Scialog conference


Nandita is a Microbiome, Neurobiology and Disease Scialog Fellow and recently participated in the annual Scialog conference to innovate and pitch new ideas about the connection between the gut and the brian.

Scialog conference

EEB149: Evolutionary Genomics

This quarter, Prof. Nandita and TA Ricky are teaching a new R-based course on Evolutionary Genomics for undergraduates. Our goals for the class are to teach students how to make evolutionary inferences from DNA sequences. We are covering topics ranging from evolutionary dynamics on short time scales within populations to longer-term evolutionary forces across across species. We conclude by examining the microbiome. Here is our syllabus.

Congratulations to Mariana Harris and Ricky Wolff!

We are very proud of Mariana Harris for receiving the UCLA GATP fellowship and Ricky Wolff for receiving honorable mention in the NSF GRFP competition!! Congrats!

Correcting for background noise improves phenotype prediction from human gut microbiome data

We are very proud to share our recent preprint:

Correcting for background noise improves phenotype prediction from human gut microbiome

Leah Briscoe, Bruna Balliu, Sriram Sankararaman, Eran Halperin and Nandita Garud

Many technical and biological variables can contribute to variation in microbiome data. For example, in this combined dataset, the study contributes more variation than colorectal disease status.

Many of the popular methods for correcting background noise in microbiome data are supervised. Unsupervised methods may be preferable because they can correct for unmeasured sources of variation. We perform a comparative analysis of background noise correction methods and find that regressing out Principal Components after applying a centered log-ratio transformation is quite effective in removing unwanted variation. In fact, if we correct for background noise, we can even improve phenotype prediction of many phenotypes.

Nandita named Allen Distinguished Investigator

Nandita has been named an Allen Distinguished Investigator! The Garud lab is excited to be collaborating with Drs. Carolina Tropini, Aida Habtezion, and Siddharth Sinha on the following grant: Distinct immune-metabolic niches in inflammatory bowel disease. Link to press release.

Comparative Population Genetics in the Human Gut Microbiome

We are delighted to share our latest preprint on:

Comparative Population Genetics in the Human Gut Microbiome

William Shoemaker, Daisy Chen, and Nandita Garud

The genetic variation in the human gut microbiome is responsible for conferring a number of crucial phenotypes like the ability to digest food and metabolize drugs. Yet, our understanding of how this variation arises and is maintained remains relatively poor. Thus, the microbiome remains a largely untapped resource, as the large number of co-existing species in this microbiome presents a unique opportunity to compare and contrast evolutionary processes across species to identify universal trends and deviations. Here we outline features of the human gut microbiome that, while not unique in isolation, as an assemblage make it a system with unparalleled potential for comparative population genomics studies. We consciously take a broad view of comparative population genetics, emphasizing how sampling a large number of species allows researchers to identify universal evolutionary dynamics in addition to new genes, which can then be leveraged to identify exceptional species that deviate from general patterns. To highlight the potential power of comparative population genetics in the microbiome, we re-analyzed patterns of purifying selection across ~40 prevalent species in the human gut microbiome to identify intriguing trends which highlight functional categories in the microbiome that may be under more or less constraint.


Welcome rotation students!

We are delighted to be working with rotation students Helen Huang, Nicole Zeltser, Jon Mah, Alejandro Espinoza, and Albert Xue this year!

Summer lab meetings

This summer we have a full house with many interns joining us, as well as new incoming PhD students including Albert Xue, Jon Mah, and Mariana Harris!  Here’s our group meeting over Zoom!


Welcome BIG students!

This summer we are delighted to welcome four new BIG (Bruins in Genomics) summer interns to our group: Shavonna Jackson, Etan Dieppa, Kevin Delao, and Maya Singh!


Etan Dieppa

Maya Singh

Shavonna Jackson

Kevin Delao


New paper on BioRxiv: Detection of hard and soft selective sweeps from Drosophila melanogaster population genomic data

Check out our latest paper on BioRxiv:

Whether hard sweeps or soft sweeps dominate adaptation has been a matter of much debate. Recently, we developed haplotype homozygosity statistics that (i) can detect both hard and soft sweeps with similar power and (ii) can classify the detected sweeps as hard or soft. The application of our method to population genomic data from a natural population of Drosophila melanogaster (DGRP) allowed us to rediscover three known cases of adaptation at the loci Ace, Cyp6g1, and CHKov1 known to be driven by soft sweeps, and detected additional candidate loci for recent and strong sweeps. Surprisingly, all of the top 50 candidates showed patterns much more consistent with soft rather than hard sweeps. Recently, Harris et al. 2018 criticized this work, suggesting that all the candidate loci detected by our haplotype statistics, including the positive controls, are unlikely to be sweeps at all and instead these haplotype patterns can be more easily explained by complex neutral demographic models. They also claim, confusingly, that these neutral non-sweeps are likely to be hard instead of soft sweeps. Here, we reanalyze the DGRP data using a range of complex admixture demographic models and reconfirm our original published results suggesting that the majority of recent and strong sweeps in D. melanogaster are first likely to be true sweeps, and second, that they do appear to be soft. Furthermore, we discuss ways to take this work forward given that the demographic models employed in such analyses are generally necessarily too simple to capture the full demographic complexity, while more realistic models are unlikely to be inferred correctly because they require fitting a very large number of free parameters.

Demographic models tested in this paper