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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.

While the samples and data from the Pima Indians of the Gila River Indian Community have been included in many international HLA workshops and conferences and have been the focus of numerous population reports and the source of novel alleles at the classical HLA loci, they have not been studied for the non-classical loci. In order to expand our HLA-disease association studies, we typed over 300 whole genome sequences from full Pima heritage members, controlled for first degree relationship, and employed recently developed computer algorithms to resolve HLA alleles. Both classical-HLA-A, -B, and -C- and non-classical- HLA-E, -F, -G, -J, -L, -W, -Y, -DPA2, -DPB2, -DMA, -DMB, -DOA, -DRB2, -DRB9, TAP1- loci were typed at the 4-field level of resolution. We present allele and selected haplotype frequencies, test the genotype distributions for population structure, discuss the issues that are created for tests of Hardy-Weinberg equilibrium over the four sample spaces of high resolution HLA typing, and address the implications for the evolution of non-classical pseudogenes that are no longer expressed in a phenotype subject to natural selection.

Background Potassium levels regulate multiple physiologic processes. The heritability of serum potassium level is moderate, with published estimates varying from 17% to 60%, suggesting genetic influences. However, the genetic determinants of potassium levels are not generally known. Methods A whole-exome sequencing association study of serum potassium levels in 5812 subjects of the Old Order Amish was performed. A dietary salt intervention in 533 Amish subjects estimated interaction between p.R642G and sodium intake. Results A cluster of variants, spanning approximately 537 kb on chromosome 16q13, was significantly associated with serum potassium levels. Among the associated variants, a known pathogenic variant of autosomal recessive Gitelman syndrome (p.R642G SLC12A3) was most likely causal; there were no homozygotes in our sample. Heterozygosity for p.R642G was also associated with lower chloride levels, but not with sodium levels. Notably, p.R642G showed a novel association with lower serum BUN levels. Heterozygotes for p.R642G had a two-fold higher rate of self-reported bone fractures and had higher resting heart rates on a low-salt diet compared with noncarriers. Conclusions This study provides evidence that heterozygosity for a pathogenic variant in SLC12A3 causing Gitelman syndrome, a canonically recessive disorder, contributes to serum potassium concentration. The findings provide insights into SLC12A3 biology and the effects of heterozygosity on electrolyte homeostasis and related subclinical phenotypes that may have implications for personalized medicine and nutrition.

The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world1. Here we describe the release of exome-sequence data for the first 49,960 study participants, revealing approximately 4 million coding variants (of which around 98.6% have a frequency of less than 1%). The data include 198,269 autosomal predicted loss-of-function (LOF) variants, a more than 14-fold increase compared to the imputed sequence. Nearly all genes (more than 97%) had at least one carrier with a LOF variant, and most genes (more than 69%) had at least ten carriers with a LOF variant. We illustrate the power of characterizing LOF variants in this population through association analyses across 1,730 phenotypes. In addition to replicating established associations, we found novel LOF variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical importance, and show that 2% of this population has a medically actionable variant. Furthermore, we characterize the penetrance of cancer in carriers of pathogenic BRCA1 and BRCA2 variants. Exome sequences from the first 49,960 participants highlight the promise of genome sequencing in large population-based studies and are now accessible to the scientific community., Exome sequences from the first 49,960 participants in the UK Biobank highlight the promise of genome sequencing in large population-based studies and are now accessible to the scientific community.

AIMS/HYPOTHESIS: Prevalence of type 2 diabetes differs among human ancestry groups, and many hypotheses invoke differential natural selection to account for these differences. We sought to assess the potential role of differential natural selection across major continental ancestry groups for diabetes and related traits, by comparison of genetic and phenotypic differences. METHODS: This was a cross-sectional comparison among 734 individuals from an urban sample (none of whom was more closely related to another than third-degree relatives), including 83 African Americans, 523 American Indians and 128 European Americans. Participants were not recruited based on diabetes status or other traits. BMI was calculated, and diabetes was diagnosed by a 75 g oral glucose tolerance test. In those with normal glucose tolerance (n=434), fasting insulin and 30 min post-load insulin, adjusted for 30 min glucose, were taken as measures of insulin resistance and secretion, respectively. Whole exome sequencing was performed, resulting in 97,388 common (minor allele frequency >5%) variants; the coancestry coefficient (F(ST)) was calculated across all markers as a measure of genetic divergence among ancestry groups. The phenotypic divergence index (P(ST)) was also calculated from the phenotypic differences and heritability (which was estimated from genetic relatedness calculated empirically across all markers in 761 American Indian participants prior to the exclusion of close relatives). Under evolutionary neutrality, the expectation is P(ST)=F(ST), while for traits under differential selection P(ST) is expected to be significantly greater than F(ST.) A bootstrap procedure was used to test the hypothesis P(ST)=F(ST.) RESULTS: With adjustment for age and sex, prevalence of type 2 diabetes was 34.0% in American Indians, 12.4% in African Americans and 10.4% in European Americans (p=2.9×10(−10) for difference among groups). Mean BMI was 36.3, 33.4 and 33.0 kg/m(2), respectively (p=1.9×10(−7)). Mean fasting insulin was 63.8, 48.4 and 45.2 pmol/l (p=9.2×10(−5)), while mean 30 min insulin was 559.8, 553.5 and 358.8 pmol/l, respectively (p=5.7×10(−8)). F(ST) across all markers was 0.130, while P(ST) for liability to diabetes, adjusted for age and sex, was 0.149 (p=0.35 for difference with F(ST)). P(ST) was 0.094 for BMI (p=0.54), 0.095 for fasting insulin (p=0.54) and 0.216 (p=0.18) for 30 min insulin. For type 2 diabetes and BMI, the maximum divergence between populations was observed between American Indians and European Americans (P(ST-MAX)=0.22, p=0.37, and P(ST-MAX)=0.14, p=0.61), which suggests that a relatively modest 22% or 14% of the genetic variance, respectively, can potentially be explained by differential selection (assuming the absence of neutral drift). CONCLUSIONS/INTERPRETATION: These analyses suggest that while type 2 diabetes and related traits differ significantly among continental ancestry groups, the differences are consistent with neutral expectations based on heritability and genetic distances. While these analyses do not exclude a modest role for natural selection, they do not support the hypothesis that differential natural selection is necessary to explain the phenotypic differences among these ancestry groups.

Applying exome sequencing to populations with unique genetic architecture has the potential to reveal novel genes and variants associated with traits and diseases. We sequenced and analyzed the exomes of 6,716 individuals from a Southwestern American Indian (SWAI) population with well-characterized metabolic traits. We found that the SWAI population has distinct allelic architecture compared to populations of European and East Asian ancestry, and there were many predicted loss-of-function (pLOF) and nonsynonymous variants that were highly enriched or private in the SWAI population. We used pLOF and nonsynonymous variants in the SWAI population to evaluate gene-burden associations of candidate genes from European genome-wide association studies (GWASs) for type 2 diabetes, body mass index, and four major plasma lipids. We found 19 significant gene-burden associations for 11 genes, providing additional evidence for prioritizing candidate effector genes of GWAS signals. Interestingly, these associations were mainly driven by pLOF and nonsynonymous variants that are unique or highly enriched in the SWAI population. Particularly, we found four pLOF or nonsynonymous variants in APOB, APOE, PCSK9, and TM6SF2 that are private or enriched in the SWAI population and associated with low-density lipoprotein (LDL) cholesterol levels. Their large estimated effects on LDL cholesterol levels suggest strong impacts on protein function and potential clinical implications of these variants in cardiovascular health. In summary, our study illustrates the utility and potential of exome sequencing in genetically unique populations, such as the SWAI population, to prioritize candidate effector genes within GWAS loci and to find additional variants in known disease genes with potential clinical impact.

The growing number of individuals who are affected by undiagnosed rare genetic disorders and participating in genomics research—to identify unknown relationships between genes and disease—enable analyses using statistical approaches, such as exome-wide or genome-wide association analyses. This chapter reviews the statistical methods historically used to study rare diseases such as linkage analysis and transmission disequilibrium tests and explores the characteristics of rare disease, research participant ascertainment and availability, and the constellation of peripheral information that may determine whether modern analytical methods may be useful and well powered to enable disease variant and gene discovery. The characteristics that drive the choice of analytical approach and the strengths and limitations of these methods for the agnostic identification of relationships between genes and rare genetic disorders are also discussed.

Although thousands of loci have been associated with human phenotypes, the role of gene-environment (GxE) interactions in determining individual risk of human diseases remains unclear. This is partly because of the severe erosion of statistical power resulting from the massive number of statistical tests required to detect such interactions. Here, we focus on improving the power of GxE tests by developing a statistical framework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait variances. When applying this framework to body mass index (BMI), we find that GxE discovery and replication rates are significantly higher when prioritizing genetic variants associated with the variance of the phenotype (vQTLs) compared to when assessing all genetic variants. Moreover, we find that vQTLs are enriched for associations with other non-BMI phenotypes having strong environmental influences, such as diabetes or ulcerative colitis. We show that GxE effects first identified in quantitative traits such as BMI can be used for GxE discovery in disease phenotypes such as diabetes. A clear conclusion is that strong GxE interactions mediate the genetic contribution to body weight and diabetes risk.

Parent-of-origin (PoO) effects refer to the differential phenotypic impact of genetic variants dependent on their parental inheritance. Genetic variants in imprinted genes can have PoO specific effects on complex traits, but these effects may be poorly captured by models that do not differentiate the parental origin of the variant. The aim of this study was to screen genome-wide imputed sequence for PoO effects on electronic health records (EHR) derived clinical traits in 134,049 individuals of European ancestry from the DiscovEHR study.Using pairwise kinship estimates from genetic data and demographic data, we identified 22,051 offspring with at least one parent present in the DiscovEHR study. We then assigned the PoO of ∼9 million variants in the heterozygous offspring using two methods. First, when one of the parental genotypes was homozygous, we determined PoO based on apparent Mendelian segregation. Second, we estimated PoO by comparing parental and offspring haplotypes around the variant allele. Using these PoO assignments, we performed genome-wide PoO association analyses across 154 quantitative traits including lab test results and biometric measures and 612 binary traits of ICD10 3-digit codes extracted from EHR in the DiscovEHR study. Out of 732 PoO associations meeting a significance threshold of P −8, we attempted to replicate 274 PoO associations in the UK Biobank study, consisting of 462,453 individuals and including 5,015 offspring with at least one parent, and replicated 9 PoO associations with nominal significance threshold P In summary, the current study characterizes PoO effects of genetic variants genome-wide on a broad range of clinical traits derived from EHR in a large population study enriched for familial relationships. Our results suggest that 1) PoO specific effects are frequently captured by a standard additive model and that 2) statistical power to detect PoO specific effects remains modest even in large studies. Nonetheless, accurately modeling PoO effects of genetic variants has the potential to improve our understanding of the mechanism of the association and finding new associations that are not captured by the additive model.

While thousands of loci have been associated with human phenotypes, the role of gene-environment (GxE) interactions in determining individual risk of human diseases remains unclear. This is partly due to the severe erosion of statistical power resulting from the massive number of statistical tests required to detect such interactions. Here, we focus on improving the power of GxE tests by developing a statistical framework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait variances. When applying this framework to body mass index (BMI), we find that GxE discovery and replication rates are significantly higher when prioritizing genetic variants associated with the variance of the phenotype (vQTLs) compared to assessing all genetic variants. Moreover, we find that vQTLs are enriched for associations with other non-BMI phenotypes having strong environmental influences, such as diabetes or ulcerative colitis. We show that GxE effects first identified in quantitative traits such as BMI can be used for GxE discovery in disease phenotypes such as diabetes. A clear conclusion is that strong GxE interactions mediate the genetic contribution to body weight and diabetes risk.

SUMMARYAging is characterized by degeneration in cellular and organismal functions leading to increased disease susceptibility and death. Although our understanding of aging biology in model systems has increased dramatically, large-scale sequencing studies to understand human aging are now just beginning. We applied exome sequencing and association analyses (ExWAS) to identify age-related variants on 58,470 participants of the DiscovEHR cohort. Linear Mixed Model regression analyses of age at last encounter revealed variants in genes known to be linked with clonal hematopoiesis of indeterminate potential, which are associated with myelodysplastic syndromes, as top signals in our analysis, suggestive of age-related somatic mutation accumulation in hematopoietic cells despite patients lacking clinical diagnoses. In addition to APOE, we identified rare DISP2 rs183775254 (p = 7.40×10−10) and ZYG11A rs74227999 (p = 2.50×10−08) variants that were negatively associated with age in either both sexes combined and females, respectively, which were replicated with directional consistency in two independent cohorts. Epigenetic mapping showed these variants are located within cell-type-specific enhancers, suggestive of important transcriptional regulatory functions. To discover variants associated with extreme age, we performed exome-sequencing on persons of Ashkenazi Jewish descent ascertained for extensive lifespans. Case-Control analyses in 525 Ashkenazi Jews cases (Males ≥ 92 years, Females ≥ 95years) were compared to 482 controls. Our results showed variants in APOE (rs429358, rs6857), and TMTC2 (rs7976168) passed Bonferroni-adjusted p-value, as well as several nominally-associated population-specific variants. Collectively, our Age-ExWAS, the largest performed to date, confirmed and identified previously unreported candidate variants associated with human age.

Applying whole exome sequencing (WES) to populations with unique genetic architecture has the potential to reveal novel genes and variants associated with traits and diseases. We sequenced and analyzed the exomes of 6,716 individuals from an American Indian population in Southwest US (Southwestern American Indian, or SWAI) with well-characterized metabolic traits. We found that individuals of SWAI have distinct allelic architecture compared to individuals with European and East Asian ancestry, with many predicted loss-of-function (pLOF) and nonsynonymous variants that were highly enriched or private in SWAI. We evaluated gene-level associations with metabolic traits using pLOF and nonsynonymous variants in SWAI. Many of the candidate genes from previous GWAS studies for body mass index, type 2 diabetes, and plasma lipid levels were associated with respective traits in SWAI. Notably, these associations were mainly driven by pLOF and nonsynonymous variants that are unique or highly enriched in American Indians, many of which have not been observed in other populations or functionally characterized. Our study illustrates the utility and potential of WES in American Indians to prioritize candidate effector genes within GWAS loci and to find novel variants in known diseases genes with potential clinical impact.

Large-scale human genetics studies are ascertaining increasing proportions of populations as they continue growing in both number and scale. As a result, the amount of cryptic relatedness within these study cohorts is growing rapidly and has significant implications on downstream analyses. We demonstrate this growth empirically among the first 92,455 exomes from the DiscovEHR cohort and, via a custom simulation framework we developed called SimProgeny, show that these measures are in-line with expectations given the underlying population and ascertainment approach. For example, we identified ∼66,000 close (first- and second-degree) relationships within DiscovEHR involving 55.6% of study participants. Our simulation results project that >70% of the cohort will be involved in these close relationships as DiscovEHR scales to 250,000 recruited individuals. We reconstructed 12,574 pedigrees using these relationships (including 2,192 nuclear families) and leveraged them for multiple applications. The pedigrees substantially improved the phasing accuracy of 20,947 rare, deleterious compound heterozygous mutations. Reconstructed nuclear families were critical for identifying 3,415 de novo mutations in ∼1,783 genes. Finally, we demonstrate the segregation of known and suspected disease-causing mutations through reconstructed pedigrees, including a tandem duplication in LDLR causing familial hypercholesterolemia. In summary, this work highlights the prevalence of cryptic relatedness expected among large healthcare population genomic studies and demonstrates several analyses that are uniquely enabled by large amounts of cryptic relatedness.

BackgroundLoss-of-function variants in the angiopoietin-like 3 gene (ANGPTL3) have been associated with decreased plasma levels of triglycerides, low-density lipoprotein (LDL) cholesterol, and high-density lipoprotein (HDL) cholesterol. It is not known whether such variants or therapeutic antagonism of ANGPTL3 are associated with a reduced risk of atherosclerotic cardiovascular disease. MethodsWe sequenced the exons of ANGPTL3 in 58,335 participants in the DiscovEHR human genetics study. We performed tests of association for loss-of-function variants in ANGPTL3 with lipid levels and with coronary artery disease in 13,102 case patients and 40,430 controls from the DiscovEHR study, with follow-up studies involving 23,317 case patients and 107,166 controls from four population studies. We also tested the effects of a human monoclonal antibody, evinacumab, against Angptl3 in dyslipidemic mice and against ANGPTL3 in healthy human volunteers with elevated levels of triglycerides or LDL cholesterol. ResultsIn th...

Background Obesity and energy expenditure (EE) are heritable and genetic variants influencing EE may contribute to the development of obesity. We sought to identify genetic variants that affect EE in American Indians, an ethnic group with high prevalence of obesity. Methods Whole-exome sequencing was performed in 373 healthy Pima Indians informative for 24-hour EE during energy balance. Genetic association analyses of all high-quality exonic variants (≥5 carriers) was performed, and those predicted to be damaging were prioritized. Results Rs752074397 introduces a premature stop codon (Cys264Ter) in DAO and demonstrated the strongest association for 24-hour EE, where the Ter allele associated with substantially lower 24-hour EE (mean lower by 268 kcal/d) and sleeping EE (by 135 kcal/d). The Ter allele has a frequency = 0.5% in Pima Indians, whereas is extremely rare in most other ethnic groups (frequency < 0.01%). In vitro functional analysis showed reduced protein levels for the truncated form of DAO consistent with increased protein degradation. DAO encodes D-amino acid oxidase, which is involved in dopamine synthesis which might explain its role in modulating EE. Conclusion Our results indicate that a nonsense mutation in DAO may influence EE in American Indians. Identification of variants that influence energy metabolism may lead to new pathways to treat human obesity. Clinical Trial Registration Number NCT00340132.

Increased LDL-cholesterol (LDL-C) and fibrinogen are independent risk factors for cardiovascular disease (CVD). We identified novel associations between an Amish-enriched missense variant (p.Asn352Ser) in a functional domain of beta-1,4-galactosyltransferase 1 (B4GALT1) and 13.5 mg/dl lower LDL-C (p=1.6E-15), and 26 mg/dl lower plasma fibrinogen (p= 9.8E-05). N-linked glycan profiling found p.Asn352Ser to be associated (p-values from 1.4E-06 to 1.0E-17) with decreased glycosylation of glycoproteins including: fibrinogen, ApoB100, immunoglobulin G (IgG), and transferrin.In vitroassays found that the mutant (352Ser) protein had 50% lower galactosyltransferase activity compared to wild type (352Asn) protein. Knockdown ofb4galt1in zebrafish embryos resulted in significantly lower LDL-C compared to control, which was fully rescued by co-expression of 352Asn humanB4GALT1mRNA but only partially rescued by co-expression of 352Ser humanB4GALT1mRNA. Our findings establishB4GALT1as a novel gene associated with lower LDL-C and fibrinogen and suggest that targeted modulation of protein glycosylation may represent a therapeutic approach to decrease CVD risk.

SUMMARYThe UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the first tranche of large-scale exome sequence data for 49,960 study participants, revealing approximately 4 million coding variants (of which ~98.4% have frequency < 1%). The data includes 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants. Nearly all genes (>97%) had ≥1 predicted loss of function carrier, and most genes (>69%) had ≥10 loss of function carriers. We illustrate the power of characterizing loss of function variation in this large population through association analyses across 1,741 phenotypes. In addition to replicating a range of established associations, we discover novel loss of function variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical significance in this population, finding that 2% of the population has a medically actionable variant. Additionally, we leverage the phenotypic data to characterize the relationship between rare BRCA1 and BRCA2 pathogenic variants and cancer risk. Exomes from the first 49,960 participants are now made accessible to the scientific community and highlight the promise offered by genomic sequencing in large-scale population-based studies.

Summary Parent-of-origin (PoO) effects refer to the differential phenotypic impacts of genetic variants dependent on their parental inheritance due to imprinting. While PoO effects can influence complex traits, they may be poorly captured by models that do not differentiate the parental origin of the variant. The aim of this study was to conduct a genome-wide screen for PoO effects on a broad range of clinical traits derived from electronic health records (EHR) in the DiscovEHR study enriched with familial relationships. Using pairwise kinship estimates from genetic data and demographic data, we identified 22,051 offspring among 134,049 individuals in the DiscovEHR study. PoO of ~9 million variants was assigned in the offspring by comparing offspring and parental genotypes and haplotypes. We then performed genome-wide PoO association analyses across 154 quantitative and 611 binary traits extracted from EHR. Of the 732 significant PoO associations identified (p < 5 × 10−8), we attempted to replicate 274 PoO associations in the UK Biobank study with 5,015 offspring and replicated 9 PoO associations (p < 0.05). In summary, our study implements a bioinformatic and statistical approach to examine PoO effects genome-wide in a large population study enriched with familial relationships and systematically characterizes PoO effects on hundreds of clinical traits derived from EHR. Our results suggest that, while the statistical power to detect PoO effects remains modest yet, accurately modeling PoO effects has the potential to find new associations that may have been missed by the standard additive model, further enhancing the mechanistic understanding of genetic influence on complex traits., Genetic variants can have differential phenotypic effects based on parent-of-origin (PoO) due to imprinting. PoO of variants can be inferred by comparing parental and offspring genetic data. Genome-wide PoO-aware association testing reveals PoO effects across numerous clinical traits. PoO statistical models can identify novel associations undetected by standard additive models.

Knowledge of the extent and distribution of linkage disequilibrium (LD) is critical to the design and interpretation of gene mapping studies. Because the demographic history of each population varies and is often not accurately known, it is necessary to empirically evaluate LD on a population-specific basis. Here we present the first genome-wide survey of LD in the Old Order Amish (OOA) of Lancaster County Pennsylvania, a closed population derived from a modest number of founders. Specifically, we present a comparison of LD between OOA individuals and US Utah participants in the International HapMap project (abbreviated CEU) using a high-density single nucleotide polymorphism (SNP) map. Overall, the allele (and haplotype) frequency distributions and LD profiles were remarkably similar between these two populations. For example, the median absolute allele frequency difference for autosomal SNPs was 0.05, with an inter-quartile range of 0.02–0.09, and for autosomal SNPs 10–20kb apart with common alleles (minor allele frequencyZ0.05), the LD measure r 2 was at least 0.8 for 15 and 14% of SNP pairs in the OOA and CEU, respectively. Moreover, tag SNPs selected from the HapMap CEU sample captured a substantial portion of the common variation in the OOA (� 88%) at r 2 Z0.8. These results suggest that the OOA and CEU may share similar LD profiles for other common but untyped SNPs. Thus, in the context of the common variant-common disease hypothesis, genetic variants discovered in gene mapping studies in the OOA may generalize to other populations. Genet. Epidemiol. 34:146–150, 2010. r 2009 Wiley-Liss, Inc.

Restriction site-associated DNA sequencing or genotyping-by-sequencing (GBS) approaches allow for rapid and cost-effective discovery and genotyping of thousands of single-nucleotide polymorphisms (SNPs) in multiple individuals. However, rigorous quality control practices are needed to avoid high levels of error and bias with these reduced representation methods. We developed a formal statistical framework for filtering spurious loci, using Mendelian inheritance patterns in nuclear families, that accommodates variable-quality genotype calls and missing data—both rampant issues with GBS data—and for identifying sex-linked SNPs. Simulations predict excellent performance of both the Mendelian filter and the sex-linkage assignment under a variety of conditions. We further evaluate our method by applying it to real GBS data and validating a subset of high-quality SNPs. These results demonstrate that our metric of Mendelian inheritance is a powerful quality filter for GBS loci that is complementary to standard coverage and Hardy–Weinberg filters. The described method, implemented in the software MendelChecker, will improve quality control during SNP discovery in nonmodel as well as model organisms.

DNA constructs for small interfering RNA (siRNA) expression in mammalian cells have the potential for longer-term target gene knockdown than synthetic siRNAs. We compared in adult mice the efficacy and longevity of target gene knockdown from siRNA expression cassettes contained in plasmids, PCR-generated linear constructs or PCR constructs containing "dumbbell" ends using the hydrodynamic delivery method. Plasmid siRNA expression constructs were more effective than PCR constructs for target gene knockdown. The efficacy of the PCR constructs was improved by addition of short extensions beyond the transcription termination signal and greatly improved by addition of dumbbell ends. Constructs containing the H1 promoter were significantly less effective in mice than those containing the U6 promoter, whereas both promoters functioned equally well in cultured cells. Target gene knockdown perdured for at least 20 weeks in mice after delivery of either PCR or plasmid siRNA expression cassettes. These results will help guide RNAi vector design.