Interacting evolutionary pressures drive mutation dynamics and health outcomes in aging blood.

Age-related clonal hematopoiesis (ARCH) is characterized by age-associated accumulation of somatic mutations in hematopoietic stem cells (HSCs) or their pluripotent descendants. HSCs harboring driver mutations will be positively selected and cells carrying these mutations will rise in frequency. While ARCH is a known risk factor for blood malignancies, such as Acute Myeloid Leukemia (AML), why some people who harbor ARCH driver mutations do not progress to AML remains unclear. Here, we model the interaction of positive and negative selection in deeply sequenced blood samples from individuals who subsequently progressed to AML, compared to healthy controls, using deep learning and population genetics. Our modeling allows us to discriminate amongst evolutionary classes with high accuracy and captures signatures of purifying selection in most individuals. Purifying selection, acting on benign or mildly damaging passenger mutations, appears to play a critical role in preventing disease-predisposing clones from rising to dominance and is associated with longer disease-free survival. Through exploring a range of evolutionary models, we show how different classes of selection shape clonal dynamics and health outcomes thus enabling us to better identify individuals at a high risk of malignancy.

High-resolution HLA allele and haplotype frequencies of the Saudi Arabian population based on 45,457 individuals and corresponding stem cell donor matching probabilities.

We estimated HLA allele and haplotype frequencies of the Saudi Arabian population from a sample of 45,457 registered stem cell donors. The most frequent HLA alleles were A*02:01g (18.5%), C*06:02g (16.1%), B*51:01g (14.1%), DRB1*07:01g (16.2%), DQB1*02:01g (30.5%), and DPB1*04:01g (33.6%). The most frequent 5-locus haplotypes were A*02:05g~C*06:02g~B*50:01g~DRB1*07:01g~DQB1*02:01g (1.73%), A*02:01g~C*06:02g~B*50:01g~DRB1*07:01g~DQB1*02:01g (1.66%), and A*26:01g~C*07:02g~B*08:01g~DRB1*03:01g~DQB1*02:01g (1.38%). Furthermore, we used the calculated haplotype frequencies to estimate stem cell donor matching probabilities for Saudi Arabian donor and patient populations under various matching requirements. These results are relevant for strategic donor registry planning in the Kingdom of Saudi Arabia.

STRUCTURE is more robust than other clustering methods in simulated mixed-ploidy populations.

Analysis of population genetic structure has become a standard approach in population genetics. In polyploid complexes, clustering analyses can elucidate the origin of polyploid populations and patterns of admixture between different cytotypes. However, combining diploid and polyploid data can theoretically lead to biased inference with (artefactual) clustering by ploidy. We used simulated mixed-ploidy (diploid-autotetraploid) data to systematically compare the performance of k-means clustering and the model-based clustering methods implemented in STRUCTURE, ADMIXTURE, FASTSTRUCTURE and INSTRUCT under different scenarios of differentiation and with different marker types. Under scenarios of strong population differentiation, the tested applications performed equally well. However, when population differentiation was weak, STRUCTURE was the only method that allowed unbiased inference with markers with limited genotypic information (co-dominant markers with unknown dosage or dominant markers). Still, since STRUCTURE was comparatively slow, the much faster but less powerful FASTSTRUCTURE provides a reasonable alternative for large datasets. Finally, although bias makes k-means clustering unsuitable for markers with incomplete genotype information, for large numbers of loci (>1000) with known dosage k-means clustering was superior to FASTSTRUCTURE in terms of power and speed. We conclude that STRUCTURE is the most robust method for the analysis of genetic structure in mixed-ploidy populations, although alternative methods should be considered under some specific conditions.

The Analysis of Polyploid Genetic Data.

Though polyploidy is an important aspect of the evolutionary genetics of both plants and animals, the development of population genetic theory of polyploids has seriously lagged behind that of diploids. This is unfortunate since the analysis of polyploid genetic data-and the interpretation of the results-requires even more scrutiny than with diploid data. This is because of several polyploidy-specific complications in segregation and genotyping such as tetrasomy, double reduction, and missing dosage information. Here, we review the theoretical and statistical aspects of the population genetics of polyploids. We discuss several widely used types of inferences, including genetic diversity, Hardy-Weinberg equilibrium, population differentiation, genetic distance, and detecting population structure. For each, we point out how the statistical approach, expected result, and interpretation differ between different ploidy levels. We also discuss for each type of inference what biases may arise from the polyploid-specific complications and how these biases can be overcome. From our overview, it is clear that the statistical toolbox that is available for the analysis of genetic data is flexible and still expanding. Modern sequencing techniques will soon be able to overcome some of the current limitations to the analysis of polyploid data, though the techniques are lagging behind those available for diploids. Furthermore, the availability of more data may aggravate the biases that can arise, and increase the risk of false inferences. Therefore, simulations such as we used throughout this review are an important tool to verify the results of analyses of polyploid genetic data.

A New Formulation of Random Genetic Drift and Its Application to the Evolution of Cell Populations.

Random genetic drift, or stochastic change in gene frequency, is a fundamental evolutionary force that is usually defined within the ideal Wright-Fisher (WF) population. However, as the theory is increasingly applied to populations that deviate strongly from the ideal model, a paradox of random drift has emerged. When drift is defined by the WF model, it becomes stronger as the population size, N, decreases. However, the intensity of competition decreases when N decreases and, hence, drift might become weaker. To resolve the paradox, we propose that random drift be defined by the variance of "individual output", V(k) [k being the progeny number of each individual with the mean of E(k)], rather than by the WF sampling. If the distribution of k is known for any population, its strength of drift relative to a WF population of the same size, N, can be calculated. Generally, E(k) and V(k) should be density dependent but their relationships are different with or without competition, leading to opposite predictions on the efficiency of random drift as N changes. We apply the "individual output" model to asexual cell populations that are either unregulated (such as tumors) or negatively density-dependent (e.g., bacteria). In such populations, the efficiency of drift could be as low as <10% of that in WF populations. Interestingly, when N is below the carrying capacity, random drift could in fact increase as N increases. Growing asexual populations, especially tumors, may therefore be genetically even more heterogeneous than the high diversity estimated by some conventional models.

Enabling high-throughput genotype-phenotype associations in the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) project as part of the Population Architecture using Genomics and Epidemiology (PAGE) study.

Genetic association studies have rapidly become a major tool for identifying the genetic basis of common human diseases. The advent of cost-effective genotyping coupled with large collections of samples linked to clinical outcomes and quantitative traits now make it possible to systematically characterize genotype-phenotype relationships in diverse populations and extensive datasets. To capitalize on these advancements, the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) project, as part of the collaborative Population Architecture using Genomics and Epidemiology (PAGE) study, accesses two collections: the National Health and Nutrition Examination Surveys (NHANES) and BioVU, Vanderbilt University's biorepository linked to de-identified electronic medical records. We describe herein the workflows for accessing and using the epidemiologic (NHANES) and clinical (BioVU) collections, where each workflow has been customized to reflect the content and data access limitations of each respective source. We also describe the process by which these data are generated, standardized, and shared for meta-analysis among the PAGE study sites. As a specific example of the use of BioVU, we describe the data mining efforts to define cases and controls for genetic association studies of common cancers in PAGE. Collectively, the efforts described here are a generalized outline for many of the successful approaches that can be used in the era of high-throughput genotype-phenotype associations for moving biomedical discovery forward to new frontiers of data generation and analysis.

Enhanced statistical tests for GWAS in admixed populations: assessment using African Americans from CARe and a Breast Cancer Consortium.

While genome-wide association studies (GWAS) have primarily examined populations of European ancestry, more recent studies often involve additional populations, including admixed populations such as African Americans and Latinos. In admixed populations, linkage disequilibrium (LD) exists both at a fine scale in ancestral populations and at a coarse scale (admixture-LD) due to chromosomal segments of distinct ancestry. Disease association statistics in admixed populations have previously considered SNP association (LD mapping) or admixture association (mapping by admixture-LD), but not both. Here, we introduce a new statistical framework for combining SNP and admixture association in case-control studies, as well as methods for local ancestry-aware imputation. We illustrate the gain in statistical power achieved by these methods by analyzing data of 6,209 unrelated African Americans from the CARe project genotyped on the Affymetrix 6.0 chip, in conjunction with both simulated and real phenotypes, as well as by analyzing the FGFR2 locus using breast cancer GWAS data from 5,761 African-American women. We show that, at typed SNPs, our method yields an 8% increase in statistical power for finding disease risk loci compared to the power achieved by standard methods in case-control studies. At imputed SNPs, we observe an 11% increase in statistical power for mapping disease loci when our local ancestry-aware imputation framework and the new scoring statistic are jointly employed. Finally, we show that our method increases statistical power in regions harboring the causal SNP in the case when the causal SNP is untyped and cannot be imputed. Our methods and our publicly available software are broadly applicable to GWAS in admixed populations.

Risk categorization for complex disorders according to genotype relative risk and precision in parameter estimates.

PURPOSE: To develop a method of genetic risk categorization based on the risk conferred by genetic variants and the precision with which risks are known. METHODS: We develop a method for risk assignment based on an "average" member of the population and their genotype, deriving empirical confidence intervals encompassing all relevant sources of variation in disease risk. An individual with risk confidence interval that does not overlap with that of the "average" individual is categorized as having higher or lower disease risk. The method is applied to data sets in Crohn's disease and type 2 diabetes. RESULTS: The proportion of the population assigned to the average risk category depends on genotype relative risk, allele frequency and sample size of the study used to estimate these parameters. For low genotype relative risks or minor allele frequency, little resolution into different risk categories may be possible. CONCLUSION: The utility of a genetic risk variant for risk categorization depends on both the magnitude of the genotype relative risk and the accuracy with which this, and other elements of risk calculation, are known. Genetic risk calculations should include an assessment of the accuracy of the risk estimation.

Level and course of FEV1 in relation to polymorphisms in NFE2L2 and KEAP1 in the general population.

BACKGROUND: The metabolism of xenobiotics plays an essential role in smoking related lung function loss and development of Chronic Obstructive Pulmonary Disease. Nuclear Factor Erythroid 2-Like 2 (NFE2L2 or NRF2) and its cytosolic repressor Kelch-like ECH-associated protein-1 (KEAP1) regulate transcription of enzymes involved in cellular detoxification processes and Nfe2l2-deficient mice develop tobacco-induced emphysema. We assessed the impact of Single Nucleotide Polymorphisms (SNPs) in both genes on the level and longitudinal course of Forced Expiratory Volume in 1 second (FEV1) in the general population. METHODS: Five NFE2L2 and three KEAP1 tagging SNPs were genotyped in the population-based Doetinchem cohort (n = 1,152) and the independent Vlagtwedde-Vlaardingen cohort (n = 1,390). On average 3 FEV1 measurements during 3 surveys, respectively 7 FEV1 measurements during 8 surveys were present. Linear Mixed Effect models were used to test cross-sectional and longitudinal genetic effects on repeated FEV1 measurements. RESULTS: In the Vlagtwedde-Vlaardingen cohort SNP rs11085735 in KEAP1 was associated with a higher FEV1 level (p = 0.02 for an additive effect), and SNP rs2364723 in NFE2L2 was associated with a lower FEV1 level (p = 0.06). The associations were even more significant in the pooled cohort analysis. No significant association of KEAP1 or NFE2L2 SNPs with FEV1 decline was observed. CONCLUSION: This is the first genetic study on variations in key antioxidant transcriptional regulators KEAP1 and NFE2L2 and lung function in a general population. It identified 2 SNPs in NFE2L2 and KEAP1 which affect the level of FEV1 in the general population. It additionally shows that NFE2L2 and KEAP1 variations are unlikely to play a role in the longitudinal course of FEV1 in the general population.

A frailty-model-based approach to estimating the age-dependent penetrance function of candidate genes using population-based case-control study designs: an application to data on the BRCA1 gene.

The population-based case-control study design is perhaps one of, if not the most, commonly used designs for investigating the genetic and environmental contributions to disease risk in epidemiological studies. Ages at onset and disease status of family members are routinely and systematically collected from the participants in this design. Considering age at onset in relatives as an outcome, this article is focused on using the family history information to obtain the hazard function, i.e., age-dependent penetrance function, of candidate genes from case-control studies. A frailty-model-based approach is proposed to accommodate the shared risk among family members that is not accounted for by observed risk factors. This approach is further extended to accommodate missing genotypes in family members and a two-phase case-control sampling design. Simulation results show that the proposed method performs well in realistic settings. Finally, a population-based two-phase case-control breast cancer study of the BRCA1 gene is used to illustrate the method.

Ecogeographic genetic epidemiology.

Complex diseases such as cancer and heart disease result from interactions between an individual's genetics and environment, i.e. their human ecology. Rates of complex diseases have consistently demonstrated geographic patterns of incidence, or spatial "clusters" of increased incidence relative to the general population. Likewise, genetic subpopulations and environmental influences are not evenly distributed across space. Merging appropriate methods from genetic epidemiology, ecology and geography will provide a more complete understanding of the spatial interactions between genetics and environment that result in spatial patterning of disease rates. Geographic information systems (GIS), which are tools designed specifically for dealing with geographic data and performing spatial analyses to determine their relationship, are key to this kind of data integration. Here the authors introduce a new interdisciplinary paradigm, ecogeographic genetic epidemiology, which uses GIS and spatial statistical analyses to layer genetic subpopulation and environmental data with disease rates and thereby discern the complex gene-environment interactions which result in spatial patterns of incidence.

CYP2A6 allele frequencies in an Iranian population.

BACKGROUND: Genetic polymorphism of CYP2A6 gene is a major causal factor in the large interindividual differences in nicotine metabolism. It may have an impact on smoking behavior and smoke-related cancer susceptibility. Until now, there are no reports of CYP2A6 allele frequencies in Iranian population. METHODS: In the present study, we investigated the frequencies of CYP2A6 alleles in 250 male Iranians. CYP2A6*2, CYP2A6*4, CYP2A6*9, and CYP2A6*12 were determined by allele-specific polymerase chain reaction. RESULTS: Frequencies of *2, *4, *9, and *12 alleles were 2.2%, 0.95%, 12.4%, and 1.34%, respectively. CONCLUSION: These results showed that the distribution of CYP2A6 alleles in Iranian population was different from those reported previously for other ethnic groups. This highlights the importance of conducting further studies to investigate the implications on smoking dependence and cancer in Iranians.

Haplotype and AGG interspersion analysis of FMR1 alleles in a Croatian population: no founder effect detected in patients with fragile X syndrome.

Several studies have suggested that fragile X syndrome (FRAXA), the most common inherited form of mental retardation, originated from a limited number of founder chromosomes. The aim of this study is to assess the genetic origin of fragile X syndrome in a Croatian population. We performed a haplotype analysis of the polymorphic loci DXS548 and FRAXAC1 in 18 unrelated fragile X and 56 control chromosomes. The AGG interspersion pattern of the FMR1 CGG repeat region was analyzed by sequencing. This is the first report on haplotype and AGG interspersion analysis of the fragile X syndrome gene in a Croatian population-the only eastern European population of Slavic origin analyzed so far. Our findings are intriguing, because they show a distinct distribution of the DXS548 and FRAXAC1 alleles in our fragile X population compared to other European fragile X populations. The DXS548/FRAXAC1 haplotype 194/154 (7-3), which is common among normal populations, was found to be the most frequent haplotype in our fragile X population as well. The AGG interspersion analysis indicated that AGG loss rather than haplotype may determine FMR1 allele instability. Our results suggest that no common ancestral X chromosome is associated with fragile X syndrome in the Croatian population studied. Further analysis of the origin of fragile X syndrome among other Slavic populations will be necessary to better define its eastern European distribution.

Testing for homogeneity of gametic disequilibrium across strata.

BACKGROUND: Assessing the non-random associations of alleles at different loci, or gametic disequilibrium, can provide clues about aspects of population histories and mating behavior and can be useful in locating disease genes. For gametic data which are available from several strata with different allele probabilities, it is necessary to verify that the strata are homogeneous in terms of gametic disequilibrium. RESULTS: Using the likelihood score theory generalized to nuisance parameters we derive a score test for homogeneity of gametic disequilibrium across several independent populations. Simulation results demonstrate that the empirical type I error rates of our score homogeneity test perform satisfactorily in the sense that they are close to the pre-chosen 0.05 nominal level. The associated power and sample size formulae are derived. We illustrate our test with a data set from a study of the cystic fibrosis transmembrane conductance regulator gene. CONCLUSION: We propose a large-sample homogeneity test on gametic disequilibrium across several independent populations based on the likelihood score theory generalized to nuisance parameters. Our simulation results show that our test is more reliable than the traditional test based on the Fisher's test of homogeneity among correlation coefficients.

Risks of cancer due to a single BRCA1 mutation in an extended Utah kindred.

OBJECTIVE: Germline mutations in the BRCA1 gene are associated with an increased risk of breast and ovarian cancer, but there is controversy about the true magnitude of these risks. Observed differences can arise from several sources, both genetic and methodological. To examine the efficiency and bias associated with different methods of risk calculation, we analyzed a single mutation in a large pedigree with known ascertainment. METHODS: Age-specific penetrance of breast and ovarian cancer was estimated using the Kaplan-Meier method and two likelihood-based approaches [maximum likelihood estimation, maximization of the logarithm of the odds (LOD) score]. Excess risk of other cancers due to the BRCA1 mutation was assessed. RESULTS: The estimated risk of breast and ovarian cancer at age 70 was 0.80 using the Kaplan-Meier approach and 0.55 using the maximum likelihood method. Both likelihood-based methods yielded similar results for the combined breast/ovarian phenotype, but using the maximum LOD score method lower estimates were obtained if only cancer at one site was considered. In the examined family, a high risk of ovarian cancer was found which might be an effect of the central location of the mutation within BRCA1. The risk of cancer at other sites than breast and ovaries was significantly elevated, but it was not possible to identify a single cancer site that could be said to be associated with the BRCA1 mutation. CONCLUSION: Estimating the penetrance of a specific mutation using different approaches, we found that both the choice of study population and statistical method affect the magnitude of the estimates.

Breast cancer screening in women at increased risk according to different family histories: an update of the Modena Study Group experience.

BACKGROUND: Breast cancer (BC) detection in women with a genetic susceptibility or strong family history is considered mandatory compared with BC screening in the general population. However, screening modalities depend on the level of risk. Here we present an update of our screening programs based on risk classification. METHODS: We defined different risk categories and surveillance strategies to identify early BC in 1325 healthy women recruited by the Modena Study Group for familial breast and ovarian cancer. Four BC risk categories included BRCA1/2 carriers, increased, intermediate, and slightly increased risk. Women who developed BC from January 1, 1994, through December 31, 2005 (N = 44) were compared with the number of expected cases matched for age and period. BRCA1/2 carriers were identified by mutational analysis. Other risk groups were defined by different levels of family history for breast or ovarian cancer (OC). The standardized incidence ratio (SIR) was used to evaluate the observed and expected ratio among groups. All statistical tests were two-sided. RESULTS: After a median follow-up of 55 months, there was a statistically significant difference between observed and expected incidence [SIR = 4.9; 95% confidence interval (CI) = 1.6 to 7.6; p < 0.001]. The incidence observed among BRCA carriers (SIR = 20.3; 95% CI = 3.1 to 83.9; P < 0.001), women at increased (SIR = 4.5; 95% CI = 1.5 to 8.3; P < 0.001) or intermediate risk (SIR = 7.0, 95% CI = 2.0 to 17.1; P = 0.0018) was higher than expected, while the difference between observed and expected among women at slightly increased risk was not statistically significant (SIR = 2.4, 95% CI = 0.9 to 8.3; P = .74). CONCLUSION: The rate of cancers detected in women at high risk according to BRCA status or strong family history, as defined according to our operational criteria, was significantly higher than expected in an age-matched general population. However, we failed to identify a greater incidence of BC in the slightly increased risk group. These results support the effectiveness of the proposed program to identify and monitor individuals at high risk, whereas prospective trials are needed for women belonging to families with sporadic BC or OC.

Simple allelic-phenotype diversity and differentiation statistics for allopolyploids.

The analysis of genetic diversity within and between populations is a routine task in the study of diploid organisms. However, population genetic studies of polyploid organisms have been hampered by difficulties associated with scoring and interpreting molecular data. This occurs because the presence of multiple alleles at each locus often precludes the measurement of genotype or allele frequencies. In allopolyploids, the problem is compounded because genetically distinct isoloci frequently share alleles. As a result, analysis of genetic diversity patterns in allopolyploids has tended to rely on the interpretation of phenotype frequencies, which loses information available from allele composition. Here, we propose the use of a simple allelic-phenotype diversity statistic (H') that measures diversity as the average number of alleles by which pairs of individuals differ. This statistic can be extended to a population differentiation measure (F'ST), which is analogous to FST. We illustrate the behaviour of these statistics using coalescent computer simulations that show that F'ST behaves in a qualitatively similar way to FST, thus providing a useful way to quantify population differentiation in allopolyploid species.

Prostate cancer incidence varies among males from different Y-chromosome lineages.

The incidence rate of prostate cancer in African-American males is two times higher than Caucasian men and ten times higher than Japanese men. The geographical specificity of Y haplogroups implies that males from different ethnic groups undoubtedly have various Y lineages with different Y-chromosomal characteristics that may affect their susceptibility or resistance to such a male-specific cancer. To confirm this hypothesis we studied the Y-chromosomal haplogroups of 92 Japanese prostate cancer patients comparing them with randomly selected 109 unrelated healthy Japanese male controls who were confirmed to be residents of the same geographical area. Males could be classified using three binary Y-chromosome markers (sex-determining region Y (SRY), YAP, 47z) into four haplogroups DE, O2b(*), O2b1, and untagged group. Our results confirmed that prostate cancer incidence varies among males from different Y-chromosome lineages. Males from DE and the untagged haplogroups are at a significantly higher risk to develop prostate cancer than O2b(*) and O2b1 haplogroups (P=0.01), odds ratio 2.17 and 95% confidence interval (1.16-4.07). Males from haplogroup DE are over-represented in the patient group showing a percentage of 41.3%. The underlying possible causes of susceptibility variations of different Y lineages for such a male-specific cancer tumorigenesis are discussed. These findings explain the lower incidence of prostate cancer in Japanese and other South East Asian males than other populations. To our knowledge, this is the first reliable study examining the association between prostate cancer and Y-chromosomal haplogroups, comparing prostate cancer patients with carefully selected matched controls.

Recombination estimation under complex evolutionary models with the coalescent composite-likelihood method.

The composite-likelihood estimator (CLE) of the population recombination rate considers only sites with exactly two alleles under a finite-sites mutation model (McVean, G. A. T., P. Awadalla, and P. Fearnhead. 2002. A coalescent-based method for detecting and estimating recombination from gene sequences. Genetics 160:1231-1241). While in such a model the identity of alleles is not considered, the CLE has been shown to be robust to minor misspecification of the underlying mutational model. However, there are many situations where the putative mutation and demographic history can be quite complex. One good example is rapidly evolving pathogens, like HIV-1. First we evaluated the performance of the CLE and the likelihood permutation test (LPT) under more complex, realistic models, including a general time reversible (GTR) substitution model, rate heterogeneity among sites (Gamma), positive selection, population growth, population structure, and noncontemporaneous sampling. Second, we relaxed some of the assumptions of the CLE allowing for a four-allele, GTR + Gamma model in an attempt to use the data more efficiently. Through simulations and the analysis of real data, we concluded that the CLE is robust to severe misspecifications of the substitution model, but underestimates the recombination rate in the presence of exponential growth, population mixture, selection, or noncontemporaneous sampling. In such cases, the use of more complex models slightly increases performance in some occasions, especially in the case of the LPT. Thus, our results provide for a more robust application of the estimation of recombination rates.