Whole-exome sequencing has been successful in identifying genetic factors contributing to familial or sporadic Parkinson’s disease (PD). However, this approach has not been applied to explore the impact of de novo mutations on PD pathogenesis. Here, we sequenced the exomes of 39 early onset patients, their parents, and 20 unaffected siblings to investigate the effects of de novo mutations on PD. We identified 12 genes with de novo mutations (MAD1L1,NUP98,PPP2CB,PKMYT1,TRIM24,CEP131,CTTNBP2,NUS1,SMPD3,MGRN1,IFI35, andRUSC2), which could be functionally relevant to PD pathogenesis. Further analyses of two independent case-control cohorts (1,852 patients and 1,565 controls in one cohort and 3,237 patients and 2,858 controls in the other) revealed thatNUS1harbors significantly more rare nonsynonymous variants (P= 1.01E-5, odds ratio = 11.3) in PD patients than in controls. Functional studies inDrosophilademonstrated that the loss ofNUS1could reduce the climbing ability, dopamine level, and number of dopaminergic neurons in 30-day-old flies and could induce apoptosis in fly brain. Together, our data suggest that de novo mutations could contribute to early onset PD pathogenesis and identifyNUS1as a candidate gene for PD.

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.

Recent studies have established that genetic diversities are mostly maintained by selection, therefore rendering the present molecular model of human origins untenable. Using improved methods and public data, we have revisited human evolution and derived an age of 1.91-1.96 million years for the first split in modern human autosomes. We found evidence of modern Y and mtDNA originating in East Asia and dispersing via hybridization with archaic humans. Analyses of autosomes, Y and mtDNA all suggest that Denisovan like humans were archaic Africans with Eurasian admixtures and ancestors of South Asia Negritos and Aboriginal Australians. Verifying our model, we found more ancestry of Southern Chinese from Hunan in Africans relative to other East Asian groups examined. These results suggest multiregional evolution of autosomes and East Asia origin of Y and mtDNA, thereby leading to a coherent account of modern human origins.

It has long been assumed that most parts of a genome and most genetic variations or SNPs are non-functional with regard to reproductive fitness. However, the collective effects of SNPs have yet to be examined by experimental science. We here developed a novel approach to examine the relationship between traits and the total amount of SNPs in panels of genetic reference populations. We identified the minor alleles (MAs) in each panel and the MA content (MAC) that each inbred strain carried for a set of SNPs with genotypes determined in these panels. MAC was nearly linearly linked to quantitative variations in numerous traits in model organisms, including life span, tumor susceptibility, learning and memory, sensitivity to alcohol and anti-psychotic drugs, and two correlated traits poor reproductive fitness and strong immunity. These results suggest that the collective effects of SNPs are functional and do affect reproductive fitness. collective effects, complex traits, minor alleles, SNPs, recombinant inbred lines, minor allele content (MAC) Citation:

Mutations in mitochondrial genome have epistatic effects on organisms depending on the nuclear background, but a role for the compatibility of mitochondrial-nuclear genomes (mit-n) in the quantitative nature of a complex trait remains unexplored. We studied a panel of recombinant inbred advanced intercrossed lines (RIAILs) of C. elegans that were established from a cross between the N2 and HW strains. We determined the HW nuclear genome content and the mitochondrial type (HW or N2) of each RIAIL strain. We found that the degree of mit-n compatibility was correlated with the lifespans but not the foraging behaviors of RIAILs. Several known aging-associated QTLs individually showed no relationship with mitotypes but collectively a weak trend consistent with a role in mit-n compatibility. By association mapping, we identified 293 SNPs that showed linkage with lifespan and a relationship with mitotypes consistent with a role in mit-n compatibility. We further found an association between mit-n compatibility and several functional characteristics of mitochondria as well as the expressions of genes involved in the respiratory oxidation pathway. The results provide the first evidence implicating mit-n compatibility in the quantitative nature of a complex trait, and may be informative to certain evolutionary puzzles on hybrids.

Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

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 SNP datasets and found higher MAC in cases relative to matched controls. A set of 25883 SNPs with MA (MAF < 0.5) more common in cases (P < 0.1) was found to have the best predictive accuracy. A weighted risk score calculated by using this set can predict 2.6% of lung cancer cases (100% specificity). These results identify a novel genetic risk element or higher MAC in lung cancer susceptibility and provide a useful genetic method to identify a small fraction of lung cancer cases.

We studied the collective effects of single nucleotide polymorphisms (SNPs) on transgenerational inheritance in Caenorhabditis elegans recombinant inbred advanced intercross lines (RIAILs) and yeast segregants. We divided the RIAILs and segregants into two groups of high and low minor allele content (MAC). RIAILs with higher MAC needed less generations of benzaldehyde training to gain a stable olfactory imprint and showed a greater change from normal after benzaldehyde training. Yeast segregants with higher MAC showed a more dramatic shortening of the lag phase length after ethanol exposure. The short lag phase as acquired by ethanol training was more dramatically lost after recovery in ethanol free medium for the high MAC group. We also found a preferential association between MAC and traits linked with higher number of additive QTLs. These results suggest a role for the collective effects of SNPs in transgenerational inheritance, and may help explain human variations in disease susceptibility.