most frequent read types were determined and analyzed L and S allele frequencies were calculated (Supplementary Fig. S1e).
Spisak S., Tisza V., Nuzzo P.V., Seo J.H., Pataki B., Ribli D., Sztupinszki Z., Bell C., Rohanizadegan M., Stillman D.R., Alaiwi S.A., Bartels A.H., Papp M., Shetty A., Abbasi F., Lin X., Lawrenson K., Gayther S.A., Pomerantz M., Baca S., Solymosi N., Csabai I., Szallasi Z., Gusev A, & Freedman M.L. (2023). A biallelic multiple nucleotide length polymorphism explains functional causality at 5p15.33 prostate cancer risk locus. Nature Communications, 14, 5118.
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Publication 2023
Amish DNA-Directed DNA Polymerase Females Genome Germ Line Males Oligonucleotides

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8
Haplotypecaller by Agilent Technologies
HaplotypeCaller is a software tool developed by Agilent Technologies for the analysis of genetic data. It is designed to identify and call genetic variations, specifically haplotypes, from sequencing data. The core function of HaplotypeCaller is to detect and genotype genetic variants, including single nucleotide polymorphisms (SNPs) and small insertions and deletions (indels), in DNA samples.
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Sureselect whole exome v6 by Agilent Technologies
The Agilent SureSelect Whole Exome v6 is a targeted enrichment solution for capturing the human exome. It is designed to provide comprehensive coverage of the protein-coding regions of the genome.
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Anti α smooth muscle actin α sma by Merck Group
Sourced in United States, Japan
Anti-α-smooth muscle actin (α-SMA) is a laboratory product used to detect the presence of α-smooth muscle actin, a protein found in smooth muscle cells. It is commonly used in research applications to identify and quantify smooth muscle cells in various tissue samples.

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