Scoring the collective effects of SNPs: association of minor alleles with complex traits in model organisms

The time and place of origin of South Caucasian languages: insights into past human societies, ecosystems and human population genetics

This study re-examines the linguistic phylogeny of the South Caucasian linguistic family (aka the Kartvelian linguistic family) and attempts to identify its Urheimat. We apply Bayesian phylogenetics to infer a dated phylogeny of the South Caucasian languages. We infer the Urheimat and the reasons for the split of the Kartvelian languages by taking into consideration (1) the past distribution ranges of wildlife elements whose names can be traced back to proto-Kartvelian roots, (2) the distribution ranges of past cultures and (3) the genetic variations of past and extant human populations. Our best-fit Bayesian phylogenetic model is in agreement with the widely accepted topology suggested by previous studies. However, in contrast to these studies, our model suggests earlier mean split dates, according to which the divergence between Svan and Karto-Zan occurred in the early Copper Age, while Georgian and Zan diverged in the early Iron Age. The split of Zan into Megrelian and Laz is widely attributed to the spread of Georgian and/or Georgian speakers in the seventh-eighth centuries CE. Our analyses place the Kartvelian Urheimat in an area that largely intersects the Colchis glacial refugium in the South Caucasus. The divergence of Kartvelian languages is strongly associated with differences in the rate of technological expansions in relation to landscape heterogeneity, as well as the emergence of state-run communities. Neolithic societies could not colonize dense forests, whereas Copper Age societies made limited progress in this regard, but not to the same degree of success achieved by Bronze and Iron Age societies. The paper also discusses the importance of glacial refugia in laying the foundation for linguistic families and where Indo-European languages might have originated.

Race-specific association of an IRGM risk allele with cytokine expression in human subjects

Immunity-related GTPase family M (IRGM), located on human chromosome 5q33.1, encodes a protein that promotes autophagy and suppresses the innate immune response. The minor allele of rs13361189 (-4299T>C), a single nucleotide polymorphism in the IRGM promoter, has been associated with several diseases, including Crohn's disease and tuberculosis. Although patterns of linkage disequilibrium and minor allele frequency for this polymorphism differ dramatically between subjects of European and African descent, studies of rs13361189 have predominantly been conducted in Europeans and the mechanism of association is poorly understood. We recruited a cohort of 68 individuals (30 White, 34 African American, 4 other race) with varying rs13361189 genotypes and assessed a panel of immune response measures including whole blood cytokine induction following ex vivo stimulation with Toll-like Receptor ligands. Minor allele carriers were found to have increased serum immunoglobulin M, C-reactive protein, and circulating CD8+ T cells. No differences in whole blood cytokines were observed between minor allele carriers and non-carriers in the overall study population; however, minor allele status was associated with increased induction of a subset of cytokines among African American subjects, and decreased induction among White subjects. These findings underline the importance of broad racial inclusion in genetic studies of immunity.

The collective effects of genetic variants and complex traits

Traditional approaches in studying the genetics of complex traits have focused on identifying specific genetic variants. However, the collective effects of variants have remained largely unexplored. Here, we evaluated whether traits could be influenced by the collective effects of variants across the entire protein coding-region of the genome or the entire genome. We studied the UK Biobank exome sequencing data of 167,246 individuals as well as the genome-wide SNP array data of 408,868 individuals. We calculated for each individual four different measures of genetic variation such as heterozygosity and number of variants and two different measures of the overall deleteriousness of all variants, and performed correlations with 17 representative traits that have been studied previously. Linear regression analysis was performed with adjustment for age, sex, and genetic principal components. The results showed a high correlation among the six different measures and an inverse association of two well-correlated traits (educational attainment and height) with the total number of all variants as well as the overall deleteriousness of all variants. We have also categorized the genes based on whether they are expressed in the brain and found that the association with educational attainment only held for the brain-expressed genes. No other traits examined showed a significant correlation with the brain-expressed genes. The study demonstrates that common traits could be studied by analyzing the overall genetic variation and suggests that educational attainment is inversely related to genetic variation.

Enrichment in conservative amino acid changes among fixed and standing missense variations in slowly evolving proteins

The process of molecular evolution has many elements that are not yet fully understood. Evolutionary rates are known to vary among protein coding and noncoding DNAs, and most of the observed changes in amino acid or nucleotide sequences are assumed to be non-adaptive by the neutral theory of molecular evolution. However, it remains unclear whether fixed and standing missense changes in slowly evolving proteins are more or less neutral compared to those in fast evolving genes. Here, based on the evolutionary rates as inferred from identity scores between orthologs in human and Rhesus Macaques (Macaca mulatta), we found that the fraction of conservative substitutions between species was significantly higher in their slowly evolving proteins. Similar results were obtained by using four different methods of scoring conservative substitutions, including three that remove the impact of substitution probability, where conservative changes require fewer mutations. We also examined the single nucleotide polymorphisms (SNPs) by using the 1000 Genomes Project data and found that missense SNPs in slowly evolving proteins also had a higher fraction of conservative changes, especially for common SNPs, consistent with more non-conservative substitutions and hence stronger natural selection for SNPs, particularly rare ones, in fast evolving proteins. These results suggest that fixed and standing missense variants in slowly evolving proteins are more likely to be neutral.

The relationship between the minor allele content and Alzheimer's disease

Alzheimer's disease (AD) is a chronic neurodegenerative disease. The genetic risk factors of AD remain better understood. Using previously published dataset of common single nucleotide polymorphisms (SNPs), we studied the association between the minor allele content (MAC) in an individual and AD. We found that AD patients have higher average MAC values than matched controls. We identified a risk prediction model that could predict 2.19% of AD cases. We also identified 49 genes whose expression levels correlated with both MAC and AD. By pathway and process enrichment analyses, these genes were found in pathways or processes closely related to AD. Our study suggests that AD may be linked with too many genetic variations over a threshold. The method of correlations with both MAC and traits appears to be effective in high efficiency identification of target genes for complex traits.

Identification of Specific Nuclear Genetic Loci and Genes That Interact With the Mitochondrial Genome and Contribute to Fecundity in Caenorhabditis elegans

Previous studies have found that fecundity is a multigenic trait regulated, in part, by mitochondrial-nuclear (mit-n) genetic interactions. However, the identification of specific nuclear genetic loci or genes interacting with the mitochondrial genome and contributing to the quantitative trait fecundity is an unsolved issue. Here, a panel of recombinant inbred advanced intercrossed lines (RIAILs), established from a cross between the N2 and CB4856 strains of C. elegans, were used to characterize the underlying genetic basis of mit-n genetic interactions related to fecundity. Sixty-seven single nucleotide polymorphisms (SNPs) were identified by association mapping to be linked with fecundity among 115 SNPs linked to mitotype. This indicated significant epistatic effects between nuclear and mitochondria genetics on fecundity. In addition, two specific nuclear genetic loci interacting with the mitochondrial genome and contributing to fecundity were identified. A significant reduction in fecundity was observed in the RIAILs that carried CB4856 mitochondria and a N2 genotype at locus 1 or a CB4856 genotype at locus 2 relative to the wild-type strains. Then, a hybrid strain (CNC10) was established, which was bred as homoplasmic for the CB4856 mtDNA genome and N2 genotype at locus 1 in the CB4856 nuclear background. The mean fecundity of CNC10 was half the fecundity of the control strain. Several functional characteristics of the mitochondria in CNC10 were also influenced by mit-n interactions. Overall, experimental evidence was presented that specific nuclear genetic loci or genes have interactions with the mitochondrial genome and are associated with fecundity. In total, 18 genes were identified using integrative approaches to have interactions with the mitochondrial genome and to contribute to fecundity.

Collective effects of common single nucleotide polymorphisms and genetic risk prediction in type 1 diabetes

Type 1 diabetes (T1D) is a common autoimmune disease and may be related to multiple genetic and environmental risk factors. Previous genetic studies have focused on looking for individual polymorphic risk variants. Here, we studied the overall levels of genetic diversity in T1D patients by making use of a previously published study including 1865 cases and 2828 reference samples with genotyping data for 500 K common single nucleotide polymorphisms (SNPs). We determined the minor allele (MA) status of each SNP in the reference samples and calculated the total number of MAs or minor allele contents (MAC) of each individual. We found the average MAC of cases to be greater than that of the reference samples. By focusing on MAs with strong linkage to cases, we further identified a set of 112 SNPs that could predict 19.19% of cases. These results suggest that overall genetic variation over a threshold level may be a risk factor in T1D and provide a new genetic method for predicting the disorder.

Collective effects of common SNPs and risk prediction in lung cancer

Lung cancer is the leading cause of cancer deaths in both men and women in the US. While most sporadic lung cancer cases are related to environmental factors such as smoking, genetic susceptibility may also play an important role and a number of lung cancer associated single-nucleotide polymorphisms (SNPs) have been identified although many remain to be found. The collective effects of genome-wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have been linked with quantitative variations of complex traits and diseases. Here we studied MAC in lung cancer using previously published SNPs data sets (US and Finland samples) and found higher MAC in cases relative to matched controls. A set of 5400 SNPs with MA (MAF < 0.5) more common in cases (P < 0.08) and linkage disequilibrium (LD) r² = 0.3 was found to have the best predictive accuracy. These results identify higher MAC in lung cancer susceptibility and provide a meaningful genetic method to identify those at risk of lung cancer.

Enrichment of minor allele of SNPs and genetic prediction of type 2 diabetes risk in British population

Type 2 diabetes (T2D) is a complex disorder characterized by high blood sugar, insulin resistance, and relative lack of insulin. The collective effects of genome wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have been linked with quantitative variations of complex traits and diseases. Here we studied MAC in T2D using previously published SNP datasets and found higher MAC in cases relative to matched controls. A set of 357 SNPs was found to have the best predictive accuracy in a British population. A weighted risk score calculated by using this set produced an area under the curve (AUC) score of 0.86, which is comparable to risk models built by phenotypic markers. These results identify a novel genetic risk element in T2D susceptibility and provide a potentially useful genetic method to identify individuals with high risk of T2D.

Close genetic relationships between a spousal pair with autism-affected children and high minor allele content in cases in autism-associated SNPs

Parents of children affected with autism spectrum disorders (ASD) often have mild forms of autistic-like characteristics. Past studies have focused on searching for individual genetic risk loci of ASD. Here we studied the overall properties of the genomes of ASD trios by using previously published genome-wide data for common SNPs. The pairwise genetic distance (PGD) between a spousal pair with ASD-affected children was found smaller than that of a random pair selected among the spouses in the ASD trios, and spousal relatedness correlated with severe forms of ASD. Furthermore, for a set of 970 ASD associated SNPs, cases showed higher homozygous minor allele content than parents. These results indicate new genetic elements in the broad phenotypes of parents with ASD-affected offspring and in ASD pathogenesis.

New thoughts on an old riddle: What determines genetic diversity within and between species?

The question of what determines genetic diversity has long remained unsolved by the modern evolutionary theory (MET). However, it has not deterred researchers from producing interpretations of genetic diversity by using MET. We examine the two observations of genetic diversity made in the 1960s that contributed to the development of MET. The interpretations of these observations by MET are widely known to be inadequate. We review the recent progress of an alternative framework, the maximum genetic diversity (MGD) hypothesis, that uses axioms and natural selection to explain the vast majority of genetic diversity as being at equilibrium that is largely determined by organismal complexity. The MGD hypothesis absorbs the proven virtues of MET and considers its assumptions relevant only to a much more limited scope. This new synthesis has accounted for the overlooked phenomenon of progression towards higher complexity, and more importantly, been instrumental in directing productive research.

Copy number variations play important roles in heredity of common diseases: a novel method to calculate heritability of a polymorphism

“Missing heritability” in genome wide association studies, the failure to account for a considerable fraction of heritability by the variants detected, is a current puzzle in human genetics. For solving this puzzle the involvement of genetic variants like rare single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) has been proposed. Many papers have published estimating the heritability of sets of polymorphisms, however, there has been no paper discussing the estimation of a heritability of a single polymorphism. Here I show a simple but rational method to calculate heritability of an individual polymorphism, h_p². Using this method, I carried out a trial calculation of h_p² of CNVs and SNPs using published data. It turned out that h_p² of some CNVs is quite large. Noteworthy examples were that about 25% of the heritability of type 2 diabetes mellitus and about 15% of the heritability of schizophrenia could be accounted for by one CNV and by four CNVs, respectively. The results suggest that a large part of missing heritability could be accounted for by re-evaluating the CNVs which have been already found and by searching novel CNVs with large h_p².

Collective effects of common SNPs in foraging decisions in Caenorhabditis elegans and an integrative method of identification of candidate genes

Optimal foraging decision is a quantitative flexible behavior, which describes the time at which animals choose to abandon a depleting food supply. The total minor allele content (MAC) in an individual has been shown to correlate with quantitative variations in complex traits. We have studied the role of MAC in the decision to leave a food lawn in recombinant inbred advanced intercross lines (RIAILs) of Caenorhabditis elegans. We found a strong link between MAC and the food lawn leaving rates (Spearman r = 0.4, P = 0.005). We identified 28 genes of unknown functions whose expression levels correlated with both MAC and leaving rates. When examined by RNAi experiments, 8 of 10 tested among the 28 affected leaving rates, whereas only 2 of 9 did among genes that were only associated with leaving rates but not MAC (8/10 vs 2/9, P < 0.05). The results establish a link between MAC and the foraging behavior and identify 8 genes that may play a role in linking MAC with the quantitative nature of the trait. The method of correlations with both MAC and traits may find broad applications in high efficiency identification of target genes for other complex traits in model organisms and humans.

Enrichment of Minor Alleles of Common SNPs and Improved Risk Prediction for Parkinson's Disease

Parkinson disease (PD) is the second most common neurodegenerative disorder in the aged population and thought to involve many genetic loci. While a number of individual single nucleotide polymorphisms (SNPs) have been linked with PD, many remain to be found and no known markers or combinations of them have a useful predictive value for sporadic PD cases. The collective effects of genome wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have recently been shown to be linked with quantitative variations of numerous complex traits in model organisms with higher MAC more likely linked with lower fitness. Here we found that PD cases had higher MAC than matched controls. A set of 37564 SNPs with MA (MAF < 0.4) more common in cases (P < 0.05) was found to have the best predictive accuracy. A weighted risk score calculated by using this set can predict 2% of PD cases (100% specificity), which is comparable to using familial PD genes to identify familial PD cases. These results suggest a novel genetic component in PD and provide a useful genetic method to identify a small fraction of PD cases.