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

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.

Multi-biomarker is an early-stage predictor for progression of Coronavirus disease 2019 (COVID-19) infection

Coronavirus disease 2019 (COVID-19) has spread widely in the communities in many countries. Although most of the mild patients could be cured by their body's ability to self-heal, many patients quickly progressed to severe disease and had to undergo treatment in the intensive care unit (ICU). Thus, it is very important to effectively predict which patients with mild disease are more likely to progress to severe disease. A total of 72 patients hospitalized with COVID-19 in Shandong Provincial Public Health Clinical Center and 1141 patients included in the published papers were enrolled in this study. We determined that the combination of interleukin-6 (IL-6), Neutrophil (NEUT), and Natural Killer (NK) cells had the highest prediction accuracy (with 75% sensitivity and 95% specificity) for progression of COVID-19 infection. A binomial regression equation that accounted for a multiple risk score for the combination of IL-6, NEUT, and NK was also established. The multiple risk score is a good indicator for early stratification of mild patients into risk categories, which is very important for adjusting the treatment plan and preventing death.

Genome-wide genetic diversity yields insights into genomic responses of candidate climate-selected loci in an Andean wetland plant

Assessing population evolutionary potential has become a central tenet of conservation biology. Since adaptive responses require allelic variation at functional genes, consensus has grown that genetic variation at genes under selection is a better surrogate for adaptive evolutionary potential than neutral genetic diversity. Although consistent with prevailing theory, this argument lacks empirical support and ignores recent theoretical advances questioning the very concept of neutral genetic diversity. In this study, we quantified genome-wide responses of single nucleotide polymorphism loci linked to climatic factors over a strong latitudinal gradient in natural populations of the high Andean wetland plant, Carex gayana, and then assessed whether genetic variation of candidate climate-selected loci better predicted their genome-wide responses than genetic variation of non-candidate loci. Contrary to this expectation, genomic responses of climate-linked loci only related significantly to environmental variables and genetic diversity of non-candidate loci. The effects of genome-wide genetic diversity detected in this study may be a result of either the combined influence of small effect variants or neutral and demographic factors altering the adaptive evolutionary potential of C. gayana populations. Regardless of the processes involved, our results redeem genome-wide genetic diversity as a potentially useful indicator of population adaptive evolutionary potential.

Prediction of short-term neonatal complications in preterm infants using exome-wide genetic variation and gestational age: a pilot study

BACKGROUND

Preterm birth is the leading cause of mortality and morbidity in young children, with over a million deaths per year worldwide arising from neonatal complications (NCs). NCs are moderately heritable although the genetic causes are largely unknown. Therefore, we investigated the impact of accumulated genetic variation (burden) on NCs in non-Hispanic White (NHW) and non-Hispanic Black (NHB) preterm infants.

METHODS

We sequenced 182 exomes from infants with gestational ages from 26 to 31 weeks. These infants were cared for in the same time period and hospital environment. Eighty-one preterm infants did not develop NCs, whereas 101 developed at least one severe complication. We measured the effect of burden at the single-gene and exome-wide levels and derived a polygenic risk score (PRS) from the top 10 genes to predict NCs.

RESULTS

Burden across the exome was associated with NCs in NHW (p = 0.05) preterm infants suggesting that multiple genes influence susceptibility. In a post hoc analysis, we find that PRS alone predicts NCs (AUC = 0.67) and that PRS is uncorrelated with GA ([Formula: see text] = 0.05; p = 0.53). When PRS and GA at birth are combined, the AUC is 0.87.

CONCLUSIONS

Our results support the hypothesis that genetic burden influences NCs in NHW preterm infants.

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.

Accumulation of minor alleles and risk prediction in schizophrenia

Schizophrenia is a common neuropsychiatric disorder with a lifetime risk of 1%. Accumulation of common polygenic variations has been found to be an important risk factor. Recent studies showed a role for the enrichment of minor alleles (MAs) of SNPs in complex diseases such as Parkinson’s disease. Here we similarly studied the role of genome wide MAs in schizophrenia using public datasets. Relative to matched controls, schizophrenia cases showed higher average values in minor allele content (MAC) or the average amount of MAs per subject. By risk prediction analysis based on weighted genetic risk score (wGRS) of MAs, we identified an optimal MA set consisting of 23 238 variants that could be used to predict 3.14% of schizophrenia cases, which is comparable to using 22q11 deletion to detect schizophrenia cases. Pathway enrichment analysis of these SNPs identified 30 pathways with false discovery rate (FDR) <0.02 and of significant P-value, most of which are known to be linked with schizophrenia and other neurological disorders. These results suggest that MAs accumulation may be a risk factor to schizophrenia and provide a method to genetically screen for this disease.

Genetic equidistance at nucleotide level

The genetic equidistance phenomenon shows complex taxa to be all approximately equidistant to a less complex species in amino acid percentage identity. The overlooked mystery was re-interpreted by the maximum genetic diversity hypothesis (MGD). Here, we studied 14 proteomes and their coding DNA sequences (CDS) to see if the equidistance phenomenon also holds at the CDS level. We found that the outgroup taxon was equidistant to the two more complex taxa species. When two sister taxa were compared to human as the outgroup, the more complex taxon was closer to human, confirming species complexity to be the primary determinant of MGD. Finally, we found the fraction of overlap sites to be inversely correlated with CDS conservation, indicating saturation to be more common in less conserved DNAs. These results establish the genetic equidistance phenomenon to be universal at the DNA level and provide additional evidence for the MGD theory.

Rules for resolving Mendelian inconsistencies in nuclear pedigrees typed for two-allele markers

Gene-mapping studies, regularly, rely on examination for Mendelian transmission of marker alleles in a pedigree as a way of screening for genotyping errors and mutations. For analysis of family data sets, it is, usually, necessary to resolve or remove the genotyping errors prior to consideration. At the Center of Inherited Disease Research (CIDR), to deal with their large-scale data flow, they formalized their data cleaning approach in a set of rules based on PedCheck output. We scrutinize via carefully designed simulations that how well CIDR’s data cleaning rules work in practice. We found that genotype errors in siblings are detected more often than in parents for less polymorphic SNPs and vice versa for more polymorphic SNPs. Through computer simulations, we conclude that some of the CIDR’s rules work poorly in some circumstances, and we suggest a set of modified data cleaning rules that may work better than CIDR’s rules.

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.

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.