Digital Dermatoglyphic Patterns of Eskimo and Amerindian Populations: Relationships between Geographic, Dermatoglyphic, Genetic, and Linguistic Distances

This is the published version. Copyright 1992 Wayne State University Press.Dermatoglyphic traits have been used to assess population affinities and structure. Here, we describe the digital patterns of four Eskimo populations from Alaska: two Yupik-speaking villages from St. Lawrence Island and two Inupik groups presently residing on mainland Alaska. For a broader evolutionary perspective, these four Eskimo populations are compared to other Inuit groups, to North American Indian populations, and to Siberian aggregates. The genetic structures of 18 New and Old World populations were explored using /?-matrix plots and Wright's FST values. The relationships between dermatoglyphic, blood genetic, geographic, and linguistic distances were assessed by comparing matrices through Mantel correlations and through partial and multiple correlations. Statistically significant relationships between dermatoglyphics and genetics, genetics and geography, and geography and language were revealed. In addition, significant correlations between dermatoglyphics and geography, with linguistic variation constant, were noted for females but not for males. These results attest to the usefulness of dermatoglyphics in resolving various evolutionary questions concerning normal human variation

Population Structure of the Chenchu and Other South Indian Tribal Groups: Relationships between Genetic, Anthropometric, Dermatoglyphic, Geographic, and Linguistic Distances

This is the published version. Copyright 1994 Wayne State University Press.We describe the genetic structure and interrelationships of nine south Indian tribal groups (seven from Andhra Pradesh and two from the adjoining states of Tamil Nadu and Kerala) using seven polymorphic loci (ABO, MN, RH, PGM, ACP, PGD, and LDH). R matrix analysis indicates that the Andhra Pradesh tribes are clustered and that the Kadar and Irula are genetically isolated from them. This dispersion of populations has been explained by the combination of relatively high frequencies of the alleles RH D and MN M in the Kadar and the relatively high proportions of the allele PGM*2 in the Irula. The Mahaboobnagar Chenchu subgroup is isolated from other Telugu-speaking groups because of high frequencies of the PGM*1 and ACP*A alleles. The regression of mean per locus heterozygosity (//) on distance from the gene frequency centroid (r„) reveals considerable levels of external gene flow among the Lam-badi, the Yerukula, and the two Chenchu subgroups and more homogeneity in the Kolam, Koya, Yanadi, Irula, and Kadar. Mantel statistics were used to assess the relative effects of nonbiological processes (i.e., language and geography) on the morphological and genetic patterns of these subdivided populations. The significance of correlations was determined between different data sets (genetic, dermatoglyphic, anthropometric, geographic, and linguistic) at three levels involving nine, six, and five populations. Although multiple correlation analysis reveals significant combined effects of geography and language on genetics, anthropometrics, and dermato-glyphics, highly significant partial correlations suggest strong effects of geography on both anthropometry and genetics. Our analysis indicates that geographic factors have an overwhelming effect on the genetic differentiation of the south Indian tribal groups

Effect of genotype × alcoholism interaction on linkage analysis of an alcoholism-related quantitative phenotype

Studies have shown that genetic and environmental factors and their interactions affect several alcoholism phenotypes. Genotype × alcoholism (G×A) interaction refers to the environmental (alcoholic and non-alcoholic) influences on the autosomal genes contributing to variation in an alcoholism-related quantitative phenotype. The purpose of this study was to examine the effects of G×A interaction on the detection of linkage for alcoholism-related phenotypes. We used phenotypic and genotypic data from the Collaborative Study on the Genetics of Alcoholism relating to 1,388 subjects as part of Genetic Analysis Workshop 14 problem 1. We analyzed the MXDRNK phenotype to detect G×A interaction using SOLAR. Upon detecting significant interaction, we conducted variance-component linkage analyses using microsatellite marker data. For maximum number of drinks per a 24 hour period, the highest LODs were observed on chromosomes 1, 4, and 13 without G×A interaction. Interaction analysis yielded four regions on chromosomes 1, 4, 13, and 15. On chromosome 4, a maximum LOD of 1.5 at the same location as the initial analysis was obtained after incorporating G×A interaction effects. However, after correcting for extra parameters, the LOD score was reduced to a corrected LOD of 1.1, which is similar to the LOD observed in the non-interaction analysis. Thus, we see little differences in LOD scores, while some linkage regions showed large differences in the magnitudes of estimated quantitative trait loci heritabilities between the alcoholic and non-alcoholic groups. These potential hints of differences in genetic effect may influence future analyses of variants under these linkage peaks

Heritability of anthropometric phenotypes in caste populations of Visakhapatnam, India

This is the publisher's version, also available electronically from http://digitalcommons.wayne.edu/humbiol/vol74/iss3/1/.In this study, we used anthropometric data from six Andhra caste populations to examine heritability patterns of 23 anthropometric phenotypes (linear, craniofacial, and soft tissue measures) with special reference to caste differences. We obtained anthropometric data from 342 nuclear families from Brahmin, Reddy, Telaga, Nagara, Ag. Kshatriya, and Mala castes of Visakhapatnam, India. These caste groups represent the existing hierarchical stratification of Indian populations. We used a variance components approach to determine the heritability (h2) of these 23 anthropometric phenotypes (height, weight, BMI, etc.). The sample consisted of 1918 individuals ranging in age from 6 to 72 years (mean = 21.5, S.D. = 13.8). The heritabilities (h2 ± S.E.) for all anthropometric traits for the entire sample were significant (p < 0.0001) and varied from 0.25 ± 0.05 (BMI) to 0.61 ± 0.05 (bizygomatic breadth) after accounting for sex, age, and caste effects. Since data on socioeconomic and nutritional covariates were available for a subset of families, we repeated the genetic analyses using this subset, which has yielded higher heritabilities ranging from 0.21 ± 0.16 (head breadth) to 0.72 ± 0.18 (nasal breadth). In general, craniofacial measurements exhibited higher h2 compared to linear measures. Breadth measurements and circumferences yielded more or less similar heritabilities. Age and sex effects were significant (p < 0.0001) for most of the traits, while the effects of caste, socioeconomic status, and nutritional status were inconsistent across the traits. In conclusion, anthropometric phenotypes examined in this study are under appreciable additive genetic influences

Benchmarking Relatedness Inference Methods with Genome-Wide Data from Thousands of Relatives

Inferring relatedness from genomic data is an essential component of genetic association studies, population genetics, forensics, and genealogy. While numerous methods exist for inferring relatedness, thorough evaluation of these approaches in real data has been lacking. Here, we report an assessment of 12 state-of-the-art pairwise relatedness inference methods using a data set with 2485 individuals contained in several large pedigrees that span up to six generations. We find that all methods have high accuracy (92–99%) when detecting first- and second-degree relationships, but their accuracy dwindles to \u3c43% for seventh-degree relationships. However, most identical by descent (IBD) segment-based methods inferred seventh-degree relatives correct to within one relatedness degree for \u3e76% of relative pairs. Overall, the most accurate methods are Estimation of Recent Shared Ancestry (ERSA) and approaches that compute total IBD sharing using the output from GERMLINE and Refined IBD to infer relatedness. Combining information from the most accurate methods provides little accuracy improvement, indicating that novel approaches, such as new methods that leverage relatedness signals from multiple samples, are needed to achieve a sizeable jump in performance

Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls

Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (P = 6.1 × 10−3) and candidate genes from knockout mice (P = 5.2 × 10−3). Within our study, the strongest T2D gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000–185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts

Physical activity and FTO genotype by physical activity interactive influences on obesity

Background: Although the effect of the fat mass and obesity-associated (FTO) gene on adiposity is well established, there is a lack of evidence whether physical activity (PA) modifies the effect of FTO variants on obesity in Latino populations. Therefore, the purpose of this study was to examine PA influences and interactive effects between FTO variants and PA on measures of adiposity in Latinos. Results: After controlling for age and sex, participants who did not engage in regular PA exhibited higher BMI, fat mass, HC, and WC with statistical significance (P \u3c 0.001). Although significant associations between the three FTO genotypes and adiposity measures were found, none of the FTO genotype by PA interaction assessments revealed nominally significant associations. However, several of such interactive influences exhibited considerable trend towards association. Conclusions: These data suggest that adiposity measures are associated with PA and FTO variants in Latinos, but the impact of their interactive influences on these obesity measures appear to be minimal. Future studies with large sample sizes may help to determine whether individuals with specific FTO variants exhibit differential responses to PA interventions

Genetic diversity fuels gene discovery for tobacco and alcohol use

Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury1,2,3,4. These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries5. Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction