Natural proteome diversity links aneuploidy tolerance to protein turnover.

Accessing the natural genetic diversity of species unveils hidden genetic traits, clarifies gene functions and allows the generalizability of laboratory findings to be assessed. One notable discovery made in natural isolates of Saccharomyces cerevisiae is that aneuploidy-an imbalance in chromosome copy numbers-is frequent1,2 (around 20%), which seems to contradict the substantial fitness costs and transient nature of aneuploidy when it is engineered in the laboratory3-5. Here we generate a proteomic resource and merge it with genomic1 and transcriptomic6 data for 796 euploid and aneuploid natural isolates. We find that natural and lab-generated aneuploids differ specifically at the proteome. In lab-generated aneuploids, some proteins-especially subunits of protein complexes-show reduced expression, but the overall protein levels correspond to the aneuploid gene dosage. By contrast, in natural isolates, more than 70% of proteins encoded on aneuploid chromosomes are dosage compensated, and average protein levels are shifted towards the euploid state chromosome-wide. At the molecular level, we detect an induction of structural components of the proteasome, increased levels of ubiquitination, and reveal an interdependency of protein turnover rates and attenuation. Our study thus highlights the role of protein turnover in mediating aneuploidy tolerance, and shows the utility of exploiting the natural diversity of species to attain generalizable molecular insights into complex biological processes.

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.

The pseudoentropy of allele frequency trajectories, the persistence of variation, and the effective population size.

To concisely describe how genetic variation, at individual loci or across whole genomes, changes over time, and to follow transitory allelic changes, we introduce a quantity related to entropy, that we term pseudoentropy. This quantity emerges in a diffusion analysis of the mean time a mutation segregates in a population. For a neutral locus with an arbitrary number of alleles, the mean time of segregation is generally proportional to the pseudoentropy of initial allele frequencies. After the initial time point, pseudoentropy generally decreases, but other behaviours are possible, depending on the genetic diversity and selective forces present. For a biallelic locus, pseudoentropy and entropy coincide, but they are distinct quantities with more than two alleles. Thus for populations with multiple biallelic loci, the language of entropy suffices. Then entropy, combined across loci, serves as a concise description of genetic variation. We used individual based simulations to explore how this entropy behaves under different evolutionary scenarios. In agreement with predictions, the entropy associated with unlinked neutral loci decreases over time. However, deviations from free recombination and neutrality have clear and informative effects on the entropy's behaviour over time. Analysis of publicly available data of a natural D. melanogaster population, that had been sampled over seven years, using a sliding-window approach, yielded considerable variation in entropy trajectories of different genomic regions. These mostly follow a pattern that suggests a substantial effective population size and a limited effect of positive selection on genome-wide diversity over short time scales.

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.

High Nucleotide Diversity Accompanies Differential DNA Methylation in Naturally Diverging Populations.

Epigenetic mechanisms such as DNA methylation (DNAme) are thought to comprise an invaluable adaptive toolkit in the early stages of local adaptation, especially when genetic diversity is constrained. However, the link between genetic diversity and DNAme has been scarcely examined in natural populations, despite its potential to shed light on the evolutionary forces acting on methylation state. Here, we analyzed reduced-representation bisulfite sequencing and whole-genome pool-seq data from marine and freshwater stickleback populations to examine the relationship between DNAme variation (between- and within-population) and nucleotide diversity in the context of freshwater adaptation. We find that sites that are differentially methylated between populations have higher underlying standing genetic variation, with diversity higher among sites that gained methylation in freshwater than those that lost it. Strikingly, although nucleotide diversity is generally lower in the freshwater population as expected from a population bottleneck, this is not the case for sites that lost methylation, which instead have elevated nucleotide diversity in freshwater compared with marine. Subsequently, we show that nucleotide diversity is higher among sites with ancestrally variable methylation and also positively correlates with the sensitivity to environmentally induced methylation change. The results suggest that as selection on the control of methylation state becomes relaxed, so too does selection against mutations at the sites themselves. Increased epigenetic variance in a population is therefore likely to precede genetic diversification.

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.

Signatures of Selection on Mitonuclear Integrated Genes Uncover Hidden Mitogenomic Variation in Fur Seals.

Nuclear copies of mitochondrial genes (numts) are commonplace in vertebrate genomes and have been characterized in many species. However, relatively little attention has been paid to understanding their evolutionary origins and to disentangling alternative sources of insertions. Numts containing genes with intact mitochondrial reading frames represent good candidates for this purpose. The sequences of the genes they contain can be compared with their mitochondrial homologs to characterize synonymous to nonsynonymous substitution rates, which can shed light on the selection pressures these genes have been subjected to. Here, we characterize 25 numts in the Antarctic fur seal (Arctocephalus gazella) genome. Among those containing genes with intact mitochondrial reading frames, three carry multiple substitutions in comparison to their mitochondrial homologs. Our analyses reveal that one represents a historic insertion subjected to strong purifying selection since it colonized the Otarioidea in a genomic region enriched in retrotransposons. By contrast, the other two numts appear to be more recent and their large number of substitutions can be attributed to noncanonical insertions, either the integration of heteroplasmic mtDNA or hybridization. Our study sheds new light on the evolutionary history of pinniped numts and uncovers the presence of hidden sources of mitonuclear variation.

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.

Correcting Bias in Allele Frequency Estimates Due to an Observation Threshold: A Markov Chain Analysis.

There are many problems in biology and related disciplines involving stochasticity, where a signal can only be detected when it lies above a threshold level, while signals lying below threshold are simply not detected. A consequence is that the detected signal is conditioned to lie above threshold, and is not representative of the actual signal. In this work, we present some general results for the conditioning that occurs due to the existence of such an observational threshold. We show that this conditioning is relevant, for example, to gene-frequency trajectories, where many loci in the genome are simultaneously measured in a given generation. Such a threshold can lead to severe biases of allele frequency estimates under purifying selection. In the analysis presented, within the context of Markov chains such as the Wright-Fisher model, we address two key questions: (1) "What is a natural measure of the strength of the conditioning associated with an observation threshold?" (2) "What is a principled way to correct for the effects of the conditioning?". We answer the first question in terms of a proportion. Starting with a large number of trajectories, the relevant quantity is the proportion of these trajectories that are above threshold at a later time and hence are detected. The smaller the value of this proportion, the stronger the effects of conditioning. We provide an approximate analytical answer to the second question, that corrects the bias produced by an observation threshold, and performs to reasonable accuracy in the Wright-Fisher model for biologically plausible parameter values.