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Differences in cancer genomes between racial groups may impact tumor biology and health disparities. However, the discovery of race-associated mutations is constrained by the limited representation and sample size of different racial groups in prior genomic studies. We evaluated the influence of race on the frequency of gene mutations using the Genomics, Evidence, Neoplasia, Information, Exchange database, a large genomic dataset aggregated from clinical sequencing. Matched cohort analyses were used to identify histology-specific race-associated mutations including increased TERT promoter mutations in Black and Asian patients with gliomas and bladder cancers, and a decreased frequency of mutations in DNA repair pathway genes and subunits of the SWI/SNF chromatin complex in Asian and Black patients across multiple cancer types. The distribution of actionable mutations in oncogenes was also race-specific, demonstrating how targeted therapies may have a disparate impact on racial groups. Down-sampling analyses indicate that larger sample sizes are likely to discover more race-associated mutations. These results provide a resource to understand differences in cancer genomes between racial groups which may inform the design of clinical studies and patient recruitment strategies in biomarker trials., (© The Author(s) 2024. Published by Oxford University Press.)

The prevalence and severity of SLE have been found to vary across populations of different ancestries. This review explores whether these differences can be explained by the genetic aetiology of the condition. Large genetic studies suggest that populations of different ancestry share the same risk loci but individual risk alleles are more common in some, leading to a higher prevalence and severity and an earlier onset of the condition. Despite many of the loci being shared across populations, some have been found to be ancestry specific and these are hypothesized to have undergone differential selective pressure in recent human history. Additionally, the effectiveness of some of the drugs used in SLE has been found to vary across ancestries, which might affect progression of the disease, but it is unclear whether these differences are pharmacogenetic. We concluded that to understand the full role of genetics in the risk, presentation and response to treatment of SLE, larger studies including individuals from a wider representation of ancestries will be required., (© The Author(s) 2022. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Primary aldosteronism (PA) is a common cause of secondary hypertension. Recent technological advances in genetic analysis have provided a better understanding of the molecular pathogenesis of this disease. The application of next-generation sequencing has resulted in the identification of somatic mutations in aldosterone-producing adenoma (APA), a major subtype of PA. Based on the recent findings using a sequencing method that selectively targets the tumor region where aldosterone synthase (CYP11B2) is expressed, the vast majority of APAs appear to harbor a somatic mutation in one of the aldosterone-driver genes, including KCNJ5, ATP1A1, ATP2B3, CACNA1D, CACNA1H, and CLCN2. Mutations in these genes alter intracellular ion homeostasis and enhance aldosterone production. In a small subset of APAs, somatic activating mutations in the CTNNB1 gene, which encodes β-catenin, have also been detected. Accumulating evidence suggests that race and sex impact the somatic mutation spectrum of APA. Specifically, somatic mutations in the KCNJ5 gene, encoding an inwardly rectifying K+ channel, are common in APAs from Asian populations as well as women regardless of race. Associations between APA histology, genotype, and patient clinical characteristics have also been proposed, suggesting a potential need to consider race and sex for the management of PA patients. Herein, we review recent findings regarding somatic mutations in APA and discuss potential roles of race and sex on the pathophysiology of APA as well as possible clinical implications.

Background: Minimal residual disease (MRD) after induction therapy is one of the strongest prognostic factors in childhood acute lymphoblastic leukemia (ALL), and MRD-directed treatment intensification improves survival. Little is known about the effects of inherited genetic variants on interpatient variability in MRD., Methods: A genome-wide association study was performed on 2597 children on the Children's Oncology Group AALL0232 trial for high-risk B-cell ALL. Association between genotype and end-of-induction MRD levels was evaluated for 863 370 single nucleotide polymorphisms (SNPs), adjusting for genetic ancestry and treatment strata. Top variants were further evaluated in a validation cohort of 491 patients from the Children's Oncology Group P9905 and 6 ALL trials. The independent prognostic value of single nucleotide polymorphisms was determined in multivariable analyses. All statistical tests were 2-sided., Results: In the discovery genome-wide association study, we identified a genome-wide significant association at the GATA3 locus (rs3824662, odds ratio [OR] = 1.58, 95% confidence interval [CI] = 1.35 to 1.84; P = 1.15 × 10-8 as a dichotomous variable). This association was replicated in the validation cohort (P = .003, MRD as a dichotomous variable). The rs3824662 risk allele independently predicted ALL relapse after adjusting for age, white blood cell count, and leukemia DNA index (P = .04 and .007 in the discovery and validation cohort, respectively) and remained prognostic when the analyses were restricted to MRD-negative patients (P = .04 and .03 for the discovery and validation cohorts, respectively)., Conclusion: Inherited GATA3 variant rs3824662 strongly influences ALL response to remission induction therapy and is associated with relapse. This work highlights the potential utility of germline variants in upfront risk stratification in ALL., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Context: Polycystic ovary syndrome (PCOS) is one of the leading causes of infertility, yet current diagnostic criteria are ineffective at identifying patients whose symptoms reside outside strict diagnostic criteria. As a result, PCOS is underdiagnosed and its etiology is poorly understood., Objective: We aim to characterize the phenotypic spectrum of PCOS clinical features within and across racial and ethnic groups., Methods: We developed a strictly defined PCOS algorithm (PCOSkeyword-strict) using the International Classification of Diseases, ninth and tenth revisions and keywords mined from clinical notes in electronic health records (EHRs) data. We then systematically relaxed the inclusion criteria to evaluate the change in epidemiological and genetic associations resulting in 3 subsequent algorithms (PCOScoded-broad, PCOScoded-strict, and PCOSkeyword-broad). We evaluated the performance of each phenotyping approach and characterized prominent clinical features observed in racially and ethnically diverse PCOS patients., Results: The best performance came from the PCOScoded-strict algorithm, with a positive predictive value of 98%. Individuals classified as cases by this algorithm had significantly higher body mass index (BMI), insulin levels, free testosterone values, and genetic risk scores for PCOS, compared to controls. Median BMI was higher in African American females with PCOS compared to White and Hispanic females with PCOS., Conclusions: PCOS symptoms are observed across a severity spectrum that parallels the continuous genetic liability to PCOS in the general population. Racial and ethnic group differences exist in PCOS symptomology and metabolic health across different phenotyping strategies., (© Endocrine Society 2020.)

Genome-wide association studies have uncovered thousands of genetic variants that are associated with a wide variety of human traits. Knowledge of how trait-associated variants are distributed within and between populations can provide insight into the genetic basis of group-specific phenotypic differences, particularly for health-related traits. We analyzed the genetic divergence levels for 1) individual trait-associated variants and 2) collections of variants that function together to encode polygenic traits, between two neighboring populations in Colombia that have distinct demographic profiles: Antioquia (Mestizo) and Chocó (Afro-Colombian). Genetic ancestry analysis showed 62% European, 32% Native American, and 6% African ancestry for Antioquia compared with 76% African, 10% European, and 14% Native American ancestry for Chocó, consistent with demography and previous results. Ancestry differences can confound cross-population comparison of polygenic risk scores (PRS); however, we did not find any systematic bias in PRS distributions for the two populations studied here, and population-specific differences in PRS were, for the most part, small and symmetrically distributed around zero. Both genetic differentiation at individual trait-associated single nucleotide polymorphisms and population-specific PRS differences between Antioquia and Chocó largely reflected anthropometric phenotypic differences that can be readily observed between the populations along with reported disease prevalence differences. Cases where population-specific differences in genetic risk did not align with observed trait (disease) prevalence point to the importance of environmental contributions to phenotypic variance, for both infectious and complex, common disease. The results reported here are distributed via a web-based platform for searching trait-associated variants and PRS divergence levels at http://map.chocogen.com (last accessed August 12, 2020)., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

The Dominican Republic is one of the two countries on the Hispaniola island, which is part of the Antilles. Hispaniola was affected by the European colonization and massive deportation of African slaves since the XVI century and these events heavily shaped the genetic composition of the present-day population. To shed light about the effect of the European rules, we analyzed 92 single nucleotide polymorphisms on the Y chromosome in 182 Dominican individuals from three different locations. The Dominican Y haplogroup composition was characterized by an excess of northern African/European lineages (59%), followed by the African clades (38%), whereas the Native-American lineages were rare (3%). The comparison with the mitochondrial DNA variability, dominated by African clades, revealed a sex-biased admixture pattern, in line with the colonial society dominated by European men. When other Caribbean and non-Caribbean former colonies were also considered, we noted a difference between territories under a Spanish rule (like the Dominican Republic) and British/French rule, with the former characterized by an excess of European Y lineages reflecting the more permissive Iberian legislation about mixed people and slavery. Finally, we analyzed the distribution in Africa of the Dominican lineages with a putative African origin, mainly focusing on central and western Africa, which were the main sources of African slaves. We found that most (83%) of the African lineages observed in Santo Domingo have a central African ancestry, suggesting that most of the slaves were deported from regions., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Positive selection causes beneficial alleles to rise to high frequency, resulting in a selective sweep of the diversity surrounding the selected sites. Accordingly, the signature of a selective sweep in an ancestral population may still remain in its descendants. Identifying signatures of selection in the ancestor that are shared among its descendants is important to contextualize the timing of a sweep, but few methods exist for this purpose. We introduce the statistic SS-H12, which can identify genomic regions under shared positive selection across populations and is based on the theory of the expected haplotype homozygosity statistic H12, which detects recent hard and soft sweeps from the presence of high-frequency haplotypes. SS-H12 is distinct from comparable statistics because it requires a minimum of only two populations, and properly identifies and differentiates between independent convergent sweeps and true ancestral sweeps, with high power and robustness to a variety of demographic models. Furthermore, we can apply SS-H12 in conjunction with the ratio of statistics we term [Formula: see text] and [Formula: see text] to further classify identified shared sweeps as hard or soft. Finally, we identified both previously reported and novel shared sweep candidates from human whole-genome sequences. Previously reported candidates include the well-characterized ancestral sweeps at LCT and SLC24A5 in Indo-Europeans, as well as GPHN worldwide. Novel candidates include an ancestral sweep at RGS18 in sub-Saharan Africans involved in regulating the platelet response and implicated in sudden cardiac death, and a convergent sweep at C2CD5 between European and East Asian populations that may explain their different insulin responses., (Copyright © 2020 by the Genetics Society of America.)

Genetic ancestry is a critical co-factor to study phenotype-genotype associations using cohorts of human subjects. Most publicly available molecular datasets are, however, missing this information or only share self-reported race and ethnicity, representing a limitation to identify and repurpose datasets to investigate the contribution of ancestry to diseases and traits. We propose an analytical framework to enrich the metadata from publicly available cohorts with genetic ancestry information and a resulting diversity score at continental resolution, calculated directly from the data. We illustrate this framework using The Cancer Genome Atlas datasets searched through the DataMed Data Discovery Index. Data repositories and contributors can use this framework to provide genetic diversity measurements for controlled access datasets, minimizing the work involved in requesting a dataset that may ultimately prove inadequate for a researcher's purpose. With the increasing global scale of human genetics research, studies on disease risk and susceptibility would benefit greatly from the adequate estimation and sharing of genetic diversity in publicly available datasets following a framework such as the one presented., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Estimates of different ancestral proportions in admixed populations are very important in population genetics studies, especially for the detection of population substructure effects in studies of case-control associations. Brazil is one of the most heterogeneous countries in the world, both from a socio-cultural and a genetic point of view. In this work, we investigated a previously developed set of 61 ancestry informative markers (AIM), aiming to estimate the proportions of four different ancestral groups (African, European, Native American and Asian) in Brazilian populations. To the best of our knowledge, this is the first study to use a set of AIM to investigate the genetic contribution of all four main parental populations to the Brazilian population, including Asian contribution. All selected markers were genotyped through multiplex PCR and capillary electrophoresis. The set was able to successfully differentiate the four ancestral populations (represented by 939 individuals) and identify their genetic contributions to the Brazilian population. In addition, it was used to estimate individual interethnic admixture of 1050 individuals from the Southeast region of Brazil and it showed that these individuals present a higher European ancestry contribution, followed by African, Asian and Native American ancestry contributions. Therefore, the 61 AIM set has proved to be a valuable tool to estimate individual and global ancestry proportions in populations mainly formed by these four groups. Our findings highlight the importance of using sets of AIM to evaluate population substructure in studies carried in admixed populations, in order to avoid misinterpretation of results., (Copyright © 2018 Andrade et al.)

Illumina genotyping arrays have powered thousands of large-scale genome-wide association studies over the past decade. Yet, because of the tremendous volume and complicated genetic assumptions of Illumina genotyping data, processing and quality control (QC) of these data remain a challenge. Thorough QC ensures the accurate identification of single-nucleotide polymorphisms and is required for the correct interpretation of genetic association results. By processing genotyping data on > 100 000 subjects from >10 major Illumina genotyping arrays, we have accumulated extensive experience in handling some of the most peculiar scenarios related to the processing and QC of Illumina genotyping data. Here, we describe strategies for processing Illumina genotyping data from the raw data to an analysis ready format, and we elaborate on the necessary QC procedures required at each processing step. High-quality Illumina genotyping data sets can be obtained by following our detailed QC strategies.

Natural epigenetic diversity has been suggested as a key mechanism in microevolutionary processes due to its capability to create phenotypic variability within individuals and populations. It constitutes an important reservoir of variation potentially useful for rapid adaptation in response to environmental stimuli. The analysis of population epigenetic structure represents a possible tool to study human adaptation and to identify external factors that are able to naturally shape human DNA methylation variability. The aim of this study is to investigate the dynamics that create epigenetic diversity between and within different human groups. To this end, we first used publicly available epigenome-wide data to explore population-specific DNA methylation changes that occur at macro-geographic scales. Results from this analysis suggest that nutrients, UVA exposure and pathogens load might represent the main environmental factors able to shape DNA methylation profiles. Then, we evaluated DNA methylation of candidate genes (KRTCAP3, MAD1L1, and BRSK2), emerged from the previous analysis, in individuals belonging to different populations from Morocco, Nigeria, Philippines, China, and Italy, but living in the same Italian city. DNA methylation of the BRSK2 gene is significantly different between Moroccans and Nigerians (pairwise t-test: CpG 6 P-value = 5.2*10 (-) (3); CpG 9 P-value = 2.6*10 (-) (3); CpG 10 P-value = 3.1*10 (-) (3); CpG 11 P-value = 2.8*10 (-) (3)). Comprehensively, these results suggest that DNA methylation diversity is a source of variability in human groups at macro and microgeographical scales and that population demographic and adaptive histories, as well as the individual ancestry, actually influence DNA methylation profiles., (© The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Objectives: To apply techniques for ancestry and sex computation from next-generation sequencing (NGS) data as an approach to confirm sample identity and detect sample processing errors., Methods: We combined a principal component analysis method with k-nearest neighbors classification to compute the ancestry of patients undergoing NGS testing. By combining this calculation with X chromosome copy number data, we determined the sex and ancestry of patients for comparison with self-report. We also modeled the sensitivity of this technique in detecting sample processing errors., Results: We applied this technique to 859 patient samples with reliable self-report data. Our k-nearest neighbors ancestry screen had an accuracy of 98.7% for patients reporting a single ancestry. Visual inspection of principal component plots was consistent with self-report in 99.6% of single-ancestry and mixed-ancestry patients. Our model demonstrates that approximately two-thirds of potential sample swaps could be detected in our patient population using this technique., Conclusions: Patient ancestry can be estimated from NGS data incidentally sequenced in targeted panels, enabling an inexpensive quality control method when coupled with patient self-report., (© American Society for Clinical Pathology, 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Differences in internal dose of nicotine and tobacco-derived carcinogens among ethnic/racial groups have been observed. In this study, we explicitly examined the relationships between genetic ancestries (genome-wide average) and 19 tobacco-derived biomarkers in smokers from 3 admixed groups in the Multiethnic Cohort Study (1993-present), namely, African ancestry in African Americans (n = 362), Amerindian ancestry in Latinos (n = 437), and Asian and Native Hawaiian ancestries in Native Hawaiians (n = 300). After multiple comparison adjustment, both African and Asian ancestries were significantly related to a greater level of free cotinine; African ancestry was also significantly related to lower cotinine glucuronidation (P's < 0.00156). The predicted decrease in cotinine glucuronidation was 8.6% (P = 4.5 × 10(-6)) per a 20% increase in African ancestry. Follow-up admixture mapping revealed that African ancestry in a 12-Mb region on chromosome 4q was related to lower cotinine glucuronidation (P's < 2.7 × 10(-7), smallest P = 1.5 × 10(-9)), although this is the same region reported in our previous genome-wide association study. Our results implicate a genetic ancestral component in the observed ethnic/racial variation in nicotine metabolism. Further studies are needed to identify the underlying genetic variation that could potentially be ethnic/racial specific., (© The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Bone mineral density (BMD) is a highly heritable trait used both for the diagnosis of osteoporosis in adults and to assess bone health in children. Ethnic differences in BMD have been documented, with markedly higher levels in individuals of African descent, which partially explain disparity in osteoporosis risk across populations. To date, 63 independent genetic variants have been associated with BMD in adults of Northern-European ancestry. Here, we demonstrate that at least 61 of these variants are predictive of BMD early in life by studying their compound effect within two multiethnic pediatric cohorts. Furthermore, we show that within these cohorts and across populations worldwide the frequency of those alleles associated with increased BMD is systematically elevated in individuals of Sub-Saharan African ancestry. The amount of differentiation in the BMD genetic scores among Sub-Saharan and non-Sub-Saharan populations together with neutrality tests, suggest that these allelic differences are compatible with the hypothesis of selective pressures acting on the genetic determinants of BMD. These findings constitute an explorative contribution to the role of selection on ethnic BMD differences and likely a new example of polygenic adaptation acting on a human trait., (© The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

Using genome-wide genotypes, we characterized the genetic structure of 103,006 participants in the Kaiser Permanente Northern California multi-ethnic Genetic Epidemiology Research on Adult Health and Aging Cohort and analyzed the relationship to self-reported race/ethnicity. Participants endorsed any of 23 race/ethnicity/nationality categories, which were collapsed into seven major race/ethnicity groups. By self-report the cohort is 80.8% white and 19.2% minority; 93.8% endorsed a single race/ethnicity group, while 6.2% endorsed two or more. Principal component (PC) and admixture analyses were generally consistent with prior studies. Approximately 17% of subjects had genetic ancestry from more than one continent, and 12% were genetically admixed, considering only nonadjacent geographical origins. Self-reported whites were spread on a continuum along the first two PCs, indicating extensive mixing among European nationalities. Self-identified East Asian nationalities correlated with genetic clustering, consistent with extensive endogamy. Individuals of mixed East Asian-European genetic ancestry were easily identified; we also observed a modest amount of European genetic ancestry in individuals self-identified as Filipinos. Self-reported African Americans and Latinos showed extensive European and African genetic ancestry, and Native American genetic ancestry for the latter. Among 3741 genetically identified parent-child pairs, 93% were concordant for self-reported race/ethnicity; among 2018 genetically identified full-sib pairs, 96% were concordant; the lower rate for parent-child pairs was largely due to intermarriage. The parent-child pairs revealed a trend toward increasing exogamy over time; the presence in the cohort of individuals endorsing multiple race/ethnicity categories creates interesting challenges and future opportunities for genetic epidemiologic studies., (Copyright © 2015 by the Genetics Society of America.)

Motivation: A basic problem of broad public and scientific interest is to use the DNA of an individual to infer the genomic ancestries of the parents. In particular, we are often interested in the fraction of each parent's genome that comes from specific ancestries (e.g. European, African, Native American, etc). This has many applications ranging from understanding the inheritance of ancestry-related risks and traits to quantifying human assortative mating patterns., Results: We model the problem of parental genomic ancestry inference as a pooled semi-Markov process. We develop a general mathematical framework for pooled semi-Markov processes and construct efficient inference algorithms for these models. Applying our inference algorithm to genotype data from 231 Mexican trios and 258 Puerto Rican trios where we have the true genomic ancestry of each parent, we demonstrate that our method accurately infers parameters of the semi-Markov processes and parents' genomic ancestries. We additionally validated the method on simulations. Our model of pooled semi-Markov process and inference algorithms may be of independent interest in other settings in genomics and machine learning., (© The Author 2015. Published by Oxford University Press.)

Context: It is well established that ethnicity is associated with the phenotype of polycystic ovary syndrome (PCOS). Self-reported ethnicity was shown to be an inaccurate proxy for ethnic origin in other disease traits, and it remains unclear how in PCOS patients self-reported ethnicity compares with a biological proxy such as genetic ancestry., Objective: We compared the impact of self-reported ethnicity versus genetic ancestry on PCOS and tested which of these 2 classifications better predicts the variability in phenotypic characteristics of PCOS., Patients: A total of 1499 PCOS patients from The Netherlands, comprising 11 self-reported ethnic groups of European, African, American, and Asian descent were genotyped with the Illumina 610K Quad BeadChip and merged with the data genotyped with the Illumina HumanHap650K available for the reference panel collected by the Human Genome Diversity Project (HGDP), in a collaboration with the Centre Etude Polymorphism Humain (CEPH), including 53 populations for ancestry reference., Main Outcome Measures: Algorithms for inferring genetic relationships among individuals, including multidimensional scaling and ADMIXTURE, were applied to recover genetic ancestry for each individual. Regression analysis was used to determine the best predictor for the variability in PCOS characteristics., Results: The association between self-reported ethnicity and genetic ancestry was moderate. For amenorrhea, total follicle count, body mass index, SHBG, dehydroepiandrosterone sulfate, and insulin, mainly genetic ancestry clusters ended up in the final models (P values < .004), indicating that they explain a larger proportion of variability of these PCOS characteristics compared with self-reported ethnicity. Especially variability of insulin levels seems predominantly explained by genetic ancestry., Conclusions: Self-reported ancestry is not a perfect proxy for genetic ancestry in patients with PCOS, emphasizing that by using genetic ancestry data instead of self-reported ethnicity, PCOS-relevant misclassification can be avoided. Moreover, because genetic ancestry explained a larger proportion of phenotypic variability associated with PCOS than self-reported ethnicity, future studies should focus on genetic ancestry verification of PCOS patients for research questions and treatment as well as preventive strategies in these women.

Prostate cancer (PCa) is the second most frequently diagnosed malignancy in men. Genome-wide association studies (GWAS) has been highly successful in discovering susceptibility loci for prostate cancer. Currently, more than twenty GWAS have identified more than fifty common variants associated with susceptibility with PCa. Yet with the increase in loci, voices from the scientific society are calling for more. In this review, we summarize current findings, discuss the common problems troubling current studies and shed light upon possible breakthroughs in the future. GWAS is the beginning of something wonderful. Although we are quite near the end of the beginning, post-GWAS studies are just taking off and future studies are needed extensively. It is believed that in the future GWAS information will be helpful to build a comprehensive system intergraded with PCa prevention, diagnosis, molecular classification, personalized therapy.

Common obesity risk variants have been associated with macronutrient intake; however, these associations' generalizability across populations has not been demonstrated. We investigated the associations between 6 obesity risk variants in (or near) the NEGR1, TMEM18, BDNF, FTO, MC4R, and KCTD15 genes and macronutrient intake (carbohydrate, protein, ethanol, and fat) in 3 Population Architecture using Genomics and Epidemiology (PAGE) studies: the Multiethnic Cohort Study (1993-2006) (n = 19,529), the Atherosclerosis Risk in Communities Study (1987-1989) (n = 11,114), and the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) Study, which accesses data from the Third National Health and Nutrition Examination Survey (1991-1994) (n = 6,347). We used linear regression, with adjustment for age, sex, and ethnicity, to estimate the associations between obesity risk genotypes and macronutrient intake. A fixed-effects meta-analysis model showed that the FTO rs8050136 A allele (n = 36,973) was positively associated with percentage of calories derived from fat (βmeta = 0.2244 (standard error, 0.0548); P = 4 × 10(-5)) and inversely associated with percentage of calories derived from carbohydrate (βmeta = -0.2796 (standard error, 0.0709); P = 8 × 10(-5)). In the Multiethnic Cohort Study, percentage of calories from fat assessed at baseline was a partial mediator of the rs8050136 effect on body mass index (weight (kg)/height (m)(2)) obtained at 10 years of follow-up (mediation of effect = 0.0823 kg/m(2), 95% confidence interval: 0.0559, 0.1128). Our data provide additional evidence that the association of FTO with obesity is partially mediated by dietary intake.

Motivation: Inference of ancestry using genetic data is motivated by applications in genetic association studies, population genetics and personal genomics. Here, we provide methods and software for improved ancestry inference using genome-wide single nucleotide polymorphism (SNP) weights from external reference panels. This approach makes it possible to leverage the rich ancestry information that is available from large external reference panels, without the administrative and computational complexities of re-analyzing the raw genotype data from the reference panel in subsequent studies., Results: We extensively validate our approach in multiple African American, Latino American and European American datasets, making use of genome-wide SNP weights derived from large reference panels, including HapMap 3 populations and 6546 European Americans from the Framingham Heart Study. We show empirically that our approach provides much greater accuracy than either the prevailing ancestry-informative marker (AIM) approach or the analysis of genome-wide target genotypes without a reference panel. For example, in an independent set of 1636 European American genome-wide association study samples, we attained prediction accuracy (R(2)) of 1.000 and 0.994 for the first two principal components using our method, compared with 0.418 and 0.407 using 150 published AIMs or 0.955 and 0.003 by applying principal component analysis directly to the target samples. We finally show that the higher accuracy in inferring ancestry using our method leads to more effective correction for population stratification in association studies., Availability: The SNPweights software is available online at http://www.hsph.harvard.edu/faculty/alkes-price/software/., Supplementary Information: Supplementary data are available at Bioinformatics online.

Tumour-associated antigen human epidermal growth factor receptor 2 (HER2) is over-expressed in 25-30% of breast cancer patients and is associated with poor prognosis. Naturally occurring anti-HER2 antibody responses have been described in patients with HER2 over-expressing tumours. There is significant interindividual variability in antibody responsiveness, but the host genetic factors responsible for this variability are poorly understood. The aim of the present investigation was to determine whether immunoglobulin genetic markers [GM (genetic determinants of γ chains)] and Fcγ receptor (FcγR) alleles contribute to the magnitude of natural antibody responsiveness to HER2 in patients with breast cancer. A total of 855 breast cancer patients from Japan and Brazil were genotyped for several GM and FcγR alleles. They were also characterized for immunoglobulin (Ig)G antibodies to HER2. In white subjects (n = 263), GM 23-carriers had higher levels of anti-HER2 antibodies than non-carriers of this allele (p = 0·004). At the GM 5/21 locus, the homozygotes for the GM 5 allele had higher levels of anti-HER2 antibodies than the other two genotypes (P = 0·0067). In black subjects (n = 42), FcγRIIa-histidine/histidine homozygotes and FcγRIIIa-phenylalanine/valine heterozygotes were associated with high antibody responses (P = 0·0071 and 0·0275, respectively). FcγR genotypes in white subjects and GM genotypes in black subjects were not associated with anti-HER2 antibody responses. No significant associations were found in other study groups. These racially restricted contributions of GM and FcγR genotypes to humoral immunity to HER2 have potential implications for immunotherapy of breast cancer., (© 2012 British Society for Immunology.)

Population mixture is an important process in biology. We present a suite of methods for learning about population mixtures, implemented in a software package called ADMIXTOOLS, that support formal tests for whether mixture occurred and make it possible to infer proportions and dates of mixture. We also describe the development of a new single nucleotide polymorphism (SNP) array consisting of 629,433 sites with clearly documented ascertainment that was specifically designed for population genetic analyses and that we genotyped in 934 individuals from 53 diverse populations. To illustrate the methods, we give a number of examples that provide new insights about the history of human admixture. The most striking finding is a clear signal of admixture into northern Europe, with one ancestral population related to present-day Basques and Sardinians and the other related to present-day populations of northeast Asia and the Americas. This likely reflects a history of admixture between Neolithic migrants and the indigenous Mesolithic population of Europe, consistent with recent analyses of ancient bones from Sweden and the sequencing of the genome of the Tyrolean "Iceman."

Genes and proteins rarely act in isolation, but they rather operate as components of complex networks of interacting molecules. Therefore, for understanding their evolution, it may be helpful to take into account the interaction networks in which they participate. It has been shown that selective constraints acting on genes depend on the position that they occupy in the network. Less understood is how the impact of local adaptation at the intraspecific level is affected by the network structure. Here, we analyzed the patterns of molecular evolution of 67 genes involved in the insulin/target of rapamycin (TOR) signal transduction pathway. This well-characterized pathway plays a key role in fundamental processes such as energetic metabolism, growth, reproduction, and aging and is involved in metabolic disorders such as obesity, insulin resistance, and diabetes. For that purpose, we combined genotype data from worldwide human populations with current knowledge of the structure and function of the pathway. We identified the footprint of recent positive selection in nine of the studied genomic regions. Most of the adaptation signals were observed among Middle East and North African, European, and Central South Asian populations. We found that positive selection preferentially targets the most central elements in the pathway, in contrast to previous observations in the whole human interactome. This observation indicates that the impact of positive selection on genes involved in the insulin/TOR pathway is affected by the pathway structure.

Recent studies implicate single nucleotide polymorphisms (SNPs) within the 8q24 region as a risk factor for prostate cancer (PCa). New developments suggest that 8q24 encodes regulators of the nearby MYC gene, a known oncogene. In order to better understand the implications of SNPs in this region, we performed meta-analyses, stratified by race, of seven SNPs and one microsatellite marker previously identified as risk loci on the 8q24 region of the genome. In addition, we reviewed the literature examining the possible associations between these polymorphisms and clinicopathological features of PCa. The results of the meta-analyses indicate that rs6983267, rs1447295, rs6983561, rs7837688, rs16901979, and DG8S737 are significantly associated with a higher risk for PCa for at least one race, whereas the variants rs13254738 and rs7000448 are not. The degree of association and frequency of the causative allele varied among men of different races. Though several studies have demonstrated an association between certain 8q24 SNPs and clinicopathological features of the disease, review of this topic revealed conflicting results.

Recent work on the Neandertal genome has raised the possibility of admixture between Neandertals and the expanding population of Homo sapiens who left Africa between 80 and 50 Kya (thousand years ago) to colonize the rest of the world. Here, we provide evidence of a notable presence (9% overall) of a Neandertal-derived X chromosome segment among all contemporary human populations outside Africa. Our analysis of 6,092 X-chromosomes from all inhabited continents supports earlier contentions that a mosaic of lineages of different time depths and different geographic provenance could have contributed to the genetic constitution of modern humans. It indicates a very early admixture between expanding African migrants and Neandertals prior to or very early on the route of the out-of-Africa expansion that led to the successful colonization of the planet.

In the past decade, a number of case-control studies have been carried out to investigate the relationship between the HHEX polymorphism and type 2 diabetes (T2D). However, the results have been inconclusive. To investigate this inconsistency, we performed a meta-analysis of all available studies dealing with the relationship between the HHEX polymorphism and T2D. In total, 22 association studies on two HHEX polymorphisms (rs1111875 and rs7923837) and risk of T2D published before April 2010, including a total of 36 695 T2D cases and 51 800 controls were included. We also explored potential sources of heterogeneity. In a combined analysis, the summary per-allele odds ratio (OR) for T2D of the rs1111875 and rs7923837 polymorphism was 1.17 [95% confidence interval (CI): 1.13-1.21] and 1.23 (95% CI: 1.18-1.28), respectively. The haplotype analysis also showed significant association in the pooled international populations with an OR of 1.19 (95% CI: 1.15-1.22). In the subgroup analysis by ethnicity, significantly increased risks were found in Asians and Caucasians for these polymorphisms in almost all genetic models. Subgroup analysis also showed that ethnicity is the main source of heterogeneity between pooled studies. This meta-analysis demonstrated that the risk allele of HHEX polymorphisms (rs1111875 and rs7923837) is a risk factor for developing T2D. However, additional very large-scale studies are warranted to provide conclusive evidence on the effects of the HHEX gene on risk of T2D.

Synonymous mutations are considered to be "silent" as they do not affect protein sequence. However, different silent codons have different translation efficiency (TE), which raises the question to what extent such mutations are really neutral. We perform the first genome-wide study of natural selection operating on TE in recent human evolution, surveying 13,798 synonymous single nucleotide polymorphisms (SNPs) in 1,198 unrelated individuals from 11 populations. We find evidence for both negative and positive selection on TE, as measured based on differentiation in allele frequencies between populations. Notably, the likelihood of an SNP to be targeted by positive or negative selection is correlated with the magnitude of its effect on the TE of the corresponding protein. Furthermore, negative selection acting against changes in TE is more marked in highly expressed genes, highly interacting proteins, complex members, and regulatory genes. It is also more common in functional regions and in the initial segments of highly expressed genes. Positive selection targeting sites with a large effect on TE is stronger in lowly interacting proteins and in regulatory genes. Similarly, essential genes are enriched for negative TE selection while underrepresented for positive TE selection. Taken together, these results point to the significant role of TE as a selective force operating in humans and hence underscore the importance of considering silent SNPs in interpreting associations with complex human diseases. Testifying to this potential, we describe two synonymous SNPs that may have clinical implications in phenylketonuria and in Best's macular dystrophy due to TE differences between alleles.

Although there is agreement in that the Bering Strait was the entry point for the initial colonization of the American continent, there is considerable uncertainty regarding the timing and pattern of human migration from Asia to America. In order to perform a statistical assessment of the relative probability of alternative migration scenarios and to estimate key demographic parameters associated with them, we used an approximate Bayesian computation framework to analyze a data set of 401 autosomal microsatellite loci typed in 29 native American populations. A major finding is that a single, discrete, wave of colonization is highly inconsistent with observed levels of genetic diversity. A scenario with two discrete migration waves is also not supported by the data. The current genetic diversity of Amerindian populations is best explained by a third model involving recurrent gene flow between Asia and America, after initial colonization. We estimate that this colonization involved about 100 individuals and occurred some 13,000 years ago, in agreement with well-established archeological data.

Traditional methods for analyzing population structure, such as the Structure program, ignore the influence of the effect of allele mutations between the ancestral and current alleles of genetic markers, which can dramatically influence the accuracy of the structural estimation of current populations. Studying these effects can also reveal additional information about population evolution such as the divergence time and migration history of admixed populations. We propose mStruct, an admixture of population-specific mixtures of inheritance models that addresses the task of structure inference and mutation estimation jointly through a hierarchical Bayesian framework, and a variational algorithm for inference. We validated our method on synthetic data and used it to analyze the Human Genome Diversity Project-Centre d'Etude du Polymorphisme Humain (HGDP-CEPH) cell line panel of microsatellites and HGDP single-nucleotide polymorphism (SNP) data. A comparison of the structural maps of world populations estimated by mStruct and Structure is presented, and we also report potentially interesting mutation patterns in world populations estimated by mStruct.

Mutations in the human gene ALMS1 result in Alström Syndrome, which presents with early childhood obesity and insulin resistance leading to Type 2 diabetes. Previous genomewide scans for selection in the HapMap data based on linkage disequilibrium and population structure suggest that ALMS1 was subject to recent positive selection. Through a detailed population genomic analysis of existing genomewide data sets and new resequencing data obtained in geographically diverse populations, we find that the signature of selection at ALMS1 is considerably more complex than what would be expected for an idealized model of a selective sweep acting on a newly arisen advantageous mutation. Specifically, we observed three highly divergent and globally dispersed haplogroups, two of which carry a set of seven derived nonsynonymous single nucleotide polymorphisms that are nearly fixed in Asian populations. Our data suggest that the interaction of human demographic history and positive selection on standing variation in Eurasian populations approximately 15 thousand years ago parsimoniously explains the spectrum of extant ALMS1 variation. These results provide new insights into the evolutionary history of ALMS1 in humans and suggest that selective events identified in genomewide scans may be more complex than currently appreciated.

Context: In mice, scavenger receptor class B, type I (SR-BI) receptor protein deficiency is associated with elevated high-density lipoprotein (HDL)-cholesterol (HDL-C) levels., Objective: Our objective was to determine the relationship between SR-BI protein and HDL-C levels in humans., Design: This was a prospective study of adults with hyperalphalipoproteinemia. Fasting blood was obtained for lipid and lipoprotein measurement, genomic DNA, and monocyte-derived macrophages. SR-BI protein levels were measured by Western blots, and SR-BI activity was measured by cholesteryl ester (CE) uptake of each donor's radiolabeled HDL with their monocyte-derived macrophages, or by degradation and specific cell association of dual-labeled HDL in vitro., Setting: The study was performed in a tertiary university teaching hospital., Results: The mean age was 57.2 +/- 10.9 yr (n = 65). SR-BI protein levels were inversely associated with HDL-C levels (P < 0.002), HDL particle size (P < 0.05), and positively associated with CE uptake (P < 0.004); there was no association with plasma apolipoprotein levels. SR-BI protein levels (P = 0.01) were independent predictors of HDL-C levels. Subjects who were carriers of the A allele for the rs4238001 (glycine to serine at position 2) polymorphism [single nucleotide polymorphism (SNP)] had lower SR-BI protein levels (P = 0.01), whereas carriers of the C allele for the rs2278986 SNP also had lower SR-BI protein levels (P = 0.02). Body mass index (P = 0.05), rs4238001 (P = 0.01), and rs2278986 (P = 0.01) SNPs were independent predictors of SR-BI protein levels. In vitro studies of murine macrophages stably expressing the glycine to serine at position 2 SNP showed less degradation (P < 0.0004) and specific cell association (P < 0.0004) of [(125)I, (3)H]-CE-labeled HDL., Conclusions: SR-BI protein has an independent effect on HDL-C levels in women with hyperalphalipoproteinemia. Two SNPs were significantly associated with lower SR-BI protein levels.

In recent years approximate Bayesian computation (ABC) methods have become popular in population genetics as an alternative to full-likelihood methods to make inferences under complex demographic models. Most ABC methods rely on the choice of a set of summary statistics to extract information from the data. In this article we tested the use of the full allelic distribution directly in an ABC framework. Although the ABC techniques are becoming more widely used, there is still uncertainty over how they perform in comparison with full-likelihood methods. We thus conducted a simulation study and provide a detailed examination of ABC in comparison with full likelihood in the case of a model of admixture. This model assumes that two parental populations mixed at a certain time in the past, creating a hybrid population, and that the three populations then evolve under pure drift. Several aspects of ABC methodology were investigated, such as the effect of the distance metric chosen to measure the similarity between simulated and observed data sets. Results show that in general ABC provides good approximations to the posterior distributions obtained with the full-likelihood method. This suggests that it is possible to apply ABC using allele frequencies to make inferences in cases where it is difficult to select a set of suitable summary statistics and when the complexity of the model or the size of the data set makes it computationally prohibitive to use full-likelihood methods.

We estimate parameters of a general isolation-with-migration model using resequence data from mitochondrial DNA (mtDNA), the Y chromosome, and two loci on the X chromosome in samples of 25-50 individuals from each of 10 human populations. Application of a coalescent-based Markov chain Monte Carlo technique allows simultaneous inference of divergence times, rates of gene flow, as well as changes in effective population size. Results from comparisons between sub-Saharan African and Eurasian populations estimate that 1500 individuals founded the ancestral Eurasian population approximately 40 thousand years ago (KYA). Furthermore, these small Eurasian founding populations appear to have grown much more dramatically than either African or Oceanian populations. Analyses of sub-Saharan African populations provide little evidence for a history of population bottlenecks and suggest that they began diverging from one another upward of 50 KYA. We surmise that ancestral African populations had already been geographically structured prior to the founding of ancestral Eurasian populations. African populations are shown to experience low levels of mitochondrial DNA gene flow, but high levels of Y chromosome gene flow. In particular, Y chromosome gene flow appears to be asymmetric, i.e., from the Bantu-speaking population into other African populations. Conversely, mitochondrial gene flow is more extensive between non-African populations, but appears to be absent between European and Asian populations.

Background: Previous research suggests that the G315A single-nucleotide polymorphism in the ornithine decarboxylase (ODC) gene may be a genetic marker for risk of colorectal neoplasia and may also modify the association of aspirin use with risk., Methods: We tested these hypotheses among participants in the Aspirin/Folate Polyp Prevention Study who were randomly assigned to placebo or to aspirin treatment (81 or 325 mg daily) and followed for 3 years for the occurrence of new adenomas. Genomic DNA from 973 subjects was analyzed for ODC genotype. Relative risks (RRs) and 95% confidence intervals (CIs) were calculated to test the association between ODC genotype and adenoma occurrence and interactions with aspirin treatment. All statistical tests were two-sided., Results: Of the 973 subjects, 54% were homozygous wild-type (GG), 7% were homozygous variant (AA), and 39% were heterozygous individuals; the allele frequencies varied statistically significantly by race and ethnicity. Among these subjects, the absolute risk of any adenoma was 45% and the risk of an advanced lesion was 10%. Overall, no association was found between ODC genotype and the occurrence of new adenomas, but genotype did modify the effect of aspirin on adenoma risk. Although aspirin treatment had no protective effect among subjects with a GG genotype, among subjects with at least one A allele, it was associated with statistically significant reduced risks of any adenoma (RR = 0.77, 95% CI = 0.63 to 0.95; P = .02, P(interaction) = .04) and of advanced lesions (RR = 0.51, 95% CI = 0.29 to 0.90; P = .02, P(interaction) = .02). Among subjects with at least one A allele, 40.8% who took aspirin versus 52.9% who took placebo developed adenomas; 7.1% versus 14.0% developed advanced lesions., Conclusion: ODC genotype may modify the response to aspirin treatment for colorectal adenoma prevention.

Noncoding regions are usually less subject to natural selection than coding regions and so may be more useful for studying human evolution. The recent surveys of worldwide DNA variation in four 10-kb noncoding regions revealed many interesting but also some incongruent patterns. Here we studied another 10-kb noncoding region, which is in 6p22. Sixty-six single-nucleotide polymorphisms were found among the 122 worldwide human sequences, resulting in 46 genotypes, from which 48 haplotypes were inferred. The distribution patterns of DNA variation, genotypes, and haplotypes suggest rapid population expansion in relatively recent times. The levels of polymorphism within human populations and divergence between humans and chimpanzees at this locus were generally similar to those for the other four noncoding regions. Fu and Li's tests rejected the neutrality assumption in the total sample and in the African sample but Tajima's test did not reject neutrality. A detailed examination of the contributions of various types of mutations to the parameters used in the neutrality tests clarified the discrepancy between these test results. The age estimates suggest a relatively young history in this region. Combining three autosomal noncoding regions, we estimated the long-term effective population size of humans to be 11,000 +/- 2800 using Tajima's estimator and 17,600 +/- 4700 using Watterson's estimator and the age of the most recent common ancestor to be 860,000 +/- 258,000 years ago.

This study investigated the reliability of self-reported ancestry by comparing the interview responses of probands and their siblings. A total of 546 sibling pairs were ascertained in a family-based study of susceptibility genes for Parkinson's disease and asked to identify maternal and paternal countries of origin. Probands were recruited prospectively from the Department of Neurology of the Mayo Clinic in Rochester, Minnesota, from June 1, 1996, through May 31, 2005. Probands resided within Minnesota or one of the four surrounding states (Wisconsin, Iowa, South Dakota, North Dakota). Only 49 percent of these sibling pairs, primarily Caucasian, agreed completely on the countries of origin of both parents. The agreement increased to 68 percent when named countries were postcoded into six population genetic clusters (as previously defined by microsatellite markers). Self-reported ancestry may not be a reliable method to reduce the possible impact of population stratification in genetic association studies of outbred populations, such as in the United States.

Genetic variations in DNA repair genes are thought to modulate DNA repair capacity and are suggested to be related to cancer risk. However, epidemiologic findings have been inconsistent. The authors conducted meta-analyses of associations between genes in the base excision repair pathway and cancer risk, focusing on three key genes: 8-oxoguanine DNA glycosylase (OGG1), apurinic/apyrimidinic endonuclease (APE1/APEX1), and x-ray repair cross-complementing group 1 (XRCC1). They found increased lung cancer risk among subjects carrying the OGG1 Cys/Cys genotype (odds ratio (OR) = 1.24, 95% confidence interval (CI): 1.01, 1.53), using 3,253 cases and 3,371 controls from seven studies; this is consistent with experimental evidence that this isoform exhibits decreased activity. They found a protective effect of the XRCC1 194Trp allele for tobacco-related cancers (OR = 0.86, 95% CI: 0.77, 0.95), using 4,895 cases and 5,977 controls from 16 studies; this is compatible with evidence of lower mutagen sensitivity for this allele. The XRCC1 399Gln/399Gln genotype was associated with increased risk of tobacco-related cancers among light smokers (OR = 1.38, 95% CI: 0.99, 1.94) but decreased risk among heavy smokers (OR = 0.71, 95% CI: 0.51, 0.99), suggesting effect modification by tobacco smoking. There was no association between cancer risk and the APE1/APEX1 Asp148Glu and XRCC1 Arg280His polymorphisms. Recommendations for future studies include pooling of individual data to facilitate evaluation of multigenic effects and detailed analysis of effect modification by environmental exposure.

Leptin is an important regulator of the mass of adipose tissue and of body weight; it operates by inhibiting food intake and stimulating energy expenditure. Some polymorphic genes involved in the regulation of leptin-the leptin gene (LEP A19G), the leptin receptor gene (LEPR Q223R, K109R, and K656N), and the peroxisome proliferator-activated receptor-gamma gene (PPARG P12A and C161T)--have been investigated as possible factors associated with obesity. Allelic frequencies of these polymorphisms show ethnic variation. The authors performed a meta-analysis of the available data on the association between these polymorphisms and obesity based on case-control studies. Odds ratios and 95% confidence intervals for obesity associated with leptin polymorphisms were calculated by using both fixed- and random-effects models. Results suggest no evidence of association between the genes under study and obesity. The lack of association could be due to the complex pathogenesis of obesity, which involves a number of genetic and environmental factors. Large studies including testing of multiple genes in both obese and lean subjects, with epidemiologic data on dietary habits in different ethnic groups, are necessary to better understand the role of leptin in regulating weight in human populations.

To develop new strategies for searching for genetic associations with complex human diseases, we analyzed 2784 single-nucleotide polymorphisms (SNPs) in 396 protein-coding genes involved in biological processes relevant to cancer and other complex diseases, with respect to gene diversity within samples of individuals representing the three major historic human populations (African, European, and Asian) and with respect to interpopulation genetic distance. Reduced levels of both intrapopulation gene diversity and interpopulation genetic distance were seen in the case of SNPs located within the 5'-UTR and at nonsynonymous SNPs, causing radical changes to protein structure. Reduction of gene diversity at SNP loci in these categories was evidence of purifying selection acting at these sites, which in turn causes a reduction in interpopulation divergence. By contrast, a small number of SNP sites in these categories revealed unusually high genetic distances between the two most diverged populations (African and Asian); these loci may have historically been subject to divergent selection pressures.

Because GnRH and its receptor (GnRHR) are pivotal regulators of the reproductive endocrine axis and mutations in GNRHR lead to hypogonadotropic hypogonadism, we investigated whether genetic variation in GNRHR or GNRH1 affects pubertal timing in the general population. To screen for missense mutations in these genes that might affect pubertal timing, we resequenced the coding regions of these genes in 48 probands with late but otherwise normal pubertal development. No missense variants were found in either gene, except for a previously identified single nucleotide polymorphism (SNP) in GNRH1 that was not associated with late pubertal development. To search for common variants that might affect pubertal timing, we took a haplotype-based association approach. To identify common haplotypes in these genes, we genotyped 41 SNPs in DNA from commercially available European-derived multigenerational pedigrees and participants in a multiethnic cohort (MEC). Two blocks of strong linkage disequilibrium were identified that spanned GNRHR and one was identified spanning GNRH1; within each block, more than 80% of chromosomes carried one of a few common haplotypes. A set of haplotype-tagging SNPs that mark these common haplotypes in all five ethnic groups within the MEC were defined and used to perform association studies among 125 trios (probands with late pubertal development and their parents) and 506 women from the MEC who had early (menarche < 11 yr of age, n = 216) or late (menarche > or = 15 yr of age, n = 290) pubertal development. Three SNPs in GNRHR showed modest association with late pubertal development in the trios; among the 506 women, a different SNP was associated with late menarche, and one rare haplotype was associated with early age of menarche. All of the observed associations were relatively modest and only nominally statistically significant; replication is needed to determine their validity. We conclude that genetic variation in GNRH1 and GNRHR is not likely to be a substantial modulator of pubertal timing in the general population.

Studies of human DNA sequence polymorphism reveal a range of diversity patterns throughout the genome. This variation among loci may be due to natural selection, demographic influences, and/or different sampling strategies. Here we build on a continuing study of noncoding regions on the X chromosome in a panel of 41 globally sampled humans representing African and non-African populations by examining patterns of DNA sequence variation at four loci (APXL, AMELX, TNFSF5, and RRM2P4) and comparing these patterns with those previously reported at six loci in the same panel of 41 individuals. We also include comparisons with patterns of noncoding variation seen at five additional X-linked loci that were sequenced in similar global panels. We find that, while almost all loci show a reduction in non-African diversity, the magnitude of the reduction varies substantially across loci. The large observed variance in non-African levels of diversity results in the rejection of a neutral model of molecular evolution with a multi-locus HKA test under both a constant size and a bottleneck model. In non-Africans, some loci harbor an excess of rare mutations over neutral equilibrium predictions, while other loci show no such deviation in the distribution of mutation frequencies. We also observe a positive relationship between recombination rate and frequency spectra in our non-African, but not in our African, sample. These results indicate that a simple out-of-Africa bottleneck model is not sufficient to explain the observed patterns of sequence variation and that diversity-reducing selection acting at a subset of loci and/or a more complex neutral model must be invoked., (Copyright 2004 Genetics Society of America)

The SNP500Cancer Database provides sequence and genotype assay information for candidate single nucleotide polymorphisms (SNPs) useful in mapping complex diseases, such as cancer. The database is an integral component of the NCI's Cancer Genome Anatomy Project. SNP500Cancer provides bi-directional sequencing information on a set of control DNA samples derived from anonymized subjects (102 Coriell samples representing four self-described ethnic groups: African/African-American, Caucasian, Hispanic and Pacific Rim). All SNPs are chosen from public databases and reports, and the choice of genes includes a bias towards non-synonymous and promoter SNPs in genes that have been implicated in one or more cancers. The web site is searchable by gene, chromosome, gene ontology pathway and by known dbSNP ID. As of July 2003, the database contains over 3400 SNPs, 2490 of which have been sequenced in the SNP500Cancer population. For each analyzed SNP, gene location and over 200 bp of surrounding annotated sequence (including nearby SNPs) are provided, with frequency information in total and per subpopulation, and calculation of Hardy-Weinberg Equilibrium (HWE) for each subpopulation. Sequence validated SNPs with minor allele frequency > 5% are entered into a high-throughput pipeline for genotyping analysis to determine concordance for the same 102 samples. The website provides the conditions for validated genotyping assays. SNP500Cancer provides an invaluable resource for investigators to select SNPs for analysis, design genotyping assays using validated sequence data, choose selected assays already validated on one or more genotyping platforms, and select reference standards for genotyping assays. The SNP500Cancer Database is freely accessible via the web page at http://snp500cancer.nci.nih.gov/.

HbVar (http://globin.cse.psu.edu/globin/hbvar/) is a relational database developed by a multi-center academic effort to provide up-to-date and high quality information on the genomic sequence changes leading to hemoglobin variants and all types of thalassemia and hemoglobinopathies. Extensive information is recorded for each variant and mutation, including sequence alterations, biochemical and hematological effects, associated pathology, ethnic occurrence and references. In addition to the regular updates to entries, we report two significant advances: (i) The frequencies for a large number of mutations causing beta-thalassemia in at-risk populations have been extracted from the published literature and made available for the user to query upon. (ii) HbVar has been linked with the GALA (Genome Alignment and Annotation database, available at http://globin.cse.psu.edu/gala/) so that users can combine information on hemoglobin variants and thalassemia mutations with a wide spectrum of genomic data. It also expands the capacity to view and analyze the data, using tools within GALA and the University of California at Santa Cruz (UCSC) Genome Browser.

In most western countries prostate cancer is the most commonly diagnosed non-skin cancer in men. Despite its high morbidity and mortality the etiology of prostate cancer remains obscure. The involvement of androgens has been examined extensively in prostate carcinogenesis but the results of most epidemiological studies remain inconclusive. This review focuses on current perspectives of androgen levels and polymorphisms in androgen-related genes. Racial differences in genetic polymorphisms that have a role in the biosynthesis and metabolism of androgens may partly account for racial differences in prostate cancer risk. Reasons are also given for inconsistent results in molecular epidemiological studies and insights and directions for future research are discussed. The development of a polygenic model for prostate cancer, incorporating multiple loci from the individual genes, may maximize the chance of identifying individuals with high-risk genotypes, resulting in better preventive, diagnostic, and therapeutic strategies.