{"id":112,"date":"2021-04-15T18:34:48","date_gmt":"2021-04-15T18:34:48","guid":{"rendered":"https:\/\/labs.icahn.mssm.edu\/oreillylab\/?page_id=112"},"modified":"2025-05-01T03:59:34","modified_gmt":"2025-05-01T03:59:34","slug":"home","status":"publish","type":"page","link":"https:\/\/labs.icahn.mssm.edu\/oreillylab\/","title":{"rendered":"Home"},"content":{"rendered":"

[et_pb_section fb_built=”1″ fullwidth=”on” _builder_version=”4.9.0″ _module_preset=”default”][et_pb_fullwidth_slider _builder_version=”4.9.0″ _module_preset=”default” header_text_color=”#2b2b2b” header_text_shadow_style=”preset1″][et_pb_slide heading=”The PRS Lab” _builder_version=”4.9.0″ _module_preset=”default” header_level=”h1″ background_color=”RGBA(0,0,0,0)” background_enable_color=”on” background_image=”https:\/\/labs.icahn.mssm.edu\/oreillylab\/wp-content\/uploads\/sites\/380\/2021\/04\/central_park.png” background_enable_image=”on” text_orientation=”center” width=”100%” max_width=”100%” border_radii_image=”on|12px|12px|12px|12px” inline_fonts=”Times New Roman,Arial” sticky_transition=”on”]<\/p>\n

Methods Development & Applications of Polygenic Risk Scores<\/em><\/span><\/strong><\/span><\/h4>\n

[\/et_pb_slide][\/et_pb_fullwidth_slider][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”home” _builder_version=”4.9.0″ _module_preset=”default” background_color=”rgba(132,132,132,0.08)”][et_pb_row column_structure=”3_5,2_5″ _builder_version=”4.9.0″ _module_preset=”default” custom_padding=”||0px|||”][et_pb_column type=”3_5″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text admin_label=”Text” _builder_version=”4.9.0″ _module_preset=”default”]<\/p>\n

Welcome To Our Lab<\/h2>\n

Our lab develops statistical and computational approaches (see Software<\/a>) to understand how human genetic variation, in combination with the environment, leads to disease. Given their proxy for genetic liability itself, our primary focus is on the theory and application of Polygenic Risk Scores (PRS).\u00a0In 2015, we published the popular PRS software, PRSice <\/em><\/a>(‘precise’), followed in 2019 by PRSice-2\u00a0<\/em>(PRSice<\/em> website here<\/a>). We have run several PRS workshops (eg. our PRS Summer School<\/a>) and in 2020\u00a0we published our Guide to PRS paper<\/a>, with accompanying PRS tutorial<\/a>.<\/p>\n

We believe that genetic liability to disease is more complex than implied by the additive model of present polygenic risk scores, that the interplay between the genome and the environment in causing disease needs to be better understood, and that analysis of diverse populations across diverse environments can provide the greatest power to understand the causes of disease.<\/p>\n

The research in our lab follows 4 key themes:<\/p>\n

1) Pathway-specific, function-informed, polygenic risk scores<\/b><\/p>\n

<\/b>2) Polygenic risk scores for diverse and admixed populations<\/b><\/p>\n

<\/b>3) Using genetics to infer the environmental<\/em>\u00a0causes of disease<\/b><\/p>\n

4) The Statistical Genetics of Brain Disorders<\/b><\/p>\n

We need to bring together the fields of statistical genetics, GWAS, functional genomics, population genetics and epidemiology in order to understand how individual genetic profiles combine with the environment to produce human traits and disease – so if you are interested in any of these fields and the research of our lab then please feel free to\u00a0email Paul<\/strong> (paul.oreilly@mssm.edu<\/a>) to discuss more or to enquire about our open student and postdoc positions<\/strong>.<\/span><\/p>\n

 <\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”2_5″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text admin_label=”Text” _builder_version=”4.9.0″ _module_preset=”default” min_height=”62px” custom_padding=”3px|||||”]<\/p>\n

Contact Us<\/h1>\n

<\/h4>\n

O’Reilly Lab<\/strong><\/span><\/h4>\n

[\/et_pb_text][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” min_height=”125px” custom_margin=”-23px||100px|||” custom_padding=”||45px|||”]<\/p>\n

Location<\/strong>: Department of\u00a0Genetics and Genomic Sciences,\u00a0Icahn School of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York<\/span><\/p>\n

Email<\/strong>: paul.oreilly@mssm.edu<\/a>\u00a0<\/span><\/p>\n

[\/et_pb_text][et_pb_slider _builder_version=”4.9.0″ _module_preset=”default” header_level=”h4″ header_text_align=”left” header_text_color=”#000000″ background_color=”rgba(132,132,132,0.12)” min_height=”161px” custom_margin=”-99px|||||” custom_padding=”246px||246px||true|false” auto=”on” custom_css_slide_description=”text-shadow:none !important” custom_css_slide_title=”text-shadow:none !important”][et_pb_slide button_text=”Click Here” button_link=”https:\/\/labs.icahn.mssm.edu\/minervalab\/data-ark\/” url_new_window=”on” _builder_version=”4.9.0″ _module_preset=”default” header_level=”h4″ header_text_align=”left” header_text_color=”rgba(0,0,0,0.68)” body_text_color=”rgba(0,1,117,0.7)” link_option_url=”https:\/\/labs.icahn.mssm.edu\/minervalab\/data-ark\/” link_option_url_new_window=”on” custom_css_slide_title=”text-shadow:none !important” custom_css_slide_description=”text-shadow:none !important” sticky_transition=”on”]<\/p>\n

Initiatives we’re involved in:<\/strong><\/em><\/span><\/h4>\n

 <\/p>\n

 <\/p>\n

\"\"<\/strong><\/span><\/p>\n

Data Ark:<\/strong>\u00a0a Mount Sinai Data Commons, which the O’Reilly Lab helped to set sail!<\/span><\/p>\n

[\/et_pb_slide][et_pb_slide button_text=”Click Here” button_link=”https:\/\/icahn.mssm.edu\/about\/diversity\/ceye” url_new_window=”on” _builder_version=”4.9.0″ _module_preset=”default” header_level=”h4″ header_text_align=”left” header_text_color=”rgba(0,0,0,0.68)” body_text_color=”rgba(0,1,117,0.7)” body_font_size=”17px” background_enable_color=”on” link_option_url=”https:\/\/icahn.mssm.edu\/about\/diversity\/ceye” link_option_url_new_window=”on” custom_css_slide_description=”text-shadow:none !important” sticky_transition=”on”]<\/p>\n

Initiatives we’re involved in:<\/strong><\/em><\/span><\/h4>\n

 <\/p>\n

\"\"<\/strong><\/p>\n

CEYE<\/strong>\u00a0provides research experience for talented NYC high school students from underrepresented backgrounds.<\/p>\n

[\/et_pb_slide][\/et_pb_slider][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”project2″ _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF”][et_pb_row _builder_version=”4.9.0″ _module_preset=”default”][et_pb_column type=”4_4″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default”]<\/p>\n

Lab Themes<\/h1>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_5,2_5″ _builder_version=”4.9.0″ _module_preset=”default” min_height=”402px” custom_margin=”|auto|4px|auto||” custom_padding=”2px||0px|||”][et_pb_column type=”3_5″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/oreillylab\/wp-content\/uploads\/sites\/380\/2021\/05\/pathPRSim5.png” _builder_version=”4.9.0″ _module_preset=”default”][\/et_pb_image][\/et_pb_column][et_pb_column type=”2_5″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” min_height=”467px” custom_padding=”||0px|||”]<\/p>\n

Pathway-specific, function-informed, PRS<\/span><\/h4>\n

The standard polygenic model, which assumes that everyone lies on a linear spectrum from low to high risk, is an over-simplification given the known heterogeneity of disease and functional sub-structure of the genome.\u00a0<\/span>A primary focus in our lab is on developing pathway-specific, function (i.e. multi-omics) informed, polygenic risk scores. We believe that these will better reflect how genetic liability manifests and leads to disease, as compared to standard PRS that only consider genome-wide aggregated risk irrespective of specific genetic profiles. Moreover, we believe that pathway-specific PRS will pave a clearer path towards stratified medicine given their focus on functional variability of individual genetic profiles.<\/span><\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_5,2_5″ _builder_version=”4.9.0″ _module_preset=”default” min_height=”531px” custom_padding=”71px|||||”][et_pb_column type=”3_5″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/oreillylab\/wp-content\/uploads\/sites\/380\/2021\/04\/acePRS.png” title_text=”acePRS” _builder_version=”4.9.0″ _module_preset=”default” width=”100%” min_height=”428px” custom_margin=”|-88px||10px||” custom_padding=”|0px|0px|0px||”][\/et_pb_image][\/et_pb_column][et_pb_column type=”2_5″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” text_text_color=”#0c71c3″]<\/p>\n

PRS for diverse and admixed populations<\/strong><\/span><\/h4>\n

PRS are mostly derived from European-ancestry GWAS, making their predictive power lower when computed in individuals of non-European ancestry. This problem has been widely reported in recent years, yet PRS in e.g. recent African-ancestry individuals are still often computed using\u00a0European GWAS and PRS methods applied to European data. This takes\u00a0no account for known population genetic factors affecting the data, such as: LD, genetic drift, natural selection and G*E interactions. The clinical utility and aetiological insights provided by PRS may have limited relevance to individuals of recent African and other non-European ancestry unless PRS methods are developed specifically for application to diverse and admixed populations. Moreover, performing research in diverse populations living in diverse locations is an ideal way to better understand the genetic and environmental causes of disease.<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_5,2_5″ _builder_version=”4.9.0″ _module_preset=”default” custom_margin=”28px|auto||auto||” custom_padding=”51px|||||”][et_pb_column type=”3_5″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/oreillylab\/wp-content\/uploads\/sites\/380\/2021\/04\/Screen-Shot-2021-04-19-at-11.43.46-2.png” title_text=”Screen Shot 2021-04-19 at 11.43.46″ _builder_version=”4.9.0″ _module_preset=”default” transform_scale=”80%|80%” width=”89.3%” min_height=”415px” custom_margin=”-33px|86px||||” custom_padding=”0px|0px|0px|||”][\/et_pb_image][\/et_pb_column][et_pb_column type=”2_5″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” width=”121.1%” min_height=”533px” custom_margin=”|||-81px||” custom_padding=”||0px|85px||”]<\/p>\n

Using genetics to infer the\u00a0environmental\u00a0<\/em>causes of disease<\/span><\/h4>\n

Almost all environmental risk factors for disease have a genetic component, which itself must be a genetic component of the disease. This creates a complex interplay between genetic and environmental causes of disease, which needs to be investigated carefully in order to disentangle the effects of each. While genetic variants that are convincingly associated with disease (controlling for pop. structure) have the convenient feature that they must be causal, they may only be causal because of environmental risk factors that they interact with or trigger, and so could be non-causal in other environmental settings or if the environment changes (e.g. due to health policy intervention or social changes). However, because of this, genetics provides an opportunity to test the causality between putative\u00a0environmental\u00a0<\/em>risk factors and disease – but only if the complex network between genetics and the environment (both cellular and society level) is accounted for and accurately modelled.<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”Team” _builder_version=”4.9.0″ _module_preset=”default” background_color=”rgba(179,221,229,0.42)”][et_pb_row _builder_version=”4.9.0″ _module_preset=”default” custom_margin=”|auto|-153px|auto||”][et_pb_column type=”4_4″ _builder_version=”4.9.0″ _module_preset=”default”][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.9.0″ _module_preset=”default” locked=”off”][et_pb_column type=”4_4″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” min_height=”37px”]<\/p>\n

Team<\/h1>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ use_custom_gutter=”on” module_id=”bios” _builder_version=”4.9.0″ background_color=”#ffffff” min_height=”250px” custom_margin=”108px|auto||auto||” custom_padding=”0px||0px|||” animation_style=”fade” border_radii=”on|20px|20px|20px|20px” box_shadow_style=”preset3″ box_shadow_vertical=”17px” box_shadow_blur=”41px” box_shadow_color=”rgba(175,196,204,0.18)”][et_pb_column type=”3_4″ _builder_version=”3.25″ background_color=”#ffffff” custom_padding=”60px||60px|5%” custom_padding_tablet=”40px|40px|40px|40px” custom_padding_phone=”” custom_padding_last_edited=”on|tablet” padding_tablet=”40px|40px|40px|40px” padding_last_edited=”on|tablet” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.0″ text_font=”||||||||” text_font_size=”18px” text_line_height=”1.8em” header_font=”||||||||” header_4_font=”Fredoka One||||||||” header_4_text_color=”#262f71″ header_4_font_size=”26px” custom_padding=”||0px|||” animation_style=”fade”]<\/p>\n

Paul O’Reilly<\/span><\/strong><\/span><\/h3>\n

I am a Professor in Statistical Genetics at the Icahn School of Medicine at Mount Sinai NYC<\/a>, having joined in 2019 from King\u2019s College London (previously at Imperial College London, where I did my PhD supervised by\u00a0David Balding<\/a>\u00a0and where I became faculty in 2011). In order to gain insights into how the human genome evolves and gives rise to disease, I have developed Statistical Genetics methods and software in Genetic Epidemiology (eg. multi-trait GWAS:\u00a0MultiPhen<\/a>,\u00a0Polygenic Risk Scores:\u00a0PRSice<\/a>) and Population Genetics (eg. detecting selection:\u00a0Ped\/Pop method<\/a>, simulating inversions: invertFREGENE<\/a>), while my applied work focuses on the statistical genetics of brain disorders.\u00a0<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/oreillylab\/wp-content\/uploads\/sites\/380\/2021\/03\/paul.png” title_text=”paul” align=”right” force_fullwidth=”on” _builder_version=”4.9.0″ min_height=”223px” custom_margin=”-1px|||||” animation_style=”fade” always_center_on_mobile=”off”][\/et_pb_image][et_pb_text _builder_version=”4.9.0″ _module_preset=”default”]<\/p>\n

Email: paul.oreilly@mssm.edu<\/a><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ use_custom_gutter=”on” _builder_version=”3.25″ background_color=”#ffffff” custom_margin=”60px|||” custom_padding=”0px||0px|||” animation_style=”fade” border_radii=”on|20px|20px|20px|20px” box_shadow_style=”preset3″ box_shadow_vertical=”17px” box_shadow_blur=”41px” box_shadow_color=”rgba(175,196,204,0.18)” locked=”off”][et_pb_column type=”3_4″ _builder_version=”3.25″ background_color=”#ffffff” custom_padding=”60px||60px|5%” custom_padding_tablet=”40px|40px|40px|40px” custom_padding_phone=”” custom_padding_last_edited=”on|tablet” padding_tablet=”40px|40px|40px|40px” padding_last_edited=”on|tablet” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.0″ text_font=”||||||||” text_font_size=”18px” text_line_height=”1.8em” header_font=”||||||||” header_4_font=”Fredoka One||||||||” header_4_text_color=”#262f71″ header_4_font_size=”26px” animation_style=”fade”]<\/p>\n

Beatrice Wu<\/span><\/h3>\n

I\u00a0am a computional scientist focused on the application of statistical genetics methods and software to brain disorders, in particular schizophrenia and Alzheimer’s disease. My PhD focused on identifying genetic risk factors of schizophrenia, supervised by Pak Sham<\/a> at the University of Hong Kong. I joined the O’Reilly in 2016 at King’s College London and have since focused on the application of polygenic scores to psychiatric disorders and Alzheimer’s disease.<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/oreillylab\/wp-content\/uploads\/sites\/380\/2021\/03\/Screen-Shot-2020-12-10-at-11.32.58-AM.png” title_text=”Screen Shot 2020-12-10 at 11.32.58 AM” _builder_version=”4.9.0″ _module_preset=”default” animation_style=”fade”][\/et_pb_image][et_pb_text _builder_version=”4.9.0″ _module_preset=”default”]<\/p>\n

Email: heiman.wu@mssm.edu<\/a><\/p>\n

[\/et_pb_text][et_pb_blurb use_icon=”on” _builder_version=”4.9.0″ _module_preset=”default”][\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ use_custom_gutter=”on” _builder_version=”3.25″ background_color=”#ffffff” custom_margin=”60px|||” custom_padding=”0px||0px|” animation_style=”fade” border_radii=”on|20px|20px|20px|20px” box_shadow_style=”preset3″ box_shadow_vertical=”17px” box_shadow_blur=”41px” box_shadow_color=”rgba(175,196,204,0.18)”][et_pb_column type=”3_4″ _builder_version=”3.25″ background_color=”#ffffff” custom_padding=”60px||60px|5%” custom_padding_tablet=”40px|40px|40px|40px” custom_padding_phone=”” custom_padding_last_edited=”on|tablet” padding_tablet=”40px|40px|40px|40px” padding_last_edited=”on|tablet” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.0″ text_font=”||||||||” text_font_size=”18px” text_line_height=”1.8em” header_font=”||||||||” header_4_font=”Fredoka One||||||||” header_4_text_color=”#262f71″ header_4_font_size=”26px” animation_style=”fade”]<\/p>\n

Clive Hoggart<\/span><\/h3>\n

I am a statistician focused on Bayesian approaches and predictive modelling in statistical genetics, having completed a PhD in Bayesian methods in forensic science supervised by Adrian Smith<\/a>. I have produced numerous methods in statistical genetics, \u00a0including methods for admixture mapping (including software ADMIXMAP), novel methodology and software for the joint modelling of all SNPs genome-wide in genetic association studies (HyperLASSO), estimation of missing heritability attributable to allelic heterogeneity and identification of parent-of-origin effects using genetic data from unrelated individuals. Since moving to the\u00a0Icahn School of Medicine in 2020, my research focus has been on methods to improve the portability of polygenic risk scores across different populations and ancestries.<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/oreillylab\/wp-content\/uploads\/sites\/380\/2021\/03\/Screen-Shot-2020-12-10-at-11.42.07-AM.png” title_text=”Screen Shot 2020-12-10 at 11.42.07 AM” align=”right” force_fullwidth=”on” _builder_version=”4.9.0″ animation_style=”fade” always_center_on_mobile=”off”][\/et_pb_image][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” custom_padding=”|||0px||”]<\/p>\n

Email:\u00a0clive.hoggart@mssm.edu<\/a><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ use_custom_gutter=”on” _builder_version=”4.9.0″ background_color=”#ffffff” custom_margin=”60px|||” custom_padding=”0px||0px|” animation_style=”fade” hover_enabled=”0″ border_radii=”on|20px|20px|20px|20px” box_shadow_style=”preset3″ box_shadow_vertical=”17px” box_shadow_blur=”41px” box_shadow_color=”rgba(175,196,204,0.18)” sticky_enabled=”0″][et_pb_column type=”3_4″ _builder_version=”3.25″ background_color=”#ffffff” custom_padding=”60px||60px|5%” custom_padding_tablet=”40px|40px|40px|40px” custom_padding_phone=”” custom_padding_last_edited=”on|tablet” padding_tablet=”40px|40px|40px|40px” padding_last_edited=”on|tablet” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.0″ text_font=”||||||||” text_font_size=”18px” text_line_height=”1.8em” header_font=”||||||||” header_4_font=”Fredoka One||||||||” header_4_text_color=”#262f71″ header_4_font_size=”26px” animation_style=”fade” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n

Conrad Iyegbe<\/span><\/h3>\n

I have core training in molecular and quantitative genetic analysis from King’s College London (PhD in Genetics) and postdoctoral research training in psychiatric research. My previous research interests combine methods development with a focused interest in understanding how gene-environment interplay shapes risk for disease at statistical and functional levels. I currently work on strategies to improve polygenic risk score performance across global populations. I co-lead an annual training workshop on polygenic risk score analysis, run in conjunction with Wellcome Connecting Science. As part of my commitment to improving health outcomes on a global level I co-chair the Africa division of the Psychiatric Genomics Consortium<\/a>\u00a0which was created in September 2022 to implement a strategic agenda for psychiatric genomics research in Africa.<\/p>\n

<\/div>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/oreillylab\/wp-content\/uploads\/sites\/380\/2021\/03\/Screen-Shot-2020-12-10-at-11.28.22-AM.png” title_text=”Screen Shot 2020-12-10 at 11.28.22 AM” align=”right” force_fullwidth=”on” _builder_version=”4.9.0″ animation_style=”fade” always_center_on_mobile=”off”][\/et_pb_image][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” custom_margin=”|||-6px||”]<\/p>\n

Email:\u00a0conrad.iyegbe@mssm.edu<\/a><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ use_custom_gutter=”on” _builder_version=”4.9.0″ background_color=”#ffffff” custom_margin=”60px|||” custom_padding=”0px||0px|” animation_style=”fade” border_radii=”on|20px|20px|20px|20px” box_shadow_style=”preset3″ box_shadow_vertical=”17px” box_shadow_blur=”41px” box_shadow_color=”rgba(175,196,204,0.18)”][et_pb_column type=”3_4″ _builder_version=”3.25″ background_color=”#ffffff” custom_padding=”60px||60px|5%” custom_padding_tablet=”40px|40px|40px|40px” custom_padding_phone=”” custom_padding_last_edited=”on|tablet” padding_tablet=”40px|40px|40px|40px” padding_last_edited=”on|tablet” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.0″ text_font=”||||||||” text_font_size=”18px” text_line_height=”1.8em” header_font=”||||||||” header_4_font=”Fredoka One||||||||” header_4_text_color=”#262f71″ header_4_font_size=”26px” custom_padding=”1px|||||” animation_style=”fade”]<\/p>\n

Judit Garc\u00eda-Gonz\u00e1lez<\/span><\/h3>\n

I am a computational biologist with a background in experimental and statistical genetics research. I received my PhD in genetics from Queen Mary University of London, where I investigated the interplay between smoking and genetic vulnerability to psychiatric disorders using animal models and statistical genetics approaches in human cohorts. In late 2020, I joined the O’Reilly lab to develop methods that incorporate functional genomic information to polygenic risk scores, with a focus on psychiatric disorders.<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/oreillylab\/wp-content\/uploads\/sites\/380\/2024\/01\/JGG.png” title_text=”JGG” align=”right” force_fullwidth=”on” _builder_version=”4.9.0″ animation_style=”fade” always_center_on_mobile=”off”][\/et_pb_image][et_pb_text _builder_version=”4.9.0″ _module_preset=”default”]<\/p>\n

Email:\u00a0judit.garciagonzalez@mssm.edu<\/a><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ use_custom_gutter=”on” _builder_version=”4.9.0″ background_color=”#ffffff” custom_margin=”60px|||” custom_padding=”0px||0px|” animation_style=”fade” border_radii=”on|20px|20px|20px|20px” box_shadow_style=”preset3″ box_shadow_vertical=”17px” box_shadow_blur=”41px” box_shadow_color=”rgba(175,196,204,0.18)” locked=”off”][et_pb_column type=”3_4″ _builder_version=”3.25″ background_color=”#ffffff” custom_padding=”60px||60px|5%” custom_padding_tablet=”40px|40px|40px|40px” custom_padding_phone=”” custom_padding_last_edited=”on|tablet” padding_tablet=”40px|40px|40px|40px” padding_last_edited=”on|tablet” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.0″ text_font=”||||||||” text_font_size=”18px” text_line_height=”1.8em” header_font=”||||||||” header_4_font=”Fredoka One||||||||” header_4_text_color=”#262f71″ header_4_font_size=”26px” custom_padding=”1px|||||” animation_style=”fade”]<\/p>\n

Anil Ori<\/span><\/h3>\n

As a scientist, my <\/span>work is motivated by <\/span>a<\/span> commitment to improving health<\/span>
outcomes and addressing health disparities<\/span>. I specialize broadly in <\/span>
medical and population genomics<\/span>, and health equity. I have joined the <\/span>
Icahn School of Medicine <\/span>at Mount Sinai NYC and the PRS lab in the <\/span>
summer of 2022 and am currently investigating<\/span> how evolutionary genetic <\/span>
processes, such as selection, can help us understand the genetic <\/span>
architecture of complex traits.<\/span> This is important as a better understanding <\/span>
of trait architecture can for example improve downstream genetic <\/span>
analyses, such as gene discovery and PRS predictions. <\/span> Additionally, I am <\/span>
affiliated with the Department of Psychiatry at the University Amsterdam <\/span>
Medical Center in the Netherlands, where I <\/span>co-<\/span>lead a recently funded <\/span>
project measuring population representation, specifically in terms of <\/span>
racial\/ethnic composition, across <\/span>medical<\/span> cohorts in the Netherlands<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/oreillylab\/wp-content\/uploads\/sites\/380\/2023\/02\/20220827_102316-scaled.jpg” title_text=”20220827_102316″ align=”right” force_fullwidth=”on” _builder_version=”4.9.0″ animation_style=”fade” always_center_on_mobile=”off”][\/et_pb_image][et_pb_text _builder_version=”4.9.0″ _module_preset=”default”]<\/p>\n

Email:\u00a0anil.ori@mssm.edu<\/a><\/p>\n

Twitter:\u00a0@anilpsori<\/span><\/a><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ use_custom_gutter=”on” _builder_version=”3.25″ background_color=”#ffffff” custom_margin=”60px|auto|60px|auto|true|” custom_padding=”0px||0px|” animation_style=”fade” border_radii=”on|20px|20px|20px|20px” box_shadow_style=”preset3″ box_shadow_vertical=”17px” box_shadow_blur=”41px” box_shadow_color=”rgba(175,196,204,0.18)”][et_pb_column type=”3_4″ _builder_version=”3.25″ background_color=”#ffffff” custom_padding=”60px||60px|5%” custom_padding_tablet=”40px|40px|40px|40px” custom_padding_phone=”” custom_padding_last_edited=”on|tablet” padding_tablet=”40px|40px|40px|40px” padding_last_edited=”on|tablet” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.0″ text_font=”||||||||” text_font_size=”18px” text_line_height=”1.8em” header_font=”||||||||” header_4_font=”Fredoka One||||||||” header_4_text_color=”#262f71″ header_4_font_size=”26px” custom_padding=”0px|||||” animation_style=”fade”]<\/p>\n

Lathan Liou<\/span><\/h3>\n

I am a medical student at the Icahn School of Medicine at Mount Sinai. I received my M.Phil. in genetic epidemiology from the University of Cambridge, where I studied the application of cardiovascular polygenic risk scores to cancer cohorts under the supervision of Paul Pharoah.<\/a>\u00a0I stayed to work at the MRC Biostatistics Unit<\/a> where I helped develop the onlineFDR framework<\/a>\u00a0with David Robertson<\/a>. I then worked as a data scientist at Merck. I joined the O’Reilly Lab to study how to apply statistical genetics to improve clinical decision-making.<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/oreillylab\/wp-content\/uploads\/sites\/380\/2023\/02\/Liou-Lathan.jpg” title_text=”Liou, Lathan” align=”right” force_fullwidth=”on” _builder_version=”4.9.0″ animation_style=”fade” always_center_on_mobile=”off”][\/et_pb_image][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” text_line_height=”1.6em” width=”100%” max_width=”100%” custom_margin=”0px||||false|false” custom_padding=”0px|||0px|false|false”]<\/p>\n

Email: lathan.liou@icahn.mssm.edu<\/a><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”3_4,1_4″ use_custom_gutter=”on” _builder_version=”3.25″ background_color=”#ffffff” custom_margin=”60px|auto|60px|auto|true|” custom_padding=”0px||0px|” animation_style=”fade” border_radii=”on|20px|20px|20px|20px” box_shadow_style=”preset3″ box_shadow_vertical=”17px” box_shadow_blur=”41px” box_shadow_color=”rgba(175,196,204,0.18)” locked=”off”][et_pb_column type=”3_4″ _builder_version=”3.25″ background_color=”#ffffff” custom_padding=”60px||60px|5%” custom_padding_tablet=”40px|40px|40px|40px” custom_padding_phone=”” custom_padding_last_edited=”on|tablet” padding_tablet=”40px|40px|40px|40px” padding_last_edited=”on|tablet” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.9.0″ text_font=”||||||||” text_font_size=”18px” text_line_height=”1.8em” header_font=”||||||||” header_4_font=”Fredoka One||||||||” header_4_text_color=”#262f71″ header_4_font_size=”26px” custom_padding=”0px|||||” animation_style=”fade”]<\/p>\n

Alanna Cote<\/span><\/h3>\n

I am a computational researcher with a background in functional genomics and transcriptomics of brain disorders. I completed my PhD at the Icahn School of Medicine supervised by Dr. Laura Huckins<\/a>, with a focus on quantifying artifact- and genotype- driven patterns of co-expression in the prefrontal cortex. I joined the O\u2019Reilly Lab in early 2024, where my focus has been on developing methods to incorporate functional information in pathway-specific PRS.<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/oreillylab\/wp-content\/uploads\/sites\/380\/2024\/01\/Photo.jpg” title_text=”Alanna Cote” align=”right” force_fullwidth=”on” _builder_version=”4.9.0″ animation_style=”fade” always_center_on_mobile=”off”][\/et_pb_image][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” text_line_height=”1.6em” width=”100%” max_width=”100%” custom_margin=”0px||||false|false” custom_padding=”0px|||0px|false|false”]<\/p>\n

Email: alanna.cote@icahn.mssm.edu<\/a><\/p>\n

Twitter: @AlannaCote2<\/a><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”pub” _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” background_image=”https:\/\/labs.icahn.mssm.edu\/oreillylab\/wp-content\/uploads\/sites\/380\/2021\/04\/nyc_background.jpg”][et_pb_row module_id=”research” _builder_version=”4.9.0″ _module_preset=”default” custom_margin=”-76px|auto||auto||” custom_padding=”||46px|||”][et_pb_column type=”4_4″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default”]<\/p>\n

Selected Publications<\/h1>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.9.0″ _module_preset=”default” min_height=”513px” custom_margin=”24px|auto||auto||” custom_padding=”34px||34px||true|” locked=”off”][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”|7px|||false|false” link_option_url=”https:\/\/www.nature.com\/articles\/s41537-020-00131-2″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

 <\/p>\n

BridgePRS leverages shared genetic effects across ancestries to increase polygenic risk score portability<\/span><\/strong><\/a><\/span><\/p>\n

Hoggart CJ<\/b>,…, O\u2019Reilly PF<\/strong>. 2023. Nature Genetics.<\/em><\/p>\n

\"Fig.\u00a0<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”|7px|||false|false” link_option_url=”https:\/\/www.nature.com\/articles\/s41537-020-00131-2″ border_color_all=”#000000″ box_shadow_style=”preset2″ locked=”off”]<\/p>\n

 <\/p>\n

Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort<\/span><\/strong><\/span><\/a><\/span><\/p>\n

Priv\u00e9 F,…, O\u2019Reilly PF,\u00a0<\/strong>Vilhj\u00e1lmsson BJ. 2022. Am J Hum Genet.<\/em><\/p>\n

\"An\u00a0<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.9.0″ _module_preset=”default” min_height=”436px” custom_margin=”24px|auto||auto||” custom_padding=”34px||34px||true|” locked=”off”][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”|7px|||false|false” link_option_url=”https:\/\/www.nature.com\/articles\/s41537-020-00131-2″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

 <\/p>\n

EraSOR: a software tool to eliminate inflation caused by sample overlap in polygenic score analyses<\/span><\/strong><\/a><\/span><\/p>\n

Choi SW<\/strong>, Mak TS, Hoggart CJ<\/strong>, O\u2019Reilly PF<\/strong>. 2023.\u00a0Gigascience.<\/em><\/p>\n

\"\"<\/em><\/p>\n

\u00a0<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” link_option_url=”https:\/\/www.nature.com\/articles\/s41596-020-0353-1″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

 <\/p>\n

PRSet: Pathway-based polygenic risk score analyses and software<\/span><\/strong><\/a><\/span><\/p>\n

Choi SW*, Garc\u00eda-Gonz\u00e1lez J*…,<\/strong>O\u2019Reilly PF<\/strong>. 2023.\u00a0 Plos Genetics.<\/em><\/p>\n

<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.9.0″ _module_preset=”default” min_height=”513px” custom_margin=”24px|auto||auto||” custom_padding=”34px||34px||false|” locked=”off”][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”|7px|||false|false” link_option_url=”https:\/\/www.nature.com\/articles\/s41537-020-00131-2″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

 <\/p>\n

Trumpet plots: visualizing the relationship between allele frequency and effect size in genetic association studies<\/span><\/strong><\/a><\/span><\/p>\n

Corte L, Liou L<\/strong>,\u00a0O\u2019Reilly PF, Garcia-Gonzalez J<\/strong>. 2023.\u00a0Gigabyte.<\/em><\/p>\n

\"An\u00a0<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” link_option_url=”https:\/\/www.nature.com\/articles\/s41596-020-0353-1″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

 <\/p>\n

Sibling Similarity Can Reveal Key Insights Into Genetic Architecture<\/span><\/strong><\/a><\/span><\/p>\n

Souaiaia, T, Wu HM<\/strong>, Hoggart CJ,\u00a0<\/strong>O\u2019Reilly PF<\/strong>. 2023. \u00a0eLife.<\/em><\/p>\n

<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.9.0″ _module_preset=”default” min_height=”513px” custom_margin=”24px|auto||auto||” custom_padding=”34px||34px||false|” locked=”off”][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”|7px|||false|false” link_option_url=”https:\/\/www.nature.com\/articles\/s41537-020-00131-2″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

 <\/p>\n

Heterogeneous effects of genetic risk for Alzheimer’s disease on the phenome<\/span><\/strong><\/a><\/span><\/p>\n

Wu HM<\/strong>, Goate A,\u00a0O\u2019Reilly<\/strong>. 2021. Translational Psychiatry.<\/em><\/p>\n

\"figure\u00a0<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” link_option_url=”https:\/\/www.nature.com\/articles\/s41596-020-0353-1″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

 <\/p>\n

Genetic origins of schizophrenia find common ground<\/span><\/strong><\/a><\/span><\/p>\n

Iyegbe CO &\u00a0<\/strong>O\u2019Reilly<\/strong>. 2022.\u00a0 Nature.<\/em><\/p>\n

\"Figure<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.9.0″ _module_preset=”default” min_height=”772px” custom_margin=”24px|auto||auto||” custom_padding=”34px|||||”][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”|7px|||false|false” link_option_url=”https:\/\/www.nature.com\/articles\/s41537-020-00131-2″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

 <\/p>\n

Investigating the effects of genetic risk of schizophrenia on behavioural traits<\/span><\/strong><\/span><\/p>\n

Socrates A,\u2026, O\u2019Reilly PF<\/strong>. 2021.\u00a0NPJ Schizophrenia.<\/em><\/p>\n

\u00a0\"\"\u00a0<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” link_option_url=”https:\/\/www.nature.com\/articles\/s41596-020-0353-1″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

Tutorial: a Guide to Performing Polygenic Risk Score Analyses<\/span><\/p>\n

Choi SW<\/strong>, Mak TS, O\u2019Reilly PF<\/strong>. 2020. \u00a0Nature Protocols<\/em><\/p>\n

\"\"<\/em><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.9.0″ _module_preset=”default” custom_padding=”46px|||||”][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” link_option_url=”https:\/\/academic.oup.com\/gigascience\/article\/8\/7\/giz082\/5532407″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

<\/h4>\n

PRSice-2: Polygenic Risk Score Software for large-scale data<\/a><\/span><\/h4>\n
\n

Choi<\/strong>\u00a0SW\u00a0<\/strong>&\u00a0O\u2019Reilly PF<\/strong>. 2019.\u00a0GigaScience<\/em><\/p>\n

\"\"<\/p>\n

\u00a0<\/span><\/p>\n<\/div>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” link_option_url=”https:\/\/academic.oup.com\/bioinformatics\/article\/31\/9\/1466\/200539″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

<\/h4>\n

PRSice: Polygenic Risk Score Software<\/a><\/span><\/h4>\n

Euesden J, Lewis CM, O\u2019Reilly PF<\/strong>. 2015. Bioinformatics<\/em><\/p>\n

\n

\u00a0\"\"<\/p>\n

 <\/p>\n<\/div>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”|7px||0px|false|false” link_option_url=”https:\/\/www.nature.com\/articles\/s41398-020-01009-8″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

The effects of polygenic risk for psychiatric disorders and smoking behaviour on psychotic experiences in UK Biobank<\/a><\/span>\u00a0<\/span><\/span><\/p>\n

Garc\u00eda-Gonz\u00e1lez J\u00a0<\/strong>et al. 2020. Translational Psychiatry<\/em><\/p>\n

\"\"<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” link_option_url=”https:\/\/www.nature.com\/articles\/s41576-018-0020-3″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

 <\/p>\n

Using genetic data to strengthen causal inference in observational research<\/span><\/strong><\/span><\/p>\n

Pingualt JB, O\u2019Reilly PF<\/strong> et al. 2018.\u00a0Nature Reviews Genetics<\/em><\/p>\n

\u00a0\"\"\u00a0<\/span><\/p>\n

 <\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”|16px|||false|false” link_option_url=”https:\/\/link.springer.com\/article\/10.1007\/s00127-014-0823-2″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

 <\/p>\n

The emerging molecular architecture of schizophrenia, polygenic risk scores and the clinical implications for GxE research<\/span><\/strong><\/span><\/p>\n

\u00a0Iyegbe C<\/strong>\u00a0et al. 2014. Social Psych. and Psych. Research<\/em>\u00a0<\/span><\/p>\n

\"\"<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” link_option_url=”https:\/\/jamanetwork.com\/journals\/jamapsychiatry\/fullarticle\/2712600″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

 <\/p>\n

Effect of Damaging Rare Mutations in Synapse-Related Gene Sets on Response to Short-term Antipsychotic Medication in Chinese Patients With Schizophrenia: A Randomized Clinical Trial<\/span><\/strong><\/span><\/p>\n

Wang Q*, Wu HM<\/strong>*\u00a0<\/strong>et al. 2018.\u00a0JAMA Psychiatry<\/em>\u00a0\"\"\u00a0<\/span><\/p>\n

 <\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” link_option_url=”https:\/\/www.nature.com\/articles\/srep38837″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

 <\/p>\n

Multivariate simulation framework reveals performance of multi-trait GWAS<\/span><\/strong><\/span><\/p>\n

Porter HF and O\u2019Reilly PF<\/strong>. 2017.\u00a0Scientific Reports<\/em><\/p>\n

\u00a0\"An<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” link_option_url=”https:\/\/journals.plos.org\/plosone\/article?id=10.1371\/journal.pone.0034861″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

MultiPhen: Joint Model of Multiple Phenotypes Can Increase Discovery in GWAS<\/a><\/p>\n

O\u2019Reilly PF<\/strong>\u00a0et al. 2012.\u00a0<\/span>PLOS ONE<\/em><\/p>\n

 <\/p>\n

\n

\"\"<\/p>\n<\/div>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”|4px|||false|false” link_option_url=”https:\/\/journals.plos.org\/plosgenetics\/article?id=10.1371\/journal.pgen.1004508″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

 <\/p>\n

Novel approach identifies SNPs in SLC2A10 and KCNK9 with evidence for parent-of-origin effect on body mass index<\/span><\/strong><\/span><\/p>\n

Hoggart CJ<\/strong> et al. 2014. PLoS Genetics<\/em><\/p>\n

\u00a0<\/span>\"\"\u00a0<\/span><\/p>\n

 <\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” link_option_url=”https:\/\/www.nature.com\/articles\/ng.922″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

 <\/p>\n

Genome-Wide Association Study Identifies Six New Loci Influencing Pulse Pressure and Mean Arterial Pressure<\/span><\/strong><\/p>\n

Wain*, Verwoert*, O’Reilly<\/strong>*, Shi*, Johnson* et al. 2011. Nature Genetics<\/em>\u00a0<\/span><\/p>\n

\"\"\u00a0<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”|14px|||false|false” link_option_url=”https:\/\/genome.cshlp.org\/content\/18\/8\/1304.long” border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

 <\/p>\n

Confounding between recombination and selection, and the Ped\/Pop method for detecting selection<\/span><\/strong><\/span><\/p>\n

O\u2019Reilly PF<\/strong>, Birney E, Balding DJ. 2008. Genome Research<\/em><\/p>\n

\u00a0\"\"<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”|4px|||false|false” link_option_url=”https:\/\/academic.oup.com\/bioinformatics\/article\/26\/6\/838\/244528″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

 <\/p>\n

InvertFREGENE: Software for Simulating Inversions in Population Genetic Data<\/span><\/strong><\/span><\/p>\n

O\u2019Reilly PF<\/strong>, Coin LCM, Hoggart CJ<\/strong>. 2010. Bioinformatics<\/em><\/p>\n

\u00a0\"\"\u00a0<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”|16px|||false|false” link_option_url=”https:\/\/www.nature.com\/articles\/ng.918″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

Admixture provides new insights into recombination<\/strong><\/span><\/p>\n

O’Reilly PF<\/strong> & Balding DJ. 2011. Nature Genetics<\/i><\/span><\/p>\n

\"\"<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” link_option_url=”https:\/\/academic.oup.com\/genetics\/article\/190\/2\/669\/6064104″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

 <\/p>\n

Fine-scale estimation of location of birth from genome-wide SNP data<\/span><\/strong>\u00a0<\/span><\/p>\n

Hoggart CJ*, O\u2019Reilly PF*<\/strong> et al. 2012. Genetics<\/em><\/p>\n

\u00a0<\/span>\"\"<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”|4px|||false|false” link_option_url=”https:\/\/journals.plos.org\/plosgenetics\/article?id=10.1371\/journal.pgen.1000130″ border_color_all=”#000000″ box_shadow_style=”preset2″]<\/p>\n

Simultaneous Analysis of all SNPs in Genome-Wide and re\u2013sequencing association studies<\/span><\/strong><\/p>\n

Hoggart CJ<\/strong>, Whittaker JC, De Iorio M, Balding DJ. 2008. PLoS Genetics<\/em><\/p>\n

\"\"<\/p>\n

 <\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.9.0″ _module_preset=”default”][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ module_id=”softwares” _builder_version=”4.9.0″ _module_preset=”default” background_color=”rgba(132,132,132,0.08)” background_enable_image=”off”][et_pb_row _builder_version=”4.9.0″ _module_preset=”default” min_height=”876.5px” custom_padding=”||88px|||”][et_pb_column type=”4_4″ _builder_version=”4.9.0″ _module_preset=”default”][et_pb_text _builder_version=”4.9.0″ _module_preset=”default” custom_margin=”||25px|||”]<\/p>\n

Software<\/strong><\/span><\/h1>\n

 <\/p>\n

[\/et_pb_text][et_pb_text admin_label=”Text” _builder_version=”4.9.0″ _module_preset=”default” background_color=”#FFFFFF” custom_margin=”-41px|||||” border_radii=”on|12px|12px|12px|12px”]\u00a0 \u00a0\u00a0<\/b><\/span><\/p>\n

\u00a0 \u00a0 \u00a0Methods and software for GWAS and PRS analyses:<\/b><\/span>\u00a0<\/span><\/p>\n