Body Mass Index and Birth Weight Improve Polygenic Risk Score for Type 2 Diabetes.

One of the major challenges in the post-genomic era is elucidating the genetic basis of human diseases. In recent years, studies have shown that polygenic risk scores (), based on aggregated information from millions of variants across the human genome, can estimate individual risk for common diseases. In practice, the current medical practice still predominantly relies on physiological and clinical indicators to assess personal disease risk.

R869C mutation in molecular motor gene is involved in dementia with Lewy bodies.

Introduction The: -N370S mutation is one of the most frequent risk factors for dementia with Lewy bodies (DLB) and Parkinson's disease (PD). We looked for genetic variations that contribute to the outcome in N370S-carriers, whether PD or DLB.

Methods: Whole-genome sequencing of 95 Ashkenazi-N370S-carriers affected with either DLB (n = 19) or PD (n = 76) was performed, and 564 genes related to dementia and PD analyzed.

The genetic history of Cochin Jews from India.

Cochin Jews form a small and unique community on the Malabar coast in southwest India. While the arrival time of any putative Jewish ancestors of the community has been speculated to have taken place as far back as biblical times (King Solomon's era), a Jewish community in the Malabar coast has been documented only since the 9th century CE. Here, we explore the genetic history of Cochin Jews by collecting and genotyping 21 community members and combining the data with that of 707 individuals from 72 other Indian, Jewish, and Pakistani populations, together with additional individuals from worldwide populations.

The Genetics of Bene Israel from India Reveals Both Substantial Jewish and Indian Ancestry.

The Bene Israel Jewish community from West India is a unique population whose history before the 18th century remains largely unknown. Bene Israel members consider themselves as descendants of Jews, yet the identity of Jewish ancestors and their arrival time to India are unknown, with speculations on arrival time varying between the 8th century BCE and the 6th century CE. Here, we characterize the genetic history of Bene Israel by collecting and genotyping 18 Bene Israel individuals.

Accounting for eXentricities: analysis of the X chromosome in GWAS reveals X-linked genes implicated in autoimmune diseases.

Many complex human diseases are highly sexually dimorphic, suggesting a potential contribution of the X chromosome to disease risk. However, the X chromosome has been neglected or incorrectly analyzed in most genome-wide association studies (GWAS). We present tailored analytical methods and software that facilitate X-wide association studies (XWAS), which we further applied to reanalyze data from 16 GWAS of different autoimmune and related diseases (AID).

Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer.

Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end we present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. We build >280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner.

Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality.

Synthetic lethality occurs when the inhibition of two genes is lethal while the inhibition of each single gene is not. It can be harnessed to selectively treat cancer by identifying inactive genes in a given cancer and targeting their synthetic lethal (SL) partners. We present a data-driven computational pipeline for the genome-wide identification of SL interactions in cancer by analyzing large volumes of cancer genomic data.

A genome-wide systematic analysis reveals different and predictive proliferation expression signatures of cancerous vs. non-cancerous cells.

Understanding cell proliferation mechanisms has been a long-lasting goal of the scientific community and specifically of cancer researchers. Previous genome-scale studies of cancer proliferation determinants have mainly relied on knockdown screens aimed to gauge their effects on cancer growth. This powerful approach has several limitations such as off-target effects, partial knockdown, and masking effects due to functional backups.

Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks.

The prioritization of candidate disease-causing genes is a fundamental challenge in the post-genomic era. Current state of the art methods exploit a protein-protein interaction (PPI) network for this task. They are based on the observation that genes causing phenotypically-similar diseases tend to lie close to one another in a PPI network.

Hippocampus neuronal metabolic gene expression outperforms whole tissue data in accurately predicting Alzheimer's disease progression.

Numerous metabolic alterations are associated with the impairment of brain cells in Alzheimer's disease (AD). Here we use gene expression microarrays of both whole hippocampus tissue and hippocampal neurons of AD patients to investigate the ability of metabolic gene expression to predict AD progression and its cognitive decline. We find that the prediction accuracy of different AD stages is markedly higher when using neuronal expression data (0.

Selection for translation efficiency on synonymous polymorphisms in recent human evolution.

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.

Translation efficiency is determined by both codon bias and folding energy.

Synonymous mutations do not alter the protein produced yet can have a significant effect on protein levels. The mechanisms by which this effect is achieved are controversial; although some previous studies have suggested that codon bias is the most important determinant of translation efficiency, a recent study suggested that mRNA folding at the beginning of genes is the dominant factor via its effect on translation initiation. Using the Escherichia coli and Saccharomyces cerevisiae transcriptomes, we conducted a genome-scale study aiming at dissecting the determinants of translation efficiency.

Translation efficiency in humans: tissue specificity, global optimization and differences between developmental stages.

Various studies in unicellular and multicellular organisms have shown that codon bias plays a significant role in translation efficiency (TE) by co-adaptation to the tRNA pool. Yet, in humans and other mammals the role of codon bias is still an open question, with contradictory results from different studies. Here we address this question, performing a large-scale tissue-specific analysis of TE in humans, using the tRNA Adaptation Index (tAI) as a direct measure for TE.

TP53 cancerous mutations exhibit selection for translation efficiency.

The tumor suppressor gene TP53 is known to be a key regulator in cancer, and more than half of human cancers exhibit mutations in this gene. Recent evidence shows that point mutations in TP53 not only disrupt its function but also possess gain-of-function and dominant-negative effects on wild-type copies, thus making the mutated gene an oncogene. Hence, this brings about the possibility that TP53 mutations may be under selection for increasing the overall translation efficiency (TE) of defected TP53 in cancerous cells.